CN111737993A - Method for extracting health state of equipment from fault defect text of power distribution network equipment - Google Patents
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Abstract
The invention discloses a method for extracting the health state of equipment from a fault defect text of power distribution network equipment. Segmenting words of fault defect texts of the power distribution network equipment, and vectorizing; constructing a training set and a test set; calculating the similarity between the two fault defect texts in the test set and the training set; calculating the optimal value number k of the k-nearest neighbor algorithm, testing each fault defect text in the set, and selecting the k fault defect texts with the maximum similarity in the training set; and calculating the health state of each fault defect text in the test set, taking the health state of the fault defect text in the test set as the weighted average sum of the health states of the k fault defect texts, and then weighting and summing to obtain the final health state. The invention realizes the self-learning mapping from the fault defect text to the health state data, changes the existing mode of evaluating the fault/defect grade, extracts the health state of the equipment from the fault defect text on the basis of the k-nearest neighbor algorithm, and accurately obtains the data of the health state of the whole equipment.
Description
Technical Field
The invention belongs to a method for combining natural language processing and power distribution network equipment data information in the field of intelligent electric power operation and detection, and particularly relates to a method for extracting equipment health state from fault defect texts of power distribution network equipment.
Background
With the acceleration of the power grid information process, a lot of unstructured and semi-structured data are accumulated in the database of the power grid enterprise. As one of the most typical, most complex unstructured data, the analysis of textual data has been a hot problem in the field of data mining.
In the maintenance and repair link of the power system, a large number of maintenance test records, inspection defect elimination records, fault and defect description reports, event sequence records and the like are recorded. The logs and reports mainly appear in the form of short Chinese texts with numbers and letter symbols, which are called as texts for short), contain rich historical running state information, overhaul effect information, reliability information and the like of the equipment, and have great benefits for objectively evaluating the development process of the health state of the equipment.
However, the above information has not been sufficiently mined due to the text having characteristics of being ambiguous, difficult to segment, ambiguous, noisy, and the like. The Chinese text processing of the power grid currently belongs to the starting stage. The intuitive reliability statistical information is mined from the Chinese text information, and complex information mining technology and skillful mining process need to be explored. Chinese text mining has long been recognized as an important and difficult technique. Especially, when it is applied to various professional fields, it is difficult to closely combine with the knowledge of the professional fields. In the field of electric power, foreign scholars provide a basis for utilizing a machine learning method to mine massive historical defect data for a new york power grid, so that power equipment fault prediction and preventive maintenance are provided. However, the chinese text is very different from the english text, and not only there is no space between words, but also the part of speech and the syntactic structure are very different, and the processing method for handling the english text is not feasible. Other texts in the power system, such as fault defect texts, are manually input, the syntax structure is complex, and the main and predicate guest components are difficult to accurately divide, so that the processing is complex and difficult.
The equipment defect classification and analysis statistics work to be carried out by power grid enterprises every year is usually carried out manually, so that the workload is high, time and labor are consumed, and the correctness of the classification and statistics work is difficult to verify due to subjective factors and experience differences. Therefore, data analysis mining based on fault defect texts is very important and urgent.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a mapping method based on fault defect text to equipment health state evaluation.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the method comprises the following steps:
1) performing word segmentation processing on each fault defect text of the power distribution network equipment to obtain word segmentation results;
the fault defect text refers to text data which are input into a computer by different maintenance/overhaul personnel at different time aiming at power distribution network equipment of a power grid system.
2) Vectorizing the fault defect text;
3) constructing a training set and a testing set of fault defect texts of the power distribution network equipment;
collecting all fault defect texts of known power distribution network equipment, vectorizing according to the method in the step 2), classifying according to the health state, forming a fault defect text library, and using the fault defect text library as a training set; vectorizing all fault defect texts of the power distribution network equipment to be tested according to the method in the step 2) to form a test set;
4) calculating the similarity between every two fault defect texts in the test set and the training set;
5) calculating the optimal value of the optimal value number k in the k-nearest neighbor algorithm, and then selecting k fault defect texts with the maximum similarity from the training set aiming at each fault defect text in the test set;
in the conventional method, the values of the parameter k are all preset and specified. According to the method, the optimal k value is calculated according to the training set of the fault defect text, so that the prediction accuracy rate on the training set is highest.
6) Calculating the Health state results HI (Health Index) of each fault defect text in the test set, wherein HI is a decimal number between 0 and 1, namely HI is more than or equal to 0 and less than or equal to 1, 0 and 1 respectively represent equipment fault and complete Health, and taking the Health state of each fault defect text in the test set as the weighted average sum of the Health states corresponding to k fault defect texts in the training set obtained in the step (5); and then weighting and summing the health states of the fault defect texts in the test set to obtain the final health state H of the power distribution network equipment.
The step 5) is as follows:
5.1) dividing the health state HI into 3 types, wherein the corresponding regions are respectively [0,0.33 ], [0.33,0.67 ], and [0.67,1], setting the initial value of the optimal value number k to be 20, and setting the initial value of the number n of selected fault defect texts of the fault defect texts in the training set;
5.2) aiming at the vectorization result of n fault defect texts in the training set, wherein n is 0-J, and k fault defect texts with the highest similarity are selected from the training set according to the similarity obtained by calculation in the step 4);
5.3) taking the category to which the health state HI corresponding to the k fault defect texts belongs to carry out average calculation to obtain a prediction category, and then judging:
if the corresponding category of the fault defect text in the training set is consistent with the prediction category, the fault defect text is considered to be correct, otherwise, the fault defect text is considered to be wrong;
5.4) counting the corresponding accuracy of the n fault defect texts;
5.5) if the accuracy of the current iteration is obviously improved by 20 percent or more than the accuracy of the last iteration, reducing the k value by 1, and returning to the step 5.2) to repeat the step process;
if the accuracy of the current iteration is not obviously improved by 20% or more than the accuracy of the last iteration, the current k value is an optimal value;
5.6) aiming at each fault defect text in the test set, selecting k fault defect texts with the maximum similarity in the training set.
In the step 6), the weighted average sum of the health states corresponding to the k fault defect texts in the training set obtained in the step (5) is used as the health state of each fault defect text in the test set by adopting the following formula:
wherein, HIxRepresenting the health state of the x-th fault defect sample in the k fault defect texts selected in the training set;
representing selected w-th in the test setiHealth status of individual fault defect samples.
The step 1) adopts a hidden Markov model-based Chinese word segmentation technology (HMM-CWS) method to carry out the preprocessing of text word segmentation.
And 2) corresponding each word after word segmentation to one dimension in the vector space, sorting according to word frequency, eliminating repeated words, obtaining a non-repeated word sequence, and forming a complete vector space by the non-repeated word sequence.
And 4) calculating the similarity between each fault defect text in the test set and each fault defect text in the training set according to a similarity measurement formula.
The invention has the beneficial effects that:
the method realizes self-learning mapping from the fault defect text to the health state data, changes the existing mode of evaluating the fault/defect grade, extracts the equipment health state from the fault defect text on the basis of the k-nearest neighbor algorithm, and provides a basis for power grid enterprises to accurately obtain the data of the whole equipment health state.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
The embodiment of the invention and the implementation process thereof are as follows:
according to the embodiment, different maintenance/repair personnel record the fault conditions of the power distribution network equipment of the power grid system at different times and input the fault conditions into a computer in a text form to form fault defect text data of the power distribution network equipment.
1) Performing word segmentation processing on each fault defect text of the power distribution network equipment to obtain word segmentation results;
specifically, a hidden Markov model-based Chinese word segmentation technology (HMM-CWS) method is adopted to carry out preprocessing of text word segmentation.
2) Vectorizing the fault defect text;
and step 1) corresponding each word after word segmentation to one dimension in a vector space, sorting according to word frequency, eliminating repeated words, obtaining a non-repeated word sequence, wherein the non-repeated word sequence forms a complete vector space.
The complete vector space W is formed as shown in detail belowALL：
WALL＝[wij]IxJ
Wherein, wijRepresenting the weight, w, between the ith fault defect text in the test set and the jth fault defect text in the training setij0 or 1. When w isijWhen the word vector is 1, the word vector is contained in the text, otherwise, the word vector is 0; i represents the total number of defective text in the test set, J represents the total number of defective text in the training set, and]IxJthe size of the representation matrix is I × J.
3) Constructing a training set and a testing set of fault defect texts of the power distribution network equipment;
collecting all fault defect texts of known power distribution network equipment, vectorizing according to the method in the step 2), classifying according to the health state, forming a fault defect text library, and using the fault defect text library as a training set, wherein each fault defect text corresponds to a specific health state classification; vectorizing all fault defect texts of the power distribution network equipment to be tested according to the method in the step 2) to form a test set; the test set is a vectorized fault defect text set to be classified.
In particular implementations, the health status-based classifications are specifically classified into three categories, full health, sub-health, and failure.
4) Calculating the similarity between every two fault defect texts in the test set and the training set;
specifically, the similarity between each fault defect text in the test set and each fault defect text in the training set is calculated according to a similarity measurement formula.
The similarity formula is as follows:
wherein S isijFor similarity between the ith fault defect text in the test set and the jth fault defect text in the training set, wiIs a feature vector of the text i, wjIs the feature vector of text j, M is the dimension of the vector, wil、wjlIs a vector wi、wjThe l dimensional value of (1).
5) Calculating the optimal value of the optimal value number k in the k-nearest neighbor algorithm, and then selecting k fault defect texts with the maximum similarity from the training set aiming at each fault defect text in the test set;
5.1) dividing the Health state HI (HI is a decimal number between 0 and 1, namely HI is more than or equal to 0 and less than or equal to 1, and 0 and 1 respectively represent equipment failure and complete Health) into 3 types, wherein the corresponding zone regions are respectively [0,0.33 ], [0.33,0.67 ], [0.67,1], and the initial value of the optimal value number k is 20;
5.2) aiming at the result of vectorization of n fault defect texts in the training set, the value range of n is an integer of [1, J ], and k fault defect texts with the highest similarity are selected from the training set according to the similarity obtained by calculation in the step 4);
5.3) taking the category to which the health state HI corresponding to the k fault defect texts belongs to carry out average calculation to obtain a prediction category, and then judging:
if the corresponding category of the fault defect text in the training set is consistent with the prediction category, the fault defect text is considered to be correct, otherwise, the fault defect text is considered to be wrong;
5.4) counting the corresponding accuracy of the n fault defect texts;
5.5) if the accuracy of the current iteration is 20% or more higher than the accuracy of the last iteration, reducing the k value by 1, and returning to the step 5.2) to repeat the step process;
if the accuracy of the current iteration is not increased by 20% or more than the accuracy of the last iteration, the current k value is an optimal value;
5.6) aiming at each fault defect text in the test set, selecting k fault defect texts with the maximum similarity in the training set.
6) Calculating a health state result HI of each fault defect text in the test set, and taking the health state of each fault defect text in the test set as the weighted average sum of the health states corresponding to the k fault defect texts in the training set obtained in the step (5); and then weighting and summing the health states of the fault defect texts in the test set to obtain the final health state HI of the power distribution network equipment.
And (3) taking the weighted average sum of the health states corresponding to the k fault defect texts in the training set obtained in the step (5) as the health state of each fault defect text in the test set by adopting the following formula:
wherein, HIxRepresenting the health state of the x-th fault defect sample in the k fault defect texts selected in the training set;
representing selected w-th in the test setiHealth status of individual fault defect samples.
Claims (6)
1. A method for extracting the health state of equipment from fault defect texts of power distribution network equipment is characterized by comprising the following steps of: the method comprises the following steps:
1) performing word segmentation processing on each fault defect text of the power distribution network equipment to obtain word segmentation results;
2) vectorizing the fault defect text;
3) constructing a training set and a testing set of fault defect texts of the power distribution network equipment;
collecting all fault defect texts of known power distribution network equipment, vectorizing according to the method in the step 2), classifying according to the health state, forming a fault defect text library, and using the fault defect text library as a training set; vectorizing all fault defect texts of the power distribution network equipment to be tested according to the method in the step 2) to form a test set;
4) calculating the similarity between every two fault defect texts in the test set and the training set;
5) calculating the optimal value of the optimal value number k in the k-nearest neighbor algorithm, and then selecting k fault defect texts with the maximum similarity from the training set aiming at each fault defect text in the test set;
6) calculating the health state results HI of each fault defect text in the test set, wherein HI is a decimal number between 0 and 1, namely HI is more than or equal to 0 and less than or equal to 1, 0 and 1 respectively represent equipment fault and complete health, and taking the health state of each fault defect text in the test set as the weighted average sum of the health states corresponding to k fault defect texts in the training set obtained in the step (5); and then weighting and summing the health states of the fault defect texts in the test set to obtain the final health state H of the power distribution network equipment.
2. The method for extracting the health status of the fault defect text of the power distribution network equipment according to claim 1, wherein the method comprises the following steps: the step 5) is as follows:
5.1) dividing the health state HI into 3 types, wherein the corresponding regions are respectively [0,0.33 ], [0.33,0.67 ], and [0.67,1], setting the initial value of the optimal value number k to be 20, and setting the initial value of the number n of selected fault defect texts of the fault defect texts in the training set;
5.2) aiming at the vectorization result of n fault defect texts in the training set, wherein n is 0-J, and k fault defect texts with the highest similarity are selected from the training set according to the similarity obtained by calculation in the step 4);
5.3) taking the category to which the health state HI corresponding to the k fault defect texts belongs to carry out average calculation to obtain a prediction category, and then judging:
if the corresponding category of the fault defect text in the training set is consistent with the prediction category, the fault defect text is considered to be correct, otherwise, the fault defect text is considered to be wrong;
5.4) counting the corresponding accuracy of the n fault defect texts;
5.5) if the accuracy of the current iteration is obviously improved by 20 percent or more than the accuracy of the last iteration, reducing the k value by 1, and returning to the step 5.2) to repeat the step process;
if the accuracy of the current iteration is not obviously improved by 20% or more than the accuracy of the last iteration, the current k value is an optimal value;
5.6) aiming at each fault defect text in the test set, selecting k fault defect texts with the maximum similarity in the training set.
3. The method for extracting the health status of the fault defect text of the power distribution network equipment according to claim 1, wherein the method comprises the following steps: in the step 6), the weighted average sum of the health states corresponding to the k fault defect texts in the training set obtained in the step (5) is used as the health state of each fault defect text in the test set by adopting the following formula:
wherein, HIxRepresenting the health state of the x-th fault defect sample in the k fault defect texts selected in the training set;
representing selected w-th in the test setiHealth status of individual fault defect samples.
4. The method for extracting the health status of the fault defect text of the power distribution network equipment according to claim 1, wherein the method comprises the following steps: the step 1) adopts a hidden Markov model-based Chinese word segmentation technology (HMM-CWS) method to carry out the preprocessing of text word segmentation.
5. The method for extracting the health status of the fault defect text of the power distribution network equipment according to claim 1, wherein the method comprises the following steps: and 2) corresponding each word after word segmentation to one dimension in the vector space, sorting according to word frequency, eliminating repeated words, obtaining a non-repeated word sequence, and forming a complete vector space by the non-repeated word sequence.
6. The method for extracting the health status of the fault defect text of the power distribution network equipment according to claim 1, wherein the method comprises the following steps: and 4) calculating the similarity between each fault defect text in the test set and each fault defect text in the training set according to a similarity measurement formula.
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| CN112925668A (en) * | 2021-02-25 | 2021-06-08 | 北京百度网讯科技有限公司 | Server health evaluation method, device, equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112580875A (en) * | 2020-12-21 | 2021-03-30 | 泉州装备制造研究所 | Fault prediction method and system for power distribution device |
| CN112580875B (en) * | 2020-12-21 | 2022-06-24 | 泉州装备制造研究所 | Fault prediction method and system for power distribution device |
| CN112925668A (en) * | 2021-02-25 | 2021-06-08 | 北京百度网讯科技有限公司 | Server health evaluation method, device, equipment and storage medium |
| CN112925668B (en) * | 2021-02-25 | 2024-04-05 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for evaluating server health |
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