CN113554366B - Classification supervision method for disinfection product production enterprises and related equipment - Google Patents
Classification supervision method for disinfection product production enterprises and related equipment Download PDFInfo
- Publication number
- CN113554366B CN113554366B CN202111111913.7A CN202111111913A CN113554366B CN 113554366 B CN113554366 B CN 113554366B CN 202111111913 A CN202111111913 A CN 202111111913A CN 113554366 B CN113554366 B CN 113554366B
- Authority
- CN
- China
- Prior art keywords
- index data
- enterprise
- production
- score
- self
- 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.)
- Active
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 194
- 238000004659 sterilization and disinfection Methods 0.000 title claims abstract description 94
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000013210 evaluation model Methods 0.000 claims abstract description 40
- 238000012549 training Methods 0.000 claims abstract description 22
- 230000002159 abnormal effect Effects 0.000 claims description 32
- 238000007689 inspection Methods 0.000 claims description 22
- 230000001954 sterilising effect Effects 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 14
- 230000004044 response Effects 0.000 claims description 12
- 239000002131 composite material Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 2
- 239000000047 product Substances 0.000 description 81
- 238000003062 neural network model Methods 0.000 description 18
- 238000004891 communication Methods 0.000 description 10
- 238000012797 qualification Methods 0.000 description 10
- 238000001514 detection method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
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
- G06Q10/0639—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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- 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
- G06Q10/06395—Quality analysis or management
-
- 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
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Operations Research (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Primary Health Care (AREA)
- Probability & Statistics with Applications (AREA)
- Fuzzy Systems (AREA)
- Manufacturing & Machinery (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Apparatus For Disinfection Or Sterilisation (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present disclosure provides a classification supervision method and related equipment for a disinfection product manufacturing enterprise, the method comprises the steps of firstly obtaining production index data and management index data of the disinfection product manufacturing enterprise; obtaining a production score of the disinfection product manufacturing enterprise through a production index evaluation model obtained through training and based on the production index data; obtaining a management score of the disinfection product manufacturing enterprise through a management index evaluation model obtained through training and based on the management index data; obtaining a comprehensive score of the disinfection product manufacturing enterprise based on the production score and the management score; classifying the disinfection product production enterprises according to the comprehensive scores to obtain classification results, generating supervision information based on the classification results, and finally outputting the supervision information and the classification results, so that the classification accuracy of the enterprises is improved, and the supervision efficiency of the enterprises is further improved.
Description
Technical Field
The disclosure relates to the technical field of classified supervision of manufacturing enterprises, in particular to a classified supervision method and related equipment for a disinfection product manufacturing enterprise.
Background
At present, various disinfection products play irreplaceable roles in the society, and the quality of disinfection product production enterprises directly influences the quality of the disinfection products.
With the development of artificial intelligence and neural networks, part of the classification supervision work of a plurality of disinfection product production enterprises is completed by the neural network model, so that the supervision pressure of relevant departments is greatly reduced, and the influence of artificial subjective factors on supervision fairness is avoided. However, at present, the classification of the disinfection product manufacturing enterprises through the neural network model is single, and generally, all index data of the disinfection product manufacturing enterprises are input into the same network model, and then an evaluation result is output. Therefore, the accuracy of the classification result is not high, enterprises with different qualities cannot be distinguished accurately, and further the supervision effect on the sterilization product production enterprises cannot be achieved.
Disclosure of Invention
In view of the above, the present disclosure is directed to a method and related apparatus for monitoring and classifying disinfection product manufacturers.
Based on the above purpose, the present disclosure provides a method for classified supervision of a sterilization product manufacturing enterprise, comprising:
acquiring production index data and management index data of the disinfection product production enterprise;
obtaining a production score of the disinfection product manufacturing enterprise through a production index evaluation model obtained through training and based on the production index data;
obtaining a management score of the disinfection product manufacturing enterprise through a management index evaluation model obtained through training and based on the management index data;
obtaining a comprehensive score of the disinfection product manufacturing enterprise based on the production score and the management score;
classifying the disinfection product manufacturing enterprises based on the comprehensive scores, generating supervision information based on classification results, and outputting the supervision information and the classification results.
Accordingly, the present disclosure also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, the processor implementing the method for class supervision of a sterilization product manufacturing enterprise as described above when executing the program.
