CN111340652A - Meal diet safety dynamic supervision method based on big data analysis - Google Patents
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Abstract
The invention discloses a meal safety dynamic supervision method based on big data analysis, which comprises the following steps: the data access is used for accessing catering industry data including government supervision department data, catering unit self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic machine account data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology; and the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score.
Description
Technical Field
The invention belongs to the technical field of big data analysis, and particularly relates to a food safety dynamic supervision system and method based on big data analysis.
Background
People eat as days, with the development of economy and society, the frequency of eating by residents is more and more, and the demand for food and beverage is more and more abundant and various. In order to ensure the healthy development of the catering industry, the supervision department needs to dynamically track the operation conditions of each catering owner in real time, and find out problems or potential problems and need to follow up to solve the problems in time.
In the prior art, the catering industry has annual rating ABC and quantitative rating (face egg) management for the rating of catering enterprises. However, whether annual rating or quantitative grading is adopted, the evaluation is carried out by depending on the subjectivity of the supervisors after the site inspection of the catering enterprises, and the method has great limitation: firstly, a supervisor needs to go to a site for inspection and needs a large amount of manpower for supporting; secondly, the powerful support of high-frequency sampling data is lacked, and the objective is not achieved; thirdly, the method has short-term property and cannot dynamically and continuously reflect the actual state of the enterprise; fourthly, the annual rating and the quantitative grading are only one result display, and the interactivity and the participation sense are lacked; and fifthly, the annual rating and the quantitative grading only reflect one enterprise, and the regional situation cannot be integrally evaluated in the on-point analysis stage.
With the development of scientific and technological means such as the Internet of things, artificial intelligence, cloud video and the like, the problems faced by the supervision of the catering industry can be effectively solved.
Disclosure of Invention
In view of the above technical problems, the present invention is directed to providing a meal plan dynamic supervision method based on big data analysis.
In order to solve the technical problems, the invention adopts the following technical scheme:
one aspect of the embodiments of the present invention provides a method for dynamically supervising food and drink safety based on big data analysis, including:
the data access is used for accessing catering industry data including government supervision department data, catering unit self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic machine account data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology;
and the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score.
Preferably, further comprising:
risk sorting, namely dividing catering enterprises into high-risk units, general-risk units and low-risk units according to the types of risk factors;
generating a supervision instruction, and carrying out 50% random selection of inspection objects, random selection of inspection personnel and spot inspection on high-risk units; carrying out 40% random selection of inspection objects, random selection of inspection personnel and random inspection on general risk units; the low-risk units are subjected to 10% random selection of examination objects and random selection of examination personnel and spot examination.
Preferably, the self-checking data of the catering unit class comprises daily morning check, body temperature measurement, morning check state and card punching data of the staff.
Another aspect of the embodiments of the present invention provides a four-color dynamic food safety code management method based on changes in food safety index, including:
the data access is used for accessing catering industry data including government supervision department data, enterprise self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic ledger data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology;
the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score;
generating a four-color dynamic food safety code, wherein the four-color dynamic food safety code is used for generating a colored two-dimensional code which represents food safety indication of an enterprise according to the obtained safety score of the food enterprise, and a gray code represents the food enterprise which is not evaluated; green code represents a low risk catering enterprise; yellow code represents a middle risk catering enterprise; red numbers represent high-risk catering enterprises.
Preferably, the information in the food safety code comprises the numerical value of the food safety index and the food safety risk condition of the catering enterprise.
Preferably, the self-checking data of the catering unit class comprises daily morning check, body temperature measurement, morning check state and card punching data of the staff.
Preferably, the food security code is displayed to the public and is synchronously displayed on the outsourcing meal ordering platform.
The embodiment of the invention also provides a regional five-color dynamic food security code management method based on the risk classification proportion of catering main bodies, which comprises the following steps:
the data access is used for accessing catering industry data including government supervision department data, enterprise self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic machine account data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology;
the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score;
risk sorting, namely dividing catering enterprises into high-risk units, general-risk units and low-risk units according to the types of risk factors;
the five-color dynamic food security code is generated according to the number and proportion of food security risk enterprises in the region to present food security states in the region, red represents high risk, orange represents high risk, yellow represents medium risk, blue represents low risk, and green represents low risk.
Preferably, the self-checking data of the catering unit class comprises daily morning check, body temperature measurement, morning check state and card punching data of the staff.
