CN115730605A - Data Analysis Method Based on Multidimensional Information - Google Patents

Abstract

The invention provides a data analysis method based on multi-dimensional information. The method comprises the following steps: acquiring a service database and a target service analysis request; the service database at least comprises multidimensional data of the target service; acquiring a service logic of the target service based on the target service analysis request; extracting the associated data of the target service from a service database based on the service logic; acquiring an analysis algorithm corresponding to a 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. Therefore, 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, the target service can be automatically analyzed, the analysis result required by the user is obtained, and the user can conveniently extract the required information from a large amount of data and effectively use the information.

Description

Data Analysis Method Based on Multidimensional Information

technical field

The invention relates to the technical field of data analysis, in particular to a data analysis method based on multidimensional information.

Background technique

In the era of digital economy, data has become an important factor of production. Through the collection, storage, reorganization and analysis and modeling of data, the important value and laws hidden in the data are gradually revealed. An important driving force for sustainable development. At present, some users will obtain and store a large amount of data in the process of production and operation, but do not know how to use the data to obtain effective information; for users who understand data analysis, the process of analyzing data is complicated and takes a long time , not convenient enough.

Therefore, there is a need for a data analysis method that is widely applicable and easy to use.

Contents of the invention

The embodiment of the present invention provides a data analysis method based on multi-dimensional information to solve the problem of inconvenient use of the current data analysis method.

In the first aspect, the embodiment of the present invention provides a data analysis method based on multidimensional information, including:

Obtain a business database and a target business analysis request; wherein, the business database includes at least multidimensional data of the target business;

Obtain the business logic of the target business based on the target business analysis request;

Extract the associated data of the target business from the business database based on the business logic;

Obtain the analysis algorithm corresponding to the target business;

Fill the associated data and analysis algorithm into the preset data analysis template to obtain the data analysis model corresponding to the target business;

Determine the analysis results corresponding to the target business based on the data analysis model.

In a possible implementation manner, obtaining an analysis algorithm corresponding to the target business includes:

Obtain the analysis requirements corresponding to the target business;

Determine the analysis algorithm corresponding to the target business based on the analysis requirements and the type of the target business.

In a possible implementation manner, the analysis requirements corresponding to the target business are obtained, including:

Determine the analysis requirements corresponding to the target business based on the type of target business and at least one of the following:

Machine Learning, Semantic Analysis and Knowledge Graphs.

In a possible implementation, the analysis algorithm corresponding to the target business is determined based on the analysis requirements and the type of the target business, including:

Determine the analysis algorithm corresponding to the target business based on the analysis requirements, the type of the target business and at least one of the following:

Machine Learning, Semantic Analysis and Knowledge Graphs.

In a possible implementation manner, before filling the associated data and the analysis algorithm into the preset data analysis template, the method further includes:

Perform data cleaning on linked data;

Fill associated data and analysis algorithms into preset data analysis templates, including:

Fill the associated data and analysis algorithm after data cleaning into the preset data analysis template.

In a possible implementation manner, before filling the associated data and the analysis algorithm into the preset data analysis template, the method further includes:

Correct the associated data;

Fill associated data and analysis algorithms into preset data analysis templates, including:

Fill the corrected associated data and analysis algorithm into the preset data analysis template.

In a possible implementation, after determining the analysis results corresponding to the target business based on the data analysis model, the method further includes:

Obtain the visualization form selected by the user;

Fill the analysis results into the display template corresponding to the visualization form, and obtain the visual analysis results corresponding to the target business.

In the second aspect, the embodiment of the present invention provides a data analysis platform based on multidimensional information, including:

A data acquisition module, configured to acquire a business database and a target business analysis request; wherein, the business database includes at least multidimensional data of the target business;

A business logic acquisition module, configured to acquire the business logic of the target business based on the target business analysis request;

The associated data extraction module is used to extract the associated data of the target business in the business database based on the business logic;

The analysis algorithm determination module is used to obtain the analysis algorithm corresponding to the target business;

The analysis model building module is used to fill the associated data and analysis algorithm into the preset data analysis template to obtain the data analysis model corresponding to the target business;

The target business analysis module is configured to determine an analysis result corresponding to the target business based on the data analysis model.

