Data query method, device and equipment based on APaaS platform and medium

Abstract

The invention provides a data query method, device and equipment based on an APaaS platform and a medium, and relates to the technical field of data query, the method comprises the following steps: obtaining a target query session initiated by a user; based on the target query session, determining a target entity list of an application platform as a service ApaaS platform through a vector database; based on the target entity list, determining an entity association relationship corresponding to the target entity list through an ApaaS platform; and determining a data query result corresponding to the target query session based on the entity association relationship, the target entity list and the conversational business intelligence model. According to the method, an underlying data table is not needed, management and maintenance of technicians are not needed, common users can easily use and maintain ChatBI, seamless integration of user permission and an APaaS platform is achieved, differentiated data display based on the users is supported, the ChatBI available for everyone is achieved, the operation and maintenance cost is reduced, and meanwhile the flexibility of the user permission is ensured.

Description

Technical Field

[0001] This invention relates to the field of data query technology, and in particular to a data query method, apparatus, device and medium based on the APaaS platform. Background Technology

[0002] Chat Business Intelligence (ChatBI) is a deep integration of Large Language Model (LLM) and Business Intelligence (BI) platform. Essentially, it is a business intelligence system based on natural language interaction.

[0003] The current implementation of ChatBI mainly relies on converting natural language into structured query language (SQL) or on text to domain-specific language (text2DSL) technology, and finally displays the data by querying the underlying data table through SQL.

[0004] Currently, mainstream data query methods based on ChatBI generally rely on the integration of underlying data tables with large models, lacking data management flexibility and placing high demands on administrators and users. The use of ChatBI depends on technical personnel for maintenance and management, making it difficult for non-technical personnel to use and manage it. This increases maintenance costs and raises the barrier to entry, resulting in inflexible user permissions. Summary of the Invention

[0005] This invention provides a data query method, apparatus, device, and medium based on an Application Platform as a Service (APaaS) platform. It addresses the shortcomings of existing technologies that place high demands on administrators and users, making it impossible for non-technical personnel to use and manage ChatBI. This increases maintenance costs and raises the barrier to entry, resulting in inflexible user permissions. The invention's solution eliminates the need for underlying data tables and does not rely on technical personnel for management and maintenance. Ordinary users can easily use and maintain ChatBI. Seamless integration of user permissions with the APaaS platform supports user-based differentiated data display, achieving "ChatBI for everyone." This reduces usage and maintenance costs while ensuring flexible user permissions.

[0006] This invention provides a data query method based on the APaaS platform, comprising the following steps.

[0007] Retrieve the target query session initiated by the user; Based on the target query session, the target entity list of the Application Platform as a Service (ApaaS) platform is determined through a vector database; Based on the target entity list, the entity association relationships corresponding to the target entity list are determined through the ApaaS platform; Based on the entity relationships, the target entity list, and the conversational business intelligence model, the data query results corresponding to the target query session are determined.

[0008] According to the data query method based on the APaaS platform provided by the present invention, the step of obtaining the target query session initiated by the user includes: Obtain the query information input by the user; When the query information is associated with context information, the query information and the context information are sent to the conversational business intelligence model to obtain the target query session output by the conversational business intelligence model; If the query information is not associated with context information, the query information will be identified as the target query session.

[0009] According to the present invention, a data query method based on an APaaS platform is provided, wherein determining the target entity list of the Application Platform as a Service (APaaS) platform based on the target query session through a vector database includes: Based on the target query session, the initial entity list of the ApaaS platform and the corresponding filtering information are queried through the vector database; The target entity list is determined based on the initial entity list and the filtering information.

[0010] According to the present invention, a data query method based on an APaaS platform is provided, wherein the target entity list includes field metadata descriptions; The process of determining the data query results corresponding to the target query session based on the entity associations, the target entity list, and the conversational business intelligence model includes: Input the entity association and the field metadata description into the conversational business intelligence model to obtain the dimension information and filtering information output by the conversational business intelligence model; The data query results are determined based on the entity relationships, the dimension information, and the filtering information.

[0011] According to the present invention, a data query method based on an APaaS platform is provided, wherein determining the data query result based on the entity association, the dimension information, and the filtering information includes: Generate a query statement based on the entity relationships, the dimension information, and the filtering information; Based on the query statement, a data query is performed in the preset database to obtain the initial query results; Based on the initial query results, a chart is drawn to obtain the data query results in chart format.

