CN110189057A - A kind of the food enterprises risk stratification early warning system and method - Google Patents
A kind of the food enterprises risk stratification early warning system and method Download PDFInfo
- Publication number
- CN110189057A CN110189057A CN201910501177.2A CN201910501177A CN110189057A CN 110189057 A CN110189057 A CN 110189057A CN 201910501177 A CN201910501177 A CN 201910501177A CN 110189057 A CN110189057 A CN 110189057A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- enterprise
- model
- food
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 235000013305 food Nutrition 0.000 title claims abstract description 54
- 238000013517 stratification Methods 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 title claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 22
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 238000009825 accumulation Methods 0.000 claims abstract description 8
- 238000007726 management method Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000013500 data storage Methods 0.000 claims description 5
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 230000002265 prevention Effects 0.000 abstract description 2
- WSFSSNUMVMOOMR-UHFFFAOYSA-N Formaldehyde Chemical compound O=C WSFSSNUMVMOOMR-UHFFFAOYSA-N 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000008821 health effect Effects 0.000 description 2
- 210000004218 nerve net Anatomy 0.000 description 2
- 229920000877 Melamine resin Polymers 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 235000013405 beer Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- JDSHMPZPIAZGSV-UHFFFAOYSA-N melamine Chemical compound NC1=NC(N)=NC(N)=N1 JDSHMPZPIAZGSV-UHFFFAOYSA-N 0.000 description 1
- 239000008267 milk Substances 0.000 description 1
- 210000004080 milk Anatomy 0.000 description 1
- 235000013336 milk Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 231100000252 nontoxic Toxicity 0.000 description 1
- 230000003000 nontoxic effect Effects 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 239000012188 paraffin wax Substances 0.000 description 1
- 231100000614 poison Toxicity 0.000 description 1
- 230000007096 poisonous effect Effects 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 230000005180 public health Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to food enterprise security technology area, specifically a kind of the food enterprises risk stratification early warning system and method, early warning system include sensing layer, accumulation layer, model layer, analysis layer and decision-making level；The sensing layer is provided with data acquisition module, and the accumulation layer is provided with data memory module, and data acquisition module is connect with data memory module；The model layer includes model building module, model building module is connect with data acquisition module and data memory module simultaneously, the analysis layer includes data processing module, the decision-making level includes warning module, the input terminal of data processing module is connect with model building module, and the output end of data processing module is connect with warning module.The present invention by model building module and data processing module to the food enterprises risk stratification, can discovery accurately and timely and early warning the food enterprises abnormal conditions, realization puts prevention first, preemptive Scientific control theory.
Description
Technical field
The present invention relates to food enterprise security technology area, specifically a kind of the food enterprises risk stratification early warning system
And method.
Background technique
Food safety refers to that food is nontoxic, harmless, meets the nutritional requirement that should have, and does not cause any urgency to human health
Property, subacute or chronic hazard, according to promise food safety again define, food safety is that " poisonous and harmful substances are to people in food
The public health problem of body health effect ".
China's food safety affair frequently occurs at present, the things such as paraffin rice, formaldehyde beer, melamine milk powder
Part deepens the trust crisis of food products market increasingly, causes to give a heavy blow to food service industry development, not only constrains China's food
The development of industry influences the international image of country.Meanwhile China's food enterprise is numerous, situation is complicated, and supervision resource is opposite to be had
Limit, it is difficult to supervision comprehensively.
Therefore, for the above status, there is an urgent need to develop a kind of the food enterprises risk stratification early warning system and method,
To overcome the shortcomings of in currently practical application.
Summary of the invention
It is above-mentioned to solve the purpose of the present invention is to provide a kind of the food enterprises risk stratification early warning system and method
The problem of being proposed in background technique.
To achieve the above object, the invention provides the following technical scheme:
A kind of the food enterprises risk stratification early warning system, including sensing layer, accumulation layer, model layer, analysis layer and certainly
Plan layer；The sensing layer is provided with data acquisition module, and the accumulation layer is provided with data memory module, data acquisition module with
Data memory module connection；
The model layer includes model building module, and model building module stores mould with data acquisition module and data simultaneously
Block connection, model building module are used to handle company information data, enterprise's creation data and assessment regular data, utilize
Risk Metrics model carries out reliability and ANALYSIS OF AVAILABILITY ON to sample companies data, rejects unreasonable index, correction model, by wind
Dangerous grade is divided into tetra- grades of A, B, C, D from low height, obtains the food enterprises classification hierarchy model；
The analysis layer includes data processing module, and the decision-making level includes warning module, the input of data processing module
End is connect with model building module, and the output end of data processing module is connect with warning module, and data processing module passes through model
It establishes module and receives specified company information data and enterprise's creation data, and by neural network algorithm to the company information number
It is analyzed and processed according to enterprise creation data, obtains risk class numerical value, be divided into A, B, C, D for the risk class numerical value is corresponding
Level-one in four risk class determines the classification of risks grade of food enterprise.