As can be seen from the above, the method for monitoring the disinfection product manufacturing enterprise in a classified manner provided by the present disclosure first obtains production index data and management index data of the disinfection product manufacturing enterprise; obtaining a production score of the disinfection product manufacturing enterprise through a production index evaluation model obtained through training and based on the production index data; obtaining a management score of the disinfection product manufacturing enterprise through a management index evaluation model obtained through training and based on the management index data; obtaining a comprehensive score of the disinfection product manufacturing enterprise based on the production score and the management score; classifying the disinfection product production enterprises according to the comprehensive scores to obtain classification results, generating supervision information based on the classification results, and finally outputting the supervision information and the classification results, so that when the disinfection product production enterprises are supervised in a classification mode through a neural network model, production index data and management index data are graded through different models independently to obtain two non-interfering grading results, and then the two grading results are combined to serve as the basis of enterprise classification, thereby improving the accuracy of enterprise classification and further improving the efficiency of enterprise supervision.
Drawings
In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings needed to be used in the description of the embodiments or related technologies are briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a sterilization product manufacturing enterprise classification monitoring method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an initial neural network model for scoring index data of a sterilization product manufacturing facility, according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart diagram illustrating a method for determining self-adjusting weights according to an embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of a classification monitoring device of a sterilization product manufacturing enterprise according to an embodiment of the present disclosure;
fig. 5 is a schematic view of a specific electronic device according to an embodiment of the disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present disclosure should have a general meaning as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in the embodiments of the disclosure is not intended to indicate any order, quantity, or importance, but rather to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
As described in the background art, currently, the classification of the disinfection product manufacturers through the neural network model is single, and all index data of the disinfection product manufacturers are generally input into the same network model. Because the quantity of index data of disinfection product manufacturing enterprises is huge, and the variety is also more various, only grade it through a neural network model, receive the mutual influence between the data of different kinds of index easily to lead to whole grade accuracy not high. Further, problematic enterprises cannot be sorted out, thereby increasing the supervision workload. The inventor of the present disclosure finds that various index data of a disinfection product production enterprise can be roughly divided into production index data and management index data, then two models for scoring the production index data and the management index data are respectively trained, so that scoring results are more targeted and do not interfere with each other, further the scoring accuracy is improved, then the scoring results of the two models are combined to obtain a comprehensive score of each enterprise, each disinfection product production enterprise can be classified through the comprehensive score, supervision information is generated according to the classification results, finally, the supervision information and the classification results are output, a user can take different supervision measures for different enterprises by referring to the output results, and the supervision efficiency is further improved.
Referring to fig. 1, a schematic flow chart of a classification supervision method of a sterilization product manufacturing enterprise according to an embodiment of the present disclosure includes the following steps:
and S101, acquiring production index data and management index data of the disinfection product production enterprise.
In specific implementation, the production index data of the disinfection product production enterprise comprises various production qualification data of the enterprise and various index data detected by detection equipment arranged on the site of the enterprise. The production qualification data specifically comprises: whether a business license is provided, whether a license for franchise is provided, whether a business license is expired, etc. The production qualification data can be obtained from data disclosed on official platforms of related departments through a web crawler, or an enterprise can actively upload qualification certificates through a supervision system and serve as the production qualification data of the enterprise after artificial intelligent verification. The detection equipment arranged on the enterprise site generally is the detection equipment arranged by a third party or a quality inspection department in a disinfection product production enterprise, the data detected by the equipment can be directly uploaded to a monitoring system through a network, and the detected enterprise can not modify the data of the equipment. The management index data of the disinfection product production enterprise comprises quality data of enterprise personnel, field environment condition data of the enterprise and the like, the management index data can be filled in on a supervision system by the enterprise, a third party organization and a supervision department according to preset inspection items, and then the management index data is obtained from the supervision system.
It should be noted that the division of the production index data and the management index data of the sterilization product manufacturing enterprise is only one division method that can be implemented, and those skilled in the art can supplement and modify the division result as needed. Production indicator data in the present disclosure generally refers to indicator data that can be objectively and quantitatively evaluated, such as some business qualification data (with or without the result being unique), or data that is directly detected by a detection device, such as the concentration of a certain exhaust gas. Management index data in the present disclosure generally refers to index data that is not easily evaluated objectively and quantitatively, for example, how good or bad the environmental sanitation of an enterprise is, and how high or low the quality of the enterprise's job.
