CN109933049B - Power dispatching log fault classification method and system - Google Patents
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Abstract
The disclosure provides a power dispatching log fault classification method and system. The power dispatching log fault classification method comprises the following steps: normalizing the format and the template of the power dispatching log; inquiring construction content in the power dispatching log, and extracting construction position information as characteristic value information; inputting the extracted characteristic value information into a trained classifier, and outputting a fault classification result; the classifier is generated based on a decision tree learning algorithm, and the training process is as follows: dividing the extracted normalized characteristic value information of the power dispatching log with the known fault type into a training set and a testing set; and training the classifier by using the training set until the precision of the classification result of the classifier tested by the test set reaches the preset requirement.
Description
Technical Field
The disclosure belongs to the field of power dispatching log fault classification, and particularly relates to a power dispatching log fault classification method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The inventor finds that the formats and templates of the current power dispatching logs are various, the fault types recorded in different power dispatching logs are different, the manual classification task amount is large, and the problem of inaccurate classification is caused by low classification speed and even missing of key information due to non-uniform formats in the existing classification method.
Disclosure of Invention
In order to solve the above problem, a first aspect of the present disclosure provides a power scheduling log fault classification method, which performs fault classification on a power scheduling log by using a classifier generated based on a decision tree learning algorithm, and can improve classification efficiency and accuracy.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
a power dispatching log fault classification method comprises the following steps:
normalizing the format and the template of the power dispatching log;
inquiring construction contents in the power dispatching log, and extracting construction position information in the construction contents as characteristic value information;
inputting the extracted characteristic value information into a trained classifier, and outputting a fault classification result;
the classifier is generated based on a decision tree learning algorithm, and the training process is as follows:
dividing the extracted normalized characteristic value information of the power dispatching log with the known fault type into a training set and a testing set;
and training the classifier by using the training set until the precision of the classification result of the classifier tested by the test set reaches the preset requirement.
In order to solve the above problem, a second aspect of the present disclosure provides a power dispatching log fault classification system, which performs fault classification on a power dispatching log by using a classifier generated based on a decision tree learning algorithm, and can improve classification efficiency and accuracy.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
a power dispatch log fault classification system, comprising:
the normalization module is used for normalizing the format and the template of the power dispatching log;
the characteristic extraction module is used for inquiring construction contents in the power dispatching log and extracting construction position information in the construction contents as characteristic value information;
the fault classification module is used for inputting the extracted characteristic value information into a trained classifier and outputting a fault classification result;
the fault classification module further comprises: a classifier training module to:
the classifier is generated based on a decision tree learning algorithm;
dividing the extracted normalized characteristic value information of the power dispatching log with the known fault type into a training set and a testing set;
and training the classifier by using the training set until the precision of the classification result of the classifier tested by the test set reaches the preset requirement.
In order to solve the above-mentioned problems, a third aspect of the present disclosure provides a computer-readable storage medium for fault classification of a power scheduling log using a classifier generated based on a decision tree learning algorithm, which can improve classification efficiency and accuracy.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
a computer readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps in the power dispatch log fault classification method described above.
In order to solve the above problem, a fourth aspect of the present disclosure provides a computer device that classifies a fault of a power scheduling log by using a classifier generated based on a decision tree learning algorithm, and that can improve classification efficiency and accuracy.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the power dispatch log fault classification method when executing the program.
The beneficial effects of this disclosure are:
according to the method and the system for classifying the faults of the power dispatching logs, the formats and the templates of the power dispatching logs are normalized; inquiring construction contents in the power dispatching log, and extracting construction position information in the construction contents as characteristic value information; inputting the extracted characteristic value information into a trained classifier, and outputting a fault classification result; the classifier is generated based on a decision tree learning algorithm, and is used for classifying the faults of the power dispatching logs, so that the efficiency and the accuracy of the classification of the faults of the power dispatching logs are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of a method for classifying faults of a power dispatching log according to an embodiment of the present disclosure.
Fig. 2 is a schematic structural diagram of a power dispatching log fault classification system provided by an embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Fig. 1 is a flowchart of a method for classifying faults of a power dispatching log according to an embodiment of the present disclosure.
As shown in fig. 1, a method for classifying faults of a power scheduling log of the present embodiment includes:
s101: and normalizing the format and the template of the power dispatching log.
In a specific implementation, in step S101, the normalization process of the format and the template of the power scheduling log is as follows:
s1011: setting a format and a template of a normalized power dispatching log; the template of the normalized power dispatching log comprises command content and event-recording information, wherein the event-recording information comprises a power failure range and construction content;
s1012: format conversion of the power dispatching log;
and setting the normalized power dispatching log format as an excel format.
The current power dispatching log is in a word format, extracting key characters and related expression characters of the word format, and filling the key characters and the related expression characters to corresponding positions of the excel format.
Therefore, the efficiency and the accuracy of later-stage fault classification can be improved.
S1013: and identifying command content, power failure range and construction content of the power dispatching log, and correspondingly filling corresponding content into the normalized template.
It should be noted that, the template of the normalized power scheduling log may include a command number, a command issuing time, an annotation time, a required completion time, a reporting time, and a command recipient, in addition to the command content and the event record information.
According to the embodiment, the power dispatching logs are subjected to normalization processing, so that the formats of the power dispatching logs are consistent with the templates, the efficiency of inquiring the construction contents in the power dispatching logs can be improved, the construction position information can be accurately extracted, the omission of the construction position information is avoided, and the accuracy of final fault classification is ensured.
S102: and inquiring construction contents in the power dispatching log, and extracting construction position information in the construction contents as characteristic value information.
In specific implementation, the step S102 queries the construction content in the power dispatching log, and the process of extracting the construction position information as the characteristic value information includes:
s1021: converting construction contents in the power dispatching log into characters, and screening out punctuations;
s1022: and extracting information associated with the construction position in the characters of the punctuation marks as characteristic value information.
Therefore, the processing time of redundant information is saved, and the efficiency of fault classification of the power dispatching log is improved.
S103: and inputting the extracted characteristic value information into the trained classifier, and outputting a fault classification result.
The classifier is generated based on a decision tree learning algorithm, and the training process is as follows:
dividing the extracted normalized characteristic value information of the power dispatching log with the known fault type into a training set and a testing set;
and training the classifier by using the training set until the precision of the classification result of the classifier tested by the test set reaches the preset requirement.
Specifically, the fault types of the power dispatching log comprise:
line faults, transformer faults, bus faults, and other faults.
According to the fault classification method for the power dispatching logs, the formats and templates of the power dispatching logs are normalized; inquiring construction contents in the power dispatching log, and extracting construction position information in the construction contents as characteristic value information; inputting the extracted characteristic value information into a trained classifier, and outputting a fault classification result; the classifier is generated based on a decision tree learning algorithm, and is used for classifying the faults of the power dispatching logs, so that the efficiency and the accuracy of the classification of the faults of the power dispatching logs are improved.
Fig. 2 is a schematic structural diagram of a power dispatching log fault classification system provided by an embodiment of the present disclosure.
As shown in fig. 2, a power dispatching log fault classification system of the present embodiment includes:
(1) and the normalization module is used for normalizing the format and the template of the power scheduling log.
In a specific implementation, the normalization module further includes:
the normalization setting module is used for setting the format and the template of the normalized power dispatching log; the template of the normalized power dispatching log comprises command content and event-recording information, wherein the event-recording information comprises a power failure range and construction content;
the format conversion determining module is used for converting the format of the power dispatching log;
and the template conversion module is used for identifying the command content, the power failure range and the construction content of the power dispatching log and correspondingly filling the corresponding content into the normalized template.
(2) And the characteristic extraction module is used for inquiring the construction content in the power dispatching log and extracting the construction position information in the power dispatching log as characteristic value information.
In a specific implementation, the feature extraction module is configured to:
converting construction contents in the power dispatching log into characters, and screening out punctuations;
and extracting information associated with the construction position in the characters of the punctuation marks as characteristic value information.
(3) The fault classification module is used for inputting the extracted characteristic value information into a trained classifier and outputting a fault classification result;
the fault classification module further comprises: a classifier training module to:
the classifier is generated based on a decision tree learning algorithm;
dividing the extracted normalized characteristic value information of the power dispatching log with the known fault type into a training set and a testing set;
and training the classifier by using the training set until the precision of the classification result of the classifier tested by the test set reaches the preset requirement.
Wherein, the fault type of the power dispatching log comprises:
line faults, transformer faults, bus faults, and other faults.
The power dispatching log fault classification system of the embodiment normalizes the format and the template of the power dispatching log; inquiring construction contents in the power dispatching log, and extracting construction position information in the construction contents as characteristic value information; inputting the extracted characteristic value information into a trained classifier, and outputting a fault classification result; the classifier is generated based on a decision tree learning algorithm, and is used for classifying the faults of the power dispatching logs, so that the efficiency and the accuracy of the classification of the faults of the power dispatching logs are improved.
In another embodiment, a computer readable storage medium is also provided, on which a computer program is stored, which when executed by a processor implements the steps in the power dispatch log fault classification method as shown in fig. 1.
In another embodiment, a computer device is also provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the steps in the power dispatch log fault classification method shown in fig. 1 are implemented.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (7)
1. A power dispatching log fault classification method is characterized by comprising the following steps:
normalizing the format and the template of the power dispatching log;
inquiring construction contents in the power dispatching log, and extracting construction position information in the construction contents as characteristic value information;
inputting the extracted characteristic value information into a trained classifier, and outputting a fault classification result;
the classifier is generated based on a decision tree learning algorithm, and the training process is as follows:
dividing the extracted normalized characteristic value information of the power dispatching log with the known fault type into a training set and a testing set;
training the classifier by using the training set until the precision of the classification result of the classifier tested by the test set reaches the preset requirement;
the normalization process of the format and the template of the power dispatching log is as follows:
setting a format and a template of a normalized power dispatching log; the template of the normalized power dispatching log comprises command content and event-recording information, wherein the event-recording information comprises a power failure range and construction content;
format conversion of the power dispatching log;
identifying command content, power failure range and construction content of the power dispatching log, and correspondingly filling corresponding content into a normalized template;
setting a normalized power dispatching log format as an excel format;
the format of the current power dispatching log is a word format, extracting key characters and related expression characters of the word format, and filling the key characters and the related expression characters to corresponding positions of an excel format;
the extracting the characteristic value comprises the following steps:
converting construction contents in the power dispatching log into characters, and screening out punctuations;
and extracting information associated with the construction position in the characters of the punctuation marks as characteristic value information.
2. The method for classifying the faults of the power dispatching logs as claimed in claim 1, wherein the process of inquiring the construction contents in the power dispatching logs and extracting the construction position information as the characteristic value information comprises the following steps:
converting construction contents in the power dispatching log into characters, and screening out punctuations;
and extracting information associated with the construction position in the characters of the punctuation marks as characteristic value information.
3. The method for classifying the faults of the power dispatching logs according to claim 1, wherein the fault type of the power dispatching logs comprises the following steps:
line faults, transformer faults, bus faults, and other faults.
4. A power dispatch log fault classification system, comprising:
the normalization module is used for normalizing the format and the template of the power dispatching log;
the characteristic extraction module is used for inquiring construction contents in the power dispatching log and extracting construction position information in the construction contents as characteristic value information;
the fault classification module is used for inputting the extracted characteristic value information into a trained classifier and outputting a fault classification result;
the fault classification module further comprises: a classifier training module to:
the classifier is generated based on a decision tree learning algorithm;
dividing the extracted normalized characteristic value information of the power dispatching log with the known fault type into a training set and a testing set;
training the classifier by using the training set until the precision of the classification result of the classifier tested by the test set reaches the preset requirement;
the normalization module further comprises:
the normalization setting module is used for setting the format and the template of the normalized power dispatching log; the template of the normalized power dispatching log comprises command content and event-recording information, wherein the event-recording information comprises a power failure range and construction content;
the format conversion determining module is used for converting the format of the power dispatching log;
the template conversion module is used for identifying command content, power failure range and construction content of the power dispatching log and correspondingly filling corresponding content into the normalized template; setting a normalized power dispatching log format as an excel format;
the format of the current power dispatching log is a word format, extracting key characters and related expression characters of the word format, and filling the key characters and the related expression characters to corresponding positions of an excel format;
the feature extraction module is configured to:
converting construction contents in the power dispatching log into characters, and screening out punctuations;
and extracting information associated with the construction position in the characters of the punctuation marks as characteristic value information.
5. The power dispatch log fault classification system of claim 4, wherein the fault type of the power dispatch log comprises:
line faults, transformer faults, bus faults, and other faults.
6. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps in the power dispatch log fault classification method as claimed in any one of claims 1 to 3.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the power dispatch log fault classification method of any of claims 1-3.
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