CN113886716B - Emergency disposal recommendation method and system for food safety emergencies - Google Patents
Emergency disposal recommendation method and system for food safety emergencies Download PDFInfo
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Abstract
The invention relates to an emergency treatment recommendation method and system for food safety emergencies, wherein a constructed hazard factor database, an emergency treatment case database and a standard emergency scheme database are associated to form a comprehensive database as a data set of a recommendation method, and a recommendation algorithm of an emergency scheme is formed by a hazard factor prediction model, a standard evaluation model, a backtracking model and an expert-assisted decision model, so that a decision of relevant data characteristics of the emergencies submitted by a user at the first time and the recommendation of the emergency scheme are carried out, and meanwhile, specific experts are recommended to carry out remote visual diagnosis and decision according to specific decision results, so that the problem that the user cannot obtain the emergency method at the first time in a simple manner when the emergencies are caused by food safety problems in public places and household environments is solved.
Description
Technical Field
The invention belongs to the technical field of food safety, and particularly relates to an emergency treatment recommendation method and system for food safety emergencies.
Background
Food safety emergencies are always highly valued by current health departments, and most of emergency treatment aiming at the food safety emergencies are currently processed in the market in a mode of seeking diagnosis and treatment in hospitals after finding problems or asking and answering doctors through a network on line. These approaches do not allow for timely management of emergency procedures, which may result in even worse results and even life safety hazards for the emergency patient due to the failure to obtain current emergency procedures and measures at the first time. At present, a recommendation system for creating an emergency method for food emergencies has the following problems which are not solved yet: 1. the lack of the database means that the data set for sorting food safety emergencies at the present stage is not complete, wherein the data set comprises the root cause of the occurrence of the food safety emergencies, namely hazard factors, the categories of the hazard factors and the emergencies caused by the specific hazard factors, which are not related in detail; for example, biohazard factors include bacteria, fungi, viruses and natural toxins, and various subtypes exist among them, and the emergent events caused by them are also obviously different to some extent. 2. The development of a data clustering algorithm aims at the treatment mode of food safety emergencies, mostly carries out clustering through symptoms of patients, the selected data characteristics are relatively limited, a common symptom-disease supervision model of a Meiao clinic exists, the probability of a certain person suffering from the disease is diagnosed according to the symptoms and other characteristics, and a prescription which responds is recommended through the symptoms, and the like. In practical situations, although machine learning models and natural language models have been widely used, in specific projects, the selection of features in the data mining process is crucial, and the accuracy and generalization capability of prediction or recommendation are directly influenced. This is one of the main reasons why an accurate model for emergency method recommendation for food safety emergencies has not been developed yet. 3. The recommendation of the expert remote visual emergency method is an unsolvable problem for how to select a proper expert to carry out remote visual diagnosis and decision under the condition that the two problems are not solved. The accurate expert recommendation result depends on the clustering result of the hazard factor category and the name of the specific hazard factor. Therefore, specific experts in the field are recommended, and remote visual emergency method recommendation is given to the serious situation person at the first time.
Therefore, how to obtain an emergency method in a simple manner for a user in the first time when the user is confronted with a food safety emergency is an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide an emergency treatment recommendation method and system for food safety emergencies, and aims to solve the problem that a user cannot obtain an emergency method in a simple mode at the first time when the user faces the food safety emergencies, and the technical problem to be solved by the invention is realized by the following technical scheme:
in one aspect, the invention provides a method for recommending emergency treatment of food safety emergencies, which comprises the following steps:
acquiring description information of user emergency;
similarity analysis is carried out on the description information of the user emergency and data in a hazard factor database by using a hazard factor prediction model to obtain the toxic hazard factor matching degree;
if the matching degree of the poisoning hazard factor is larger than a preset matching degree threshold value, acquiring preliminary poisoning hazard factor information from the hazard factor database;
evaluating and analyzing the preliminary poisoning hazard factor information and data in a standard emergency scheme database by using a standard evaluation model to obtain a recommendation score of a standard emergency scheme;
if the recommended score of the standard emergency scheme is smaller than a preset score threshold value, matching and analyzing the description information of the user emergency and data in an emergency disposal case database by using a backtracking model to obtain a first case matching result;
if the first case matching result is a successful matching case, acquiring target poisoning hazard factor information and a target case treatment scheme according to the first case matching result, wherein the target poisoning hazard factor information comprises a target poisoning hazard factor name and a target poisoning hazard factor category;
and performing decision analysis on the target poisoning hazard factor information and data in an expert information database by using an expert auxiliary decision model to obtain target expert recommendation information.
