CN117522628A - Data analysis method, apparatus, computer device, readable storage medium, and product - Google Patents
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Abstract
The application relates to a data analysis method, a data analysis device, computer equipment, a readable storage medium and a product, and relates to the technical field of power grids. The method comprises the following steps: responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment; determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters; and carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction. By adopting the method, the data analysis efficiency can be improved.
Description
Technical Field
The present disclosure relates to the field of power grid technologies, and in particular, to a data analysis method, apparatus, computer device, readable storage medium, and product.
Background
Along with the rapid development of big data, the electric power enterprises start to gradually digitize and transform, and new technologies such as the Internet, artificial intelligence, big data, the Internet of things and the like based on a cloud platform are deeply applied, so that higher requirements are put forward on the quality and management capability of digital resources.
At present, data analysis in power grid equipment is usually performed manually, however, the data volume in the power grid equipment is huge, and manual operation is time-consuming and labor-consuming and the accuracy of the data analysis cannot be guaranteed.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a data analysis method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve the accuracy of data analysis.
In a first aspect, the present application provides a data analysis method. The method comprises the following steps:
responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment;
determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
and carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
In one embodiment, after the step of obtaining the parameter analysis result corresponding to the data analysis instruction, the method further includes:
Packaging the parameter analysis result into a correction instruction, and sending the correction instruction to a display device, wherein the correction instruction is used for indicating the display device to display the parameter analysis result and collecting target correction parameters selected by a user based on the parameter analysis result;
receiving the target correction parameters returned by the display equipment based on the correction instruction;
and carrying out equipment parameter correction on the target equipment based on the target correction parameters to obtain an equipment parameter correction result.
In one embodiment, the device parameter correction results include parameter type correction results;
and performing device parameter correction on the target device based on the target correction parameter to obtain a device parameter correction result, including:
acquiring target equipment parameters corresponding to the target correction parameters;
and carrying out parameter type correction on the target equipment parameter based on the parameter correction type corresponding to the target correction parameter to obtain a parameter type correction result.
In one embodiment, the parameter types include a multi-dimensional parameter and a single-dimensional parameter; the determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter comprises the following steps:
If the parameter type corresponding to the equipment parameter is a multi-dimensional parameter, determining a multi-dimensional parameter analysis model corresponding to the multi-dimensional parameter;
and if the parameter type corresponding to the equipment parameter is a single-dimensional parameter, determining a single-dimensional parameter analysis model corresponding to the single-dimensional parameter.
In one embodiment, the multi-dimensional parametric analysis model includes a parameter segmentation layer and a parameter analysis layer;
the step of carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction comprises the following steps:
carrying out multi-dimensional segmentation on the equipment parameters through the parameter segmentation layer to obtain multi-dimensional parameters corresponding to the equipment parameters;
and carrying out isolated analysis on the multidimensional parameters by using the parameter analysis layer to obtain an isolated analysis result corresponding to the equipment parameters, and taking the isolated analysis result as the parameter analysis result, wherein the isolated analysis result is used for representing whether the isolated parameters exist in the equipment parameters.
In one embodiment, the single-dimensional parametric analytical model comprises a first analytical model and a second analytical model;
And performing parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the equipment parameters, and further comprising:
performing extremum analysis on the equipment parameters by using the first analysis model to obtain a first extremum analysis result corresponding to the equipment parameters, and performing extremum analysis on the equipment parameters by using the second analysis model to obtain a second extremum analysis result corresponding to the equipment parameters;
and taking the first extreme value analysis result and the second extreme value analysis result as the parameter analysis result.
In a second aspect, the present application also provides a data analysis device. The device comprises:
the parameter acquisition module is used for responding to a data analysis instruction aiming at target equipment and acquiring equipment parameters corresponding to the target equipment;
the model determining module is used for determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
and the data analysis module is used for carrying out parameter analysis on the equipment parameters by utilizing the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment;
determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
and carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment;
determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
And carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment;
determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
and carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
The data analysis method, the data analysis device, the computer equipment, the computer readable storage medium and the computer program product firstly respond to a data analysis instruction aiming at target equipment to acquire equipment parameters corresponding to the target equipment, wherein the equipment parameters are used as data supports for data analysis of the target equipment. And then determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is pre-established for different types of equipment parameters, and carrying out parameter analysis on the equipment parameter by utilizing the parameter analysis model to obtain a parameter analysis result corresponding to a data analysis instruction, so that the data analysis of various equipment parameters can be carried out in a targeted manner, the accuracy of the data analysis can be improved, the efficiency of the data analysis can be improved, abnormal data in the equipment parameter can be quickly found, and the fault monitoring on the target equipment is facilitated.
