CN117573402A - Operation and maintenance data analysis method, operation and maintenance data analysis device, computer equipment and storage medium - Google Patents
Operation and maintenance data analysis method, operation and maintenance data analysis device, computer equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/079—Root cause analysis, i.e. error or fault diagnosis
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Abstract
The application relates to the field of intelligent operation and maintenance, and provides an operation and maintenance data analysis method, an operation and maintenance data analysis device, operation and maintenance data analysis equipment and a computer storage medium, wherein the operation and maintenance data analysis method comprises the following steps: acquiring at least one piece of operation and maintenance index data, and determining target operation and maintenance data and default operation and maintenance data except the target operation and maintenance data from at least one piece of operation and maintenance index data; based on the default operation and maintenance data, predicting the target operation and maintenance data through a preset operation and maintenance data prediction model to obtain target prediction data; comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data; and determining operation and maintenance index data corresponding to the fault root according to the target error values. The operation and maintenance data analysis method can be used for maintaining the financial system, and determining the fault root cause when the financial system fails by predicting the specific operation and maintenance data and comparing the predicted value with the actual value.
Description
Technical Field
The present disclosure relates to the field of intelligent operation and maintenance, and in particular, to a method and apparatus for analyzing operation and maintenance data, a computer device, and a storage medium.
Background
Root cause analysis refers to identifying and locating root causes of system faults, and belongs to one key link in intelligent operation and maintenance. For example, in a financial system including a plurality of pieces of operation and maintenance data, a fault of one piece of operation and maintenance data affects other pieces of operation and maintenance data, so that values of the plurality of pieces of operation and maintenance data are at abnormal levels, and therefore, in order to extract a fault cause from a large amount of complex fault information, operation and maintenance data such as monitoring data, performance index data and the like in the system need to be collected first, and a root cause analysis model needs to be trained based on various algorithms. However, this method requires the operation and maintenance engineer to provide a label to the operation and maintenance data, which consumes high labor costs. There is a need for a fault analysis method that solves the above problems.
Disclosure of Invention
The main purpose of the application is to provide a method, a device, equipment and a computer storage medium for analyzing operation and maintenance data, aiming at improving the operation and maintenance efficiency of a system.
In a first aspect, the present application provides a method of analyzing a dimension of a motion, the method of analyzing a dimension of a motion comprising the steps of:
acquiring at least one piece of operation and maintenance index data, and determining target operation and maintenance data and default operation and maintenance data except the target operation and maintenance data from at least one piece of operation and maintenance index data;
based on the default operation and maintenance data, predicting the target operation and maintenance data through a preset operation and maintenance data prediction model to obtain target prediction data;
comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data;
and determining operation and maintenance index data corresponding to the fault root according to the target error values.
In a second aspect, the present application also provides a fortune dimension analysis device including:
the operation and maintenance data acquisition module is used for acquiring at least one operation and maintenance index data, and determining target operation and maintenance data and default operation and maintenance data except the target operation and maintenance data from at least one operation and maintenance index data;
the operation and maintenance data prediction module is used for predicting the target operation and maintenance data through a preset operation and maintenance data prediction model based on the default operation and maintenance data to obtain target prediction data;
the prediction result comparison module is used for comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data;
and the fault root positioning module is used for determining operation and maintenance index data corresponding to the fault root according to the target error values of the items.
In a third aspect, the present application also provides a computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements a method of operation and data analysis as described above.
In a fourth aspect, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the operation and data analysis method as described above.
The application provides a method, a device, equipment and a computer storage medium for analyzing operation and maintenance data, wherein the method comprises the steps of obtaining at least one operation and maintenance index data, and determining target operation and maintenance data and default operation and maintenance data except for the target operation and maintenance data from at least one operation and maintenance index data; based on the default operation and maintenance data, predicting the target operation and maintenance data through a preset operation and maintenance data prediction model to obtain target prediction data; comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data; and determining operation and maintenance index data corresponding to the fault root according to the target error values. Since the specific operation and maintenance data are predicted and the predicted value is compared with the actual value, the fault root cause when the financial system fails can be determined.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for analyzing operation and maintenance data according to an embodiment of the present application;
FIG. 2 is a view of a scenario illustrating a method of analysis of a motion data according to an embodiment of the present application;
FIG. 3 is a view of another use scenario of a method for analysis of motion data according to one embodiment of the present application;
FIG. 4 is a schematic block diagram of a motion and data analysis device according to an embodiment of the present application;
fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
The embodiment of the application provides a method, a device, computer equipment and a computer readable storage medium for analyzing operation and data.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flow chart of a method for analyzing operation and data according to an embodiment of the present application. The operation and data analysis method can be used in a terminal or a server, for example, the terminal or the server running a financial system, so as to position the fault root cause when the financial system fails. The terminal can be electronic equipment such as a mobile phone, a tablet personal computer, a notebook computer, a desktop computer, a personal digital assistant, wearable equipment and the like; the server may be an independent server, a server cluster, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), basic cloud computing services such as big data and artificial intelligence platforms, and the like.