[0012] According to the present invention, a data query method based on an APaaS platform is provided, the method further includes: The data query results and preset analysis prompts are input into the conversational business intelligence model to obtain the in-depth analysis results corresponding to the data query results output by the conversational business intelligence model.

[0013] The present invention also provides a data query device based on the APaaS platform, comprising the following modules: The acquisition module is used to acquire the target query session initiated by the user; The determination module is used to determine the target entity list of the Application Platform as a Service (ApaaS) platform based on the target query session through a vector database. The relationship module is used to determine the entity association relationships corresponding to the target entity list through the ApaaS platform based on the target entity list. The query module is used to determine the data query results corresponding to the target query session based on the entity association, the target entity list, and the conversational business intelligence model.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data query method based on the APaaS platform as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data query method based on the APaaS platform as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the data query method based on the APaaS platform as described above.

[0017] This invention provides a data query method, apparatus, device, and medium based on an APaaS platform. The method involves: acquiring a target query session initiated by a user; determining a target entity list for the Application Platform as a Service (APaaS) platform based on the target query session using a vector database; determining the entity relationships corresponding to the target entity list through the APaaS platform; and determining the data query results corresponding to the target query session based on the entity relationships, the target entity list, and a conversational business intelligence model. This invention's technical solution does not require underlying data tables and does not rely on technical personnel for management and maintenance. Ordinary users can easily use and maintain ChatBI. Seamless integration of user permissions with the APaaS platform supports differentiated data display based on users, achieving "ChatBI for everyone," reducing usage and maintenance costs while ensuring the flexibility of user permissions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is one of the flowcharts illustrating the data query method based on the APaaS platform provided by this invention.

[0020] Figure 2 This is the second flowchart of the data query method based on the APaaS platform provided by the present invention.

[0021] Figure 3 This is a schematic diagram of the structure of the data query device based on the APaaS platform provided by the present invention.

[0022] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] To address the aforementioned problems in the prior art, this invention provides a data query method based on the APaaS platform. Figure 1This is one of the flowcharts illustrating the data query method based on the APaaS platform provided by this invention, such as... Figure 1 As shown, the method includes the following steps 110 to 140.

[0025] Step 110: Obtain the target query session initiated by the user.

[0026] Specifically, users can input query information according to their query needs, and the executing entity of this invention can obtain the target query session corresponding to the query information initiated by the user. The target query session can be in the form of natural language.

[0027] In one embodiment, obtaining the user-initiated target query session includes: Obtain the query information input by the user; When the query information is associated with context information, the query information and the context information are sent to the conversational business intelligence model to obtain the target query session output by the conversational business intelligence model; If the query information is not associated with context information, the query information will be identified as the target query session.

[0028] Specifically, Figure 2 This is the second flowchart illustrating the data query method based on the APaaS platform provided by this invention, as shown below. Figure 2 As shown, users can input query information. The execution entity of this invention can determine whether the query information is associated with context. If the user inputs other contextual information besides the query information, it is determined that the query information is associated with contextual information. Furthermore, the query information and contextual information can be sent to the conversational business intelligence model. The conversational business intelligence model can parse the intent of the query information and contextual information, reassemble the query information and contextual information to generate a target query session, and thus obtain the target query session output by the conversational business intelligence model. If the user does not input any other contextual information besides the query information, it is determined that the query information is not associated with contextual information, and further, the query information can be directly identified as the target query session. The conversational business intelligence model can be a general ChatBI, or it can be obtained by fine-tuning a general ChatBI based on sample session data and corresponding tags.

[0029] In the above embodiments, when the query information is associated with contextual information, a target query session is generated based on ChatBI. This target query session can more clearly and accurately represent the content that the user wants to query, thus enhancing the user experience.

[0030] Step 120: Based on the target query session, determine the target entity list of the Application Platform as a Service (ApaaS) platform through the vector database.

