CN113706002A - Food safety knowledge base-based supervision platform, method and storage medium - Google Patents
Food safety knowledge base-based supervision platform, method and storage medium Download PDFInfo
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Abstract
The invention relates to a supervision platform, a supervision method and a storage medium based on a food safety knowledge base, wherein the supervision method comprises the following steps: acquiring various industrial chain data, and establishing a food safety knowledge base according to the various industrial chain data; integrating query data of keywords to be searched in the food safety knowledge base by adopting a knowledge graph; and carrying out early warning based on the query result of the knowledge graph. The invention establishes an enterprise production application database and is intelligently associated with a food safety knowledge base, thereby realizing the quality safety supervision of the whole chain of food production.
Description
Technical Field
The invention relates to the technical field of food information, in particular to a supervision platform, a supervision method and a storage medium based on a food safety knowledge base.
Background
With the development of economy and the improvement of the living standard of people, the demand of people for food is changed from quantity to quality. The food quality safety is a basic guarantee for healthy life and is an important factor for improving the international market competitiveness of food in China. Due to the lack of multi-source and scientific food safety data, the food safety emergency management in China faces many challenges. The method integrates and analyzes data in the food production and processing process by establishing a food related database, and provides basis for risk assessment and supervision management of food safety, such as a food safety hazard database (Wangpeng and other 2020), a food safety enterprise database (Zhang Jingyu and other 2020), a food circulation database (Xuhuixin and other 2020) and the like, but has more influence factors and wider sources of food quality safety, still lacks of various channel source information, a comprehensive and multidimensional food quality safety database with multiple angles and multiple indexes, and lacks of a rapid and automatic intelligent management system.
The mode of the knowledge map is beneficial to intelligent application research of a high-latitude multi-channel database, the current related knowledge map and the knowledge base lack multi-source and high-dimensional food safety knowledge, the functions of knowledge inquiry, fusion and output are lacked between the related knowledge map and the knowledge base, and real-time updating and application of knowledge in the food production process cannot be realized. The food industry chain data analysis and knowledge mining are completed through multi-source and high-dimensional information of a network space comprehensive society, food enterprises, supervision and detection mechanisms, Internet media, consumers and the like, fragmented information is aggregated into a knowledge network map, and a more comprehensive and scientific food quality safety knowledge base is established according to production and processing logics. The method has the advantages that the whole-chain multi-source information risk assessment is carried out on an industrial chain, the scientification and precision level of emergency management decision is improved, meanwhile, the content in the knowledge base is inquired through the knowledge map, more comprehensive and scientific related knowledge is arranged and output, even new knowledge is output, the situation that mutual communication information channels among departments are unsmooth and unequal is broken, and the food risk is reduced. In conclusion, how to adopt multi-source and high-dimensional information to comprehensively and efficiently and intelligently supervise the food industry chain is an urgent problem to be solved.
Disclosure of Invention
In view of the above, it is necessary to provide a monitoring platform, a method and a storage medium based on a food safety knowledge base, so as to solve the problems of single source of food safety monitoring data and unsmooth information channel in the prior art.
The invention provides a food safety knowledge base-based supervision platform, which comprises a database construction system, a knowledge map construction system and a safety supervision system, wherein:
the database construction system is used for acquiring various industrial chain data and establishing a food safety knowledge base according to the various industrial chain data;
the knowledge map construction system is used for integrating query data of keywords to be queried in the food safety knowledge base by adopting a knowledge map;
and the safety supervision system is used for carrying out early warning on the basis of the query result of the knowledge graph.
The invention also provides a supervision method based on the food safety knowledge base-based supervision platform of claim 1, wherein the supervision method comprises the following steps:
acquiring various industrial chain data, and establishing a food safety knowledge base according to the various industrial chain data;
integrating query data of keywords to be searched in the food safety knowledge base by adopting a knowledge graph;
and carrying out early warning based on the query result of the knowledge graph.
Further, the establishing a food safety knowledge base according to the various industry chain data comprises:
performing data sorting and data integration on the multiple industrial chain data to generate a plurality of standard libraries, wherein the data sorting comprises at least one of extraction, cleaning, completion, conversion and summarization, and the data integration comprises data fusion on the multiple industrial chain data to generate new information data;
and constructing the food safety knowledge base based on the plurality of standard bases.
Further, the step of integrating query data of the keywords to be searched in the food safety knowledge base by using the knowledge graph comprises the following steps:
acquiring a target to be searched, related fields and keywords;
determining a secondary database according to the target to be searched, the related field and the keyword and the query result in the food safety knowledge base, and re-expanding the food safety knowledge base according to the secondary database;
and determining a query knowledge graph associated with the relevant fields in the re-expanded food safety knowledge base by using the knowledge graph.
Further, the determining a secondary database according to the query result of the object to be searched, the related field and the keyword in the food safety knowledge base comprises:
establishing superior words, inferior words, synonyms and related words of the related fields according to the target to be searched, the related fields and the keywords;
associating information related to the relevant fields in the food safety knowledge base based on the hypernyms, hyponyms, synonyms and relevant words of the relevant fields;
and constructing the secondary database according to the related information of the related fields in the food safety knowledge base.
Further, the determining, using the knowledge-graph, a query knowledge-graph associated with the relevant fields in the re-expanded food safety knowledge base comprises:
searching in the re-expanded food safety knowledge base by using a knowledge map, and determining superior words, inferior words, synonyms and related words which are associated with the related fields;
and establishing the query knowledge graph associated with the related fields according to the hypernyms, the hyponyms, the synonyms and the related words.
Further, the integration of the query data of the keywords to be searched in the food safety knowledge base by using the knowledge graph further comprises:
and carrying out data fusion on the different query data to form a relative risk value, a node risk index and a comprehensive risk index, wherein:
the relative risk value is expressed by the following formula:
in the above formula, PijRepresenting the relative risk value of the jth index of the ith node; a represents an actual measurement value of the j index of the i node; b represents the limit value of the j index of the ith node; xijThe detection frequency or the period achievement rate of the jth index of the ith node is represented; c represents the actual detection frequency or period of the jth index of the ith node; d represents a prescribed detection frequency or period of the jth index of the ith node; i represents the ith node, i is 1,2,3, …, m is the number of nodes; j represents j index, j is 1,2,3,4, …, n, n is index number;
the node risk index is expressed by the following formula:
in the above formula, FiRepresenting a risk index of the ith node; [ P ]ij]MAXRepresenting the maximum value of the single index risk index in the ith node; e represents the classification coefficient of the multi-source information, the biological type is e1, the chemical type is e2, and the physical type is e 3; y isiIndicating the index detection rate of the ith node; f represents the number of detection indexes of the ith node; g represents the total number of detection indexes of the ith node;
the composite risk index is expressed by the following formula:
in the above formula, F represents the composite risk index; [ F ]i]MAXRepresenting the maximum risk index of a full chain node; z represents the density of the master nodes of the full chain; h represents the actual number of master nodes in the full chain; k indicates the number of master nodes for the full chain.