Referring to fig. 2, fig. 2 is a usage scenario diagram provided in an embodiment of the present application. As shown in fig. 2, the server acquires a plurality of pieces of operation and maintenance data of the financial system: operation and maintenance data 1, operation and maintenance data 2, … … and operation and maintenance data n are respectively analyzed to determine operation and maintenance data corresponding to at least one fault root cause.
As shown in fig. 1, the operation and data analysis method includes steps S101 to S104.
Step S101, at least one piece of operation and maintenance index data is obtained, and a target operation and maintenance data and default operation and maintenance data except the target operation and maintenance data are determined from at least one piece of operation and maintenance index data.
The operation and maintenance data analysis method provided by the embodiment of the application can be applied to a financial system, wherein the financial system can be an open source container cluster management system deployed based on cloud service, for example, an access system deployed based on Kubernetes, but is not limited to the above.
Referring to fig. 3, fig. 3 is a view of another usage scenario of a method for analyzing operation and data according to an embodiment of the present application.
As shown in fig. 3, the service is a server system for deploying the financial system, and the pod is a logical host required for running various applications in the financial system; the node represents a host running a specific program, and may be a virtual machine or a physical machine. Wherein, a service may include a plurality of pod, a pod may be associated with a plurality of nodes, and each node may be associated with a different node at the same time, which is not limited herein.
Illustratively, the acquiring at least one piece of operation and maintenance index data may be respectively acquiring operation and maintenance index data from different nodes of the financial system. For example, one piece of operation and maintenance index data is obtained from the service, each pod and each node in fig. 3, respectively, to obtain 9 pieces of operation and maintenance index data. Of course, the present invention is not limited to this, and a plurality of pieces of operation and maintenance index data may be acquired from each node, and the present invention is not limited to this.
For example, because the correlation of each operation and maintenance index data in the financial system is higher, the operation and maintenance index data are mutually dependent and mutually influenced, in order to determine the operation and maintenance index data corresponding to the fault root cause, each operation and maintenance index data can be sequentially determined as target operation and maintenance data, and each operation and maintenance index data is respectively analyzed.
Illustratively, the operation and maintenance index data other than the target operation and maintenance data is taken as default operation and maintenance data.
In some embodiments, the acquiring at least one piece of operation and maintenance index data includes: acquiring a time sequence corresponding to each operation and maintenance index; normalizing the numerical values in the time sequence corresponding to each operation and maintenance index to obtain index data; and respectively adding a preset offset value to each index data to obtain the operation and maintenance index data.
For example, the operation and maintenance index data may be time sequence data, that is, the time sequence corresponding to each operation and maintenance index respectively includes the values of the operation and maintenance index data collected at different moments.
By way of example, the values in the time sequence corresponding to the operation and maintenance indexes are normalized, so that the accuracy of the operation and maintenance index data is improved. Specifically, the index data is obtained by the following formula:
wherein X is min Representing the minimum value, X, of the operation and maintenance index in the time series max The maximum value of the operation and maintenance index in the time series is represented, X represents the numerical value of the operation and maintenance index before normalization, and X' represents index data obtained after normalization.
By adding preset offset values to each index data, the spatial distribution of the curve obtained by mapping each index data on the plane coordinate on the ordinate is staggered, so that the subsequent data analysis is facilitated. Specifically, for example, 9 index data items in total are taken as an example, offset data of 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, and 0 may be added to the index data, and the present invention is not limited thereto.
And step S102, predicting the target operation and maintenance data through a preset operation and maintenance data prediction model based on the default operation and maintenance data to obtain target prediction data.
Illustratively, due to the correlation between the items of operation and maintenance index data, the target operation and maintenance data can be predicted according to the numerical level of the default operation and maintenance data. Wherein the default operation and maintenance data may be a plurality of items of data, and the target operation and maintenance data may be one item of data.
In some embodiments, the method further comprises: acquiring at least one sample index data, and determining target sample data and default sample data except the target sample data from the at least one sample index data; and performing self-supervision training on a preset operation and data prediction model based on the target sample data and the default sample data so that the operation and data prediction model can predict the target sample data according to the default sample data.
The motion and data prediction model may be implemented by a neural network, for example, by processing the sample index data through an encoder-decoder structure, compressing the time sequence of the motion and data into a hidden vector through an encoder, and the decoder may be capable of generating an output sequence from the hidden vector through a cyclic neural network or a self-attention model, and the decoder and the encoder may be, for example, a convolutional neural network codec, but are not limited thereto.
Illustratively, the self-supervision training is performed on the preset operation and maintenance data prediction model according to target sample data and default sample data in the sample index data. Specifically, hiding target sample data, predicting the target sample data according to default sample data through a motion and data prediction model, comparing the predicted data with the target sample data, and adjusting the motion and data prediction model according to a comparison result until the difference value between the motion and data prediction model and the target sample data is smaller than a preset difference value.
And step 103, comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data.
The target error value is obtained by comparing the target prediction data predicted by the motion data prediction model with the actual target motion data, so as to determine whether the target prediction data is accurate according to the size of the target error value.
For example, if the target prediction data is time series data, the target error value may be determined according to an average value of differences between the target prediction data and the target operation data at each time. Specifically, the target error value of the target operation and maintenance data may be determined by the following formula:
wherein Var is i Representing a target error value when the ith operation and maintenance index data is the target operation and maintenance data, L representing the data amount in the time series of the target operation and maintenance data, y ik Representing the value, x, of the kth target prediction data predicted by the motion data prediction model ik Representing the value of the actual kth target operational dimension data.
And step S104, according to the target error values of the items, determining operation and maintenance index data corresponding to the fault root cause.
For example, when the operation and maintenance index data is abnormal, predicting the target operation and maintenance data based on the default operation and maintenance data, if the difference between the predicted target prediction data and the actual target operation and maintenance data is smaller, indicating that the abnormality of the target operation and maintenance data is influenced by the default operation and maintenance data; otherwise, if the difference between the predicted target prediction data and the actual target operation and maintenance data is larger, the exception of the target operation and maintenance data is not caused by the default operation and maintenance data, but the target operation and maintenance data itself has the exception, namely the exception of the target operation and maintenance data is the root cause of the fault. Therefore, the target operation and maintenance data having a large difference from the target prediction data is determined as operation and maintenance index data corresponding to the root cause of the failure.
In some embodiments, the determining the operation and maintenance index data corresponding to the fault root cause according to the target error values includes: and determining the operation and maintenance index data with the maximum error value of at least one item as the operation and maintenance index data corresponding to the fault root cause.
For example, the operation and maintenance index data corresponding to the fault root cause may be one or more items, and therefore, at least one item of operation and maintenance index data with the largest target error value is determined as the operation and maintenance index data corresponding to the fault root cause in each item of operation and maintenance index data.
In some embodiments, the determining the operation and maintenance index data with the maximum error value of the at least one item index as the operation and maintenance index data corresponding to the fault root cause includes: determining operation and maintenance index data with at least one item index error value larger than a preset error value as operation and maintenance index data corresponding to a fault root cause; or sorting the operation and maintenance index data according to the size of the target error value, and determining the operation and maintenance index data with the preset quantity as the operation and maintenance index data corresponding to the fault root cause according to the sorting result.
For example, a preset error value serving as an error value threshold may be preset for the target error value, and when the target error value is greater than the preset error value, the corresponding operation and maintenance index data is considered to belong to the operation and maintenance index data corresponding to the fault root cause.
For example, the first n pieces of operation and maintenance index data with the largest target error value may be determined as operation and maintenance index data corresponding to the fault root cause, which is not limited herein.
In some embodiments, the method further comprises: and calculating the credibility of the target prediction data according to the target error value of the default operation and maintenance data based on a preset credibility checking algorithm.
Illustratively, each piece of operation and maintenance index data is used as target operation and maintenance data, and target predicted values corresponding to each piece of operation and maintenance index data and target error values between each piece of operation and maintenance index data and the corresponding target predicted values are obtained.
Illustratively, the reliability of the target prediction data is determined according to the restoring effect of the operation and data prediction model on each default operation and maintenance data, so that a user can determine whether the analysis result of the fault root cause is accurate according to the reliability of the target prediction data.
In some embodiments, the calculating the credibility of the target prediction data according to the target error value of the default operation and maintenance data based on the preset credibility checking algorithm includes: and determining the credibility of the target prediction data according to the sum of the inverse values of the target error values of the default operation and maintenance data.
Illustratively, the larger the target error value of the default operation and maintenance data is, the more inaccurate the prediction effect of the operation and maintenance data prediction model is, the smaller the credibility of the target prediction data is; on the contrary, the smaller the target error value of the default operation and maintenance data is, the more accurate the prediction effect of the operation and maintenance data prediction model is, and the greater the credibility of the target prediction data is.
Thus, the confidence level of the target prediction data is determined based on the sum of the inverse of the target error values. Specifically, the reliability of the target prediction data may be determined according to the following formula:
wherein, confidence i Representing the credibility of the target prediction data when the ith operation and maintenance index data is taken as the target operation and maintenance data, var j And representing the target error value of the jth operation and maintenance index data, wherein i is not equal to j, namely the credibility of the target prediction data is the sum of the inverse values of the target error values of the default operation and maintenance data.
According to the operation and maintenance data analysis method provided by the embodiment, at least one operation and maintenance index data is obtained, and target operation and maintenance data and default operation and maintenance data except the target operation and maintenance data are determined from at least one operation and maintenance index data; based on the default operation and maintenance data, predicting the target operation and maintenance data through a preset operation and maintenance data prediction model to obtain target prediction data; comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data; and determining operation and maintenance index data corresponding to the fault root according to the target error values. The method can be used for maintaining the financial system, and determining the fault root cause when the financial system breaks down by predicting specific operation data and comparing the predicted value with the actual value.
Referring to fig. 4, fig. 4 is a schematic diagram of a operation and data analysis device according to an embodiment of the present application, where the operation and data analysis device may be configured in a server or a terminal to perform the operation and data analysis method described above.
As shown in fig. 4, the operation and data analysis device includes: the system comprises a fortune dimension acquisition module 110, a fortune dimension prediction module 120, a prediction result comparison module 130 and a fault root cause positioning module 140.
An operation and maintenance data obtaining module 110, configured to obtain at least one operation and maintenance index data, and determine a target operation and maintenance data from at least one operation and maintenance index data, and default operation and maintenance data other than the target operation and maintenance data;
the operation and data prediction module 120 is configured to predict, based on the default operation and data, the target operation and data through a preset operation and data prediction model, to obtain target prediction data;
a prediction result comparison module 130, configured to compare the numerical difference between the target prediction data and the target operation and maintenance data, so as to obtain a target error value of the target operation and maintenance data;
the fault root positioning module 140 is configured to determine operation and maintenance index data corresponding to the fault root according to the target error values.
Illustratively, the fault root location module 140 includes: and a fault data determining module.
And the fault data determining module is used for determining the operation and maintenance index data with the maximum error value of at least one item as the operation and maintenance index data corresponding to the fault root cause.
Illustratively, the fault data determination module includes: the first fault data determining module or the second fault data determining module.
The first fault data determining module is used for determining operation and maintenance index data with at least one item index error value larger than a preset error value as operation and maintenance index data corresponding to a fault root cause;
and the second fault data determining module is used for sequencing the operation and maintenance index data according to the size of the target error value, and determining a preset number of operation and maintenance index data as operation and maintenance index data corresponding to a fault root cause according to a sequencing result.
Illustratively, the operation and data analysis device further includes: and a credibility determination module.
The credibility determining module is used for calculating the credibility of the target prediction data according to the target error value of the default operation and maintenance data based on a preset credibility checking algorithm.
Illustratively, the credibility determination module comprises: and a reliability calculation sub-module.
And the credibility calculation submodule is used for determining the credibility of the target prediction data according to the sum of the inverse values of the target error values of the default operation and maintenance data.
Illustratively, the fortune dimension acquisition module 110 includes: the device comprises a time sequence acquisition module, a time sequence normalization module and a bias value adding module.
The time sequence acquisition module is used for acquiring time sequences corresponding to various operation and maintenance indexes;
the time sequence normalization module is used for normalizing the numerical values in the time sequence corresponding to each operation and maintenance index to obtain index data;
and the offset value adding module is used for respectively adding preset offset values to each index data to obtain the operation and maintenance index data.
Illustratively, the operation and data analysis device further includes: and the sample data acquisition module and the prediction model training module.
The sample data acquisition module is used for acquiring at least one item of sample index data, determining target sample data from the at least one item of sample index data and default sample data except the target sample data;
and the prediction model training module is used for performing self-supervision training on a preset operation and maintenance data prediction model based on the target sample data and the default sample data so that the operation and maintenance data prediction model can predict the target sample data according to the default sample data.
It should be noted that, for convenience and brevity of description, specific working processes of the above-described apparatus and each module, unit may refer to corresponding processes in the foregoing method embodiments, which are not repeated herein.
The methods and apparatus of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The above-described methods, apparatus, and computer program products may be embodied in a computer program that is executed on a computer device as shown.
Referring to fig. 5, fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device may be a server or a terminal.
As shown in fig. 5, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a storage medium and an internal memory.
The storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause a processor to perform any of a number of methods of analysis of a dimension of motion.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a storage medium that, when executed by a processor, causes the processor to perform any of a number of methods of analysis of the data of the operation.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein in one embodiment the processor is configured to run a computer program stored in the memory to implement the steps of:
acquiring at least one piece of operation and maintenance index data, and determining target operation and maintenance data and default operation and maintenance data except the target operation and maintenance data from at least one piece of operation and maintenance index data;
based on the default operation and maintenance data, predicting the target operation and maintenance data through a preset operation and maintenance data prediction model to obtain target prediction data;
comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data;
and determining operation and maintenance index data corresponding to the fault root according to the target error values.
It should be noted that, for convenience and brevity of description, the specific working process of the operation and dimension analysis described above may refer to the corresponding process in the operation and dimension analysis control method embodiment, and will not be described in detail herein.
Embodiments of the present application also provide a computer readable storage medium having a computer program stored thereon, where the computer program includes program instructions that, when executed, implement a method that may refer to various embodiments of the present application's operation data analysis method.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which are provided on the computer device.
It is to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments. While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of operation and data analysis, the method comprising:
acquiring at least one piece of operation and maintenance index data, and determining target operation and maintenance data and default operation and maintenance data except the target operation and maintenance data from at least one piece of operation and maintenance index data;
based on the default operation and maintenance data, predicting the target operation and maintenance data through a preset operation and maintenance data prediction model to obtain target prediction data;
comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data;
and determining operation and maintenance index data corresponding to the fault root according to the target error values.
2. The operation and maintenance data analysis method according to claim 1, wherein determining operation and maintenance index data corresponding to a fault root according to the target error values includes:
and determining the operation and maintenance index data with the maximum error value of at least one item as the operation and maintenance index data corresponding to the fault root cause.
3. The operation and maintenance data analysis method according to claim 2, wherein the determining the operation and maintenance index data with the maximum error value of at least one item index as the operation and maintenance index data corresponding to the fault root cause includes:
determining operation and maintenance index data with at least one item index error value larger than a preset error value as operation and maintenance index data corresponding to a fault root cause; or alternatively
And sequencing the operation and maintenance index data according to the size of the target error value, and determining the operation and maintenance index data with the preset quantity as operation and maintenance index data corresponding to the fault root cause according to the sequencing result.
4. A method of operation and data analysis according to claim 1, further comprising:
and calculating the credibility of the target prediction data according to the target error value of the default operation and maintenance data based on a preset credibility checking algorithm.
5. The operation and maintenance data analysis method according to claim 4, wherein the calculating the reliability of the target prediction data based on the target error value of the default operation and maintenance data according to the preset reliability checking algorithm includes:
and determining the credibility of the target prediction data according to the sum of the inverse values of the target error values of the default operation and maintenance data.
6. The operation and maintenance data analysis method according to claim 1, wherein the acquiring at least one operation and maintenance index data includes:
acquiring a time sequence corresponding to each operation and maintenance index;
normalizing the numerical values in the time sequence corresponding to each operation and maintenance index to obtain index data;
and respectively adding a preset offset value to each index data to obtain the operation and maintenance index data.
7. A method of analyzing a dimension of motion as in any of claims 1-6, further comprising:
acquiring at least one sample index data, and determining target sample data and default sample data except the target sample data from at least one sample index data;
and performing self-supervision training on a preset operation and data prediction model based on the target sample data and the default sample data so that the operation and data prediction model can predict the target sample data according to the default sample data.
8. A fortune dimension analysis device, characterized in that the fortune dimension analysis device comprises:
the operation and maintenance data acquisition module is used for acquiring at least one operation and maintenance index data, and determining target operation and maintenance data and default operation and maintenance data except the target operation and maintenance data from at least one operation and maintenance index data;
the operation and maintenance data prediction module is used for predicting the target operation and maintenance data through a preset operation and maintenance data prediction model based on the default operation and maintenance data to obtain target prediction data;
the prediction result comparison module is used for comparing the numerical value difference between the target prediction data and the target operation and maintenance data to obtain a target error value of the target operation and maintenance data;
and the fault root positioning module is used for determining operation and maintenance index data corresponding to the fault root according to the target error values of the items.
9. A computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the steps of the dimension analysis method of any of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the operation data analysis method according to any of claims 1 to 7.
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