CN117435583A - On-line acquisition data verification method for production line - Google Patents

On-line acquisition data verification method for production line Download PDF

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Publication number
CN117435583A
CN117435583A CN202311449582.7A CN202311449582A CN117435583A CN 117435583 A CN117435583 A CN 117435583A CN 202311449582 A CN202311449582 A CN 202311449582A CN 117435583 A CN117435583 A CN 117435583A
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data
database
hash value
character string
value
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陈文正
张兵
史志宪
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Hefei Yougao Internet Of Things Identification Equipment Co ltd
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Hefei Yougao Internet Of Things Identification Equipment Co ltd
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    • 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
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
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    • G06F16/22—Indexing; Data structures therefor; Storage structures
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    • G06F16/2255—Hash tables
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • 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

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Abstract

The invention relates to data verification, in particular to an online acquisition data verification method of a production line, which comprises the steps of pre-storing standard data into a first database, acquiring real-time data aiming at different process links in the production line, and storing the acquired data into a second database; determining a first target hash value of a first database and a second target hash value of a second database; comparing the first target hash value with the second target hash value, and obtaining a first online data verification result based on a comparison result; processing a first data table of a first database to obtain a first feature matrix and a first MD5 feature value related to the DQL; processing a second data table of the second database to obtain a second feature matrix and a second MD5 feature value related to the DQL; the technical scheme provided by the invention can effectively overcome the defect that the online data acquisition of the production line cannot be accurately checked in the prior art.

