CN116862231A - Risk assessment system and method for pet production full link - Google Patents
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Abstract
The invention relates to a risk assessment system and a risk assessment method for a pet food production full link, and belongs to the technical field of internet service. According to the invention, production service data of a pet food production full-link is collected through an RFID tag collection technology, stored in an internal buffer area of an RFID reader-writer, and the collected data is subjected to dirty data processing and compliance verification through an ETL interface and pumped to a database. Establishing a risk assessment attribute set and a risk assessment index set for production service data in a database, constructing a decision matrix, performing deblurring, normalization and standardization on the decision matrix, calculating the processed matrix by using an entropy weight method to obtain a feature vector of the risk assessment attribute, and finally obtaining a risk assessment value of a production link in a pet production full link by calculating Euclidean distance between the feature vector and an ideal vector solution, thereby providing decision assistance for management of a pet production enterprise and enabling management of the production link to be more targeted.
Description
Technical Field
The invention belongs to the technical field of internet service, and particularly relates to a risk assessment system and a risk assessment method for a pet food production full link.
Background
Currently, the pet food production field involves a number of links and parties, including raw material suppliers, factories, brands and consumers, in the pet food production full chain. These parties may use different information systems or data standards, have difficulty directly interfacing and comparing data, and may have situations where data transfer is not timely, accurate or comprehensive, resulting in scattered sources of data, and difficult integration and sharing.
Meanwhile, in the whole link from raw material production to delivery of finished products to consumers by terminal node enterprises, each link may become a source hidden trouble causing food safety problems. The existing production links tend to be refined and subdivided, difficulty is brought to factory management, and the risk assessment workload of the production links is complex.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a risk assessment system and a risk assessment method for pet food production full links, which are used for acquiring production service data of the pet food production full links through an RFID tag acquisition technology, storing the production service data in an internal cache area of an RFID reader-writer, carrying out dirty data processing and compliance verification on the acquired data through an ETL interface, and pumping the dirty data to a database. Establishing a risk assessment attribute set and a risk assessment index set for production service data in a database, constructing a decision matrix, performing deblurring, normalization and standardization on the decision matrix, and calculating the processed matrix by using an entropy weight method to obtain a feature vector of the risk assessment attribute. And finally, calculating the Euclidean distance between the feature vector and the ideal vector solution to obtain a risk evaluation value of the production link in the pet food production full link.
The aim of the invention can be achieved by the following technical scheme:
s1: the RFID reader-writer acquires production service data of a pet food production full-link acquired by the RFID tag;
s2: the ETL interface performs dirty data checking and processing on the production service data, wherein the dirty data checking comprises non-NULL checking, date type checking, value type checking, logic foreign key checking and value domain checking;
s3: the ETL interface performs compliance verification on the production service data, and calls CTL.PKG_FBI_AUTO_ETL.INC_ODS_BOUND on the production service data passing verification to store the service data into a service database, wherein the compliance check comprises table presence check and increment type check;
s4: establishing a risk evaluation attribute set A= { a m M=1, 2,..x }, risk assessment index set i= { I n I n=1, 2,..y }, the index i in the risk assessment index set n For attribute a in the risk assessment attribute set m Is a rating value lambda of (2) m Constructing a decision matrix lambda for risk assessment values, for each term lambda mn Proceeding withObtaining a defuzzified matrix P;
s5: obtaining each term P in the defuzzified matrix P ij The corresponding risk evaluation attribute a m When the risk assessment attribute a is classified m When belonging to the efficiency class, evaluating the attribute a of the risk m Corresponding p ij UsingPerforming standardized calculation when the risk evaluation attribute a m When belonging to the quality class, evaluating the attribute a of the risk m Corresponding p ij Use->Performing standardized calculation to obtain a standardized matrix H= (H) ij ) m×n ；
S6: each term H in the normalization matrix H ij Through the process ofNormalization calculation to obtain a normalization matrix W ij ＝(w ij ) m×n Weighting each item in the normalized matrix by using a method, wherein the risk evaluation attribute a m The entropy weight of (a) forms a feature vector of the risk evaluation attribute, and the risk evaluation attribute a is preset m Calculating the Euclidean distance between the ideal vector solution and the feature vector to obtain the risk evaluation attribute a m Risk value of (a), the risk evaluation attribute a of the production link m And all the risk evaluation values of the production links are obtained through addition.
As a preferable technical scheme of the invention, the RFID tag in S1 is attached to all materials of the pet food production full link, and comprises the following steps: raw materials, products in process and finished products, wherein the deployment position of the RFID reader covers all RFID labels.
Preferably, the specific method for processing the dirty data in step S2 is as follows:
s201: creating a connection between the reader and the database;
s202: reading each row of data in a table list record table in an internal cache area of the RFID reader, and inquiring column details by taking each row of data in the table list record table as table name information;
s203: checking the data of the column details, storing the row marked as dirty data into a specified dirty data storage table, checking whether the dirty data storage table reaches a predefined upper storage limit, deleting old data if yes, and circulating the contents of S202 and S203 if not.
Preferably, the specific method for checking the compliance in the step S3 is as follows:
s301: checking whether a source table in an internal cache area of the RFID reader-writer and a target table in a database exist or not;
s302: and checking the main key of the source table, setting a validity flag bit in the source table, wherein the validity flag bit of the row data of the source table without compliance problem is 'Y'.
Preferably, the specific method of the weighting flow in step S6 is as follows:
s601: for each term W in the normalized matrix W ij Calculating attribute entropy
S602: entropy calculation of the attributesObtaining the entropy weight of the attribute entropy;
s603: repeating S601 and S602, and evaluating the attribute a for the risk m Is set as each of the risk evaluation index i n Calculating the entropy weight, wherein the entropy weight forms the risk evaluation attribute a m Feature vector w= (w) 1 ，w 2 ，...，w n )。
And (6) the Euclidean distance calculation formula of the ideal vector solution and the characteristic vector in the step (S) is as follows: wherein the ideal vector solution is +.>
A pet production full-link risk assessment system, comprising:
the RFID data acquisition module is used for acquiring the production service data acquired by the RFID tag;
the ETL interface is used for carrying out dirty data inspection and compliance inspection on the data stored in the RFID reader-writer and pumping the data meeting the compliance requirement from the RFID reader-writer to the database;
and the risk evaluation module is used for connecting the database, and calculating the business data in the database to obtain a risk evaluation value of the production link of the pet food production full link.
The beneficial effects of the invention are as follows:
(1) The RFID label covering the pet food production full link is used for realizing more comprehensive and accurate acquisition of the business data of the pet food production full link, and the business data of each key link is not missed;
(2) The triangle fuzzy data is used for defuzzification treatment, the entropy weight method is used for defining the weight of the risk evaluation attribute, and finally, the risk of each production link of pet food production is obtained, so that decision assistance effect is provided for the management of pet food production enterprises, and the management of the production links is more targeted.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention for achieving the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects according to the invention with reference to the attached drawings and the preferred embodiment.
As shown in fig. 1, the working principle and the use flow of the invention are as follows:
s1: the RFID reader-writer acquires production service data of a pet food production full-link acquired by the RFID tag;
s2: the ETL interface performs dirty data checking and processing on the production service data, wherein the dirty data checking comprises non-NULL checking, date type checking, value type checking, logic foreign key checking and value domain checking;
s3: the ETL interface performs compliance verification on the production service data, and calls CTL.PKG_FBI_AUTO_ETL.INC_ODS_BOUND on the production service data passing verification to store the service data into a service database, wherein the compliance check comprises table presence check and increment type check;
s4: establishing a risk evaluation attribute set A= { a m M=1, 2,..x }, risk assessment index set i= { I n I n=1, 2,..y }, the index i in the risk assessment index set n For attribute a in the risk assessment attribute set m Is a rating value lambda of (2) m Constructing a decision matrix lambda for risk assessment values, for each term lambda mn Proceeding withObtaining a defuzzified matrix P;
s5: obtaining each term P in the defuzzified matrix P ij The corresponding risk evaluation attribute a m When the risk assessment attribute a is classified m When belonging to the efficiency class, evaluating the attribute a of the risk m Corresponding p ij UsingPerforming standardized calculation when the risk evaluation attribute a m When belonging to the quality class, evaluating the attribute a of the risk m Corresponding p ij Use->Performing standardized calculation to obtain a standardized matrix H= (H) ij ) m×n ；
S6: each term H in the normalization matrix H ij Through the process ofNormalization calculation to obtain a normalization matrix W ij ＝(w ij ) m×n Weighting each item in the normalized matrix by using a method, wherein the risk evaluation attribute a m The entropy weight of (a) forms a feature vector of the risk evaluation attribute, and the risk evaluation attribute a is preset m Calculating the Euclidean distance between the ideal vector solution and the feature vector to obtain the risk evaluation attribute a m Risk value of (a), the risk evaluation attribute a of the production link m And all the risk evaluation values of the production links are obtained through addition.
Specifically, the RFID tag is attached to an object to be tracked, wherein the object to be tracked refers to materials, including raw materials, products and finished products, which appear in the pet food production process. For raw materials, information that can be acquired by RFID includes shipping time, supply lot, material code, consignee, and raw material cargo space of the raw materials; for an article, the information that can be obtained by RFID includes the production lot, production time, shift, operator and raw material bar codes of the article; for finished goods, the information that can be obtained by RFID includes production lot, time of labor, finished product bar code, finished product location, quality report, time of shipment, lot of shipment, quantity, consignee, and final sales bar code. And the RFID reader-writer is deployed to ensure the power supply and the network of the RFID reader-writer, and the RFID reader-writer needs to cover all RFID tags. The range where the RFID reader/writer is deployed needs to cover includes: the device comprises a raw material warehouse entry port, a raw material cargo space, a workshop line side bin cargo space, a raw material feeding port, a process entry and exit port, a finished product warehouse entry port and a finished product cargo space.
In this embodiment, from rfid protocol import RFIDDevice is called in code; creating an RFID reader-writer device instance and an RFID tag instance by using an RFID ()' statement, registering tags in the reader-writer, and connecting the reader-writer through an rfid.open () function, wherein the RFID reader-writer device instance and the RFID tag instance are connected with the reader-writer through an rfid.open () function, and using uid = rfid.scan (); data=rfid.read_block (uid, block_number) to read tag data and write tag data through rfid.write_block (uid, block_number, data_to_write), the written data being stored in an internal buffer area of the RFID reader/writer.
Checking the TABLE information of each row of data corresponding to the CTL.AUTO_CHECK_TABLE_LIST in an internal buffer area of the RFID reader, inquiring column information in the CTL.AUTO_CHECK_COL_LIST according to the TABLE name in the TABLE information, performing corresponding data CHECK on each column information, storing the row marked as dirty data in a designated position, checking whether the corresponding dirty data storage TABLE reaches a predefined upper storage limit, and deleting old data if the corresponding dirty data storage TABLE reaches the predefined upper storage limit.
Calling a TABLE which is legal and has an active flag of 'Y' in the CTL.AUTO_ETL_TABLE_LIST, and generating incremental information into an incremental configuration TABLE CTL.AUTO_ETL_INC_CONTROL_ODS in a respective incremental mode;
and calling a CTL.PKG_FBI_AUTO_ETL.RUN_STG_ETL process, reading incremental information corresponding to the CTL.AUTO_ETL_INC_CONTROL_ODS, taking columns from the CTL.AUTO_ETL_COL_LIST, replacing, dynamically generating an SQL statement for extracting data from a source database, and dynamically executing the statement to realize incremental extraction from a data source to a data warehouse.
Establishing a risk evaluation attribute set A= { a m M=1, 2,..x }, risk assessment index set i= { I n N=1, 2,..y }, a total of X risk assessment attributes, each risk assessment attribute has Y risk assessment indexes, index i n For attribute a m The risk evaluation value of (C) is lambda mn All lambda mn Constituting a decision matrix Λ= [ λ ] mn ] X×Y ：
Calling conn=sqlite 3.Connect ('database file. Db') and cursor=conn. Cursor () to connect with a database, and acquiring event type data in service data as risk assessment attribute a m Event type associated attribute value as risk assessment index i n Calculating a set of business data for each risk assessment attribute a m Risk assessment index i of (2) n The maximum value, the minimum value and the average value of the service data form the triangle fuzzy number of the service dataWherein the method comprises the steps ofThe lower limit value, the most probable value and the upper limit value of the standard evaluation opinion are respectively represented. Under the condition of not affecting standard opinion, adopting the optimal deblurring performance method to carry out deblurring processing operation on the matrix lambda, and obtaining the matrix lambda by +.> To obtain a matrix p= (P ij ) m×n Then pass-> Normalizing the matrix P to obtain i= (I) xy ) m×n Weighting by entropy weighting method, and setting +.>After defining the entropy of the ith evaluation attribute, the entropy weight definition of the ith attribute can be obtained, and the formula is as follows: />In the risk evaluation matrix i= (I xy ) m×n Finding the best result of each risk evaluation index, i.e. +.> The risk index is called as an ideal solution of the risk index, euclidean distance of the ideal solution E is respectively summed with each risk index, and the Euclidean distance is used as a risk value of each risk index> C y The smaller, the description i n The smaller the index risk, the larger the reverse, from which i can be found n Risk value of index, i n The risk values of the indexes are added to obtain each risk assessment attribute a m Is a risk level of (2).