As a further solution of the present invention: data acquisition module is used to acquire the company information data of enterprise, enterprise's life
Data and assessment regular data are produced, and the company information data collected and enterprise's creation data are passed into data storage mould
Block is stored and forms database.
As a further solution of the present invention: the company information data includes the management position of enterprise, production and operation rule
Mould, food classification, management system, safety management ability and implementation situation data.
As a further solution of the present invention: the data processing module is raw to specified company information data and enterprise
It produces and first establishes tetra- grades of a, b, c, d before data are handled, the corresponding threshold values of each grade.
As a further solution of the present invention: tetra- grades of described a, b, c, d are corresponding in turn to tetra- risks of A, B, C, D etc.
Grade.
As a further solution of the present invention: the warning module carries out early warning for the enterprise excessively high to risk class.
A kind of the food enterprises risk stratification method for early warning, which comprises the following steps:
S1, several enterprises are chosen as sample, passes through the company information data of data collecting module collected enterprise, enterprise's life
Data and assessment regular data are produced, and the company information data collected and enterprise's creation data are passed into data storage mould
Block is stored and is formed database, provides data basis for the analysis of subsequent data；
S2, the assessment regular data collected is passed to by model building module by data acquisition module, data are deposited
It stores up module and the company information data of storage and enterprise's creation data is passed into model building module, model building module is to enterprise
Information data, enterprise's creation data and assessment regular data are handled, using risk Metrics model, to sample companies data into
Row reliability and ANALYSIS OF AVAILABILITY ON, reject unreasonable index, and risk class is divided into A, B, C, D tetra- from low height by correction model
Grade obtains the food enterprises classification hierarchy model；
S3, specified company information data and enterprise's creation data are received by model building module, and pass through nerve net
Network algorithm is analyzed and processed the company information data and enterprise's creation data, obtains risk class numerical value, by the risk etc.
The corresponding level-one being divided into tetra- risk class of A, B, C, D of value of series, determines the classification of risks grade of the food enterprise；
S4, early warning is carried out using the warning module enterprise excessively high to risk class, enterprise is reminded to be rectified and improved.
Compared with prior art, the beneficial effects of the present invention are: the present invention passes through model building module to company information number
It is handled according to, enterprise's creation data and assessment regular data, using risk Metrics model, reliability is carried out to sample companies data
And ANALYSIS OF AVAILABILITY ON, unreasonable index is rejected, risk class is divided into tetra- grades of A, B, C, D from low height, obtained by correction model
To the food enterprises classification hierarchy model, also pass through neural network algorithm to the company information data using data processing module
It is analyzed and processed with enterprise creation data, obtains risk class numerical value, be divided into A, B, C, D tetra- for the risk class numerical value is corresponding
Level-one in a risk class determines the classification of risks grade of food enterprise, can be quasi- to the food enterprises risk stratification
Really timely discovery and early warning the food enterprises abnormal conditions, realization put prevention first, preemptive Scientific control theory,
It is of great significance for improving food safety standard, recovery market confidence, enhancing government authority.
Detailed description of the invention
Fig. 1 is the structural block diagram of the food enterprises risk stratification early warning system.
In figure: 1- data acquisition module, 2- data memory module, 3- model building module, 4- data processing module, 5- are pre-
Alert module.
Specific embodiment
The technical solution of the patent is explained in further detail With reference to embodiment.
The embodiment of this patent is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and is only used for explaining this patent, and cannot be understood as a limitation of this patent.
In the description of this patent, it is to be understood that term " center ", "upper", "lower", "front", "rear", " left side ",
The orientation or positional relationship of the instructions such as " right side ", "vertical", "horizontal", "top", "bottom", "inner", "outside" is based on the figure
Orientation or positional relationship, be merely for convenience of description this patent and simplify description, rather than the device of indication or suggestion meaning or
Element must have a particular orientation, be constructed and operated in a specific orientation, therefore should not be understood as the limitation to this patent.
