Intelligent enterprise data analysis platform, method and program product

Abstract

The invention relates to the technical field of data analysis, in particular to an enterprise data intelligent analysis platform and method and a program product, and the method comprises the steps that a BI model setting module obtains component selection information, generates a BI model according to a selected component, and comprises the steps: determining an analysis method according to the component; determining a data demand table according to an analysis method; according to the data demand table, calling a corresponding data interface to obtain parameter data; combining the selected components to generate a BI model; the BI model sharing module shares a BI model; obtaining BI model adjustment information, and adjusting the corresponding BI model according to the BI model adjustment information; and the BI model application module adopts a BI model to analyze the acquired enterprise data, displays analysis results and forms a billboard according to all the analysis results. According to the scheme, different users can be assisted to obtain the BI model which is suitable for the use environment and meets the requirements of the users, resources and time of the users are saved, and timely analysis of data is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and specifically to an enterprise data intelligent analysis platform, method and program product. Background Art

[0002] BI models generally refer to various mathematical, statistical, and predictive analysis models used in Business Intelligence (BI) systems. These models help organizations extract valuable information from data to support the decision-making process. BI models can integrate multi-source heterogeneous data and reveal patterns and trends behind the data through advanced analysis techniques (such as machine learning, statistical analysis, etc.), thereby helping enterprises optimize business processes, improve operational efficiency, and enhance market competitiveness.

[0003] There are many types of BI models, but different enterprises and different application scenarios may have different BI models set. The design of BI models is a complex process, involving multiple steps and considerations, including requirement confirmation, data acquisition, identification, integration, data modeling, code programming, interaction design, testing, deployment, etc., and corresponding adjustments need to be made in combination with the actual situation during specific implementation. For some small-scale enterprises, there are simply not enough resources to develop and design BI models suitable for themselves, and existing BI models may not be applicable. Moreover, the development and design of BI models consume a large amount of resources and take a long time to develop, which is not conducive to timely data analysis. Summary of the Invention

[0004] One of the purposes of the present invention is to provide an enterprise data intelligent analysis platform, which can assist different users in obtaining BI models suitable for their usage environments and meeting their needs, save users' resources and time, and is conducive to timely data analysis.

[0005] The first basic solution provided by the present invention: An enterprise data intelligent analysis platform, comprising:

[0006] A BI model setting module, configured to obtain component selection information and generate a BI model according to the selected components; wherein generating a BI model according to the selected components includes: determining an analysis method according to the components; determining a data requirement table according to the analysis method; calling a corresponding data interface according to the data requirement table to obtain parameter data; and combining the selected components to generate a BI model;

[0007] A BI model sharing module, configured to share the BI model;

[0008] The BI module setting module is further configured to obtain BI model adjustment information and adjust the corresponding BI model according to the BI model adjustment information;

[0009] The BI model application module is used to analyze the collected enterprise data by using the BI model, display the analysis results, and form a dashboard based on all the analysis results.

[0010] Beneficial effects: Through the BI model setting module, the BI model is designed and generated. Then, through the BI model sharing module, the BI model can be shared. Other users' designed and shared BI models can be directly adopted, and one's own designed BI model can also be shared for other users. When calling the BI model in the BI model sharing module, it can also be adaptively adjusted through the BI model setting module to make it more suitable for the user's current usage environment. Finally, through the BI model application module, the BI model is used to analyze the collected enterprise data, display the analysis results, and form a dashboard based on all the analysis results, which is convenient for users to view the analysis results.

[0011] Through this solution, the shared analysis BI model can be replicated and applied to any industry. Thus, even small-scale enterprises can possess professional data analysis and problem discovery capabilities, summarize various data, and form a dashboard.

