Data analysis method and device based on artificial intelligence, electronic equipment, computer readable storage medium and computer program product

Abstract

The invention provides a data analysis method and device based on artificial intelligence, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps that a data analysis interface provided by a data analysis platform is displayed, and the data analysis interface comprises an intelligent assistant; in response to a trigger operation for the intelligent assistant, displaying a first session interface for a session between the target object and the intelligent assistant; in response to a first message sending operation triggered by the target object based on the first session interface, a first message sent by the target object is displayed in the first session interface, and the first message is used for describing the data analysis task; a first data analysis result sent by the intelligent assistant is displayed in the first session interface, and the first data analysis result is obtained by analyzing data included in the data analysis interface by the intelligent assistant based on the first message. According to the method and the device, the data analysis capability of the data analysis platform can be enhanced, and more friendly user experience is provided.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence and data analysis technology, and in particular to an artificial intelligence-based data analysis method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] A data analysis platform is a technology platform based on cloud computing, big data, artificial intelligence (AI), and other technologies to support data analysis and business decision-making for businesses, organizations, and other institutions. The platform integrates multiple data sources, data mining, data visualization, and other tools to help users transform data into useful information, thereby better guiding business development.

[0003] However, in related technologies, data analysis platforms usually provide static or preset analysis, which is not flexible enough for users to conduct ad hoc queries on specific issues. In addition, complex data analysis functions usually require users to have professional data analysis skills, resulting in a high user threshold. Summary of the Invention

[0004] The embodiments of the present application provide an artificial intelligence-based data analysis method, device, electronic device, computer-readable storage medium, and computer program product, which can enhance the data analysis capabilities of a data analysis platform and provide a more user-friendly experience.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] The present invention provides an artificial intelligence-based data analysis method, including:

[0007] Displaying a data analysis interface provided by a data analysis platform, wherein the data analysis interface includes an intelligent assistant;

[0008] In response to a triggering operation on the intelligent assistant, displaying a first conversation interface for a target object to have a conversation with the intelligent assistant;

[0009] In response to a first message sending operation triggered by the target object based on the first conversation interface, displaying a first message sent by the target object in the first conversation interface, wherein the first message is used to describe the data analysis task;

[0010] A first data analysis result sent by the intelligent assistant is displayed in the first conversation interface, wherein the first data analysis result is obtained by the intelligent assistant analyzing the data included in the data analysis interface based on the first message.

[0011] The present invention provides an artificial intelligence-based data analysis device, comprising:

[0012] A display module, configured to display a data analysis interface provided by the data analysis platform, wherein the data analysis interface includes an intelligent assistant;

[0013] The display module is further configured to display a first conversation interface for a target object to have a conversation with the intelligent assistant in response to a triggering operation on the intelligent assistant;

[0014] The display module is further configured to display a first message sent by the target object in the first conversation interface in response to a first message sending operation triggered by the target object based on the first conversation interface, wherein the first message is used to describe the data analysis task;

[0015] The display module is further configured to display a first data analysis result sent by the intelligent assistant in the first conversation interface, wherein the first data analysis result is obtained by the intelligent assistant analyzing the data included in the data analysis interface based on the first message.

[0016] An embodiment of the present application provides an electronic device, including:

[0017] a memory for storing executable instructions;

[0018] The processor is used to implement the artificial intelligence-based data analysis method provided in the embodiment of the present application when executing the executable instructions stored in the memory.

[0019] An embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions for implementing the artificial intelligence-based data analysis method provided in an embodiment of the present application when executed by a processor.

[0020] An embodiment of the present application provides a computer program product, including a computer program or computer executable instructions, which, when executed by a processor, implements the artificial intelligence-based data analysis method provided in an embodiment of the present application.

