CN114692758A - Power communication fault analysis method and device, terminal equipment and medium - Google Patents
Power communication fault analysis method and device, terminal equipment and medium Download PDFInfo
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Abstract
The application discloses a power communication fault analysis method, a device, a terminal device and a medium, wherein the method comprises the following steps: collecting historical fault information of power communication, determining fault reasons and diagnosis strategies of the historical fault information, and constructing a database; classifying the historical fault information by using a clustering algorithm; constructing an original fault diagnosis model, and training the original fault diagnosis model by using a classification result to generate a target fault diagnosis model; and inputting the power communication data to be analyzed into the target fault diagnosis model, and matching the output fault information with the database to obtain fault reasons and diagnosis strategies of the output fault information. According to the power communication fault analysis method and system, the power communication fault is analyzed based on the artificial neural network, the complexity and subjectivity of manual analysis are avoided, the power communication fault can be accurately and intelligently analyzed, and then the reliability of power communication is effectively improved.
Description
Technical Field
The present application relates to the field of power communication technologies, and in particular, to a method, an apparatus, a terminal device, and a medium for analyzing a power communication fault.
Background
The power communication network is an integral part of a modern power grid, is one of three main pillars for safe, stable, economic and high-quality operation of the power grid, improves the communication quality of the power communication network, increases the reliability of the power communication network, is a consistent requirement of national power grid companies on the power communication network, and can run through the continuous process of the life cycle of the whole power communication network. Therein, reliability problems of the power communication network are evaluated, often closely related to the failure analysis thereof. In the electric power communication network, the category of fault measurement can be divided into two layers of equipment fault and network fault, and the network fault is the deep reflection of the equipment fault. However, the existing method for evaluating the power communication fault usually depends on manual evaluation, and has strong subjectivity, and obviously, the method has the advantages of large workload, long consumed time, easy error, and failure of the telecommunication network cannot be effectively analyzed, so that the occurrence of the fault cannot be avoided.
Disclosure of Invention
The application aims to provide a power communication fault analysis method, a power communication fault analysis device, a terminal device and a medium, so as to solve the problems that the existing power communication fault analysis is long in time consumption and low in accuracy.
In order to achieve the above object, the present application provides a power communication fault analysis method, including:
collecting historical fault information of power communication, determining fault reasons and diagnosis strategies of the historical fault information, and constructing a database;
classifying the historical fault information by using a clustering algorithm;
constructing an original fault diagnosis model, and training the original fault diagnosis model by using a classification result to generate a target fault diagnosis model;
and inputting the power communication data to be analyzed into the target fault diagnosis model, and matching the output fault information with the database to obtain fault reasons and diagnosis strategies of the output fault information.
Further, preferably, the constructing the original fault diagnosis model includes:
an original fault diagnosis model having a three-layer structure is constructed based on an artificial neural network, wherein,
the first layer is an input layer, the second layer is a fault diagnosis layer and is used for detecting and diagnosing faults of known fault types, and the third layer is an adaptive detection and diagnosis layer and comprises the step of identifying and diagnosing unknown fault types which cannot be identified by the second layer in an adaptive mode.
Further, preferably, the diagnostic algorithm of the fault diagnosis layer is:
defining the characteristic vector of the data to be detected as S ═ S1,s2,s3,...snAn ith fault recognizer in the fault diagnosis layer is Z ═ Zi1,zi2,zi3,...zin}；
The fusion degree of the data characteristic vector and the fault identifier is as follows:
in the formula, thetatRepresents the weight ratio of the kth characteristic parameter and satisfies 0<D(s,zi)<1；
After transformation, the following are obtained:
in the formula, 0<f(s,zi)<1。
Further, it is preferable that the criterion for determining whether or not the power communication failure occurs is:
when f (s, z)i)∈(0.67,0.89), it is judged that the power communication is failed.