[0012] Specifically, when the BI model setting module designs and generates the BI model, it does not start from the bottom-level code coding. There are components involving various analysis methods in the BI model. Users can directly select the corresponding components according to the analysis requirements and actual application scenarios. Based on the components, the analysis methods can be determined, and then the data requirement table and the corresponding data interfaces can be determined to obtain parameter data. By combining the selected components, the BI model can be generated. It supports the access of different systems and can automatically generate corresponding input and output data interfaces (i.e., API interfaces) for other systems to access according to the analysis, without re-coding. And users can also make more user-suitable adjustments to the generated BI model through the BI model setting module. While ensuring that the generated model is suitable for the user's current environment and meets the user's data analysis requirements, it does not require users to consume a large amount of resources and time to develop and design a BI model from the bottom up, which is beneficial to the timely analysis of data.

[0013] In summary, this solution can assist different users in obtaining BI models suitable for their usage environments and meeting their needs, save users' resources and time, and is beneficial to the timely analysis of data.

[0014] Furthermore, the BI model application module is also used to analyze the problems existing in the enterprise by using the AI model according to the analysis results, generate problems to be rectified and processed, and publish them to the external problem system. According to the analysis results of the BI model, through the AI model, problems to be rectified and processed are automatically generated to deeply dig into enterprise data, improve data utilization rate, and for small-scale enterprises without professional data analysis systems or personnel, it can assist them in data analysis and problem discovery.

[0015] Furthermore, one or more analysis methods are included in the component, or an empty component is called to customize the analysis method in the component. Analysis methods can be preset in the component for easy direct invocation, or analysis methods can be customized in the component to meet the different needs of users for analysis methods.

[0016] Furthermore, for the components selected in the combination to generate a BI model, it includes: combining the selected components according to the correlation between parameter data, or combining them according to a custom process relationship to generate a BI model. The combination between components can be based on the correlation of parameter data designed for the components or can be user-defined, so that a multi-process BI model is generated by the combination of components, integrating various analysis methods to ensure that the generated BI model meets the user's requirements.

[0017] Furthermore, for the shared BI model, it includes:

[0018] Obtaining BI model sharing selection information, and according to the BI model sharing selection information, obtaining the corresponding BI model and uploading it to the shared space of the BI model sharing module;

[0019] Obtaining BI model selection information, and according to the BI model selection information, obtaining the corresponding BI model for use.

[0020] For the shared BI model, one can share the BI model designed by oneself with other users, or obtain the BI models shared by other users, so that users can help each other and expand the application scope of the BI model.

[0021] The second object of the present invention is to provide an enterprise data intelligent analysis method, which can assist different users in obtaining BI models suitable for their usage environments and meeting their requirements, save the resources and time of users, and is beneficial to the timely analysis of data.

[0022] The present invention provides Basic Solution Two: An enterprise data intelligent analysis method, including the following content:

[0023] Obtaining component selection information, and generating a BI model according to the selected components; wherein generating a BI model according to the selected components includes: determining the analysis method according to the components; determining the data requirement table according to the analysis method; calling the corresponding data interface according to the data requirement table to obtain parameter data; combining the selected components to generate a BI model;

[0024] Sharing the BI model;

[0025] Obtaining BI model adjustment information, and adjusting the corresponding BI model according to the BI model adjustment information;

[0026] Adopt the BI model to analyze the collected enterprise data, display the analysis results, and form a dashboard based on all the analysis results.

[0027] This solution can assist different users in obtaining BI models suitable for their usage environments and meeting their needs, saving users' resources and time, and facilitating the timely analysis of data.

[0028] Furthermore, it also includes: According to the analysis results, adopt the AI model to analyze the problems existing in the enterprise, generate problems to be rectified and processed, and publish them to an external problem system. According to the analysis results of the BI model, through the AI model, automatically generate problems to be rectified and processed, conduct in-depth exploration of enterprise data, improve data utilization rate, and for enterprises with small scales that do not have professional data analysis systems or personnel, it can assist them in data analysis and problem discovery.

[0029] Furthermore, the components include one or more analysis methods, or call an empty component to customize analysis methods in the components;

[0030] The selected components generate a BI model, including: Combining the selected components according to the correlation between parameter data, or combining them according to the self-defined process relationship to generate a BI model.