[0021] The embodiments of the present application have the following beneficial effects:

[0022] By adding an intelligent assistant to the data analysis platform, the target subject, after triggering the intelligent assistant, can provide an intuitive conversational interface for the target subject to describe using natural language. The intelligent assistant can understand the data analysis task proposed by the target subject and analyze the data included in the data analysis interface in conjunction with the target subject's data analysis task. In other words, the intelligent assistant can guide the target subject in performing complex data analysis without requiring the target subject to possess specialized data analysis skills, thereby enhancing the data analysis capabilities of the data analysis platform and providing a more user-friendly user experience. In addition, because the intelligent assistant can guide the target subject in performing complex data analysis, it can improve the target subject's data analysis capabilities, thereby reducing ineffective data analysis processing on the data analysis platform and improving the resource utilization of the data analysis platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the interface of the data analysis platform provided by the relevant technology;

[0024] Figure 2 1 is a schematic diagram of the architecture of an artificial intelligence-based data analysis system 100 provided in an embodiment of the present application;

[0025] Figure 3 is a structural diagram of an electronic device 500 provided in an embodiment of the present application;

[0026] Figure 4 This is a flow chart of an artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0027] Figure 5 This is a flow chart of an artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0028] Figure 6 This is a flow chart of an artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0029] Figure 7 This is a flow chart of an artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0030] Figures 8A to 8H This is a schematic diagram of an application scenario of the artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0031] Figure 9 This is a flow chart of an artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0032] Figure 10 This is a process diagram of the artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0033] Figure 11 This is a process diagram of the artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0034] Figure 12 This is a schematic diagram of an application scenario of the artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0035] Figure 13 This is a flow chart of an artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0036] Figure 14 This is a schematic diagram of an application scenario of the artificial intelligence-based data analysis method provided in an embodiment of the present application;

[0037] Figure 15 This is a schematic diagram of an application scenario of the artificial intelligence-based data analysis method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0039] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0040] It is understandable that in the embodiments of the present application, when user information and other related data are involved, when the embodiments of the present application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0041] In the following description, the terms "first\second\..." are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understandable that "first\second\..." can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0043] Before further explaining the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0044] 1) In response to: used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed can be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0045] 2) Large Language Model (LLM): LLM is a type of natural language processing technology based on deep learning. Its main purpose is to enable machines to better understand and generate human natural language text, such as articles, conversations, and searches.

[0046] 3) Retrieval Augmented Generation (RAG): This optimizes the output of large language models so that they reference authoritative knowledge bases outside of the training data before generating responses. RAG introduces an information retrieval component that leverages user input to first extract information from new data sources. Both the user query and related information are fed to the LLM, which uses this new knowledge and its training data to create better responses.

[0047] Artificial Intelligence (AI) is the theory, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. AI also involves studying the design principles and implementation methods of various intelligent machines, enabling them to possess the capabilities of perception, reasoning, and decision-making.

[0048] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, pre-trained models, operating / interaction systems, and mechatronics. Pre-trained models, also known as large models or basic models, can be fine-tuned and widely applied to downstream tasks across various AI disciplines. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0049] like Figure 1 As shown in Figure 1, the data analysis platform provided by relevant technologies generally includes the following core components:

[0050] 1) Data Integration: These data analytics platforms can connect to a variety of data sources, such as databases, application programming interfaces (APIs), and file uploads, and support the integration of data from these different sources into a unified platform.

[0051] 2) Data preprocessing: Provides tools for data cleaning, conversion, and integration to make raw data suitable for analysis;

[0052] 3) Data visualization: Using charts, dashboards, and reporting functions, data is presented to users in a visually friendly way so that they can quickly understand and communicate the analysis results;

[0053] 4) Analytics and Business Intelligence (BI): Support for Structured Query Language (SQL) queries, a drag-and-drop interface, and several BI tools allow users to explore data, identify trends, and generate reports.

[0054] 5) Advanced analytics: Some data analytics platforms may include machine learning and predictive modeling tools to perform more complex data analysis and gain deeper insights.

[0055] 6) Collaboration and Management: Supports team collaboration and provides user rights management functions to ensure data security.

[0056] It can be seen that the data analysis platforms provided by relevant technologies usually have the main goal of improving data visualization, report generation and a certain degree of user-friendliness. At the same time, some data analysis platforms have also begun to penetrate advanced analysis and certain forms of artificial intelligence functions.