Description

On-line acquisition data verification method for production line
Technical Field
The invention relates to data verification, in particular to an online acquisition data verification method for a production line.
Background
An automated production line refers to a production organization form of an automated machine system for implementing a product manufacturing process, which is further developed on a continuous production line basis. In an automated production line, production objects are automatically transferred from one device to another, and corresponding production links are automatically completed by the devices, and the tasks of workers are only to adjust, supervise and manage the production line, so that the production process is highly continuous.
In each procedure link of an automatic production line, a large amount of online acquisition data can be generated, and the production efficiency and quality of the product can be effectively controlled by checking the online acquisition data. However, due to the large data volume of the online collected data, how to realize accurate verification of the online collected data is a current urgent problem to be solved.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects existing in the prior art, the invention provides an online data acquisition verification method for a production line, which can effectively overcome the defect that the online data acquisition of the production line cannot be accurately verified in the prior art.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
an online acquisition data verification method of a production line comprises the following steps:
s1, pre-storing standard data into a first database, acquiring real-time data aiming at different process links in a production line, and storing acquired data into a second database;
s2, determining a first target hash value of a first database and a second target hash value of a second database;
s3, comparing the first target hash value with the second target hash value, and obtaining a first online data verification result based on a comparison result;
s4, processing a first data table of the first database to obtain a first feature matrix and a first MD5 feature value related to the DQL;
s5, processing a second data table of a second database to obtain a second feature matrix and a second MD5 feature value related to the DQL;
s6, comparing the first characteristic matrix with the second characteristic matrix, and comparing the first MD5 characteristic value with the second MD5 characteristic value, and obtaining a second online data verification result based on the comparison result;
and S7, obtaining a final online data verification result according to the first online data verification result and the second online data verification result, and realizing effective verification of online data of the production line.
Preferably, determining the first target hash value of the first database in S2 includes:
calculating a first hash value of the single-column data;
calculating a second hash value of the single data table according to the first hash value;
calculating third hash values of all the data tables according to the second hash values;
a first target hash value of the first database is calculated based on the third hash value and the data object type.
Preferably, the calculating the first hash value of the single column of data includes:
determining a first character string of the single-column data, and calculating a first hash value corresponding to the single-column data according to the first character string;
the column data includes field type, field name, field value and index number.
Preferably, the calculating the second hash value of the single data table according to the first hash value includes:
determining a second character string of the single data table, and calculating a second hash value corresponding to the single data table according to the second character string;
wherein the second string contains the first hash value of all single column data in the single data table.
Preferably, the calculating the third hash value of all the data tables according to the second hash value includes:
determining a third character string of the first database, and calculating third hash values corresponding to all the data tables according to the third character string;
the third character string comprises second hash values of all data tables in the first database.
Preferably, the calculating the first target hash value of the first database according to the third hash value and the data object type includes:
determining a character string of the data object type in the first database, and combining the character string with the third character string to form a fourth character string;
and calculating the hash value of the fourth character string to obtain a first target hash value of the first database.
Preferably, in S3, comparing the first target hash value with the second target hash value, and obtaining a first online data verification result based on the comparison result includes:
if the first target hash value is equal to the second target hash value, judging that the online data check is passed;
if the first target hash value is not equal to the second target hash value, judging that the online data verification fails, and comparing the fourth character string, the third character string, the second character string and the first character string of the first database with the fourth character string, the third character string, the second character string and the first character string of the second database in sequence to judge the inconsistent target objects.
Preferably, in S4, the processing the first data table of the first database to obtain a first feature matrix related to the DQL includes:
acquiring all preset special fields in a first data table, classifying and calculating all the special fields, and determining the number of each special field;
and constructing a first characteristic matrix related to the DQL according to the number of each special field.
Preferably, in S4, processing the first data table of the first database to obtain a first MD5 feature value includes:
and exporting the first data table into a first data file according to a preset separator, and generating a first MD5 characteristic value according to the first data file.
Preferably, in S6, comparing the first feature matrix with the second feature matrix, and comparing the first MD5 feature value with the second MD5 feature value, and obtaining a second online data verification result based on the comparison result includes:
comparing the first characteristic matrix and the first MD5 characteristic value corresponding to each data table in the first database with the second characteristic matrix and the second MD5 characteristic value corresponding to each data table in the second database;
if the feature matrix and the feature value between all the data tables in the first database and the second database are equal, judging that the online data check is passed;
if the feature matrix and the feature value among all the data tables in the first database and the second database are not equal, judging that the online data verification is not passed.
(III) beneficial effects
Compared with the prior art, the online acquisition data verification method for the production line provided by the invention has the following beneficial effects:
1) Determining a first target hash value of a first database and a second target hash value of a second database, comparing the first target hash value with the second target hash value, and obtaining a first online data verification result based on the comparison result, so that whether online acquisition data passes verification can be judged according to the target hash values of the first database and the second database;