[0031] Analysis methods can be preset in the components for easy direct calling, or analysis methods can be customized in the components to suit different user needs for analysis methods. The combination between components can be based on the correlation of parameter data designed for the components, or can be user-defined, so as to generate a multi-process BI model from the combination of components, integrating various analysis methods to ensure that the generated BI model meets user needs.

[0032] Furthermore, the shared BI model includes:

[0033] Obtain BI model sharing selection information, and according to the BI model sharing selection information, obtain the corresponding BI model and upload it to the shared space;

[0034] Obtain BI model selection information, and according to the BI model selection information, obtain the corresponding BI model for use.

[0035] The shared BI model can share the BI models designed by itself with other users, and can also obtain the BI models shared by other users, so that users can help each other and expand the application scope of the BI model.

[0036] The third object of the present invention is to provide a program product that can assist different users in obtaining BI models suitable for their usage environments and meeting their needs, saving users' resources and time, and facilitating the timely analysis of data.

[0037] The present invention provides Basic Solution 3: A program product that, when executed by a processor, implements the steps of the above-mentioned enterprise data intelligent analysis method. This facilitates the application and promotion of the enterprise data intelligent analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a logic block diagram of an embodiment of an enterprise data intelligent analysis platform of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following is a further detailed description through specific embodiments:

[0040] The embodiment is basically as shown in the attached Figure 1 figures: An enterprise data intelligent analysis platform, including: a BI model setting module, a BI model sharing module, and a BI model application module;

[0041] The BI module setting module is used to obtain component selection information, generate a BI model according to the selected components, and store it; among them, the component selection information is custom-selected by the user according to needs. Specifically, the components include one or more analysis methods, or an empty component is called to customize the analysis method in the components; among them, the analysis method is the processing logic of parameter data and the relationships between them, generally a function model, such as a function model for obtaining various enterprise data or a combination thereof;

[0042] Among them, generating a BI model according to the selected components includes:

[0043] Determine the analysis method according to the components;

[0044] Determine the data requirement table according to the analysis method; specifically, each analysis method involves different parameter data. According to the analysis method, determine the parameter data involved by it as the required data, so as to generate a data requirement table; the data requirement table is a document for describing information such as the required parameter data, attributes, data sources, and their relationships;

[0045] According to the data requirement table, call the corresponding data interface to obtain parameter data; specifically, according to the parameter data determined in the data requirement table and its data source, determine the data interface that needs to be called, and obtain the parameter data by calling the data interface, where the data interface is the corresponding input and output API interface;

[0046] Components for combined selection are used to generate a BI model. Specifically, the selected components are combined according to the correlation relationships between parameter data or according to custom process relationships to generate a BI model. Among them, when combining according to the correlation relationships between parameter data, for example, when the BI model to be generated is an ABC analysis model, and the selected components A, B, C, and D are involved in analysis methods of calculating total sales, calculating cumulative total sales, calculating the proportion of cumulative total sales, and dividing ABC products according to the proportion. According to the relationships between the parameter data among them, it can be determined that the inputs and outputs of components A, B, C, and D are connected in sequence to generate an ABC analysis model.

[0047] The BI model sharing module is used to share BI models. Specifically, it obtains BI model sharing selection information, and according to the BI model sharing selection information, obtains the corresponding BI model and uploads it to the shared space of the BI model sharing module to share the designed BI model with other enterprises. At the same time, it obtains BI model selection information, and according to the BI model selection information, obtains the corresponding BI model for use without re-coding.

[0048] The BI module setting module is also used to obtain BI model adjustment information and adjust the corresponding BI model according to the BI model adjustment information. Among them, adjusting the BI model includes, but is not limited to, adjusting analysis methods, data interfaces, parameter data, and component relationships.

[0049] The BI model application module is used to analyze the collected enterprise data using the BI model, display the analysis results, and form a dashboard according to all the analysis results.

[0050] It is also used to analyze the problems existing in the enterprise using an AI model according to the analysis results, generate problems to be rectified and processed, and publish them to an external problem system.

[0051] Specifically, the AI model is selected according to requirements or all AI models are pre-stored, and then according to the analysis results, the AI model with a matching degree higher than the preset matching degree is retrieved to analyze the problems existing in the enterprise.

[0052] AI models include, but are not limited to: classification models (such as logistic regression, random forest) for predicting binary outcomes, such as whether a customer will churn; clustering models (such as K-means) for grouping similar customers or products; regression models (such as linear regression, XGBoost) for predicting continuous values, such as sales volume or cost; anomaly detection models (such as Isolation Forest) for identifying abnormal behaviors or events; important feature analysis models for finding the input features that have the greatest impact on the output, which may indicate key problem areas for the enterprise; trend analysis models for viewing trends over time to understand whether there are deteriorating or improving problems; association rule learning models for exploring the relationships between different variables and discovering causal connections that may not have been noticed.

[0053] This solution can assist different users in obtaining BI models suitable for their usage environments and meeting their needs, saving users' resources and time, and facilitating timely data analysis.

[0054] This embodiment also provides an enterprise data intelligent analysis method, including the following content:

[0055] Obtain component selection information, generate a BI model according to the selected components, and store it; wherein the component selection information is custom-selected by the user according to their needs. Specifically, the components include one or more analysis methods, or an empty component is called to customize the analysis method in the component; wherein the analysis method is the processing logic of parameter data and the relationships between them, generally a function model, such as a function model for obtaining various enterprise data or a combination thereof;

[0056] Among them, generating a BI model according to the selected components includes:

[0057] Determine the analysis method according to the components;

[0058] Determine the data requirement table according to the analysis method; specifically, each analysis method involves different parameter data. Determine the parameter data involved according to the analysis method as the required data, thereby generating a data requirement table; the data requirement table is a document for describing information such as the required parameter data, attributes, data sources, and their relationships;

[0059] According to the data requirement table, call the corresponding data interface to obtain the parameter data; specifically, according to the parameter data determined in the data requirement table and their data sources, determine the data interface that needs to be called, and obtain the parameter data by calling the data interface, where the data interface is the corresponding input and output API interface;

[0060] Combine the selected components to generate a BI model. Specifically, combine the selected components according to the correlation relationship between parameter data or according to a custom process relationship to generate a BI model. Among them, when combining according to the correlation relationship between parameter data, for example: if a BI model to be generated is an ABC analysis model, and the selected components A, B, C, and D are involved in analysis methods such as calculating total sales, calculating cumulative total sales, calculating the proportion of cumulative total sales, and dividing ABC products according to the proportion. According to the relationship between the parameter data among them, it can be determined that the inputs and outputs of components A, B, C, and D are connected in sequence and combined to generate an ABC analysis model.

[0061] Share the BI model. Specifically, obtain the BI model sharing selection information, and according to the BI model sharing selection information, obtain the corresponding BI model and upload it to the shared space to share the designed BI model with other enterprises. At the same time, obtain the BI model selection information, and according to the BI model selection information, obtain the corresponding BI model for use without re-coding.

[0062] Obtain the BI model adjustment information and adjust the corresponding BI model according to the BI model adjustment information. Among them, adjusting the BI model includes, but is not limited to: adjusting analysis methods, data interfaces, parameter data, and component relationships.

[0063] Adopt the BI model to analyze the collected enterprise data, display the analysis results, and form a dashboard according to all the analysis results.

[0064] According to the analysis results, use the AI model to analyze the problems existing in the enterprise, generate problems to be rectified and processed, and publish them to an external problem system.

[0065] Specifically, the AI model selects according to requirements or pre-stores all AI models, and then according to the analysis results, retrieves the AI model with a matching degree higher than the preset matching degree to analyze the problems existing in the enterprise.

[0066] AI models include, but are not limited to: classification models (such as logistic regression, random forest) for predicting binary results, such as whether a customer will churn; clustering models (such as K-means) for grouping similar customers or products; regression models (such as linear regression, XGBoost) for predicting continuous values, such as sales volume or cost; anomaly detection models (such as Isolation Forest) for identifying abnormal behaviors or events; important feature analysis models for finding the input features that have the greatest impact on the output, which may indicate the key problem areas of the enterprise; trend analysis models for viewing trends over time to understand whether there are problems getting worse or improving; association rule learning models for exploring the relationships between different variables and discovering causal links that may not have been noticed.