[0057] However, during the implementation of the embodiments of the present application, the applicant discovered that the data analysis platform provided by the related art still has the following shortcomings:

[0058] 1) Limited interactivity: Data analysis platforms in related technologies provide static or pre-set analyses and are often not flexible enough to handle ad hoc queries on specific questions users want to answer.

[0059] 2) Insufficient analytical depth: Although data visualization helps understand complex data sets, related technologies often do not provide in-depth data interpretation or predictive analysis;

[0060] 3) High user threshold: Complex data analysis functions usually require users to have professional data analysis skills, which is a barrier to entry for many non-technical users;

[0061] 4) Low efficiency in processing big data: The data analysis platforms provided by related technologies may be inefficient or unable to process extremely large data sets;

[0062] 5) Limited diversity of data sources: Solutions provided by related technologies are difficult to adapt to the rapidly growing and changing types of data sources, especially the integration and analysis of unstructured data;

[0063] 6) Insufficient personalization and customization: It is difficult for users to personalize and customize analytics and reports without programming or deep technical knowledge.

[0064] In view of this, the present application provides an artificial intelligence-based data analysis method to solve the above problems, specifically including:

[0065] 1) Increased interactivity: Through AI-driven conversational interfaces, users can query data using natural language, and the data analysis platform can understand the user's questions and provide corresponding analysis results;

[0066] 2) Provide in-depth analysis: Integrated machine learning models (such as AI large language models) can provide predictive analysis, data interpretation, trend discovery, and in-depth insights;

[0067] 3) Lower user barriers to entry: The intuitive user interface and intelligent assistant can guide users through complex data analysis without requiring specialized data analysis skills;

[0068] 4) Strengthening big data processing: Utilizing the latest data processing technologies and architectures to improve the ability to process large data sets;

[0069] 5) Processing diverse data sources: Through powerful data fusion capabilities, multiple data sources, including unstructured data, can be integrated and analyzed;

[0070] 6) Personalization and customization: The intelligent assistant can be configured according to the user’s specific business needs and analytical goals, providing highly customized analysis and reporting.

[0071] That is to say, the embodiments of the present application provide a data analysis method, device, electronic device, computer-readable storage medium and computer program product based on artificial intelligence, which can enhance the data analysis capabilities of the data analysis platform and provide a more user-friendly experience. The electronic device provided by the embodiment of the present application is described below. The electronic device provided by the embodiment of the present application can be implemented as a terminal device, or can be implemented in collaboration with a server and a terminal device. The following is an example of an artificial intelligence-based data analysis method provided by an embodiment of the present application being implemented in collaboration with a server and a terminal device.

[0072] For example, see Figure 2 , Figure 2 This is a schematic diagram of the architecture of the artificial intelligence-based data analysis system 100 provided in an embodiment of the present application, which is used to support and enhance the data analysis capabilities of the data analysis platform while providing a more user-friendly application, such as Figure 1 As shown, the artificial intelligence-based data analysis system 100 includes: a server 200 (such as a data analysis platform), a network 300 and a terminal device 400, wherein the network 300 can be a local area network or a wide area network, or a combination of the two, the terminal device 400 is a user-associated terminal device, and a client 410 runs on the terminal device 400. The client 410 can be various types of clients, for example, it can be a data analysis client provided by the data analysis platform or a browser.

[0073] The following description will be made by taking the client 410 as a browser as an example.