2) Processing a first data table of a first database to obtain a first feature matrix and a first MD5 feature value related to the DQL, processing a second data table of a second database to obtain a second feature matrix and a second MD5 feature value related to the DQL, comparing the first feature matrix with the second feature matrix, the first MD5 feature value with the second MD5 feature value, and obtaining a second online data verification result based on the comparison result, so that whether online acquisition data passes verification can be judged according to the feature matrix and the MD5 feature value of the first database and the second database;
3) And obtaining a final online data verification result according to the first online data verification result and the second online data verification result, so that the online collected data of a large number of production lines can be accurately verified.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic flow chart of the present invention;
fig. 2 is a flowchart illustrating a process for determining a first target hash value of a first database according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An online data acquisition verification method for a production line is shown in fig. 1, wherein (1) standard data is stored in a first database in advance, real-time data acquisition is performed for different process links in the production line, and acquired data is stored in a second database.
(2) A first target hash value of the first database and a second target hash value of the second database are determined.
As shown in fig. 2, determining a first target hash value for a first database includes:
calculating a first hash value of the single-column data;
calculating a second hash value of the single data table according to the first hash value;
calculating third hash values of all the data tables according to the second hash values;
a first target hash value of the first database is calculated based on the third hash value and the data object type.
1) Calculating a first hash value of the single column of data, comprising:
determining a first character string of the single-column data, and calculating a first hash value corresponding to the single-column data according to the first character string;
the column data includes field type, field name, field value and index number.
2) Calculating a second hash value of the single data table based on the first hash value, comprising:
determining a second character string of the single data table, and calculating a second hash value corresponding to the single data table according to the second character string;
wherein the second string contains the first hash value of all single column data in the single data table.
3) Calculating third hash values of all data tables according to the second hash values, including:
determining a third character string of the first database, and calculating third hash values corresponding to all the data tables according to the third character string;
the third character string comprises second hash values of all data tables in the first database.
4) Calculating a first target hash value of the first database according to the third hash value and the data object type, including:
determining a character string of the data object type in the first database, and combining the character string with the third character string to form a fourth character string;
and calculating the hash value of the fourth character string to obtain a first target hash value of the first database.
(3) Comparing the first target hash value with the second target hash value, and obtaining a first online data verification result based on the comparison result, wherein the method specifically comprises the following steps:
if the first target hash value is equal to the second target hash value, judging that the online data check is passed;
if the first target hash value is not equal to the second target hash value, judging that the online data verification fails, and comparing the fourth character string, the third character string, the second character string and the first character string of the first database with the fourth character string, the third character string, the second character string and the first character string of the second database in sequence to judge the inconsistent target objects.
According to the technical scheme, the first target hash value of the first database and the second target hash value of the second database are determined, the first target hash value and the second target hash value are compared, and the first online data verification result is obtained based on the comparison result, so that whether the online collected data passes through verification can be judged according to the target hash values of the first database and the second database.
As shown in fig. 1, (4) the first data table of the first database is processed to obtain a first feature matrix and a first MD5 feature value related to DQL.
1) Processing the first data table of the first database to obtain a first feature matrix related to the DQL, including:
acquiring all preset special fields in a first data table, classifying and calculating all the special fields, and determining the number of each special field;
and constructing a first characteristic matrix related to the DQL according to the number of each special field.
2) Processing the first data table of the first database to obtain a first MD5 characteristic value, wherein the processing comprises the following steps:
and exporting the first data table into a first data file according to a preset separator, and generating a first MD5 characteristic value according to the first data file.
(5) And processing a second data table of the second database to obtain a second characteristic matrix and a second MD5 characteristic value related to the DQL.
(6) Comparing the first feature matrix with the second feature matrix, and comparing the first MD5 feature value with the second MD5 feature value, and obtaining a second online data verification result based on the comparison result, wherein the method specifically comprises the following steps:
comparing the first characteristic matrix and the first MD5 characteristic value corresponding to each data table in the first database with the second characteristic matrix and the second MD5 characteristic value corresponding to each data table in the second database;
if the feature matrix and the feature value between all the data tables in the first database and the second database are equal, judging that the online data check is passed;
if the feature matrix and the feature value among all the data tables in the first database and the second database are not equal, judging that the online data verification is not passed.
According to the technical scheme, the first data table of the first database is processed to obtain the first feature matrix and the first MD5 feature value related to the DQL, the second data table of the second database is processed to obtain the second feature matrix and the second MD5 feature value related to the DQL, the first feature matrix and the second feature matrix, the first MD5 feature value and the second MD5 feature value are compared, and a second online data verification result is obtained based on the comparison result, so that whether online acquisition data passes through verification can be judged according to the feature matrix and the MD5 feature value of the first database and the second database.
(7) And obtaining a final online data verification result according to the first online data verification result and the second online data verification result, and realizing effective verification of online data of the production line.