Further, the early warning based on the query result of the knowledge graph comprises:
acquiring parameters of each node of a food industry chain;
inquiring in the inquiring knowledge graph according to the relevant fields of the food industry chain, and determining standard indexes corresponding to the parameters of each node;
and performing early warning and tracing according to the node parameters and the corresponding standard indexes.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements a food safety knowledge base-based supervision method as described above.
Compared with the prior art, the invention has the beneficial effects that: firstly, effectively acquiring alarm information and fire station information so as to effectively feed back the situations of an alarm site and a fire station; then, based on the alarm information, determining the most appropriate required fire fighting force under the alarm, and based on the information of the fire station, determining the combination of the power of fire fighting of different fire stations; furthermore, by utilizing an intelligent dispatching model, combining information of the fire stations, required fire fighting force and various fire station fighting combinations, determining a corresponding recommended dispatching scheme, and ensuring timeliness and effectiveness of fire fighting dispatching; finally, the deviation is corrected by adopting a manual intervention mode, and the accuracy is ensured. In conclusion, the invention solves the problem that the fire rescue force is dispatched according to subjective experience and lacks scientificity and systematicness, combines the alarm information and the fire station information, completes efficient and timely fire fighting dispatch, collects the alarm data and the fire station data, applies a big data analysis technology, automatically recommends the dispatch scheme through an intelligent alarm receiving and processing system, and improves the strength dispatch scientificity and the strength dispatch efficiency.
Drawings
FIG. 1 is a schematic structural diagram of an intelligent supervision platform based on a food safety knowledge base provided by the invention;
FIG. 2 is a schematic flow chart of an embodiment of an intelligent supervision method based on a food safety knowledge base according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of step S1 in FIG. 2 according to the present invention;
FIG. 4 is a schematic structural diagram of an embodiment of a knowledge base of food safety provided by the present invention;
FIG. 5 is a flowchart illustrating an embodiment of step S2 in FIG. 2 according to the present invention;
FIG. 6 is a flowchart illustrating an embodiment of step S22 in FIG. 5 according to the present invention;
FIG. 7 is a flowchart illustrating an embodiment of step S23 in FIG. 5 according to the present invention;
FIG. 8 is a flowchart illustrating an embodiment of a risk database provided by the present invention;
fig. 9 is a flowchart illustrating an embodiment of step S3 in fig. 2 according to the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Further, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Reference throughout this specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the described embodiments can be combined with other embodiments.
The invention provides a supervision platform, a supervision method and a storage medium based on a food safety knowledge base, which are applied to food information monitoring, prewarning risks existing in nodes in each food industry chain and providing a new idea for improving food supervision efficiency. The following are detailed below:
the embodiment of the invention provides an intelligent supervision platform based on a food safety knowledge base, and when being seen in combination with fig. 1, fig. 1 is a schematic structural diagram of the intelligent supervision platform based on the food safety knowledge base provided by the invention, the intelligent supervision platform based on the food safety knowledge base comprises a database construction system, a knowledge graph construction system and a safety supervision system, wherein:
the database construction system is used for acquiring various industrial chain data and establishing a food safety knowledge base according to the various industrial chain data;
the knowledge graph construction system is used for integrating query data of keywords to be searched in the food safety knowledge base in a knowledge graph mode;
and the safety supervision system is used for carrying out early warning on the basis of the query result of the knowledge graph.
In the embodiment of the invention, a database construction system is arranged, data related to food quality safety is collected and sorted according to the characteristics of food quality safety information to form a database, and a food safety knowledge base is established according to reasonable database combination; by setting a knowledge graph construction system, carrying out structured processing on data in the food production process, establishing intelligent association between food safety data and a knowledge base in a knowledge graph mode, and calculating the risk level of multi-source information to carry out risk assessment; by setting the safety supervision system, an enterprise production application database is established and intelligently associated with the food safety knowledge base, so that the full-chain quality safety supervision of food is realized, and effective early warning is carried out.
An embodiment of the present invention further provides a monitoring method based on the monitoring platform based on the food safety knowledge base, and referring to fig. 2, fig. 2 is a schematic flow chart of an embodiment of an intelligent monitoring method based on the food safety knowledge base, provided by the present invention, and the above intelligent monitoring method based on the food safety knowledge base includes steps S1 to S3, where:
in step S1, acquiring various industrial chain data, and establishing a food safety knowledge base according to the various industrial chain data;
in step S2, integrating query data of the keywords to be queried in the food safety knowledge base by using a knowledge graph;
in step S3, an early warning is given based on the query result of the knowledge graph.
In the embodiment of the invention, firstly, according to the characteristics of food quality safety information, data related to food quality safety is collected and sorted to form a database, and a food safety knowledge base is established according to reasonable database combination; further, data in the food production process are subjected to structured processing, intelligent association between food quality safety data and a knowledge base is established in a knowledge graph mode, and risk levels of multi-source information are calculated for risk assessment; and finally, establishing an enterprise production application database and intelligently associating the database with a food safety knowledge base to realize the full-chain quality safety supervision of the food so as to carry out effective early warning.
It should be noted that the invention establishes a food safety assessment system of multi-source information (including physical information, chemical information and biological information), collects and records related data in the food industry chain relatively widely and accurately, classifies and summarizes the data into a knowledge base, provides a data sharing platform for enterprises, regulatory agencies and the public, reduces food quality safety hazards and reduces cost. And intelligently associating the data in a knowledge graph mode to obtain the optimal food quality safety supervision result. By establishing a food quality safety assessment system with multi-source information, nodes in an industrial chain can be assessed more scientifically. The food quality safety is determined by all the influencing factors in the industrial chain, so that the food quality safety supervision of the whole chain is required, and the food quality safety of China is ensured by establishing a related knowledge base, providing intelligent data association fusion, risk assessment and supervision management of the whole chain for the food.
As a preferred embodiment, referring to fig. 3, fig. 3 is a schematic flowchart of an embodiment of step S1 in fig. 2 provided by the present invention, where the step S1 includes steps S11 to S12, where:
in step S11, performing data sorting and data integration on the multiple types of industry chain data to generate multiple standard libraries, where the data sorting includes at least one of extraction, cleaning, completion, conversion, and summarization, and the data integration includes performing data fusion on the multiple types of industry chain data to generate new information data;
in step S12, the food safety knowledge base is constructed based on the plurality of standard bases.
In the embodiment of the invention, a plurality of corresponding standard libraries are formed by collecting various industrial chain information so as to effectively construct a food safety knowledge base.