The invention has the following beneficial effects that:
(1) the catering department is warned. The responsible personnel of the catering units and the superior administrative departments thereof can master the responsibility implementation condition of the main body of the unit at any time through the index condition, accurately find the problems and reasons, take effective measures in time, prevent the problems from progressing and eliminate the hidden troubles in the sprouting state.
(2) And the supervision efficiency is improved. The supervision department takes the body responsibility fulfillment indexes of catering units in the jurisdiction as reference, so that the enforcement, inspection and supervision and spot inspection strength of the catering units with the red indexes and the yellow indexes is practically increased, the hitting precision is really improved, the supervision efficiency is greatly improved, and the problem of insufficient supervision strength is solved.
(3) Promote the society to cure the diseases together. When the general consumers choose to eat, the related information such as the main responsibility achievement index, the enterprise integrity, the supervision and inspection result and the like of the catering enterprise can be obtained in a convenient mode, the social co-management and co-treatment of food safety management can be comprehensively and deeply participated, and the establishment work of national food safety demonstration cities is promoted together.
Drawings
Fig. 1 is a schematic diagram of a meal plan dynamic supervision method based on big data analysis according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, a method for dynamically supervising food and drink safety based on big data analysis according to an embodiment of the present invention is shown, including the following steps:
and data access, wherein a plurality of interfaces are provided for accessing catering industry data including government regulatory department data, enterprise self-inspection data and social evaluation data, and the data access is dynamically adjusted according to regulatory requirements. For example, government regulatory department data may include palm law enforcement system regulatory inspection data, food operation approval system and 'three-small one-sharing' filing system data, business license registration system data, food-borne disease risk monitoring system data, 'dual-system' detection traceability system data, food tableware supervision and random inspection system data, practitioner electronic health certificate system data, administrative punishment case system data, network meal ordering certificate comparison system data, kitchen waste disposal system data, and the like. The enterprise self-inspection data can comprise electronic standing book data of an intelligent catering management system, main body self-inspection data, video snapshot data of an intelligent sunshine kitchen system, sensing data of an environmental device Internet of things, AI (information input) analysis system data of online catering over-range operation, practitioner training system data, food material distribution informatization traceability system data and the like. The social evaluation data comprises public opinion monitoring system data, third-party collaborating system data, consumption comment data, consumer complaint reporting system data and the like.
And the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score. The risk factors of the food safety index are counted according to the risk factors, for example, under the item of the management license risk item, the risk factors of license non-disclosure, license non-certification management, food operation license exceeding the valid period and beyond-range management are included. The personnel management project item comprises health certificate-free employees, overdue health certificate of employees, food safety knowledge training, regular training of food safety managers, morning check record of employees and risk factors of centralized morning check of employees. The operation behavior items include risk factors that no work clothes are worn during operation on duty, no hat is worn during operation on duty, smoking is performed in a kitchen, a mobile phone is out of specification, a garbage can is not covered, a cutting board in a special room is not disinfected, clothes are not changed for the second time when the garbage can enters the special room, hands are not washed and disinfected when the garbage can enters the special room, and people in the special room do not wear a mask. The method comprises the following steps of carrying out food storage nonstandard, ticket searching and evidence searching without real implementation, unqualified food detection, food risk comparison warning, food spoilage and deterioration, abnormal odor of food, nonstandard additive management, out-of-range, out-of-limit use of food additives, illegal addition of non-edible substances, use of expired food and non-compliance of food label identification and other risk factors under the food quality safety item. The items of the tableware disinfection items comprise risk factors such as no tableware disinfection equipment, abnormal use of the disinfection equipment, unclean tableware, nonstandard tableware cleanness, unqualified tableware spot inspection and the like. Under the item of food safety management, risk factors such as non-establishment of a food safety system, non-in-place self-check of food safety, unclean environmental sanitation, kitchen waste treatment and the like are included. And other list of risk factors such as being penalized by administration, developing food-borne illness, and suspected food-borne illness. The specific evaluation mode is as follows:
(1) the prandial diet security subject responsibility actualization index types are as follows: dividing the current index and the monthly index into two types; the current index is a real-time dynamic index of the main body responsibility implementation condition of the catering unit in the same month, and the monthly index is an index of the main body responsibility implementation condition of the catering unit in a natural month.
