CN112163012B - Power grid line fault phase selection method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a power grid line fault phase selection method, a device, electronic equipment and a storage medium, belonging to the field of power grid fault diagnosis, wherein the method comprises the following steps: s1, acquiring a plurality of fault information sequences corresponding to a power grid line during a fault period, wherein the fault information sequences comprise a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence; s2, acquiring gradient similarity of sampling points in each two fault information sequences, and constructing gradient similarity matrixes corresponding to the plurality of fault information sequences; s3, performing visualization processing on the gradient similarity matrix to construct a visualization graph corresponding to the plurality of fault information sequences; and S4, inputting the visual graph into a convolutional neural network model to perform fault phase selection on the grid line during the fault. The invention omits the step of marking the occurrence time of the fault, simplifies the phase selection flow path of the power grid line fault, and improves the accuracy of the phase selection of the power grid line fault.
Description
Technical Field
The invention belongs to the field of power grid fault diagnosis, and in particular relates to a power grid line fault phase selection method, a device, electronic equipment and a storage medium.
Background
After the power grid line has short-circuit fault, the fault phase of the power grid line can be timely and accurately judged, and the method has important significance for timely clearing the fault and guaranteeing safe and stable operation of the power grid.
In recent years, deep learning technology, particularly convolutional neural network (convolutional neural network, CNN) technology, has been developed rapidly, and has been widely used in the fields of power grid fault diagnosis, optimization operation, and the like.
However, in the prior art, the neural network model is used for carrying out fault phase selection on the power grid line, the occurrence time of the fault needs to be accurately marked, and once the occurrence time of the fault is marked with errors, the phenomenon of error judgment of the fault phase is easy to occur.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a power grid line fault phase selection method, a device, electronic equipment and a storage medium, which aim to simplify the fault identification process and improve the fault phase identification probability at the same time, thereby solving the technical problems of low accuracy and complex operation flow of power grid line fault phase selection by using a neural network model in the prior art.
To achieve the above object, according to one aspect of the present invention, there is provided a power grid line fault phase selection method, apparatus, electronic device, and storage medium.
A power grid line fault phase selection method, comprising:
s1, acquiring a plurality of fault information sequences corresponding to a power grid line during a fault period, wherein the fault information sequences comprise a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence;
s2, acquiring gradient similarity of sampling points in each two fault information sequences, and constructing gradient similarity matrixes corresponding to the plurality of fault information sequences;
s3, performing visualization processing on the gradient similarity matrix to construct a visualization graph corresponding to the plurality of fault information sequences;
and S4, inputting the visual graph into a convolutional neural network model to perform fault phase selection on the grid line during the fault.
In one embodiment, the step S2 specifically includes:
s201, gradient vectors of sampling points in each two fault information sequences are obtained;
s202, acquiring Manhattan distances of every two gradient vectors to identify the similarity of every two gradient vectors; and constructing the gradient similarity matrix by taking the Manhattan distance of each gradient vector as an element in the gradient similarity matrix.
In one embodiment, the step S201 specifically includes:
any two fault information sequences theta i And theta j ，i,j＝1,2,…,m，θ i And theta j The corresponding time sequences are respectively marked asAnd->Acquired->And->The gradient vectors of the middle sampling points are respectively as follows:
wherein,for theta i Corresponding->Is +.>For theta j Corresponding->N=1, …, N; />Is->Gradient of the nth sampling point; />Is->Gradient of nth sampling point, θ 1 -θ m Is deltat, m is the number of said fault information sequences;
the step S202 specifically includes:
manhattan distance MD for obtaining every two gradient vectors i,j Description of the inventionAnd->Similarity between: />
Constructing the gradient similarity matrix GS-matrix by using the acquired Manhattan distances:
in one embodiment, the step S3 specifically includes:
s301, mapping each Manhattan distance in the gradient similarity matrix into a corresponding color level;
s302, constructing the visual graph by utilizing each color level corresponding to the gradient similarity matrix.
In one embodiment, the step S301 specifically includes:
mapping each Manhattan distance into a corresponding color level according to a preset relation, wherein the preset relation is as follows:
wherein,is MD (machine direction) i,j Is a color level of (2); INT is a rounding symbol; />And->The maximum and minimum values of all elements in the GS-matrix, respectively.
In one embodiment, before the step S4, the power grid line fault phase selection method further includes:
setting the input layer structure of the convolutional neural network model to be the same as the size structure of the visual graph;
and setting an output layer structure of the convolutional neural network model according to the number of the fault phases.
