CN116821087A - Power transmission line fault database construction method, device, terminal and storage medium - Google Patents
Power transmission line fault database construction method, device, terminal and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of power failure database construction, in particular to a power transmission line failure database construction method, a device, a terminal and a storage medium; then, carrying out influence analysis on the abnormal data set and the plurality of observation data sets to obtain a plurality of factor data sets; then constructing a fault label according to the characteristic data of the plurality of factor data sets and the abnormal data sets; and finally, associating and adding the fault labels, the plurality of abnormal data sets and the plurality of factor data sets into a database. According to the embodiment of the application, the characteristics of the observation data set are extracted through the characteristic extraction queue, the influence analysis on the fault formation is carried out based on the extracted characteristics, the observation data which has obvious influence on the fault formation can be reserved, the irrelevant data is deleted, the cause of the fault formation can be analyzed through the factor data, the data volume is small, and the misleading of people referring to the database can not be caused.
Description
Technical Field
The application relates to the technical field of power failure database construction, in particular to a power transmission line failure database construction method, a device, a terminal and a storage medium.
Background
Faults of the transmission line have a great relationship with the erection area, form, season and climate of the transmission line. Common faults of the power transmission line include windage faults, pollution flashover faults and icing faults.
The cause, prevention and maintenance method of the power transmission line faults have important significance for the design and construction of the power transmission line, so that a power transmission line fault database is necessary to be constructed, and references are provided for the transformation and maintenance of the existing power transmission line.
Because transmission line faults are associated with a variety of potential factors, it is often difficult to determine the relevance of influencing factors to transmission line faults when constructing a database, and the data of the database can be misleading to technicians when the actual cause of the fault is not provided. In addition, because of the wide variety of fault conditions, the database is constructed by aiming at the problem of classifying faults and facilitating retrieval.
Based on the above, a construction method of the transmission line fault database needs to be developed and designed.
Disclosure of Invention
The embodiment of the application provides a method, a device, a terminal and a storage medium for constructing a power transmission line fault database, which are used for solving the problem of poor accuracy of fault reasons of the fault database in the prior art.
In a first aspect, an embodiment of the present application provides a method for constructing a power transmission line fault database, including:
acquiring an abnormal data set of a power transmission line and a plurality of observation data sets corresponding to the abnormal data set, wherein the abnormal data set comprises a section of the power transmission line, fault data of the power transmission line and abnormal data before fault formation;
performing influence analysis on the abnormal data set and the plurality of observation data sets, and deleting irrelevant observation data sets to obtain a plurality of factor data sets;
constructing a fault label according to the plurality of factor data sets and the characteristic data of the abnormal data set;
and correlating the fault labels, the plurality of abnormal data sets and the plurality of factor data sets, and adding the fault labels, the plurality of abnormal data sets and the plurality of factor data sets into a fault database of a section to which the power transmission line belongs.
In one possible implementation manner, the performing an impact analysis on the abnormal data set and the plurality of observation data sets, and pruning irrelevant observation data sets to obtain a plurality of factor data sets includes:
obtaining a plurality of data feature extraction queues;
for each of the plurality of observation data sets, performing the steps of:
extracting a plurality of feature vectors of the dataset by using the plurality of data feature extraction queues;
respectively carrying out influence analysis on the abnormal data set of the corresponding data set by utilizing the plurality of characteristic vectors to obtain a plurality of influence coefficients;
and determining whether the observed data set is reserved according to the influence coefficients.
In one possible implementation, the data feature extraction queue includes at least one of: the extracting the feature vectors of the data set by using the plurality of data feature extracting queues comprises the following steps:
for each data feature extraction queue, the following steps are performed:
initializing an extraction position;
and a data extraction step: extracting a plurality of data with the same number as the data characteristic queues from the data set according to the extraction position;
taking the data as vectors, and calculating the vector products of the data and the data characteristic queues;
adding the vector product to the feature vector;
and if the extraction position does not reach the final position of the data set, moving the extraction position according to a preset moving step distance, and jumping to the data extraction step.
In one possible implementation manner, the performing, by using the plurality of feature vectors, an influence analysis on the abnormal data set of the corresponding data set to obtain a plurality of influence coefficients includes:
for each feature vector of the plurality of feature vectors, performing the steps of:
calculating an influence coefficient according to a first formula, a feature vector and an abnormal data set of a corresponding data set, wherein the first formula is as follows:
wherein INF is an influence coefficient, feat (N-N) is an (N-N) th element of a feature vector, EXC (M-N) is an (M-N) th element of an abnormal data set, n+1 elements are contained in the feature vector, and M+1 data are contained in the abnormal data set.
In one possible implementation manner, the constructing a fault tag according to the feature data of the plurality of factor data sets and the anomaly data set includes:
acquiring a plurality of feature vectors according to the plurality of factor data sets;
pooling the abnormal data set and the plurality of feature vectors to obtain an abnormal feature value and a plurality of factor feature values of the plurality of feature vectors;
constructing a fault tag vector according to the abnormal characteristic values and the plurality of factor characteristic values;
adding fault tag vectors into tag classes, wherein the tag classes comprise a plurality of tag vectors, and the tag classes are obtained based on similarity clusters of the tag vectors;
and determining the label of the class according to the class center of the label class.
In one possible implementation, the tag class is obtained based on a similarity cluster of tag vectors, including:
obtaining a plurality of tag vectors and separation thresholds;
selecting vectors to be classified: randomly selecting one label vector from a plurality of label vectors which are not clustered as a vector to be classified;
and a target vector searching step: searching a target vector from a plurality of label vectors which are not clustered, wherein the similarity difference between the target vector and the vector to be classified is smaller than a separation threshold value;
if the target vector is found, adding the target vector into the class to which the vector to be classified belongs, taking the target vector as the vector to be classified, and jumping to the target vector searching step;
otherwise, jumping to the vector selection step to be classified.
In one possible implementation, the searching for the target vector from the plurality of label vectors that are not clustered includes:
searching a target vector from a plurality of label vectors which are not clustered according to a second formula, wherein the second formula is as follows:
where SIM is the similarity, LBL (j) is the j-th element of the non-clustered tag vector, LBL tbs (j) SPC for the j-th element of the vector to be classified TH For the separation threshold, J is the total number of elements of the vector to be categorized.
In a second aspect, an embodiment of the present application provides a power transmission line fault database construction apparatus, configured to implement the power transmission line fault database construction method according to the first aspect or any one of possible implementation manners of the first aspect, where the power transmission line fault database construction apparatus includes:
the system comprises an observation data acquisition module, a data acquisition module and a data processing module, wherein the observation data acquisition module is used for acquiring an abnormal data set of a power transmission line and a plurality of observation data sets corresponding to the abnormal data set, and the abnormal data set comprises a section of the power transmission line, fault data of the power transmission line and abnormal data before fault formation;
the data deleting module is used for carrying out influence analysis on the abnormal data set and the plurality of observation data sets, deleting irrelevant observation data sets and obtaining a plurality of factor data sets;
the fault label construction module is used for constructing a fault label according to the plurality of factor data sets and the characteristic data of the abnormal data set;
the method comprises the steps of,
and the database construction module is used for associating the fault labels, the plurality of abnormal data sets and the plurality of factor data sets and adding the fault labels, the plurality of abnormal data sets and the plurality of factor data sets into a fault database of a section to which the power transmission line belongs.
In a third aspect, an embodiment of the present application provides a terminal, including a memory and a processor, where the memory stores a computer program executable on the processor, and where the processor implements the steps of the method according to the first aspect or any one of the possible implementations of the first aspect when the processor executes the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
Compared with the prior art, the embodiment of the application has the beneficial effects that:
the application relates to an embodiment of a power transmission line fault database construction method, which comprises the steps of firstly, acquiring an abnormal data set of a power transmission line and a plurality of observation data sets corresponding to the abnormal data set, wherein the abnormal data set comprises a section of the power transmission line, fault data of the power transmission line and abnormal data before fault formation; then, carrying out influence analysis on the abnormal data set and the plurality of observation data sets, and deleting irrelevant observation data sets to obtain a plurality of factor data sets; then, constructing a fault label according to the plurality of factor data sets and the characteristic data of the abnormal data set; and finally, correlating the fault labels, the plurality of abnormal data sets and the plurality of factor data sets, and adding the fault labels, the plurality of abnormal data sets and the plurality of factor data sets into a fault database of a section to which the power transmission line belongs. According to the embodiment of the application, the characteristics of the observation data set are extracted through the characteristic extraction queue, then, the influence analysis on the fault formation is carried out based on the extracted characteristics, the observation data which has obvious influence on the fault formation can be reserved, and the irrelevant data is deleted, so that the cause of the fault formation can be analyzed through the factor data, the data quantity is small, and the misleading cannot be caused for people referring to the database. According to the embodiment of the application, the label vector is constructed through the characteristics, the fault data is classified based on the classification method, so that the faults and the fault causes can be conveniently searched, and the searching efficiency is higher.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments or the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for constructing a transmission line fault database according to an embodiment of the present application;
FIG. 2 is a flow chart of extracting feature vectors of a dataset using a data feature extraction queue provided by an embodiment of the present application;
fig. 3 is a functional block diagram of a power transmission line fault database construction device according to an embodiment of the present application;
fig. 4 is a functional block diagram of a terminal according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following description will be made with reference to the accompanying drawings.
The following describes in detail the embodiments of the present application, and the present embodiment is implemented on the premise of the technical solution of the present application, and a detailed implementation manner and a specific operation procedure are given, but the protection scope of the present application is not limited to the following embodiments.
Fig. 1 is a flowchart of a method for constructing a power transmission line fault database according to an embodiment of the present application.
As shown in fig. 1, a flowchart of an implementation of a method for constructing a power transmission line fault database according to an embodiment of the present application is shown, and the details are as follows:
in step 101, an abnormal data set of the power transmission line and a plurality of observation data sets corresponding to the abnormal data set are obtained, wherein the abnormal data set comprises a section of the power transmission line, fault data of the power transmission line and abnormal data before fault formation.
In step 102, the anomaly data set and the plurality of observation data sets are subjected to influence analysis, and the irrelevant observation data sets are pruned to obtain a plurality of factor data sets.
In some embodiments, the step 102 includes:
obtaining a plurality of data feature extraction queues;
for each of the plurality of observation data sets, performing the steps of:
extracting a plurality of feature vectors of the dataset by using the plurality of data feature extraction queues;
respectively carrying out influence analysis on the abnormal data set of the corresponding data set by utilizing the plurality of characteristic vectors to obtain a plurality of influence coefficients;
and determining whether the observed data set is reserved according to the influence coefficients.
In some implementations, the data feature extraction queue includes at least one of: the extracting the feature vectors of the data set by using the plurality of data feature extracting queues comprises the following steps:
for each data feature extraction queue, the following steps are performed:
initializing an extraction position;
and a data extraction step: extracting a plurality of data with the same number as the data characteristic queues from the data set according to the extraction position;
taking the data as vectors, and calculating the vector products of the data and the data characteristic queues;
adding the vector product to the feature vector;
and if the extraction position does not reach the final position of the data set, moving the extraction position according to a preset moving step distance, and jumping to the data extraction step.
In some embodiments, the performing, with the plurality of feature vectors, an influence analysis on the abnormal data set of the corresponding data set to obtain a plurality of influence coefficients includes:
for each feature vector of the plurality of feature vectors, performing the steps of:
calculating an influence coefficient according to a first formula, a feature vector and an abnormal data set of a corresponding data set, wherein the first formula is as follows:
wherein INF is an influence coefficient, feat (N-N) is an (N-N) th element of a feature vector, EXC (M-N) is an (M-N) th element of an abnormal data set, n+1 elements are contained in the feature vector, and M+1 data are contained in the abnormal data set.
Illustratively, the abnormal data set includes a status of the transmission line before the fault occurs and a status of the fault, and in some embodiments, data of a fault point in a time period in which the fault occurs is obtained. Accordingly, the observation data set is observation data acquired during the period based on the plurality of observation points. For example, if the line fault is a windage fault and occurs in the N to n+1 time period, then multiple node line windage anomalies should be acquired during this time period. Wind speed information for a plurality of time nodes should also be acquired as observation data based on this period.
The above examples illustrate only some of the faults that are relatively easy to determine the cause, and in fact some of the faults are related to a variety of factors, and these factors are not obvious. The embodiment of the application provides that the observation data influencing faults can be determined based on the observation data and the abnormal data by performing influence analysis, so that the cause of the faults is determined, irrelevant data is deleted on the basis, and only relevant data is reserved as factor data.
The observation data set actually contains various information, and the influence of the observation data set on fault formation can be determined by analyzing the observation data set from multiple dimensions. One way to extract information is to use a feature extraction queue, such as a differential queue, an accumulation queue, or a fuzzy queue, which can extract differential features, accumulation features, or fuzzy features, respectively. (-1, 1) is a differential queue, when two data of an observation data set and the differential queue perform a vector product operation, the difference of the observation data set is obtained, which represents the variation condition of the observation data with time, and actually, the differential operation of the whole data set extracts two data through the vector product, and then, after translating a certain unit, the vector product operation is performed until the data of the data set is completely extracted. As shown in fig. 2, which illustrates the procedure of this vector product operation, first, a vector product operation is performed using the feature extraction queue 201 and the data of the first position 202 of the data set, a first data 203 is obtained, and this data is added to the feature vector, then, the first position 202 is translated to the second position 204, a vector product operation is performed again, a second data 205 is obtained, and this is repeated until the end of the data set is reached. As with this process, (1, 1) is an accumulation queue, the accumulation sum feature of three data is extracted after the vector product operation, (1/3 ) is a fuzzy queue, and the vector product operation extracts fuzzy data (average of three elements) of three data.
It can be seen that different features can be extracted through different data feature extraction queues, and whether the observed data affect the formation of faults can be determined by performing influence analysis on the features. One way of influence analysis is to calculate the influence coefficient by a first formula:
wherein INF is an influence coefficient, feat (N-N) is an (N-N) th element of a feature vector, EXC (M-N) is an (M-N) th element of an abnormal data set, n+1 elements are contained in the feature vector, and M+1 data are contained in the abnormal data set.
By this formula, the calculated influence coefficients are in the interval-1 to 1, and the closer to-1, the stronger the negative correlation with the cause of the fault formation is, and the closer to-1, the stronger the positive correlation with the fault formation is, and we should keep those observed datasets whose absolute values are larger than the influence threshold, and take these datasets as factor datasets.
In step 103, a fault signature is constructed from the plurality of factor datasets and the feature data of the anomaly dataset.
In some embodiments, the step 103 includes:
acquiring a plurality of feature vectors according to the plurality of factor data sets;
pooling the abnormal data set and the plurality of feature vectors to obtain an abnormal feature value and a plurality of factor feature values of the plurality of feature vectors;
constructing a fault tag vector according to the abnormal characteristic values and the plurality of factor characteristic values;
adding fault tag vectors into tag classes, wherein the tag classes comprise a plurality of tag vectors, and the tag classes are obtained based on similarity clusters of the tag vectors;
and determining the label of the class according to the class center of the label class.
In some embodiments, the tag class is obtained based on a similarity cluster of tag vectors, comprising:
obtaining a plurality of tag vectors and separation thresholds;
selecting vectors to be classified: randomly selecting one label vector from a plurality of label vectors which are not clustered as a vector to be classified;
and a target vector searching step: searching a target vector from a plurality of label vectors which are not clustered, wherein the similarity difference between the target vector and the vector to be classified is smaller than a separation threshold value;
if the target vector is found, adding the target vector into the class to which the vector to be classified belongs, taking the target vector as the vector to be classified, and jumping to the target vector searching step;
otherwise, jumping to the vector selection step to be classified.
In some embodiments, the searching for the target vector from the plurality of label vectors that are not clustered comprises:
searching a target vector from a plurality of label vectors which are not clustered according to a second formula, wherein the second formula is as follows:
where SIM is the similarity, LBL (j) is the j-th element of the non-clustered tag vector, LBL tbs (j) SPC for the j-th element of the vector to be classified TH For the separation threshold, J is the total number of elements of the vector to be categorized.
Illustratively, after the factors affecting fault formation are found, a label is also required to be constructed for the combination of the factor data and the anomaly data to facilitate the search and categorization. The feature vector obtained in the foregoing step when performing factor data analysis is the basis for performing labeling, and since the feature vector data is more, it is generally necessary to perform pooling operation, for example, maximum pooling is performed by extracting the largest element in the feature vector, and average pooling operation is performed by extracting the average value of a plurality of elements in the feature vector. Through the steps, a plurality of characteristic values corresponding to a plurality of abnormal characteristic vectors and abnormal characteristic values corresponding to an abnormal data set can be extracted, the characteristic values are arranged according to a preset sequence, a fault tag vector is obtained, and the fault tag vector is classified into a tag class, and the tag is determined according to the tag class.
In practice, a tag class is obtained by clustering a plurality of tag vectors, and the tag vectors in the class always find a tag whose distance is smaller than a separation threshold, and in one embodiment, the manner in which the plurality of vectors in the class are determined mainly depends on a second formula:
where SIM is the similarity, LBL (j) is the j-th element of the non-clustered tag vector, LBL tbs (j) SPC for the j-th element of the vector to be classified TH For the separation threshold, J is the total number of elements of the vector to be categorized.
In fact, the clustering process is similar to a ferrule mode, a point with a distance not greater than a separation threshold value from the starting point is found by the starting point, if the point is found, the point is used as the starting point to continue to be found by the separation threshold value until a new point cannot be found, the clustering is completed, then the next clustering process is carried out until all vectors are clustered completely.
When the fault label vector is classified, the label of the vector (the similarity with other vectors in the class and the smallest vector) in the center of the class can be selected as the label to identify the abnormal data set and the factor data set.
In step 104, the fault tag, the plurality of abnormal data sets and the plurality of factor data sets are associated and added to a fault database of the section to which the transmission line belongs.
The fault tag identification anomaly data set and the factor data set are finally added to a fault database of the section to which the transmission line belongs. When searching, the fault label can search the fault data.
The application relates to an embodiment of a power transmission line fault database construction method, which comprises the steps of firstly, acquiring an abnormal data set of a power transmission line and a plurality of observation data sets corresponding to the abnormal data set, wherein the abnormal data set comprises a section of the power transmission line, fault data of the power transmission line and abnormal data before fault formation; then, carrying out influence analysis on the abnormal data set and the plurality of observation data sets, and deleting irrelevant observation data sets to obtain a plurality of factor data sets; then, constructing a fault label according to the plurality of factor data sets and the characteristic data of the abnormal data set; and finally, correlating the fault labels, the plurality of abnormal data sets and the plurality of factor data sets, and adding the fault labels, the plurality of abnormal data sets and the plurality of factor data sets into a fault database of a section to which the power transmission line belongs. According to the embodiment of the application, the characteristics of the observation data set are extracted through the characteristic extraction queue, then, the influence analysis on the fault formation is carried out based on the extracted characteristics, the observation data which has obvious influence on the fault formation can be reserved, and the irrelevant data is deleted, so that the cause of the fault formation can be analyzed through the factor data, the data quantity is small, and the misleading cannot be caused for people referring to the database. According to the embodiment of the application, the label vector is constructed through the characteristics, the fault data is classified based on the classification method, so that the faults and the fault causes can be conveniently searched, and the searching efficiency is higher.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
The following are device embodiments of the application, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 is a functional block diagram of a transmission line fault database construction apparatus according to an embodiment of the present application, and referring to fig. 3, the transmission line fault database construction apparatus 3 includes: an observation data acquisition module 301, a data pruning module 303, a failure tag construction module 303, and a database construction module 304, wherein:
an observation data obtaining module 301, configured to obtain an abnormal data set of a power transmission line and a plurality of observation data sets corresponding to the abnormal data set, where the abnormal data set includes a section of the power transmission line, fault data of the power transmission line, and abnormal data before fault formation;
a data pruning module 303, configured to perform an impact analysis on the abnormal data set and the plurality of observation data sets, prune the irrelevant observation data sets, and obtain a plurality of factor data sets;
a fault tag construction module 303, configured to construct a fault tag according to the multiple factor datasets and feature data of the anomaly dataset;
the database construction module 304 is configured to associate the fault tag, the plurality of abnormal data sets, and the plurality of factor data sets, and add the fault tag, the plurality of abnormal data sets, and the plurality of factor data sets to a fault database of a section to which the power transmission line belongs.
Fig. 4 is a functional block diagram of a terminal according to an embodiment of the present application. As shown in fig. 4, the terminal 4 of this embodiment includes: a processor 400 and a memory 401, said memory 401 having stored therein a computer program 402 executable on said processor 400. The steps in the above-mentioned method and embodiment for constructing a database of faults of electric transmission lines, such as steps 101 to 104 shown in fig. 1, are implemented when the processor 400 executes the computer program 402.
By way of example, the computer program 402 may be partitioned into one or more modules/units that are stored in the memory 401 and executed by the processor 400 to accomplish the present application.
The terminal 4 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal 4 may include, but is not limited to, a processor 400, a memory 401. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the terminal 4 and is not limiting of the terminal 4, and may include more or fewer components than shown, or may combine some components, or different components, e.g., the terminal 4 may further include input-output devices, network access devices, buses, etc.
The processor 400 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 401 may be an internal storage unit of the terminal 4, for example, a hard disk or a memory of the terminal 4. The memory 401 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal 4. Further, the memory 401 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 401 is used for storing the computer program 402 and other programs and data required by the terminal 4. The memory 401 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, and will not be described herein again.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and the details or descriptions of other embodiments may be referred to for those parts of an embodiment that are not described in detail or are described in detail.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on this understanding, the present application may also be implemented by implementing all or part of the procedures in the methods of the above embodiments, or by instructing the relevant hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may be implemented by implementing the steps of the embodiments of the methods and apparatuses described above when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present application, and are not limited thereto; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and they should be included in the protection scope of the present application.
Claims (10)
1. The utility model provides a transmission line fault database construction method which is characterized in that the method comprises the following steps:
acquiring an abnormal data set of a power transmission line and a plurality of observation data sets corresponding to the abnormal data set, wherein the abnormal data set comprises a section of the power transmission line, fault data of the power transmission line and abnormal data before fault formation;
performing influence analysis on the abnormal data set and the plurality of observation data sets, and deleting irrelevant observation data sets to obtain a plurality of factor data sets;
constructing a fault label according to the plurality of factor data sets and the characteristic data of the abnormal data set;
and correlating the fault labels, the plurality of abnormal data sets and the plurality of factor data sets, and adding the fault labels, the plurality of abnormal data sets and the plurality of factor data sets into a fault database of a section to which the power transmission line belongs.
2. The method for constructing a power transmission line fault database according to claim 1, wherein the performing an influence analysis on the abnormal data set and the plurality of observation data sets, and performing pruning on unrelated observation data sets to obtain a plurality of factor data sets, includes:
obtaining a plurality of data feature extraction queues;
for each of the plurality of observation data sets, performing the steps of:
extracting a plurality of feature vectors of the dataset by using the plurality of data feature extraction queues;
respectively carrying out influence analysis on the abnormal data set of the corresponding data set by utilizing the plurality of characteristic vectors to obtain a plurality of influence coefficients;
and determining whether the observed data set is reserved according to the influence coefficients.
3. The transmission line fault database construction method according to claim 2, wherein the data feature extraction queue includes at least one of: the extracting the feature vectors of the data set by using the plurality of data feature extracting queues comprises the following steps:
for each data feature extraction queue, the following steps are performed:
initializing an extraction position;
and a data extraction step: extracting a plurality of data with the same number as the data characteristic queues from the data set according to the extraction position;
taking the data as vectors, and calculating the vector products of the data and the data characteristic queues;
adding the vector product to the feature vector;
and if the extraction position does not reach the final position of the data set, moving the extraction position according to a preset moving step distance, and jumping to the data extraction step.
4. The method for constructing a power transmission line fault database according to claim 2, wherein the performing, by using the plurality of feature vectors, an influence analysis on an abnormal data set of a corresponding data set, respectively, to obtain a plurality of influence coefficients includes:
for each feature vector of the plurality of feature vectors, performing the steps of:
calculating an influence coefficient according to a first formula, a feature vector and an abnormal data set of a corresponding data set, wherein the first formula is as follows:
wherein INF is an influence coefficient, feat (-N) is an (-N) th element of a feature vector, EXC (M-N) is an (-N) th element of an abnormal data set, n+1 elements are contained in the feature vector, and M+1 data are contained in the abnormal data set.
5. The power transmission line fault database construction method according to any one of claims 1 to 4, wherein the constructing a fault signature from the plurality of factor data sets and the characteristic data of the anomaly data set includes:
acquiring a plurality of feature vectors according to the plurality of factor data sets;
pooling the abnormal data set and the plurality of feature vectors to obtain an abnormal feature value and a plurality of factor feature values of the plurality of feature vectors;
constructing a fault tag vector according to the abnormal characteristic values and the plurality of factor characteristic values;
adding fault tag vectors into tag classes, wherein the tag classes comprise a plurality of tag vectors, and the tag classes are obtained based on similarity clusters of the tag vectors;
and determining the label of the class according to the class center of the label class.
6. The transmission line fault database construction method according to claim 5, wherein the tag class is obtained based on a similarity cluster of tag vectors, comprising:
obtaining a plurality of tag vectors and separation thresholds;
selecting vectors to be classified: randomly selecting one label vector from a plurality of label vectors which are not clustered as a vector to be classified;
and a target vector searching step: searching a target vector from a plurality of label vectors which are not clustered, wherein the similarity difference between the target vector and the vector to be classified is smaller than a separation threshold value;
if the target vector is found, adding the target vector into the class to which the vector to be classified belongs, taking the target vector as the vector to be classified, and jumping to the target vector searching step;
otherwise, jumping to the vector selection step to be classified.
7. The transmission line fault database construction method according to claim 6, wherein finding a target vector from among a plurality of label vectors that are not clustered comprises:
searching a target vector from a plurality of label vectors which are not clustered according to a second formula, wherein the second formula is as follows:
where SIM is the similarity, LBL (j) is the j-th element of the non-clustered tag vector, LBL tbs (j) SPC for the j-th element of the vector to be classified TH For the separation threshold, J is the total number of elements of the vector to be categorized.
8. A transmission line fault database construction apparatus for implementing the transmission line fault database construction method according to any one of claims 1 to 7, the transmission line fault database construction apparatus comprising:
the system comprises an observation data acquisition module, a data acquisition module and a data processing module, wherein the observation data acquisition module is used for acquiring an abnormal data set of a power transmission line and a plurality of observation data sets corresponding to the abnormal data set, and the abnormal data set comprises a section of the power transmission line, fault data of the power transmission line and abnormal data before fault formation;
the data deleting module is used for carrying out influence analysis on the abnormal data set and the plurality of observation data sets, deleting irrelevant observation data sets and obtaining a plurality of factor data sets;
the fault label construction module is used for constructing a fault label according to the plurality of factor data sets and the characteristic data of the abnormal data set;
the method comprises the steps of,
and the database construction module is used for associating the fault labels, the plurality of abnormal data sets and the plurality of factor data sets and adding the fault labels, the plurality of abnormal data sets and the plurality of factor data sets into a fault database of a section to which the power transmission line belongs.
9. A terminal comprising a memory and a processor, the memory having stored therein a computer program executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 7.
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| CN117132266A (en) * | 2023-10-25 | 2023-11-28 | 山东四季汽车服务有限公司 | Block chain-based automobile service security guarantee method and system |
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| CN117132266A (en) * | 2023-10-25 | 2023-11-28 | 山东四季汽车服务有限公司 | Block chain-based automobile service security guarantee method and system |
| CN117132266B (en) * | 2023-10-25 | 2024-08-27 | 山东四季汽车服务有限公司 | Block chain-based automobile service security guarantee method and system |
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