And S102, obtaining a production score of the disinfection product production enterprise through a production index evaluation model obtained through training and based on the production index data.
In specific implementation, an initial neural network model is selected from existing neural network models, and optionally, referring to fig. 2, the initial neural network model includes an input layer, a hidden layer, and an output layer, each layer includes a plurality of neurons, and the number of the neurons can be selected as needed, which is not limited herein. Optionally, the activation function of the initial neural network model is a Sigmoid function. After the initial neural network model is determined, the existing initial model is trained through a large amount of production index sample data, and then the production index evaluation model can be obtained. Optionally, during model training, the sample index data of each sterile product manufacturing enterprise is input into the initial neural network model, and then the model outputs the prediction score of each sterile product manufacturing enterprise, and optionally, during training of the initial neural network model, a cross entropy loss function is used to calibrate the trained prediction result. Optionally, the formula of the cross entropy loss function is:
wherein y' represents a test value output by the training model, and y represents a preset target value.
And after the production index data of the disinfection product production enterprises to be classified are obtained, the production index data are input into the production index evaluation model, the production index evaluation model evaluates the production index data of the disinfection product production enterprises, and a production score is output. The production index evaluation model is only completed through production index sample data during training, so that the production index evaluation model can evaluate the production index data more accurately.
In order to further improve the scoring efficiency of the production index evaluation model, in some embodiments, the production score of the sterilized product manufacturing enterprise is obtained through the production index data and based on the production index evaluation model obtained through training; the method comprises the following steps:
averagely dividing the production index data into a plurality of groups of index data sets;
for each group of index data sets, acquiring abnormal index data and non-abnormal index data in the index data sets, determining a value coefficient of the index data sets through a preset value function and based on the abnormal index data and the non-abnormal index data, and determining value ranking of the index data sets in all the index data sets based on the value coefficient;
and inputting all index data sets in preset ranks into the production index evaluation model to obtain the production scores of the disinfection product production enterprises.
During specific implementation, because the quantity of production index data of disinfection product manufacturing enterprises is huge, if directly input all production index data that acquire to production index evaluation model, can influence the efficiency of production index evaluation model undoubtedly, simultaneously, if production index evaluation model has obtained a large amount of worthless information, still can influence the accuracy of scoring result when influencing efficiency undoubtedly. Therefore, before the production index data is input into the production index evaluation model, the production index data is averagely divided into a plurality of groups of index data sets, and optionally, the ratio of the production qualification data in each group of index data sets to the number of the index data detected by the detection equipment arranged on the enterprise site can be basically the same except that the number of the index data in each group of index data sets is ensured to be the same during grouping. Thereby avoiding the influence of the quantity difference among the groups on the judgment of the value coefficient. And then acquiring abnormal index data and non-abnormal index data in each group of index data set. The abnormal index data refers to unqualified production qualification data or substandard index data detected by detection equipment arranged on site. Optionally, each qualification in the production qualification data may be marked as 1, and the qualified one may be marked as 0. Meanwhile, whether each index data detected by the detection equipment arranged on site is in a preset standard range is judged, if yes, the index data is recorded as 1, and if not, the index data is recorded as 0. Then, all index data marked 1 are abnormal data, and all index data marked 0 are non-abnormal data.
After abnormal index data and non-abnormal index data in each group of index data sets are obtained, determining a value coefficient of the index data sets according to the abnormal index data and the non-abnormal index data through a preset value function, and determining value ranking of the index data sets in all the index data sets based on the value coefficient; optionally, the higher the value coefficient, the higher the value rank. And after the value ranks of all the index data sets are obtained, all the index data sets in the preset ranks are input into the production index evaluation model to obtain the production scores of the disinfection product production enterprises. The preset rank can be set according to needs, for example, the production index data of a certain disinfection product production enterprise is averagely divided into 10 groups, then the preset rank is set as the top 5, then only the index data set with the top 5 is input into the production index evaluation model, and the production index evaluation model scores the production index data of the disinfection product production enterprise according to all the index data in the index data set with the top 5. Therefore, the processing efficiency of the production index evaluation model is improved, and meanwhile, the data indexes with low value are eliminated, so that the influence of the data indexes with low value on the evaluation result of the model is avoided, and the grading accuracy of the production index evaluation model is further improved.
