CN117520621A - Data analysis method, computer device and machine-readable storage medium - Google Patents
Data analysis method, computer device and machine-readable storage medium Download PDFInfo
- Publication number
- CN117520621A CN117520621A CN202311266332.XA CN202311266332A CN117520621A CN 117520621 A CN117520621 A CN 117520621A CN 202311266332 A CN202311266332 A CN 202311266332A CN 117520621 A CN117520621 A CN 117520621A
- Authority
- CN
- China
- Prior art keywords
- data
- analyzed
- determining
- standard
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007405 data analysis Methods 0.000 title claims abstract description 88
- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000003860 storage Methods 0.000 title claims abstract description 16
- 238000004458 analytical method Methods 0.000 claims abstract description 40
- 230000002159 abnormal effect Effects 0.000 claims description 31
- 230000005856 abnormality Effects 0.000 claims description 23
- 238000012795 verification Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 9
- 239000000463 material Substances 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 238000012937 correction Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000008676 import Effects 0.000 description 2
- 238000003754 machining Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000004381 surface treatment Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/906—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/9038—Presentation of query results
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a data analysis method, computer equipment and a machine-readable storage medium, and belongs to the technical field of computers. The method comprises the following steps: analyzing a preset analysis model to determine a target data type and a model execution rule, determining data to be analyzed and standard data corresponding to the target data type based on the model execution rule, determining target standard data matched with the data to be analyzed in the standard data based on data elements and weights of the data elements of the data to be analyzed, and determining a data analysis result based on differences between the data to be analyzed and the target standard data. And analyzing the data by using an analysis model which can be adaptively adjusted based on actual demands, so that the data analysis efficiency is improved. The target standard data is determined through the weight of the data element, so that the effectiveness of data analysis is ensured, the verification between the data to be analyzed and the target standard data is automatically executed, the manual error is avoided, and the accuracy of data analysis is improved.
Description
Technical Field
The present invention relates to the field of computer technology, and in particular, to a data analysis method, a computer device, and a machine-readable storage medium.
Background
The metal work piece requires a lot of data when measuring the cost price, for which careful verification is required to ensure the accuracy of the data. The main technical method at present is to manually check, and to compare data according to the experience of the technician and the data of similar workpieces. The manual checking mode depends on the experience, cognition level, careful degree and other subjective factors of the technicians, and depends on specific technicians, so that the accuracy of various different dimension data such as materials, processing technology, management, transportation and the like is difficult to identify, the manual checking is purely performed, and the judging basis is not disclosed and transparent. The checked data cannot be traced back and used as a basis for subsequent check. The standards for data judgment are not uniform, and different departments have different maintenance rules for data and cannot be used universally. Further, the standard for data judgment cannot be dynamically adjusted, and expansion is not supported. Therefore, the existing data analysis method is low in efficiency and high in labor cost and time cost.
Disclosure of Invention
In view of the foregoing deficiencies of the prior art, it is an object of an embodiment of the present invention to provide a data analysis method, a computer device and a machine-readable storage medium.
To achieve the above object, a first aspect of the present invention provides a data analysis method, including:
analyzing a preset analysis model to determine the type of target data and a model execution rule;
determining data to be analyzed and standard data corresponding to the target data type based on a model execution rule;
determining target standard data matched with the data to be analyzed in the standard data based on the data elements of the data to be analyzed and the weights of the data elements;
and determining a data analysis result based on the difference between each piece of data to be analyzed and the target standard data.
In the embodiment of the invention, determining target standard data matched with data to be analyzed in standard data based on data elements and weights of the data elements of the data to be analyzed comprises the following steps:
determining a first data element in the data to be analyzed and a second data element in the standard data;
determining the matching quantity of the first data element and the second data element;
target criterion data is determined based on the number of matches and the weight of the first data element.
In the embodiment of the invention, before analyzing the preset analysis model to determine the target data type and the model execution rule, the method further comprises the following steps:
determining a data type and a model execution rule corresponding to the data type based on the input selection information;
and generating a preset analysis model based on the data types and the model execution rules.
In the embodiment of the invention, the data analysis method further comprises the following steps:
acquiring all stored historical data;
classifying the historical data based on preset dimension information, and determining initial standard data of each data type;
respectively correcting the initial standard data of each data type based on the first analysis requirement;
and taking the corrected initial standard data as standard data of the data type.
In the embodiment of the invention, the data analysis method further comprises the following steps:
in the absence of standard data corresponding to the target data category, standard data of the target data category is determined and stored based on the second analysis requirement.
In the embodiment of the invention, determining the data analysis result based on the difference between each data to be analyzed and the target standard data comprises the following steps:
determining a preset difference range based on a model execution rule;
under the condition that all differences are within a preset difference range, determining that the data analysis result is normal;
and determining that the data analysis result is abnormal in the case that at least one difference is not in the preset difference range.
In the embodiment of the present invention, after the step of determining that the data analysis result is data anomaly, the method further includes:
determining a data abnormality cause based on the data analysis result;
and marking all the data to be analyzed with data abnormality according to the data abnormality reasons to obtain an abnormal state mark.
In the embodiment of the invention, the data analysis method further comprises the following steps:
carrying out abnormal classification on the data to be analyzed with data abnormality according to the abnormal state mark;
acquiring data sources corresponding to each abnormal type, and marking the sources of each abnormal type based on the data sources;
outputting data analysis results of all the data to be analyzed, and outputting an abnormal state mark and a source mark based on the data to be analyzed with data abnormality.
A second aspect of the present invention provides a computer device comprising: the memory, the processor, and the program stored on the memory and executable on the processor are configured to implement the steps of the data analysis method as described in the above embodiments.
A third aspect of the invention provides a machine-readable storage medium having stored thereon instructions which, when executed by a processor, cause the processor to perform a data analysis method as described in the above embodiments.
According to the technical scheme, the preset analysis model is analyzed to determine the type of the target data and the model execution rule, the data to be analyzed and the standard data corresponding to the type of the target data are determined based on the model execution rule, the target standard data matched with the data to be analyzed in the standard data are determined based on the data elements of the data to be analyzed and the weights of the data elements, and the data analysis result is determined based on the difference between each data to be analyzed and the target standard data. And analyzing the data by using an analysis model which can be adaptively adjusted based on actual demands, so that the data analysis efficiency is improved. The target standard data is determined through the weight of the data element, so that the effectiveness of data analysis is ensured, the verification between the data to be analyzed and the target standard data is automatically executed, the manual error is avoided, and the accuracy of data analysis is improved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a data analysis method according to an embodiment of the invention;
FIG. 2 is a schematic diagram illustrating data type selection in an analytical model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of model execution rule configuration in an analytical model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of initial standard data correction according to an embodiment of the present invention.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
It should be noted that, in the embodiment of the present application, directional indications (such as up, down, left, right, front, and rear … …) are referred to, and the directional indications are merely used to explain the relative positional relationship, movement conditions, and the like between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
Fig. 1 is a flow chart of a data analysis method according to an embodiment of the invention. As shown in fig. 1, in an embodiment of the present invention, a data analysis method is provided, and the method is applied to a processor for illustration, and the method may include the following steps:
step S100, analyzing a preset analysis model to determine a target data type and a model execution rule;
in this embodiment, it should be noted that, the analysis model is a basic criterion for analyzing data, and includes a data type corresponding to the data to be analyzed, and an execution rule to be observed when the data type is analyzed, and the analysis model is preset based on actual requirements. The target data type is an analysis model including data types such as material data, management data, transportation data, and processing data. The model execution rules are execution rules included in the analysis model, such as corresponding execution time and frequency during data analysis, allowable error range of result judgment, whether generation of a data analysis report is required, and the like. The analysis model can be analyzed to determine the type of data to be analyzed and the model execution rule during analysis.
Step S200, determining data to be analyzed and standard data corresponding to the target data type based on a model execution rule;
the data to be analyzed, i.e., the data to be analyzed, is the most important data to be analyzed when data analysis is performed, and the verification standard is the standard data. The standard data are stored in a preset database in advance for different data types respectively. The model execution rule is used for limiting the time and frequency for starting to execute data analysis, inquiring the data to be analyzed corresponding to the target data type in the current service system based on the model execution rule, and inquiring the standard data corresponding to the target data type in the preset database based on the model execution rule.
Step S300, determining target standard data matched with the data to be analyzed in the standard data based on the data elements of the data to be analyzed and the weight of the data elements;
it should be noted that each piece of data to be analyzed or standard data includes one or more data elements. For example, since factors such as a material, a shape, a weight, and a length are different in the degree of influence of different data factors on an analysis result, different weights are preset for different data factors, and the larger the influence of the data factors on the analysis result is, the smaller the weight of the data factors having a smaller influence on the analysis result is. And matching the data elements of the data to be analyzed with the data elements in the standard data one by one, and finally screening the target standard data with the highest matching degree with the data to be analyzed from all the standard data according to the weight of each data element in the data to be analyzed. It is understood that there is at least one piece of data to be analyzed, and in the case that there are a plurality of pieces of data to be analyzed, the target standard data matched with each piece of data to be analyzed in the standard data is determined based on the data element and the weight of the data element of each piece of data to be analyzed.
Step S400, determining a data analysis result based on the difference between each data to be analyzed and the target standard data.
It should be noted that, the target standard data may represent a value expected by the data in a normal state, compare the data to be analyzed with the target standard data, and determine a difference between the data to be analyzed and the target standard data, so as to determine whether the data to be analyzed is abnormal, and finally obtain a data analysis result of the data to be analyzed.
In the above scheme, the preset analysis model is analyzed to determine the target data type and the model execution rule, the data to be analyzed and the standard data corresponding to the target data type are determined based on the model execution rule, the target standard data matched with the data to be analyzed in the standard data are determined based on the data elements and the weights of the data elements of the data to be analyzed, and the data analysis result is determined based on the difference between each data to be analyzed and the target standard data. And analyzing the data by using an analysis model which can be adaptively adjusted based on actual demands, so that the data analysis efficiency is improved. The target standard data is determined through the weight of the data element, so that the effectiveness of data analysis is ensured, the verification between the data to be analyzed and the target standard data is automatically executed, the manual error is avoided, and the accuracy of data analysis is improved.
In one embodiment, determining target standard data matching the data to be analyzed in the standard data based on the data elements of the data to be analyzed and the weights of the data elements includes:
determining a first data element in the data to be analyzed and a second data element in the standard data;
determining the matching quantity of the first data element and the second data element;
target criterion data is determined based on the number of matches and the weight of the first data element.
It should be noted that, the data element in the data to be analyzed is the first data element; the data elements in the standard data are second data elements. In this embodiment, further considering the number of matches between data elements, the greater the number of matches between data elements between data to be analyzed and standard data, the higher the degree of association between the data to be analyzed and the standard data can be explained. The two determining modes of determining the target standard data based on the matching quantity and the target standard data based on the weight of the first element do not have strict priority limitation, and can be adaptively adjusted based on actual conditions. For example, the priority of the number of matches may be determined to be higher, the priority of the weight of the first element may be determined to be higher, or the priorities of the two determination methods may be equal. It can be understood that, when the matching number of the first data element and the second data element is larger and the matching degree of the first data element and the second data element with higher weight is higher, the association degree of the standard data corresponding to the second data element and the data to be analyzed is higher, and the standard data with the highest association degree is regarded as the target standard data.
In this embodiment, the matching number and the weight of the first data element are considered at the same time, so that the accuracy of the target standard data used as the verification standard is ensured, the data verification error is reduced, and the effectiveness of data analysis is improved.
In one embodiment, before analyzing the preset analysis model to determine the target data type and the model execution rule, the method further comprises:
determining a data type and a model execution rule corresponding to the data type based on the input selection information;
and generating a preset analysis model based on the data types and the model execution rules.
In this embodiment, it should be noted that, the analysis model is a flexibly configurable model, and the data type that needs to be analyzed by data and the model execution rule that the data type needs to follow when analyzing data can be determined by inputting selection information. Referring to fig. 2, the option of data category is included in fig. 2, the data category is selected first, and then the refinement category under the selected category is selected, such as material data, transportation data, management data, and processing data. In one embodiment, the subdivided class may also be a further level of class classification, such as a subdivided class of material, shape, etc. under the material data. After the data types are selected, a model execution rule corresponding to which such data is executed is set, and referring to fig. 3, setting information of the model execution rule is included in fig. 3. Such as execution time and frequency, allowable error range, and whether a data analysis report needs to be generated. Different data types can be mapped with different model execution rules, and adaptive adjustment is performed based on actual operation requirements.
In this embodiment, the analytical model supports configurable, custom, and graphical processing. The method supports the customized model execution rule, abstracts the model execution rule into an analysis model through code development, and meets the data analysis requirements of different BOM (Bill of materials) structures, different materials and different dimensions through one-time code development.
In one embodiment, the data analysis method further comprises:
acquiring all stored historical data;
classifying the historical data based on preset dimension information, and determining initial standard data of each data type;
respectively correcting the initial standard data of each data type based on the first analysis requirement;
and taking the corrected initial standard data as standard data of the data type.
In this embodiment, the history data refers to standard data before the current data analysis is performed, data to be analyzed whose data analysis result is normal, and data to be analyzed whose data analysis result is abnormal but corrected for the abnormality. I.e. the history data is the value that the data expects in a normal state. The preset dimension information can comprise dimensions of regions, time, departments, versions and the like, and the historical data can be classified in time based on different preset dimension information so as to provide data standards for the data to be analyzed in different dimensions. After determining the initial standard data corresponding to each data type, the initial standard data of each data type may also be corrected based on the first analysis requirement. The first analysis requirements are determined according to the actual application, for example, data criteria specifically set for a certain project, criteria that need to be changed over time or market change, and the like. And the corrected initial standard data is used as standard data of the data type. Referring to fig. 4, fig. 4 is a visual interface for correcting the initial standard data, and for example, the brand, specification, surface treatment method, and machining method in the standard data may be corrected by correcting the brand, correction specification, correction surface treatment method, and correction machining method, respectively.
In this embodiment, the standard data is flexibly configured according to the first analysis requirement, so that the data analysis requirement of the data to be analyzed maximally meets the actual application requirement, and the practicability of data analysis is improved.
In one embodiment, the data analysis method further comprises:
in the absence of standard data corresponding to the target data category, standard data of the target data category is determined and stored based on the second analysis requirement.
When the standard data is queried based on the target data type, if the standard data cannot be queried, it is required to import the standard data corresponding to the target data type when the standard data is not present in the preset database. In this embodiment, when there is no standard data corresponding to the target data type, standard data of the target data type is required based on the second analysis, and the standard data is stored in association with the target data type. The second analysis requirement is the basis of standard data import, and can include the imported standard data itself or the data source address of the standard data to be imported.
In this embodiment, under the condition that the standard data corresponding to the target data type cannot be queried, the corresponding standard data is flexibly imported based on the second analysis requirement, so that the flexibility and adaptability of data analysis are improved.
In one embodiment, determining the data analysis result based on the differences between the respective data to be analyzed and the target standard data includes:
determining a preset difference range based on a model execution rule;
under the condition that all differences are within a preset difference range, determining that the data analysis result is normal;
and determining that the data analysis result is abnormal in the case that at least one difference is not in the preset difference range.
In this embodiment, it should be noted that, when the data to be analyzed and the target standard data are checked, a certain degree of data deviation may be allowed, and the influence of the data deviation on the product in the production and application process may be negligible. In this embodiment, the certain degree is limited by a preset difference range, and the preset difference range is limited by a model execution rule in the analysis model. After determining the differences between each piece of data to be analyzed and the target standard data, confirming that the data analysis result is normal when all the differences are within a preset difference range; if one or more differences are not within the preset difference range, determining that the data analysis result is abnormal. The difference not being within the preset difference range may include: data are unreasonable, for example length values are recorded as weight; data loss; the data is biased, e.g., out of bias range.
In this embodiment, the accuracy of data analysis is strictly controlled by limiting the allowable error range during data analysis by the preset difference range.
In one embodiment, after the step of determining that the data analysis result is data anomaly, the method further includes:
determining a data abnormality cause based on the data analysis result;
and marking all the data to be analyzed with data abnormality according to the data abnormality reasons to obtain an abnormal state mark.
In this embodiment, it should be noted that, when the data analysis result is determined to be data anomaly, the data anomaly cause is determined based on the data analysis result, and the data anomaly cause may be determined according to the data to be analyzed whose difference is not within the preset difference range. Different data anomaly reasons are defined for different data differences. And marking all the data to be analyzed with data abnormality according to the data abnormality reasons to obtain an abnormal state mark. For example, there are different abnormal state flags such as bias, data anomalies, data missing, etc.
In this embodiment, marking is performed based on the reason of data abnormality, so as to obtain different abnormal state marks, which can provide an effective reference for subsequent abnormality repair or abnormality tracing.
In one embodiment, the data analysis method further comprises:
carrying out abnormal classification on the data to be analyzed with data abnormality according to the abnormal state mark;
acquiring data sources corresponding to each abnormal type, and marking the sources of each abnormal type based on the data sources;
outputting data analysis results of all the data to be analyzed, and outputting an abnormal state mark and a source mark based on the data to be analyzed with data abnormality.
In this embodiment, it should be noted that there are multiple types of data anomalies, and the anomaly classification can be performed on the data to be analyzed based on the anomaly status flag, so that the commonality problem of the data can be found out. The source marking is carried out on each abnormal type according to the data source corresponding to each abnormal type, for example, if the data source is system push, the source marking is system push data abnormality; and (5) manually inputting the data source, and marking the source as manually inputting abnormality. Therefore, whether the data with the commonality problem come from the same data source is determined, so that the abnormal source is rapidly positioned, and the omnibearing error analysis is realized. In one embodiment, in the event that a data analysis report needs to be generated, deep causes behind the data can be mined based on the abnormal state markers and the source markers, and subsequent problems that may occur can be inferred and circumvented as suggested data analysis reports.
In this embodiment, by classifying the anomalies and marking the sources for different anomaly types, an effective reference basis is provided for tracing the data, and the comprehensiveness of data analysis is improved.
The embodiment of the invention provides a computer device, which comprises: the memory, the processor, and the program stored on the memory and executable on the processor are configured to implement the steps of the data analysis method as described in the above embodiments.
Embodiments of the present invention provide a machine-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to perform a data analysis method as described in the above embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. 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 apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method of data analysis, comprising:
analyzing a preset analysis model to determine the type of target data and a model execution rule;
determining data to be analyzed and standard data corresponding to the target data type based on the model execution rule;
determining target standard data matched with the data to be analyzed in the standard data based on the data elements of the data to be analyzed and the weights of the data elements;
and determining a data analysis result based on the difference between each piece of data to be analyzed and the target standard data.
2. The data analysis method according to claim 1, wherein the determining target standard data matching the data to be analyzed among the standard data based on the data elements of the data to be analyzed and the weights of the data elements includes:
determining a first data element in the data to be analyzed and a second data element in the standard data;
determining a number of matches of the first data element with the second data element;
target criterion data is determined based on the number of matches and the weight of the first data element.
3. The data analysis method according to claim 1, wherein before analyzing the preset analysis model to determine the target data type and the model execution rule, further comprising:
determining a data type and a model execution rule corresponding to the data type based on the input selection information;
and generating the preset analysis model based on the data types and the model execution rules.
4. The data analysis method according to claim 1, characterized in that the data analysis method further comprises:
acquiring all stored historical data;
classifying the historical data based on preset dimension information, and determining initial standard data of each data type;
respectively correcting the initial standard data of each data type based on the first analysis requirement;
and taking the corrected initial standard data as the standard data of the data type.
