CN113435307A - Operation and maintenance method, system and storage medium based on visual identification technology - Google Patents
Operation and maintenance method, system and storage medium based on visual identification technology Download PDFInfo
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Abstract
The invention discloses an operation and maintenance method and system based on a visual identification technology. The method comprises the steps that a block chain main node collects a network link hardware state diagram in real time, groups the network link hardware state diagram according to the data execution rate of each block chain sub-node, and sends each group of network link hardware state diagrams to each corresponding block chain sub-node; each block chain child node extracts fault features from the hardware state diagram according to a visual identification technology, inputs the fault features into a pre-constructed link abnormity model to identify fault types, calculates the severity of each fault type and returns the severity to the block chain main node; and summarizing the block chain main nodes to process the faults. By adopting the technical scheme, a large amount of hardware state diagrams are subjected to distributed recognition processing through the block chain technology, the speed of processing the hardware state diagrams is improved, hardware faults in the hardware state diagrams are recognized through the visual recognition technology, and the accuracy of hardware fault recognition is improved.
Description
Technical Field
The present application relates to the field of information communication, and in particular, to an operation and maintenance method, system and storage medium based on a visual identification technology.
Background
With the development and application of information technology, information systems have covered all business fields, and due to the continuous construction of information systems, the types and the quantities of hardware in network communication are large, the integration relationship is complex, and the existing hardware fault maintenance still depends on manual fault removal processing.
The visual recognition technology is a cross discipline in many fields such as artificial intelligence, neurobiology, psychophysics, computer science, image processing, pattern recognition and the like. Machine vision mainly uses a computer to simulate the visual function of a human, extracts information from an image of an objective object, processes and understands the information, and finally is used for actual detection, measurement and control. The visual recognition technology has the biggest characteristics of high speed, large information amount and multiple functions. Therefore, how to realize distributed identification processing of a large number of hardware faults through the visual identification technology becomes a problem to be solved urgently.
Disclosure of Invention
The application provides an operation and maintenance method based on a visual identification technology, which comprises the following steps:
the method comprises the steps that a block chain main node collects a network link hardware state diagram in real time, acquires the data execution rate of each block chain sub-node, groups the network link hardware state diagram according to the data execution rate of each block chain sub-node, and sends each group of network link hardware state diagrams to the corresponding block chain sub-nodes;
each block chain sub-node extracts fault characteristics from a network link hardware state diagram received by each block chain sub-node according to a visual identification technology, inputs the fault characteristics into a link abnormity model constructed in advance to identify fault types, calculates the severity of each identified fault type, and returns the fault types and the severity to a block chain main node;
and the block chain main node collects the fault types and the fault severity fed back by the block chain sub nodes and processes the faults.
The operation and maintenance method based on the visual identification technology includes that the hardware in the network link includes transmission and switching equipment, line interconnection equipment, a network adapter, a hub, a repeater, a network bridge, a router, a gateway, and a transmission medium, and the block link master node acquires each hardware state diagram in the network link in real time, and then distributes the acquired network link hardware state diagrams in a distributed manner at preset time intervals to each block link child node.
The operation and maintenance method based on the visual identification technology, wherein the network link hardware state diagrams are grouped according to the data execution rate of each block chain child node, specifically:
calculating the maximum data volume which can be processed by each block chain child node;
polling byte numbers of the hardware state diagrams in sequence, and searching a plurality of hardware equipment state diagrams closest to the maximum data volume;
and grouping all the hardware state graphs according to the maximum data volume which can be processed by each block chain child node.
The operation and maintenance method based on the visual identification technology, wherein the fault characteristics are input into a pre-constructed link anomaly model to identify the fault type, specifically comprises the following substeps:
acquiring historical hardware state diagrams of different fault types of different equipment from each block chain node in advance, constructing a fault feature vector set, inputting the fault feature vector set into a fault classification model, training the fault classification model to obtain different sub-training models, classifying the fault feature vector set by using each sub-training model respectively, and estimating a set of weights of each sub-training model according to a classification result;
searching an optimal value corresponding to each weight in the weight set, and determining a fault type through the combination of each sub-training model and the optimal value of the weight corresponding to the sub-training model to obtain a fault analysis model;
comparing the received multiple state diagrams of each network link hardware, extracting a characteristic area with morphological change as a fault characteristic, inputting the fault characteristic into a fault analysis model, and outputting a fault type.
