CN116165490A - Insulator classification method, device, equipment and storage medium - Google Patents
Insulator classification method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses an insulator classification method, device, equipment and storage medium, wherein the method comprises the following steps: acquiring initial parameters of a target sound wave frequency data and an echo state network; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix; adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter; according to the target input scaling parameters, an initial input weight matrix is adjusted to obtain a target input weight matrix: based on the echo state network, determining the classification category of the insulator according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix. The embodiment of the invention improves the detection precision of the insulator and reduces the detection cost of the insulator.
Description
Technical Field
The present invention relates to the field of insulator detection technologies, and in particular, to an insulator classification method, apparatus, device, and storage medium.
Background
The insulator is used as an indispensable insulating element in the electric power transmission line, and the running condition of the insulator influences the running safety of the power grid and the reliability of the power supply of the power grid. The fault insulator (namely the perforated insulator) in the power grid can be timely detected and replaced, so that the running safety of the power grid and the reliability of power supply of the power grid can be improved.
The traditional insulator detection technology, such as the insulator detection technology based on a support vector machine, the insulator detection technology based on a multi-layer sensor network and the like, has low detection precision. In order to improve the detection precision of the insulators, a great deal of funds are additionally spent to apply professional overhaulers to detect the insulators one by one, and the detection cost is high.
Disclosure of Invention
The invention provides an insulator classification method, an insulator classification device, insulator classification equipment and a storage medium, so that the detection precision of insulators is improved, and the detection cost of the insulators is reduced.
According to an aspect of the present invention, there is provided an insulator classification method including:
acquiring initial parameters of a target sound wave frequency data and an echo state network; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix;
Adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter;
according to the target input scaling parameters, an initial input weight matrix is adjusted to obtain a target input weight matrix:
based on the echo state network, determining the classification category of the insulator according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix.
According to another aspect of the present invention, there is provided an insulator classification device including:
the data acquisition module is used for acquiring target sound wave frequency data and initial parameters of the echo state network; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix;
the parameter determining module is used for adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter;
the matrix determining module is used for adjusting the initial input weight matrix according to the target input scaling parameter to obtain a target input weight matrix:
the class determining module is used for determining the class of the insulator according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix based on the echo state network.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the insulator classification method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the insulator classification method of any one of the embodiments of the present invention when executed.
According to the technical scheme, the target sound wave frequency data and the initial parameters of the echo state network are obtained; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix; adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter; according to the target input scaling parameters, an initial input weight matrix is adjusted to obtain a target input weight matrix: based on the echo state network, determining the classification category of the insulator according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix. According to the technical scheme, the echo state network in the optimal state after training is obtained by adjusting the initial leakage parameter, the initial input scaling parameter and the initial input weight matrix in the echo state network; by using the echo state network to classify the target sound wave frequency data, more accurate insulator classification types can be obtained, the detected insulators in the power grid can be classified more accurately, the insulators with faults in the power grid can be accurately identified (namely, perforated insulators), the detection precision of the insulators is improved, the use of human resources is reduced, the cost of additionally asking professional service personnel to detect the insulators in the power grid one by one is reduced, and the detection cost of the insulators is further reduced.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and 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 an insulator classification method according to a first embodiment of the present invention;
fig. 2 is a flowchart of an insulator classification method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an insulator classification device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing the insulator classification method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "target" and "initial" and the like in the description of the present invention and the claims and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In addition, it should be noted that, in the technical scheme of the invention, the processes of collection, storage, use, processing, transmission, provision, disclosure and the like of the target sound wave frequency data and the like all conform to the regulations of related laws and regulations and do not violate the popular regulations.
Example 1
Fig. 1 is a flowchart of an insulator classification method according to a first embodiment of the present invention, where the method may be applicable to a case of overhauling a faulty insulator in a power grid, and the method may be performed by an insulator classification device, where the device may be implemented in hardware and/or software, and may be configured in an electronic device, where the electronic device may be one of a mobile phone, a notebook computer, a desktop computer, and the like. As shown in fig. 1, the method includes:
S101, acquiring target sound wave frequency data and initial parameters of an echo state network; the initial parameters include an initial leakage parameter, an initial input scaling parameter, an initial input weight matrix, an initial cyclic weight matrix, and an initial output weight matrix.