In one embodiment, the step S1 specifically includes:
s101, collecting a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence of the head end and the tail end of each phase on a power network line in the fault period;
and S102, taking a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence of the head end and the tail end of each phase as the plurality of fault information sequences.
A power grid line fault phase selection device comprising:
the system comprises a sequence acquisition module, a fault detection module and a fault detection module, wherein the sequence acquisition module is used for acquiring a plurality of fault information sequences corresponding to a power grid line during a fault period, and the fault information sequences comprise a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence;
the matrix construction module is used for acquiring the gradient similarity of sampling points in each two fault information sequences and constructing gradient similarity matrixes corresponding to the plurality of fault information sequences;
the graphic construction module is used for carrying out visual processing on the gradient similarity matrix so as to construct visual graphics corresponding to the plurality of fault information sequences;
and the fault phase selection module is used for inputting the visual graph into a convolutional neural network model to perform fault phase selection on the power grid line during the fault.
An electronic device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
In general, compared with the prior art, the power grid line fault phase selection method provided by the invention can accurately perform fault phase selection on the power grid line without marking the fault occurrence time. The method and the device have the advantages that the step of marking the occurrence time of the fault is omitted, the phase selection flow of the power grid line fault is simplified, meanwhile, the accuracy of phase selection of the power grid line fault is improved, and the follow-up processing of the power grid line fault is facilitated.
Drawings
FIG. 1 is a flow chart of a method for grid line fault phase selection in an embodiment of the present application;
FIG. 2 is a schematic diagram of a power grid circuit in an embodiment of the present application;
FIG. 3 is a schematic diagram of a fault in an embodiment of the present application(Δt=0.3 s);
FIG. 4 is a schematic diagram of a GS-image construction process according to an embodiment of the present application;
FIG. 5 is a block diagram of a convolutional neural network model in one embodiment of the present application;
FIG. 6 is a graph comparing accuracy of the grid line fault phase selection method with that of the conventional CNN method;
FIG. 7 is a flow chart of step S2 in an embodiment of the present application;
FIG. 8 is a flow chart of step S3 in an embodiment of the present application;
FIG. 9 is a schematic diagram of a power grid line fault phase selection device according to an embodiment of the present application;
fig. 10 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application.
The same reference numbers are used throughout the drawings to reference like elements or structures, wherein:
a sequence acquisition module 901, a matrix construction module 902, a graph construction module 903, and a phase selection failure module 904.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The application provides a power grid line fault phase selection method, as shown in fig. 1, comprising the following steps: step S1 to step S4.
S1, acquiring a plurality of fault information sequences corresponding to a power grid line during a fault period, wherein the fault information sequences comprise a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence. In one embodiment, the first voltage signal time sequence, the current signal time sequence, the active power time sequence and the reactive power time sequence of each phase on the power network line during the fault period are respectively acquired, and the voltage signal time sequence, the current signal time sequence, the active power time sequence and the reactive power time sequence of the tail end are also acquired. For example, referring to FIG. 2, the grid line has two ends, a head end and a tail end, respectively, for obtaining electricity during a faultThe voltage, current, active power and reactive power time series of the head end and the tail end of the network line are time series of 4 signals in total so as to carry out the following visual processing and fault phase selection. Each power grid line has 3 phases, so that the head end of the power grid line can acquire a time sequence of 3×4=12 signals, and the tail end of the power grid line can acquire a time sequence of 3×4=12 signals, and the total time sequence of 12+12=24 signals. The voltage signal time sequence, the current signal time sequence, the active power time sequence and the reactive power time sequence of the head end and the tail end of each phase are used as a plurality of fault information sequences. The 24 signals are recorded as theta 1 -θ 24 And record theta 1 -θ 24 The corresponding fault information sequence is thatWherein N isIs at the same time +.>Each corresponding to a time length deltat.
And S2, acquiring gradient similarity of sampling points in each two fault information sequences, and constructing gradient similarity matrixes corresponding to the plurality of fault information sequences.
Specifically, gradient vectors of sampling points in each fault information sequence can be obtained to obtain a plurality of gradient vectors, and then the Manhattan distance of each two gradient vectors is calculated to identify the similarity of each two gradient vectors; and constructing a gradient similarity matrix by taking the Manhattan distance of each gradient vector as an element in the gradient similarity matrix.