[0031] Specifically, an APaaS platform is a low-code / no-code development platform that provides visual tools and pre-built components, allowing users to quickly build, deploy, and manage enterprise-level applications without writing complex code. APaaS platforms typically run in the cloud, support multi-tenant architecture, and integrate development, testing, deployment, and operation and maintenance capabilities. APaaS platforms have the following advantages: (1) Business modeling: Stores and organizes business data through objects (such as customers and orders), supports custom objects and fields, and can efficiently extend the data model; (2) Page design: Provides visual page design tools, supports the rapid construction of web and mobile pages by dragging and dropping components, and meets diverse page customization needs; (3) Process design: Provides rich process design tools, including automatic flow, approval flow, workflow, etc., and supports the automated processing of complex business processes; (4) Business logic: Supports the extension of business logic through low-code methods, and developers can write a small amount of code to implement complex business processing logic; (5) User permissions: Provides a multi-dimensional and multi-level permission management system, supports fine-grained user permission control, and ensures the security of data access.

[0032] The entity executing this invention can determine the target entity list of an Application Platform as a Service (ApaaS) platform based on a target query session using a vector database. The vector database is a database system specifically designed for storing, retrieving, and managing high-dimensional vector data (such as embedded vectors). It achieves efficient retrieval by calculating the similarity between vectors (such as cosine similarity, Euclidean distance, etc.) and is widely used in recommendation systems, semantic search, image retrieval, large model augmentation, and other scenarios. The vector database includes all entities and their corresponding information. It should be noted that the target entity list corresponds to the target query session.

[0033] In one embodiment, determining the target entity list of the Application Platform as a Service (ApaaS) platform based on the target query session using a vector database includes: Based on the target query session, the initial entity list of the ApaaS platform and the corresponding filtering information are queried through the vector database; The target entity list is determined based on the initial entity list and the filtering information.

[0034] Specifically, based on the target query session, the initial entity list of the ApaaS platform can be queried through a vector database. This initial entity list includes metadata information. Alternatively, a scalar search of the vector database can be performed to find the filtering information corresponding to the initial entity list of the ApaaS platform. This filtering information includes data information corresponding to the metadata information.

[0035] Furthermore, the target entity list can be determined based on the initial entity list and filtering information. It is easy to understand that the target entity information includes metadata information and the data information corresponding to the metadata information.

[0036] The above embodiments realize the platformization of ChatBI's data relationship model capabilities. Customers can directly utilize APaaS platform entities to automatically complete the reading of ChatBI entity relationships, significantly reducing the threshold for using and maintaining ChatBI. Furthermore, it ensures that the data accessed by users is completely consistent with their roles and permissions on the APaaS platform.

[0037] Step 130: Based on the target entity list, determine the entity association relationships corresponding to the target entity list through the ApaaS platform.

[0038] Specifically, such as Figure 2 As shown, the entity relationships corresponding to the target entity list can be determined through the ApaaS platform based on the target entity list. These entity relationships represent the relationships between the target entities in the target entity list.

[0039] Step 140: Determine the data query results corresponding to the target query session based on the entity association, the target entity list, and the conversational business intelligence model.

[0040] In one embodiment, the list of target entities includes field metadata descriptions; The process of determining the data query results corresponding to the target query session based on the entity associations, the target entity list, and the conversational business intelligence model includes: Input the entity association and the field metadata description into the conversational business intelligence model to obtain the dimension information and filtering information output by the conversational business intelligence model; The data query results are determined based on the entity relationships, the dimension information, and the filtering information.

[0041] Specifically, the target entity list includes field metadata descriptions. After obtaining the entity relationships, these relationships and field metadata descriptions can be input into the conversational business intelligence model. The conversational business intelligence model can then output the desired dimensional and filtering information through semantic parsing and intent recognition. After obtaining the dimensional and filtering information output by the conversational business intelligence model, the data query results can be determined based on the entity relationships, dimensional information, and filtering information.

[0042] For example, if the user initiates a query session with the target message "sales amount by department as of the end of this year", the corresponding dimension information is "department" and the corresponding filter information is "sales amount as of the end of this year".

[0043] In the above embodiments, dimensional information and filtering information are obtained through a conversational business intelligence model, thereby determining the data query results and further improving the accuracy of user queries.

[0044] In one embodiment, determining the data query result based on the entity association, the dimension information, and the filtering information includes: Generate a query statement based on the entity relationships, the dimension information, and the filtering information; Based on the query statement, a data query is performed in the preset database to obtain the initial query results; Based on the initial query results, a chart is drawn to obtain the data query results in chart format.

[0045] Specifically, a query statement can be generated based on entity relationships, dimension information, and filtering information. This query statement can then be used to query data in a preset database to obtain initial query results. The preset database can be any pre-defined open-source / closed-source database; this embodiment of the invention does not impose any specific limitations on it.

[0046] Furthermore, charts can be created based on the initial query results to obtain data query results in chart format. Finally, the data query results can be displayed / sent to the customer.

[0047] Alternatively, the initial query result can be directly determined as the data query result.

[0048] In the above embodiments, a query statement is generated based on entity relationships, dimension information, and filtering information. A chart is drawn based on the initial query results to obtain data query results in chart format, which facilitates user analysis and guidance.

[0049] In one embodiment, the method further includes: The data query results and preset analysis prompts are input into the conversational business intelligence model to obtain the in-depth analysis results corresponding to the data query results output by the conversational business intelligence model.

[0050] Specifically, after obtaining the data query results, the results and preset analysis prompts can be input into the conversational business intelligence model. The model can then perform in-depth analysis on the results, yielding the corresponding in-depth analysis results. These results can also be displayed or sent to the customer.

[0051] In the above embodiments, the conversational business intelligence model further generates in-depth analysis results on the target query session, which can further assist users in understanding and applying the data, and improve the efficiency of data analysis and query.

[0052] The data query method based on the APaaS platform provided by this invention involves: acquiring a target query session initiated by a user; determining a target entity list for the Application Platform as a Service (APaaS) platform based on the target query session using a vector database; determining the entity relationships corresponding to the target entity list using the APaaS platform; and determining the data query results corresponding to the target query session based on the entity relationships, the target entity list, and a conversational business intelligence model. This invention's technical solution does not rely on underlying data tables or technical personnel for management and maintenance. Ordinary users can easily use and maintain ChatBI. Seamless integration of user permissions with the APaaS platform supports user-based differentiated data display, achieving "ChatBI for everyone," reducing usage and maintenance costs while ensuring the flexibility of user permissions.

[0053] The data query device based on the APaaS platform provided by the present invention is described below. The data query device based on the APaaS platform described below can be referred to in correspondence with the data query method based on the APaaS platform described above.

[0054] Figure 3 This is a schematic diagram of the structure of the data query device based on the APaaS platform provided by the present invention, as shown below. Figure 3 As shown, the data query device 300 based on the APaaS platform includes the following modules: Module 310 is used to obtain the target query session initiated by the user; The determination module 320 is used to determine the target entity list of the Application Platform as a Service (ApaaS) platform based on the target query session through a vector database. Relationship module 330 is used to determine the entity association relationships corresponding to the target entity list through the ApaaS platform based on the target entity list; The query module 340 is used to determine the data query results corresponding to the target query session based on the entity association, the target entity list, and the conversational business intelligence model.

[0055] In one embodiment, the acquisition module 310 is specifically used for: Obtain the query information input by the user; When the query information is associated with context information, the query information and the context information are sent to the conversational business intelligence model to obtain the target query session output by the conversational business intelligence model; If the query information is not associated with context information, the query information will be identified as the target query session.

[0056] In one embodiment, the determining module 320 is specifically used for: Based on the target query session, the initial entity list of the ApaaS platform and the corresponding filtering information are queried through the vector database; The target entity list is determined based on the initial entity list and the filtering information.

[0057] In one embodiment, the target entity list includes field metadata descriptions; the query module 340 is specifically used for: Input the entity association and the field metadata description into the conversational business intelligence model to obtain the dimension information and filtering information output by the conversational business intelligence model; The data query results are determined based on the entity relationships, the dimension information, and the filtering information.

[0058] In one embodiment, the query module 340 is further configured to: Generate a query statement based on the entity relationships, the dimension information, and the filtering information; Based on the query statement, a data query is performed in the preset database to obtain the initial query results; Based on the initial query results, a chart is drawn to obtain the data query results in chart format.

[0059] In one embodiment, the data query device based on the APaaS platform further includes a deep analysis module, which is specifically used for: The data query results and preset analysis prompts are input into the conversational business intelligence model to obtain the in-depth analysis results corresponding to the data query results output by the conversational business intelligence model.

[0060] The data query device based on the APaaS platform provided by this invention obtains a target query session initiated by a user; based on the target query session, it determines the target entity list of the Application Platform as a Service (APaaS) platform through a vector database; based on the target entity list, it determines the entity relationships corresponding to the target entity list through the APaaS platform; and based on the entity relationships, the target entity list, and a conversational business intelligence model, it determines the data query results corresponding to the target query session. This invention's technical solution does not rely on underlying data tables and does not depend on the management and maintenance of technical personnel. Ordinary users can easily use and maintain ChatBI. Seamless integration of user permissions with the APaaS platform supports differentiated data display based on users, realizing "ChatBI for everyone," reducing usage and maintenance costs while ensuring the flexibility of user permissions.

[0061] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a data query method based on the APaaS platform, which includes: Retrieve the target query session initiated by the user; Based on the target query session, the target entity list of the Application Platform as a Service (ApaaS) platform is determined through a vector database; Based on the target entity list, the entity association relationships corresponding to the target entity list are determined through the ApaaS platform; Based on the entity relationships, the target entity list, and the conversational business intelligence model, the data query results corresponding to the target query session are determined.

[0062] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the data query method based on the APaaS platform provided by the above methods, the method including: Retrieve the target query session initiated by the user; Based on the target query session, the target entity list of the Application Platform as a Service (ApaaS) platform is determined through a vector database; Based on the target entity list, the entity association relationships corresponding to the target entity list are determined through the ApaaS platform; Based on the entity relationships, the target entity list, and the conversational business intelligence model, the data query results corresponding to the target query session are determined.

[0064] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data query method based on the APaaS platform provided by the above methods, the method comprising: Retrieve the target query session initiated by the user; Based on the target query session, the target entity list of the Application Platform as a Service (ApaaS) platform is determined through a vector database; Based on the target entity list, the entity association relationships corresponding to the target entity list are determined through the ApaaS platform; Based on the entity relationships, the target entity list, and the conversational business intelligence model, the data query results corresponding to the target query session are determined.

[0065] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data query method based on an APaaS platform, characterized in that, include: Retrieve the target query session initiated by the user; Based on the target query session, the target entity list of the Application Platform as a Service (ApaaS) platform is determined through a vector database; Based on the target entity list, the entity association relationships corresponding to the target entity list are determined through the ApaaS platform; Based on the entity relationships, the target entity list, and the conversational business intelligence model, the data query results corresponding to the target query session are determined.

2. The data query method based on the APaaS platform according to claim 1, characterized in that, The process of obtaining the target query session initiated by the user includes: Obtain the query information input by the user; When the query information is associated with context information, the query information and the context information are sent to the conversational business intelligence model to obtain the target query session output by the conversational business intelligence model; If the query information is not associated with context information, the query information will be identified as the target query session.

3. The data query method based on the APaaS platform according to claim 1, characterized in that, The step of determining the target entity list of the Application Platform as a Service (ApaaS) platform based on the target query session through a vector database includes: Based on the target query session, the initial entity list of the ApaaS platform and the corresponding filtering information are queried through the vector database; The target entity list is determined based on the initial entity list and the filtering information.

4. The data query method based on the APaaS platform according to claim 1, characterized in that, The target entity list includes field metadata descriptions; The step of determining the data query results corresponding to the target query session based on the entity association, the target entity list, and the conversational business intelligence model includes: Input the entity association and the field metadata description into the conversational business intelligence model to obtain the dimension information and filtering information output by the conversational business intelligence model; The data query results are determined based on the entity relationships, the dimension information, and the filtering information.

5. The data query method based on the APaaS platform according to claim 4, characterized in that, Determining the data query result based on the entity association, the dimension information, and the filtering information includes: Generate a query statement based on the entity relationships, the dimension information, and the filtering information; Based on the query statement, a data query is performed in the preset database to obtain the initial query results; Based on the initial query results, a chart is drawn to obtain the data query results in chart format.

6. The data query method based on the APaaS platform according to any one of claims 1 to 5, characterized in that, The method further includes: The data query results and preset analysis prompts are input into the conversational business intelligence model to obtain the in-depth analysis results corresponding to the data query results output by the conversational business intelligence model.

7. A data query device based on an APaaS platform, characterized in that, include: The acquisition module is used to acquire the target query session initiated by the user; The determination module is used to determine the target entity list of the Application Platform as a Service (ApaaS) platform based on the target query session through a vector database. The relationship module is used to determine the entity association relationships corresponding to the target entity list through the ApaaS platform based on the target entity list. The query module is used to determine the data query results corresponding to the target query session based on the entity association, the target entity list, and the conversational business intelligence model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the data query method based on the APaaS platform as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the data query method based on the APaaS platform as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the data query method based on the APaaS platform as described in any one of claims 1 to 6.

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