Further, preferably, after the collecting the historical fault information of the power communication, the method further includes performing data preprocessing on the historical fault information, including:
judging whether the historical fault information meets the database case standard or not;
if so, carrying out data cleaning, denoising and normalization processing on the historical fault information conforming to the database case standard;
if not, removing the historical fault information which does not accord with the database case standard.
Further, preferably, denoising the historical fault information meeting the database case standard includes:
converting the historical fault information into an electrical signal;
performing multi-layer wavelet transformation on the electric signal to obtain a low-frequency coefficient and a high-frequency coefficient of a wavelet;
performing contraction processing on the high-frequency coefficient;
performing inverse transformation on the wavelet after the contraction treatment to obtain a signal estimation value;
and carrying out signal median filtering processing on the signal estimation value to obtain denoised historical fault information.
Further, preferably, the classifying the historical fault information by using a clustering algorithm includes:
determining an initial clustering cluster centroid, an iteration number threshold and a clustering number based on the historical fault information;
calculating Manhattan distances from all data points in the historical fault information to a centroid, and distributing the data points to the centroid closest to the data points;
solving a new centroid of the cluster by using a mean function, and calculating the offset of the new centroid;
judging whether the difference value between the offset and the iteration threshold is smaller than a preset value or not;
if yes, finishing clustering;
if not, returning to the step of determining the initial clustering cluster mass center, the iteration number threshold and the clustering number.
The application also provides a power communication fault analysis device, includes:
the system comprises an information acquisition unit, a database and a data processing unit, wherein the information acquisition unit is used for acquiring historical fault information of power communication, determining fault reasons and diagnosis strategies of the historical fault information and constructing the database;
the cluster analysis unit is used for classifying the historical fault information by utilizing a clustering algorithm;
the model construction unit is used for constructing an original fault diagnosis model, training the original fault diagnosis model by using the classification result and generating a target fault diagnosis model;
and the fault analysis unit is used for inputting the electric power communication data to be analyzed into the target fault diagnosis model, matching the output fault information with the database and obtaining fault reasons and diagnosis strategies of the output fault information.
The present application further provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the power communication fault analysis method as described in any one of the above.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a power communication fault analysis method as described in any one of the above.
Compared with the prior art, the beneficial effects of this application lie in:
the application discloses a power communication fault analysis method, a device, a terminal device and a medium, wherein the method comprises the following steps: collecting historical fault information of power communication, determining fault reasons and diagnosis strategies of the historical fault information, and constructing a database; classifying the historical fault information by using a clustering algorithm; constructing an original fault diagnosis model, and training the original fault diagnosis model by using a classification result to generate a target fault diagnosis model; and inputting the power communication data to be analyzed into the target fault diagnosis model, and matching the output fault information with the database to obtain fault reasons and diagnosis strategies of the output fault information. According to the method and the device, the power communication faults are analyzed based on the artificial neural network, the complexity and subjectivity of manual analysis are avoided, the power communication faults can be accurately and intelligently analyzed, and the reliability of power communication is effectively improved.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings required to be used in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a power communication fault analysis method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an artificial neural network provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a power communication fault analysis apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
The first embodiment:
referring to fig. 1, an embodiment of the present disclosure provides a power communication fault analysis method. As shown in fig. 1, the power communication fault analysis method includes steps S10 to S40. The method comprises the following steps:
s10, collecting historical fault information of power communication, determining fault reasons and diagnosis strategies of the historical fault information, and constructing a database.
In the step, historical fault information of power communication is collected, fault reasons and diagnosis strategies corresponding to the fault information are determined, and a database is built according to the fault information, the fault reasons and the diagnosis strategies. Specifically, in this embodiment, the historical fault information may be that the transmission quality is poor, the fault cause corresponding to the historical fault information is that the anti-interference capability of the network cable is weak or the shielding effect is poor, and the diagnosis policy may be to replace the multi-core cable.
As a preferred embodiment, the fault symptom and the fault cause of the historical fault information preferably have an m → n mapping relationship, that is, it indicates that a plurality of fault symptoms may point to the same fault cause, or that one fault symptom is a result of simultaneous actions of a plurality of fault causes.