Drawings
FIG. 1 is a schematic diagram of an application scenario of a data analysis method in one embodiment;
FIG. 2 is a flow chart of a method of data analysis in one embodiment;
FIG. 3 is a flow chart of data correction in one embodiment;
FIG. 4 is a flow diagram of multidimensional data analysis in one embodiment;
FIG. 5 is a flow diagram of an orphan forest algorithm in one embodiment;
FIG. 6 is a flow diagram of one-dimensional data analysis in one embodiment;
FIG. 7 is a schematic diagram of a quartile algorithm in one embodiment;
FIG. 8 is a schematic diagram of a Grabbs algorithm in one embodiment;
FIG. 9 is a schematic diagram of an analysis and correction flow of ledger data in one embodiment;
FIG. 10 is a block diagram showing the structure of a data analysis device according to one embodiment;
FIG. 11 is an internal block diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Along with the rapid development of big data, the electric power enterprises start to gradually digitize and transform, and new technologies such as the Internet, artificial intelligence, big data, the Internet of things and the like based on a cloud platform are deeply applied, so that higher requirements are put forward on the quality and management capability of digital resources.
At present, data analysis in power grid equipment is usually performed manually, however, the data volume in the power grid equipment is huge, and manual operation is time-consuming and labor-consuming and the accuracy of the data analysis cannot be guaranteed. Resulting in lower accuracy of the current data analysis for the grid equipment.
The data analysis method provided by the embodiment of the disclosure can be applied to an application environment as shown in fig. 1. The server 102 is connected with the terminal 104, specifically, the server 102 responds to a data analysis instruction initiated by the terminal 104 and aimed at a target device to obtain a device parameter corresponding to the target device, and then determines a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the device parameter, wherein the parameter analysis model refers to a data analysis model pre-established for different types of device parameters, and finally, parameter analysis is performed on the device parameter by utilizing the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
The server 102 may be implemented as a stand-alone server or a server cluster including a plurality of servers. The terminal 104 may be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, internet of things devices, and the like.
In one embodiment, as shown in fig. 2, a data analysis method is provided, and the method is applied to the server 102 in fig. 1 for illustration, and includes the following steps:
step S202, responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment.
The target device refers to a device to be subjected to data analysis, and includes, but is not limited to, a transformer, a switching device, a cable, a transformer, a circuit breaker and the like. The data analysis instruction refers to an instruction for performing data analysis on the target device. The device parameters refer to the property parameters of the target device including, but not limited to, transformer rated capacity, switchgear parameters, cable parameters, transformer rated voltage, transformer capacitance, breaker rated current, etc.
Specifically, the server firstly acquires the equipment parameters of the target equipment as the data support for subsequent data analysis after receiving the data analysis instruction for the target equipment sent by the terminal.
Step S204, based on the parameter type corresponding to the equipment parameter, determining a parameter analysis model corresponding to the parameter type.
The parameter analysis model refers to a data analysis model which is pre-established for different types of equipment parameters. The parameter types may include a multi-dimensional parameter, which is a parameter including a plurality of fields, such as a height parameter of a transmission tower, including a total height of the tower, a call height of the tower, and the like, and a single-dimensional parameter, which means a parameter having only one field, such as voltage, current, power, and the like.
In one embodiment, the multi-dimensional parameter corresponds to the presence of a multi-dimensional parametric analytical model, and the single-dimensional parameter corresponds to the presence of a single-dimensional analytical model.
Specifically, after obtaining the device parameter of the target device, the server may determine the parameter type of the device parameter according to the number of fields included in the device parameter, if the device parameter is a multi-dimensional parameter, obtain a multi-dimensional parameter analysis model corresponding to the multi-dimensional parameter to perform data analysis on the device parameter, and if the device parameter is a single-dimensional parameter, obtain a single-dimensional parameter analysis model corresponding to the single-dimensional parameter to perform data analysis on the device parameter.