Preferably, the step of obtaining preliminary toxic hazard factor information from the hazard factor database if the toxic hazard factor matching degree is greater than a preset matching degree threshold further includes:
if the matching degree of the poisoning hazard factor is smaller than a preset matching degree threshold value, the description information of the user emergency and the data in the emergency disposal case database are subjected to matching analysis by using the backtracking model to obtain a second case matching result;
and if the second case matching result is an unsuccessful matching case, feeding back prompt information which cannot provide help to the user.
Preferably, if the recommendation score of the standard emergency scenario is lower than a preset score threshold, the step of performing matching analysis on the description information of the user emergency and the data in the emergency disposal case database by using a backtracking model to obtain a first case matching result further includes:
if the recommendation score of the standard emergency scheme is larger than a preset score threshold value, obtaining a target standard emergency scheme from the standard emergency scheme database;
and performing decision analysis on the preliminary poisoning hazard factor information and the data in the expert information database by using the expert-aided decision model to obtain target expert recommendation information.
Preferably, the step of obtaining the description information of the user emergency includes:
acquiring a description text of an emergency event input by a user;
and analyzing the description text of the emergency and extracting key features to obtain the description information of the user emergency.
Preferably, the step of obtaining the description information of the user emergency further includes:
respectively carrying out standardization processing on data sets required by the hazard factor prediction model, the standard evaluation model, the backtracking model and the expert auxiliary decision model by using a data exploratory analysis model;
and respectively carrying out variable analysis and importance analysis on the hazard factor prediction model, the standard evaluation model, the backtracking model and the expert auxiliary decision model by using a data exploratory analysis model to determine corresponding model output characteristics.
Preferably, the standard evaluation model is created using a multivariate nonlinear regression analysis method.
Preferably, the expert assistant decision model is created by adopting a collaborative filtering recommendation method.
In another aspect, the present invention further provides a system for recommending emergency handling of food safety emergencies, including:
the event acquisition module is configured to acquire description information of the user emergency;
the hazard factor prediction module is configured to perform similarity analysis on the description information of the user emergency and data in a hazard factor database by using a hazard factor prediction model to obtain a toxic hazard factor matching degree;
a hazard information obtaining module configured to obtain preliminary toxic hazard factor information from the hazard factor database if the toxic hazard factor matching degree is greater than a preset matching degree threshold;
the evaluation analysis module is configured to evaluate and analyze the preliminary poisoning hazard factor information and data in a standard emergency scheme database by using a standard evaluation model to obtain a recommendation score of a standard emergency scheme;
the case matching module is configured to perform matching analysis on the description information of the user emergency and data in an emergency disposal case database by using a backtracking model to obtain a first case matching result if the recommendation score of the standard emergency scheme is smaller than a preset score threshold value;
a target poisoning hazard factor information obtaining module configured to obtain target poisoning hazard factor information and a target case treatment scheme according to the first case matching result if the first case matching result is a successful matching case, wherein the target poisoning hazard factor information includes a target poisoning hazard factor name and a target poisoning hazard factor category;
and the expert recommending module is configured to perform decision analysis on the target poisoning hazard factor information and data in the expert information database by using an expert-aided decision model to obtain target expert recommending information.
In still another aspect, the present invention provides an electronic device including: a processor and a memory having computer readable instructions stored thereon that, when executed by the processor, implement a method for emergency treatment recommendation of a food safety emergency as described above.
In yet another aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the emergency treatment recommendation method for food safety emergencies as described above.
The emergency treatment recommendation method for the food safety emergencies comprises the steps of associating the constructed hazard factor database, the emergency treatment case database and the standard emergency scheme database into a comprehensive database serving as a data set of the recommendation method, forming a recommendation algorithm of the emergency scheme through a hazard factor prediction model, a standard evaluation model, a backtracking model and an expert-aided decision-making model, carrying out decision-making on relevant data characteristics of the emergencies submitted by users at the first time and recommending the emergency scheme, and recommending specific experts to carry out remote visual diagnosis and decision-making according to specific decision-making results. Therefore, the emergency treatment recommendation method for the food safety emergency solves the problem that a user cannot acquire an emergency method in a simple manner at the first time due to the emergency caused by the food safety problem in public places and home environments.