In a specific embodiment, after the collecting the historical fault information of the power communication, the method further includes performing data preprocessing on the historical fault information, including:
judging whether the historical fault information meets the database case standard or not;
if so, carrying out data cleaning, denoising and normalization processing on the historical fault information conforming to the database case standard;
if not, removing the historical fault information which does not accord with the database case standard.
In this embodiment, in order to unify the data format in the database, data preprocessing is usually required, and a corresponding database case standard is set, for example, each information is normalized to be between 0 and 100 by using a normalization processing method, that is, a maximum and minimum value method.
In one embodiment, denoising historical fault information conforming to a database case standard comprises the following steps:
1.1) converting the historical fault information into an electric signal;
1.2) carrying out multi-layer wavelet transformation on the electric signals, decomposing the electric signals into 3 layers, and then obtaining low-frequency coefficients and high-frequency coefficients of wavelets;
1.3) carrying out contraction processing on the obtained high-frequency coefficients of each layer, wherein the function of the contraction processing is as follows:
in the formula, TjRepresents a threshold value, γjkIs the wavelet coefficient of the j-th layer,
and j is the number of layers of wavelet decomposition, and k is the kth wavelet coefficient.
1.4) performing inverse transformation on the wavelet after the contraction treatment to obtain a signal estimation value;
1.5) carrying out signal median filtering processing on the signal estimation value to obtain denoised historical fault information.
The median filtering adopts a calculation formula as follows:
further, in this embodiment, the calculation formula adopted by the normalization processing is:
wherein x represents a parameter of the historical failure information, xminMinimum value, x, of a parameter representing historical fault informationmaxA maximum value of a parameter representing historical fault information.
S20, classifying the historical fault information by using a clustering algorithm;
after step S10 is performed, data having historical failure information in a uniform format is obtained, and this step is intended to classify these data. Specifically, the present embodiment performs data classification by using a clustering algorithm.
In one embodiment, step S20 further includes the following sub-steps:
2.1) determining the centroid of the initial cluster, an iteration threshold and the cluster number based on historical fault information;
in this step, the threshold value of the number of iterations is the maximum number of iterations in the iteration process.
2.2) calculating Manhattan distances from all data points in the historical fault information to a centroid, and distributing the data points to the centroid closest to the data points; the computing formula of the Manhattan distance is as follows:
c＝|x1-x2|+|y1-y2|；
2.3) solving a new centroid of the cluster by using a mean function, and calculating the offset of the new centroid;
2.4) judging whether the difference value between the offset and the iteration threshold is smaller than a preset value; if yes, finishing clustering; if not, returning to execute the step 2.1).
S30, constructing an original fault diagnosis model, and training the original fault diagnosis model by using the classification result to generate a target fault diagnosis model;
in this step, based on the classification result, the classified historical fault information is firstly divided into a training set and a test set, and the division ratio is preferably 7: 3.
Further, an original fault diagnosis model is constructed, specifically an original fault diagnosis model with a three-layer structure is constructed based on an artificial neural network. Here, a fully-connected neural network is preferred, as shown in fig. 2, and fig. 2 is a schematic diagram of the fully-connected neural network. The first layer of the neural network is an input layer, the second layer is a fault diagnosis layer, the fault diagnosis layer comprises fault detection and diagnosis of known fault types of which the occurrence probabilities of the faults are mutually independent, and the third layer is an adaptive detection and diagnosis layer and comprises the step of identifying and diagnosing new fault types and early faults which cannot be identified by the second layer in an adaptive mode.
Further, the diagnostic algorithm of the fault diagnosis layer is as follows:
defining the characteristic vector of the data to be detected as S ═ S1,s2,s3,...snAnd the ith fault recognizer in the fault diagnosis layer is Z ═ Zi1,zi2,zi3,...zin}；
The fusion degree of the data characteristic vector and the fault identifier is as follows:
in the formula, thetatRepresents the weight ratio of the kth characteristic parameter and satisfies 0<D(s,zi)<1；
Obtaining a final fusion degree calculation formula after conversion:
in the formula, 0<f(s,zi)<1。
Training until when a target fault diagnosis model is to be generated, comprising: and testing the trained model by using the test set, and generating a target fault diagnosis model when the output test result reaches a preset standard.
And S40, inputting the power communication data to be analyzed into the target fault diagnosis model, and matching the output fault information with the database to obtain fault reasons and diagnosis strategies of the output fault information.
In this embodiment, when fault analysis is required, only the power communication data to be analyzed needs to be collected in real time and then input to the target fault diagnosis model, so that a diagnosis result can be obtained. In this embodiment, the criteria for determining whether the power communication failure occurs are as follows: when f (s, z)i) When the value is within the range of 0.67 and 0.89, it is judged that the power communication is failed.
Finally, in order to enrich the database case, after each diagnosis, the fault information obtained from the fault diagnosis result is matched with the fault information in the database, and if the same fault information can be matched, the fault reason and the diagnosis strategy can be correspondingly found out, so that operation guidance can be provided for maintenance personnel. If the matching fails, the new case can be saved to the database as a new case, so that the effect of self-learning of the database is achieved.
According to the first embodiment of the application, the power communication fault is analyzed based on the artificial neural network, the complexity and subjectivity of manual analysis are avoided, the power communication fault can be accurately and intelligently analyzed, and the reliability of power communication is effectively improved.
Second embodiment:
the embodiment is another embodiment of the present invention, which is different from the first embodiment, and provides a verification test of an electric power communication fault analysis method based on an artificial neural network, in order to verify and explain technical effects adopted in the method, the embodiment adopts a traditional fault diagnosis method based on a BP algorithm to perform a comparison test with the method of the present invention, and compares test results by means of scientific demonstration to verify the real effect of the method.
Selecting test samples as normal data, fault data, suspected abnormal data and abnormal data, selecting 40 recording samples respectively, adopting a rapid training method when learning by adopting a BP algorithm, taking 70% of samples as training samples and 30% of samples as test samples, repeatedly observing and learning, and comparing the measured result with the result obtained by the test by using the method disclosed by the invention, wherein the comparison result is shown in the following table.
Table 1 comparative table of experimental results.
| Test specimen | BP algorithm | The method of the present application |
| Accuracy of classification | 97.26％ | 98.87％ |
| Fault precision ratio | 97.95％ | 99.14％ |
| Failure recall ratio | 82.87％ | 98.10％ |
As can be seen from the table above, compared with the traditional method, the method of the invention has stronger robustness, and the effectiveness of the method of the invention is reflected.
The third embodiment:
referring to fig. 3, in a third embodiment of the present application, there is provided a power communication fault analysis apparatus, including:
the system comprises an information acquisition unit 01, a data processing unit and a data processing unit, wherein the information acquisition unit 01 is used for acquiring historical fault information of power communication, determining fault reasons and diagnosis strategies of the historical fault information and constructing a database;
a cluster analysis unit 02 for classifying the historical fault information by using a clustering algorithm;
the model construction unit 03 is used for constructing an original fault diagnosis model according to the classification result, training the original fault diagnosis model until a preset condition is met, and generating a target fault diagnosis model;
and the fault analysis unit 04 is used for inputting the power communication data to be analyzed into the target fault diagnosis model, and matching the output fault information with the database to obtain a fault reason and a diagnosis strategy of the output fault information.
It is to be understood that the present embodiment provides a power communication failure analysis apparatus for executing the power communication failure analysis method as described in the first embodiment above. The power communication fault analysis method and the power communication fault analysis system have the advantages that the power communication fault is analyzed based on the artificial neural network, the complexity and subjectivity of manual analysis are avoided, the power communication fault can be accurately and intelligently analyzed, and the reliability of power communication is effectively improved.