The target sound wave frequency data may refer to sound wave frequency data obtained by capturing a sound wave frequency signal of the insulator to be detected. The insulator to be detected may refer to an insulator in the power grid that needs to be detected. The initial parameters may refer to parameters in the echo state network prior to training of the echo state network. The initial parameters can be preset according to actual needs, and can also be randomly generated. The initial parameters of the echo state network include an initial leakage parameter, an initial input scaling parameter, an initial input weight matrix, an initial cyclic weight matrix, and an initial output weight matrix. Wherein the initial leakage parameter (denoted as alpha 1 ) At [0,1]Take on the value of alpha 1 ∈[0,1]An initial input weight matrix (denoted as W i 1 n ) An initial cyclic weight matrix (denoted as W) and an initial output weight matrix (denoted as W out ) Are all real numbers, i.e. W i 1 n ∈R,W∈R，W out ∈R。
Specifically, capturing an acoustic frequency signal of an insulator in a power grid by adopting an ultrasonic detector to obtain target acoustic frequency data; and the initial parameters of the echo state network are preset according to the actual needs, or the initial parameters of the echo state network are randomly generated. The initial parameters of the echo state network comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial circulation weight matrix and an initial output weight matrix.
Illustratively, acquiring target sonic frequency data from a pre-configured database; and the initial parameters of the echo state network are preset according to the actual needs.
By way of example, the detection environment of an insulator in a power grid is simulated in a laboratory, and in the case that the phase-to-ground voltage of the laboratory is 7.95 kilovolts (i.e., 7.95 kV), and the phase-to-ground voltage of a power distribution system in the laboratory is 13.8kV, an ultrasonic frequency signal of the insulator within 50 seconds is captured in a pre-configured acrylic cavity using an ultrasonic detector with an ultrasonic frequency of 500 kilohertz (i.e., 500 kHz) at a position 0.4 meters from the insulator, so as to obtain target sonic frequency data; and randomly generates initial parameters of the echo state network. It should be noted that the target sonic frequency data includes sonic frequency signals of at least 50 insulators to ensure that sonic frequency signals of insulators higher than 10kHz can be captured in each ultrasonic period. Wherein, an ultrasonic wave period can be determined by the following formula:
wherein f is the ultrasonic frequency and T is the ultrasonic period. It should be noted that, the laboratory phase-to-ground voltage is 7.95kV (i.e., 7.95 kV), and the phase-to-ground voltage of the distribution system in the laboratory is 13.8kV, so as to ensure that the voltage is consistent with the voltage in the exposed insulator in the power grid, so as to simulate the voltage of the insulator in the power grid.
S102, adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter.
The target leakage parameter may be a leakage parameter obtained after the initial leakage parameter is adjusted. The target input scaling parameter may refer to an input scaling parameter that is obtained after the initial input scaling parameter is adjusted.
Specifically, the initial leakage parameter and the initial input scaling parameter in the echo state network are input into a preset grid self-adaptive direct search (Mesh adaptive direct search, MADS) algorithm, and the target leakage parameter and the target input scaling parameter are obtained through adjustment of the algorithm. The preset MADS algorithm may be one of a quadratic programming grid adaptive direct search (Quadratic programming mesh adaptive direct search, QPMADS) algorithm, a random lower triangular grid adaptive direct search (Lower triangular mesh adaptive direct search, LTMADS) algorithm, an orthogonal grid adaptive direct search (Orthogonal mesh adaptive direct search, orthoMADS) algorithm, an orthogonal triangular decomposition grid adaptive direct search (Othogonal triangularl decompositional mesh adaptive direct search, qrmmads) algorithm, and the like. The MADS algorithm is a pattern search algorithm that calculates a series of points near the optimal point for searching the optimal point in the variable space.
The initial leakage parameter and the initial input scaling parameter in the echo state network are input into a QPMADS algorithm, and the target leakage parameter and the target input scaling parameter are obtained through adjustment of the algorithm.
Illustratively, the initial leakage parameter and the initial input scaling parameter in the echo state network are input into the OrthoMADS algorithm, and the target leakage parameter and the target input scaling parameter are obtained through adjustment of the algorithm.
S103, according to the target input scaling parameters, the initial input weight matrix is adjusted, and the target input weight matrix is obtained.
The target input weight matrix may be an input weight matrix obtained by adjusting an initial input weight matrix.
Specifically, a ratio of the initial input weight matrix to the spectral radius of the echo state network reserve layer is calculated, and the product of the ratio and the target input scaling parameter is used as a target input weight matrix. Accordingly, the target input weight matrix may be determined by the following formula:
wherein,,
for initial input weight matrix,/for the matrix of weights>
λ max Is echoThe spectral radius of the state network reserve layer (i.e. the maximum value of each initial input weight matrix in the echo state network reserve layer), gamma is the target input scaling parameter, +. >
Input weight matrix for target, +.>
R is a real number.
S104, determining classification categories of insulators based on the echo state network according to the target sound wave frequency data, the target leakage parameters, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix.
The classification category of the insulator may include a category of a non-contaminated and non-perforated insulator, a non-contaminated and perforated insulator, a contaminated and non-perforated insulator, a non-perforated and impurity (salt) doped insulator, a porous wet insulator, and the like. It should be noted that, the classification category of the insulator may be preset in the echo state network, or may be obtained by pre-training the echo state network, and is not limited specifically, for example, 200 classification categories of insulators are preset in the echo state network, and the echo state network compares the input target sound wave frequency data with the 200 classification categories of insulators one by one, so as to classify the sound wave frequency signals in the target sound wave frequency data into the 200 classification categories of insulators. In the case that the classification category of the insulator has 200 kinds, the classification category of the insulator obtained through the echo state network is the most accurate.
Specifically, based on an echo state network, determining an insulator classification recognition model according to target sound wave frequency data, target leakage parameters, a target input weight matrix, an initial circulation weight matrix and an initial output weight matrix; and classifying the target sound wave frequency data by adopting an insulator classification recognition model to obtain classification categories of insulators. The insulator classification recognition model is used for classifying insulators. It can be understood that the classification category of the insulators obtained based on the echo state network considers two characteristics of the insulators in the power grid (namely, the insulators without the perforations and with impurities (added salt) and the porous wet insulators), and can distinguish the polluted insulators and the perforated insulators in the power grid, so that the perforated insulators in the power grid can be conveniently detected and replaced in time, and the running safety of the power grid and the power supply reliability of the power grid are improved.
Optionally, determining a neuron activation vector in the echo state network storage layer according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix and the initial cyclic weight matrix; determining an insulator classification recognition model according to the target sound wave frequency data, the neuron activation vector and the initial output weight matrix; and classifying the target sound wave frequency data by adopting an insulator classification recognition model to obtain classification categories of insulators.
It should be noted that, the echo state network includes an input layer, a reserve layer and an output layer, the input layer is used for inputting the target sound wave frequency data, the reserve layer is used for generating a complex dynamic space which continuously changes along with the target sound wave frequency data input by the input layer, and the output layer is used for outputting the insulator classification recognition model.
Specifically, the target sound wave frequency data is recorded as u (n), u (n) ∈R, R is a real number, and n is a positive integer; target leakage parameter is noted as alpha 2 ，α 2 ∈[0,1]The method comprises the steps of carrying out a first treatment on the surface of the Marking the target input weight matrix as
The initial cyclic weight matrix is marked as W, W epsilon R; the neuron activation vector in the echo state network reservoir is denoted as x (n), x (n) ∈r. Accordingly, the neuron activation vector (i.e., x (n)) in the echo state network reservoir may be determined by the following formula:
wherein n represents the nth sample sonic frequency data in the target sonic frequency data,
for the update of x (n), tanh () is the active function of the echo state network reservation layer. Furthermore, according to the target sound wave frequency data u (n), the neuron activation vector x (n) in the echo state network reserve layer and the initial output weight matrix, determining an insulator classification recognition model according to the following formula:
y(n)＝W out [1；u(n)；x(n)]；
Wherein y (n) is an insulator classification recognition model, y (n) ∈R, R is a real number, and W out For initially outputting the weight matrix, W out E R. Furthermore, the insulator classification recognition model is adopted to classify the target sound wave frequency data to obtain classification categories of insulators, the insulators in the power grid are classified into the categories of pollution-free and perforation-free insulators, perforation-free and impurity-free insulators (salt adding) and porous wet insulators, and the like, so that the perforated insulators in the power grid are detected according to the classification categories of the insulators, the perforated insulators in the power grid are accurately recognized, and professional maintenance staff are facilitated to replace all the perforated insulators, so that normal operation of the insulators in the power grid is guaranteed, and the safety of power grid operation and the reliability of power grid power supply are improved. According to the technical scheme, the specific method for determining the insulator classification category based on the echo state network is provided, so that the insulator classification category determination has certain regularity.
According to the technical scheme, the target sound wave frequency data and the initial parameters of the echo state network are obtained; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix; adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter; according to the target input scaling parameters, an initial input weight matrix is adjusted to obtain a target input weight matrix: based on the echo state network, determining the classification category of the insulator according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix. According to the technical scheme, the echo state network in the optimal state after training is obtained by adjusting the initial leakage parameter, the initial input scaling parameter and the initial input weight matrix in the echo state network; by using the echo state network to classify the target sound wave frequency data, more accurate insulator classification types can be obtained, the detected insulators in the power grid can be classified more accurately, the insulators with faults in the power grid can be accurately identified (namely, perforated insulators), the detection precision of the insulators is improved, the use of human resources is reduced, the cost of additionally asking professional service personnel to detect the insulators in the power grid one by one is reduced, and the detection cost of the insulators is further reduced.
Example two
Fig. 2 is a flowchart of an insulator classification method according to a second embodiment of the present invention, where on the basis of the foregoing embodiment, an alternative implementation is provided for further optimization of "adjusting an initial leakage parameter and an initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter". In the embodiments of the present invention, parts not described in detail may be referred to for related expressions of other embodiments. As shown in fig. 2, the method includes:
s201, acquiring target sound wave frequency data and initial parameters of an echo state network; the initial parameters include an initial leakage parameter, an initial input scaling parameter, an initial input weight matrix, an initial cyclic weight matrix, and an initial output weight matrix.
S202, determining a search frequency threshold according to a search direction preset by a grid self-adaptive direct search algorithm.
Wherein, the grid self-adaptive direct search algorithm can be one of QPMADS algorithm, LTMADS algorithm, orthoMADS algorithm, QRMADS algorithm and the like. The search direction depends on the grid-adaptive direct search algorithm, for example, a relatively dense direction in the variable space of the grid-adaptive direct search algorithm is taken as a preset search direction. The search number threshold may refer to a maximum number of searches in a grid-adaptive direct search algorithm. It should be noted that the grid adaptive direct search algorithm searches only one preset search direction at a time.
Specifically, counting the total number of search directions preset by the grid self-adaptive direct search algorithm, and taking the total number as a search frequency threshold of the grid self-adaptive direct search algorithm.
For example, if the grid adaptive direct search algorithm is an OrthoMADS algorithm, and the total number of search directions preset by the OrthoMADS algorithm is counted (denoted as M), the threshold of search times of the OrthoMADS algorithm is M.
S203, iteratively adjusting the initial leakage parameter and the initial input scaling parameter by adopting a grid self-adaptive direct search algorithm to obtain a target leakage parameter and a target input scaling parameter.
It should be noted that, the grid adaptive direct search algorithm determines the optimal leakage parameter and the input scaling parameter in the next search process by the following formula:
wherein M is k As a set of grid points,
for the grid size parameter, x is the point at which the search begins, V k Is real, i.e. V k E R, k is the kth grid, D z For the search direction, z is the z-th search, and z is a positive integer, i.e., z ε N. It should be noted that, when the grid adaptive direct search algorithm is the OrthoMADS algorithm, D z Is [ I ] n -I n ],I n Is an identity matrix with a dimension of n.
Optionally, inputting the initial leakage parameter and the initial input scaling parameter of the echo state network into a grid self-adaptive direct search algorithm to obtain the leakage parameter and the input scaling parameter after the first adjustment; taking the leakage parameter and the input scaling parameter as the input of a next grid self-adaptive direct search algorithm; and when the searching times are larger than the searching times threshold, taking the leakage parameter and the input scaling parameter corresponding to the searching times as the target leakage parameter and the target input scaling parameter.
Optionally, the initial leakage parameter and the initial input scaling parameter are iteratively adjusted by using a grid adaptive direct search algorithm, so as to obtain a target leakage parameter and a target input scaling parameter, which may be: under the condition that the current searching times is smaller than or equal to the searching times threshold value, inputting the current leakage parameters and the current input scaling parameters corresponding to the current searching times into the echo state network, and determining the current classification precision corresponding to the current searching times; under the condition that the current classification precision is greater than or equal to the preset target classification precision, taking the current classification precision as the new target classification precision; and under the condition that the current searching times are larger than the searching times threshold, taking the leakage parameters and the input scaling parameters corresponding to the new target classification precision as target leakage parameters and target input scaling parameters.
The preset target classification precision can be set randomly in advance, or can be set in advance according to the classification precision corresponding to the first search of the grid self-adaptive direct search algorithm. For example, if the classification accuracy corresponding to the first search of the grid adaptive direct search algorithm is Acc, the preset target classification accuracy may be Acc.
Specifically, the search number threshold is denoted as M, the current search number is denoted as z, z=1, 2, …, M, and the preset target classification accuracy is denoted as Acc 0 The method comprises the steps of carrying out a first treatment on the surface of the Based on a grid self-adaptive direct search algorithm (such as an orthoMADS algorithm), under the condition that z is less than or equal to M, the current leakage parameter corresponding to the current search times and the current input scaling parameter are input into an echo state network to obtain the current classification precision (recorded as Acc) corresponding to the current search times 1 ) The method comprises the steps of carrying out a first treatment on the surface of the If Acc 1 ≥Acc 0 Then the current classification accuracy Acc 1 As new target classification accuracy, otherwise, the new target classification accuracy is still Acc 0 The method comprises the steps of carrying out a first treatment on the surface of the In z>M, the new target classification accuracy Acc 1 Corresponding leakageThe leakage parameter and the input scaling parameter are used as the target leakage parameter and the target input scaling parameter.
It can be understood that, the initial leakage parameter and the initial input scaling parameter of the echo state network are iteratively adjusted by adopting the grid self-adaptive direct search algorithm, the classification precision corresponding to each search of the grid self-adaptive direct search algorithm is calculated by the echo state network, and the maximum value in the classification precision is used as the target classification precision; and then the leakage parameter and the input scaling parameter corresponding to the target classification precision are determined as the target leakage parameter and the target input scaling parameter, so that the adjustment of the initial leakage parameter and the initial input scaling parameter of the echo state network is realized, the initial leakage parameter and the initial input scaling parameter of the echo state network are adjusted to an optimal state, and the echo state network in the optimal state after training is further obtained, so that the more accurate insulator classification type is obtained based on the echo state network later.
Optionally, in the case that the current search is the first search, adjusting an initial leakage parameter and an initial input scaling parameter by adopting a grid self-adaptive direct search algorithm to obtain the leakage parameter and the input scaling parameter after the first processing; and inputting the leakage parameters and the input scaling parameters after the first processing into the echo state network to obtain the classification precision corresponding to the first search, and taking the classification precision corresponding to the first search as the preset target classification precision.
Illustratively, if the current search number is z (z=1, 2, …, M), M is the search number threshold M; adopting an orthoMADS algorithm to adjust the leakage parameters and the input scaling parameters of the echo state network; under the condition that z=1, inputting an initial leakage parameter and an initial input scaling parameter of the echo state network into an OrthoMADS algorithm to obtain a leakage parameter and an input scaling parameter after first processing; and inputting the leakage parameter and the input scaling parameter which are processed for the first time into an echo state network to obtain the classification precision corresponding to the first search, and taking the classification precision as the preset target classification precision. The method for determining the preset target classification precision is provided, so that the determination of the preset target classification precision is more scientific and reasonable.
S204, adjusting the initial input weight matrix according to the target input scaling parameters to obtain a target input weight matrix.
S205, determining classification categories of insulators based on the echo state network according to the target sound wave frequency data, the target leakage parameters, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix.
According to the technical scheme of the embodiment of the invention, the searching frequency threshold value is determined according to the searching direction preset by the grid self-adaptive direct searching algorithm; and iteratively adjusting the initial leakage parameter and the initial input scaling parameter by adopting a grid self-adaptive direct search algorithm to obtain a target leakage parameter and a target input scaling parameter. The specific method for adjusting the initial leakage parameter and the initial input scaling parameter in the echo state network is provided, the grid self-adaptive direct search algorithm is adopted to adjust the leakage parameter and the input scaling parameter in the echo state network to an optimal state, further the target leakage parameter and the target input scaling parameter are obtained, the input weight matrix in the echo state network is conveniently adjusted to the optimal state according to the target leakage parameter and the target input scaling parameter, and further the accuracy of the insulator classification category obtained based on the echo state network is improved.
Example III
Fig. 3 is a schematic structural diagram of an insulator classification device according to a third embodiment of the present invention, where the embodiment is applicable to a situation of overhauling a faulty insulator in a power grid, and the device may be implemented in a hardware and/or software form, and may be configured in an electronic device, where the electronic device may be one of a mobile phone, a notebook computer, a desktop computer, and the like. As shown in fig. 3, the apparatus includes:
the data acquisition module 301 is configured to acquire target acoustic frequency data and initial parameters of the echo state network; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix;
the parameter determining module 302 is configured to adjust an initial leakage parameter and an initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter;
the matrix determining module 303 is configured to adjust an initial input weight matrix according to the target input scaling parameter to obtain a target input weight matrix;
the class determining module 304 is configured to determine, based on the echo state network, a class of the insulator according to the target acoustic frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix, and the initial output weight matrix.
According to the technical scheme, the data acquisition module is used for acquiring the target sound wave frequency data and the initial parameters of the echo state network; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix; determining a target leakage parameter and a target input scaling parameter by a parameter determination module; determining a target input weight matrix through a matrix determining module; and determining the classification category of the insulator based on the echo state network according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix through the category determining module. According to the technical scheme, the echo state network in the optimal state after training is obtained by adjusting the initial leakage parameter, the initial input scaling parameter and the initial input weight matrix in the echo state network; by using the echo state network to classify the target sound wave frequency data, more accurate insulator classification types can be obtained, the detected insulators in the power grid can be classified more accurately, the insulators with faults in the power grid can be accurately identified (namely, perforated insulators), the detection precision of the insulators is improved, the use of human resources is reduced, the cost of additionally asking professional service personnel to detect the insulators in the power grid one by one is reduced, and the detection cost of the insulators is further reduced.
Optionally, the category determining module 304 includes:
the activation vector determining unit is used for determining a neuron activation vector in the echo state network reserve layer according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix and the initial cyclic weight matrix;
the identification model determining unit is used for determining an insulator classification identification model according to the target sound wave frequency data, the neuron activation vector and the initial output weight matrix;
and the classification type determining unit is used for classifying the target sound wave frequency data by adopting the insulator classification recognition model to obtain the classification type of the insulator.
Optionally, the parameter determining module 302 includes:
the frequency threshold determining unit is used for determining a searching frequency threshold according to a searching direction preset by the grid self-adaptive direct searching algorithm;
and the parameter determining unit is used for iteratively adjusting the initial leakage parameter and the initial input scaling parameter by adopting a grid self-adaptive direct search algorithm to obtain the target leakage parameter and the target input scaling parameter.
Optionally, the parameter determining unit is specifically configured to:
under the condition that the current searching times is smaller than or equal to the searching times threshold value, inputting the current leakage parameters and the current input scaling parameters corresponding to the current searching times into the echo state network, and determining the current classification precision corresponding to the current searching times; under the condition that the current classification precision is greater than or equal to the preset target classification precision, taking the current classification precision as the new target classification precision; and under the condition that the current searching times are larger than the searching times threshold, taking the leakage parameters and the input scaling parameters corresponding to the new target classification precision as target leakage parameters and target input scaling parameters.
Optionally, the apparatus further comprises:
the leakage parameter and input scaling parameter determining module is used for adjusting initial leakage parameters and initial input scaling parameters by adopting a grid self-adaptive direct search algorithm under the condition that the current search is the first search to obtain leakage parameters and input scaling parameters after the first processing;
the target classification accuracy determining module is used for inputting the leakage parameter and the input scaling parameter which are processed for the first time into the echo state network to obtain classification accuracy corresponding to the first search, and taking the classification accuracy corresponding to the first search as preset target classification accuracy.
The insulator classification device provided by the embodiment of the invention can execute the insulator classification method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the insulator classification methods.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as the insulator classification method.
In some embodiments, the insulator classification method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When the computer program is loaded into RAM13 and executed by processor 11, one or more steps of the insulator classification method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the insulator classification method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of insulator classification, the method comprising:
acquiring initial parameters of a target sound wave frequency data and an echo state network; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix;
adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter;
According to the target input scaling parameters, the initial input weight matrix is adjusted to obtain a target input weight matrix;
based on an echo state network, determining the classification category of the insulator according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix.
2. The method of claim 1, wherein the determining, based on the echo state network, a classification category of an insulator based on the target acoustic wave frequency data, the target leakage parameter, the target input weight matrix, the initial cycle weight matrix, and the initial output weight matrix comprises:
determining a neuron activation vector in an echo state network reserve layer according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix and the initial cyclic weight matrix;
determining an insulator classification recognition model according to the target sound wave frequency data, the neuron activation vector and the initial output weight matrix;
and classifying the target sound wave frequency data by adopting the insulator classification recognition model to obtain classification categories of insulators.
3. The method of claim 1, wherein said adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter comprises:
determining a search frequency threshold according to a search direction preset by a grid self-adaptive direct search algorithm;
and iteratively adjusting the initial leakage parameter and the initial input scaling parameter by adopting the grid self-adaptive direct search algorithm to obtain a target leakage parameter and a target input scaling parameter.
4. The method of claim 3, wherein iteratively adjusting the initial leakage parameter and the initial input scaling parameter using the grid-adaptive direct search algorithm to obtain a target leakage parameter and a target input scaling parameter comprises:
under the condition that the current searching times is smaller than or equal to the searching times threshold value, inputting the current leakage parameters and the current input scaling parameters corresponding to the current searching times into an echo state network, and determining the current classification precision corresponding to the current searching times;
taking the current classification precision as a new target classification precision under the condition that the current classification precision is greater than or equal to a preset target classification precision;
And under the condition that the current searching times are larger than the searching times threshold, taking the leakage parameters and the input scaling parameters corresponding to the new target classification precision as target leakage parameters and target input scaling parameters.
5. The method according to claim 4, wherein the method further comprises:
under the condition that the current search is the first search, the grid self-adaptive direct search algorithm is adopted to adjust the initial leakage parameter and the initial input scaling parameter, so that the leakage parameter and the input scaling parameter after the first processing are obtained;
and inputting the leakage parameters and the input scaling parameters after the first processing into an echo state network to obtain the classification precision corresponding to the first search, and taking the classification precision corresponding to the first search as the preset target classification precision.
6. An insulator classification device, characterized by comprising:
the data acquisition module is used for acquiring target sound wave frequency data and initial parameters of the echo state network; the initial parameters comprise initial leakage parameters, initial input scaling parameters, an initial input weight matrix, an initial cyclic weight matrix and an initial output weight matrix;
the parameter determining module is used for adjusting the initial leakage parameter and the initial input scaling parameter to obtain a target leakage parameter and a target input scaling parameter;
The matrix determining module is used for adjusting the initial input weight matrix according to the target input scaling parameter to obtain a target input weight matrix;
the category determining module is used for determining the category of the insulator according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix, the initial cyclic weight matrix and the initial output weight matrix based on the echo state network.
7. The apparatus of claim 6, wherein the category determination module comprises:
the activation vector determining unit is used for determining a neuron activation vector in an echo state network reserve layer according to the target sound wave frequency data, the target leakage parameter, the target input weight matrix and the initial cyclic weight matrix;
the identification model determining unit is used for determining an insulator classification identification model according to the target sound wave frequency data, the neuron activation vector and the initial output weight matrix;
and the classification type determining unit is used for classifying the target sound wave frequency data by adopting the insulator classification recognition model to obtain the classification type of the insulator.
8. The apparatus of claim 6, wherein the parameter determination module comprises:
the frequency threshold determining unit is used for determining a searching frequency threshold according to a searching direction preset by the grid self-adaptive direct searching algorithm;
and the parameter determining unit is used for iteratively adjusting the initial leakage parameter and the initial input scaling parameter by adopting the grid self-adaptive direct search algorithm to obtain a target leakage parameter and a target input scaling parameter.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the insulator classification method of any one of claims 1-5.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the insulator classification method of any one of claims 1-5.
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