(2) The meal diet security subject responsibility enforces the index evaluation rule: setting the monthly total score of 100 points, and adopting a reverse score mode, wherein: a. setting the value of 10 indexes of the video behaviors of the non-normative behaviors as 40 points, and deducting the corresponding value if the non-normative behaviors are found once, and ending the deduction; b. the operating environment and the equipment operation are 10 indexes, the score is set to be 20, and if the abnormal conditions occur, the corresponding score is deducted until the deduction is finished; c. 8 items of the food safety electronic account are set to have a value of 20 points, and if the items are absent or not standardized, the corresponding values are deducted by day until the deduction is finished; d. and 4 indexes are managed daily, the value is set to be 20 points, and if the corresponding requirement is not met, the corresponding value is deducted until the deduction is finished.
Specifically, for example, the operation behavior classes include: smoking (1 minute/time), playing a mobile phone (1 minute/time), not wearing a worker cap (1 minute/time), not wearing a tool (1 minute/time), not changing clothes (1 minute/time) twice, not covering a dustbin (1 minute/time), not intensively checking morning (1 minute/time, checking not less than 1 time per morning), not washing hands and disinfecting (1 minute/time), having mice (1 minute/time), poor environmental sanitation (1 minute/time, snapshotting 8 am every day), and the above data are mainly analyzed through a video AI.
The facility equipment category includes: the mouse baffle is abnormal (1 minute/time, no closing in more than 5 minutes), the ultraviolet lamp is abnormal (1 minute/time), the temperature and humidity in a special room are abnormal (1 minute/time), the temperature and humidity in a warehouse are abnormal (1 minute/time), the temperature and humidity in a sample storage refrigerator are abnormal (1 minute/time), the temperature of a cold toxicity cabinet is abnormal (1 minute/time), and a central thermometer is used (1 minute/time, the detection number is consistent with the number of samples of food).
The food safety standing book class comprises: the method comprises the following steps of missing a rope ticket and a standing book (1 minute/time), missing a disinfection standing book (1 minute/time), missing a food sample keeping standing book (1 minute/time), missing a morning check standing book (1 minute/time), missing an additive standing book (1 minute/time) and missing a kitchen waste standing book (1 minute/time).
The daily management category includes: regular self-check (10 min/min), training of whole staff (10 min/min), health certificate management (10 min/man), additive bulletin (5 min/month).
According to the deduction items, the higher the index score is, the better the responsibility implementation condition of the food security subject in the catering unit is, wherein more than 90 points (including 95 points) are green, and the good result is represented; yellow 90-80 points (including 80) are general; red color below 80 points, indicating poor.
According to the dynamic food safety supervision method based on big data analysis, a big data cloud computing technology is used, data of daily management such as irregular behaviors, site environment and equipment operation thing link sensing, electronic account data such as ticket and evidence, personnel training, self-checking implementation and the like of an AI video analysis of a catering unit in a certain period are comprehensively evaluated, and according to a preset logic, a food safety subject responsibility implementation index of the catering unit is obtained through analysis and serves as an important reference for determining the food safety management level of the catering unit.
In a specific application example, the self-checking data of the catering units comprises daily morning check, body temperature measurement, morning check state and card punching data of employees.
The morning check process may be set as follows: and the face of the employee is just facing the morning check machine identification area for face identification, the face identification is successful, and the information of the employee is displayed. And if the identification is unsuccessful, the page does not display the personnel information of the employee. Or manually selecting and selecting the staff needing morning check from the staff list, clicking a manual selection button, inputting a key search word into a search box, clicking for search, successfully searching out the corresponding staff from the list, and correctly displaying the staff information at the staff information position after clicking the selected staff.
Body temperature detection can be set as follows: the body temperature rifle aims at the forehead, presses body temperature rifle button, uses the body temperature rifle to detect body temperature. The body temperature data is displayed at the temperature of 36-37 ℃, and the treatment suggestion shows 'normal'. The body temperature data is not displayed back at the temperature of 36-37 ℃, and the treatment suggestion shows that the person is off duty and has a rest. And selecting a morning check state according to the actual condition of the staff during morning check, and lighting the corresponding processing opinions after selecting the morning check state.
And after the personnel information and the body temperature data are acquired, clicking a page to submit a morning check button, the employee successfully checks the morning today and prompts the morning check to be successful, and a morning check record list displays the morning check record of the employee. If the user clicks to check again in the morning, the page data is reset successfully, the morning check of the employee is not successful, and the morning check record list does not contain the morning check data of the employee.