Drawings
FIG. 1 is a schematic flow diagram illustrating some embodiments of a method for emergency treatment recommendation of a food safety emergency of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating an alternate embodiment of a method for emergency treatment recommendation of a food safety emergency event in accordance with the present invention;
FIG. 3 is a data flow diagram illustrating a method for emergency treatment recommendation of food safety emergencies according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the association of models of an emergency treatment recommendation method for food safety emergencies according to an embodiment of the present invention;
FIG. 5 is a database design framework diagram of an emergency treatment recommendation method for food safety emergencies implemented by the present invention;
FIG. 6 is a flow chart of a model construction of an emergency treatment recommendation method for food safety emergencies according to an embodiment of the present invention;
fig. 7 is a block diagram of some embodiments of emergency treatment recommendation systems for food safety emergencies in accordance with the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, an embodiment of the present invention provides a method for recommending emergency handling of a food safety emergency, including:
step 100: acquiring description information of user emergency;
in this step, the description information of the user emergency is obtained through the text, voice or the like uploaded by the user.
Step 200: similarity analysis is carried out on the description information of the user emergency and data in a hazard factor database by using a hazard factor prediction model to obtain the toxic hazard factor matching degree;
in the step, similarity analysis is carried out on the description information of the user emergency and data in a hazard factor database through a pre-trained hazard factor prediction model, and the model outputs a matching degree result of each hazard factor so as to obtain possible toxic hazard factors according to the matching degree. It should be noted that the hazard factor database in this embodiment includes biological hazard factor data, radioactive hazard factor data, and chemical hazard factor data.
Step 300: if the matching degree of the poisoning hazard factor is larger than a preset matching degree threshold value, acquiring preliminary poisoning hazard factor information from a hazard factor database;
in the step, possible poisoning hazard factors are screened according to a preset matching degree threshold value so as to obtain preliminary poisoning hazard factor information. It should be noted that the preset matching degree threshold is an empirical value obtained from a training experiment, and is not specifically limited herein.
Step 400: evaluating and analyzing the preliminary poisoning hazard factor information and data in a standard emergency scheme database by using a standard evaluation model to obtain a recommendation score of a standard emergency scheme;
in the step, the data in the standard emergency scheme database is standard emergency method standard data which is arranged by the country/industry, the preliminary poisoning hazard factor information and the data in the standard emergency scheme database are evaluated and analyzed through a pre-trained standard evaluation model, so that the recommendation score of each standard emergency method can be obtained, and the emergency method with high score is preferentially recommended.
Step 500: if the recommended score of the standard emergency scheme is smaller than a preset score threshold value, the description information of the user emergency and data in an emergency disposal case database are subjected to matching analysis by using a backtracking model to obtain a first case matching result;
in this step, if the recommended emergency plan cannot be obtained in the standard emergency plan database, the description information of the user emergency event needs to be matched and analyzed with the data in the emergency disposal case database again to find a matched emergency disposal case. The preset score threshold is an empirical value obtained from a training test, and is not particularly limited herein.
Step 600: if the first case matching result is a successful matching case, acquiring target poisoning hazard factor information and a target case disposal scheme according to the first case matching result, wherein the target poisoning hazard factor information comprises a target poisoning hazard factor name and a target poisoning hazard factor category;
step 700: and performing decision analysis on the target poisoning hazard factor information and data in the expert information database by using an expert-aided decision model to obtain target expert recommendation information.
In the step, the pre-trained expert assistant decision model performs decision analysis on the target poisoning hazard factor information and the data in the expert information database, so that recommended expert information is output.
According to the emergency treatment recommendation method for the food safety emergencies, the built hazard factor database, the emergency treatment case database and the standard emergency scheme database are associated to form a comprehensive database to serve as a data set of the recommendation method, then a recommendation algorithm of the emergency scheme is formed through the hazard factor prediction model, the standard evaluation model, the backtracking model and the expert-assisted decision model, decision-making of relevant data characteristics of the emergencies submitted by users in the first time and recommendation of the emergency scheme are carried out, and meanwhile specific experts are recommended to carry out remote visual diagnosis and decision-making according to specific decision-making results. The emergency treatment recommendation method for the food safety emergency solves the problem that a user cannot acquire an emergency method in a simple mode at the first time due to the emergency caused by the food safety problem in public places and family environments.