Program code embodied in a system in accordance with embodiments of the present invention may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The present invention is not limited to the above embodiments, but is not limited to the above embodiments, and any modifications, equivalents and variations made to the above embodiments according to the technical matter of the present invention can be made by those skilled in the art without departing from the scope of the technical matter of the present invention.
Claims (7)
1. A pet production full-link risk assessment method, comprising:
s1: the RFID reader-writer acquires production service data of a pet food production full-link acquired by the RFID tag;
s2: the ETL interface performs dirty data checking and processing on the production service data, wherein the dirty data checking comprises non-NULL checking, date type checking, value type checking, logic foreign key checking and value domain checking;
s3: the ETL interface performs compliance verification on the production service data, and calls CTL.PKG_FBI_AUTO_ETL.INC_ODS_BOUND on the production service data passing verification to store the service data into a service database, wherein the compliance check comprises table presence check and increment type check;
s4: establishing a risk evaluation attribute set A= { a m M=1, 2,..x }, risk assessment index set i= { I n I n=1, 2,..y }, the index i in the risk assessment index set n For attribute a in the risk assessment attribute set m Is a rating value lambda of (2) mn Constructing a decision matrix lambda for risk assessment values, for each term lambda mn Proceeding withObtaining a defuzzified matrix P;
s5: obtaining each term P in the defuzzified matrix P ij The corresponding risk evaluation attribute a m When the risk assessment attribute a is classified m When belonging to the efficiency class, evaluating the attribute a of the risk m Corresponding p ij UsingPerforming standardized calculation when the risk evaluation attribute a m When belonging to the quality class, evaluating the attribute a of the risk m Corresponding p ij UsingPerforming standardized calculation to obtain a standardized matrix H= (H) ij ) m×n ；
S6: each term H in the normalization matrix H ij Through the process ofNormalization calculation to obtain a normalization matrix W ij ＝(w ij ) m×n Weighting each item in the normalized matrix by using a method, wherein the risk evaluation attribute a m The entropy weight of (a) forms a feature vector of the risk evaluation attribute, and the risk evaluation attribute a is preset m Calculating the Euclidean distance between the ideal vector solution and the feature vector to obtain the risk evaluation attribute a m Risk value of (a), the risk evaluation attribute a of the production link m And all the risk evaluation values of the production links are obtained through addition.
2. The method of claim 1 wherein said RFID tag of said S1 is attached to all materials of said pet food production full link, comprising: raw materials, products in process and finished products, wherein the deployment position of the RFID reader covers all RFID labels.
3. The method according to claim 1, wherein the specific method for processing the dirty data in step S2 is:
s201: creating a connection between the reader and the database;
s202: reading each row of data in a table list record table in an internal cache area of the RFID reader, and inquiring column details by taking each row of data in the table list record table as table name information;
s203: checking the data of the column details, storing the row marked as dirty data into a specified dirty data storage table, checking whether the dirty data storage table reaches a predefined upper storage limit, deleting old data if yes, and circulating the contents of S202 and S203 if not.
4. The method according to claim 1, wherein the specific method of compliance checking in step S3 is:
s301: checking whether a source table in an internal cache area of the RFID reader-writer and a target table in a database exist or not;
s302: and checking the main key of the source table, setting a validity flag bit in the source table, wherein the validity flag bit of the row data of the source table without compliance problem is 'Y'.
5. The method according to claim 1, wherein the specific method of weighting in step S6 is as follows:
s601: for each term W in the normalized matrix W ij Calculating attribute entropy
S602: entropy calculation of the attributesObtaining the entropy weight of the attribute entropy;
s603: repeating S601 and S602, and evaluating the attribute a for the risk m Is set as each of the risk evaluation index i n Calculating the entropy weight, wherein the entropy weight forms the risk evaluation attribute a m Feature vector w= (w) 1 ，w 2 ，...，w n )。
6. The method according to claim 1, wherein the euclidean distance between the ideal vector solution and the feature vector in S6 is calculated by: wherein the ideal vector solution is +.>
7. A pet production full-link risk assessment system, comprising:
the RFID data acquisition module is used for acquiring the production service data acquired by the RFID tag;
the ETL interface is used for carrying out dirty data inspection and compliance inspection on the data stored in the RFID reader-writer and pumping the data meeting the compliance requirement from the RFID reader-writer to the database;
and the risk evaluation module is used for connecting the database, and calculating the business data in the database to obtain a risk evaluation value of the production link of the pet food production full link.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1952951A (en) * | 2005-10-20 | 2007-04-25 | 中国科学院自动化研究所 | Foodstuffs security management system and method realized by RF identification technique |
| CN103942671A (en) * | 2014-04-28 | 2014-07-23 | 深圳市检验检疫科学研究院 | Visual RFID (Radio Frequency Identification Devices) food logistics supply chain management and risk control method |
| CN107506905A (en) * | 2017-08-01 | 2017-12-22 | 华北电力大学 | A kind of improved Sustainable Development of Power Grid Company integrated evaluating method |
| CN108182555A (en) * | 2018-02-09 | 2018-06-19 | 重庆市农业科学院 | A kind of Safety of Food Quality risk evaluating system |
| CN110033064A (en) * | 2019-04-19 | 2019-07-19 | 清华大学天津高端装备研究院洛阳先进制造产业研发基地 | A kind of RFID middleware system applied to the retrospect of medical apparatus and instruments Life cycle |
| CN110400087A (en) * | 2019-07-31 | 2019-11-01 | 中国计量大学 | Based on the elevator safety guard system evaluation method for improving weight and variable fuzzy sets |
| CN110659814A (en) * | 2019-09-12 | 2020-01-07 | 国网山东省电力公司寿光市供电公司 | Power grid operation risk evaluation method and system based on entropy weight method |
| CN111461576A (en) * | 2020-04-27 | 2020-07-28 | 宁波市食品检验检测研究院 | Fuzzy comprehensive evaluation method for safety risk of chemical hazards in food |
| CN114386986A (en) * | 2021-12-27 | 2022-04-22 | 航天信息股份有限公司 | Product full life cycle data tracing platform |
| US20230196076A1 (en) * | 2021-03-15 | 2023-06-22 | Hohai University | Method for optimally selecting flood-control operation scheme based on temporal convolutional network |
-
2023
- 2023-07-10 CN CN202310838113.8A patent/CN116862231A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1952951A (en) * | 2005-10-20 | 2007-04-25 | 中国科学院自动化研究所 | Foodstuffs security management system and method realized by RF identification technique |
| CN103942671A (en) * | 2014-04-28 | 2014-07-23 | 深圳市检验检疫科学研究院 | Visual RFID (Radio Frequency Identification Devices) food logistics supply chain management and risk control method |
| CN107506905A (en) * | 2017-08-01 | 2017-12-22 | 华北电力大学 | A kind of improved Sustainable Development of Power Grid Company integrated evaluating method |
| CN108182555A (en) * | 2018-02-09 | 2018-06-19 | 重庆市农业科学院 | A kind of Safety of Food Quality risk evaluating system |
| CN110033064A (en) * | 2019-04-19 | 2019-07-19 | 清华大学天津高端装备研究院洛阳先进制造产业研发基地 | A kind of RFID middleware system applied to the retrospect of medical apparatus and instruments Life cycle |
| CN110400087A (en) * | 2019-07-31 | 2019-11-01 | 中国计量大学 | Based on the elevator safety guard system evaluation method for improving weight and variable fuzzy sets |
| CN110659814A (en) * | 2019-09-12 | 2020-01-07 | 国网山东省电力公司寿光市供电公司 | Power grid operation risk evaluation method and system based on entropy weight method |
| CN111461576A (en) * | 2020-04-27 | 2020-07-28 | 宁波市食品检验检测研究院 | Fuzzy comprehensive evaluation method for safety risk of chemical hazards in food |
| US20230196076A1 (en) * | 2021-03-15 | 2023-06-22 | Hohai University | Method for optimally selecting flood-control operation scheme based on temporal convolutional network |
| CN114386986A (en) * | 2021-12-27 | 2022-04-22 | 航天信息股份有限公司 | Product full life cycle data tracing platform |
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