In a specific embodiment of the present invention, taking a rice industry chain as an example, the food safety knowledge base herein is a rice food quality safety knowledge base, which means that databases are classified according to characteristics of rice product quality safety information, and include a standard base (inspection and detection, production specification standard, etc.), a product base, an enterprise basic information base, an input material base, a food attribute base, a food safety event base, etc.; quality safety supervision database, circulation database, supervision management database (sampling inspection data of supervision organization), risk analysis database produced in the production process; then carrying out database structure design, data acquisition, data storage and data arrangement; the database structure refers to the core information composition of quality safety classification of the rice products, and a rice product quality safety knowledge base is established according to reasonable database combination.
Wherein, the data acquisition comprises an acquisition mode, an acquisition object and acquisition content. The acquisition mode refers to that data are manually input or imported in batches through automation (a data acquisition device (an intelligent sensor, a quality monitor and the like) is installed in a corresponding link in the production and processing process of the rice product), and the data comprise structured data (such as preset parameters, real-time monitoring values and other numbers) and unstructured data (such as characters, pictures, audio, videos and the like); collecting data of quality safety information of rice products, including data in the production, processing and circulation processes of the rice products; the collection content comprises production and processing related standards and behavior standard, rice product information, enterprise basic information, rice product input information, rice product and food material information, material (including raw materials, semi-finished products, finished products and related products) attributes, quality safety supervision information in the production process, circulation information, supervision organization random inspection data and rice food safety events.
The data storage refers to the collection and processing of material attributes, relevant standards and specifications, food ingredients, material product information, input material information, quality safety information and relevant circulation, sales, health and public opinion data flowing into the market in all links in the rice product planting, storage, processing and circulation and sales processes by adopting a food safety big data sharing and intelligent supervision cloud platform and an intelligent knowledge center platform, and the storage is carried out in a specified storage.
The data arrangement refers to that according to the content of the rice product quality safety knowledge base, a large amount of data collected by the food safety big data sharing and intelligent supervision cloud platform and the intelligent knowledge center platform are extracted, cleaned, completed, grouped, converted and summarized, so that original disordered data are rationalized and systematized, and are arranged to form the rice product quality safety knowledge base to be stored in a food safety big data server.
In a specific embodiment of the present invention, taking the rice industry chain as an example, the food safety knowledge base here is a rice food quality safety knowledge base, and the establishment of the rice food quality safety knowledge base includes classifying the database, then performing database structure design, data acquisition, data storage, and data arrangement, wherein:
first, database classification: classifying databases according to the characteristics of the quality safety information of the rice products, wherein the databases comprise an inspection and detection standard library, a production standard library, a product library, an enterprise basic information library, an input library, a food material library, a food attribute library, a food safety event library and the like; quality safety supervision databases (data collected by intelligent instruments and data imported manually) generated in the production process, circulation databases (information of transport tools and upstream and downstream interfaces), supervision management databases (spot inspection data of supervision authorities) for supervision and management processes, and risk analysis databases;
secondly, designing a database structure: the structural design of the database refers to the composition of core information of quality safety classification of rice products, and the quality safety knowledge base of the rice products is established according to reasonable database combination, the structure of the database is seen by combining with a figure 5, and the figure 5 is a structural schematic diagram of one embodiment of the quality safety knowledge base of the rice products provided by the invention;
thirdly, data acquisition: including acquisition mode, acquisition objects and acquisition content. The acquisition mode refers to that data are manually input or imported in batches through automation (a data acquisition device (an intelligent sensor, a quality monitor and the like) is installed in a corresponding link in the production and processing process of the rice product), and the data comprise structured data (such as preset parameters, real-time monitoring values and other numbers) and unstructured data (such as characters, pictures, audio, videos and the like); collecting data of quality safety information of rice products, including data in the production, processing and circulation processes of the rice products; the collected content comprises relevant standards of inspection and detection (including local standards, industry standards, group standards and national standards), production and processing behavior specifications (including inspection specifications, legal specifications, planting specifications, processing specifications, management specifications, health specifications, technical specifications and the like), rice product information (including product name, product classification, ingredients, product standard code, producer information, factory address, food production license number, production place, quality guarantee period, storage conditions, packaging mode, eating mode, consumer hot line and the like), enterprise basic information (including enterprise name, contact mode, code, factory registration number, registration address, registration organization, affiliated industry, establishment time, business deadline, legal name, business scope and the like), rice product input object information (including input object name, input object type, business scope and the like), rice product input object information (including input object name, input object type, business scope and the like), Residue, dosage form, total content of active ingredients, usage limit, toxicity, manufacturer, address, approval for agricultural production, assay method), rice food ingredients (including material name, main origin, shelf life, energy, protein, fat, carbohydrate, dietary fiber, vitamin B1, vitamin C, vitamin E, iron, zinc, sodium, calcium, potassium, phosphorus, selenium, etc.), material (including raw material, semi-finished product, and related products) attributes (including rice variety, average length, broken rice content, processing accuracy, impurity content, defective grain content, chalkiness, moisture content, yellow rice content, intermixing rate, color and odor, taste score, amylose content, net content, bacteria, eukaryotic microorganisms, etc.), quality safety supervision data during production (including enterprise name, monitoring time, monitoring subject, etc.), quality safety supervision data during production, and quality control data for rice, Monitoring indexes, monitoring results, operators, shift, batch, monitoring methods), circulation data (including enterprise name, ex-warehouse time, product name, ex-warehouse address, transportation means, temperature, humidity, operators, destination, receiving unit, receiving time, batch, shift), monitoring organization sampling inspection data (including enterprise name, product name, detection time, detection indexes, detection values, upper and lower limits of index values, detection methods, detection mechanisms and detection frequency), rice food safety events (including occurrence time, occurrence place, type, index name, triggering symptoms, product sources, accident detailed information, processing results, social influence, hazard level and the like);
fourthly, data storage: the method comprises the steps that a food safety big data sharing and intelligent supervision cloud platform and an intelligent knowledge center platform are adopted, all data of material attributes, relevant standards and specifications, food material components, material product information, input material information and relevant circulation, health and public sentiments after the data flow into the market in all links in the rice product planting, storage and processing processes are collected and processed, and the data are stored into a specified storage;
fifthly, data sorting: according to the content of a rice product quality safety knowledge base, a large amount of data collected by a food safety big data sharing and intelligent supervision cloud platform and an intelligent knowledge center platform are extracted (the data stored in the rice food safety knowledge base is subjected to primary processing, and the data is compared with and fused with the existing data of the rice food safety knowledge base, so that the knowledge base is updated in real time), cleaned (the extracted data is identified, data files are corrected in time, whether the data are consistent or not is checked, repeated or invalid data is deleted, missing data is processed, and the like), completed (incomplete data is supplemented completely, the missing data is stored in time), grouped (the supplemented data is classified and grouped according to different attributes (including physicochemical indexes, nutritional ingredients, additives, hazard factors, production information and the like)), converted (the original data dispersed in the database is shared by the food safety big data and is supervised and intelligently grouped), and converted (the original data is dispersed in the database The platform is converted into standard and reasonable data), the data is collected, the original disordered data is structured and systematized, the standard header of the database is imported and stored in a big food safety data server, and the data is arranged to form a rice product quality safety knowledge base.