In some embodiments, the preset cost function comprises:
wherein,
a cost coefficient representing the ith set of index coefficients,P Bi representing the quantity ratio of abnormal index data in the ith group of index data sets to abnormal index data in all index data sets,P Gi and the quantity ratio of the non-abnormal index data in the ith group of index data sets to the non-abnormal index data in all the index data sets is represented.
It should be noted that, because the application scenario of the present disclosure is to perform classification supervision on disinfection product manufacturing enterprises, the solution of the present disclosure focuses on finding out abnormal enterprises with problems, so when performing evaluation through a network model, production index data is first divided into multiple sets of index data sets, and then the value of each set of index data sets is determined according to the preset value function. Of course, the value of each set of index data may also be determined by another preset value function, for example, simply determining the number ratio of abnormal index data to non-abnormal index data in each set of index data, and using the number ratio to represent the value coefficient.
S103, obtaining a management score of the disinfection product production enterprise through a management index evaluation model obtained through training and based on the management index data.
When the method is specifically implemented, the existing neural network model can be trained through a large amount of management index sample data to obtain a management index evaluation model. The process is similar to the process of training the neural network model in S102, and is not described herein. And then, inputting the management index data of the disinfection product manufacturing enterprises needing to be classified into the management index evaluation model, and outputting the management scores of the disinfection product manufacturing enterprises. It should be noted that, since sample data for training the management index evaluation model may include part of subjective factors, when the management index evaluation model is trained, the difference between the predicted result and the target result may be increased, thereby reducing the robustness of the management index evaluation model.
And S104, obtaining a comprehensive score of the disinfection product manufacturing enterprise based on the production score and the management score.
In specific implementation, after the production score and the management score are respectively obtained through a production index evaluation model and a management index evaluation model, the comprehensive score of the disinfection product production enterprise is obtained according to the production score and the management score.
In some embodiments, the composite score for the sterilization product manufacturing facility is obtained by the following formula:
S = α*S1 +（1-α）*S2 ；
wherein S represents the composite score, S1Represents said production score, S2Indicating that the management score a indicates an auto-adjusting weight.
It should be noted that the above formula is only one specific implementation manner for obtaining the comprehensive score of the disinfection product manufacturing enterprise, and those skilled in the art may select other formulas or methods to obtain the comprehensive score. For example, the production score may be summed directly with the management score to obtain a composite score. Or inputting the production scores and the management scores into a trained neural network model, and then outputting comprehensive scores by the neural network model.
S105, classifying the disinfection product production enterprises based on the comprehensive scores, generating supervision information based on classification results, and outputting the supervision information and the classification results.
In specific implementation, after the comprehensive score of the disinfection product production enterprise is obtained, the disinfection product production enterprise is classified according to the comprehensive score, supervision information is generated according to the classification result, and finally the supervision information and the classification result are output for reference of a supervision department, so that the supervision efficiency of the supervision department is improved.
In some embodiments, the classification result comprises: excellent, qualified, and unqualified enterprises; the supervision information comprises an enterprise inspection period and an enterprise inspection mode; the generating of the supervision information based on the classification result specifically includes:
in response to determining that the classification result is the excellent enterprise, generating the enterprise check period as a first preset period and generating the enterprise check mode as enterprise self-check;
in response to the fact that the classification result is the qualified enterprise, generating the enterprise inspection period as a second preset period and generating the enterprise inspection mode as that the supervision department passes through network inspection;
in response to the fact that the classification result is determined to be the unqualified enterprise, generating that the enterprise inspection period is a third preset period and generating that an enterprise inspection mode is supervision department field inspection;
the first preset period is greater than the second preset period, and the second preset period is greater than the third preset period.
It should be noted that the enterprise inspection period and the enterprise inspection mode are both related to the classification result of the enterprise, and the fewer the enterprise problems are, the longer the inspection period and the looser the inspection mode are, so that the limited supervision power is mainly applied to the enterprise with the problems. Further improving the efficiency of classification supervision of sterilization product manufacturing enterprises.
In order to more realistically reflect the actual situation of the sterilization product manufacturing facility in the composite score obtained, in some embodiments, and with reference to fig. 3, the process of determining the self-adjusting weights includes the steps of:
s201, acquiring the variance of the management index data.
In specific implementation, when the variance of the management index data is obtained, normalization processing may be performed on the obtained management index data first, so that different management index data are in the same numerical range. Then, the variance of all management index data is calculated.