In the third aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, the above first aspect or the first aspect is realized. The steps of any possible implementation method.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any one of the above first aspect or any of the possible possibilities of the first aspect can be realized. The steps to implement the method.

The beneficial effects of the data analysis method based on multidimensional information provided by the embodiment of the present invention are:

After obtaining the target service and analysis algorithm provided by the user, the present invention establishes a data analysis model according to the preset data analysis template, which can automatically analyze the target service and obtain the analysis result required by the user, which is convenient for the user to extract from a large amount of data. necessary information and to use it effectively.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the descriptions of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only of the present invention. For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without paying creative efforts.

Fig. 1 is the implementation flowchart of the data analysis method based on multi-dimensional information provided by an embodiment of the present invention;

Fig. 2 is a schematic structural diagram of a data analysis platform based on multidimensional information provided by an embodiment of the present invention;

Fig. 3 is a schematic diagram of a terminal provided by an embodiment of the present invention.

Detailed ways

In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without 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.

In order to make the purpose, technical solution and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

Referring to Fig. 1, it shows the flow chart of the implementation of the multidimensional information-based data analysis method provided by the embodiment of the present invention, which is described in detail as follows:

Step 101, acquiring a business database and a target business analysis request; wherein, the business database includes at least multidimensional data of the target business.

In this embodiment, the data analysis method based on multidimensional information can be implemented on the basis of a data analysis platform based on multidimensional information. The platform is composed of modules such as data analysis, model building, component editing, page editing, and application layout. The drag-and-drop operation of the whole process helps users to carry out data modeling and develop visualization applications with zero threshold, and provides users with data modeling and visualization analysis solutions of the whole business chain. For ease of explanation, hereinafter referred to as "Platform".

The business database in this embodiment refers to a database provided by a user, which includes data with comprehensive dimensions. The specific acquisition method can be uploaded to the platform by the user, or after the user provides the types of data that need to be included in the business database, the platform collects these data in real time through the channels provided by the user to establish the business database. The data in the business database can be more or less, but at least it needs to include the data of each dimension involved in the target business. Wherein, the target service refers to a service that the user needs to analyze, such as traffic conditions, school teaching and other services. The target service can be determined according to the target service analysis request input by the user, or can be determined by the platform according to the data category contained in the service database.

Step 102, based on the target business analysis request, acquire the business logic of the target business.

In this embodiment, the business logic of the target business refers to the running logic of the target business, through which the types of data involved in the running of the target business can be determined, as well as the influence relationship between various types of data.

Step 103, extracting the associated data of the target business from the business database based on the business logic.

In this embodiment, the associated data of the target business refers to various types of data involved in the operation of the target business. Analysis of associated data can help users determine the operating status of the target business, potential risks and other information, and assist users to make reasonable decisions.

Step 104, acquiring the analysis algorithm corresponding to the target business.

In this embodiment, when different types of target services and users have different data analysis requirements, the analysis algorithms for data analysis will also be different. The analysis algorithm can be directly specified by the user. If the user does not understand data analysis or has no clear direction of analysis, the platform can recommend an analysis algorithm to the user.

Step 105, filling the associated data and analysis algorithm into the preset data analysis template to obtain the data analysis model corresponding to the target business.

In this embodiment, the data analysis template is used to define the form of the data analysis model. For different analysis algorithms and target businesses, data analysis templates can be in different forms. Correspondingly, when selecting a data analysis template, the user can choose from various preset data analysis templates, or the platform can Make recommendations with target businesses. In this embodiment, the data analysis model is established through the data analysis template, even if the user does not understand the data analysis at all, he can quickly and conveniently obtain the required data analysis results.

Step 106, determine the analysis result corresponding to the target business based on the data analysis model.