The operation and maintenance method based on the visual identification technology is as described above, wherein the weight E is set for the number of fault types and the severity of the fault in advance1And E2Then calculating U-E1*V+E2And Y and V are total number of fault types, Y is fault severity, and fault sorting processing is carried out according to the final calculation result U.
The application also provides an operation and maintenance system based on the visual identification technology, which comprises a block chain main node and a plurality of block chain sub-nodes;
the block chain main node is used for acquiring a network link hardware state diagram in real time, acquiring the data execution rate of each block chain sub-node, grouping the network link hardware state diagram according to the data execution rate of each block chain sub-node, and sending each group of network link hardware state diagrams to the corresponding block chain sub-node;
each block chain sub-node is used for extracting fault characteristics from a network link hardware state diagram received by each block chain sub-node according to a visual identification technology, inputting the fault characteristics into a link abnormity model constructed in advance to identify fault types, calculating the severity of each identified fault type, and returning the fault types and the severity to the block chain main node;
and the block chain main node is also used for summarizing the fault types and the fault severity fed back by the block chain sub nodes and performing fault processing.
The operation and maintenance system based on the visual identification technology includes that the hardware in the network link includes transmission and switching equipment, line interconnection equipment, a network adapter, a hub, a repeater, a network bridge, a router, a gateway, and a transmission medium, and the block link master node acquires each hardware state diagram in the network link in real time, and then distributes the acquired network link hardware state diagrams in a distributed manner at preset intervals to each block link child node.
The operation and maintenance system based on the visual identification technology, wherein the block chain master node includes a hardware state diagram grouping module, which is specifically configured to calculate a maximum data volume that can be processed by each block chain child node; polling byte numbers of the hardware state diagrams in sequence, and searching a plurality of hardware equipment state diagrams closest to the maximum data volume; and grouping all the hardware state graphs according to the maximum data volume which can be processed by each block chain child node.
The operation and maintenance system based on the visual recognition technology, wherein each block chain sub-node includes a fault type recognition module, and is specifically configured to collect historical hardware state diagrams of different fault types of different devices from each block chain node in advance, construct a fault feature vector set, input the fault feature vector set into a fault classification model, train the fault classification model to obtain different sub-training models, classify the fault feature vector set by using each sub-training model, and estimate a set of weights of each sub-training model according to a classification result; searching an optimal value corresponding to each weight in the weight set, and determining a fault type through the combination of each sub-training model and the optimal value of the weight corresponding to the sub-training model to obtain a fault analysis model; comparing the received multiple state diagrams of each network link hardware, extracting a characteristic area with morphological change as a fault characteristic, inputting the fault characteristic into a fault analysis model, and outputting a fault type.
The present application further provides a computer-readable storage medium, comprising: at least one memory and at least one processor; a memory for storing one or more program instructions; a processor for executing one or more program instructions to perform any one of the operation and maintenance methods based on the visual identification technology.
The beneficial effect that this application realized is as follows: by adopting the technical scheme, a large amount of hardware state diagrams are subjected to distributed recognition processing through the block chain technology, the speed of processing the hardware state diagrams is improved, hardware faults in the hardware state diagrams are recognized through the visual recognition technology, and the accuracy of hardware fault recognition is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a flow chart of an operation and maintenance method based on a visual identification technology;
FIG. 2 is a flow chart of a method for identifying a fault type by inputting fault characteristics into a pre-constructed link anomaly model;
fig. 3 is a schematic diagram of an operation and maintenance system based on a visual identification technology.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. 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 invention.
Example one
An operation and maintenance method based on a visual identification technology is provided in an embodiment of the present application, and is used for performing operation and maintenance processing on a hardware fault existing in a network link through the visual identification technology, as shown in fig. 1, the operation and maintenance method includes:
in a data communications network, the telecommunications equipment connecting two or more data stations according to the specifications of a link protocol, called network links, the hardware in the network links including, but not limited to, transmission and switching equipment, line interconnect equipment, network adapters, hubs, repeaters, bridges, routers, gateways, transmission media, etc.;
in the embodiment of the application, the block chain main node acquires each hardware state diagram in a network link in real time, then performs distributed distribution on the acquired network link hardware state diagrams at preset time intervals, distributes the acquired network link hardware state diagrams to each block chain sub-node, and performs processing on a large number of hardware state diagrams by each block chain sub-node, so that the data processing capacity of the block chain main node can be balanced.
