CN114462735A - Intelligent pushing method for quality defect report of nuclear power plant - Google Patents

Intelligent pushing method for quality defect report of nuclear power plant Download PDF

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CN114462735A
CN114462735A CN202011240380.8A CN202011240380A CN114462735A CN 114462735 A CN114462735 A CN 114462735A CN 202011240380 A CN202011240380 A CN 202011240380A CN 114462735 A CN114462735 A CN 114462735A
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score
equipment
qdr
code
field
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张冀兰
杨逗
杨加东
张廉
汤奔
刘华
吴宝华
富会佳
胡文勇
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CNNC Nuclear Power Operation Management Co Ltd
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Abstract

The invention discloses an intelligent pushing method for quality defect reports of a nuclear power plant, which comprises the following steps: step 1: preparing data; step 2: matching rules; and step 3: a correction factor; and 4, step 4: and (6) outputting data. The invention has the beneficial effects that: by the method, how to process the defects can be accurately worked out in daily work, and the running safety of the nuclear power plant is further guaranteed. During major repairs, time can be saved and the burden on equipment responsibility engineers reduced-. The method can match the QDR of the nuclear power plant with the related QDR and push the QDR to a filler, so that the filler can quickly know the defects of the same type, required tools and instruments and problems possibly encountered in the defect treatment process, and an optimal treatment method is worked out.