According to the technical scheme, the final online data verification result is obtained according to the first online data verification result and the second online data verification result, so that the online data acquisition of a large number of production lines can be accurately verified.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. An online acquisition data verification method of a production line is characterized by comprising the following steps of: the method comprises the following steps:
s1, pre-storing standard data into a first database, acquiring real-time data aiming at different process links in a production line, and storing acquired data into a second database;
s2, determining a first target hash value of a first database and a second target hash value of a second database;
s3, comparing the first target hash value with the second target hash value, and obtaining a first online data verification result based on a comparison result;
s4, processing a first data table of the first database to obtain a first feature matrix and a first MD5 feature value related to the DQL;
s5, processing a second data table of a second database to obtain a second feature matrix and a second MD5 feature value related to the DQL;
s6, comparing the first characteristic matrix with the second characteristic matrix, and comparing the first MD5 characteristic value with the second MD5 characteristic value, and obtaining a second online data verification result based on the comparison result;
and S7, obtaining a final online data verification result according to the first online data verification result and the second online data verification result, and realizing effective verification of online data of the production line.
2. The on-line collected data verification method of a production line according to claim 1, wherein: s2, determining a first target hash value of a first database, wherein the method comprises the following steps:
calculating a first hash value of the single-column data;
calculating a second hash value of the single data table according to the first hash value;
calculating third hash values of all the data tables according to the second hash values;
a first target hash value of the first database is calculated based on the third hash value and the data object type.
3. The on-line collected data verification method of a production line according to claim 2, wherein: the calculating a first hash value of the single column of data includes:
determining a first character string of the single-column data, and calculating a first hash value corresponding to the single-column data according to the first character string;
the column data includes field type, field name, field value and index number.
4. The on-line collected data verification method of a production line according to claim 3, wherein: the calculating a second hash value of the single data table according to the first hash value comprises:
determining a second character string of the single data table, and calculating a second hash value corresponding to the single data table according to the second character string;
wherein the second string contains the first hash value of all single column data in the single data table.
5. The on-line collected data verification method of a production line according to claim 4, wherein: the calculating the third hash value of all the data tables according to the second hash value comprises the following steps:
determining a third character string of the first database, and calculating third hash values corresponding to all the data tables according to the third character string;
the third character string comprises second hash values of all data tables in the first database.
6. The on-line collected data verification method of a production line according to claim 5, wherein: the calculating the first target hash value of the first database according to the third hash value and the data object type comprises the following steps:
determining a character string of the data object type in the first database, and combining the character string with the third character string to form a fourth character string;
and calculating the hash value of the fourth character string to obtain a first target hash value of the first database.
7. The on-line collected data verification method of a production line according to claim 6, wherein: s3, comparing the first target hash value with the second target hash value, and obtaining a first online data verification result based on the comparison result, wherein the method comprises the following steps:
if the first target hash value is equal to the second target hash value, judging that the online data check is passed;
if the first target hash value is not equal to the second target hash value, judging that the online data verification fails, and comparing the fourth character string, the third character string, the second character string and the first character string of the first database with the fourth character string, the third character string, the second character string and the first character string of the second database in sequence to judge the inconsistent target objects.
8. The on-line collected data verification method of a production line according to claim 1, wherein: s4, processing a first data table of the first database to obtain a first feature matrix related to the DQL, wherein the processing comprises the following steps:
acquiring all preset special fields in a first data table, classifying and calculating all the special fields, and determining the number of each special field;
and constructing a first characteristic matrix related to the DQL according to the number of each special field.
9. The on-line collected data verification method of a production line according to claim 8, wherein: s4, processing a first data table of a first database to obtain a first MD5 characteristic value, wherein the method comprises the following steps:
and exporting the first data table into a first data file according to a preset separator, and generating a first MD5 characteristic value according to the first data file.
10. The on-line collected data verification method of a production line according to claim 9, wherein: s6, comparing the first and second feature matrixes, the first MD5 feature value and the second MD5 feature value, and obtaining a second online data verification result based on the comparison result, wherein the method comprises the following steps:
comparing the first characteristic matrix and the first MD5 characteristic value corresponding to each data table in the first database with the second characteristic matrix and the second MD5 characteristic value corresponding to each data table in the second database;
if the feature matrix and the feature value between all the data tables in the first database and the second database are equal, judging that the online data check is passed;
if the feature matrix and the feature value among all the data tables in the first database and the second database are not equal, judging that the online data verification is not passed.
CN202311449582.7A 2023-11-02 2023-11-02 On-line acquisition data verification method for production line Pending CN117435583A (en)

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Application Number Priority Date Filing Date Title
CN202311449582.7A CN117435583A (en) 2023-11-02 2023-11-02 On-line acquisition data verification method for production line

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Application Number Priority Date Filing Date Title
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CN117435583A true CN117435583A (en) 2024-01-23

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