In some embodiments, referring to fig. 2, the emergency treatment recommendation method for food safety emergencies of the present invention further includes:
step 500: if the matching degree of the poisoning hazard factors is smaller than a preset matching degree threshold value, performing matching analysis on the description information of the user emergency and the data in the emergency disposal case database by using a backtracking model to obtain a second case matching result;
in this step, if the possible toxic hazard factors cannot be found in the hazard factor database by using the hazard factor prediction model, the description information of the user emergency and the data in the emergency disposal case database need to be matched and analyzed again to find out the matched emergency disposal cases.
Step 900: and if the second case matching result is the unsuccessful matching case, feeding back prompt information which cannot provide help to the user.
In this step, if a matched case cannot be found in the emergency disposal case database, only information that the user cannot provide help can be fed back in time, and the user can choose to go to the hospital directly for medical treatment.
In some embodiments, referring to fig. 2, the emergency treatment recommendation method for food safety emergencies of the present invention further includes:
step 800: if the recommendation score of the standard emergency scheme is larger than a preset score threshold value, obtaining a target standard emergency scheme from a standard emergency scheme database;
in this step, when the recommendation score of the standard emergency scheme is greater than the preset score threshold, the recommended standard emergency scheme is obtained from the standard emergency scheme database, and the preliminary poisoning and hazard factor information is used as the target poisoning and hazard factor information.
Then, go to step 700: and performing decision analysis on the preliminary poisoning hazard factor information and data in an expert information database by using an expert-aided decision model to obtain target expert recommendation information.
Fig. 3 shows a data flow chart of an emergency treatment recommendation method for food safety emergencies according to an embodiment of the present invention, specifically, a recommendation score of an emergency method is obtained by using description information of a user (i.e., a patient) for an emergency, as an input of a hazard factor prediction model, the model outputting a category of toxic hazard factors, and a hazard factor name with a matching degree higher than a preset matching degree threshold (e.g., 85%), and then entering a standard evaluation model. And if the matching degree is lower than a preset matching degree threshold (for example, 85%), further taking the initial event description information as the input of the backtracking model to obtain a case matching result. And finally, taking the category and the name of the hazard factor as the input of an auxiliary decision-making model, and outputting recommended experts for remote visual diagnosis and decision-making. If the user inputs symptoms: "abdominal pain, colic, stomach discomfort", age: and (4) eating food for 18-22 years: the method comprises the following steps that key characteristics of uncooked beef and the like can be accurately predicted through a hazard factor prediction model, the types of hazard factors and similarity scores of hazard factors corresponding to categories can be accurately predicted, ranking is taken as a recommendation basis according to scores, and here, hazard factors of the first three ranking are extracted as recommendations, such as: salmonella, escherichia coli, clostridium perfringens. And further querying a hazard factor database to obtain a corresponding emergency method, and evaluating the emergency method according to the standard scheme scoring model. Finally, standard emergency method recommendation is obtained. Including specific experts obtained by hazard factor query correlation.
In some embodiments, step 100 of the emergency treatment recommendation method for food safety emergencies of the present invention comprises:
acquiring a description text of an emergency event input by a user;
in this step, for the voice input by the user, the voice needs to be converted into a described text.
And analyzing the description text of the emergency and extracting key features to obtain the description information of the user emergency.
In the step, the description text of the emergency can be analyzed by adopting the existing semantic analysis method, and then the key features are extracted according to the analysis result, so that the description information of the user emergency can be obtained. Symptoms such as those caused by clostridium perfringens: acute gastroenteritis type: more than 90% of patients have acute gastroenteritis such as abdominal distention, abdominal pain, diarrhea, etc. Abdominal pain can be manifested as severe colic, diarrhea is usually loose or watery stool, and sometimes mucous stool or bloody stool. Feces have foul smell and a large amount of gas is generated, and diarrhea is treated for several times to more than 10 times. Usually, abdominal distension, nausea and vomiting are caused, and generally, the body temperature is normal or low-grade fever is caused, and the symptoms of infection such as headache, dizziness, general weakness, ache and the like are caused. We performed symptom extraction by word segmentation model, and the result is:
1. abdominal distention
2. Abdominal pain
3. Diarrhea (diarrhea)
4. Nausea
5. Vomiting
6. Low heat
7. Headache (headache)
8. Dizziness (lightheadedness)
9. General weakness
10. Chills and aversion to cold
11. Exhaustion with asthenia
12. Coma
The list of symptoms of (a), converting unstructured data to structured data.