The fourth embodiment:
referring to fig. 4, a terminal device is further provided in a fourth embodiment of the present application, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the power communication fault analysis method as described above.
The processor is used for controlling the overall operation of the terminal equipment so as to complete all or part of the steps of the power communication fault analysis method. The memory is used to store various types of data to support operation at the terminal device, and these data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the power communication fault analysis method according to any one of the above embodiments, and achieve technical effects consistent with the above methods.
In another exemplary embodiment, a computer readable storage medium is also provided, which includes a computer program, which when executed by a processor, implements the steps of the power communication fault analysis method according to any one of the above embodiments. For example, the computer-readable storage medium may be the above-mentioned memory including a computer program, and the above-mentioned computer program may be executed by a processor of a terminal device to perform the power communication fault analysis method according to any one of the above-mentioned embodiments, and achieve the technical effects consistent with the above-mentioned method.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
The foregoing is a preferred embodiment of the present application, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations are also regarded as the protection scope of the present application.
Claims (10)
1. A power communication fault analysis method is characterized by comprising the following steps:
collecting historical fault information of power communication, determining fault reasons and diagnosis strategies of the historical fault information, and constructing a database;
classifying the historical fault information by using a clustering algorithm;
constructing an original fault diagnosis model, and training the original fault diagnosis model by using a classification result to generate a target fault diagnosis model;
and inputting the power communication data to be analyzed into the target fault diagnosis model, and matching the output fault information with the database to obtain fault reasons and diagnosis strategies of the output fault information.
2. The power communication fault analysis method according to claim 1, wherein the building of the raw fault diagnosis model includes:
an original fault diagnosis model having a three-layer structure is constructed based on an artificial neural network, wherein,
the first layer is an input layer, the second layer is a fault diagnosis layer and is used for detecting and diagnosing faults of known fault types, and the third layer is an adaptive detection and diagnosis layer and comprises the step of identifying and diagnosing unknown fault types which cannot be identified by the second layer in an adaptive mode.
3. The power communication fault analysis method according to claim 2, wherein the diagnosis algorithm of the fault diagnosis layer is:
defining the characteristic vector of the data to be detected as S ═ S1,s2,s3,...snAn ith fault recognizer in the fault diagnosis layer is Z ═ Zi1,zi2,zi3,...zin}；
The fusion degree of the data characteristic vector and the fault identifier is as follows:
in the formula, thetatRepresents the weight ratio of the kth characteristic parameter and satisfies 0<D(s,zi)<1；
After transformation, the following are obtained:
in the formula, 0<f(s,zi)<1。
4. The power communication failure analysis method according to claim 3, wherein the criterion for determining whether the power communication failure occurs is:
when f (s, z)i) When the value is within the range of 0.67 and 0.89, the power communication is judged to be failed.
5. The power communication fault analysis method according to claim 1, further comprising performing data preprocessing on the historical fault information after the collecting the historical fault information of the power communication, wherein the data preprocessing comprises:
judging whether the historical fault information meets the database case standard or not;
if so, carrying out data cleaning, denoising and normalization processing on the historical fault information conforming to the database case standard;
if not, removing the historical fault information which does not accord with the database case standard.
6. The power communication fault analysis method according to claim 5, wherein denoising the historical fault information conforming to the database case standard comprises:
converting the historical fault information into an electrical signal;
performing multi-layer wavelet transformation on the electric signal to obtain a low-frequency coefficient and a high-frequency coefficient of a wavelet;
performing contraction processing on the high-frequency coefficient;
performing inverse transformation on the wavelet after the contraction treatment to obtain a signal estimation value;
and carrying out signal median filtering processing on the signal estimation value to obtain denoised historical fault information.