In a specific application example, the food safety standing book record specifically includes: disinfecting the standing account, purchasing the standing account, reserving the sample standing account, checking the standing account in the morning, pesticide residue standing account, additive standing account, waste standing account, comprehensive standing account and self-checking and self-evaluating standing account. The Internet of things sensing is provided with early warning, and the types of the early warning specifically comprise hand washing sterilization, shift of the belonging side, unused tableware sterilization facilities, un-started ultraviolet lamps, abnormal refrigerator temperature, abnormal private room temperature and the like.
Example 2
On the basis of embodiment 1, the method for dynamically supervising food and drink safety based on big data analysis further comprises the following steps:
and (4) risk sorting, namely dividing the catering enterprises into high-risk units, general-risk units and low-risk units according to the types of the risk factors.
In the specific implementation process, the risk factors may be classified according to risk categories, such as negative information, important negative information, general negative information, and positive information, which are respectively deducted and added, for example, full score of 100, and boundary lines of 60 and 80. Negation negative information is a serious negation item with the highest weight, and once the serious negation item is directly marked with high risk (for example, 50 points are directly deducted, and positive information fails); emphasis on the next highest weight of negative going information; the forward information is an addend item and is used for repairing and promoting.
Generating a supervision instruction, and carrying out 50% random selection of inspection objects, random selection of inspection personnel and spot inspection on high-risk units; carrying out 40% random selection of inspection objects, random selection of inspection personnel and random inspection on general risk units; the low-risk units are subjected to 10% random selection of examination objects and random selection of examination personnel and spot examination.
Through the risk sequencing and the supervision instruction, the catering units can be scientifically and hierarchically supervised according to the comprehensive data accessed by supervision, the monitoring efficiency is greatly improved, and the supervision cost is reduced.
Example 3
On the basis of obtaining the safety score of the catering enterprise, the safety score can be used for giving certain guidance to users and regulatory agencies. Therefore, the present embodiment further provides a four-color dynamic food safety code management method based on the change of the food safety index on the basis of embodiment 1, including:
the data access is used for accessing catering industry data including government supervision department data, enterprise self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic ledger data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology;
the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score;
generating a four-color dynamic food safety code, wherein the four-color dynamic food safety code is used for generating a colored two-dimensional code which represents food safety indication of an enterprise according to the obtained safety score of the food enterprise, and a gray code represents the food enterprise which is not evaluated; green code represents a low risk catering enterprise; yellow code represents a middle risk catering enterprise; red numbers represent high-risk catering enterprises. The information in the food safety code comprises the numerical value of the food safety index and the food safety risk condition of the catering enterprise. The food safety code is displayed for the public and synchronously displayed on a take-out meal ordering platform. The food safety code is used as the basis for the supervision and inspection of government departments, the inspection frequency of high-risk enterprises is improved, and the objects of special inspection of key inspection are preferentially listed.
Example 4
On the basis of obtaining the safety score of the catering enterprise, the safety score can be used for giving certain guidance to users and regulatory agencies. Therefore, the embodiment further provides a regional five-color dynamic food security code management method based on the risk classification proportion of the catering main body on the basis of the embodiment 1, which includes:
the data access is used for accessing catering industry data including government supervision department data, enterprise self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic machine account data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology;
the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score;
risk sorting, namely dividing catering enterprises into high-risk units, general-risk units and low-risk units according to the types of risk factors;
the five-color dynamic food security code is generated according to the number and proportion of food security risk enterprises in the region to present food security states in the region, red represents high risk, orange represents high risk, yellow represents medium risk, blue represents low risk, and green represents low risk.
It can be understood by those skilled in the art that the technical details which are not mentioned in the embodiment 2, the embodiment 3 and the embodiment 4 but are the same as the embodiment 1 are the same as the process implemented in the embodiment 1.
The catering industry is a work with strong dynamic property, and the catering food safety management is a process work. The catering safety main body responsibility fulfillment index realized by the embodiment of the invention can be used for knowing the current catering unit main responsibility fulfillment status in real time, displaying the comprehensive food safety management level of catering enterprises in a certain period in the past and having a strong promotion effect on strengthening the overall process management of catering food safety. Mainly as follows:
(1) the catering department is warned. The responsible personnel of the catering units and the superior administrative departments thereof can master the responsibility implementation condition of the main body of the unit at any time through the index condition, accurately find the problems and reasons, take effective measures in time, prevent the problems from progressing and eliminate the hidden troubles in the sprouting state.