Description

Intelligent pushing method for quality defect report of nuclear power plant
Technical Field
The invention belongs to the field of maintenance of nuclear power plants, and particularly relates to an intelligent pushing method for quality defect reports of a nuclear power plant.
Background
The Quality Defect Report (QDR) of the nuclear power plant is an equipment quality abnormity report aiming at the condition that the quality of unexpected items in the maintenance process does not meet the original design requirements, and corrective measures must be made in time to repair equipment or the quality of the items to enable the quality of the items to meet the design requirements.
At present, when the QDR of the nuclear power plant is filled, a certain working experience of a filler is required, or a person with rich experience is required to guide, so that an optimal processing scheme can be worked out, and the QDR cannot be filled accurately and quickly. Especially during overhaul, the construction period is tight, the task is heavy, and sometimes the QDR filling delay causes other work delay.
Disclosure of Invention
The invention aims to provide an intelligent pushing method for a nuclear power plant quality defect report, which can match the QDR of a nuclear power plant with the related QDR and push the QDR to a reporter, so that the reporter can quickly know the problems possibly encountered in the defect treatment process, required tools and devices and the defect treatment process of the same type in the prior art, and an optimal treatment method is worked out.
The technical scheme of the invention is as follows: an intelligent pushing method for quality defect reports of nuclear power plants comprises the following steps:
step 1: preparing data;
step 2: matching rules;
and 3, step 3: a correction factor;
and 4, step 4: and (6) outputting the data.
The step 1 specifically comprises: past QDR data input: device encoding, QDR topic, device name, defect description, preliminary cause analysis,
the step 2 specifically comprises the following steps:
(1) when the reactor type is a pressurized water reactor, the main form of the equipment code is a set number (number), a system code (letter) and an equipment code (number and letter) matching rule:
1) if the "device code" field is normal, i.e. the code complies with the power plant device code rules, the matching rules are as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except that the first unit number and the middle number (the middle number refers to the NNNNEEEE NNNNNNNNNN, namely the number behind the unit U and the system SSS) are different in the equipment code, the other numbers are the same, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the score of the similarity is normalized, and the highest score wd；
e)wScore of＝wa/wb/wc+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
2) When the 'equipment code' field is abnormal and the 'QDR theme' and the 'defect description' have normal equipment codes, the equipment codes of the 'QDR theme' and the 'defect description' field need to be extracted, and when a plurality of normal equipment codes are contained, all the equipment codes are extracted, and the equipment codes are in a one-to-many relationship, and the matching principle is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same in the equipment code, and the score is wb；
c) Except that the first unit number and the middle number (the middle number refers to the NNNNEEEE NNNNNNNNNN, namely the number behind the unit U and the system SSS) are different in the equipment code, the other numbers are the same, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the score of the similarity is normalized, and the highest score is obtainedScore value wd；
e)wScore of＝MAX(wa，wb，wc)+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
3) When the equipment coding field is abnormal and no equipment coding meeting the regulation is available in the QDR theme and the defect description, the fields of the QDR theme, the equipment name and the preliminary reason analysis are learned according to the semantic similarity, and the fields higher than w are pushed through normalization processingLimiting the scoreThe data of (1).
(2) When the reactor type is a pressurized water reactor, the main form of the equipment code is a system code (letter) + "-" + equipment position number (letter, number, - + "-" + type code (letter), and the matching rule is as follows:
1) if the equipment code field is normal, namely the code conforms to the power plant equipment code rule, the matching rule is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the equipment position number, the equipment codes are all the same, and the score is wb；
c) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wc；
d)wScore of＝wa/wb+wcPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
2) When the 'equipment code' field is abnormal and the 'QDR theme' and the 'defect description' have normal equipment codes, the equipment codes of the 'QDR theme' and the 'defect description' field need to be extracted, and when a plurality of normal equipment codes are contained, all the equipment codes are extracted, and the equipment codes are in a one-to-many relationship, and the matching principle is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the equipment position number, the equipment codes are all the same, and the score is wb；
c) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wc；
d)wScore of＝MAX(wa，wb)+wcPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
3) When the 'equipment code' field is abnormal and no equipment code meeting the regulation is available in the 'QDR theme' and the 'defect description', the 'QDR theme', 'equipment name' and 'preliminary cause analysis' field are learned according to the semantic similarity, and here, the field is required to be normalized and pushed to be higher than wLimiting the scoreThe data of (1).
(3) When the heap type is a heavy water heap, the main form of the equipment code is a unit number (number) + "-" + system code (number) + "-" + equipment code, and the matching principle is as follows:
1) if the equipment code field is normal, namely the code conforms to the power plant equipment code rule, the matching rule is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except for the first unit number in the device code, if-SSSSS-EE (for example, U-SSSSS-EE-QQQ, i.e., after unit U-SSSSS-EE, i.e., "-" + system code + "-" + english letter (possibly one, two or three), and after english letter, if "-" is terminated) is the same, the other is different, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝wa/wb/wc+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
2) When the 'equipment code' field is abnormal and the 'QDR theme' and the 'defect description' have normal equipment codes, the equipment codes of the 'QDR theme' and the 'defect description' field need to be extracted, and when a plurality of normal equipment codes are contained, all the equipment codes are extracted, and the equipment codes are in a one-to-many relationship, and the matching principle is as follows:
a) the device codes are all identical (same device), with a score of wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except for the first unit number in the device code, if-SSSSS-EE (for example, U-SSSSS-EE-QQQ, i.e., after unit U-SSSSS-EE, i.e., "-" + system code + "-" + english letter (possibly one, two or three), and after english letter, if "-" is terminated) is the same, the other is different, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝MAX(wa，wb，wc)+wdPush only wScore ofAt wLimiting the scoreAnd the data of the above scores are recorded,