In a specific embodiment of the invention, taking the rice industry chain as an example, the food safety knowledge base is a rice food quality safety knowledge base, and the establishment of the rice product quality safety knowledge base is based on a food safety big data sharing and intelligent supervision cloud platform and an intelligent knowledge center platform. The food safety big data sharing and intelligent supervision platform comprises all contents of food safety big data, the knowledge base is used as one of the plates, required information is collected through an artificial or intelligent sensor, and the required information is stored in the server after structured processing. The rice product quality safety knowledge base establishes a detection standard base, a production standard base, a product base, an enterprise basic information base, an input material base, a food material base and a food attribute base; a quality safety supervision database and a circulation database generated in the production process; the supervising management database, the risk analysis database, the food safety event library and other 12 databases in the supervising process can perform functions of inquiring and being called and analyzed by other databases, all information can form correlation through the application of the intelligent knowledge center platform, and the information can be correlated through superior words, inferior words, synonyms, related words and the like. Wherein, the rice food quality safety knowledge base includes:
first, the library of detection criteria is examined: the inspection and detection standard library is a database mainly established by laws and regulations and national standards of various foods, is mainly a national standard of detection limits of some substances in the foods, and information of the standard library comprises a standard number, a standard type, a detection index, an index type, an upper limit value, a lower limit value and a detection method. The standard types comprise national standard, group standard, local standard and enterprise standard; the index types comprise technical requirements, sensory requirements, physical and chemical indexes, microorganism limit, pollutant limit, mycotoxin limit, pesticide residue limit and food additives;
second, production specification standard library: the production standard library is a database collection of the production, processing, management and other specifications of enterprises in the processing and production links, provides technical reference for the actual production and processing processes of the enterprises, and is beneficial to standardizing the production and processing technologies of the enterprises. The production standard library comprises enterprise names, standard serial numbers, classifications, detection links, detection objects, detection indexes, detection results and detection methods; the classification 1 and the classification 2 respectively represent standard types and standard categories, the standard types comprise local standards, industrial standards, group standards and national standards, and the standard categories comprise inspection specifications, legal specifications, planting specifications, processing specifications, management specifications, health specifications and technical specifications;
thirdly, product library: the information of the product database comprises a product name, an ingredient table, a product standard code, a producer, an address, a food production license number, a production place, a quality guarantee period, storage conditions, a packaging mode, an eating mode and a consumer hot line, and the categories 1 and 2 are respectively a raw material type and a product type. Wherein the raw materials include polished rice, brown rice, colored rice, compound rice, and glutinous rice; the product types comprise puffed food, instant food, steamed food and instant food;
fourthly, enterprise basic information base: the basic information base of the enterprise is a database which is mainly established by various rice and related product companies and aims to collect basic information of a plurality of domestic enterprises, supplement and perfect a product base and a standard base. The information of the enterprise basic information base comprises an enterprise name, a classification, a social credit code, a registration number, a registration authority, a service range, an address, a legal person and an enterprise telephone, wherein the classifications 1 and 2 are respectively an enterprise type and an industry. The basic information of the enterprise is divided into a limited responsibility company (foreign legal person control stock), a limited responsibility company (natural person control stock), a limited responsibility company (listed), a limited responsibility company (not listed) and other limited responsibility companies according to types; the method is divided into service industry, wholesale industry, agricultural and sideline product processing industry, business service industry, catering, hotels, agriculture, public welfare organization, transportation industry and the like according to the industry;
fifth, input library: the inputs comprise substances used or added in the planting, storing, processing and circulating processes of the rice products, wherein the agricultural inputs are substances used or added in the planting process of agricultural products, and the commercial inputs comprising fertilizers, pesticides and other pesticides are mainly disinfectants in the storing process and food additives in the production and processing process. The fertilization and pesticide spraying are the most important links in the rice product planting process and are most prone to harm, so that pesticides and fertilizers are strictly used according to relevant national laws and regulations, and the use of food additives and the like is strictly controlled, so that the safety hazard of the rice products can be greatly reduced. The establishment of the input warehouse is beneficial to standardizing the use of the rice product input, and the input warehouse is used together with a product warehouse, a standard warehouse and the like, so that the safety of the rice product is guaranteed. The input material library comprises input material names, classifications, residues, dosage forms, total content of active ingredients, use limit, toxicity, manufacturers, addresses, agricultural production approval numbers and detection methods. The categories 1 and 2 refer to input types and input types, the input types comprise pesticides, fertilizers and other pesticides, and the input types are divided into pesticides, herbicides, bactericides, growth regulators, pesticide auxiliaries, compound fertilizers, phosphate fertilizers, potassium fertilizers and nitrogen fertilizers;
sixth, the rice product food material warehouse: the rice product food material library is a collection of basic information of rice and raw materials of rice products, and the raw materials of the rice products are mainly rice, so that the nutrition safety information of the rice is particularly important. The food material library comprises material name, production place, shelf life, basic nutritional components such as energy, protein, fat, carbohydrate, dietary fiber, main vitamins such as vitamin B1 and vitamin E, and various trace elements such as iron, zinc, sodium, calcium, potassium, selenium and phosphorus;
seventh, rice product food attribute library: the rice product food attribute library is a set of rice product material attributes and distinguishes nutrition safety information from a food material library, and the attribute library mainly aims at basic evaluation indexes of rice products, provides basis for control of finished products and production and processing operation specifications of enterprises, and also provides reference indexes for purchasing of other enterprises. The rice product food attribute library comprises rice types, average length, broken rice content, processing precision, impurity content, imperfect grain content, chalkiness degree, moisture content, yellow grain content, intermixing rate, color and smell, taste evaluation value, amylose content and net content;
eighth, enterprise quality safety supervision database: the enterprise quality safety supervision database is mainly a set of self-monitoring data of an enterprise in the production process, the production and processing processes of the enterprise are recorded by using video monitoring, intelligent sensors and the like, the self-management of the enterprise is facilitated, and a basis is provided for the self-checking and traceability of the enterprise after food safety hazards appear. The quality safety supervision database comprises enterprise names, monitoring time, monitoring objects, monitoring indexes, monitoring results, operating personnel, shift, batch and monitoring methods;
ninth, enterprise circulation database: the enterprise circulation database is a database consisting of the production and transmission processes of enterprise products and the cross information established by departments, and the full-chain hazard tracing of the products, the standard management of the enterprises and the safety of circulation links can be realized by combining the quality supervision database and the like. The circulation database comprises enterprise names, delivery time, product names, delivery addresses, transportation tools, temperature, humidity, operators, destinations, receiving units, receiving time, batches and shifts;
tenth, supervising the management database: the supervision and management database is a database for sampling and detecting products on enterprises or markets by a detection mechanism, is beneficial to strengthening supervision and control of a supervision and management bureau and the like on the products produced by the enterprises, ensures the personal safety of consumers, and can perfect the management and production and processing specifications of the enterprises by being combined with the enterprise quality safety supervision database and the like. The supervision and management database comprises enterprise names, product names, detection time, detection indexes, detection values, upper and lower limits of the index values, a detection method, a detection mechanism and detection frequency;
eleventh, food safety event library: the food safety event library is a database of safety accidents caused by rice and products thereof, provides reference basis for safety analysis of rice food by collecting safety events, researches multiple places and reasons of the safety events of the rice products by the database, and takes related measures to prevent the safety events of the rice products. The food safety event library comprises event names, occurrence time, places, types, index contents, triggering symptoms, product sources, accident detailed information, processing results and social influences.