Specifically, when performing data distribution, the master node of the block chain needs to first obtain the data execution rate of each sub node of the block chain, and each sub node of the block chain calculates the data execution rate according to the following formula:
where M is the data execution rate of each blockchain child node, LiThe residual storage length of the ith storage area in the block chain child node is shown, n is the total number of the storage areas, m is the security level of the hardware state diagram, and the value of i is the slave
L is the total byte number of the network link hardware state diagram, Lj' is the total length of the jth memory area, and j takes the value of 1-n; lambda [ alpha ]1Storing capability weights, λ, for the block chain child nodes2Calculating power weights for the block chain sub-nodes, and ν is the current CPU utilization of the block chain sub-nodes.
Then the main node of the block chain carries out data grouping according to the data execution rate of each sub node of the block chain, specifically, the maximum data volume which can be processed by each sub node of the block chain is calculated firstly, namely
floor is a rounded down function, MiFor the data execution rate of the ith blockchain child node,
calculating the maximum data volume L which can be processed by each block chain child node for the sum of the data execution rates of all the block chain child nodes, wherein T is the total number of the block chain child nodesimaxThen polling the byte number of the hardware state diagram in sequence to find the maximum data quantity LimaxGrouping all the hardware state diagrams according to the maximum data volume which can be processed by each block chain child node, and sending each group of network link hardware state diagrams to each corresponding block chain child node; for example, the hardware state diagram of a hub, repeater, bridge is distributed toThe first block chain child node distributes the hardware state diagrams of the transmission and exchange equipment and the circuit interconnection equipment to the second block chain child node, and distributes the hardware state diagrams of the network adapter, the router, the gateway and the transmission medium to the third block chain child node.
in the embodiment of the present application, as shown in fig. 2, inputting the fault characteristics into a link abnormality model that is constructed in advance to identify the fault type specifically includes the following sub-steps:
specifically, inputting a fault feature vector set into a classification model, and training a sub-training model by using the feature vector set; classifying the feature vector set by using the sub-training model to obtain a classification result, estimating a weight set of the sub-training model according to the classification result, and specifically adopting a formula
Estimating a set of weights [ mu ] of a sub-training model1，μ2，μ3……μTWherein argmin is
The set of μ with the minimum value.
specifically, calculating an optimal value corresponding to each weight in a set of weights of each sub-training model through a particle swarm optimization algorithm; and determining the fault type through the optimal value combination of each sub-training model and the corresponding weight thereof to obtain a fault analysis model.
And step 230, comparing the received multiple state diagrams of each network link hardware, extracting a characteristic area with morphological changes from the state diagrams as a fault characteristic, inputting the fault characteristic into a fault analysis model, and outputting a fault type.
Referring back to fig. 1, in step 130, each block chain child node calculates the severity of each identified fault type, and returns the fault type and the severity to the block chain master node;
specifically, the severity of each fault type is calculated using the following equation:
wherein Y represents the severity of the fault type;
indicates the total number of the jth fault types,
indicates the total number of all fault types, R, of all devicesijA limit threshold representing the jth fault type in the ith hardware device.
specifically, after receiving the fault types and the severity of the fault types fed back by all the blockchain child nodes, the blockchain master node considers that the number of the faults and the severity of the faults have influence on the sequence of fault processing, and therefore sets weights E for the number of the fault types and the severity of the faults in advance1And E2，E1+E21, then calculate U-E1*V+E2Y, V are soAnd (4) carrying out fault sorting processing according to a final calculation result U, wherein the total number of the fault types is Y, and the fault severity is Y.
Example two
The second embodiment of the present application provides an operation and maintenance system based on a visual identification technology, as shown in fig. 3, which includes a blockchain main node and a plurality of blockchain sub-nodes, and in order to improve the processing capability of the blockchain main node for a plurality of network link hardware state diagrams, the network link hardware state diagrams are distributed to each blockchain sub-node for data distributed processing.
