CN115907289A - Intelligent supervision method and system for food quality and production safety - Google Patents
Intelligent supervision method and system for food quality and production safety Download PDFInfo
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Abstract
The invention provides an intelligent supervision method and system for food quality and production safety, wherein the method comprises the following steps: constructing a classification system, wherein the classification system comprises a food quality classification system and a production safety classification system; acquiring historical data, and preprocessing the historical data to obtain preprocessed data; classifying and clustering the preprocessed data by using a classification system to obtain analysis data; constructing a database according to the analysis data; the database comprises a food quality database and a production safety database; and acquiring data to be supervised, and comparing the data to be supervised with the database to obtain the food grade or the production grade of the data to be supervised. The method aims to meet the product compliance requirements of the food industry, a unified classification system and a database are constructed, the food quality and the production safety are automatically identified, and the defect that the food quality and the production safety cannot be effectively supervised in the prior art is overcome.
Description
Technical Field
The invention belongs to the technical field of food safety, and particularly relates to an intelligent supervision method and system for food quality and production safety.
Background
With the diversification and development of food, food safety is more and more concerned by people in order to reduce hidden danger of diseases and prevent food poisoning. The food safety mainly refers to discussing the food quality and production safety in the processes of food processing, storage, sale and the like. Therefore, after the food production is finished, the quality of the produced food needs to be detected, and whether risks exist in the production environment, raw and auxiliary materials purchasing and using, production process, product inspection, packaging labels and the like.
However, the current detection method mainly comprises spot check, wherein a part of food is manually extracted to detect how the quality of the food is, whether the production of a factory is in compliance or not is randomly spot-checked, and the detection process is also manually judged by a detector, so that the problem of inaccurate detection result due to manual negligence or insufficient experience can occur, and the spot check mode is time-consuming and labor-consuming, so that the food quality and the production safety can still not be effectively supervised.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent supervision method and system for food quality and production safety, and solves the defect that the food quality and the production safety cannot be effectively supervised in the prior art.
In a first aspect, an intelligent supervision method for food quality and production safety comprises the following steps:
constructing a classification system, wherein the classification system comprises a food quality classification system and a production safety classification system;
acquiring historical data, and preprocessing the historical data to obtain preprocessed data;
classifying and clustering the preprocessed data by using a classification system to obtain analysis data;
constructing a database according to the analysis data; the database comprises a food quality database and a production safety database;
and acquiring data to be supervised, and comparing the data to be supervised with the database to obtain the food grade or the production grade of the data to be supervised.
Further, the constructing of the classification system specifically includes:
acquiring standard regulations, wherein the standard regulations comprise food industry regulations and production safety regulations of different countries, regions, international organizations and industry associations;
analyzing a standard rule and extracting keywords; the keywords comprise food grades and corresponding judging conditions, production grades and corresponding judging conditions;
constructing a food quality classification system with a tree structure according to the affiliations of all food grades;
and constructing a production safety classification system with a tree structure according to the affiliations of all the production levels.
Furthermore, the food grade or the production grade in the classification system is provided with a classification code.
Further, classifying and clustering the preprocessed data by using a classification system specifically comprises:
analyzing the preprocessed data to obtain real-time conditions in the preprocessed data;
clustering the real-time conditions, and combining the same real-time conditions;
acquiring judgment conditions which are met by the combined real-time conditions to obtain food grades or production grades corresponding to the judgment conditions;
the pre-processing data, the corresponding food grade or production grade is defined as analysis data.
Further, comparing the data to be supervised with the database to obtain the food grade or production grade of the data to be supervised specifically comprises:
matching the data to be supervised with a database;
when matching to the food grade or production grade in the database, to obtain the food grade or production grade of the data to be supervised.
Further, after obtaining the food grade or production grade of the data to be regulated, the method further comprises the following steps:
constructing a decision base; the decision library comprises at least one decision scheme;
when the food grade or the production grade of the data to be supervised does not meet the standard, calling a decision scheme corresponding to the food grade and the production grade from the decision library;
and outputting the decision scheme.
In a second aspect, an intelligent monitoring system for food quality and production safety comprises:
a system construction unit: the method is used for constructing a classification system, wherein the classification system comprises a food quality classification system and a production safety classification system;
a database construction unit: the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring historical data and preprocessing the historical data to obtain preprocessed data; classifying and clustering the preprocessed data by using a classification system to obtain analysis data; constructing a database according to the analysis data; the database comprises a food quality database and a production safety database;
a supervision unit: the monitoring system is used for acquiring data to be monitored and comparing the data to be monitored with the database to obtain the food grade or the production grade of the data to be monitored.
Further, the architecture unit is specifically configured to:
acquiring standard regulations, wherein the standard regulations comprise food industry regulations and production safety regulations of different countries, regions, international organizations and industry associations;
analyzing a standard rule and extracting keywords; the keywords comprise food grades and corresponding judging conditions, production grades and corresponding judging conditions;
constructing a food quality classification system with a tree structure according to the affiliations of all food grades;
and constructing a production safety classification system with a tree structure according to the affiliations of all the production levels.
Further, the database construction unit is specifically configured to:
analyzing the preprocessed data to obtain real-time conditions in the preprocessed data;
clustering the real-time conditions, and combining the same real-time conditions;
acquiring judgment conditions which are met by the combined real-time conditions to obtain food grades or production grades corresponding to the judgment conditions;
the pre-processing data, the corresponding food grade or production grade is defined as analysis data.
Further, still include:
a decision unit: for constructing a decision library; the decision library comprises at least one decision scheme; when the food grade or the production grade of the data to be supervised does not meet the standard, calling a decision scheme corresponding to the food grade and the production grade from the decision library; and outputting the decision scheme.
According to the technical scheme, the intelligent supervision method and system for food quality and production safety provided by the invention are used for constructing a unified classification system and a database in order to meet the product compliance requirements of the food industry, automatically identifying the food quality and the production safety, and overcoming the defect that the food quality and the production safety cannot be effectively supervised in the prior art.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a flowchart of an intelligent supervision method according to an embodiment.
Fig. 2 is a flowchart of a classification system building method according to an embodiment.
Fig. 3 is a flowchart of a classification clustering method according to an embodiment.
Fig. 4 is a block diagram of modules of an intelligent supervision system according to an embodiment.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby. It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the present invention belongs.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Example (b):
an intelligent supervision method for food quality and production safety is disclosed, referring to fig. 1, comprising:
s1: constructing a classification system, wherein the classification system comprises a food quality classification system and a production safety classification system;
s2: acquiring historical data, and preprocessing the historical data to obtain preprocessed data;
s3: classifying and clustering the preprocessed data by using a classification system to obtain analysis data;
s4: constructing a database according to the analysis data; the database comprises a food quality database and a production safety database;
s5: and acquiring data to be supervised, and comparing the data to be supervised with the database to obtain the food grade or the production grade of the data to be supervised.
In the embodiment, the method takes meeting the product compliance requirements of the food industry as an entry point, researches information such as food supervision regulations and standards of different countries, regions, international organizations and industry associations all over the world, and constructs a unified classification system by taking industry quality and safety control as classification principles. For example, a food quality classification system may include which first major categories the food grade is classified into, which second major categories below which first major categories the food grade is classified into, and so on. The production safety classification system may include which first major categories the production grades are classified into, which second major categories below which first major categories the production grades are classified into, and so on.
In the embodiment, the method is based on food classification, food quality and safety attribute classification, and the historical data of different countries and regions in the world under different supervision modes, supervision requirements, supervision indexes and other conditions are researched and extracted, and the database is constructed after the historical data is preprocessed, classified and clustered. For example, the method can utilize OpenRefine data cleaning technology to clean historical data, complete the preprocessing of the historical data, utilize data statistical analysis technology to preprocess the data to classify and cluster the content characteristics, and construct a food quality database and a production safety database.
In the embodiment, the method can perform intelligent identification and judgment on food quality and production safety by comparing the data to be supervised with the database, and realizes the functions of food quality supervision, production safety supervision and the like.
The intelligent supervision method for the food quality and the production safety aims to meet the product compliance requirements of the food industry, a unified classification system and a database are constructed, the food quality and the production safety are automatically identified, and the defect that the food quality and the production safety cannot be effectively supervised in the prior art is overcome.
Further, in some embodiments, referring to fig. 2, constructing the taxonomy specifically includes:
s11: acquiring standard regulations, wherein the standard regulations comprise food industry regulations and production safety regulations of different countries, regions, international organizations and industry associations;
s12: analyzing a standard rule and extracting keywords; the keywords comprise food grades and corresponding judging conditions, production grades and corresponding judging conditions;
s13: constructing a food quality classification system with a tree structure according to the affiliations of all food grades;
s14: and constructing a production safety classification system with a tree structure according to the affiliations of all the production levels.
In this example, the method reads food industry regulations and production safety regulations of different countries, regions, international organizations, industry associations, including food quality requirement national standards, association standards, safety production standards, etc., when constructing a classification system. Then, the method analyzes the standard regulation and extracts keywords, wherein the keywords comprise food grades and corresponding judging conditions, production grades and corresponding judging conditions; for example, the keywords in the food industry regulations include a food grade a and a determination condition a, a food grade B and a determination condition B, and the like. The keywords in the production safety regulations include a production grade C and a judgment condition C, and a production grade D and a judgment condition D. And finally, constructing a classification system according to the affiliated relationship between the food grade and the production grade, for example, if the food grade A and the food grade B are in a same-level relationship, the food grade A and the food grade B are used as two father nodes in the classification system of the food quality. And if the production level C is a lower characteristic of the production level D, the production level D is used as a father node of a production safety classification system, and the production level C is used as a child node under the production level D.
Further, in some embodiments, the food grade or the production grade in the classification system is provided with a classification code.
In this embodiment, the method may further set a unique classification code for each level in the classification system, so as to distinguish different levels in different classification systems.
Further, in some embodiments, referring to fig. 3, classifying and clustering the preprocessed data by using the classification system specifically includes:
s21: analyzing the preprocessed data to obtain real-time conditions in the preprocessed data;
s22: clustering the real-time conditions, and combining the same real-time conditions;
s23: acquiring judgment conditions which are met by the combined real-time conditions to obtain food grades or production grades corresponding to the judgment conditions;
s24: the pre-processing data, the corresponding food grade or production grade is defined as analysis data.
In this embodiment, when performing classification and clustering, the method first extracts real-time conditions in the pre-processing data, for example, the real-time conditions are information such as the amount of each ingredient, the production flow, and the production environment. And then combining the same real-time conditions to avoid repeated matching in the subsequent matching process, and during matching, matching the real-time conditions with the classification system to obtain the judgment conditions which are consistent with the real-time conditions, thereby obtaining the food grade or the production grade corresponding to the judgment conditions, for example, if the implementation conditions are consistent with the judgment conditions B, the food grade of the preprocessed data is the food grade B, and if the implementation conditions are consistent with the judgment conditions D, the production grade of the preprocessed data is the production grade D. The pre-processing data, the corresponding food grade or production grade is defined as analysis data.
Further, in some embodiments, comparing the data to be supervised with the database to obtain the food grade or production grade of the data to be supervised specifically comprises:
matching the data to be supervised with a database;
when the food grade or the production grade in the database is matched, the food grade or the production grade of the data to be supervised is obtained.
In the embodiment, when the method identifies the food grade or the production grade of the data to be supervised, the data to be supervised is matched with the database, and the matched food grade or production grade is defined as the food grade or the production grade of the data to be supervised.
Further, in some embodiments, after obtaining the food grade or production grade of the data to be regulated, the method further comprises:
constructing a decision base; the decision base comprises at least one decision scheme;
when the food grade or the production grade of the data to be supervised does not meet the standard, calling a decision scheme corresponding to the food grade and the production grade from the decision library;
and outputting the decision scheme.
In this embodiment, the method may further provide a solution for the food quality problem and the production safety problem, for example, when the food quality is not compliant, the corresponding decision-making scheme is queried and output to the user, and the user may adjust the production process, the storage process, and the like in time according to the decision-making scheme, so that the food quality reaches the standard, and the merchant is helped to solve the problems of the food ingredient compliance, the tag compliance, and the like. Wherein each food problem and production problem in the decision library can correspond to a decision scheme.
An intelligent supervision system for food quality and production safety, see fig. 4, comprises:
system construction unit 1: the method is used for constructing a classification system, wherein the classification system comprises a food quality classification system and a production safety classification system;
the database construction unit 2: the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring historical data and preprocessing the historical data to obtain preprocessed data; classifying and clustering the preprocessed data by using a classification system to obtain analysis data; constructing a database according to the analysis data; the database comprises a food quality database and a production safety database;
the supervision unit 3: the monitoring system is used for acquiring data to be monitored and comparing the data to be monitored with the database to obtain the food grade or the production grade of the data to be monitored.
Further, in some embodiments, the architecture unit 1 is specifically configured to:
acquiring standard regulations, wherein the standard regulations comprise food industry regulations and production safety regulations of different countries, regions, international organizations and industry associations;
analyzing a standard rule and extracting keywords; the keywords comprise food grades and corresponding judging conditions, production grades and corresponding judging conditions;
constructing a food quality classification system with a tree structure according to the affiliations of all food grades;
and constructing a production safety classification system with a tree structure according to the affiliations of all the production levels.
Further, in some embodiments, the database construction unit 2 is specifically configured to:
analyzing the preprocessed data to obtain real-time conditions in the preprocessed data;
clustering the real-time conditions, and combining the same real-time conditions;
acquiring judgment conditions which are met by the combined real-time conditions to obtain food grades or production grades corresponding to the judgment conditions;
the pre-processing data, the corresponding food grade or production grade is defined as analysis data.
Further, in some embodiments, the method further comprises:
a decision unit: for constructing a decision library; the decision library comprises at least one decision scheme; when the food grade or the production grade of the data to be supervised does not meet the standard, calling a decision scheme corresponding to the food grade and the production grade from the decision library; and outputting the decision scheme.
For a brief description of the system provided by the embodiment of the present invention, reference may be made to the corresponding contents in the foregoing embodiment where no mention is made in the embodiment.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. An intelligent supervision method for food quality and production safety is characterized by comprising the following steps:
constructing a classification system, wherein the classification system comprises a food quality classification system and a production safety classification system;
acquiring historical data, and preprocessing the historical data to obtain preprocessed data;
classifying and clustering the preprocessed data by using the classification system to obtain analysis data;
building a database according to the analysis data; the database comprises a food quality database and a production safety database;
and acquiring data to be supervised, and comparing the data to be supervised with the database to obtain the food grade or the production grade of the data to be supervised.
2. The intelligent supervision method for food quality and production safety according to claim 1, wherein the constructing a classification system specifically comprises:
acquiring standard regulations, wherein the standard regulations comprise food industry regulations and production safety regulations of different countries, regions, international organizations and industry associations;
analyzing the standard rule and extracting key words; the keywords comprise the food grade and a corresponding judgment condition, and the production grade and a corresponding judgment condition;
constructing the food quality classification system with a tree structure according to the affiliations of all the food grades;
and constructing the production safety classification system with a tree structure according to the affiliations of all the production levels.
3. The intelligent supervision method for food quality and production safety according to claim 2, characterized in that,
and the food grade or the production grade in the classification system is provided with a classification code.
4. The intelligent supervision method for food quality and production safety according to claim 2, wherein the classifying and clustering the preprocessed data by the classification system specifically comprises:
analyzing the preprocessed data to obtain real-time conditions in the preprocessed data;
clustering the real-time conditions, and combining the same real-time conditions;
acquiring the judgment condition which is met by the combined real-time condition, and acquiring the food grade or production grade corresponding to the judgment condition;
defining the pre-processing data, the corresponding food grade or production grade as the analysis data.
5. The intelligent monitoring method for food quality and production safety according to claim 4, wherein comparing the data to be monitored with the database to obtain the food grade or the production grade of the data to be monitored specifically comprises:
matching the data to be supervised with the database;
when matching to the food grade or production grade in the database, to obtain the food grade or production grade of the data to be supervised.
6. The intelligent supervision method for food quality and production safety according to claim 1, further comprising, after the obtaining of the food grade or production grade of the data to be supervised:
constructing a decision base; the decision library comprises at least one decision scheme;
when the food grade or the production grade of the data to be supervised does not meet the standard, calling a decision scheme corresponding to the food grade and the production grade from the decision library;
and outputting the decision scheme.
7. The utility model provides an intelligent supervisory systems of food quality and production safety which characterized in that includes:
a system construction unit: the method is used for constructing a classification system, wherein the classification system comprises a food quality classification system and a production safety classification system;
a database construction unit: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring historical data and preprocessing the historical data to obtain preprocessed data; classifying and clustering the preprocessed data by using the classification system to obtain analysis data; building a database according to the analysis data; the database comprises a food quality database and a production safety database;
a supervision unit: the monitoring system is used for acquiring data to be monitored and comparing the data to be monitored with the database to obtain the food grade or the production grade of the data to be monitored.
8. The intelligent food quality and production safety supervision system according to claim 7, wherein the system building unit is specifically configured to:
acquiring standard regulations, wherein the standard regulations comprise food industry regulations and production safety regulations of different countries, regions, international organizations and industry associations;
analyzing the standard regulation and extracting keywords; the keywords comprise the food grade and a corresponding judgment condition, and the production grade and a corresponding judgment condition;
constructing the food quality classification system with a tree structure according to the affiliations of all the food grades;
and constructing the production safety classification system with a tree structure according to the affiliations of all the production levels.
9. The intelligent food quality and production safety supervision system according to claim 8, wherein the database construction unit is specifically configured to:
analyzing the preprocessed data to obtain real-time conditions in the preprocessed data;
clustering the real-time conditions, and combining the same real-time conditions;
acquiring the judgment condition which is met by the combined real-time condition, and acquiring the food grade or production grade corresponding to the judgment condition;
defining the pre-processing data, the corresponding food grade or production grade as the analytical data.
10. The intelligent food quality and production safety supervision system according to claim 8, further comprising:
a decision unit: for constructing a decision library; the decision library comprises at least one decision scheme; when the food grade or the production grade of the data to be supervised does not reach the standard, calling a decision scheme corresponding to the food grade and the production grade from the decision library; and outputting the decision scheme.
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