Data analysis method and device, processor and storage medium
By integrating and analyzing data locally, the low data security problem caused by data analysis methods relying on public platforms is solved, and strict data security protection is achieved.
Patent Information
- Application Number
- CN202411145760.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-20
- Publication Date
- 2025-01-14
AI Technical Summary
Existing data analysis methods rely on public platforms, resulting in low data security, risk of unauthorized access or theft, and it is difficult to meet the needs of high security.
By acquiring and integrating original historical data sets, generating standardized data sets, and performing data analysis on local data analysis devices, using the user's data analysis instructions on the preset display interface for data analysis operations, ensuring that data processing and analysis are strictly controlled to be carried out in a private environment.
It effectively guarantees data security and privacy protection, avoids interference from third parties, and solves the problem of low data security.
Smart Images
Figure CN119312380A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data analysis technology, and in particular to a data analysis method, a data analysis device, a processor and a storage medium. Background Art
[0002] In related technologies, data analysis and storage are usually performed on public platforms. However, in some scenarios, the specific data required for data analysis may be highly confidential. In this case, if these data are still uploaded to the public platform for analysis and storage, they may be at risk of being accessed or stolen by unauthorized personnel, thereby causing data leakage and privacy protection issues, and even having a negative impact on subsequent work. Therefore, existing data analysis and storage methods are difficult to meet the demand for high security. Summary of the invention
[0003] The purpose of the embodiments of the present application is to provide a data analysis method, a data analysis device, a processor and a storage medium to solve the problem that the existing data analysis method relies on a public platform to realize its analysis function and has low data security.
[0004] In order to achieve the above-mentioned object, the present application provides a data analysis method in a first aspect, which is applied to a data analysis device, and the method comprises: Acquire multiple original historical data sets, and integrate each original historical data set to obtain and store multiple standardized data sets; wherein the standardized data set includes multiple analyzable parameters and multiple parameter values corresponding to each analyzable parameter; When a user issues a data analysis instruction on a preset display interface of the data analysis device, analysis information contained in the data analysis instruction is obtained; wherein the analysis information includes a target analysis rule and a target analysis object set, the target analysis object set includes at least one target analysis parameter and identification information corresponding to each target analysis parameter, and the identification information is identification information of a standardized data set to which the target analysis parameter belongs; Based on the target analysis rules, data analysis is performed according to the target analysis object set to obtain data analysis results.
[0005] In the embodiment of the present application, each original historical data set is integrated to obtain and store multiple standardized data sets, including: For each original historical data set, determine whether there is a data set to be merged corresponding to the original historical data set in multiple original historical data sets. If so, merge the original historical data set with its corresponding data set to be merged to obtain a standardized data set. If not, determine the original historical data set as a standardized data set; wherein the analyzable parameters contained in the data set to be merged and its corresponding original historical data set are exactly the same.
[0006] In the embodiment of the present application, each original historical data set is integrated to obtain and store multiple standardized data sets, and further includes: For each standardized data set, determine whether to perform parameter merging on the standardized data set. If so, determine multiple target merging parameters based on the analyzable parameters contained in the standardized data set. Based on the target merging parameters, determine new analyzable parameters and their corresponding parameter values, and update the standardized data set; wherein the parameter values corresponding to the new analyzable parameters include the parameter values corresponding to each target merging parameter of the new analyzable parameters.
[0007] In the embodiment of the present application, based on the target analysis rule, data analysis is performed according to the target analysis object set to obtain data analysis results, including: Determine the target parameter value corresponding to each target analysis parameter according to the identification information corresponding to each target analysis parameter; If the target analysis rule is the first preset analysis rule, then for each target analysis parameter, according to the preset reference parameter value corresponding to the target analysis parameter, data analysis is performed on the target parameter value corresponding to the target analysis parameter to obtain a data analysis result set corresponding to the target analysis parameter; If the target analysis rule is the second preset analysis rule, then based on the preset operation logic, numerical operations are performed according to the target parameter values corresponding to each target analysis parameter to obtain a data analysis result set; The data analysis results include a data analysis result set.
[0008] In the embodiment of the present application, after performing data analysis based on the target analysis rule and obtaining the data analysis result according to the target analysis object set, the method further includes: When there are data analysis results that do not meet the preset conditions in the data analysis result set, data cleaning is performed on the target parameter values corresponding to at least part of the target analysis parameters to obtain new target parameter values, and the new target parameter values are used as the target parameter values currently corresponding to the target analysis parameters; Based on the target analysis rules, data analysis is performed according to the target parameter values currently corresponding to each target analysis parameter to obtain a new set of data analysis results.
[0009] In an embodiment of the present application, data cleaning is performed on target parameter values corresponding to at least some target analysis parameters, including: If the target parameter value contains a preset Chinese field, replace the preset Chinese field in the target parameter value with a preset English field; or / and If the target parameter value contains an integer, convert the integer in the target parameter value to a floating point number; or / and If there are preset characters in the target parameter value, the preset characters in the target parameter value are deleted.
[0010] In an embodiment of the present application, the method further includes: Displaying the data analysis results on the data analysis device; Based on the data analysis results, a data analysis report is generated and stored.
[0011] A second aspect of the present application provides a data analysis device, which is configured in a data analysis device, and the device includes: A data integration module is used to obtain multiple original historical data sets, and integrate each original historical data set to obtain and store multiple standardized data sets; wherein the standardized data set includes multiple analyzable parameters and multiple parameter values corresponding to each analyzable parameter; An analysis target acquisition module, used for acquiring analysis information contained in the data analysis instruction when the user issues a data analysis instruction on a preset display interface of the data analysis device; wherein the analysis information includes a target analysis rule and a target analysis object set, the target analysis object set includes at least one target analysis parameter and identification information corresponding to each target analysis parameter, and the identification information is identification information of a standardized data set to which the target analysis parameter belongs; The data analysis module is used to perform data analysis based on the target analysis rules and the target analysis object set to obtain data analysis results.
[0012] A third aspect of the present application provides a processor configured to execute the above-mentioned data analysis method.
[0013] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configure the processor to execute the above-mentioned data analysis method.
[0014] The data analysis method, device, processor and storage medium provided by the present application integrate the original historical data sets involved in the data analysis to obtain and store the standardized data sets, then obtain the data analysis instructions issued by the user on the preset display interface of the data analysis device, and use the data analysis device to implement the data analysis operation locally according to the data analysis instructions. The data processing (such as data set integration) and analysis of the present application are carried out in a strictly controlled private environment, which fully guarantees data security and privacy protection, avoids interference from third parties, and effectively solves the problem that the existing data analysis methods rely on public platforms to implement their analysis functions and have low data security.
[0015] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings: Figure 1 The flowchart of the data analysis method according to the embodiment of the present application is schematically shown; Figure 2 The structure block diagram of the data analysis device according to the embodiment of the present application is schematically shown; Figure 3 The internal structure diagram of the computer device according to the embodiment of the present application is schematically shown.
[0017] Description of Reference Numerals A01-processor; A02-network interface; A03-internal memory; A04-display screen; A05-input device; A06-non-volatile storage medium; B01-operating system; B02-computer program. DETAILED DESCRIPTION
[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0019] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.
[0020] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.
[0021] In related technologies, data analysis and storage are usually performed on public platforms. However, in some scenarios, specific data required for data analysis may be highly confidential. For example, the hardware data and software data of the distributed control system (DCS) during operation are subject to confidentiality requirements, and during the testing process, data analysis is required based on the design inputs provided by different design institutes. In this case, if these data are still uploaded to a public platform for analysis and storage, they may be at risk of being accessed or stolen by unauthorized personnel, thereby causing data leakage and privacy protection issues, and even having a negative impact on subsequent work. Therefore, existing data analysis and storage methods are difficult to meet the demand for high security.
[0022] In view of the fact that the existing data analysis methods in the related art rely on a public platform to realize their analysis functions, and there is a problem of low data security, the present application provides a data analysis method, device, processor and storage medium. The data analysis method, device, processor and storage medium provided by the present application are described in detail below in conjunction with the accompanying drawings through specific examples and implementation methods.
[0023] In addition, it should be noted that the public platform mentioned in this application refers to an online service environment provided by a third party and open to a wide range of users, such as a cloud service platform and a data analysis platform.
[0024] Figure 1 The flowchart of the data analysis method of the embodiment of the present application is schematically shown. Figure 1 As shown, in one embodiment of the present application, a data analysis method is provided, which is applied to a data analysis device, and the method may include the following steps.
[0025] Step 200: Acquire multiple original historical data sets, and integrate each of the original historical data sets to obtain and store multiple standardized data sets.
[0026] The original historical data set includes a plurality of analyzable parameters and a plurality of parameter values corresponding to each of the analyzable parameters.
[0027] Taking DCS as an example, its original historical data set includes the parameter values of various hardware parameters at different times and the parameter values of various software parameters at different times during the historical operation of DCS. It can be understood that each hardware parameter and each software parameter of DCS are analyzable parameters of DCS, and the DCS can be deeply understood according to the parameter values of the analyzable parameters of DCS.
[0028] In this embodiment, after obtaining multiple original historical data sets, in order to integrate the original historical data sets to obtain multiple standardized data sets, the step 200 may include: Step 210, for each original historical data set, determine whether there is a data set to be merged corresponding to the original historical data set in multiple original historical data sets. If so, merge the original historical data set with its corresponding data set to be merged to obtain a standardized data set. If not, determine the original historical data set as a standardized data set.
[0029] The analyzable parameters contained in the data set to be merged and the corresponding original historical data set are exactly the same. The original historical data set and its corresponding data set to be merged are merged to represent the contents of the original historical data set and its corresponding data set to be merged into a single data set, namely, a standardized data set. In this embodiment, the standardized data set includes multiple analyzable parameters and multiple parameter values corresponding to each analyzable parameter.
[0030] Specifically, when the analyzable parameters contained in multiple original historical data sets are exactly the same, these original historical data sets are characterized as historical data generated by the same target object (such as DCS) in different time periods. At this time, these original historical data sets can be merged to facilitate subsequent data analysis and ensure the integrity of the data analysis.
[0031] Optionally, step 200 may further include: Step 220, for each standardized data set, determine whether to perform parameter merging on the standardized data set. If so, determine multiple target merging parameters based on the analyzable parameters contained in the standardized data set, determine new analyzable parameters and their corresponding parameter values based on the target merging parameters, and update the standardized data set.
[0032] The parameter value corresponding to the new analyzable parameter includes the parameter value corresponding to each target combined parameter of the new analyzable parameter.
[0033] Specifically, in some application scenarios, some analyzable parameters in the determined standardized data set may need to be merged to meet actual data analysis needs. Based on this, after determining the standardized data set, some analyzable parameters in the standardized data set may be merged according to preset merging rules to generate new analyzable parameters and their corresponding parameter values.
[0034] In this embodiment, for any standardized data set, after determining its new analyzable parameters and their corresponding parameter values, the standardized data set can be updated in the form of key-value pairs by retaining the new analyzable parameters and their corresponding parameter values, as well as the parameter values corresponding to each target combined parameter of the new analyzable parameter, in the standardized data set at the same time, or by deleting the parameter values corresponding to each target combined parameter of the new analyzable parameter from the standardized data set, and retaining the new analyzable parameters and their corresponding parameter values in the standardized data set in the form of key-value pairs. In this embodiment, the update method of the standardized data set depends on the specific data analysis requirements.
[0035] Step 400: When a user issues a data analysis instruction on a preset display interface of the data analysis device, analysis information included in the data analysis instruction is obtained.
[0036] Specifically, the user can operate in the preset display interface of the data analysis device according to specific data analysis requirements, and then generate data analysis instructions.
[0037] The analysis information includes target analysis rules and a target analysis object set, the target analysis object set includes at least one target analysis parameter and identification information corresponding to each target analysis parameter, and the identification information is the identification information of the standardized data set to which the target analysis parameter belongs. It is worth mentioning that in this embodiment, the target analysis parameter is an analyzable parameter contained in the standardized data set.
[0038] In this embodiment, the data analysis instruction may include multiple analysis information, that is, the user may issue multiple data analysis tasks at the same time. When there are multiple data analysis tasks, the data analysis device may execute multiple data analysis tasks simultaneously (i.e., execute step 600), or execute each data analysis task in sequence.
[0039] Step 600, based on the target analysis rules, data analysis is performed according to the target analysis object set to obtain data analysis results.
[0040] In this embodiment, step 600 may include: Step 610: Determine the target parameter value corresponding to each target analysis parameter according to the identification information corresponding to each target analysis parameter.
[0041] In this embodiment, each standardized data set has unique identification information (such as a data set name), and the target parameter value corresponding to the target analysis parameter is the parameter value corresponding to the target analysis parameter in the standardized data set whose identification information is the identification information corresponding to the target analysis parameter.
[0042] Step 620, if the target analysis rule is the first preset analysis rule, then for each target analysis parameter, according to the preset reference parameter value corresponding to the target analysis parameter, data analysis is performed on the target parameter value corresponding to the target analysis parameter to obtain a data analysis result set corresponding to the target analysis parameter.
[0043] The first preset analysis rule represents that each parameter value corresponding to the target analysis parameter is numerically compared with the preset reference parameter value corresponding to the target analysis parameter to obtain a data analysis result set corresponding to the target analysis parameter.
[0044] In this embodiment, when the target analysis rule is the first preset analysis rule, the analysis information also includes the comparison type corresponding to each target analysis parameter. For example, when the comparison type is equal, the data analysis of the target parameter value corresponding to the target analysis parameter is specifically to determine whether the target parameter value corresponding to the target analysis parameter is equal to the preset reference parameter value corresponding to the target analysis parameter, thereby obtaining a data analysis result set corresponding to the target analysis parameter.
[0045] Step 630: If the target analysis rule is the second preset analysis rule, numerical operations are performed based on the preset operation logic according to the target parameter values corresponding to each target analysis parameter to obtain a data analysis result set.
[0046] The second preset analysis rule represents performing numerical operations on the target parameter values corresponding to each target analysis parameter at the same time to obtain a data analysis result set.
[0047] In this embodiment, when the target analysis rule is the second preset analysis rule, the analysis information also includes a preset operation logic among multiple preset operation logics, and numerical operations are performed according to the target parameter values corresponding to each target analysis parameter. Specifically, according to the preset operation logic selected by the user, numerical operations are performed on the target parameter values corresponding to each target analysis parameter at the same time, thereby obtaining a set of data analysis results.
[0048] In this embodiment, the data analysis result includes a data analysis result set.
[0049] Optionally, after step 600, the method may further include: Step 810, when there are data analysis results in the data analysis result set that do not meet the preset conditions, data cleaning is performed on the target parameter values corresponding to at least some of the target analysis parameters to obtain new target parameter values, and the new target parameter values are used as the target parameter values currently corresponding to the target analysis parameters.
[0050] In this embodiment, in step 810, the method of performing data cleaning on the target parameter values corresponding to at least part of the target analysis parameters may include: if the target parameter value contains a preset Chinese field, replacing the preset Chinese field in the target parameter value with a preset English field; or / and if the target parameter value contains an integer, converting the integer in the target parameter value into a floating point number; or / and if there are preset characters (such as a space) in the target parameter value, deleting the preset characters in the target parameter value.
[0051] Step 820, based on the target analysis rules, data analysis is performed according to the target parameter values currently corresponding to each target analysis parameter to obtain a new data analysis result set.
[0052] It can be understood that when a new data analysis result set is obtained, the data analysis result includes the new data analysis result set.
[0053] Specifically, since the parameter values in the standardized data set may be inconsistent or have text format problems due to data filling and other reasons, in order to improve the accuracy of data analysis, this embodiment may perform data cleaning on the target parameter values corresponding to at least some of the target analysis parameters when there are data analysis results that do not meet the preset conditions in the data analysis result set. It is worth mentioning that in this embodiment, the target analysis parameters corresponding to the target parameter values for which data cleaning is performed are the target analysis parameters involved in the data analysis results that do not meet the preset conditions.
[0054] Optionally, after step 600 or step 820, the method may further include: Step 910: Display the data analysis results on the data analysis device so that the user can view the data analysis results in time.
[0055] Step 920, based on the data analysis results, a data analysis report is generated and stored to facilitate later review, further analysis, and making of relevant decisions.
[0056] It is not difficult to see that the data analysis method provided in this embodiment first integrates the original historical data sets involved in the data analysis to obtain and store the standardized data sets, then obtains the data analysis instructions issued by the user on the preset display interface of the data analysis device, and uses the data analysis device to implement the data analysis operation locally according to the data analysis instructions. The data processing (such as data set integration, data cleaning) and analysis of this application are all carried out in a strictly controlled private environment, which fully guarantees data security and privacy protection and avoids interference from third parties.
[0057] Figure 1 FIG. 1 is a flow chart of a data analysis method in one embodiment. It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.
[0058] Figure 2 The structure block diagram of the data analysis device of the embodiment of the present application is schematically shown. Figure 2 As shown, in one embodiment of the present application, a data analysis device is provided, which is configured in a data analysis device, and the device may include: The data integration module is used to obtain multiple original historical data sets, and integrate each original historical data set to obtain and store multiple standardized data sets; wherein the standardized data set includes multiple analyzable parameters and multiple parameter values corresponding to each analyzable parameter.
[0059] An analysis target acquisition module is used to obtain analysis information contained in a data analysis instruction when a user issues a data analysis instruction on a preset display interface of the data analysis device; wherein the analysis information includes a target analysis rule and a target analysis object set, the target analysis object set includes at least one target analysis parameter and identification information corresponding to each target analysis parameter, and the identification information is the identification information of the standardized data set to which the target analysis parameter belongs.
[0060] The data analysis module is used to perform data analysis based on the target analysis rules and the target analysis object set to obtain data analysis results.
[0061] In this embodiment, the data integration module may include: The data integration unit is used to determine, for each original historical data set, whether there is a data set to be merged corresponding to the original historical data set in multiple original historical data sets; if so, merge the original historical data set with its corresponding data set to be merged to obtain a standardized data set; if not, determine the original historical data set as a standardized data set; wherein the analyzable parameters contained in the data set to be merged and its corresponding original historical data set are exactly the same.
[0062] In this embodiment, the data integration module may further include: The data merging unit is used to determine, for each standardized data set, whether to perform parameter merging on the standardized data set. If so, multiple target merging parameters are determined based on the analyzable parameters included in the standardized data set. New analyzable parameters and their corresponding parameter values are determined based on the target merging parameters, and the standardized data set is updated; wherein the parameter values corresponding to the new analyzable parameters include the parameter values corresponding to the respective target merging parameters of the new analyzable parameters.
[0063] In this embodiment, the data analysis module may include: The target parameter value determination unit is used to determine the target parameter value corresponding to each target analysis parameter according to the identification information corresponding to each target analysis parameter.
[0064] The first data analysis unit is used to, if the target analysis rule is the first preset analysis rule, perform data analysis on the target parameter value corresponding to each target analysis parameter according to the preset reference parameter value corresponding to the target analysis parameter to obtain a data analysis result set corresponding to the target analysis parameter.
[0065] The second data analysis unit is used to perform numerical operations according to target parameter values corresponding to each target analysis parameter based on preset operation logic to obtain a data analysis result set if the target analysis rule is the second preset analysis rule.
[0066] In this embodiment, the data analysis module may further include: A data cleaning unit is used to perform data cleaning on target parameter values corresponding to at least part of the target analysis parameters when there are data analysis results that do not meet preset conditions in the data analysis result set, obtain new target parameter values, and use the new target parameter values as the target parameter values currently corresponding to the target analysis parameters.
[0067] The third data analysis unit is used to perform data analysis based on the target analysis rules and the target parameter values currently corresponding to each target analysis parameter to obtain a new data analysis result set.
[0068] In this embodiment, the data cleaning unit is specifically used to: if the target parameter value contains a preset Chinese field, replace the preset Chinese field in the target parameter value with a preset English field; or / and if the target parameter value contains an integer, convert the integer in the target parameter value into a floating point number; or / and if there are preset characters in the target parameter value, delete the preset characters in the target parameter value.
[0069] In this embodiment, the device may further include: A result display module is used to display the data analysis result on the data analysis device.
[0070] The report generation module is used to generate and store data analysis reports based on data analysis results.
[0071] In this embodiment, the data analysis device includes a processor and a memory. The data integration module, the analysis target acquisition module and the data analysis module are all stored in the memory as program units, and the processor executes the above program modules stored in the memory to implement corresponding functions.
[0072] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the above data analysis method can be implemented by adjusting kernel parameters.
[0073] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.
[0074] An embodiment of the present application provides a processor configured to execute the above-mentioned data analysis method.
[0075] An embodiment of the present application provides a machine-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned data analysis method.
[0076] Figure 3 The internal structure diagram of the computer device of the embodiment of the present application is schematically shown. Figure 3 As shown, in one embodiment of the present application, a computer device is provided, which may be a terminal. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, a data analysis method is implemented. The display screen A04 of the computer device may be a liquid crystal display or an electronic ink display, and the input device A05 of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.
[0077] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0078] In one embodiment, the data analysis device provided by the present application can be implemented in the form of a computer program. The computer program can be Figure 3 The computer device shown in the figure is run. The memory of the computer device can store various program modules constituting the data analysis device, for example, Figure 2 The data integration module, analysis target acquisition module and data analysis module shown in the figure. The computer program composed of each program module enables the processor to execute the steps of the data analysis method of each embodiment of the present application described in this specification.
[0079] Figure 3 The computer device shown can be Figure 2 The data integration module in the data analysis device shown executes step 200 , the analysis target acquisition module executes step 400 , and the data analysis module executes step 600 .
[0080] The present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: A plurality of original historical data sets are obtained, and each of the original historical data sets are integrated to obtain and store a plurality of standardized data sets; wherein the standardized data sets include a plurality of analyzable parameters and a plurality of parameter values corresponding to each of the analyzable parameters.
[0081] When the user issues a data analysis instruction on the preset display interface of the data analysis device, the analysis information contained in the data analysis instruction is obtained; wherein the analysis information includes target analysis rules and a target analysis object set, the target analysis object set includes at least one target analysis parameter and identification information corresponding to each target analysis parameter, and the identification information is the identification information of the standardized data set to which the target analysis parameter belongs.
[0082] Based on the target analysis rules, data analysis is performed according to the target analysis object set to obtain data analysis results.
[0083] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0084] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0085] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0087] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0088] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.
[0089] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0090] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0091] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A data analysis method, characterized in that: Applied to a data analysis device, the method comprises: Acquire multiple original historical data sets, and integrate each original historical data set to obtain and store multiple standardized data sets; wherein the standardized data set includes multiple analyzable parameters and multiple parameter values corresponding to each analyzable parameter; When a user issues a data analysis instruction on a preset display interface of the data analysis device, analysis information contained in the data analysis instruction is obtained; wherein the analysis information includes a target analysis rule and a target analysis object set, the target analysis object set includes at least one target analysis parameter and identification information corresponding to each target analysis parameter, and the identification information is identification information of a standardized data set to which the target analysis parameter belongs; Based on the target analysis rules, data analysis is performed according to the target analysis object set to obtain data analysis results.