In some embodiments, step 100 of the emergency treatment recommendation method for food safety emergencies of the present invention further includes:
respectively carrying out standardized processing on data sets required by training of the hazard factor prediction model, the standard evaluation model, the backtracking model and the expert auxiliary decision model by using a data exploratory analysis model;
and respectively carrying out variable analysis and importance analysis on the hazard factor prediction model, the standard evaluation model, the backtracking model and the expert auxiliary decision model by using the data exploratory analysis model to determine corresponding model output characteristics.
Specifically, an original data set is provided in a document and picture mode, and a characteristic word is constructed by semantic word segmentation at first, a form is designed, and finally the characteristic word is recorded into a MySQL database; for the text contents of which most fields of data belong to character string types, word segmentation needs to be carried out again to form a word segmentation array, and coding standardization is carried out through an Onehot label; for selection of output characteristics of the model, univariate analysis, multivariate analysis, and importance analysis are required for each characteristic. Thus, the data exploratory analysis model works mainly as follows: 1) process optimization and data analysis reporting on food safety data sets. And converting the unstructured data into structured data, and simultaneously performing univariate analysis and multivariate analysis on the characteristics. 2) Performing importance analysis and variable selection aiming at a data set of the hazard factor prediction model, determining a training set and a testing set of the model, constructing the model, and training and optimizing the model; 3) and (3) performing importance analysis and variable selection aiming at a data set of the backtracking model, determining a training set and a testing set of the model, constructing the model and training and optimizing the model. 4) And performing importance analysis and variable selection aiming at a data set of the standard evaluation model, determining a training set and a testing set of the model, creating the model, and training and optimizing the model. 6) And (3) performing importance analysis and variable selection aiming at a data set of an expert-aided decision model, determining a training set and a test set of the model, constructing the model and training and optimizing the model. The association relationship between the models is specifically shown in fig. 4.
Optionally, in the emergency treatment recommendation method for food safety emergencies according to the embodiment of the present invention, a random forest method is used to create a hazard factor prediction model.
Optionally, in the emergency disposal recommendation method for food safety emergencies according to the embodiment of the present invention, a backtracking model is created by using a decision tree method.
Optionally, in the emergency treatment recommendation method for food safety emergencies according to the embodiment of the present invention, a multiple nonlinear regression analysis method is used to create a standard evaluation model.
Optionally, the expert-aided decision making model is created by adopting a collaborative filtering recommendation method in the embodiment of the present invention.
It should be noted that, in the present embodiment, the hazard factor prediction model, the backtracking model, the standard evaluation model, and the expert-aided decision model may be respectively created by using other machine learning methods, which are not illustrated here.
Fig. 5 shows a database design framework diagram of the emergency treatment recommendation method for food safety emergencies implemented by the present invention. In the embodiment, a comprehensive database is established, namely a regional and localized food safety emergency and emergency treatment standard database is established, and a MySQL database management system is established to realize the functions of recording and retrieving a self-owned data set; and interfacing with other databases of the emergency system of food safety emergencies. The main contents include the following: 1) creation of a biological hazard factor database 2) creation of a radioactive hazard factor database 3) creation of a chemical hazard factor database 4) creation of an emergency case database (i.e., an emergency treatment case database) 5) creation of a standard emergency method specification database (i.e., a standard emergency plan database) 6) creation of an expert information database 7) creation and query of a comprehensive database 8) import, download, preview, editing and other functions of a management end of the database.
The specific environment configuration is as follows:
1. server side hardware environment:
a WEB server: CPU 2E 5-2630 V42.2GHz 10 cores; a 128GB memory; 4 × 900GB 10K HDD hard disk; and (4) a Raid card.
A file server: CPU 2 × E5-2630 v42.2ghz 10 cores CPU, 128GB memory, 2 × 900GB 10K HDD +6 × 8TB 7.2K HDD hard disk; and (4) a Raid card.
2. Server software environment:
operating the system: CentOS 7
A database: MySQL
A development kit: JDK8
A WEB server: tomcat
3. Client hardware environment:
a CPU: above P3. Memory: more than 256M. Hard disk: above 20G. Resolution ratio: recommended use of 1024 x 768 pixels
4. Client software environment:
operating the system: one of Microsoft Windows2000 Professional, Microsoft window2000 server, Microsoft window XP Professional, and Windows 7 Professional is selected. The browser: firefox, Chrome, or versions above.