The block chain main node is used for acquiring a network link hardware state diagram in real time, acquiring the data execution rate of each block chain sub-node, grouping the network link hardware state diagram according to the data execution rate of each block chain sub-node, and sending each group of network link hardware state diagrams to the corresponding block chain sub-node;
each block chain sub-node extracts fault characteristics from a network link hardware state diagram received by each block chain sub-node according to a visual identification technology, inputs the fault characteristics into a link abnormity model constructed in advance to identify fault types, calculates the severity of each identified fault type, and returns the fault types and the severity of each fault to a block chain main node;
and the block chain main node performs fault merging processing according to the fed back fault type and the severity of each fault.
The hardware in the network link comprises transmission and exchange equipment, line interconnection equipment, a network adapter, a hub, a repeater, a network bridge, a router, a gateway and a transmission medium, and the block chain master node acquires each hardware state diagram in the network link in real time, and then distributes the acquired network link hardware state diagrams in a distributed manner at preset time intervals to each block chain child node.
The block chain main node comprises a hardware state diagram grouping module which is specifically used for calculating the maximum data volume which can be processed by each block chain sub node; polling byte numbers of the hardware state diagrams in sequence, and searching a plurality of hardware equipment state diagrams closest to the maximum data volume; and grouping all the hardware state graphs according to the maximum data volume which can be processed by each block chain child node.
Each block chain sub-node comprises a fault type identification module, and is specifically used for acquiring historical hardware state diagrams of different fault types of different equipment from each block chain node in advance, constructing a fault feature vector set, inputting the fault feature vector set into a fault classification model, training the fault classification model to obtain different sub-training models, classifying the fault feature vector set by using each sub-training model respectively, and estimating a weight set of each sub-training model according to a classification result; searching an optimal value corresponding to each weight in the weight set, and determining a fault type through the combination of each sub-training model and the optimal value of the weight corresponding to the sub-training model to obtain a fault analysis model; comparing the received multiple state diagrams of each network link hardware, extracting a characteristic area with morphological change as a fault characteristic, inputting the fault characteristic into a fault analysis model, and outputting a fault type.
Corresponding to the above embodiments, an embodiment of the present invention provides a computer storage medium, including: at least one memory and at least one processor;
the memory is used for storing one or more program instructions;
the processor is used for executing one or more program instructions to execute an operation and maintenance method based on the visual identification technology.
In accordance with the embodiments described above, the present invention provides a computer-readable storage medium, which contains one or more program instructions for executing, by a processor, a method for operation and maintenance based on a visual identification technology.
The disclosed embodiments of the present invention provide a computer-readable storage medium having stored therein computer program instructions which, when run on a computer, cause the computer to perform the above-described method.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. An operation and maintenance method based on a visual identification technology is characterized by comprising the following steps:
the method comprises the steps that a block chain main node collects a network link hardware state diagram in real time, acquires the data execution rate of each block chain sub-node, groups the network link hardware state diagram according to the data execution rate of each block chain sub-node, and sends each group of network link hardware state diagrams to the corresponding block chain sub-nodes;
each block chain sub-node extracts fault characteristics from a network link hardware state diagram received by each block chain sub-node according to a visual identification technology, inputs the fault characteristics into a link abnormity model constructed in advance to identify fault types, calculates the severity of each identified fault type, and returns the fault types and the severity to a block chain main node;
and the block chain main node collects the fault types and the fault severity fed back by the block chain sub nodes and processes the faults.
2. The operation and maintenance method based on the visual identification technology as claimed in claim 1, wherein the hardware in the network link includes a transmission and switching device, a line interconnection device, a network adapter, a hub, a repeater, a network bridge, a router, a gateway, and a transmission medium, the block chain master node obtains each hardware state diagram in the network link in real time, and then distributes the collected network link hardware state diagrams in a distributed manner to each block chain child node at preset time intervals.
3. The operation and maintenance method based on the visual identification technology as claimed in claim 1, wherein the network link hardware state diagrams are grouped according to the data execution rate of each blockchain child node, specifically:
calculating the maximum data volume which can be processed by each block chain child node;
polling byte numbers of the hardware state diagrams in sequence, and searching a plurality of hardware equipment state diagrams closest to the maximum data volume;
and grouping all the hardware state graphs according to the maximum data volume which can be processed by each block chain child node.