3) when the 'equipment code' field is abnormal and no equipment code meeting the regulation is available in the 'QDR theme' and the 'defect description', the 'QDR theme', 'equipment name' and 'preliminary cause analysis' field are learned according to the semantic similarity, and here, the field is required to be normalized and pushed to be higher than wLimiting the scoreThe data of (1).
The step 3 specifically comprises:
the output QDR can be sorted from high to low according to the scores, if the scores of the two QDRs are the same, the QDR is judged according to fields of 'QC verification opinion' and 'equipment responsibility engineer closing opinion', and if the fields contain NCR (NCR transfer), QDR (QDR transfer) and worksheet transfer, the score of the QDR is additionally increased by wWeight scoresThe total score is: w is aTotal score＝wScore of+wWeight scores。
The step 4 specifically comprises:
the pushed QDR display fields are: QDR number, QDR theme, device code, device name, status.
The invention has the beneficial effects that: by the method, how to process the defects can be accurately worked out in daily work, and the running safety of the nuclear power plant is further guaranteed. During the overhaul period, the time can be saved, and the burden of equipment responsibility engineers is relieved.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
An intelligent pushing method for quality defect reports of nuclear power plants comprises the following steps:
step 1: data preparation
The method specifically comprises the following steps: past QDR data input: device coding, QDR topic, device name, defect description, preliminary cause analysis.
Step 2: matching rules
(1) When the reactor type is a pressurized water reactor, the main form of the equipment code is a set number (number), a system code (letter) and an equipment code (number and letter) matching rule:
1) if the "equipment code" field is normal, the code conforms to the power plant equipment code rule. The matching rule is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except that the first unit number and the middle number (the middle number refers to the NNNNEEEE NNNNNNNNNN, namely the number behind the unit U and the system SSS) are different in the equipment code, the other numbers are the same, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝wa/wb/wc+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
2) When the device code field is abnormal and there is a normal device code in the QDR topic and the defect description, the device code of the QDR topic and the defect description field needs to be extracted. When the code contains a plurality of normal equipment codes, all the codes are extracted. Here, the relationship is one-to-many, and the matching principle is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except that the first unit number and the middle number (the middle number refers to the NNNNEEEE NNNNNNNNNN, namely the number behind the unit U and the system SSS) are different in the equipment code, the other numbers are the same, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝MAX(wa，wb，wc)+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
3) When the equipment coding field is abnormal and no equipment coding meeting the regulation is available in the QDR theme and the defect description, the fields of the QDR theme, the equipment name and the preliminary reason analysis are learned according to the semantic similarity, and the fields higher than w are pushed through normalization processingLimiting the scoreThe data of (1).
(2) When the reactor type is a pressurized water reactor, the main form of the equipment code is a system code (letter) + "-" + equipment position number (letter, number, - + "-" + type code (letter), and the matching rule is as follows:
1) if the equipment code field is normal, namely the code conforms to the power plant equipment code rule, the matching rule is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the equipment position number, the equipment codes are all the same, and the score is wb；
c) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wc；
d)wScore of＝wa/wb+wcPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
2) When the 'equipment code' field is abnormal and the 'QDR theme' and the 'defect description' have normal equipment codes, the equipment codes of the 'QDR theme' and the 'defect description' field need to be extracted, and when a plurality of normal equipment codes are contained, all the equipment codes are extracted, and the equipment codes are in a one-to-many relationship, and the matching principle is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the equipment position number, the equipment codes are all the same, and the score is wb；
c) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wc；
d)wScore of＝MAX(wa，wb)+wcPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
3) When the 'equipment code' field is abnormal and no equipment code meeting the regulation is available in the 'QDR theme' and the 'defect description', the 'QDR theme', 'equipment name' and 'preliminary cause analysis' field are learned according to the semantic similarity, and here, the field is required to be normalized and pushed to be higher than wLimiting the scoreThe data of (1).
(3) When the heap type is a heavy water heap, the main form of the equipment code is a unit number (number) + "-" + system code (number) + "-" + equipment code, and the matching principle is as follows:
1) if the "equipment code" field is normal, the code conforms to the power plant equipment code rule. The matching rules are as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except for the first unit number in the device code, if-SSSSS-EE (for example, U-SSSSS-EE-QQQ, i.e., after unit U-SSSSS-EE, i.e., "-" + system code + "-" + english letter (possibly one, two or three), and after english letter, if "-" is terminated) is the same, the other is different, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝wa/wb/wc+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
2) When the device code field is abnormal and there is a normal device code in the QDR topic and the defect description, the device code of the QDR topic and the defect description field needs to be extracted. When the code contains a plurality of normal equipment codes, all the codes are extracted. Here, the relationship is one-to-many, and the matching principle is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except for the first unit number in the device code, if-SSSSS-EE (for example, U-SSSSS-EE-QQQ, i.e., after unit U-SSSSS-EE, i.e., "-" + system code + "-" + english letter (possibly one, two or three), and after english letter, if "-" is terminated) is the same, the other is different, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score is obtainedwd；
e)wScore of＝MAX(wa，wb，wc)+wdPush only wScore ofAt wLimiting the scoreAnd data scored above.
3) When the 'equipment code' field is abnormal and no equipment code meeting the regulation is available in the 'QDR theme' and the 'defect description', the 'QDR theme', 'equipment name' and 'preliminary cause analysis' field are learned according to the semantic similarity, and here, the field is required to be normalized and pushed to be higher than wLimiting the scoreThe data of (1).
And step 3: correction factor
The output QDR can be sorted from high to low according to the scores, if the scores of the two QDRs are the same, the QDR is judged according to fields of 'QC verification opinion' and 'equipment responsibility engineer closing opinion', and if the fields contain NCR (NCR transfer), QDR (QDR transfer) and worksheet transfer, the score of the QDR is additionally increased by wWeight scoresThe total score is: w is aTotal score＝wScore of+wWeight scores。
And 4, step 4: data output
The pushed QDR display fields are: QDR number, QDR theme, device code, device name, status.