In the embodiment, the data section is integrally designed to the model end and the final remote visualization end, and the final emergency treatment recommendation of the food safety emergency is used as app software for a user. The whole process is carried out on line in real time after the user inputs and inquires the result and finally the expert is remotely diagnosed on line. When a user faces an emergency, the corresponding hazard factor category, hazard factor name and response emergency method and expert recommendation can be obtained by simply outputting necessary characteristic data required by the system. According to the embodiment, the user can be timely recommended to the emergency method, and unnecessary loss is avoided.
Fig. 6 shows a model construction flow chart of the emergency treatment recommendation method for food safety emergencies according to the embodiment of the present invention, and the construction flows of the hazard factor prediction model, the backtracking model, the standard evaluation model and the expert-aided decision-making model are substantially the same, and the general process is as follows: firstly, extracting data from a corresponding database, performing necessary data filling and data segmentation processing, then performing importance analysis and variable selection on a data set of a corresponding model, determining a training set and a test set of the model, then performing model development, optimizing by using the test set after the model development is completed, adjusting model parameters, finally evaluating the performance of the model, and performing online deployment when the performance of the model meets design requirements.
In this embodiment, GridSearchCV may be used to perform model parameter optimization, and a SHAP visualization tool is used to explain the influence of each feature variable on the prediction result.
On the other hand, referring to fig. 7, an embodiment of the present invention further provides an emergency treatment recommendation system 1 for food safety emergencies, including:
an event obtaining module 10 configured to obtain description information of a user emergency;
the hazard factor prediction module 20 is configured to perform similarity analysis on the description information of the user emergency and data in the hazard factor database by using a hazard factor prediction model to obtain a toxic hazard factor matching degree;
a hazard information obtaining module 30 configured to obtain preliminary toxic hazard factor information from the hazard factor database if the toxic hazard factor matching degree is greater than a preset matching degree threshold;
the evaluation analysis module 40 is configured to perform evaluation analysis on the preliminary poisoning hazard factor information and the data in the standard emergency scheme database by using a standard evaluation model to obtain a recommendation score of the standard emergency scheme;
the case matching module 50 is configured to perform matching analysis on the description information of the user emergency and the data in the emergency disposal case database by using a backtracking model to obtain a first case matching result if the recommendation score of the standard emergency scheme is smaller than a preset score threshold value;
a target poisoning hazard information obtaining module 60 configured to, if the first case matching result is a successful matching case, obtain target poisoning hazard factor information and a target case disposal plan according to the first case matching result, where the target poisoning hazard factor information includes a target poisoning hazard factor name and a target poisoning hazard factor category;
and the expert recommending module 70 is configured to perform decision analysis on the target poisoning hazard factor information and data in the expert information database by using an expert-aided decision model to obtain target expert recommending information.
The specific details of each module of the emergency disposal recommendation system for food safety emergencies are described in detail in the corresponding emergency disposal recommendation method for food safety emergencies, and therefore are not described herein again.
In another aspect, an embodiment of the present invention further provides an electronic device, including: the emergency treatment recommendation system comprises a processor and a memory, wherein computer readable instructions are stored on the memory, and when executed by the processor, the emergency treatment recommendation method for the food safety emergencies is realized.
Specifically, the memory and the processor can be general-purpose memory and processor, which are not limited in particular, and when the processor executes the computer readable instructions stored in the memory, the emergency treatment recommendation method for food safety emergencies described in the above embodiments can be performed.
In still another aspect, 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 emergency treatment recommendation method for a food safety emergency of the above-mentioned embodiment is implemented.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
It should be noted that the above detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than those illustrated or otherwise described herein.
Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may also be oriented in other different ways, such as by rotating it 90 degrees or at other orientations, and the spatially relative descriptors used herein interpreted accordingly.