In a specific embodiment of the present invention, taking the rice food industry chain as an example, the food safety knowledge base here is a rice food quality safety knowledge base, and the corresponding data acquisition refers to introducing food quality safety data into a corresponding system or memory by establishing a standardized header for a specific acquisition object by using a suitable big data acquisition mode. The method comprises the steps of collecting modes, collecting contents, collecting objects and the like;
the collection mode refers to that data is manually input or imported in batches through automation (a data collection device (an intelligent sensor, a quality monitor and the like) is installed in a corresponding link in the rice production and processing process), and the data comprises structured data (such as preset parameters, real-time monitoring values and other numbers) and unstructured data (such as characters, pictures, audio, videos and the like). The rice processing data acquisition mode is shown in table 1:
TABLE 1
The collection objects are rice food quality safety related data and comprise links such as planting, storage, processing, circulation and consumption, and the collection objects of the rice production and processing industrial chain are shown in the following table 2:
TABLE 2
In table 2, the meanings of the relevant keywords are as follows:
person (responsible person): such as seed purchasers, recording personnel, operators, etc.
Machine (equipment, facility, technical parameters of the plant): for example, the technical parameters of the cleaning sieve in the processing and cleaning link are as follows:
the removal rate of shoulder stones, the removal rate of large impurities, the removal rate of small impurities, complete grains in impurities and the like.
Material preparation: the materials include rice seed, paddy, brown rice, and finished rice.
Material properties, product information, etc. need to be collected.
The method comprises the following steps: numbering of laws and regulations, rules and regulations, standard specifications, etc., application ranges, keywords, limits, etc.
Public opinion analysis: after the product flows into the market, the comprehensive popularity, the food safety accident news and the like generated by social networks such as the Xinlang microblog, the WeChat and the Baidu are generated.
The collection content comprises relevant standards for inspection and detection, production and processing behavior specifications, rice product information, enterprise basic information, rice product input information, rice food material components, material attributes, quality safety supervision data in the production process, circulation data, supervision organization sampling inspection data and rice food safety event data.
As a preferred embodiment, referring to fig. 5, fig. 5 is a schematic flowchart of an embodiment of step S2 in fig. 2 provided by the present invention, where the step S2 includes steps S21 to S23, where:
in step S21, an object to be looked up, a related field, and a keyword are acquired;
in step S22, determining a secondary database according to the query result of the object to be queried, the relevant field, and the keyword in the food safety knowledge base, and re-expanding the food safety knowledge base according to the secondary database;
in step S23, a query knowledge-graph associated with the relevant fields in the re-expanded food safety knowledge base is determined using the knowledge-graph.
In the embodiment of the invention, the query knowledge graph of different key words is effectively formed so as to facilitate the query of key indexes in the following.
As a preferred embodiment, referring to fig. 6, fig. 6 is a schematic flowchart of an embodiment of step S22 in fig. 5 provided by the present invention, where the step S22 includes steps S221 to S223, where:
in step S221, an hypernym, a hyponym, a synonym, and a related word of the related field are established according to the target to be searched, the related field, and the keyword;
in step S222, associating the related information of the related fields in the food safety knowledge base based on the hypernyms, hyponyms, synonyms and related words of the related fields;
in step S223, the secondary database is constructed according to the information related to the relevant fields in the food safety knowledge base.
In the embodiment of the invention, a secondary database is formed according to the superior word, the inferior word, the synonym and the related word of the related field, and the food safety knowledge base is effectively expanded.
As a preferred embodiment, referring to fig. 7, fig. 7 is a schematic flowchart of an embodiment of step S23 in fig. 5 provided by the present invention, where the step S23 further includes steps S231 to S232, where:
in step S231, searching in the re-expanded food safety knowledge base using the knowledge map, and determining hypernyms, hyponyms, synonyms, and related words associated with the related fields;
in step S232, the query knowledge graph associated with the related fields is established according to the hypernym, the hyponym, the synonym, and the related word.
In the embodiment of the invention, the related associated words are effectively associated by utilizing the query knowledge graph.
As a preferred embodiment, the step S2 further includes:
and carrying out data fusion on the different query data to form a relative risk value, a node risk index and a comprehensive risk index, wherein:
the relative risk value is expressed by the following formula:
in the above formula, PijRepresenting the relative risk value of the jth index of the ith node; a represents an actual measurement value of the j index of the i node; b represents the limit value of the j index of the ith node; xijThe detection frequency or the period achievement rate of the jth index of the ith node is represented; c represents the actual detection frequency or cycle of the j index of the i nodeA period; d represents a prescribed detection frequency or period of the jth index of the ith node; i represents the ith node, i is 1,2,3, …, m is the number of nodes; j represents j index, j is 1,2,3,4, …, n, n is index number;
the node risk index is expressed by the following formula:
in the above formula, FiRepresenting a risk index of the ith node; [ P ]ij]MAXRepresenting the maximum value of the single index risk index in the ith node; e represents the classification coefficient of the multi-source information, the biological type is e1, the chemical type is e2, and the physical type is e 3; y isiIndicating the index detection rate of the ith node; f represents the number of detection indexes of the ith node; g represents the total number of detection indexes of the ith node;
the composite risk index is expressed by the following formula:
in the above formula, F represents the composite risk index; [ F ]i]MAXRepresenting the maximum risk index of a full chain node; z represents the density of the master nodes of the full chain; h represents the actual number of master nodes in the full chain; k indicates the number of master nodes for the full chain.
In the embodiment of the invention, different indexes of the industrial chain are formed based on multi-aspect collected information, and the risks of the industrial chain are reflected.
In a specific embodiment of the invention, the specified detection frequency d of cadmium in the rice industry chain planting node harvesting sub-node is 3 times/h, the actual detection frequency c is 4 times/h, and the detection value a of a certain time is 0.15mg/kg, then the index detection standard-reaching rate is calculated by using the formula:
wherein, the index limit value b is 0.2mg/kg, and the relative risk value of cadmium in the 'planting' node 'harvesting' child node is calculated by the formula:
wherein, detected a plurality of indexes in the rice industry chain "stores up" node: the aflatoxin risk index is 0.8, the impurity content risk index is 0.6, the moisture content risk index is 0.6, the humidity risk index is 0.4, and the insect pest risk index is 0.2. All indexes of the node are detected according to the standard, Y is equal to 1, and the maximum value [ P ] of the risk index of the single index in the nodeij]MAXIf aflatoxin is 0.8 and is a chemical hazard, and e2 of aflatoxin of a storage node is 1.2, the storage node risk index is calculated by using the following formula:
wherein, if in the risk assessment of the whole chain of the rice industry chain, 5 nodes of planting, storing, processing, selling and circulating are stipulated, wherein 4 nodes of planting, storing, processing and circulating are monitored, the due node number k is 5, the actual main node number h of the whole chain is 4, the risk index of the planting node is 0.6, the risk index of the storing node is 0.8, the risk index of the processing node is 0.6, the risk index of the circulating node is 0.4, and the maximum risk index [ F ] of the whole chain node is 0.4i]MAXWind collecting and storing nodeAnd the risk index is 0.8, the comprehensive risk index of the rice industry chain is calculated by the formula:
Z＝h/k＝4/5＝0.8
as a preferred embodiment, the step S2 further includes: and determining the risk state and the risk grade according to the risk index of the node, the occurrence frequency of the risk and the toxicological effect. In the embodiment of the present invention, it is,
in a specific embodiment of the invention, the food data evaluation module gives a risk evaluation and early warning result judgment by establishing a multi-source information rice product quality safety risk evaluation and early warning model, and the specific implementation steps are as follows:
(1) the relative risk value of the jth index of the ith node is a single-index relative risk value PijIndicating that the risk index of the jth node is FjThe full chain composite risk index is denoted by F. Wherein the relative risk value can be used
Calculating; the risk index of the node can be used
Calculating, wherein e is a multi-source information classification coefficient; the biological type is e1, the chemical type is e2, and the physical type is e 3; f is the composite risk index by
Calculated, z is the full chain master node density by
And (4) calculating.
Note that, for example, when PiWhen j is more than or equal to 1, the single index detection exceeds the standard and is unqualified, and when P is greater than or equal to 1iAnd when j is less than 1, the single index is detected to be qualified. Taking the above specific numerical values as examples, firstIf the specified detection frequency d of cadmium in the rice harvesting link is 3 times/h and the actual detection frequency c is 4 times/h, the index detection frequency standard reaching rate X is calculated to be 1.33 by using the ratio of the two frequencies, and then the index standard reaching rate X is multiplied by the ratio of the index measured value a (0.15mg/kg) and the index limit value b (0.2mg/kg) to obtain PijIs 0.56; then, when the node detects a plurality of indexes (the aflatoxin risk index is 0.8, the impurity content risk index is 0.6, the moisture content risk index is 0.6, the humidity risk index is 0.4, and the insect pest risk index is 0.2), the maximum risk index (the aflatoxin risk index is 0.8) is taken as [ Pij]MAXThe value of (1) is that aflatoxin is a chemical hazard, e2 is taken as 1.2 for aflatoxin of a 'storage' node, and all indexes of the node are detected according to the specification, so that the index detection rate Y is 1, and the node risk index F can be effectively calculated by using the formulaiIs 0.96; finally, the industry chain stipulates 5 nodes of 'planting', 'storing', 'processing', 'selling' and 'circulating', wherein 4 nodes of 'planting', 'storing', 'processing' and 'circulating' are monitored, the risk index of the planting node is 0.6, the risk index of the storing node is 0.8, the risk index of the processing node is 0.6, the risk index of the circulating node is 0.4, the comprehensive risk index F is determined by the product of the maximum risk index of the full-chain node and the reciprocal of the density z of the full-chain main node, wherein the density z of the full-chain main node is the quotient of the actual main node number h (the value is 4) of the full-chain and the main node number k (the value is 5) of the full-chain, the density z of the full-chain main node is calculated to be 0.8, the reciprocal is 1/0.8, the maximum risk index of the full-chain node is the risk index of the storing node 0.8, calculating to obtain a value of 1 for the comprehensive risk index F;
(2) and the rice product quality safety risk assessment and early warning model with multi-source information calculates the input data, and calculates the risk grade according to the relative risk value, the risk index or the comprehensive risk index. And each node adopts a food safety risk analysis table to carry out food safety risk management, risk description, risk quantification, risk grade determination and deviation rectification measures.
According to the risk index of the node and the frequency and the toxicology effect of the risk of the node, the risk grades are classified into four grades of extremely high, medium and low, and the risk state is described as the following table 3.
TABLE 3
And the risk score is used for risk evaluation of product nodes and finished products, and the risk score D is calculated according to the description of the risk state and a certain proportion:
D＝0.5A+0.3B+0.2C
wherein, the risk grades are divided into 1 grade, 2 grade, 3 grade and 4 grade, and the risk grades and the risk description are shown in table 4:
TABLE 4
| Risk score D | ＜0.50 | 0.50～0.74 | 0.75～0.90 | ＞0.90 | |
| Risk description | Low risk | Moderate risk | High risk | Extremely high risk | |
| | Level | 1 | Stage 2 | Grade 3 | 4 stage |
In a specific embodiment of the invention, the multi-source information rice product quality safety risk assessment result is taken as an example, and is associated and fused with the existing database to complete the fusion and combination of knowledge. In the rice food risk assessment, the risk assessment is mainly carried out on detection indexes of all nodes, wherein the detection indexes comprise material attributes of rice raw materials, pesticide residues, chemical fertilizer residues, microorganisms, mycotoxins in the production process, the temperature and humidity of circulation links and the like. When the detection index is input, the detection index is associated with a corresponding database, such as a food attribute library, an inspection standard library and a circulation database, and related feature data is extracted, and the data is fused through the above logic and then is subjected to structuring processing to form a risk database, as shown in fig. 8, where fig. 8 is a schematic flow diagram of an embodiment of the risk database provided by the present invention.
In a specific embodiment of the invention, a keyword 'Wangwang snow cake' is input in the intelligent knowledge center, and the intelligent knowledge center database searches a series of associated information including product information, input information, superior words, inferior words and related word information of the Wangwang snow cake. Wherein the product information of the ham wet by rain comprises a batching table, a product standard code, a producer, an address, a food production license number, a production place, a quality guarantee period, storage conditions, a packaging mode, an eating mode and a consumer hot line; the input information comprises input name, classification, residue, dosage form, total content of effective components, use limit, toxicity, manufacturer, address, agricultural production approval number and detection method. The superior word of the Wangwang snow cake is a rice product and comprises information such as material attributes and product information production specifications, a product information database comprises numerous products such as vermicelli, rice noodles, rice crust, rice noodles and the like, and the products can be continuously associated to form a series of data; the lower-level words of the Wangwang snow cake are investment, circulation and the like, and comprise additives, a transport means and the like, the additives are continuously associated with information of the additives, inspection and detection standards, manufacturers and the like, and the transport means is associated with knowledge information of transporters, temperature, humidity and the like; the related words of the Wangwang snow cake are Wangwang Xianbei, brown rice roll and the like, and similarly, more related knowledge of the quality of the puffed rice can be related and emitted.