(2) And the supervision efficiency is improved. The supervision department takes the body responsibility fulfillment indexes of catering units in the jurisdiction as reference, so that the enforcement, inspection and supervision and spot inspection strength of the catering units with the red indexes and the yellow indexes is practically increased, the hitting precision is really improved, the supervision efficiency is greatly improved, and the problem of insufficient supervision strength is solved.
(3) Promote the society to cure the diseases together. When the general consumers choose to eat, the related information such as the main responsibility achievement index, the enterprise integrity, the supervision and inspection result and the like of the catering enterprise can be obtained in a convenient mode, the social co-management and co-treatment of food safety management can be comprehensively and deeply participated, and the establishment work of national food safety demonstration cities is promoted together.
It is to be understood that the exemplary embodiments described herein are illustrative and not restrictive. Although one or more embodiments of the present invention have been described with reference to the accompanying drawings, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.
Claims (9)
1. A dynamic meal and diet safety supervision method based on big data analysis is characterized by comprising the following steps:
the data access is used for accessing catering industry data including government supervision department data, catering unit self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic machine account data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology;
and the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score.
2. The method for dynamically supervising food and drink safety based on big data analysis of claim 1, further comprising:
risk sorting, namely dividing catering enterprises into high-risk units, general-risk units and low-risk units according to the types of risk factors;
generating a supervision instruction, and carrying out 50% random selection of inspection objects, random selection of inspection personnel and spot inspection on high-risk units; carrying out 40% random selection of inspection objects, random selection of inspection personnel and random inspection on general risk units; the low-risk units are subjected to 10% random selection of examination objects and random selection of examination personnel and spot examination.
3. The dynamic meal-diet security supervision method based on big data analysis as claimed in claim 1 or 2, characterized in that the self-test data of catering unit class includes daily morning test, body temperature measurement, morning test status and card punching data of employees.
4. A four-color dynamic food safety code management method based on food safety index change is characterized by comprising the following steps:
the data access is used for accessing catering industry data including government supervision department data, enterprise self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic ledger data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology;
the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score;
generating a four-color dynamic food safety code, wherein the four-color dynamic food safety code is used for generating a colored two-dimensional code which represents food safety indication of an enterprise according to the obtained safety score of the food enterprise, and a gray code represents the food enterprise which is not evaluated; green code represents a low risk catering enterprise; yellow code represents a middle risk catering enterprise; red numbers represent high-risk catering enterprises.
5. The four-color dynamic foodware code management method based on changes in foodware index as claimed in claim 4 wherein the information in the foodware code includes the numerical value of the foodware index, the food safety risk status of the catering enterprise.
6. The four-color dynamic food safety code management method based on changes in food safety indexes as claimed in claim 4, wherein the self-inspection data of the catering unit class comprises daily morning inspection, body temperature measurement, morning inspection state and card punching data of employees.
7. The four-color dynamic food safety code management method based on food safety index change according to claim 4 or 5, characterized in that the food safety code is displayed to the public and synchronously displayed on a take-out meal ordering platform.
8. A management method for a five-color dynamic food security code generated based on a risk classification proportion of a catering main body is characterized by comprising the following steps:
the data access is used for accessing catering industry data including government supervision department data, enterprise self-inspection data and social evaluation data, and comprehensively evaluating data of catering units including AI video analysis non-standard behaviors, site environment and equipment operation thing union sensing, electronic machine account data, personnel training and self-inspection implementation daily management in a certain period by applying a big data cloud computing technology;
the food safety index risk factor statistics is used for carrying out risk classification and quantitative grading on the accessed data according to the food safety risk factor statistics and the established risk model to obtain the food enterprise safety score;
risk sorting, namely dividing catering enterprises into high-risk units, general-risk units and low-risk units according to the types of risk factors;
the five-color dynamic food security code is generated according to the number and proportion of food security risk enterprises in the region to present food security states in the region, red represents high risk, orange represents high risk, yellow represents medium risk, blue represents low risk, and green represents low risk.
9. The regional five-color dynamic food security code management method based on catering main body risk classification proportion as claimed in claim 8, wherein the catering unit class self-check data includes daily morning check, body temperature measurement, morning check state and card punching data of staff.
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