Describing 24 failure information sequences as an example, first, for any two θ i And theta j (i, j=1, 2, …, 24) with corresponding time sequences of respectivelyAnd->Assume thatθ 1 -θ 24 Is Δt, then +.>And->The gradient of each sampling point is:
in the above-mentioned method, the step of,and->Respectively->And->A gradient of each of the sampling points in the (c),and->Are vectors; />Is->N=1, …, N; />Is->Is +.>Is->Gradient of the nth sampling point; />Is->Gradient of the nth sample point. Wherein N is->Is at the same time +.>Each corresponding to a time length deltat. Taking Δt=0.3 s, n=0.3×1000=300 can be obtained since the sampling frequency is 1000Hz in the embodiment, as shown in fig. 3, showing +_ in a certain fault>Wherein the part enclosed by the black frame is at=0.3 s +.>
Further, manhattan distance description is utilizedAnd->Similarity of (a), i.e
Wherein MD is i,j To be described by Manhattan distanceAnd->Similarity between them. Further, a gradient similarity matrix GS-matrix is constructed, i.e
And S3, carrying out visualization processing on the gradient similarity matrix to construct a plurality of visualization graphs corresponding to the fault information sequences.
Specifically, the GS-matrix is subjected to visualization processing, namely, the GS-image is constructed. In GS-image, the gradation of a pixel in a figure is an integer ranging from 0 to 255, for a total of 256 gradations. Defining the mapping relation between the elements in GS-matrix and the pixel color levels in GS-image, such as
In the above-mentioned method, the step of,is MD (machine direction) i,j Is a color level of (2); INT is a rounding symbol; />And->The maximum and minimum values of all elements in the GS-matrix, respectively. Finally, the construction of the gradient similarity visual graph GS-image is realized. As shown in fig. 4, the faults in all 3 subgraphs occur at 0s, and the time window Δt=0.3 s of the three subgraphs. It follows that the starting points of the three sub-graph time windows are different, but the generated GS-images are very similar. Therefore, the proposed method does not need to mark the fault occurrence time, and can generate similar GS-image as long as the time window can wrap the electrical signal for a period of time after the fault occurrence, and is used for the fault phase selection based on CNN.
And S4, inputting the visual graph into a convolutional neural network model to perform fault phase selection on the power grid line during the fault.
Specifically, the visual pattern is input into a trained convolutional neural network model, the convolutional neural network model can identify the visual pattern and then output an identification result, namely a fault phase selection result. The plurality of fault phases are provided, and the convolutional neural network model is used for identifying a visual pattern to select one fault phase from the plurality of fault phases. For example, the fault phase may be of class 10, such as an a-phase short ground, a B-phase short ground, a C-phase short ground, an AB-phase short ground, an AC-phase short ground, a BC-phase short ground, an AB-phase short, an AC-phase short, a BC-phase short, an ABC-phase short ground.
In one embodiment, before step S4, the grid line fault phase selection method further includes: the input layer structure of the convolutional neural network model is set to be the same as the size structure of the visual graph. And setting an output layer structure of the convolutional neural network model according to the number of the fault phases. If the size of the GS-image is large as other parameters such as mxm, the structure of the convolutional neural network model input layer is set to mxm, for example, for the structure of the convolutional neural network model input layer, the size of the GS-image is 24×24, so the structure of the CNN input layer is set to 24×24. The structure of the output layer is set according to the number of types of the failed phase selection, for example, since 10 types are common in the failed phase selection problem, the structure of the CNN output layer is set to 10×1. In this embodiment, the internal structure of the CNN is shown in fig. 5. In addition, fig. 5 is a schematic diagram of the structure of the CNN of the present embodiment.
| Parameters (parameters) | Numerical value |
| Convolution kernel size in first convolution layer | 7×7 |
| Pool core size in first pool layer | 2×2 |
| Number of convolution kernels/pooling kernels in first convolution layer/pooling layer | 3 |
| Convolution kernel size in second convolution layer | 4×4 |
| Pool core size in second pool layer | 2×2 |
| Number of convolution kernels/pooling kernels in second convolution layer/pooling layer | 6 |
TABLE 1 CNN internal Structure parameters
Further, taking the GS-image as the input of the CNN, and outputting the CNN as a fault phase selection result of the power grid line. As shown in fig. 6, when the fault time stamp error is 0, the proposed method and the conventional CNN method both obtain more accurate fault phase selection results; when a fault time marking error occurs, only the method is accurate; furthermore, the greater the fault time marking error, the lower the accuracy of the conventional method, while the proposed method is substantially unaffected.
In one embodiment, as shown in fig. 7, step S2 specifically includes: s201, gradient vectors of sampling points in each fault information sequence are obtained; s202, acquiring Manhattan distances of every two gradient vectors to identify similarity of every two gradient vectors; and constructing a gradient similarity matrix by taking the Manhattan distance of each gradient vector as an element in the gradient similarity matrix.