4. The operation and maintenance method based on the visual identification technology as claimed in claim 1, wherein the fault feature is input into a pre-constructed link anomaly model to identify the fault type, and the operation and maintenance method specifically comprises the following sub-steps:
acquiring historical hardware state diagrams of different fault types of different equipment from each block chain node in advance, constructing a fault feature vector set, inputting the fault feature vector set into a fault classification model, training the fault classification model to obtain different sub-training models, classifying the fault feature vector set by using each sub-training model respectively, and estimating a set of weights of each sub-training model according to a classification result;
searching an optimal value corresponding to each weight in the weight set, and determining a fault type through the combination of each sub-training model and the optimal value of the weight corresponding to the sub-training model to obtain a fault analysis model;
comparing the received multiple state diagrams of each network link hardware, extracting a characteristic area with morphological change as a fault characteristic, inputting the fault characteristic into a fault analysis model, and outputting a fault type.
5. The operation and maintenance method based on visual identification technology as claimed in claim 1, wherein the weight E is set for the number of types of failure and the severity of failure in advance1And E2Then calculating U-E1*V+E2And Y and V are total number of fault types, Y is fault severity, and fault sorting processing is carried out according to the final calculation result U.
6. An operation and maintenance system based on a visual identification technology is characterized by comprising a block chain main node and a plurality of block chain sub nodes;
the block chain main node is used for acquiring a network link hardware state diagram in real time, acquiring the data execution rate of each block chain sub-node, grouping the network link hardware state diagram according to the data execution rate of each block chain sub-node, and sending each group of network link hardware state diagrams to the corresponding block chain sub-node;
each block chain sub-node is used for extracting fault characteristics from a network link hardware state diagram received by each block chain sub-node according to a visual identification technology, inputting the fault characteristics into a link abnormity model constructed in advance to identify fault types, calculating the severity of each identified fault type, and returning the fault types and the severity to the block chain main node;
and the block chain main node is also used for summarizing the fault types and the fault severity fed back by the block chain sub nodes and performing fault processing.
7. The operation and maintenance system based on visual identification technology of claim 6, wherein the hardware in the network link includes transmission and switching equipment, line interconnection equipment, network adapters, hubs, repeaters, bridges, routers, gateways, and transmission media, and the blockchain master node acquires each hardware state diagram in the network link in real time, and then distributes the acquired network link hardware state diagrams in a distributed manner at preset time intervals to each blockchain child node.
8. The operation and maintenance system based on visual identification technology of claim 6, wherein the blockchain master node comprises a hardware state diagram grouping module, specifically configured to calculate a maximum amount of data that each blockchain child node can process; polling byte numbers of the hardware state diagrams in sequence, and searching a plurality of hardware equipment state diagrams closest to the maximum data volume; and grouping all the hardware state graphs according to the maximum data volume which can be processed by each block chain child node.
9. The operation and maintenance system based on visual recognition technology as claimed in claim 6, wherein each block chain sub-node comprises a fault type recognition module, specifically configured to collect historical hardware state diagrams of different fault types of different devices from each block chain node in advance, construct a fault feature vector set, input the fault feature vector set into a fault classification model, train the fault classification model to obtain different sub-training models, classify the fault feature vector set by using each sub-training model, and estimate a set of weights of each sub-training model according to a classification result; searching an optimal value corresponding to each weight in the weight set, and determining a fault type through the combination of each sub-training model and the optimal value of the weight corresponding to the sub-training model to obtain a fault analysis model; comparing the received multiple state diagrams of each network link hardware, extracting a characteristic area with morphological change as a fault characteristic, inputting the fault characteristic into a fault analysis model, and outputting a fault type.
10. A computer-readable storage medium, comprising: at least one memory and at least one processor;
a memory for storing one or more program instructions;
a processor for executing one or more program instructions to perform a method of operation based on visual identification technology as claimed in any one of claims 1 to 5.
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| CN114265930A (en) * | 2021-11-19 | 2022-04-01 | 国电南京自动化股份有限公司 | Low-voltage user fault report repairing and processing method based on event extraction |
| CN114465873A (en) * | 2022-01-21 | 2022-05-10 | 无锡软美信息科技有限公司 | Method and storage medium for blockchain exception node discovery and repair |
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