In the foregoing detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, like numerals typically identify like components, unless context dictates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for recommending emergency treatment of food safety emergencies is characterized by comprising the following steps:
acquiring description information of user emergency;
similarity analysis is carried out on the description information of the user emergency and data in a hazard factor database by using a hazard factor prediction model to obtain the toxic hazard factor matching degree;
if the matching degree of the poisoning hazard factor is larger than a preset matching degree threshold value, acquiring preliminary poisoning hazard factor information from the hazard factor database;
evaluating and analyzing the preliminary poisoning hazard factor information and data in a standard emergency scheme database by using a standard evaluation model to obtain a recommendation score of a standard emergency scheme;
if the recommended score of the standard emergency scheme is smaller than a preset score threshold value, matching and analyzing the description information of the user emergency and data in an emergency disposal case database by using a backtracking model to obtain a first case matching result;
if the first case matching result is a successful matching case, acquiring target poisoning hazard factor information and a target case treatment scheme according to the first case matching result, wherein the target poisoning hazard factor information comprises a target poisoning hazard factor name and a target poisoning hazard factor category;
performing decision analysis on the target poisoning hazard factor information and data in an expert information database by using an expert auxiliary decision model to obtain target expert recommendation information;
wherein the method further comprises:
if the matching degree of the poisoning hazard factor is smaller than a preset matching degree threshold value, the description information of the user emergency and the data in the emergency disposal case database are subjected to matching analysis by using the backtracking model to obtain a second case matching result;
if the second case matching result is an unsuccessful matching case, feeding back prompt information which cannot provide help to the user;
if the recommendation score of the standard emergency scheme is larger than a preset score threshold value, obtaining a target standard emergency scheme from the standard emergency scheme database;
and performing decision analysis on the preliminary poisoning hazard factor information and the data in the expert information database by using the expert-aided decision model to obtain target expert recommendation information.
2. The emergency treatment recommendation method for food safety emergencies according to claim 1, wherein the step of obtaining description information of user emergencies comprises:
acquiring a description text of an emergency event input by a user;
and analyzing the description text of the emergency and extracting key features to obtain the description information of the user emergency.
3. The emergency treatment recommendation method for food safety emergencies according to claim 2, wherein the step of obtaining description information of the user emergencies is further preceded by the steps of:
respectively carrying out standardization processing on data sets required by the hazard factor prediction model, the standard evaluation model, the backtracking model and the expert auxiliary decision model by using a data exploratory analysis model;
and respectively carrying out variable analysis and importance analysis on the hazard factor prediction model, the standard evaluation model, the backtracking model and the expert auxiliary decision model by using a data exploratory analysis model to determine corresponding model output characteristics.
4. The emergency treatment recommendation method for food safety emergencies according to claim 3, characterized in that the standard evaluation model is created by a multivariate nonlinear regression analysis method.
5. The emergency treatment recommendation method for food safety emergencies according to claim 3, characterized in that the expert-aided decision-making model is created by a collaborative filtering recommendation method.
6. An emergency treatment recommendation system for food safety emergencies, comprising:
the event acquisition module is configured to acquire description information of the user emergency;
the hazard factor prediction module is configured to perform similarity analysis on the description information of the user emergency and data in a hazard factor database by using a hazard factor prediction model to obtain a toxic hazard factor matching degree;
a hazard information obtaining module configured to obtain preliminary toxic hazard factor information from the hazard factor database if the toxic hazard factor matching degree is greater than a preset matching degree threshold;
the evaluation analysis module is configured to evaluate and analyze the preliminary poisoning hazard factor information and data in a standard emergency scheme database by using a standard evaluation model to obtain a recommendation score of a standard emergency scheme;
the case matching module is configured to perform matching analysis on the description information of the user emergency and data in an emergency disposal case database by using a backtracking model to obtain a first case matching result if the recommendation score of the standard emergency scheme is smaller than a preset score threshold value;
a target poisoning hazard factor information obtaining module configured to obtain target poisoning hazard factor information and a target case treatment scheme according to the first case matching result if the first case matching result is a successful matching case, wherein the target poisoning hazard factor information includes a target poisoning hazard factor name and a target poisoning hazard factor category;
the expert recommending module is configured to perform decision analysis on the target poisoning hazard factor information and data in an expert information database by using an expert auxiliary decision model to obtain target expert recommending information;
wherein the system is further specifically configured to:
if the matching degree of the poisoning hazard factor is smaller than a preset matching degree threshold value, the description information of the user emergency and the data in the emergency disposal case database are subjected to matching analysis by using the backtracking model to obtain a second case matching result;
if the second case matching result is an unsuccessful matching case, feeding back prompt information which cannot provide help to the user;
if the recommendation score of the standard emergency scheme is larger than a preset score threshold value, obtaining a target standard emergency scheme from the standard emergency scheme database;
and performing decision analysis on the preliminary poisoning hazard factor information and the data in the expert information database by using the expert-aided decision model to obtain target expert recommendation information.
7. An electronic device, comprising: a processor and a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method of emergency treatment recommendation for a food safety emergency as claimed in any one of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for emergency treatment recommendation of a food safety emergency as claimed in any one of claims 1 to 5.