S202, determining initial self-adjusting weight based on the variance and a preset weight coefficient.
In specific implementation, because the management index data is inevitably affected by subjective factors of people, when the management index data is acquired, the reliability of the management index data is judged by the variance, when the variance is large, the obvious difference exists between different management index data of the enterprise, which is closer to the actual situation of a disinfection product production enterprise, each management index data is relatively more objective, and when the variance is small, the obvious difference does not exist between different management index data of the enterprise, namely the management index data are at an average level, which is obviously not in line with the actual management situation of the disinfection product production enterprise. Therefore, to further improve the accuracy of the classification of the enterprise, the self-adjusting weights may be controlled by the variance. Therefore, when the enterprises are classified, the influence of subjective factors on the classification result is further reduced. Optionally, the initial self-adjusting weight is determined by the variance and a preset weight coefficient. The preset weight coefficient is mainly used for processing the variance to enable the initial self-adjusting weight to be in a reasonable range, for example, between 0 and 1, and specific values can be set as required.
S203, determining the self-adjusting weight based on the initial self-adjusting weight and a preset threshold value.
In specific implementation, the enterprise index data of the present disclosure is divided into production index data and management index data, and the production index data belongs to more objective index data, so that the weight occupied by the management score needs to be limited. Namely, the self-adjusting weight is determined according to the initial self-adjusting weight obtained by calculation and a preset threshold value. The preset threshold may be set as needed, and is not limited herein.
In some embodiments, the determining the self-adjustment weight based on the initial self-adjustment weight and a preset threshold specifically includes:
in response to determining that the initial self-adjustment weight is less than a preset threshold, determining the initial self-adjustment weight as the self-adjustment weight;
in response to determining that the initial self-adjustment weight is not less than a preset threshold, determining the preset threshold as the self-adjustment weight.
In specific implementation, when the initial self-adjustment weight is smaller than the preset threshold, it indicates that the weight occupied by the management score at this time is in a reasonable range, and the initial self-adjustment weight may be determined as the self-adjustment weight. When the initial self-adjusting weight is not smaller than a preset threshold, in order to avoid the situation that the weight occupied by the management score is too high, the preset threshold is determined as the self-adjusting weight.
In consideration of the fact that the self-adjustment weight is determined based on the initial self-adjustment weight and the preset threshold, all management scores with the initial self-adjustment weight larger than the preset threshold have the same weight value. For example, the initial self-adjustment weight calculated by enterprise a is 41%, the initial self-adjustment weight calculated by enterprise B is 45%, and if the preset threshold is 40%, the final self-adjustment weights obtained by enterprises a and B are both 40%. This is clearly not fair to business B. Therefore, in some embodiments, the determining the self-adjustment weight based on the initial self-adjustment weight and a preset threshold specifically includes:
and in response to the fact that the initial self-adjusting weight is not smaller than a preset threshold value, mapping the initial self-adjusting weight into a preset numerical range, and determining the initial self-adjusting weight as the self-adjusting weight based on the mapped initial self-adjusting weight and the preset threshold value.
In specific implementation, when the initial self-adjusting weight is not less than the preset threshold, the initial self-adjusting weight is mapped into a preset numerical range, the preset numerical range can be set as required, and the maximum value in the preset numerical range is less than the initial self-adjusting weight. Alternatively, the initial self-adjustment weight may be mapped into a preset numerical range by multiplying the initial self-adjustment weight by a preset attenuation coefficient. And then adding the mapped initial self-adjusting weight to a preset threshold value, and using the added sum as the self-adjusting weight. For example, if the calculated initial self-adjustment weight of enterprise a is 41%, the calculated initial self-adjustment weight of enterprise B is 45%, and the preset threshold is 40% and the preset attenuation coefficient is 0.1, then the self-adjustment weight of enterprise a is 40% +4.1% and the self-adjustment weight of enterprise B is 40% + 4.5%. Thereby avoiding that the self-tuning weights last determined by enterprises a and B are the same. The accuracy of enterprise classification is further improved.
The classified supervision method for the disinfection product production enterprises, provided by the disclosure, comprises the steps of firstly obtaining production index data and management index data of the disinfection product production enterprises; obtaining a production score of the disinfection product manufacturing enterprise through a production index evaluation model obtained through training and based on the production index data; obtaining a management score of the disinfection product manufacturing enterprise through a management index evaluation model obtained through training and based on the management index data; obtaining a comprehensive score of the disinfection product manufacturing enterprise based on the production score and the management score; classifying the disinfection product production enterprises according to the comprehensive scores to obtain classification results, generating supervision information based on the classification results, and finally outputting the supervision information and the classification results, so that when the disinfection product production enterprises are supervised in a classification mode through a neural network model, production index data and management index data are graded through different models independently to obtain two non-interfering grading results, and then the two grading results are combined to serve as the basis of enterprise classification, thereby improving the accuracy of enterprise classification and further improving the efficiency of enterprise supervision.
It should be noted that the method of the embodiments of the present disclosure may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the devices may interact with each other to complete the method.
It should be noted that the above describes some embodiments of the disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same inventive concept, the invention also provides a classification monitoring device of a disinfection product manufacturing enterprise corresponding to the method of any embodiment.
Referring to fig. 4, the classification supervision apparatus of the sterilization product manufacturing enterprise includes:
an obtaining module 301, configured to obtain production index data and management index data of the disinfection product manufacturing enterprise;
a production scoring module 302 for obtaining a production score of the disinfection product manufacturing enterprise through a production index evaluation model obtained through training and based on the production index data;
the management scoring module 303 is used for obtaining a management score of the disinfection product manufacturing enterprise through a management index evaluation model obtained through training and based on the management index data;
a comprehensive scoring module 304, which obtains a comprehensive score of the disinfection product manufacturing enterprise based on the production score and the management score;
and a classification supervision module 305 for classifying the disinfection product manufacturing enterprises based on the comprehensive scores, generating supervision information based on classification results, and outputting the supervision information and the classification results.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations of the present disclosure.
The device of the above embodiment is used for implementing the classification supervision method of the disinfection product manufacturing enterprise corresponding to any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above embodiments, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the program, the method for supervising and classifying of a sterilization product manufacturing enterprise according to any of the above embodiments is implemented.
Fig. 5 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device of the above embodiment is used for implementing the classification supervision method of the disinfection product manufacturing enterprise corresponding to any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the present disclosure, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the present disclosure, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present disclosure are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that the embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The disclosed embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalents, improvements, and the like that may be made within the spirit and principles of the embodiments of the disclosure are intended to be included within the scope of the disclosure.
Claims (7)
1. A method of classified supervision of a sterilization product manufacturing enterprise, comprising:
acquiring production index data and management index data of the disinfection product production enterprise;
obtaining a production score of the disinfection product manufacturing enterprise through a production index evaluation model obtained through training and based on the production index data;
obtaining a management score of the disinfection product manufacturing enterprise through a management index evaluation model obtained through training and based on the management index data;
obtaining a comprehensive score of the disinfection product manufacturing enterprise based on the production score, the management score and the self-adjusting weight; wherein the process of determining the self-adjusting weights comprises:
acquiring the variance of the management index data;
determining an initial self-adjusting weight based on the variance and a preset weight coefficient;
determining the self-adjusting weight based on the initial self-adjusting weight and a preset threshold value;
classifying the disinfection product manufacturing enterprises based on the comprehensive scores, generating supervision information based on classification results, and outputting the supervision information and the classification results;
obtaining a production score of the disinfection product manufacturing enterprise through the production index data and based on a production index evaluation model obtained through training; the method comprises the following steps:
averagely dividing the production index data into a plurality of groups of index data sets;
for each group of index data sets, acquiring abnormal index data and non-abnormal index data in the index data sets, determining a value coefficient of the index data sets through a preset value function and based on the abnormal index data and the non-abnormal index data, and determining value ranking of the index data sets in all the index data sets based on the value coefficient;
inputting all index data sets in a preset rank into the production index evaluation model to obtain a production score of the disinfection product production enterprise;
wherein the determining a cost coefficient of the set of metric data based on the abnormal metric data and the non-abnormal metric data by a preset cost function comprises:
and judging the quantity ratio of the abnormal index data to the non-abnormal index data in each group of index data set, and expressing the value coefficient by using the quantity ratio.
2. The method of claim 1, wherein the composite score of the sterilization product manufacturing facility is obtained by the following formula:
S =α*S1 +（1-α）*S2；
wherein S represents the composite score, S1Represents said production score, S2Indicating that the management score a indicates an auto-adjusting weight.