5. The data analysis method according to claim 1, characterized in that the data analysis method further comprises:
and determining and storing the standard data of the target data category based on the second analysis requirement in the condition that the standard data corresponding to the target data category does not exist.
6. The data analysis method according to claim 1, wherein the determining of the data analysis result based on the difference between each of the data to be analyzed and the target standard data includes:
determining a preset difference range based on the model execution rule;
under the condition that all the differences are within the preset difference range, determining that the data analysis result is normal;
and determining that the data analysis result is abnormal under the condition that at least one difference is not in the preset difference range.
7. The method according to claim 6, wherein after the step of determining that the data analysis result is data anomaly, further comprising:
determining a data abnormality cause based on the data analysis result;
and marking all the data to be analyzed with data abnormality according to the data abnormality reasons to obtain an abnormal state mark.
8. The data analysis method according to claim 7, characterized in that the data analysis method further comprises:
carrying out abnormal classification on the data to be analyzed with data abnormality according to the abnormal state mark;
acquiring data sources corresponding to each abnormal type, and marking the sources of each abnormal type based on the data sources;
outputting data analysis results of all data to be analyzed, and outputting the abnormal state mark and the source mark based on the data to be analyzed with data abnormality.
9. A computer device, the computer device comprising: memory, a processor and a program stored on the memory and executable on the processor, the program being configured to implement the steps of the data analysis method according to any one of claims 1 to 8.
10. A machine-readable storage medium having stored thereon instructions for causing a machine to perform the steps of the data analysis method of any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311266332.XA CN117520621A (en) | 2023-09-27 | 2023-09-27 | Data analysis method, computer device and machine-readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311266332.XA CN117520621A (en) | 2023-09-27 | 2023-09-27 | Data analysis method, computer device and machine-readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117520621A true CN117520621A (en) | 2024-02-06 |
Family
ID=89744593
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311266332.XA Pending CN117520621A (en) | 2023-09-27 | 2023-09-27 | Data analysis method, computer device and machine-readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117520621A (en) |
-
2023
- 2023-09-27 CN CN202311266332.XA patent/CN117520621A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112732641A (en) | Electronic file filing method, device and medium | |
| CN110929965A (en) | Project risk assessment method and device | |
| CN109360064B (en) | Furniture customized order process refinishing method, electronic equipment and storage medium | |
| CN111209206A (en) | Automatic test method and system for software product | |
| CN111026080A (en) | Hardware-in-loop test method and device for controller | |
| CN117853018A (en) | MES-based accessory inventory adjustment method, system, equipment and medium | |
| CN115619091A (en) | Medicine industry material inspection release control method and device and storage medium | |
| CN117520621A (en) | Data analysis method, computer device and machine-readable storage medium | |
| US20080027833A1 (en) | Method for optimizing sample size for inventory management processes | |
| KR20210045385A (en) | automatic loading warehouse interlocking manufacturing execution system | |
| CN113407650A (en) | Part classification method and device, electronic equipment and storage medium | |
| CN115618828B (en) | Data interaction method and system applied to industrial Internet | |
| CN117522257A (en) | Method for purchasing sample, sample purchasing system and processor | |
| CN114070737B (en) | Method and device for checking configuration data of equipment, storage medium and electronic equipment | |
| CN116415912A (en) | Code management device and method for managing codes | |
| CN116166615A (en) | Data archiving method, equipment and medium for business system | |
| CN106855804B (en) | Code change management and control method and device | |
| CN114138774A (en) | Data processing method and device, storage medium and electronic equipment | |
| CN110554671A (en) | automatic point inspection method and system for product production details | |
| CN117593017A (en) | Electronic product traceability method | |
| CN112486823B (en) | Error code verification method and device, electronic equipment and readable storage medium | |
| CN116797136A (en) | Data analysis method, processor and machine-readable storage medium | |
| CN114511236A (en) | Audit assessment method and audit assessment device | |
| CN115098374A (en) | Test environment monitoring method and device, electronic equipment and storage medium | |
| CN115526539A (en) | Product production inspection method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |