CN114090647A - Power communication equipment defect relevance analysis method and defect checking method - Google Patents
Power communication equipment defect relevance analysis method and defect checking method Download PDFInfo
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Abstract
The invention discloses a method for analyzing the relevance of defects of electric power communication equipment and a method for checking the defects, which comprise the following steps: acquiring historical electric power communication equipment defect data in an electric power communication equipment transmission network to generate an electric power communication equipment defect original database; according to a data discrete rule determined in advance based on the defect characteristics of the power communication equipment, carrying out quantization processing on a defect original database of the power communication equipment to generate a defect-influence factor database of the power communication equipment; and introducing a pre-constructed improved Apriori algorithm model for analysis to obtain frequent item sets with different support degrees and confidence degrees and two strong rules. The advantages are that: the method is based on a physical model of the electric power communication equipment, improves the traditional Apriori algorithm according to the characteristics of an equipment defect database, performs relevance analysis on the defects of the electric power communication equipment based on the improved Apriori algorithm, and effectively identifies and detects the fault reason and the fault point location of the electric power communication equipment.
Description
Technical Field
The invention relates to a method for analyzing the relevance of defects of electric power communication equipment and a method for checking the defects, and belongs to the technical field of electric power communication.
Background
With the development of energy internet, power communication equipment is continuously increased and serves as an important infrastructure for ensuring the safe operation of a power system, transmission channels with various types and rates are provided for secondary equipment of power grid, such as stable control and telecontrol and the like, the influence of the operation state of the transmission channels on a power grid safety and stability control device is gradually increased, and the defect analysis of the communication equipment becomes a key factor influencing the safe and stable operation of the power grid, so that the intelligent and efficient equipment fault analysis is the guarantee for the stable operation of large-scale power communication equipment connected to the power grid.
Along with the continuous expansion of electric power communication scale, original communication equipment overhauls working method and is difficult to support the pressure that communication equipment's large-scale input brought, and the equipment is many and complicated has brought very big work burden for the maintainer, has also brought certain risk for the electric wire netting operation simultaneously.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method for analyzing the relevance of the defects of the electric power communication equipment and a method for checking the defects, and can solve the technical problems of insufficient capability of identifying the causes of the defects of the electric power communication equipment and detecting the positions of fault points in the prior art.
In order to solve the above technical problem, the present invention provides a method for analyzing defect association of power communication equipment, including:
collecting historical electric power communication equipment defect data in an electric power communication equipment transmission network, carrying out classification analysis on the historical electric power communication equipment defect data, and generating an electric power communication equipment defect original database;
according to a data discrete rule determined in advance based on the defect characteristics of the power communication equipment, carrying out quantization processing on a defect original database of the power communication equipment to generate a defect-influence factor database of the power communication equipment;
and importing the defect-influence factor database of the power communication equipment into a pre-constructed improved Apriori algorithm model for analysis to obtain a frequent item set with different support degrees and confidence degrees and two strong rules, wherein the two strong rules are incidence relations between the influence factors and the defects and between the defects and the fault positions of the defective equipment.
Further, in the above-mentioned case,
the method for acquiring the historical power communication equipment defect data in the power communication equipment transmission network, performing classification analysis on the historical power communication equipment defect data, and generating a power communication equipment defect original database comprises the following steps:
collecting historical electric power communication equipment defect data of an electric power communication equipment transmission network, classifying and analyzing various equipment defects and influence factors thereof in the historical electric power communication equipment defect data to obtain mapping relations among the electric power communication equipment defect influence factors, defect types and fault modules, and generating an electric power communication equipment defect original database.
Further, the generating a power communication device defect-influence factor database by performing quantization processing on the power communication device defect original database according to a data discrete rule determined in advance based on the power communication device defect characteristics includes:
analyzing the effectiveness of the collected electric power communication equipment defects based on the electric power communication equipment defect characteristics to obtain each data set E, wherein E is { signal loss, tail fiber interruption, excessive error code, light power overload and environment temperature change }, then coding and integrating each data set E, classifying the defect types by letters, and classifying influence factors by numbers to form a data set E' consisting of letters and numbers; and importing each data set E' into an improved Apriori algorithm, finding out a defect frequent item set with higher support degree by calculating the support degree of the equipment defect candidate item set and the confidence degree between the influence factors and the equipment defects, and forming strong association between the influence factors with high confidence degree and the defects to generate a power communication equipment defect-influence factor database.
Further, the step of importing the defect-influence factor database of the power communication device into a pre-constructed improved Apriori algorithm model for analysis to obtain frequent item sets with different support degrees and confidence degrees and two strong rules includes:
the method comprises the steps that the electric power communication equipment defect-influence factor database is subjected to hierarchical processing according to a circuit layer, a channel layer and a transmission medium layer, the mapping relation among electric power communication equipment defect influence factors is determined through the electric power communication equipment defect-influence factor database after hierarchical processing, the electric power communication equipment defect-influence factor database is mapped into a Boolean matrix which only contains elements of 0 and 1 and is arranged in a hierarchical mode according to the mapping relation, each row in the Boolean matrix is a defect database, and each column is a defect type;
and introducing the Boolean matrix into a pre-constructed improved Apriori algorithm model for analysis to obtain frequent item sets with different support degrees and confidence degrees and two strong rules.
Further, the introducing the boolean matrix into a pre-constructed improved Apriori algorithm model for analysis to obtain frequent item sets with different support degrees and confidence degrees and two strong rules includes:
performing pruning operation by comparing the occurrence frequency of each column of '1' elements of the Boolean matrix with a preset threshold value, and performing self-connection operation by performing logic AND operation on the defect type item columns of the Boolean matrix after the pruning operation to generate a frequent item set;
and repeating the pruning operation and the self-connection operation, and terminating under the condition to obtain a frequent item set with different support degrees and confidence degrees and two strong rules.
A power communication equipment defect checking method comprises the following steps:
acquiring a frequent item set and two strong rules of different support degrees and confidence degrees determined by the electric power communication equipment defect relevance analysis method;
and preferentially checking the defects of the equipment with high support degree during preventive state maintenance and fault maintenance according to the frequent item sets with different support degrees and confidence degrees and two strong rules, and preferentially checking the influence factors with high confidence degrees and fault parts when the power communication equipment breaks down.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any one of the power communication device defect correlation analysis methods or the power communication device defect review method.
A computing device, comprising, in combination,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the power communication device fault correlation analysis methods or the power communication device fault troubleshooting method.
The invention achieves the following beneficial effects:
the method is an effective method for identifying and detecting the fault reason and fault point positioning of the power communication equipment, and is based on a physical model of the power communication equipment, combines the equipment, human factors and an external environment, improves the traditional Apriori algorithm according to the characteristics of an equipment defect database, performs relevance analysis on the defects of the power communication equipment based on the improved Apriori algorithm, and extracts specific strong relevance as a fault tracing basis.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a diagram of a defect analysis of the power communication equipment;
FIG. 3 is a flow chart of the modified Apriori algorithm for generating frequent item sets;
fig. 4 is a rule diagram of defect data of the power communication device.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a method for analyzing a defect association of an electric power communication device solves the technical problems of the prior art that the capability of identifying the cause of a defect of an electric power communication device and detecting the position of a fault point is insufficient.
The invention adopts the following technical scheme for realizing the aim of the invention:
step 1: and constructing a physical model of the power communication equipment, and analyzing the defect characteristics and the influence factors of the power communication equipment. The power communication equipment transmission network is layered, the power communication equipment transmission network comprises a circuit layer, a channel layer and a transmission medium layer, a basis is provided for the follow-up layered introduction of the defect database into the improved Apriori algorithm, and the complexity of the algorithm is reduced. The circuit layer is service-oriented and can directly provide service bearing of communication service SDH for users, the channel layer can be further divided into a high-order channel layer and a low-order channel layer, and the segment layer in the transmission medium layer is divided into a multiplexing segment and a regeneration segment, so that a basis is provided for introducing the encoded and integrated defect database into an improved Apriori algorithm in a layering manner.
The method comprises the steps of collecting defect data of local equipment, carrying out classification analysis on various defects and influence factors of the local equipment according to characteristics to obtain a defect influence factor system of the power communication equipment and a mapping relation between the defect influence factor system and the defect influence factor system, and generating a defect original database.
Step 2: and formulating a data discrete rule according to the defect characteristics of the equipment, and carrying out quantitative processing on the original defect data to generate a power communication equipment defect-influence factor database. The method overcomes the contradiction of data fusion algorithm, carries out quantitative coding on the original data, and establishes a defect analysis model of the power communication equipment according to a data model and a physical model and by combining association rules.
And step 3: and improving the algorithm according to the characteristics of the database, analyzing and processing the defects and the influence factors of the power communication equipment based on the improved Apriori algorithm, and generating a frequent item set. And (3) introducing the quantized and coded defect database into an improved Apriori algorithm for analysis to obtain frequent item sets with different support degrees and confidence degrees and two strong rules which are respectively incidence relations between the influence factors and the defects and between the defects and the fault parts of the defective equipment.
And 4, step 4: and tracing the source defect reason and positioning the fault through the strong association between the equipment defect and the influencing factor as well as the fault module. And judging and identifying the causes and the positions of fault points of the defects by calculating the support degree and the confidence degree of the absence database and preferentially matching the influence factors with high confidence degrees and the fault modules.
Further, as shown in fig. 2, in step 1, taking a Synchronous Digital Hierarchy (SDH) transmission device as an example, a layer model and a basic multiplexing mapping structure of the SDH transmission device are analyzed, for example, receiving information at a far end of a multiplexing section and mismatch and error code information detected at a receiving end of a high-order channel are collected, and a fault possibly occurring on a transmission route and an affinity relationship with some modules are analyzed through the layer model and the multiplexing mapping structure of the SDH transmission device, so as to establish a physical model of the SDH transmission device. The method comprises the steps of collecting defect data of local electric power communication equipment, taking each piece of data as a sample, selecting n data samples, analyzing defect characteristics and influence factors of the defect characteristics, wherein the defect characteristics comprise a plurality of defects such as signal loss, frame loss and excessive error codes and a plurality of influence factors such as tail fiber interruption, light power overload and environmental temperature change, generating a multi-dimensional influence factor system, and taking the multi-dimensional influence factor system as an original database of the defects of the electric power communication equipment.
Further, in step 2, the collected defects of the power communication device are analyzed for validity based on the characteristics of the defects of the power communication device, so as to obtain each data set E, E ═ signal loss, pigtail interruption, excessive bit errors, optical power overload, and ambient temperature change }, then each data set E is encoded and integrated, the defect types are classified by letters, the influencing factors are classified by numbers, data composed of letters and numbers are formed, each data set E' is led into an improved Apriori algorithm, the defect candidate set with higher support is found out by calculating the support of the device defect candidate set and the confidence between the influencing factors and the device defects, and the influencing factors with high confidence and the defects form strong association, so as to generate a defect-influencing factor database of the power communication device.
Further, in step 3, firstly, the defect data is processed hierarchically, then the device defect database is preprocessed and mapped into a boolean matrix only containing 0 and 1, one device defect record is used as a row, each type of defect is used as a column, a candidate frequent item set is generated by a union set of each column, then pruning operation is performed according to a minimum threshold value, logical and operation is performed on the defect item columns to perform connection operation, the operation is sequentially performed, the candidate frequent item set is continuously updated, and finally the frequent item set is obtained according to the minimum threshold value.
Further, in the step 3, according to the association rule, the mapping relationship between the defect of the power communication device and the influence factor is represented as an influence factor a
Defect B and faulty Module C
And (3) influencing the factor A, mapping the defect database into a Boolean matrix, introducing the Boolean matrix into an improved Apriori algorithm for analysis and processing, generating an equipment defect frequent item set according to the support degree, and generating strong association according to the confidence degree, wherein the strong association comprises strong association relations between the influencing factor and the defect and between the defect and the fault part of the defective equipment.
Further, in the step 4, according to the obtained frequent item set and the strong association, the defect of the device with high support degree is preferentially checked in the process of preventing the condition maintenance and the fault maintenance, and when the power communication device breaks down, the influence factors with high confidence degree and the fault part are preferentially checked, so that the source fault occurrence reason and the fault point position are traced, and the load loss and the economic loss are reduced to the minimum.
In the embodiment, the present invention is described with SDH transmission equipment as a specific embodiment:
and acquiring defect data of local equipment, and classifying and analyzing various defects and influence factors of the equipment according to the characteristics. According to an SDH transmission device model, the performance and the operation parameters of the SDH transmission device are analyzed, and the defects of the device easily generated at different channel layers are analyzed, so that the multi-dimensional influence factors of the power communication device can be generally divided into two aspects of external factors and internal factors. External factors include environmental temperature and humidity changes, optical fiber line quality, manual misoperation, quakeproof and dustproof conditions, extreme natural disasters and the like. The manual misoperation is mostly the wrong connection of the optical fiber direction and the wrong setting of the clock source parameters. The internal factors refer to equipment originating factors causing alarm defects, and include equipment operation time, optical power overload, network layer message errors, equipment manufacturer types, equipment load degree and the like. There are many drawbacks to power communication devices, which can be classified as signal loss, frame loss, excessive bit errors, trace marker mismatch, pointer loss, clock failure, signal degradation, link failure, etc., as shown in fig. 1.
The collected defects of the communication equipment are sorted and summarized to obtain 800 effective defect sets. The classification induction defect types mainly comprise 8 types, including signal loss, frame loss, excessive error codes, track identification mismatch, pointer loss, clock failure, signal degradation and link failure; the influencing factors mainly include 9 types, which are respectively tail fiber interruption, optical power overload, network layer message error, environment temperature change, equipment operation duration, equipment manufacturing parameters, environment humidity change, extreme natural disasters and equipment load degree. Sample data is numbered, wherein nine types of influencing factors such as tail fiber interruption, optical power overload and the like are numbered by 1-9, and eight types of defects such as signal loss, frame loss and the like are numbered by A-H. And analyzing the collected defect data by adopting a defect relevance analysis method of the improved Apriori algorithm for the power communication equipment. And mapping the equipment defects and the influence factors in each record into a Boolean matrix through coding, setting the minimum support threshold and the minimum confidence threshold of the improved Apriori algorithm to be 10% and 50% respectively due to the large data volume of the sample set, obtaining a frequent item set through the circulating self-connection and pruning of the frequent item set, and mining strong rules according to confidence indexes.
The algorithm is improved, so that the defect data of the power communication equipment can be analyzed more quickly. Firstly, the defect data is processed in a layered mode and divided into a channel layer and a transmission medium layer, wherein the channel layer is subdivided into a low order and a high order, and a section layer in the transmission medium layer is subdivided into a multiplexing section and a regeneration section. The device defect database is then preprocessed and mapped into a matrix containing only "0" and "1" elements, i.e., a boolean matrix. The matrix row represents a piece of defect data, and the column represents defect classification and influence factors; if B isi×j1 means that defect j appears in the ith data, if Bi×jThe value of 0 indicates that the defect j does not appear in the ith piece of data, and thus the matrix B is composed of elements "0" and "1", and a communication device defect boolean matrix is generated. For example, five existing device defect records correspond to five device defects, which are respectively signal loss, frame loss, track mark mismatch, pointer loss, and signal degradation, and are sequentially numbered A, B, C, D, E, where each record has a corresponding defect that is 1, and otherwise is 0, so that a 5 × 5 boolean matrix can be formed, as shown below:
as shown in fig. 3, the Apriori algorithm is modified, and before the frequent item set is connected, the correction is performed according to the definition that if the number of single defect items j contained in the k-dimensional frequent item set is smaller than the dimension k, the defect items j cannot appear in the frequent k +1 item set. Specifically, a candidate frequent C-1 item set is generated by a union set of defective items in each column of a power communication equipment defect Boolean matrix B; counting the number of occurrences of each column of '1' elements of the Boolean matrix B, and performing pruning operation, namely if j columns of '1' elements occur for the number SjN x a% or less, deleting the column of the Boolean matrix, namely deleting the defect item, and generating a frequent L-1 item set; then to frequent L-1 item setAnd performing a connection operation, namely performing logic AND operation on the defective item columns of the Boolean matrix B to generate a candidate frequent C-2 item set. For example, for the matrix described above, the minimum threshold is set to 20%, i.e., SjAnd when the signal degradation defect is less than or equal to 1, deleting the E column of the matrix, namely, carrying out logic AND operation on the new matrix to obtain the following matrix:
and comparing the '1' of each sub-element of the candidate frequent C-2 item set with a set minimum threshold, deleting if the sub-element of the candidate frequent C-2 item set does not meet the set minimum threshold, and updating the frequent L-2 item set. And sequentially carrying out pruning and connecting operations until the L-k item set is updated.
A series of frequent item sets are obtained by improving an Apriori algorithm, 23 effective strong rules which can be used for analyzing defect mechanisms and providing a correlation analysis method and meet the minimum threshold condition are screened out according to the strong rules mined from the frequent item sets, and the obtained results are shown in table 1.
Table 1 strong rules for defective data of communication device
According to the calculation results of the support degree and the confidence degree, the elements are divided according to the obtained strong rule result, and a defect data rule base of the power communication equipment can be constructed by classifying, inducing and summarizing, as shown in fig. 4.
The present invention also provides a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any one of the methods of power communication device fault correlation analysis or the method of power communication device fault troubleshooting.