As a preferred embodiment, referring to fig. 9 in combination, fig. 9 is a schematic flowchart of an embodiment of step S3 in fig. 2 provided by the present invention, where the step S3 includes steps S31 to S33, where:
in step S31, acquiring parameters of each node of the food industry chain;
in step S32, according to the relevant fields of the food industry chain, querying in the query knowledge graph to determine the standard indexes corresponding to the parameters of each node;
in step S33, early warning and tracing are performed according to the node parameters and the corresponding standard indexes.
In the embodiment of the invention, the knowledge graph is used for effective index comparison, so that early warning tracing is completed.
In a specific embodiment of the invention, the rice product production full-chain risk monitoring system comprises the establishment of a quality safety monitoring model, the establishment of an enterprise basic information database, a quality safety supervision database and a circulation database, and the risk prediction, risk assessment, risk early warning, full-chain traceability and supervision decision analysis based on the rice product full-chain information. Taking rice food as an example, the full-chain information of the rice food comprises planting, storing, processing, circulating and other nodes, and the planting nodes comprise seed purchasing, seedling raising, cultivation, harvesting and other sub-nodes; the storage comprises sub-nodes of transportation, receiving, drying, storage and the like; processing comprises sub-nodes of receiving, cleaning, ridge and valley, rice milling, polishing, packaging and the like; the circulation comprises sub-nodes of transportation, receiving, selling, health and the like;
taking monitoring of heavy metal mercury content in planting nodes as an example, detecting the content of mercury in soil by using a heavy metal detector, comparing detected mercury content data with a detection standard database, and performing risk assessment, wherein the mercury content of a paddy field with a pH value of 6.5-7.5 needs to be less than 0.6mg/kg, if the mercury content of the soil is greater than 0.6mg/kg, the soil is seriously polluted by heavy metal and is not suitable for planting, an alarm is given, a responsible person is informed to take treatment measures on the soil, and if the mercury content of the soil is less than 0.6mg/kg, the soil is less polluted and is suitable for planting. If the detection value is close to 0.6mg/kg for a long time, an early warning is sent out to remind related responsible persons to pay attention to the mercury content value in the subsequent links. Enterprises also need to extract relevant knowledge from the enterprise basic information base and the quality safety supervision management database to reduce the content of mercury;
wherein, if a food safety event occurs, emergency treatment such as recalling and the like should be carried out. And detecting the hazard factors of the retained samples according to the reverse sequence of the industrial chain until a hazard source is found, and determining a hazard sample, a liability subject and a liability person. For example, a rice metallic mercury poisoning event occurs in a certain place, firstly, immediate recall processing is carried out, whether metallic mercury exceeds the standard in the same node batch is searched, and according to the metallic mercury detection index, a bottom layer node and a sub-node are reversely searched until a hazard source is found; after the material is processed in the node, new information can be generated, and the information and the material are completely or partially transmitted to the next node or nodes; the direction of information transfer is a one-way direction from the source of planting to consumption.
According to the harm sample batch of the harm points, forward tracing of an industrial chain is carried out, all non-compliant products are searched, and recalling is carried out. For example, when a certain batch of processing node ridge valley link detects that heavy metal mercury exceeds the standard, the name of a lower-level material, a lower-level receiving unit and time are determined by inquiring a circulation database and the like according to the material flow direction of an inlet node and an outlet node of an industrial chain, the lower-level unit is informed to stop receiving transaction contents and implement recall on all flowed related transaction contents, and after the recall, the metal mercury is detected according to batches, and a damage terminal and a damage source are determined.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for intelligent supervision based on a food safety knowledge base as described above is implemented.
The embodiment of the invention also provides a computing device, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the program, the intelligent supervision method based on the food safety knowledge base is realized.
The invention discloses an intelligent supervision platform, a method and a storage medium based on a food safety knowledge base, wherein firstly, warning information and fire station information are effectively acquired so as to effectively feed back the situations of a warning scene and a fire station; then, based on the alarm information, determining the most appropriate required fire fighting force under the alarm, and based on the information of the fire station, determining the combination of the power of fire fighting of different fire stations; furthermore, by utilizing an intelligent dispatching model, combining information of the fire stations, required fire fighting force and various fire station fighting combinations, determining a corresponding recommended dispatching scheme, and ensuring timeliness and effectiveness of fire fighting dispatching; finally, the deviation is corrected by adopting a manual intervention mode, and the accuracy is ensured.
According to the technical scheme, the problem that fire rescue force is dispatched according to subjective experience and lacks scientificity and systematicness is solved, the fire-fighting dispatch is efficiently and timely completed by combining the alarm information and the fire station information, the alarm data and the fire station data are collected, the dispatch scheme is automatically recommended by the intelligent alarm receiving and processing system by applying a big data analysis technology, and the strength dispatch scientificity and the strength dispatch efficiency are improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. The utility model provides a supervision platform based on food safety knowledge base which characterized in that, includes database construction system, knowledge map construction system and safety supervision system, wherein:
the database construction system is used for acquiring various industrial chain data and establishing a food safety knowledge base according to the various industrial chain data;
the knowledge map construction system is used for integrating query data of keywords to be queried in the food safety knowledge base by adopting a knowledge map;
and the safety supervision system is used for carrying out early warning on the basis of the query result of the knowledge graph.
2. A method for supervising a food safety knowledge base-based supervision platform according to claim 1, wherein the method comprises:
acquiring various industrial chain data, and establishing a food safety knowledge base according to the various industrial chain data;
integrating query data of keywords to be searched in the food safety knowledge base by adopting a knowledge graph;
and carrying out early warning based on the query result of the knowledge graph.
3. The method of claim 2, wherein the establishing a food safety knowledge base according to the plurality of industry chain data comprises:
performing data sorting and data integration on the multiple industrial chain data to generate a plurality of standard libraries, wherein the data sorting comprises at least one of extraction, cleaning, completion, conversion and summarization, and the data integration comprises data fusion on the multiple industrial chain data to generate new information data;
and constructing the food safety knowledge base based on the plurality of standard bases.
4. The food safety knowledge base-based supervision method according to claim 2, wherein the integrating the query data of the keywords to be searched in the food safety knowledge base by using the knowledge graph comprises:
acquiring a target to be searched, related fields and keywords;
determining a secondary database according to the target to be searched, the related field and the keyword and the query result in the food safety knowledge base, and re-expanding the food safety knowledge base according to the secondary database;
and determining a query knowledge graph associated with the relevant fields in the re-expanded food safety knowledge base by using the knowledge graph.
5. The food safety knowledge base-based supervision method according to claim 4, wherein the determining of the secondary database according to the query result of the object to be queried, the related field and the keyword in the food safety knowledge base comprises:
establishing superior words, inferior words, synonyms and related words of the related fields according to the target to be searched, the related fields and the keywords;
associating information related to the relevant fields in the food safety knowledge base based on the hypernyms, hyponyms, synonyms and relevant words of the relevant fields;
and constructing the secondary database according to the related information of the related fields in the food safety knowledge base.
6. The method of claim 4, wherein the determining the query knowledge-graph associated with the relevant field in the re-expanded food safety knowledge-base using a knowledge-graph comprises:
searching in the re-expanded food safety knowledge base by using a knowledge map, and determining superior words, inferior words, synonyms and related words which are associated with the related fields;
and establishing the query knowledge graph associated with the related fields according to the hypernyms, the hyponyms, the synonyms and the related words.
7. The food safety knowledge base-based supervision method according to claim 4, wherein the integrating query data of the keywords to be searched in the food safety knowledge base by using the knowledge graph further comprises:
and carrying out data fusion on the different query data to form a relative risk value, a node risk index and a comprehensive risk index, wherein:
the relative risk value is expressed by the following formula:
in the above formula, PijRepresenting the relative risk value of the jth index of the ith node; a represents an actual measurement value of the j index of the i node; b represents the limit value of the j index of the ith node; xijThe detection frequency or the period achievement rate of the jth index of the ith node is represented; c represents the actual detection frequency or period of the jth index of the ith node; d represents a prescribed detection frequency or period of the jth index of the ith node; i represents the ith node, i is 1,2,3, …, m is the number of nodes; j represents j index, j is 1,2,3,4, …, n, n is index number;
the node risk index is expressed by the following formula:
in the above formula, FiRepresenting a risk index of the ith node; [ P ]ij]MAXRepresenting the maximum value of the single index risk index in the ith node; e represents the classification coefficient of the multi-source information, the biological type is e1, the chemical type is e2, and the physical type is e 3; y isiIndicating the index detection rate of the ith node; f represents the number of detection indexes of the ith node(ii) a g represents the total number of detection indexes of the ith node;
the composite risk index is expressed by the following formula:
in the above formula, F represents the composite risk index; [ F ]i]MAXRepresenting the maximum risk index of a full chain node; z represents the density of the master nodes of the full chain; h represents the actual number of master nodes in the full chain; k indicates the number of master nodes for the full chain.
8. The food safety knowledge base-based supervision method according to claim 7, wherein the integrating query data of the keywords to be searched in the food safety knowledge base by using a knowledge graph further comprises:
and determining the risk state and the risk grade according to the risk index of the node, the occurrence frequency of the risk and the toxicological effect.
9. The food safety knowledge base-based supervision method according to claim 4, wherein the early warning based on the query result of the knowledge graph comprises:
acquiring parameters of each node of a food industry chain;
inquiring in the inquiring knowledge graph according to the relevant fields of the food industry chain, and determining standard indexes corresponding to the parameters of each node;
and performing early warning and tracing according to the node parameters and the corresponding standard indexes.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of food safety knowledge base-based supervision according to any one of claims 2-9.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115470092A (en) * | 2022-11-14 | 2022-12-13 | 成都银光软件有限公司 | Food monitoring and displaying method and system based on multi-task distribution |
| CN115684510A (en) * | 2023-01-04 | 2023-02-03 | 中储粮成都储藏研究院有限公司 | Grain intelligent sampling inspection method |
| CN115759256A (en) * | 2022-11-24 | 2023-03-07 | 中安华邦(北京)安全生产技术研究院股份有限公司 | Method, system, medium and equipment for constructing safety production digital knowledge base |
| CN116167781A (en) * | 2023-04-26 | 2023-05-26 | 宗申·比亚乔佛山摩托车企业有限公司 | Commodity traceability data processing method based on artificial intelligence and cloud platform |
| CN116777273A (en) * | 2023-06-16 | 2023-09-19 | 佛山市朗全电器有限公司 | Grain quality monitoring method and system based on Internet of things |
| CN118134432A (en) * | 2024-04-30 | 2024-06-04 | 元尔科技(无锡)有限公司 | Cooperative supervision method, system, electronic equipment and medium for food safety |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018036239A1 (en) * | 2016-08-24 | 2018-03-01 | 慧科讯业有限公司 | Method, apparatus and system for monitoring internet media events based on industry knowledge mapping database |
| CN110781315A (en) * | 2019-10-16 | 2020-02-11 | 华中农业大学 | Food safety knowledge map and construction method of related intelligent question-answering system |
| CN112116277A (en) * | 2020-10-10 | 2020-12-22 | 华中农业大学 | Full-chain food safety big data acquisition method |
-
2021
- 2021-08-20 CN CN202110962942.8A patent/CN113706002A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018036239A1 (en) * | 2016-08-24 | 2018-03-01 | 慧科讯业有限公司 | Method, apparatus and system for monitoring internet media events based on industry knowledge mapping database |
| CN110781315A (en) * | 2019-10-16 | 2020-02-11 | 华中农业大学 | Food safety knowledge map and construction method of related intelligent question-answering system |
| CN112116277A (en) * | 2020-10-10 | 2020-12-22 | 华中农业大学 | Full-chain food safety big data acquisition method |
Non-Patent Citations (2)
| Title |
|---|
| 吴晓娟 等: "方便米饭安全生产关键监管数据标准化方法的研究", 食品与机械, no. 09, pages 97 - 102 * |
| 陶启 等: "加强大数据在食品安全中应用研究", 中国食品卫生杂志, no. 03, pages 7 - 11 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115470092A (en) * | 2022-11-14 | 2022-12-13 | 成都银光软件有限公司 | Food monitoring and displaying method and system based on multi-task distribution |
| CN115759256A (en) * | 2022-11-24 | 2023-03-07 | 中安华邦(北京)安全生产技术研究院股份有限公司 | Method, system, medium and equipment for constructing safety production digital knowledge base |
| CN115684510A (en) * | 2023-01-04 | 2023-02-03 | 中储粮成都储藏研究院有限公司 | Grain intelligent sampling inspection method |
| CN115684510B (en) * | 2023-01-04 | 2023-04-07 | 中储粮成都储藏研究院有限公司 | Grain intelligent sampling inspection method |
| CN116167781A (en) * | 2023-04-26 | 2023-05-26 | 宗申·比亚乔佛山摩托车企业有限公司 | Commodity traceability data processing method based on artificial intelligence and cloud platform |
| CN116777273A (en) * | 2023-06-16 | 2023-09-19 | 佛山市朗全电器有限公司 | Grain quality monitoring method and system based on Internet of things |
| CN118134432A (en) * | 2024-04-30 | 2024-06-04 | 元尔科技(无锡)有限公司 | Cooperative supervision method, system, electronic equipment and medium for food safety |
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