In one embodiment, step S201 specifically includes:
any two fault information sequences theta i And theta j (i,j＝1,2,…,m)，θ i And theta j The corresponding time sequences are respectively marked asAnd->Acquired->And->The gradient vectors of the middle sampling points are respectively as follows:
wherein,for theta i Corresponding->N=1, …, N; />For theta j Corresponding->N=1, …, N); />Is->Gradient of the nth sampling point; />Is->Gradient of nth sampling point, θ 1 -θ m Is Δt, m is the number of fault information sequences.
The step S202 specifically includes:
obtaining any two laddersManhattan distance MD of a degree vector i,j For describingAnd->Similarity between:
then constructing a gradient similarity matrix GS-matrix by using the acquired Manhattan distances:
in one embodiment, as shown in fig. 8, step S3 specifically includes: s301, mapping each Manhattan distance in the gradient similarity matrix into a corresponding color level. S302, constructing a visual graph by utilizing each tone scale corresponding to the gradient similarity matrix.
Specifically, the gradient similarity matrix is subjected to visualization processing, namely, each element in the matrix is mapped to one of the color steps 0-255, and finally, the matrix is mapped to one graph, namely, the gradient similarity matrix is converted to a visualization graph, the visualization graph carries fault state information, and the visualization graph is identified to obtain fault phases.
In one embodiment, step S301 specifically includes: mapping each Manhattan distance into a corresponding color level according to a preset relation, wherein the preset relation is as follows:
wherein,is MD (machine direction) i,j Is a color level of (2); INT is a rounding symbol; />And->The maximum and minimum values of all elements in the GS-matrix, respectively.
Specifically, the GS-matrix is subjected to visualization processing, namely, the GS-image is constructed. In GS-image, the gradation of a pixel in a figure is an integer ranging from 0 to 255, for a total of 256 gradations. Defining the mapping relation between the elements in GS-matrix and the pixel color level in GS-image, i.eWherein (1)>Is MD (machine direction) i,j Is a color level of (2); INT is a rounding symbol; />And->The maximum and minimum values of all elements in the GS-matrix, respectively. The above formula finally realizes the construction of the gradient similarity visual graphics GS-image.
The embodiment of the application further provides a power grid line fault phase selection device, as shown in fig. 9, including: a sequence acquisition module 901, a matrix construction module 902, a graph construction module 903, and a phase selection failure module 904. The sequence obtaining module 901 is configured to obtain a plurality of fault information sequences corresponding to the power grid lines during the fault period, where the fault information sequences include a voltage signal time sequence, a current signal time sequence, an active power time sequence, and a reactive power time sequence. The matrix construction module 902 is configured to obtain gradient similarities of sampling points in each two fault information sequences, and construct gradient similarity matrices corresponding to the plurality of fault information sequences. The graph construction module 903 is configured to perform visualization processing on the gradient similarity matrix to construct a visual graph corresponding to the plurality of fault information sequences. The fault phase selection module 904 is configured to input the visual graphic into the convolutional neural network model to perform fault phase selection on the grid line during the fault.
In one embodiment, the matrix construction module 902 is configured to obtain gradient vectors of sampling points in each fault information sequence; acquiring Manhattan distances of every two gradient vectors to identify similarity of every two gradient vectors; and constructing a gradient similarity matrix by taking the Manhattan distance of each gradient vector as an element in the gradient similarity matrix.
In one embodiment, the graph construction module 903 is configured to map each manhattan distance in the gradient similarity matrix to a corresponding color level; and constructing a visual graph by utilizing each color level corresponding to the gradient similarity matrix.
The division of each module in the above-mentioned power grid line fault phase selection device is only used for illustration, and in other embodiments, the power grid line fault phase selection device may be divided into different modules according to the needs, so as to complete all or part of the functions of the above-mentioned power grid line fault phase selection device.
For specific limitations on the grid line fault phase selection device, reference may be made to the above limitation on the grid line fault phase selection method, and no further description is given here. The modules in the power grid line fault phase selection device can be all or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the electronic device, or may be stored in software in a memory of the computer device, so that the processor may call and execute operations corresponding to the above modules.
Fig. 10 is a schematic diagram of an internal structure of an electronic device in one embodiment. As shown in fig. 10, the electronic device includes a processor and a memory connected through a system bus. Wherein the processor is configured to provide computing and control capabilities to support operation of the entire electronic device. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program is executable by a processor for implementing a grid line fault phase selection method as provided in the various embodiments below. The internal memory provides a cached operating environment for operating system computer programs in the non-volatile storage medium.
The implementation of each module in the power grid line fault phase selection device provided in the embodiments of the present application may be in the form of a computer program. The computer program may run on a terminal or a server. Program modules of the computer program may be stored in the memory of the electronic device. Which when executed by a processor, performs the steps of the methods described in the embodiments of the present application.
Embodiments of the present application also provide a computer-readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions that, when executed by one or more processors, cause the processors to perform the steps of a grid line fault phase selection method.
A computer program product containing instructions that, when run on a computer, cause the computer to perform a grid line fault phase selection method.
Any reference to memory, storage, database, or other medium used herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. A power grid line fault phase selection method, comprising:
s1, acquiring a plurality of fault information sequences corresponding to a power grid line during a fault period, wherein the fault information sequences comprise a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence;
s2, acquiring gradient similarity of sampling points in each two fault information sequences, and constructing gradient similarity matrixes corresponding to the plurality of fault information sequences;
s3, performing visualization processing on the gradient similarity matrix to construct a visualization graph corresponding to the plurality of fault information sequences;
s4, inputting the visual graph into a convolutional neural network model to perform fault phase selection on the power grid line during the fault period;
the step S2 specifically includes: s201, gradient vectors of sampling points in each fault information sequence are obtained; s202, acquiring Manhattan distances of every two gradient vectors to identify the similarity of every two gradient vectors; constructing the gradient similarity matrix by taking the Manhattan distance of each gradient vector as an element in the gradient similarity matrix;
the step S201 specifically includes: any two fault information sequences theta i And theta j ，i,j＝1,2,…,m，θ i And theta j The corresponding time sequences are respectively marked asAnd->Acquired->And->The gradient vectors of the middle sampling points are respectively as follows:
wherein,for theta i Corresponding->Is +.>For theta j Corresponding->N=1, …, N, m is the number of the fault information sequences, N is the number of the sampling points of the m fault information sequences; />Is->Gradient of the nth sampling point; />Is->Gradient of nth sampling point, θ 1 -θ m Is deltat;
the step S202 specifically includes:
manhattan distance MD for obtaining every two gradient vectors i,j Description of the inventionAnd->Similarity between:constructing the gradient similarity matrix GS-matrix by using the acquired Manhattan distances:
2. the grid line fault phase selection method according to claim 1, wherein the step S3 specifically comprises:
s301, mapping each Manhattan distance in the gradient similarity matrix into a corresponding color level;
s302, constructing the visual graph by utilizing each color level corresponding to the gradient similarity matrix.
3. The grid line fault phase selection method according to claim 2, wherein the step S301 specifically includes:
mapping each Manhattan distance into a corresponding color level according to a preset relation, wherein the preset relation is as follows:
wherein,is MD (machine direction) i,j Is a color level of (2); INT is a rounding symbol; />And->The maximum and minimum values of all elements in the GS-matrix, respectively.
4. A grid line fault phase selection method as claimed in any one of claims 1 to 3, wherein prior to step S4, the grid line fault phase selection method further comprises:
setting the input layer structure of the convolutional neural network model to be the same as the size structure of the visual graph;
and setting an output layer structure of the convolutional neural network model according to the number of the fault phases.
5. A power grid line fault phase selection method according to any one of claims 1 to 3, wherein step S1 specifically comprises:
s101, collecting a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence of the head end and the tail end of each phase on a power network line in the fault period;
and S102, taking a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence of the head end and the tail end of each phase as the plurality of fault information sequences.
6. A power grid line fault phase selection apparatus for performing the power grid line fault phase selection method of any one of claims 1-5, comprising:
the system comprises a sequence acquisition module, a fault detection module and a fault detection module, wherein the sequence acquisition module is used for acquiring a plurality of fault information sequences corresponding to a power grid line during a fault period, and the fault information sequences comprise a voltage signal time sequence, a current signal time sequence, an active power time sequence and a reactive power time sequence;
the matrix construction module is used for acquiring the gradient similarity of sampling points in each two fault information sequences and constructing gradient similarity matrixes corresponding to the plurality of fault information sequences;
the graphic construction module is used for carrying out visual processing on the gradient similarity matrix so as to construct visual graphics corresponding to the plurality of fault information sequences;
and the fault phase selection module is used for inputting the visual graph into a convolutional neural network model to perform fault phase selection on the power grid line during the fault.
7. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
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| WO2018133573A1 (en) * | 2017-01-18 | 2018-07-26 | 华为技术有限公司 | Method and device for analyzing service survivability |
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