[0074] In some embodiments, a data analysis interface provided by a data analysis platform may be displayed in the human-computer interaction interface of the client 410. For example, a user may access the data analysis platform via a URL. An intelligent assistant may be displayed on the data analysis interface provided by the data analysis platform, for example, the intelligent assistant may be displayed in the lower right corner of the data analysis interface. Then, when the client 410 receives a trigger operation (e.g., a click operation) from the user for the intelligent assistant, a first conversation interface for the user and the intelligent assistant to have a conversation may be displayed in a pop-up window on the data analysis interface. Subsequently, the client 410 may display a first message sent by the user in the first conversation interface in response to a first message sending operation triggered by the user based on the first conversation interface. The first message is used to describe the data analysis task, i.e., the user can enter his or her own question in the first conversation interface. After receiving the first message sent by the user, the client 410 may send the first message to the server 200 via the network 300, so that the server 200 analyzes the data included in the data analysis interface based on the first message, obtains a first data analysis result, and returns the obtained first data analysis result to the client 410 via the network 300. Finally, the client 410 can display the first data analysis result sent by the intelligent assistant in the first conversation interface, thereby enhancing the data analysis capability of the data analysis platform while providing a more user-friendly experience.

[0075] In other embodiments, the embodiments of the present application can also be implemented with the help of cloud technology. Cloud technology refers to a hosting technology that unifies a series of resources such as hardware, software, and network within a wide area network or local area network to realize data calculation, storage, processing, and sharing.

[0076] Cloud technology is a general term for network, information, integration, management platform, and application technologies used in the cloud computing business model. It can form a resource pool that can be used flexibly and conveniently on demand. Cloud computing technology will become a key support. The backend services of technical network systems require a large amount of computing and storage resources.

[0077] For example, Figure 2The server 200 in the example can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (CDNs), and big data and artificial intelligence platforms. The terminal device 400 can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a car terminal, etc., but is not limited to this. The terminal device 400 and the server 200 can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiments of the present application.

[0078] In some embodiments, the terminal device or server can also implement the data analysis method based on artificial intelligence provided by the embodiment of the present application by running various computer executable instructions or computer programs.For example, computer executable instructions can be commands, machine instructions or software instructions at the microprogram level.The computer program can be a native program or software module in the operating system; it can be a local (Native) application (APPlication, APP), that is, a program that needs to be installed in the operating system to run, for example, it can be a data analysis client provided by a data analysis platform; it can also be a small program that can be embedded in any APP, that is, a program that only needs to be downloaded to a browser environment and can be run. In short, the above-mentioned computer executable instructions can be instructions in any form, and the above-mentioned computer program can be an application, module or plug-in in any form.

[0079] The following continues to describe the structure of the electronic device provided by the embodiment of the present application. Take the electronic device as an example, see Figure 3 , Figure 3 is a structural diagram of an electronic device 500 provided in an embodiment of the present application, Figure 3 The electronic device 500 shown includes: at least one processor 510, a memory 550, at least one network interface 520 and a user interface 530. The various components in the electronic device 500 are coupled together via a bus system 540. It is understood that the bus system 540 is used to achieve connection and communication between these components. In addition to including a data bus, the bus system 540 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 540 is not shown in FIG. Figure 3 Various buses are labeled as bus system 540 .

[0080] The processor 510 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0081] The user interface 530 includes one or more output devices 531 that enable presentation of media content, including one or more speakers and / or one or more visual display screens. The user interface 530 also includes one or more input devices 532, including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.

[0082] The memory 550 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard drives, optical drives, etc. The memory 550 may optionally include one or more storage devices that are physically remote from the processor 510.

[0083] The memory 550 includes volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be a read-only memory (ROM), and the volatile memory may be a random access memory (RAM). The memory 550 described in the embodiments of the present application is intended to include any suitable type of memory.

[0084] In some embodiments, the memory 550 can store data to support various operations, examples of which include programs, modules, and data structures, or a subset or superset thereof, as exemplified below.

[0085] Operating system 551, including system programs for processing various basic system services and performing hardware-related tasks, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and processing hardware-based tasks;

[0086] A network communication module 552 for reaching other computing devices via one or more (wired or wireless) network interfaces 520 , exemplary network interfaces 520 including Bluetooth, WiFi, and USB;

[0087] a presentation module 553 for enabling presentation of information via one or more output devices 531 (e.g., a display screen, a speaker, etc.) associated with the user interface 530 (e.g., a user interface for operating peripheral devices and displaying content and information);

[0088] The input processing module 554 is configured to detect one or more user inputs or interactions from one of the one or more input devices 532 and to translate the detected inputs or interactions.