2. The method according to claim 1, characterized in that Integrate various original historical data sets to obtain and store multiple standardized data sets, including: For each original historical data set, determine whether there is a data set to be merged corresponding to the original historical data set in multiple original historical data sets. If so, merge the original historical data set with its corresponding data set to be merged to obtain a standardized data set. If not, determine the original historical data set as a standardized data set; wherein the analyzable parameters contained in the data set to be merged and its corresponding original historical data set are exactly the same.
3. The method according to claim 2, characterized in that Integrate each original historical data set to obtain and store multiple standardized data sets, including: For each standardized data set, determine whether to perform parameter merging on the standardized data set. If so, determine multiple target merging parameters based on the analyzable parameters contained in the standardized data set. Based on the target merging parameters, determine new analyzable parameters and their corresponding parameter values, and update the standardized data set; wherein the parameter values corresponding to the new analyzable parameters include the parameter values corresponding to each target merging parameter of the new analyzable parameters.
4. The method according to claim 1, characterized in that: Based on the target analysis rules, data analysis is performed according to the target analysis object set to obtain data analysis results, including: Determine the target parameter value corresponding to each target analysis parameter according to the identification information corresponding to each target analysis parameter; If the target analysis rule is the first preset analysis rule, then for each target analysis parameter, according to the preset reference parameter value corresponding to the target analysis parameter, data analysis is performed on the target parameter value corresponding to the target analysis parameter to obtain a data analysis result set corresponding to the target analysis parameter; If the target analysis rule is the second preset analysis rule, then based on the preset operation logic, numerical operations are performed according to the target parameter values corresponding to each target analysis parameter to obtain a data analysis result set; The data analysis results include a data analysis result set.
5. The method according to claim 4, characterized in that Based on the target analysis rule, after performing data analysis according to the target analysis object set and obtaining the data analysis result, the method further includes: When there are data analysis results that do not meet the preset conditions in the data analysis result set, data cleaning is performed on the target parameter values corresponding to at least part of the target analysis parameters to obtain new target parameter values, and the new target parameter values are used as the target parameter values currently corresponding to the target analysis parameters; Based on the target analysis rules, data analysis is performed according to the target parameter values currently corresponding to each target analysis parameter to obtain a new set of data analysis results.
6. The method according to claim 5, characterized in that Performing data cleaning on target parameter values corresponding to at least some of the target analysis parameters includes: If the target parameter value contains a preset Chinese field, replace the preset Chinese field in the target parameter value with a preset English field; or / and If the target parameter value contains an integer, convert the integer in the target parameter value to a floating point number; or / and If there are preset characters in the target parameter value, the preset characters in the target parameter value are deleted.
7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Displaying the data analysis results on the data analysis device; Based on the data analysis results, a data analysis report is generated and stored.
8. A data analysis device, characterized in that: Configured in a data analysis device, the device comprises: A data integration module is used to obtain multiple original historical data sets, and integrate each original historical data set to obtain and store multiple standardized data sets; wherein the standardized data set includes multiple analyzable parameters and multiple parameter values corresponding to each analyzable parameter; An analysis target acquisition module, used for acquiring analysis information contained in the data analysis instruction when the user issues a data analysis instruction on a preset display interface of the data analysis device; wherein the analysis information includes a target analysis rule and a target analysis object set, the target analysis object set includes at least one target analysis parameter and identification information corresponding to each target analysis parameter, and the identification information is identification information of a standardized data set to which the target analysis parameter belongs; The data analysis module is used to perform data analysis based on the target analysis rules and the target analysis object set to obtain data analysis results.
9. A processor, characterized in that: The device is configured to perform the data analysis method according to any one of claims 1 to 7.
10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to perform the data analysis method according to any one of claims 1 to 7.