CN113901295B - Automatic fault detail grabbing method, equipment and storage medium based on diag system - Google Patents
Automatic fault detail grabbing method, equipment and storage medium based on diag system Download PDFInfo
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- CN113901295B CN113901295B CN202111087379.0A CN202111087379A CN113901295B CN 113901295 B CN113901295 B CN 113901295B CN 202111087379 A CN202111087379 A CN 202111087379A CN 113901295 B CN113901295 B CN 113901295B
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- 238000003860 storage Methods 0.000 title claims abstract description 5
- 230000007547 defect Effects 0.000 claims abstract description 10
- 238000004519 manufacturing process Methods 0.000 claims abstract description 9
- 230000004044 response Effects 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 7
- 208000025174 PANDAS Diseases 0.000 claims description 6
- 208000021155 Paediatric autoimmune neuropsychiatric disorders associated with streptococcal infection Diseases 0.000 claims description 6
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- 235000016496 Panda oleosa Nutrition 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 5
- 238000010923 batch production Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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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
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
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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
- G06F16/24—Querying
- G06F16/248—Presentation of query results
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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
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
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Abstract
The invention discloses a fault detail automatic grabbing method, equipment and a storage medium based on a diag system, which comprises the following steps of firstly, selecting batch inquiry or single inquiry, entering a second step if single inquiry is selected, and entering a third step if batch inquiry is selected; step two, directly inquiring fault details of a single item by acquiring a machine serial number and fault information; logging in a diag system through a crawler; traversing all machines in the uploaded production defect table, and carrying out batch inquiry on fault details to acquire detailed fault information; and fifthly, arranging fault details of all machines into a table, and outputting the table in batches. The invention has short time for capturing fault information, small log information and difficult error caused by repeated times. Fault information can be automatically fetched in batches within 10 seconds, and the result is more accurate.
Description
Technical Field
The invention relates to the technical field of automatic grabbing of fault details, in particular to an automatic grabbing method of fault details based on a diag system.
Background
When detailed fault information query is performed by utilizing the diag system, for batch production defect data, the serial number of each corresponding query machine needs to be manually input, a log of the corresponding fault is searched, and corresponding fault details are searched in the log. This process itself is very time consuming and the log information is very bulky, repeated multiple times and error prone.
Disclosure of Invention
The invention aims to solve the problems, and provides an automatic fault detail grabbing method based on a diag system, which can realize two functions of batch inquiry and single inquiry, can automatically grab fault details in batches and has more accurate results.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a fault detail automatic grabbing method based on a diag system comprises the following steps:
step one, selecting batch inquiry or single inquiry, if single inquiry is selected, entering step two, and if batch inquiry is selected, entering step three;
step two, directly inquiring fault details of a single item by acquiring a machine serial number and fault information;
logging in a diag system through a crawler;
traversing all machines in the uploaded production defect table, and carrying out batch inquiry on fault details to acquire detailed fault information;
and fifthly, arranging fault details of all machines into a table, and outputting the table in batches.
The specific method of the second step comprises the following steps:
step 2.1, selecting a corresponding factory url, and creating a session by using a third party module requests;
step 2.2, obtaining a serial number and fault information corresponding to the product, and obtaining an information page response corresponding to the detection log;
and 2.3, capturing detailed fault contents in the log information page, and outputting the detailed fault contents to the interface text box.
The specific method of the third step is as follows:
step 3.1, storing IP addresses of diag systems of different factories as dictionaries, selecting corresponding factories, and logging in the diag systems by combining account passwords through third party module requests;
and 3.2, if the login is correct, automatically storing the account password and maintaining the session, and if the login is not correct, re-executing the login operation.
The specific method of the fourth step is as follows:
step 4.1, creating an input production defect query table in the format of ". Xls" or ". Xlsx" as a table in the format of a DataFrame;
step 4.2, traversing each piece of information, and obtaining a serial number and fault information in each piece of information, wherein the fault information comprises information of detection log types and fault prompts;
step 4.3, acquiring a response of the corresponding product information page by combining the serial number;
step 4.4, extracting a log type from the fault information, and obtaining a response of a corresponding log information page;
and 4.5, positioning the fault details according to the fault prompt information in the fault information, and capturing the fault details.
In the step 4.1, the creation is performed by using a third party module pandas.
In step 4.2, each piece of information is traversed using a loc function.
In the fifth step, the fault details of all the machines are arranged into an 'xlsx' table and output in batches.