And S206, carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
The multi-dimensional parameter analysis model performs isolated analysis on the device parameters to obtain an isolated analysis result, and in an embodiment, the multi-dimensional parameter analysis model may be an isolated forest model, which is a commonly used data outlier analysis algorithm. The single-dimensional analysis model is used for carrying out extremum analysis on the equipment parameters to obtain extremum analysis results. The parameter analysis results include an isolated analysis result and an extremum analysis result.
Specifically, if the device parameter is a multidimensional parameter, the server inputs the device parameter into a multidimensional parameter analysis model for the multidimensional parameter analysis model to carry out isolated analysis on the device parameter, so as to obtain an isolated analysis result corresponding to the device parameter, wherein the isolated analysis result is used for representing whether the device parameter has an isolated parameter, and the isolated parameter refers to a parameter isolated from the device parameter and can be understood as an abnormal parameter. If the equipment parameter is a single-dimensional parameter, the server inputs the equipment parameter into a single-dimensional parameter analysis model so as to enable the single-dimensional parameter analysis model to carry out extremum analysis on the equipment parameter, an extremum analysis result corresponding to the equipment parameter is obtained, the extremum analysis result refers to a maximum value or a minimum value in the equipment parameter, and the equipment parameter can be understood as an abnormal parameter.
It should be noted that, whether the analysis is an isolation analysis or an extremum analysis, the analysis is to analyze the isolation parameter and the extremum parameter in the device parameter, and the parameters are all cleaved from the device parameter and can be considered as the abnormal parameter in the device parameter, so that a technician can quickly locate the abnormal point in the target device according to the data analysis result, thereby repairing the fault.
In this embodiment, first, in response to a data analysis instruction for a target device, a device parameter corresponding to the target device is obtained, where the device parameter is used as a data support for data analysis of the target device. And then determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is pre-established for different types of equipment parameters, and carrying out parameter analysis on the equipment parameter by utilizing the parameter analysis model to obtain a parameter analysis result corresponding to a data analysis instruction, so that the data analysis of various equipment parameters can be carried out in a targeted manner, the accuracy of the data analysis can be improved, the efficiency of the data analysis can be improved, abnormal data in the equipment parameter can be quickly found, and the fault monitoring on the target equipment is facilitated.
In one embodiment, as shown in fig. 3, after the step of obtaining the parameter analysis result corresponding to the data analysis instruction, the method further includes:
step S302, the parameter analysis result is packaged into a correction instruction, and the correction instruction is sent to a display device.
The correction instruction is used for indicating the display device to display the parameter analysis result and collecting target correction parameters selected by a user based on the parameter analysis result. Because the abnormal parameters and the normal parameters are stored in the parameter analysis result, but the abnormal parameters are determined by the model, and the abnormal parameters are not the abnormal parameters any more, in this embodiment, the parameter analysis result is packaged into a correction instruction and sent to the display device, so that the display device displays the abnormal parameters and the normal parameters to the user, the user can screen out the parameters which do not belong to the abnormal parameters at present from the abnormal parameters according to the actual application condition and return the parameters to the server, and the server can correct the parameters, namely correct the parameters into the normal parameters, so that the model can not determine the parameters into the abnormal parameters any more in the next data analysis, but regards the parameters as the normal parameters. The technical defect of low parameter correction efficiency caused by manual inspection in the traditional technology is overcome, and the efficiency of parameter correction is greatly improved. The target correction parameter refers to a parameter that needs to be subjected to parameter correction.
Specifically, the server encapsulates the parameter analysis result, which may include normal parameters and abnormal parameters, into a correction instruction. And sending the correction instruction to display equipment, wherein the display equipment can display normal parameters and abnormal parameters to a user so that the user can select parameters needing to be subjected to parameter correction in the abnormal parameters, and if the target correction parameters selected by the user are received, the display equipment returns the target correction parameters to the server so that the server can carry out subsequent parameter correction.
In an embodiment, the user may also select parameters that do not belong to the normal parameters at the moment in the normal parameters, and the server corrects these normal parameters to the abnormal parameters, so that the model determines these parameters as the abnormal parameters at the next data analysis.
Step S304, receiving the target correction parameter returned by the display device based on the correction instruction.