In this embodiment, based on the analysis results obtained by the data analysis model, the hidden connection between the associated data can be obtained, thereby helping the user to determine information such as the operating status and potential risks of the target business, and assisting the user to make a reasonable decision. At the same time, it can also provide solutions for some industries, such as transportation, medical insurance, etc.

In a possible implementation manner, obtaining an analysis algorithm corresponding to the target business includes:

Obtain the analysis requirements corresponding to the target business;

Determine the analysis algorithm corresponding to the target business based on the analysis requirements and the type of the target business.

In this embodiment, when the user does not specify an analysis algorithm, the platform may recommend an appropriate analysis algorithm or directly select an appropriate algorithm.

In a possible implementation manner, the analysis requirements corresponding to the target business are obtained, including:

Determine the analysis requirements corresponding to the target business based on the type of target business and at least one of the following:

Machine Learning, Semantic Analysis and Knowledge Graphs.

In this embodiment, 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 the analysis requirements included in the target analysis request. If the analysis requirement is not included in the target analysis request, the analysis requirement can be determined through machine learning or knowledge graph.

For example, when the platform recommends analysis requirements based on machine learning, it can input the type of target business into the neural network model used to determine the analysis requirements to determine the analysis requirements that the target business can achieve;

When the platform recommends analysis requirements based on the knowledge graph, it can determine the analysis requirements that can be realized by the target business according to the type of target business and the multigroup, where the multigroup includes various analysis requirements that can be realized by various target business types.

In a possible implementation, the analysis algorithm corresponding to the target business is determined based on the analysis requirements and the type of the target business, including:

Determine the analysis algorithm corresponding to the target business based on the analysis requirements, the type of the target business and at least one of the following:

Machine Learning, Semantic Analysis and Knowledge Graphs.

In this embodiment, the platform has built-in dozens of commonly used data analysis algorithms, and users can also combine different algorithms to analyze target services, such as optimizing neural networks through genetic algorithms. 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 the analysis algorithm included in the target analysis request. If the analysis algorithm is not included in the target analysis request, the analysis algorithm can be determined through machine learning or knowledge graph.

For example, when the platform recommends an analysis algorithm based on machine learning, it can input the type of analysis requirements and target business into the neural network model used to determine the analysis algorithm, and determine the analysis algorithm suitable for the analysis requirements and target business;

When the platform recommends an analysis algorithm based on the knowledge graph, it can determine the analysis algorithm corresponding to the analysis requirement, the type of the target business, and the triplet according to the analysis requirement, the type of the target business, and the triplet includes the analysis requirement and the type of the target business Correspondence with the analysis algorithm.

In a possible implementation manner, before filling the associated data and the analysis algorithm into the preset data analysis template, the method further includes:

Perform data cleaning on linked data;

Fill associated data and analysis algorithms into preset data analysis templates, including:

Fill the associated data and analysis algorithm after data cleaning into the preset data analysis template.

In this embodiment, there may be duplicate data, invalid values or missing values in the business database provided by the user. If the associated data is directly analyzed after extracting the associated data, the obtained analysis results may be inaccurate. Therefore, before performing data analysis, the The data is preprocessed. When performing data cleaning, it can specifically include consistency detection, invalid value and missing value detection. If many invalid values and missing values are detected, the user can be prompted to supplement relevant data to ensure the accuracy of the analysis results.

In a possible implementation manner, before filling the associated data and the analysis algorithm into the preset data analysis template, the method further includes:

Correct the associated data;

Fill associated data and analysis algorithms into preset data analysis templates, including:

Fill the corrected associated data and analysis algorithm into the preset data analysis template.

In this embodiment, many analysis algorithms need to be performed on the basis that the data distribution is similar to a normal distribution. If the skewness of the associated data is too large, it means that the skewness of the associated data distribution is too large. Direct data analysis will Can not reflect the intrinsic relationship between linked data. Therefore, it is necessary to correct the associated data. In this embodiment, the associated data can be converted by logarithmic transformation, power transformation (such as square root, square, etc.), rank transformation, reciprocal transformation, exponential transformation, etc., so that the distribution of the transformed associated data is approximately normal distribution, Improve the effectiveness of data analysis.

In a possible implementation, after determining the analysis results corresponding to the target business based on the data analysis model, the method further includes:

Obtain the visualization form selected by the user;

Fill the analysis results into the display template corresponding to the visualization form, and obtain the visual analysis results corresponding to the target business.

In this embodiment, a variety of visualization forms are preset in the platform for users to choose. Different visualization forms can meet the diverse visual needs of users, and can reflect important parts of the analysis results according to the specific settings of users.

As can be seen from the above, after obtaining the target service and analysis algorithm provided by the user, the embodiment of the present invention establishes a data analysis model according to the preset data analysis template, which can automatically analyze the target service and obtain the analysis result required by the user, which is convenient for the user Extract the required information from a large amount of data and use it effectively.

It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention.

The following are device embodiments of the present invention. For details that are not exhaustively described therein, reference may be made to the corresponding method embodiments above.

Fig. 2 shows a schematic structural diagram of a data analysis platform based on multidimensional information provided by an embodiment of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

As shown in Figure 2, the data analysis platform 2 based on multidimensional information includes:

A data acquisition module 21, configured to acquire a business database and a target business analysis request; wherein, the business database at least includes multidimensional data of the target business;

A business logic acquiring module 22, configured to acquire the business logic of the target business based on the target business analysis request;

The associated data extraction module 23 is used to extract the associated data of the target business in the business database based on the business logic;

An analysis algorithm determination module 24, configured to obtain an analysis algorithm corresponding to the target business;

The analysis model building module 25 is used to fill the associated data and analysis algorithm into the preset data analysis template to obtain the data analysis model corresponding to the target business;

The target business analysis module 26 is configured to determine an analysis result corresponding to the target business based on the data analysis model.

In a possible implementation, the analysis algorithm determination module 24 includes:

The analysis requirement determination unit is used to obtain the analysis requirement corresponding to the target business;

The analysis algorithm determining unit is configured to determine an analysis algorithm corresponding to the target service based on the analysis requirement and the type of the target service.

In a possible implementation manner, the analysis requirement determination unit is specifically used for:

Determine the analysis requirements corresponding to the target business based on the type of target business and at least one of the following:

Machine Learning, Semantic Analysis and Knowledge Graphs.

In a possible implementation, the analysis algorithm determines that the unit is specifically used for:

Determine the analysis algorithm corresponding to the target business based on the analysis requirements, the type of the target business and at least one of the following:

Machine Learning, Semantic Analysis and Knowledge Graphs.

In a possible implementation, the analysis model building module 25 is also used for:

Before filling the associated data and analysis algorithm into the preset data analysis template, perform data cleaning on the associated data;

Fill the associated data and analysis algorithm after data cleaning into the preset data analysis template.

In a possible implementation, the analysis model building module 25 is also used for:

Correct the associated data before filling the associated data and analysis algorithm into the preset data analysis template;

Fill the corrected associated data and analysis algorithm into the preset data analysis template.

In a possible implementation, the multidimensional information-based data analysis platform 2 also includes a visualization module:

Visualization modules are used to:

After determining the analysis results corresponding to the target business based on the data analysis model, obtain the visualization form selected by the user;

Fill the analysis results into the display template corresponding to the visualization form, and obtain the visual analysis results corresponding to the target business.

As can be seen from the above, after obtaining the target service and analysis algorithm provided by the user, the embodiment of the present invention establishes a data analysis model according to the preset data analysis template, which can automatically analyze the target service and obtain the analysis result required by the user, which is convenient for the user Extract the required information from a large amount of data and use it effectively.