The specific method of the fifth step comprises the following steps:
step 5.1, sorting the grasped fault detail information into a list, and adding the types of the fault information incapable of being grasped into a log incapable of being grasped, the fault information incapable of being grasped and the corresponding serial number incapable of being grasped into corresponding positions of the list according to specific grasping conditions;
and 5.1, adding the fault detail information into the DataFrame format table generated in the step 4.1 by using a pandas third party module, and storing the fault detail information into a 'xlsx' format.
An apparatus comprising a memory storing a computer program and a processor implementing steps of employing the method for automatically grabbing fault details based on a diag system when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method for automatically grabbing fault details based on a diag system.
The invention has the beneficial effects that:
the invention has short time for capturing fault information, small log information and difficult error caused by repeated times. Fault information can be automatically fetched in batches within 10 seconds, and the result is more accurate.
Drawings
FIG. 1 is a fault query system interface overview embodying the present invention;
FIG. 2 is a flow chart of the method of the present invention;
FIG. 3 is a batch fault detail acquisition flow chart.
Detailed Description
The invention will be further described with reference to the drawings and examples.
As shown in fig. 1-2, an automatic fault detail grabbing method based on a diag system includes:
step one, selecting batch inquiry or single inquiry, if single inquiry is selected, entering step two, and if batch inquiry is selected, entering step three;
step two, directly inquiring fault details of a single item by acquiring a machine serial number and fault information;
logging in a diag system through a crawler;
traversing all machines in the uploaded production defect table, and carrying out batch inquiry on fault details to acquire detailed fault information;
and fifthly, arranging fault details of all machines into a table, and outputting the table in batches.
The specific method of the second step is as follows:
step 2.1, selecting a corresponding factory url, and creating a session by using a third party module requests;
step 2.2, filling in serial numbers and fault information corresponding to the products, and obtaining information page responses corresponding to the detection logs;
and 2.3, capturing detailed fault contents in the log information page, and outputting the detailed fault contents to the interface text box.
The method for logging in the diag system through the crawler comprises the following steps:
step 3.1, storing IP addresses of diag systems of different factories as dictionaries, selecting corresponding factories when executing programs, and logging in the diag systems by combining account passwords through third party module requests;
and 3.2, if the login is correct, the account number and the password are automatically saved and the session is maintained, and if the login is not correct, the program executes login operation again.
As shown in fig. 3, the method in the fourth step is as follows:
step 4.1, using a third party module pandas to create an input production defect query table in an 'xls' or 'xlsx' format into a table in a DataFrame format, so that the table is convenient to read later;
step 4.2, arranging a DataFrame table, traversing each piece of information by using a loc function, and acquiring a serial number and fault information in each piece of information; dataFrame is a tabular data structure containing a set of ordered columns, each of which may be a different value;
step 4.3, under the session state maintained before, acquiring the response of the corresponding product information page by combining the serial number;
step 4.4, the fault information contains information for detecting the log type and fault prompt, the log type is extracted from the fault information, and the response of the corresponding log information page is obtained under the session state maintained before;
and 4.5, positioning the fault details according to the fault prompt information in the fault information, and capturing the fault details.
The method in the fifth step comprises the following steps:
step 5.1, sorting the previously captured fault detail information into a list, classifying the fault information which cannot be captured into a log which cannot be found, the fault information which cannot be found and the type of the corresponding serial number which cannot be found into the specific capturing situation, and adding the types of the fault information which cannot be found into the corresponding position of the list;
and 5.2, adding the fault detail information into the DataFrame by using a pandas third party module, and storing the fault detail information into a 'xlsx' format.
The fault query system provided by the invention can provide two functions of batch query and single query. The batch query function can directly traverse all machines in the input production defect table to perform batch query of fault details. The code implementation process is as follows: 1. sorting the production defect lookup table; 2. determining a corresponding query log according to each piece of fault information; 3. after finding out the corresponding log in the diag system, grabbing detailed fault information from the log; 4. and (5) arranging fault details of all the machines into an 'xlsx' format table, and outputting the fault details in batches. The single item inquiry function directly inquires the fault details of the single item by inputting the machine serial number and the fault information.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (8)
1. The automatic fault detail grabbing method based on the diag system is characterized by comprising the following steps of:
step one, selecting batch inquiry or single inquiry, if single inquiry is selected, entering step two, and if batch inquiry is selected, entering step three;
step two, directly inquiring fault details of a single item by acquiring a machine serial number and fault information;
logging in a diag system through a crawler;
traversing all machines in the uploaded production defect table, and carrying out batch inquiry on fault details to acquire detailed fault information;
step five, the fault details of all the machines are arranged into a table, and the table is output in batches;
the specific method of the second step comprises the following steps:
step 2.1, selecting a corresponding factory url, and creating a session by using a third party module requests;
step 2.2, obtaining a serial number and fault information corresponding to the product, and obtaining an information page response corresponding to the detection log;
step 2.3, capturing detailed fault contents in the log information page and outputting the detailed fault contents into an interface text box;
the specific method of the third step is as follows:
step 3.1, storing IP addresses of diag systems of different factories as dictionaries, selecting corresponding factories, and logging in the diag systems by combining account passwords through third party module requests;
and 3.2, if the login is correct, automatically storing the account password and maintaining the session, and if the login is not correct, re-executing the login operation.