3. The method according to claim 1, wherein the determining the self-adjustment weight based on the initial self-adjustment weight and a preset threshold specifically comprises:
in response to determining that the initial self-adjustment weight is less than a preset threshold, determining the initial self-adjustment weight as the self-adjustment weight;
in response to determining that the initial self-adjustment weight is not less than a preset threshold, determining the preset threshold as the self-adjustment weight.
4. The method according to claim 1, wherein the determining the self-adjustment weight based on the initial self-adjustment weight and a preset threshold specifically comprises:
in response to determining that the initial self-adjustment weight is not less than a preset threshold, mapping the initial self-adjustment weight to a preset numerical range, and determining the self-adjustment weight based on the mapped initial self-adjustment weight and the preset threshold.
5. The method of claim 1, wherein the preset cost function comprises:
wherein,IV i a cost coefficient representing the ith set of index coefficients,P Bi representing the quantity ratio of abnormal index data in the ith group of index data sets to abnormal index data in all index data sets,P Gi and the quantity ratio of the non-abnormal index data in the ith group of index data sets to the non-abnormal index data in all the index data sets is represented.
6. The method of claim 1, wherein the classification result comprises: excellent, qualified, and unqualified enterprises; the supervision information comprises an enterprise inspection period and an enterprise inspection mode; the generating of the supervision information based on the classification result specifically includes:
in response to determining that the classification result is the excellent enterprise, generating the enterprise check period as a first preset period and generating the enterprise check mode as enterprise self-check;
in response to the fact that the classification result is the qualified enterprise, generating the enterprise inspection period as a second preset period and generating the enterprise inspection mode as that the supervision department passes through network inspection;
in response to the fact that the classification result is determined to be the unqualified enterprise, generating that the enterprise inspection period is a third preset period and generating that an enterprise inspection mode is supervision department field inspection;
the first preset period is greater than the second preset period, and the second preset period is greater than the third preset period.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, the processor implementing the method of any one of claims 1 to 6 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111111913.7A CN113554366B (en) | 2021-09-23 | 2021-09-23 | Classification supervision method for disinfection product production enterprises and related equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111111913.7A CN113554366B (en) | 2021-09-23 | 2021-09-23 | Classification supervision method for disinfection product production enterprises and related equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113554366A CN113554366A (en) | 2021-10-26 |
| CN113554366B true CN113554366B (en) | 2021-12-14 |
Family
ID=78106539
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111111913.7A Active CN113554366B (en) | 2021-09-23 | 2021-09-23 | Classification supervision method for disinfection product production enterprises and related equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113554366B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101398836A (en) * | 2008-11-11 | 2009-04-01 | 丘雷 | Search ordering method based on subjectivity and objectivity index and weight allocation |
| CN103235983A (en) * | 2013-04-23 | 2013-08-07 | 浪潮集团山东通用软件有限公司 | Multi-dimensional automatic supplier evaluation method |
| CN106372952A (en) * | 2016-11-14 | 2017-02-01 | 北京创业公社征信服务有限公司 | Objective and subjective weight determining multi-model compositional verification-based enterprise credit assessment method and system |
| CN109377082A (en) * | 2018-11-14 | 2019-02-22 | 国网四川省电力公司经济技术研究院 | County Power Plant integrated evaluating method and device under fine-grained management background |
| CN109409628A (en) * | 2018-08-17 | 2019-03-01 | 国网浙江省电力有限公司 | Acquisition terminal production firm evaluation method based on metering big data Clustering Model |
| CN111340652A (en) * | 2020-02-27 | 2020-06-26 | 杭州祐全科技发展有限公司 | Meal diet safety dynamic supervision method based on big data analysis |
| CN111932267A (en) * | 2020-07-27 | 2020-11-13 | 中国工商银行股份有限公司 | Enterprise financial service risk prediction method and device |
| CN112819341A (en) * | 2021-02-04 | 2021-05-18 | 江苏卓易信息科技股份有限公司 | Scientific and technological type small and micro enterprise credit risk assessment method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPWO2006004131A1 (en) * | 2004-07-05 | 2008-07-31 | 株式会社アイ・ピー・ビー | Company evaluation device, company evaluation program and company evaluation method |