7. The power communication fault analysis method according to any one of claims 1 to 6, wherein the classifying the historical fault information by using a clustering algorithm comprises:
determining the centroid of an initial cluster, an iteration threshold and the cluster number based on the historical fault information;
calculating Manhattan distances from all data points in the historical fault information to a centroid, and distributing the data points to the centroid closest to the data points;
solving a new centroid of the cluster by using a mean function, and calculating the offset of the new centroid;
judging whether the difference value between the offset and the iteration number threshold is smaller than a preset value or not;
if yes, finishing clustering;
if not, returning to the step of determining the initial clustering cluster mass center, the iteration number threshold and the clustering number.
8. A power communication failure analysis apparatus, comprising:
the system comprises an information acquisition unit, a data processing unit and a data processing unit, wherein the information acquisition unit is used for acquiring historical fault information of power communication, determining fault reasons and diagnosis strategies of the historical fault information and constructing a database;
the cluster analysis unit is used for classifying the historical fault information by utilizing a clustering algorithm;
the model construction unit is used for constructing an original fault diagnosis model, training the original fault diagnosis model by using the classification result and generating a target fault diagnosis model;
and the fault analysis unit is used for inputting the electric power communication data to be analyzed into the target fault diagnosis model, matching the output fault information with the database and obtaining fault reasons and diagnosis strategies of the output fault information.
9. A terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the power communication failure analysis method of any one of claims 1-7.
10. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the power communication fault analysis method according to any one of claims 1 to 7.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115580526A (en) * | 2022-09-30 | 2023-01-06 | 中国人民解放军陆军工程大学 | Communication network fault diagnosis method, system, electronic equipment and storage medium |
| CN116245362A (en) * | 2023-03-07 | 2023-06-09 | 北京磁浮有限公司 | Urban rail contact network risk assessment method and related device |
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| CN117093405A (en) * | 2023-10-18 | 2023-11-21 | 苏州元脑智能科技有限公司 | Server fault diagnosis method, device, equipment and medium |
| CN117272152A (en) * | 2023-11-16 | 2023-12-22 | 晶科储能科技有限公司 | Energy storage system fault diagnosis method, system, electronic equipment and storage medium |
| CN118101413A (en) * | 2024-04-25 | 2024-05-28 | 中国电建集团江西省电力设计院有限公司 | Data communication network equipment fault diagnosis method and equipment based on neural network |
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| CN115580526A (en) * | 2022-09-30 | 2023-01-06 | 中国人民解放军陆军工程大学 | Communication network fault diagnosis method, system, electronic equipment and storage medium |
| CN115580526B (en) * | 2022-09-30 | 2024-03-22 | 中国人民解放军陆军工程大学 | Communication network fault diagnosis method, system, electronic equipment and storage medium |
| CN116245362A (en) * | 2023-03-07 | 2023-06-09 | 北京磁浮有限公司 | Urban rail contact network risk assessment method and related device |
| CN116245362B (en) * | 2023-03-07 | 2023-12-12 | 北京磁浮有限公司 | Urban rail contact network risk assessment method and related device |
| CN116882701A (en) * | 2023-07-27 | 2023-10-13 | 上海洲固电力科技有限公司 | Electric power material intelligent scheduling system and method based on zero-carbon mode |
| CN116882701B (en) * | 2023-07-27 | 2024-05-31 | 上海洲固电力科技有限公司 | Electric power material intelligent scheduling system and method based on zero-carbon mode |
| CN117093405A (en) * | 2023-10-18 | 2023-11-21 | 苏州元脑智能科技有限公司 | Server fault diagnosis method, device, equipment and medium |
| CN117093405B (en) * | 2023-10-18 | 2024-02-09 | 苏州元脑智能科技有限公司 | Server fault diagnosis method, device, equipment and medium |
| CN117272152A (en) * | 2023-11-16 | 2023-12-22 | 晶科储能科技有限公司 | Energy storage system fault diagnosis method, system, electronic equipment and storage medium |
| CN118101413A (en) * | 2024-04-25 | 2024-05-28 | 中国电建集团江西省电力设计院有限公司 | Data communication network equipment fault diagnosis method and equipment based on neural network |
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