A computing device, comprising, in combination,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the power communication device fault correlation analysis methods or the power communication device fault troubleshooting method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A method for analyzing defect relevance of power communication equipment is characterized by comprising the following steps:
collecting historical electric power communication equipment defect data in an electric power communication equipment transmission network, carrying out classification analysis on the historical electric power communication equipment defect data, and generating an electric power communication equipment defect original database;
according to a data discrete rule determined in advance based on the defect characteristics of the power communication equipment, carrying out quantization processing on a defect original database of the power communication equipment to generate a defect-influence factor database of the power communication equipment;
and importing the defect-influence factor database of the power communication equipment into a pre-constructed improved Apriori algorithm model for analysis to obtain a frequent item set with different support degrees and confidence degrees and two strong rules, wherein the two strong rules are incidence relations between the influence factors and the defects and between the defects and the fault positions of the defective equipment.
2. The power communication device defect correlation analysis method according to claim 1,
the method for acquiring the historical power communication equipment defect data in the power communication equipment transmission network, performing classification analysis on the historical power communication equipment defect data, and generating a power communication equipment defect original database comprises the following steps:
collecting historical electric power communication equipment defect data of an electric power communication equipment transmission network, classifying and analyzing various equipment defects and influence factors thereof in the historical electric power communication equipment defect data to obtain mapping relations among the electric power communication equipment defect influence factors, defect types and fault modules, and generating an electric power communication equipment defect original database.
3. The method for analyzing the relevance of the defects of the power communication equipment according to claim 1, wherein the step of performing quantization processing on the original database of the defects of the power communication equipment according to a data discrete rule determined in advance based on the characteristics of the defects of the power communication equipment to generate the database of the defects-influencing factors of the power communication equipment comprises the following steps:
analyzing the effectiveness of the collected electric power communication equipment defects based on the electric power communication equipment defect characteristics to obtain each data set E, wherein E is { signal loss, tail fiber interruption, excessive error code, light power overload and environment temperature change }, then coding and integrating each data set E, classifying the defect types by letters, and classifying influence factors by numbers to form a data set E' consisting of letters and numbers; and importing each data set E' into an improved Apriori algorithm, finding out a defect frequent item set with higher support degree by calculating the support degree of the equipment defect candidate item set and the confidence degree between the influence factors and the equipment defects, and forming strong association between the influence factors with high confidence degree and the defects to generate a power communication equipment defect-influence factor database.
4. The method according to claim 3, wherein the step of importing the defect-influence factor database of the power communication device into a pre-constructed improved Apriori algorithm model for analysis to obtain a frequent item set with different support degrees and confidence degrees and two strong rules comprises:
the method comprises the steps that the electric power communication equipment defect-influence factor database is subjected to hierarchical processing according to a circuit layer, a channel layer and a transmission medium layer, the mapping relation among electric power communication equipment defect influence factors is determined through the electric power communication equipment defect-influence factor database after hierarchical processing, the electric power communication equipment defect-influence factor database is mapped into a Boolean matrix which only contains elements of 0 and 1 and is arranged in a hierarchical mode according to the mapping relation, each row in the Boolean matrix is a defect database, and each column is a defect type;
and introducing the Boolean matrix into a pre-constructed improved Apriori algorithm model for analysis to obtain frequent item sets with different support degrees and confidence degrees and two strong rules.
5. The method according to claim 4, wherein the introducing the Boolean matrix into a pre-constructed improved Apriori algorithm model for analysis to obtain frequent item sets with different support degrees and confidence degrees and two strong rules comprises:
performing pruning operation by comparing the occurrence frequency of each column of '1' elements of the Boolean matrix with a preset threshold value, and performing self-connection operation by performing logic AND operation on the defect type item columns of the Boolean matrix after the pruning operation to generate a frequent item set;
and repeating the pruning operation and the self-connection operation, and terminating under the condition to obtain a frequent item set with different support degrees and confidence degrees and two strong rules.
6. A method for troubleshooting defects of power communication equipment is characterized by comprising the following steps:
acquiring a frequent item set and two strong rules of different support degrees and confidence degrees determined by the method for analyzing the defect relevance of the power communication equipment according to claim 1;
and preferentially checking the defects of the equipment with high support degree during preventive state maintenance and fault maintenance according to the frequent item sets with different support degrees and confidence degrees and two strong rules, and preferentially checking the influence factors with high confidence degrees and fault parts when the power communication equipment breaks down.
7. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-5 or the method of claim 6.
8. A computing device, comprising,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-5 or the method of claim 6.
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