| CN107944761A (en) * | 2017-12-18 | 2018-04-20 | 广东广业开元科技有限公司 | Early warning and monitoring analysis method is complained based on artificial intelligence protection of consumers' rights index enterprise |
| CN112884590A (en) * | 2021-01-26 | 2021-06-01 | 浙江工业大学 | Power grid enterprise financing decision method based on machine learning algorithm |
| CN113360358B (en) * | 2021-06-25 | 2022-05-27 | 杭州优云软件有限公司 | Method and system for adaptively calculating IT intelligent operation and maintenance health index |
-
2021
- 2021-09-23 CN CN202111111913.7A patent/CN113554366B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101398836A (en) * | 2008-11-11 | 2009-04-01 | 丘雷 | Search ordering method based on subjectivity and objectivity index and weight allocation |
| CN103235983A (en) * | 2013-04-23 | 2013-08-07 | 浪潮集团山东通用软件有限公司 | Multi-dimensional automatic supplier evaluation method |
| CN106372952A (en) * | 2016-11-14 | 2017-02-01 | 北京创业公社征信服务有限公司 | Objective and subjective weight determining multi-model compositional verification-based enterprise credit assessment method and system |
| CN109409628A (en) * | 2018-08-17 | 2019-03-01 | 国网浙江省电力有限公司 | Acquisition terminal production firm evaluation method based on metering big data Clustering Model |
| CN109377082A (en) * | 2018-11-14 | 2019-02-22 | 国网四川省电力公司经济技术研究院 | County Power Plant integrated evaluating method and device under fine-grained management background |
| CN111340652A (en) * | 2020-02-27 | 2020-06-26 | 杭州祐全科技发展有限公司 | Meal diet safety dynamic supervision method based on big data analysis |
| CN111932267A (en) * | 2020-07-27 | 2020-11-13 | 中国工商银行股份有限公司 | Enterprise financial service risk prediction method and device |
| CN112819341A (en) * | 2021-02-04 | 2021-05-18 | 江苏卓易信息科技股份有限公司 | Scientific and technological type small and micro enterprise credit risk assessment method |
Non-Patent Citations (1)
| Title |
|---|
| 消毒产品生产企业分类监督综合评价试点工作探讨;段亚波 等;《中国消毒学杂志》;20200331;第37卷(第3期);参见第211-212页，第215页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113554366A (en) | 2021-10-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hatefi et al. | A common weight MCDA–DEA approach to construct composite indicators | |
| US20140278738A1 (en) | Systems and methods for unified scoring | |
| CN108563548A (en) | Method for detecting abnormality and device | |
| CN108090516A (en) | Automatically generate the method and system of the feature of machine learning sample | |
| CN108052979A (en) | The method, apparatus and equipment merged to model predication value | |
| CN109976998B (en) | Software defect prediction method and device and electronic equipment | |
| CN107991097A (en) | A kind of Method for Bearing Fault Diagnosis based on multiple dimensioned symbolic dynamics entropy | |
| CN104517023A (en) | obesity gene detection and evaluation system and data processing method | |
| CN106485585A (en) | Method and system for ranking | |
| CN107220505A (en) | Child development capability assessment method and device | |
| CN111125868A (en) | Multi-stress life evaluation method and device for electronic product | |
| CN109009084A (en) | QRS wave group calibration method, device, equipment and medium for multi-lead electrocardiosignal | |
| CN110888625A (en) | Method for controlling code quality based on demand change and project risk | |
| CN113554366B (en) | Classification supervision method for disinfection product production enterprises and related equipment | |
| CN115565020A (en) | Tooth surface damage identification method and device based on improved neural network | |
| CN113033757A (en) | Method, apparatus and computer-readable storage medium for testing operator precision in neural networks | |
| CN114970357A (en) | Energy-saving effect evaluation method, system, device and storage medium | |
| CN112486808B (en) | System testing method and device, electronic equipment and storage medium | |
| DE112014006799T5 (en) | Method for measuring a user behavior consistency level based on a complex correspondence system | |
| López-Martín et al. | Support vector regression for predicting the productivity of higher education graduate students from individually developed software projects | |
| CN114510405B (en) | Index data evaluation method, apparatus, device, storage medium, and program product | |
| Qin et al. | Remaining useful life estimation of bearing via temporal convolutional networks enhanced by a gated convolutional unit | |
| CN114331237A (en) | Sewage treatment quality evaluation method and device based on AHP-entropy weight method | |
| CN114239738A (en) | Medical data classification method for small sample and related equipment | |
| CN112747813A (en) | Automatic part weighing and counting system and method |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |