CN115730605B - Data analysis method based on multidimensional information - Google Patents
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Abstract
The invention provides a data analysis method based on multidimensional information. The method comprises the following steps: acquiring a service database and a target service analysis request; wherein the service database at least comprises multidimensional data of the target service; acquiring service logic of a target service based on the target service analysis request; extracting the associated data of the target service from a service database based on service logic; acquiring an analysis algorithm corresponding to the target service; filling the associated data and the analysis algorithm into a preset data analysis template to obtain a data analysis model corresponding to the target service; and determining an analysis result corresponding to the target service based on the data analysis model. As can be seen from the above, after the target service and the analysis algorithm provided by the user are obtained, the embodiment of the invention establishes the data analysis model according to the preset data analysis template, so that the target service can be automatically analyzed to obtain the analysis result required by the user, and the user can conveniently extract the required information from a large amount of data and effectively use the information.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a data analysis method based on multidimensional information.
Background
In the digital economic age, data has become an important production element, and through the collection, storage, reorganization and analysis modeling of data, the important value and law hidden in the data are gradually revealed, and are becoming important driving forces for tissue transformation upgrading and sustainable development. At present, a large amount of data can be obtained and stored in the production operation process by some users, but the users are not aware of how to obtain effective information by using the data; for users who know data analysis, the process of analyzing the data is complex and more time-consuming, which is inconvenient.
Therefore, a data analysis method with a wide application range and convenient use is needed.
Disclosure of Invention
The embodiment of the invention provides a data analysis method based on multidimensional information, which aims to solve the problem that the conventional data analysis method is inconvenient to use.
In a first aspect, an embodiment of the present invention provides a data analysis method based on multidimensional information, including:
acquiring a service database and a target service analysis request; wherein the service database at least comprises multidimensional data of the target service;
acquiring service logic of a target service based on the target service analysis request;
extracting the associated data of the target service from a service database based on service logic;
acquiring an analysis algorithm corresponding to the target service;
filling the associated data and the analysis algorithm into a preset data analysis template to obtain a data analysis model corresponding to the target service;
and determining an analysis result corresponding to the target service based on the data analysis model.
In one possible implementation manner, obtaining an analysis algorithm corresponding to the target service includes:
acquiring analysis requirements corresponding to target services;
and determining an analysis algorithm corresponding to the target service based on the analysis requirement and the type of the target service.
In one possible implementation manner, obtaining the analysis requirement corresponding to the target service includes:
determining an analysis requirement corresponding to the target service based on the type of the target service and at least one of the following:
machine learning, semantic analysis, and knowledge-graph.
In one possible implementation, determining an analysis algorithm corresponding to the target service based on the analysis requirements and the type of the target service includes:
determining an analysis algorithm corresponding to the target service based on the analysis requirement, the type of the target service and at least one of the following:
machine learning, semantic analysis, and knowledge-graph.
In one possible implementation, before populating the association data and analysis algorithm into the preset data analysis template, the method further comprises:
carrying out data cleaning on the associated data;
filling the associated data and the analysis algorithm into a preset data analysis template, wherein the method comprises the following steps:
and filling the association data and analysis algorithm after data cleaning into a preset data analysis template.
In one possible implementation, before populating the association data and analysis algorithm into the preset data analysis template, the method further comprises:
correcting the deviation of the associated data;
filling the associated data and the analysis algorithm into a preset data analysis template, wherein the method comprises the following steps:
and filling the corrected associated data and the analysis algorithm into a preset data analysis template.
In one possible implementation manner, after determining the analysis result corresponding to the target service based on the data analysis model, the method further includes:
obtaining a visual form selected by a user;
and filling the analysis result into a display template corresponding to the visual form to obtain a visual analysis result corresponding to the target service.
In a second aspect, an embodiment of the present invention provides a data analysis platform based on multidimensional information, including:
the data acquisition module is used for acquiring a service database and a target service analysis request; wherein the service database at least comprises multidimensional data of the target service;
the service logic acquisition module is used for acquiring the service logic of the target service based on the target service analysis request;
the associated data extraction module is used for extracting associated data of the target service from the service database based on the service logic;
the analysis algorithm determining module is used for acquiring an analysis algorithm corresponding to the target service;
the analysis model building module is used for filling the associated data and the analysis algorithm into a preset data analysis template to obtain a data analysis model corresponding to the target service;
and the target service analysis module is used for determining an analysis result corresponding to the target service based on the data analysis model.
In a third aspect, an embodiment of the present invention provides a terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is executed by the processor.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which when executed by a processor performs the steps of the method of the first aspect or any one of the possible implementations of the first aspect.
The data analysis method based on the multidimensional information provided by the embodiment of the invention has the beneficial effects that:
after the target service and the analysis algorithm provided by the user are obtained, the data analysis model is established according to the preset data analysis template, so that the target service can be automatically analyzed to obtain the analysis result required by the user, and the user can conveniently extract the required information from a large amount of data and effectively use the information.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an implementation of a method for multidimensional information based data analysis in accordance with an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a data analysis platform based on multidimensional information according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an implementation of a multidimensional information based data analysis method according to an embodiment of the present invention is shown, and the details are as follows:
step 101, acquiring a service database and a target service analysis request; wherein the service database comprises at least multidimensional data of the target service.
In this embodiment, the data analysis method based on the multidimensional information may be implemented on the basis of a data analysis platform based on the multidimensional information, where the platform is composed of modules such as data analysis, model construction, component editing, page editing, and application arrangement, so that the full-flow drag operation can be implemented, the user can be helped to perform data modeling and develop visual application with zero threshold, and a full-service-chain data modeling and visual analysis solution is provided for the user. For ease of explanation, hereinafter simply referred to as a "platform".
The service database in this embodiment refers to a database provided by a user, and includes data with comprehensive dimensions. The specific acquisition mode can be that the user uploads the data to the platform, or after the user provides each data category needed to be contained in the service database, the platform collects the data in real time through a channel provided by the user so as to establish the service database. The data in the business database may be more or less, but at least needs to include the dimensions of the data that the target business involves. The target service refers to a service that a user needs to analyze, such as traffic conditions, school teaching, and the like. The target service may be determined according to a target service analysis request input by a user, or may be determined by the platform according to a data category contained in the service database.
Step 102, based on the target service analysis request, the service logic of the target service is obtained.
In this embodiment, the service logic of the target service refers to the operation logic of the target service, and it can determine which types of data are involved in the target service during operation and the influence relationship between the types of data.
And 103, extracting the associated data of the target service from the service database based on the service logic.
In this embodiment, the associated data of the target service refers to various types of data related to the target service during operation. The associated data is analyzed, so that the user can be helped to determine the running condition, potential risk and other information of the target service, and the user is helped to make a reasonable decision.
Step 104, obtaining an analysis algorithm corresponding to the target service.
In this embodiment, the analysis algorithm for performing data analysis is also different for different types of target services and when the user has different data analysis requirements. The analysis algorithm may be specified directly by the user and may be recommended by the platform to the analysis algorithm if the user does not know the data analysis or does not have an explicit analysis direction.
And 105, filling the associated data and the analysis algorithm into a preset data analysis template to obtain a data analysis model corresponding to the target service.
In this embodiment, the data analysis templates are used to define the form of the data analysis model. The data analysis templates can be in different forms according to different analysis algorithms and target services, and correspondingly, when the data analysis templates are selected, the user can select among the preset data analysis templates, and the platform can recommend the data analysis templates according to the analysis algorithms and the target services. In the embodiment, the data analysis model is built through the data analysis template, so that a user can quickly and conveniently obtain a required data analysis result even if the user does not know the data analysis at all.
And 106, determining an analysis result corresponding to the target service based on the data analysis model.
In this embodiment, the hidden link between the associated data can be obtained based on the analysis result obtained by the data analysis model, so as to help the user determine the information such as the running condition and the potential risk of the target service, and assist the user in making a reasonable decision. Meanwhile, the method can provide solutions for some industries, such as traffic, medical insurance and the like.
In one possible implementation manner, obtaining an analysis algorithm corresponding to the target service includes:
acquiring analysis requirements corresponding to target services;
and determining an analysis algorithm corresponding to the target service based on the analysis requirement and the type of the target service.
In this embodiment, when the user does not specify the analysis algorithm, the platform may recommend an appropriate analysis algorithm or directly select an appropriate algorithm.
In one possible implementation manner, obtaining the analysis requirement corresponding to the target service includes:
determining an analysis requirement corresponding to the target service based on the type of the target service and at least one of the following:
machine learning, semantic analysis, and knowledge-graph.
In this embodiment, if the user inputs the target analysis request in the platform in text form, the platform may perform semantic analysis on the target analysis request, and extract the analysis requirements included in the target analysis request. If the analysis requirements are not included in the target analysis request, the analysis requirements may be determined by machine learning or knowledge-graph.
For example, when recommending an analysis requirement based on machine learning, the platform may input the type of the target service into a neural network model for determining the analysis requirement, and determine the analysis requirement that the target service can achieve;
when recommending analysis requirements based on the knowledge graph, the platform can determine the analysis requirements which can be realized by the target service according to the type of the target service and the multiple groups, wherein the multiple groups comprise multiple analysis requirements which can be realized by the types of various target services.
In one possible implementation, determining an analysis algorithm corresponding to the target service based on the analysis requirements and the type of the target service includes:
determining an analysis algorithm corresponding to the target service based on the analysis requirement, the type of the target service and at least one of the following:
machine learning, semantic analysis, and knowledge-graph.
In this embodiment, several tens of commonly used data analysis algorithms are built in the platform, and the user may also combine different algorithms to analyze the target service, for example, optimize the neural network through a genetic algorithm. If the user inputs the target analysis request in the platform in the form of text, the platform can perform semantic analysis on the target analysis request and extract an analysis algorithm contained in the target analysis request. If the analysis algorithm is not included in the target analysis request, the analysis algorithm may be determined by machine learning or knowledge-graph.
For example, when recommending an analysis algorithm based on machine learning, the platform may input the types of analysis requirements and target services into a neural network model for determining the analysis algorithm, determine an analysis algorithm suitable for analyzing the requirements and target services;
when the platform recommends the analysis algorithm based on the knowledge graph, the analysis algorithm corresponding to the analysis requirement and the type of the target service can be determined according to the analysis requirement, the type of the target service and the triplet, wherein the triplet comprises the corresponding relation of the analysis requirement, the type of the target service and the analysis algorithm.
In one possible implementation, before populating the association data and analysis algorithm into the preset data analysis template, the method further comprises:
carrying out data cleaning on the associated data;
filling the associated data and the analysis algorithm into a preset data analysis template, wherein the method comprises the following steps:
and filling the association data and analysis algorithm after data cleaning into a preset data analysis template.
In this embodiment, the service database provided by the user may have duplicate data, invalid values, missing values, etc., and if the analysis is directly performed after extracting the associated data, the analysis result obtained may be inaccurate, so that the data is preprocessed before the data analysis is performed. The data cleaning may specifically include consistency detection, invalid value detection and missing value detection. If the invalid value and the missing value are detected to be more, the user can be prompted to supplement the related data so as to ensure the accuracy of the analysis result.
In one possible implementation, before populating the association data and analysis algorithm into the preset data analysis template, the method further comprises:
correcting the deviation of the associated data;
filling the associated data and the analysis algorithm into a preset data analysis template, wherein the method comprises the following steps:
and filling the corrected associated data and the analysis algorithm into a preset data analysis template.
In this embodiment, many analysis algorithms are required to be performed on the basis that the data distribution is similar to the normal distribution, and if the deviation of the associated data is too large, which indicates that the deviation degree of the associated data distribution form is too large, the inherent relation between the associated data cannot be represented by directly performing the data analysis. The correlation data is rectified. In this embodiment, the associated data may be converted by means of logarithmic transformation, power transformation (such as root-open number, square, etc.), rank transformation, reciprocal transformation, exponential transformation, etc., so that the distribution of the converted associated data approximates to normal distribution, and the effect of data analysis is improved.
In one possible implementation manner, after determining the analysis result corresponding to the target service based on the data analysis model, the method further includes:
obtaining a visual form selected by a user;
and filling the analysis result into a display template corresponding to the visual form to obtain a visual analysis result corresponding to the target service.
In this embodiment, multiple visual forms are preset in the platform for the user to select, and different visual forms can meet the diversified visual requirements of the user, and an important part in the analysis result can be reflected according to the specific setting of the user.
As can be seen from the above, after the target service and the analysis algorithm provided by the user are obtained, the embodiment of the invention establishes the data analysis model according to the preset data analysis template, so that the target service can be automatically analyzed to obtain the analysis result required by the user, and the user can conveniently extract the required information from a large amount of data and effectively use the information.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 2 is a schematic structural diagram of a data analysis platform based on multidimensional information according to an embodiment of the present invention, and for convenience of explanation, only a portion relevant to the embodiment of the present invention is shown, which is described in detail below:
as shown in fig. 2, the multidimensional information-based data analysis platform 2 includes:
a data acquisition module 21 for acquiring a service database and a target service analysis request; wherein the service database at least comprises multidimensional data of the target service;
a service logic obtaining module 22, configured to obtain a service logic of a target service based on the target service analysis request;
a related data extraction module 23, configured to extract related data of the target service from the service database based on the service logic;
the analysis algorithm determining module 24 is configured to obtain an analysis algorithm corresponding to the target service;
the analysis model building module 25 is configured to fill the association data and the analysis algorithm into a preset data analysis template to obtain a data analysis model corresponding to the target service;
the target service analysis module 26 is configured to determine an analysis result corresponding to the target service based on the data analysis model.
In one possible implementation, the analysis algorithm determination module 24 includes:
the analysis demand determining unit is used for obtaining the analysis demand corresponding to the target service;
and the analysis algorithm determining unit is used for determining an analysis algorithm corresponding to the target service based on the analysis requirement and the type of the target service.
In one possible implementation, the analysis demand determination unit is specifically configured to:
determining an analysis requirement corresponding to the target service based on the type of the target service and at least one of the following:
machine learning, semantic analysis, and knowledge-graph.
In one possible implementation, the analysis algorithm determination unit is specifically configured to:
determining an analysis algorithm corresponding to the target service based on the analysis requirement, the type of the target service and at least one of the following:
machine learning, semantic analysis, and knowledge-graph.
In one possible implementation, the analysis model building module 25 is further configured to:
before filling the associated data and the analysis algorithm into a preset data analysis template, carrying out data cleaning on the associated data;
and filling the association data and analysis algorithm after data cleaning into a preset data analysis template.
In one possible implementation, the analysis model building module 25 is further configured to:
correcting the deviation of the associated data before filling the associated data and the analysis algorithm into a preset data analysis template;
and filling the corrected associated data and the analysis algorithm into a preset data analysis template.
In one possible implementation, the multidimensional information based data analysis platform 2 further comprises a visualization module:
the visualization module is used for:
after determining an analysis result corresponding to the target service based on the data analysis model, obtaining a visual form selected by a user;
and filling the analysis result into a display template corresponding to the visual form to obtain a visual analysis result corresponding to the target service.
As can be seen from the above, after the target service and the analysis algorithm provided by the user are obtained, the embodiment of the invention establishes the data analysis model according to the preset data analysis template, so that the target service can be automatically analyzed to obtain the analysis result required by the user, and the user can conveniently extract the required information from a large amount of data and effectively use the information.
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 3, the terminal 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32 stored in said memory 31 and executable on said processor 30. The steps of the various embodiments of the multidimensional information based data analysis method described above, such as steps 101 through 106 shown in fig. 1, are implemented by the processor 30 when executing the computer program 32. Alternatively, the processor 30 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules/units 21-26 shown in fig. 2, when executing the computer program 32.
Illustratively, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 32 in the terminal 3. For example, the computer program 32 may be split into the modules/units 21 to 26 shown in fig. 2.
The terminal 3 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal 3 may include, but is not limited to, a processor 30, a memory 31. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the terminal 3 and does not constitute a limitation of the terminal 3, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal may further include an input-output device, a network access device, a bus, etc.
The processor 30 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 31 may be an external storage device of the terminal 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used for storing the computer program as well as other programs and data required by the terminal. The memory 31 may also be used for temporarily storing data that has been output or is to be output.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of each functional unit and module is illustrated, and in practical application, the above-mentioned functional allocation may be performed by different functional units and modules according to needs, i.e. the internal structure of the platform is divided into different functional units or modules, so as to perform all or part of the above-mentioned functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by instructing related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of each of the data analysis method embodiments based on multidimensional information when being executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (6)
1. A method for data analysis based on multidimensional information, comprising:
acquiring a service database and a target service analysis request; wherein the service database at least comprises multidimensional real-time data of a target service; the target business is determined according to the data category contained in the business database, and comprises traffic road conditions and school teaching;
acquiring service logic of the target service based on the target service analysis request;
extracting the associated data of the target service from the service database based on the service logic;
acquiring an analysis algorithm corresponding to the target service;
filling the associated data and the analysis algorithm into a preset data analysis template to obtain a data analysis model corresponding to the target service;
determining an analysis result corresponding to the target service based on the data analysis model;
the analysis algorithm for obtaining the target service comprises the following steps:
acquiring analysis requirements corresponding to the target service;
determining an analysis algorithm corresponding to the target service based on the analysis requirement and the type of the target service;
the obtaining the analysis requirement corresponding to the target service includes:
determining the analysis requirement which can be realized by the target service according to the type and the multiple groups of the target service; wherein the multiple groups contain various analysis requirements which can be realized by the types of various target services;
the obtaining the analysis requirement corresponding to the target service includes:
if a user inputs a target analysis request in a text form, carrying out semantic analysis on the target analysis request, and extracting analysis requirements in the target analysis request;
if the target analysis request does not contain analysis requirements, determining the analysis requirements corresponding to the target service through machine learning or a knowledge graph based on the type of the target service;
before said populating the association data and the analysis algorithm into a pre-set data analysis template, the method further comprises:
performing data cleaning on the associated data; the data cleaning comprises consistency detection, invalid value detection and missing value detection;
the filling the associated data and the analysis algorithm into a preset data analysis template comprises the following steps:
filling the association data after data cleaning and the analysis algorithm into a preset data analysis template;
before said populating the association data and the analysis algorithm into a pre-set data analysis template, the method further comprises:
correcting the deviation of the associated data;
the filling the associated data and the analysis algorithm into a preset data analysis template comprises the following steps:
and filling the corrected associated data and the analysis algorithm into a preset data analysis template.
2. The multidimensional information based data analysis method of claim 1, wherein the determining an analysis algorithm corresponding to the target service based on the analysis requirement and the type of target service comprises:
determining an analysis algorithm corresponding to the target service based on the analysis requirement, the type of the target service and at least one of the following:
machine learning, semantic analysis, and knowledge-graph.
3. The multidimensional information based data analysis method of claim 1 or 2, wherein after the determination of the analysis result corresponding to the target service based on the data analysis model, the method further comprises:
obtaining a visual form selected by a user;
and filling the analysis result into the display template corresponding to the visual form to obtain the visual analysis result corresponding to the target service.
4. A multidimensional information based data analysis platform, comprising:
the data acquisition module is used for acquiring a service database and a target service analysis request; wherein the service database at least comprises multidimensional real-time data of a target service; the target business is determined according to the data category contained in the business database, and comprises traffic road conditions and school teaching;
the service logic acquisition module is used for acquiring the service logic of the target service based on the target service analysis request;
the associated data extraction module is used for extracting the associated data of the target service from the service database based on the service logic;
the analysis algorithm determining module is used for acquiring an analysis algorithm corresponding to the target service;
the analysis model building module is used for filling the associated data and the analysis algorithm into a preset data analysis template to obtain a data analysis model corresponding to the target service;
the target service analysis module is used for determining an analysis result corresponding to the target service based on the data analysis model;
the analysis algorithm determining module is specifically configured to:
acquiring analysis requirements corresponding to the target service;
determining an analysis algorithm corresponding to the target service based on the analysis requirement and the type of the target service;
the analysis algorithm determining module is specifically configured to:
determining the analysis requirement which can be realized by the target service according to the type and the multiple groups of the target service; wherein the multiple groups contain various analysis requirements which can be realized by the types of various target services;
the analysis algorithm determining module is specifically configured to:
if a user inputs a target analysis request in a text form, carrying out semantic analysis on the target analysis request, and extracting analysis requirements in the target analysis request;
if the target analysis request does not contain analysis requirements, determining the analysis requirements corresponding to the target service through machine learning or a knowledge graph based on the type of the target service;
the analysis model establishment module is also used for:
before the association data and the analysis algorithm are filled into a preset data analysis template, carrying out data cleaning on the association data; the data cleaning comprises consistency detection, invalid value detection and missing value detection;
filling the association data after data cleaning and the analysis algorithm into a preset data analysis template;
the analysis model establishment module is also used for:
correcting the deviation of the associated data before the associated data and the analysis algorithm are filled into a preset data analysis template;
and filling the corrected associated data and the analysis algorithm into a preset data analysis template.
5. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 3 when the computer program is executed.
6. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 3.
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