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| CN202111473542.7A CN113886716B (en) | 2021-11-30 | 2021-11-30 | Emergency disposal recommendation method and system for food safety emergencies |
| PCT/CN2022/131071 WO2023098445A1 (en) | 2021-11-30 | 2022-11-10 | Emergency disposal recommendation method and system for emergencies associated with food safety |
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| CN113886716B (en) * | 2021-11-30 | 2022-04-05 | 国家食品安全风险评估中心 | Emergency disposal recommendation method and system for food safety emergencies |
| CN116992160B (en) * | 2023-09-27 | 2023-12-12 | 长春中医药大学 | Clinical care training emergency plan recommendation method for sanitary event |
| CN117350288B (en) * | 2023-12-01 | 2024-05-03 | 浙商银行股份有限公司 | Case matching-based network security operation auxiliary decision-making method, system and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014084438A1 (en) * | 2012-11-30 | 2014-06-05 | 대한민국 (식품의약품안전청장) | Online system and method for managing food safety |
| CN110889635A (en) * | 2019-11-29 | 2020-03-17 | 北京金和网络股份有限公司 | Method for performing emergency drilling on food safety event processing |
| CN111738549A (en) * | 2020-05-21 | 2020-10-02 | 平安国际智慧城市科技股份有限公司 | Food safety risk assessment method, device, equipment and storage medium |
| CN113032493A (en) * | 2019-12-05 | 2021-06-25 | 天津科技大学 | Food safety emergency disposal flow knowledge graph construction method |
| CN113052441A (en) * | 2021-03-09 | 2021-06-29 | 中国安全生产科学研究院 | Emergency food supply risk analysis method |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2541976A1 (en) * | 2002-10-08 | 2004-04-22 | Food Security Systems, L.L.C. | System and method for identifying a food event, tracking the food product, and assessing risks and costs associated with intervention |
| JP2004341983A (en) * | 2003-05-19 | 2004-12-02 | Nec Fielding Ltd | Resident health management system, method vending machine and program |
| CN104408569A (en) * | 2014-11-28 | 2015-03-11 | 东莞中国科学院云计算产业技术创新与育成中心 | Implementation method for plan-based multi-target aid decision-making platform |
| CN106202514A (en) * | 2016-07-21 | 2016-12-07 | 北京邮电大学 | Accident based on Agent is across the search method of media information and system |
| CN106504161A (en) * | 2016-10-13 | 2017-03-15 | 广州市莱曼信息技术有限公司 | A kind of animal health risk management method and system |
| WO2019222471A1 (en) * | 2018-05-17 | 2019-11-21 | Ecolab Usa Inc. | Food safety risk and sanitation compliance tracking |
| US20210035679A1 (en) * | 2019-07-30 | 2021-02-04 | Experian Health, Inc. | Social determinants of health solution |
| CN113886716B (en) * | 2021-11-30 | 2022-04-05 | 国家食品安全风险评估中心 | Emergency disposal recommendation method and system for food safety emergencies |
-
2021
- 2021-11-30 CN CN202111473542.7A patent/CN113886716B/en active Active
-
2022
- 2022-11-10 WO PCT/CN2022/131071 patent/WO2023098445A1/en unknown
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014084438A1 (en) * | 2012-11-30 | 2014-06-05 | 대한민국 (식품의약품안전청장) | Online system and method for managing food safety |
| CN110889635A (en) * | 2019-11-29 | 2020-03-17 | 北京金和网络股份有限公司 | Method for performing emergency drilling on food safety event processing |
| CN113032493A (en) * | 2019-12-05 | 2021-06-25 | 天津科技大学 | Food safety emergency disposal flow knowledge graph construction method |
| CN111738549A (en) * | 2020-05-21 | 2020-10-02 | 平安国际智慧城市科技股份有限公司 | Food safety risk assessment method, device, equipment and storage medium |
| CN113052441A (en) * | 2021-03-09 | 2021-06-29 | 中国安全生产科学研究院 | Emergency food supply risk analysis method |
Non-Patent Citations (3)
| Title |
|---|
| 基于内容分析法的技术专家在食品安全风险管理中的作用及其限度研究;吴建勋;《河南工业大学学报(社会科学版)》;20180331;第14卷(第2期);全文 * |
| 基层突发公共卫生事件应急体系应对能力评估工具的开发、应用与评估模型的探索性研究;赵琦;《中国博士学位论文全文数据库 医药卫生科技辑》;20101215;全文 * |
| 韩继磊.食品生产企业质量安全突发事件预警及应急决策研究.《中国优秀硕士学位论文全文数据库 经济与管理科学辑》.2018, * |
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