[0067] This embodiment also provides a program product which, when executed by a processor, implements the steps of the above enterprise data intelligent analysis method.

[0068] If the above-described text method is implemented in the form of software functional units and sold or used as an independent product, it can be stored in a storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0069] The above are only embodiments of the present invention. Common knowledge such as specific structures and characteristics known in the art is not described in detail here. Those of ordinary skill in the art know all the common technical knowledge in the technical field to which the invention pertains before the application date or priority date, can know all the prior art in this field, and have the ability to apply conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application, complete and implement this solution in combination with their own capabilities. Some typical well-known structures or well-known methods should not be an obstacle for those of ordinary skill in the art to implement this application. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be subject to the content of its claims, and the specific implementation manners described in the specification can be used to interpret the content of the claims.

Claims

1. An enterprise data intelligent analysis platform, characterized in that: The BI model setting module is used to obtain component selection information and generate a BI model according to the selected components; wherein generating a BI model according to the selected components includes: determining an analysis method according to the components; determining a data requirement table according to the analysis method; calling a corresponding data interface according to the data requirement table to obtain parameter data; and combining the selected components to generate a BI model; BI model sharing module, used to share BI models; The BI module setting module is also used to obtain BI model adjustment information and adjust the corresponding BI model according to the BI model adjustment information; The BI model application module is used to adopt the BI model to analyze the collected enterprise data, display the analysis results, and form a dashboard based on all the analysis results.

2. The enterprise data intelligent analysis platform according to claim 1, characterized in that: The BI model application module is also used to analyze existing problems of the enterprise using an AI model based on the analysis results, and generate problems to be rectified and published to an external problem system.

3. The enterprise data intelligent analysis platform according to claim 1, characterized in that: The component includes one or more analysis methods, or calls an empty component to customize the analysis method in the component.

4. The enterprise data intelligent analysis platform according to claim 1, characterized in that: The combining selected components to generate a BI model includes: combining the selected components according to correlations between parameter data, or combining them according to user-defined process relationships, to generate a BI model.

5. The enterprise data intelligent analysis platform according to claim 1, characterized in that: The shared BI model includes: Obtain BI model sharing selection information, obtain the corresponding BI model according to the BI model sharing selection information, and upload it to the shared space of the BI model sharing module; Obtain BI model selection information, and obtain the corresponding BI model based on the BI model selection information for use.

6. A method for intelligent analysis of enterprise data, characterized in that: It includes the following: Obtaining component selection information, and generating a BI model according to the selected components; wherein generating a BI model according to the selected components includes: determining an analysis method according to the components; determining a data requirement table according to the analysis method; calling a corresponding data interface according to the data requirement table to obtain parameter data; and combining the selected components to generate a BI model; Shared BI models; Obtain BI model adjustment information, and adjust the corresponding BI model according to the BI model adjustment information; The BI model is used to analyze the collected enterprise data, display the analysis results, and form a dashboard based on all the analysis results.

7. The enterprise data intelligent analysis method according to claim 6, characterized in that: Also includes: Based on the analysis results, AI models are used to analyze existing problems in the enterprise, generate issues to be rectified, and publish them to an external problem system.

8. The enterprise data intelligent analysis method according to claim 6, characterized in that: The component includes one or more analysis methods, or calls an empty component to customize the analysis method in the component; The combining selected components to generate a BI model includes: combining the selected components according to correlations between parameter data, or combining them according to user-defined process relationships, to generate a BI model.

9. The enterprise data intelligent analysis method according to claim 6, characterized in that: The shared BI model includes: Obtain BI model sharing selection information, obtain the corresponding BI model based on the BI model sharing selection information, and upload it to the shared space; Obtain BI model selection information, and obtain the corresponding BI model based on the BI model selection information for use.

10. A program product, characterized in that When executed by a processor, the steps of the enterprise data intelligent analysis method as described in any one of claims 6 to 9 are implemented.

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