Fig. 3 is a schematic diagram of a terminal provided by 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 the memory 31 and operable on the processor 30 . When the processor 30 executes the computer program 32, it implements the steps in the above embodiments of the data analysis method based on multidimensional information, such as steps 101 to 106 shown in FIG. 1 . Alternatively, when the processor 30 executes the computer program 32, it realizes the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules/units 21 to 26 shown in FIG. 2 .

Exemplarily, the computer program 32 can be divided into one or more modules/units, and the one or more modules/units are stored in the memory 31 and executed by the processor 30 to complete this invention. The one or more modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the terminal 3 . For example, the computer program 32 may be divided 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, a palmtop computer, or a cloud server. The terminal 3 may include, but not limited to, a processor 30 and a memory 31 . Those skilled in the art can understand that FIG. 3 is only an example of the terminal 3 and does not constitute a limitation to the terminal 3. It may include more or less components than shown in the figure, or combine some components, or different components, such as The terminal may also include an input and output device, a network access device, a bus, and the like.

The so-called processor 30 may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

The storage 31 may be an internal storage unit of the terminal 3 , such as a hard disk or memory of the terminal 3 . The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk equipped on the terminal 3, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, Flash Card (Flash Card), etc. Further, the memory 31 may also include both an internal storage unit of the terminal 3 and an external storage device. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units, Module completion means that the internal structure of the platform is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into one processing unit, or each unit may exist separately physically, or two or more units may be integrated into one unit, and the above-mentioned integrated units may adopt hardware It can also be implemented in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the above system, reference may be made to the corresponding process in the foregoing method embodiments, and details will not be repeated here.

In the above-mentioned embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not detailed or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed device/terminal and method may be implemented in other ways. For example, the device/terminal embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or Components may be combined or integrated into another system, or some features may be omitted, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated module/unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention realizes all or part of the processes in the methods of the above embodiments, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps in the above embodiments of the data analysis method based on multidimensional information can be realized. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (Read-Only Memory, ROM) , random access memory (Random Access Memory, RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, computer-readable media Excluding electrical carrier signals and telecommunication signals.

The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still carry out the foregoing embodiments Modifications to the technical solutions recorded in the examples, or equivalent replacement of some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should be included in within the protection scope of the present invention.

Claims (10)

1. A data analysis method based on multi-dimensional information is characterized by comprising 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 the service logic of the target service based on the target service analysis request;

extracting the associated data of the target service in 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;

and determining an analysis result corresponding to the target service based on the data analysis model.

2. The method according to claim 1, wherein the obtaining of the analysis algorithm corresponding to the target service comprises:

acquiring an analysis requirement corresponding to the target service;

and determining an analysis algorithm corresponding to the target service based on the analysis requirement and the type of the target service.

3. The method according to claim 2, wherein the obtaining of the analysis requirement corresponding to the target service comprises:

determining an analysis requirement corresponding to the target service based on the type of the target service and at least one of the following items:

machine learning, semantic analysis, and knowledge-graph.

4. The method according to claim 2, wherein the determining an analysis algorithm corresponding to the target service based on the analysis requirement and the type of the 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 items:

machine learning, semantic analysis, and knowledge-graph.

5. The method of claim 1, wherein prior to the populating the associated data and the analysis algorithm into a preset data analysis template, the method further comprises:

performing data cleaning on the associated data;

the filling of the associated data and the analysis algorithm into a preset data analysis template comprises:

and filling the associated data after data cleaning and the analysis algorithm into a preset data analysis template.

6. The method of claim 1, wherein prior to the populating the associated data and the analysis algorithm into a preset data analysis template, the method further comprises:

correcting the associated data;

the filling the associated data and the analysis algorithm into a preset data analysis template comprises:

and filling the corrected associated data and the analysis algorithm into a preset data analysis template.

7. The method for analyzing data based on multidimensional information according to any of claims 1 to 6, wherein after the determining the analysis result corresponding to the target service based on the data analysis model, the method further comprises:

acquiring 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.

8. A multi-dimensional 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 data of the target service;

a service logic obtaining module, configured to obtain a 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 establishing 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.

9. 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 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

Images