In the description of this patent, it should be noted that unless otherwise clearly defined and limited, term " installation ", " phase
Even ", " connection ", " setting " shall be understood in a broad sense, for example, it may be being fixedly linked, being arranged, may be a detachable connection, set
It sets, or is integrally connected, is arranged.For the ordinary skill in the art, above-mentioned art can be understood as the case may be
The concrete meaning of language in this patent.
Embodiment 1
Referring to Fig. 1, in the embodiment of the present invention, a kind of the food enterprises risk stratification early warning system, including sensing layer,
Accumulation layer, model layer, analysis layer and decision-making level；The sensing layer is provided with data acquisition module 1, and the accumulation layer is provided with number
According to memory module 2, data acquisition module 1 is connect with data memory module 2, and data acquisition module 1 is used to acquire the enterprise of enterprise
Information data, enterprise's creation data and assessment regular data, and by the company information data collected and enterprise's creation data
It passes to data memory module 2 and is stored and formed database, provide data basis for the analysis of subsequent data, it is same with this
When, the data in data memory module 2 are continuously available update, so that the precision of database be made to be continuously available promotion；
Specifically, the company information data includes management position, production scale, the food of enterprise in the present embodiment
Category not, management system, safety management ability and carry out the data such as situation；
The model layer includes model building module 3, and model building module 3 is deposited with data acquisition module 1 and data simultaneously
It stores up module 2 to connect, the assessment regular data collected is passed to model building module 3, data storage by data acquisition module 1
The company information data of storage and enterprise's creation data are passed to model building module 3 by module 2, analyze mould for subsequent data
The foundation of type provides data basis；The model building module 3 is used to advise company information data, enterprise's creation data and assessment
Then data are handled, and using risk Metrics model, are carried out reliability and ANALYSIS OF AVAILABILITY ON to sample companies data, are rejected unreasonable
Risk class is divided into tetra- grades of A, B, C, D from low height by index, correction model, obtains the food enterprises classification classification
Model；
The analysis layer includes data processing module 4, and the decision-making level includes warning module 5, data processing module 4 it is defeated
Enter end to connect with model building module 3, the output end of data processing module 4 is connect with warning module 5, and data processing module 4 is logical
It crosses model building module 3 and receives specified company information data and enterprise's creation data, and by neural network algorithm to the enterprise
Industry information data and enterprise's creation data are analyzed and processed, and obtain risk class numerical value, by corresponding stroke of the risk class numerical value
Enter the level-one in tetra- risk class of A, B, C, D, the classification of risks grade of food enterprise is determined with this；
Specifically, the data processing module 4 is producing number to specified company information data and enterprise in the present embodiment
According to first establishing tetra- grades of a, b, c, d before being handled, the corresponding threshold values of each grade, tetra- grades of described a, b, c, d according to
Tetra- secondary corresponding A, B, C, D risk class, for determining the classification of risks grade of food enterprise, it is assumed that 4 benefit of data processing module
The risk class numerical value obtained after being handled with neural algorithm specified company information data and enterprise's creation data is F, F
When less than threshold values a, the risk class of the enterprise is A, and when F is greater than threshold values a and is less than threshold values b, the risk class of the enterprise is
For B, and so on；
The warning module 5 carries out early warning for the enterprise excessively high to risk class, and enterprise is reminded to be rectified and improved, it is ensured that food
The risk class of product enterprise is fallen into controllable risk class.
Embodiment 2
A kind of the food enterprises risk stratification method for early warning, comprising the following steps:
S1, several enterprises are chosen as sample, company information data, the enterprise of enterprise is acquired by data acquisition module 1
Creation data and assessment regular data, and the company information data collected and enterprise's creation data are passed into data storage
Module 2 is stored and is formed database, provides data basis for the analysis of subsequent data；
S2, the assessment regular data collected is passed to by model building module 3, data by data acquisition module 1
The company information data of storage and enterprise's creation data are passed to model building module 3, model building module 3 by memory module 2
Company information data, enterprise's creation data and assessment regular data are handled, using risk Metrics model, to sample companies
Data carry out reliability and ANALYSIS OF AVAILABILITY ON, reject unreasonable index, correction model, by risk class from low height be divided into A, B, C,
Tetra- grades of D obtain the food enterprises classification hierarchy model；
S3, specified company information data and enterprise's creation data are received by model building module 3, and pass through nerve net
Network algorithm is analyzed and processed the company information data and enterprise's creation data, obtains risk class numerical value, by the risk etc.
The corresponding level-one being divided into tetra- risk class of A, B, C, D of value of series, determines the classification of risks grade of the food enterprise；
S4, early warning is carried out using the enterprise excessively high to risk class of warning module 5, enterprise is reminded to be rectified and improved.
The above are merely the preferred embodiment of the present invention, it is noted that for those skilled in the art, not
Under the premise of being detached from present inventive concept, several modifications and improvements can also be made, these also should be considered as protection model of the invention
It encloses, these all will not influence the effect and patent practicability that the present invention is implemented.
Claims (7)
1. a kind of the food enterprises risk stratification early warning system, which is characterized in that including sensing layer, accumulation layer, model layer, divide
Analyse layer and decision-making level；The sensing layer is provided with data acquisition module (1), and the accumulation layer is provided with data memory module (2),
Data acquisition module (1) is connect with data memory module (2)；
The model layer includes model building module (3), model building module (3) simultaneously with data acquisition module (1) and data
Memory module (2) connection, model building module (3) are used for company information data, enterprise's creation data and assessment regular data
It is handled, using risk Metrics model, reliability and ANALYSIS OF AVAILABILITY ON is carried out to sample companies data, reject unreasonable index,
Risk class is divided into tetra- grades of A, B, C, D from low height by correction model, obtains the food enterprises classification hierarchy model；
The analysis layer includes data processing module (4), and the decision-making level includes warning module (5), data processing module (4)
Input terminal is connect with model building module (3), and the output end of data processing module (4) is connect with warning module (5), data processing
Module (4) receives specified company information data and enterprise's creation data by model building module (3), and passes through neural network
Algorithm is analyzed and processed the company information data and enterprise's creation data, obtains risk class numerical value, by the risk class
The corresponding level-one being divided into tetra- risk class of A, B, C, D of numerical value, determines the classification of risks grade of food enterprise.
2. the food enterprises risk stratification early warning system according to claim 1, which is characterized in that data acquisition module
(1) for acquiring company information data, enterprise's creation data and the assessment regular data of enterprise, and the enterprise collected is believed
Breath data and enterprise's creation data pass to data memory module (2) and are stored and form database.
3. the food enterprises risk stratification early warning system according to claim 2, which is characterized in that the company information
Data include management position, production scale, food classification, management system, safety management ability and the implementation situation of enterprise
Data.
4. the food enterprises risk stratification early warning system according to claim 1, which is characterized in that the data processing
Module (4) first establishes tetra- grades of a, b, c, d before handling specified company information data and enterprise's creation data, often
The corresponding threshold values of a grade.
5. the food enterprises risk stratification early warning system according to claim 4, which is characterized in that described a, b, c, d
Four grades are corresponding in turn to tetra- risk class of A, B, C, D.
6. the food enterprises risk stratification early warning system according to claim 1, which is characterized in that the warning module
(5) early warning is carried out for the enterprise excessively high to risk class.
7. a kind of the food enterprises risk stratification method for early warning, which comprises the following steps:
S1, several enterprises are chosen as sample, company information data, the enterprise's life of enterprise is acquired by data acquisition module (1)
Data and assessment regular data are produced, and the company information data collected and enterprise's creation data are passed into data storage mould
Block (2) is stored and is formed database, provides data basis for the analysis of subsequent data；
S2, the assessment regular data collected is passed to by model building module (3), data by data acquisition module (1)
The company information data of storage and enterprise's creation data are passed to model building module (3), model foundation mould by memory module (2)
Block (3) handles company information data, enterprise's creation data and assessment regular data, using risk Metrics model, to sample
This business data carries out reliability and ANALYSIS OF AVAILABILITY ON, rejects unreasonable index, risk class is divided by correction model from low height
A, tetra- grades of B, C, D obtain the food enterprises classification hierarchy model；
S3, specified company information data and enterprise's creation data are received by model building module (3), and pass through neural network
Algorithm is analyzed and processed the company information data and enterprise's creation data, obtains risk class numerical value, by the risk class
The corresponding level-one being divided into tetra- risk class of A, B, C, D of numerical value, determines the classification of risks grade of the food enterprise；
S4, early warning is carried out using warning module (5) enterprise excessively high to risk class, enterprise is reminded to be rectified and improved.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910501177.2A CN110189057A (en) | 2019-06-11 | 2019-06-11 | A kind of the food enterprises risk stratification early warning system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910501177.2A CN110189057A (en) | 2019-06-11 | 2019-06-11 | A kind of the food enterprises risk stratification early warning system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110189057A true CN110189057A (en) | 2019-08-30 |
Family
ID=67721201
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910501177.2A Pending CN110189057A (en) | 2019-06-11 | 2019-06-11 | A kind of the food enterprises risk stratification early warning system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110189057A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112258081A (en) * | 2020-11-04 | 2021-01-22 | 成都市食品药品检验研究院 | Food enterprise personalized risk grading management and control method and system |
| CN114140095A (en) * | 2021-12-13 | 2022-03-04 | 成都市食品检验研究院 | System for intelligently generating key control measures of food production and operation enterprises |
| CN115526546A (en) * | 2022-11-08 | 2022-12-27 | 成都市食品检验研究院 | Risk grading intelligent management and control system for food enterprises |
| CN116596300A (en) * | 2023-04-19 | 2023-08-15 | 浙江物芯数科信息产业有限公司 | Intelligent supervision method, system, equipment and medium for food production enterprises |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202472731U (en) * | 2011-12-29 | 2012-10-03 | 钟安清 | Food safety risk early warning system |
| CN109829631A (en) * | 2019-01-14 | 2019-05-31 | 北京中兴通网络科技股份有限公司 | A kind of business risk early warning analysis method and system based on memory network |
-
2019
- 2019-06-11 CN CN201910501177.2A patent/CN110189057A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202472731U (en) * | 2011-12-29 | 2012-10-03 | 钟安清 | Food safety risk early warning system |
| CN109829631A (en) * | 2019-01-14 | 2019-05-31 | 北京中兴通网络科技股份有限公司 | A kind of business risk early warning analysis method and system based on memory network |
Non-Patent Citations (1)
| Title |
|---|
| 李强等: ""我国食品企业生产控制水平的评价方法研究"", 《中国食物与营养》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112258081A (en) * | 2020-11-04 | 2021-01-22 | 成都市食品药品检验研究院 | Food enterprise personalized risk grading management and control method and system |
| CN114140095A (en) * | 2021-12-13 | 2022-03-04 | 成都市食品检验研究院 | System for intelligently generating key control measures of food production and operation enterprises |
| CN115526546A (en) * | 2022-11-08 | 2022-12-27 | 成都市食品检验研究院 | Risk grading intelligent management and control system for food enterprises |
| CN116596300A (en) * | 2023-04-19 | 2023-08-15 | 浙江物芯数科信息产业有限公司 | Intelligent supervision method, system, equipment and medium for food production enterprises |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110189057A (en) | A kind of the food enterprises risk stratification early warning system and method | |
| CN107436277B (en) | The single index data quality control method differentiated based on similarity distance | |
| CN101465059B (en) | Early-warning system for inspecting and judging urban road traffic safety situation | |
| CN105426441B (en) | A kind of automatic preprocess method of time series | |
| CN108765004A (en) | A method of user's electricity stealing is identified based on data mining | |
| CN103177114B (en) | Based on the shift learning sorting technique across data field differentiating stream shape | |
| Smith et al. | Data–driven methodology for signal timing plan development: A computational approach | |
| CN109376613A (en) | Video brainpower watch and control system based on big data and depth learning technology | |
| CN110781873A (en) | Driver fatigue grade identification method based on bimodal feature fusion | |
| CN109670449B (en) | Vehicle illegal judgment method based on vertical snapshot mode | |
| CN110490150A (en) | A kind of automatic auditing system of picture violating the regulations and method based on vehicle retrieval | |
| CN110781872A (en) | Driver fatigue grade recognition system with bimodal feature fusion | |
| CN109766956A (en) | Method for detecting abnormality based on express delivery big data | |
| CN112084716B (en) | Red tide prediction and early warning method based on eutrophication comprehensive evaluation | |
| CN103575279A (en) | Flight path correlating method and system based on fuzzy information | |
| CN111476979A (en) | Intelligent security and stability maintenance method and system based on multi-model analysis | |
| CN112511351A (en) | Security situation prediction method and system based on MES identification data intercommunication system | |
| CN110062393A (en) | A kind of network difference cell intelligent analysis method | |
| CN109166012B (en) | Method and device for classifying users in travel reservation class and pushing information | |
| CN113721299B (en) | Subway security inspection mode configuration management method | |
| CN116614366B (en) | Industrial Internet optimization method and system based on edge calculation | |
| CN107730717B (en) | A kind of suspicious card identification method of public transport based on feature extraction | |
| CN102157061A (en) | Keyword-statistic-based traffic event identifying method | |
| CN116665305A (en) | Method and system for detecting worker behaviors based on computer vision and knowledge graph | |
| CN106127602A (en) | A kind of stealing discrimination method based on yojan outlier algorithm and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190830 |