Step S306, performing device parameter correction on the target device based on the target correction parameter, to obtain a device parameter correction result.
The device parameter correction result includes a parameter type correction result, that is, the device parameter correction means to correct the parameter type of the device parameter, and it should be noted that the parameter type is different from the parameter type in step S204, and the parameter type refers to whether the device parameter belongs to an abnormal parameter or a normal parameter. In order to distinguish from the parameter type in step S204, the parameter type in the present embodiment is replaced with the description of the target type
Specifically, after receiving a parameter which is returned by the display device and needs to be subjected to parameter correction, the server directly carries out parameter type correction on the target device to obtain a parameter type correction result. Parameter type correction results refer to results used to characterize whether a device parameter is corrected, and may include correction completed and correction not completed.
In an embodiment, after the server receives the parameter to be corrected returned by the display device, the server only needs to correct the opposite type of parameter because the target type of the device parameter only includes the normal parameter and the abnormal parameter. That is, the server acquires the target device parameter corresponding to the target correction parameter, corrects the target device parameter to a normal parameter if the target device parameter is an abnormal parameter, and corrects the target device parameter to an abnormal parameter if the target device parameter is a normal parameter.
In this embodiment, by correcting the device parameters of the target device, for the parameter determined to be non-abnormal by the user, the next model analysis will treat the parameter as a normal parameter, and for the parameter determined to be abnormal by the user, the next model analysis will treat the parameter as an abnormal parameter, so that the technical defect of low parameter correction efficiency caused by manual inspection in the conventional technology is overcome, and the efficiency of parameter correction is greatly improved.
In one embodiment, as shown in fig. 4, the multi-dimensional parametric analysis model includes a parameter segmentation layer and a parameter analysis layer;
the step of carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction comprises the following steps:
step S402, performing multidimensional segmentation on the device parameter through the parameter segmentation layer, to obtain a multidimensional parameter corresponding to the device parameter.
And step S404, performing isolated analysis on the multidimensional parameter by using the parameter analysis layer to obtain an isolated analysis result corresponding to the equipment parameter, and taking the isolated analysis result as the parameter analysis result.
The parameter segmentation layer refers to a network layer for cutting equipment parameters, and the earlier the equipment parameters are cut and separated out by random repetition of the cutting, the larger the outlier probability is, the larger the probability of becoming an isolated parameter is. The orphan analysis results are used to characterize whether orphan parameters exist among the device parameters. The presence of an isolated parameter means that there is an abnormal parameter in the device parameters. The parameter analysis layer is used for performing isolated analysis on the multi-dimensional parameters, namely analyzing whether isolated parameters exist in the multi-dimensional parameters.
In particular, reference may be made to the parameter segmentation schematic of fig. 5. The server inputs the equipment parameters into a multi-dimensional parameter analysis model, the equipment parameters are firstly circulated to a parameter segmentation layer, the parameter segmentation layer carries out random repeated cutting processing on the equipment parameters, in the random cutting process, isolated parameters such as A points can be rapidly distributed into an independent space with a high probability, and otherwise, parameters in clusters such as B points and C points are difficult to rapidly distribute into the independent space. In the cutting process, parameters can form a binary tree, and the earlier the object which is independent due to cutting, the shallower the depth on the binary tree, the larger the outlier probability.
In fig. 5, the data set refers to the device parameters just entering the parameter splitting layer, the device parameters are cut for the first time to obtain a data set 1 and a data set 2, the data set 1 refers to the parameter set containing the point a, the data set 2 refers to the parameter set except the point a, the data set 1 is cut for the second time to obtain a data point a and a data set 3, the data set 3 refers to the parameter set except the point a in the data set 1, the data set 3 is cut for the third time to obtain a data set 4 and a data set 5, and the data set 5 is cut for the fourth time to obtain a data point B and a data point C.
In this embodiment, for the device parameter with the multi-dimensional parameter type, the device parameter is subjected to data analysis by using an isolated analysis mode, so as to analyze whether an isolated parameter, that is, an abnormal parameter exists in the device parameter, thereby improving the accuracy of data analysis.
In one embodiment, as shown in fig. 6, the single-dimensional parametric analysis model includes a first analysis model and a second analysis model;
and performing parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the equipment parameters, and further comprising:
step S602, performing extremum analysis on the device parameter by using the first analysis model to obtain a first extremum analysis result corresponding to the device parameter, and performing extremum analysis on the device parameter by using the second analysis model to obtain a second extremum analysis result corresponding to the device parameter.
Step S604, using the first extremum analysis result and the second extremum analysis result together as the parameter analysis result.
The first analysis model refers to a data analysis model for analyzing a maximum value or a minimum value in equipment parameters, and may be a quartile algorithm model.
In one embodiment, reference may be made to the box diagram schematic of the quartile algorithm of fig. 7, which is a statistically applied algorithm that sorts the data from small to large into quartiles, wherein the data at the three split point positions is referred to as the lower quartile (Q1, 25%), the middle quartile (Q2, 50% of the digits), the upper quartile (Q3, 75% of the digits, wherein 25% of the digits refer to the digits at the last 25% (percent) position, 50% of the digits refer to the digits at the 50% position, 75% of the digits refer to the digits at the first 75% position, and the distance between the upper quartile and the lower quartile is referred to as the quartile range (IQR). Wherein, the quarter bit distance (IQR) =Q3-Q1, the upper edge value=Q3+1.5 IQR, the lower edge value=Q1-1.5 IQR, the outlier (maximum) > the upper edge value, the outlier (minimum) < the lower edge value,
the second analysis model refers to a parameter for analyzing the deviation from the average value among the device parameters, and may be a glabros algorithm model.
In one embodiment, referring to the schematic diagram of the glabros algorithm in fig. 8, specifically, firstly, data is prepared, that is, the parameters of the device are input into the glabros algorithm model, then the model may arrange the data in descending order or ascending order, calculate the average value of the parameters, then lock the suspicious value, that is, filter the parameters far away from the average value, calculate the current critical value, which is a value for evaluating whether the suspicious value is determined to be an abnormal parameter, and preset a standard critical value, compare the current critical value corresponding to the suspicious value with the standard critical value, and if the current critical value exceeds the standard critical value, determine that the suspicious value is the abnormal parameter.
Specifically, when the equipment parameter is a single-dimensional parameter, the server inputs the equipment parameter into a single-dimensional parameter analysis model, namely, the first analysis model and the second analysis model respectively perform data analysis to obtain a first extremum analysis result output by the first analysis model, wherein the first extremum analysis result is used for representing a maximum value or a minimum value in the equipment parameter, and a second extremum analysis result output by the second analysis model is obtained, and the second extremum analysis result is used for representing a suspicious parameter deviating from an average value in equipment reference. And the first extreme value analysis result and the second extreme value analysis result are used as parameter analysis results together
In this embodiment, the first analysis model and the second analysis model are combined to perform data analysis on the single-dimensional parameters, so that accuracy of data analysis is improved.
In an embodiment, the data rule analysis may be further performed on the device parameters of the target device based on the Rete algorithm model, that is, performing enumeration value checking, data range checking, parameter consistency checking, and the like on the device parameters. The Rete algorithm is a forward rule fast matching algorithm, and the matching speed is irrelevant to the number of rules. Rete is latin, corresponding to english being net, i.e. network. The Rete algorithm performs pattern matching by forming a Rete network, and improves the data rule analysis efficiency by utilizing two characteristics of the rule-based system, namely, the time redundancy (Temporal redundancy) and the structural similarity (structural similarity).
In an embodiment, as shown in fig. 9, the target device may be a ledger device, and the device parameter may be ledger data. Specifically, the server responds to a data analysis instruction aiming at the ledger equipment, obtains ledger data corresponding to the ledger equipment, and determines a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the ledger data, namely, if the ledger data is multi-dimensional data, the data analysis is carried out on the ledger data by using an isolated forest algorithm model in a data analysis tool, and if the ledger data is single-dimensional data, the data analysis is carried out by using a four-bit algorithm model in the data analysis tool and combining with a Grabbs algorithm model. And enumeration value checking, data range checking and parameter consistency checking can be performed on the ledger data by using a Rete algorithm model.
Then, the server may send the parameter analysis result to the display device, instruct the display device to display the parameter analysis result, collect the target correction parameter selected by the user based on the parameter analysis result, and return the target correction parameter to the server, where the server may correct the ledger data of the ledger device based on the target correction parameter.
In the embodiment, through carrying out targeted data analysis on the ledger data, the accuracy of the ledger data analysis is improved, the efficiency of the ledger data analysis is also improved, abnormal data in the ledger data is quickly found, and fault monitoring is conveniently carried out on the ledger equipment. In addition, by correcting the ledger data, the parameters which are determined to be non-abnormal by the user are regarded as normal parameters by the next model analysis, and the parameters which are determined to be abnormal by the user are regarded as abnormal parameters by the next model analysis, so that the technical defect of low correction efficiency of the ledger data caused by manual inspection in the traditional technology is overcome, and the correction efficiency of the ledger data is greatly improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a data analysis device for realizing the data analysis method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in one or more embodiments of the data analysis device provided below may refer to the limitation of the data analysis method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 10, there is provided a data analysis apparatus including: a parameter acquisition module 1002, a model determination module 1004, and a data analysis module 1006, wherein:
a parameter obtaining module 1002, configured to obtain, in response to a data analysis instruction for a target device, a device parameter corresponding to the target device;
a model determining module 1004, configured to determine a parameter analysis model corresponding to a parameter type based on the parameter type corresponding to the device parameter, where the parameter analysis model refers to a data analysis model that is pre-established for different types of device parameters;
and the data analysis module 1006 is configured to perform parameter analysis on the device parameter by using the parameter analysis model, so as to obtain a parameter analysis result corresponding to the data analysis instruction.
In one embodiment, the data analysis device further comprises:
the instruction sending module is used for packaging the parameter analysis result into a correction instruction and sending the correction instruction to display equipment, wherein the correction instruction is used for indicating the display equipment to display the parameter analysis result and collecting target correction parameters selected by a user based on the parameter analysis result;
The correction parameter receiving module is used for receiving the target correction parameters returned by the display equipment based on the correction instruction;
and the parameter correction module is used for carrying out equipment parameter correction on the target equipment based on the target correction parameters to obtain an equipment parameter correction result.
In one embodiment, the parameter correction module is further configured to:
acquiring target equipment parameters corresponding to the target correction parameters;
and carrying out parameter type correction on the target equipment parameter based on the parameter correction type corresponding to the target correction parameter to obtain a parameter type correction result.
In one embodiment, the model determination module 1004 is further configured to:
if the parameter type corresponding to the equipment parameter is a multi-dimensional parameter, determining a multi-dimensional parameter analysis model corresponding to the multi-dimensional parameter;
and if the parameter type corresponding to the equipment parameter is a single-dimensional parameter, determining a single-dimensional parameter analysis model corresponding to the single-dimensional parameter.
In one embodiment, the data analysis module 1006 is further configured to:
carrying out multi-dimensional segmentation on the equipment parameters through the parameter segmentation layer to obtain multi-dimensional parameters corresponding to the equipment parameters;
And carrying out isolated analysis on the multidimensional parameters by using the parameter analysis layer to obtain an isolated analysis result corresponding to the equipment parameters, and taking the isolated analysis result as the parameter analysis result, wherein the isolated analysis result is used for representing whether the isolated parameters exist in the equipment parameters.
In one embodiment, the data analysis module 1006 is further configured to:
performing extremum analysis on the equipment parameters by using the first analysis model to obtain a first extremum analysis result corresponding to the equipment parameters, and performing extremum analysis on the equipment parameters by using the second analysis model to obtain a second extremum analysis result corresponding to the equipment parameters;
and taking the first extreme value analysis result and the second extreme value analysis result as the parameter analysis result.
The respective modules in the above-described data analysis apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 11. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing item recommendation data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data analysis method.
It will be appreciated by those skilled in the art that the structure shown in fig. 11 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment;
determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
and carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
In one embodiment, the processor when executing the computer program further performs the steps of:
packaging the parameter analysis result into a correction instruction, and sending the correction instruction to a display device, wherein the correction instruction is used for indicating the display device to display the parameter analysis result and collecting target correction parameters selected by a user based on the parameter analysis result;
receiving the target correction parameters returned by the display equipment based on the correction instruction;
And carrying out equipment parameter correction on the target equipment based on the target correction parameters to obtain an equipment parameter correction result.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring target equipment parameters corresponding to the target correction parameters;
and carrying out parameter type correction on the target equipment parameter based on the parameter correction type corresponding to the target correction parameter to obtain a parameter type correction result.
In one embodiment, the processor when executing the computer program further performs the steps of:
if the parameter type corresponding to the equipment parameter is a multi-dimensional parameter, determining a multi-dimensional parameter analysis model corresponding to the multi-dimensional parameter;
and if the parameter type corresponding to the equipment parameter is a single-dimensional parameter, determining a single-dimensional parameter analysis model corresponding to the single-dimensional parameter.
In one embodiment, the processor when executing the computer program further performs the steps of:
carrying out multi-dimensional segmentation on the equipment parameters through the parameter segmentation layer to obtain multi-dimensional parameters corresponding to the equipment parameters;
and carrying out isolated analysis on the multidimensional parameters by using the parameter analysis layer to obtain an isolated analysis result corresponding to the equipment parameters, and taking the isolated analysis result as the parameter analysis result, wherein the isolated analysis result is used for representing whether the isolated parameters exist in the equipment parameters.
In one embodiment, the processor when executing the computer program further performs the steps of:
performing extremum analysis on the equipment parameters by using the first analysis model to obtain a first extremum analysis result corresponding to the equipment parameters, and performing extremum analysis on the equipment parameters by using the second analysis model to obtain a second extremum analysis result corresponding to the equipment parameters;
and taking the first extreme value analysis result and the second extreme value analysis result as the parameter analysis result.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment;
determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
and carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
In one embodiment, the computer program when executed by the processor further performs the steps of:
packaging the parameter analysis result into a correction instruction, and sending the correction instruction to a display device, wherein the correction instruction is used for indicating the display device to display the parameter analysis result and collecting target correction parameters selected by a user based on the parameter analysis result;
receiving the target correction parameters returned by the display equipment based on the correction instruction;
and carrying out equipment parameter correction on the target equipment based on the target correction parameters to obtain an equipment parameter correction result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring target equipment parameters corresponding to the target correction parameters;
and carrying out parameter type correction on the target equipment parameter based on the parameter correction type corresponding to the target correction parameter to obtain a parameter type correction result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the parameter type corresponding to the equipment parameter is a multi-dimensional parameter, determining a multi-dimensional parameter analysis model corresponding to the multi-dimensional parameter;
And if the parameter type corresponding to the equipment parameter is a single-dimensional parameter, determining a single-dimensional parameter analysis model corresponding to the single-dimensional parameter.
In one embodiment, the computer program when executed by the processor further performs the steps of:
carrying out multi-dimensional segmentation on the equipment parameters through the parameter segmentation layer to obtain multi-dimensional parameters corresponding to the equipment parameters;
and carrying out isolated analysis on the multidimensional parameters by using the parameter analysis layer to obtain an isolated analysis result corresponding to the equipment parameters, and taking the isolated analysis result as the parameter analysis result, wherein the isolated analysis result is used for representing whether the isolated parameters exist in the equipment parameters.
In one embodiment, the computer program when executed by the processor further performs the steps of:
performing extremum analysis on the equipment parameters by using the first analysis model to obtain a first extremum analysis result corresponding to the equipment parameters, and performing extremum analysis on the equipment parameters by using the second analysis model to obtain a second extremum analysis result corresponding to the equipment parameters;
and taking the first extreme value analysis result and the second extreme value analysis result as the parameter analysis result.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment;
determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
and carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
In one embodiment, the computer program when executed by the processor further performs the steps of:
packaging the parameter analysis result into a correction instruction, and sending the correction instruction to a display device, wherein the correction instruction is used for indicating the display device to display the parameter analysis result and collecting target correction parameters selected by a user based on the parameter analysis result;
receiving the target correction parameters returned by the display equipment based on the correction instruction;
And carrying out equipment parameter correction on the target equipment based on the target correction parameters to obtain an equipment parameter correction result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring target equipment parameters corresponding to the target correction parameters;
and carrying out parameter type correction on the target equipment parameter based on the parameter correction type corresponding to the target correction parameter to obtain a parameter type correction result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the parameter type corresponding to the equipment parameter is a multi-dimensional parameter, determining a multi-dimensional parameter analysis model corresponding to the multi-dimensional parameter;
and if the parameter type corresponding to the equipment parameter is a single-dimensional parameter, determining a single-dimensional parameter analysis model corresponding to the single-dimensional parameter.
In one embodiment, the computer program when executed by the processor further performs the steps of:
carrying out multi-dimensional segmentation on the equipment parameters through the parameter segmentation layer to obtain multi-dimensional parameters corresponding to the equipment parameters;
and carrying out isolated analysis on the multidimensional parameters by using the parameter analysis layer to obtain an isolated analysis result corresponding to the equipment parameters, and taking the isolated analysis result as the parameter analysis result, wherein the isolated analysis result is used for representing whether the isolated parameters exist in the equipment parameters.
In one embodiment, the computer program when executed by the processor further performs the steps of:
performing extremum analysis on the equipment parameters by using the first analysis model to obtain a first extremum analysis result corresponding to the equipment parameters, and performing extremum analysis on the equipment parameters by using the second analysis model to obtain a second extremum analysis result corresponding to the equipment parameters;
and taking the first extreme value analysis result and the second extreme value analysis result as the parameter analysis result.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of data analysis, the method comprising:
responding to a data analysis instruction aiming at target equipment, and acquiring equipment parameters corresponding to the target equipment;
determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
And carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
2. The method according to claim 1, further comprising, after the step of obtaining the parameter analysis result corresponding to the data analysis instruction:
packaging the parameter analysis result into a correction instruction, and sending the correction instruction to a display device, wherein the correction instruction is used for indicating the display device to display the parameter analysis result and collecting target correction parameters selected by a user based on the parameter analysis result;
receiving the target correction parameters returned by the display equipment based on the correction instruction;
and carrying out equipment parameter correction on the target equipment based on the target correction parameters to obtain an equipment parameter correction result.
3. The method of claim 2, wherein the device parameter correction results comprise parameter type correction results;
and performing device parameter correction on the target device based on the target correction parameter to obtain a device parameter correction result, including:
acquiring target equipment parameters corresponding to the target correction parameters;
And carrying out parameter type correction on the target equipment parameter based on the parameter correction type corresponding to the target correction parameter to obtain a parameter type correction result.
4. The method of claim 1, wherein the parameter types include a multi-dimensional parameter and a single-dimensional parameter; the determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter comprises the following steps:
if the parameter type corresponding to the equipment parameter is a multi-dimensional parameter, determining a multi-dimensional parameter analysis model corresponding to the multi-dimensional parameter;
and if the parameter type corresponding to the equipment parameter is a single-dimensional parameter, determining a single-dimensional parameter analysis model corresponding to the single-dimensional parameter.
5. The method of claim 4, wherein the multi-dimensional parametric analysis model comprises a parametric segmentation layer and a parametric analysis layer;
the step of carrying out parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction comprises the following steps:
carrying out multi-dimensional segmentation on the equipment parameters through the parameter segmentation layer to obtain multi-dimensional parameters corresponding to the equipment parameters;
And carrying out isolated analysis on the multidimensional parameters by using the parameter analysis layer to obtain an isolated analysis result corresponding to the equipment parameters, and taking the isolated analysis result as the parameter analysis result, wherein the isolated analysis result is used for representing whether the isolated parameters exist in the equipment parameters.
6. The method of claim 4, wherein the single-dimensional parametric analytical model comprises a first analytical model and a second analytical model;
and performing parameter analysis on the equipment parameters by using the parameter analysis model to obtain a parameter analysis result corresponding to the equipment parameters, and further comprising:
performing extremum analysis on the equipment parameters by using the first analysis model to obtain a first extremum analysis result corresponding to the equipment parameters, and performing extremum analysis on the equipment parameters by using the second analysis model to obtain a second extremum analysis result corresponding to the equipment parameters;
and taking the first extreme value analysis result and the second extreme value analysis result as the parameter analysis result.
7. A data analysis device, the device comprising:
the parameter acquisition module is used for responding to a data analysis instruction aiming at target equipment and acquiring equipment parameters corresponding to the target equipment;
The model determining module is used for determining a parameter analysis model corresponding to the parameter type based on the parameter type corresponding to the equipment parameter, wherein the parameter analysis model refers to a data analysis model which is built in advance for different types of equipment parameters;
and the data analysis module is used for carrying out parameter analysis on the equipment parameters by utilizing the parameter analysis model to obtain a parameter analysis result corresponding to the data analysis instruction.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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