Claims (9)

1. An intelligent pushing method for quality defect reports of nuclear power plants is characterized by comprising the following steps:
step 1: preparing data;
step 2: matching rules;
and step 3: a correction factor;
and 4, step 4: and (6) outputting the data.
2. The intelligent pushing method for the quality defect report of the nuclear power plant according to claim 1, wherein the step 1 specifically comprises: past QDR data input: device coding, QDR topic, device name, defect description, preliminary cause analysis.
3. The intelligent pushing method for quality defect reports of nuclear power plants according to claim 1, wherein the step 2 specifically comprises the following steps:
(1) when the reactor type is a pressurized water reactor, the main form of the equipment code is a set number (number), a system code (letter) and an equipment code (number and letter) matching rule:
1) if the "device code" field is normal, i.e. the code complies with the power plant device code rules, the matching rules are as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except that the first unit number and the middle number (the middle number refers to the NNNNEEEE NNNNNNNNNN, namely the number behind the unit U and the system SSS) are different in the equipment code, the other numbers are the same, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝wa/wb/wc+wdPush only wScore ofAt wLimiting the scoreAnd the above-scored data;
2) when the 'equipment code' field is abnormal and the 'QDR theme' and the 'defect description' have normal equipment codes, the equipment codes of the 'QDR theme' and the 'defect description' field need to be extracted, and when a plurality of normal equipment codes are contained, all the equipment codes are extracted, and the equipment codes are in a one-to-many relationship, and the matching principle is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except that the first unit number and the middle number (the middle number refers to the NNNNEEEE NNNNNNNNNN, namely the number behind the unit U and the system SSS) are different in the equipment code, the other numbers are the same, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝MAX(wa，wb，wc)+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
3) When the equipment coding field is abnormal and no equipment coding meeting the regulation is available in the QDR theme and the defect description, the fields of the QDR theme, the equipment name and the preliminary reason analysis are learned according to the semantic similarity, and the fields higher than w are pushed through normalization processingLimiting the scoreThe data of (a);
(2) when the reactor type is a pressurized water reactor, the main form of the equipment code is a system code (letter) + "-" + equipment position number (letter, number, - + "-" + type code (letter), and the matching rule is as follows:
1) if the equipment code field is normal, namely the code conforms to the power plant equipment code rule, the matching rule is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the equipment position number, the equipment codes are all the same, and the score is wb；
c) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wc；
d)wScore of＝wa/wb+wcPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
2) When the 'equipment code' field is abnormal and the 'QDR theme' and the 'defect description' have normal equipment codes, the equipment codes of the 'QDR theme' and the 'defect description' field need to be extracted, and when a plurality of normal equipment codes are contained, all the equipment codes are extracted, and the equipment codes are in a one-to-many relationship, and the matching principle is as follows:
a) device codes are all identical (same device), scoresIs wa；
b) Except the equipment position number, the equipment codes are all the same, and the score is wb；
c) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wc；
d)wScore of＝MAX(wa，wb)+wcPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
3) When the 'equipment code' field is abnormal and no equipment code meeting the regulation is available in the 'QDR theme' and the 'defect description', the 'QDR theme', 'equipment name' and 'preliminary cause analysis' field are learned according to the semantic similarity, and here, the field is required to be normalized and pushed to be higher than wLimiting the scoreThe data of (1).
(3) When the heap type is a heavy water heap, the main form of the equipment code is a unit number (number) + "-" + system code (number) + "-" + equipment code, and the matching principle is as follows:
1) if the equipment code field is normal, namely the code conforms to the power plant equipment code rule, the matching rule is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except for the first unit number in the device code, if-SSSSS-EE (for example, U-SSSSS-EE-QQQ, i.e., after unit U-SSSSS-EE, i.e., "-" + system code + "-" + english letter (possibly one, two or three), and after english letter, if "-" is terminated) is the same, the other is different, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝wa/wb/wc+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
2) When the 'equipment code' field is abnormal and the 'QDR theme' and the 'defect description' have normal equipment codes, the equipment codes of the 'QDR theme' and the 'defect description' field need to be extracted, and when a plurality of normal equipment codes are contained, all the equipment codes are extracted, and the equipment codes are in a one-to-many relationship, and the matching principle is as follows:
a) the device codes are all identical (same device), and the score is wa；
b) Except the first unit number, all the other units are the same, and the score is wb；
c) Except for the first unit number in the device code, if-SSSSSS-EE (such as U-SSSSSSSS-EE-QQQ, i.e. -SSSSSSSS-EE-behind the unit U, i.e. "-" + system code + "-" + English letter (possibly one, two or three), and digit or- "-behind English letter is terminated) is the same, the other is different, and the score is wc；
d) Other contents of the 'QDR topic' field and 'primary reason analysis' are matched through semantic similarity, the scores of the similarity are normalized, and the highest score wd；
e)wScore of＝MAX(wa，wb，wc)+wdPush only wScore ofAt wLimiting the scoreAnd the above fractional data.
3) When the 'equipment code' field is abnormal and no equipment code meeting the regulation is available in the 'QDR theme' and the 'defect description', the 'QDR theme', 'equipment name' and 'preliminary cause analysis' field are learned according to the semantic similarity, and here, the field is required to be normalized and pushed to be higher than wLimiting the scoreThe data of (1).
4. The intelligent pushing method for quality defect reports of nuclear power plants according to claim 1, wherein the step 3 specifically comprises:
the output QDRs are sorted from high to low according to the scores, if the scores of the two QDRs are the same, the opinions are verified according to QC, and equipment responsibility engineering is carried outThe teacher closes the opinion' field judgment, if the field contains NCR, QDR and worksheet, the score of QDR is additionally increased by wWeight scoresThe total score is: w is aTotal score＝wScore of+wWeight scores。
5. The intelligent pushing method for quality defect reports of nuclear power plants according to claim 1, wherein the step 4 specifically comprises:
the pushed QDR display fields are: QDR number.
6. The intelligent pushing method for quality defect reports of nuclear power plants according to claim 1, wherein the step 4 specifically comprises:
the pushed QDR display fields are: the QDR topic.
7. The intelligent pushing method for quality defect reports of nuclear power plants according to claim 1, wherein the step 4 specifically comprises:
the pushed QDR display fields are: and (5) encoding the equipment.
8. The intelligent pushing method for quality defect reports of nuclear power plants according to claim 1, wherein the step 4 specifically comprises:
the pushed QDR display fields are: the name of the device.
9. The intelligent pushing method for quality defect reports of nuclear power plants according to claim 1, wherein the step 4 specifically comprises:
the pushed QDR display fields are: the device status.
CN202011240380.8A 2020-11-09 2020-11-09 Intelligent pushing method for quality defect report of nuclear power plant Pending CN114462735A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115098624A (en) * 2022-06-10 2022-09-23 中核核电运行管理有限公司 Accurate matching method for NCR and external event information