2. The automatic fault detail grabbing method based on the diag system as claimed in claim 1, wherein the specific method of the fourth step is as follows:
step 4.1, creating an input production defect query table in the format of ". Xls" or ". Xlsx" as a table in the format of a DataFrame;
step 4.2, traversing each piece of information, and obtaining a serial number and fault information in each piece of information, wherein the fault information comprises information of detection log types and fault prompts;
step 4.3, acquiring a response of the corresponding product information page by combining the serial number;
step 4.4, extracting a log type from the fault information, and obtaining a response of a corresponding log information page;
and 4.5, positioning the fault details according to the fault prompt information in the fault information, and capturing the fault details.
3. The automatic fault detail grabbing method based on the diag system as claimed in claim 2, wherein in the step 4.1, the creation is performed by using a third party module pandas.
4. The automatic fault detail grabbing method based on the diag system as claimed in claim 2, wherein in the step 4.2, each piece of information is traversed by using a loc function.
5. The automatic fault detail grabbing method based on the diag system as claimed in claim 1, wherein in the fifth step, the fault details of each machine are organized into an 'xlsx' form and are output in batches.
6. The automatic fault detail grabbing method based on the diag system as claimed in claim 4, wherein the specific method of the fifth step comprises the following steps:
step 5.1, sorting the grasped fault detail information into a list, and adding the types of the fault information incapable of being grasped into a log incapable of being grasped, the fault information incapable of being grasped and the corresponding serial number incapable of being grasped into corresponding positions of the list according to specific grasping conditions;
and 5.1, adding the fault detail information into the DataFrame format table generated in the step 4.1 by using a pandas third party module, and storing the fault detail information into a 'xlsx' format.
7. An apparatus comprising a memory and a processor, said memory storing a computer program, characterized in that said processor, when executing said computer program, implements the steps of a method for automatically grabbing fault details based on a diag system as claimed in any one of claims 1-6.
8. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of a fault detail automatic grabbing method based on a diag system as claimed in any one of claims 1-6.
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| JPH05346913A (en) * | 1992-06-15 | 1993-12-27 | Hitachi Ltd | Fault information storing system |
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| JP2017107372A (en) * | 2015-12-09 | 2017-06-15 | 三菱電機株式会社 | Failure symptom detection system and failure symptom detection method |
| CN108170582A (en) * | 2017-12-28 | 2018-06-15 | 政采云有限公司 | System mode querying method and device, computer readable storage medium |
| CN110070191A (en) * | 2019-04-11 | 2019-07-30 | 苏州浪潮智能科技有限公司 | A kind of method and system carrying out failure control based on MES system and diagnostic system |
| CN111861113A (en) * | 2020-06-12 | 2020-10-30 | 苏州浪潮智能科技有限公司 | MES system-based server manufacturing system and method |
-
2021
- 2021-09-16 CN CN202111087379.0A patent/CN113901295B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05346913A (en) * | 1992-06-15 | 1993-12-27 | Hitachi Ltd | Fault information storing system |
| CN104598973A (en) * | 2015-01-08 | 2015-05-06 | 北京红马传媒文化发展有限公司 | Ticket purchase registry system and method |
| JP2017107372A (en) * | 2015-12-09 | 2017-06-15 | 三菱電機株式会社 | Failure symptom detection system and failure symptom detection method |
| CN108170582A (en) * | 2017-12-28 | 2018-06-15 | 政采云有限公司 | System mode querying method and device, computer readable storage medium |
| CN110070191A (en) * | 2019-04-11 | 2019-07-30 | 苏州浪潮智能科技有限公司 | A kind of method and system carrying out failure control based on MES system and diagnostic system |
| CN111861113A (en) * | 2020-06-12 | 2020-10-30 | 苏州浪潮智能科技有限公司 | MES system-based server manufacturing system and method |
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