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FI905428A0 (en) * 1989-11-02 1990-11-01 Combustion Eng FOERBAETTRAD KONTROLLENHET VID ATOMKRAFTVERK.
WO2000008577A1 (en) * 1998-07-31 2000-02-17 Westinghouse Energy Systems Europe - Wese A method for interrelating safety related documents of a production plant
KR20020030148A (en) * 2000-10-16 2002-04-24 길형보 Parts and product defect handling mathod
AU4391801A (en) * 2001-05-16 2002-11-21 Requisite Technology Inc. Method and apparatus for analyzing the quality of the content of a database
CN108256713A (en) * 2016-12-29 2018-07-06 中核核电运行管理有限公司 Nuclear power plant system measure of supervision based on ERDB
CN109492106A (en) * 2018-11-13 2019-03-19 扬州大学 Text code combined automatic classification method for defect reasons
KR102017162B1 (en) * 2018-12-05 2019-10-14 한국수력원자력 주식회사 Predictive diagnosis method and system of nuclear power plant equipment
CN111898808A (en) * 2020-07-15 2020-11-06 苏州热工研究院有限公司 Nuclear power plant in-service inspection data management and analysis method

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FI905428A0 (en) * 1989-11-02 1990-11-01 Combustion Eng FOERBAETTRAD KONTROLLENHET VID ATOMKRAFTVERK.
WO2000008577A1 (en) * 1998-07-31 2000-02-17 Westinghouse Energy Systems Europe - Wese A method for interrelating safety related documents of a production plant
KR20020030148A (en) * 2000-10-16 2002-04-24 길형보 Parts and product defect handling mathod
AU4391801A (en) * 2001-05-16 2002-11-21 Requisite Technology Inc. Method and apparatus for analyzing the quality of the content of a database
CN108256713A (en) * 2016-12-29 2018-07-06 中核核电运行管理有限公司 Nuclear power plant system measure of supervision based on ERDB
CN109492106A (en) * 2018-11-13 2019-03-19 扬州大学 Text code combined automatic classification method for defect reasons
KR102017162B1 (en) * 2018-12-05 2019-10-14 한국수력원자력 주식회사 Predictive diagnosis method and system of nuclear power plant equipment
CN111898808A (en) * 2020-07-15 2020-11-06 苏州热工研究院有限公司 Nuclear power plant in-service inspection data management and analysis method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115098624A (en) * 2022-06-10 2022-09-23 中核核电运行管理有限公司 Accurate matching method for NCR and external event information

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