[0089] In some embodiments, the apparatus provided in the embodiments of the present application may be implemented in software. Figure 3 The data analysis device 555 based on artificial intelligence stored in the memory 550 is shown. It can be software in the form of programs and plug-ins, including the following software modules: display module 5551, adjustment module 5552, configuration module 5553, storage module 5554, identification module 5555, control module 5556 and acquisition module 5557. These modules are logical and can be arbitrarily combined or further split according to the functions implemented. It should be pointed out that in Figure 3 For the sake of convenience, all the above modules are shown at once, but it should not be regarded as excluding the implementation of the artificial intelligence-based data analysis device 555 that only includes the display module 5551. The functions of each module will be explained below.

[0090] The artificial intelligence-based data analysis method provided in the embodiment of the present application will be specifically described below in combination with the exemplary application and implementation of the terminal device provided in the embodiment of the present application.

[0091] See also Figure 4 , Figure 4 This is a flow chart of the data analysis method based on artificial intelligence provided by the embodiment of the present application, which will be combined with Figure 4 The steps shown are explained.

[0092] It should be noted that Figure 4 The illustrated methods can be executed by various computer programs running on a terminal device, not limited to a client. For example, they can also be executed by the operating system, software module, script, and applet described above. Therefore, the examples below using a client should not be considered as limiting the embodiments of the present application. In addition, for ease of description, the following description does not specifically distinguish between a terminal device and a client running on the terminal device.

[0093] In step 101, a data analysis interface provided by a data analysis platform is displayed.

[0094] Here, the data analysis interface may include an intelligent assistant. For example, the intelligent assistant may be displayed in the lower right corner of the data analysis interface, that is, the intelligent assistant may be resident in the lower right corner of the data analysis interface.

[0095] In some embodiments, step 101 can be implemented as follows: in response to receiving a uniform resource locator input by the target object, displaying a data analysis interface provided by the data analysis platform, wherein the uniform resource locator input by the target object is a web page address corresponding to the data analysis platform.

[0096] For example, taking the target object as user A and the client as a browser, user A can access the data analysis platform through a URL. For example, user A can enter the URL corresponding to the data analysis platform in the address bar of the browser. After receiving the URL entered by user A, the browser can parse the URL entered by user A and jump to the data analysis interface provided by the data analysis platform based on the parsing result. An intelligent assistant can be displayed in the data analysis interface, for example, the intelligent assistant can be displayed in the lower right corner of the data analysis interface.

[0097] It should be noted that the user can set the display position of the smart assistant on the data analysis interface. For example, the user can move the smart assistant from the lower right corner to the upper right corner of the data analysis interface. This embodiment of the present application does not specifically limit this.

[0098] In step 102, in response to a trigger operation on the intelligent assistant, a first conversation interface for a target object to have a conversation with the intelligent assistant is displayed.

[0099] In some embodiments, taking user A as the target user, upon receiving a trigger operation (e.g., a click operation, a swipe operation, etc.) from user A on the intelligent assistant displayed in the data analysis interface, a first conversation interface for user A to converse with the intelligent assistant may be displayed in a pop-up window in the data analysis interface. For example, the first conversation interface may be displayed floating on the right side of the data analysis interface. Of course, the first conversation interface may also be displayed independently of the data analysis interface, for example, the data analysis interface and the first conversation interface may be displayed separately in a split-screen manner, which is not specifically limited in this embodiment of the present application.

[0100] For example, when a user clicks on the smart assistant displayed in the data analysis interface, the size of the terminal device's screen can be detected. When the size of the terminal device's screen is larger than a size threshold, the data analysis interface and the first conversation interface can be displayed independently in a split-screen manner; when the size of the terminal device's screen is smaller than the size threshold, the first conversation interface can be displayed floating on the data analysis interface in a pop-up window.

[0101] In step 103 , in response to a first message sending operation triggered by the target object based on the first conversation interface, the first message sent by the target object is displayed in the first conversation interface.

[0102] Here, the first message may be used to describe a data analysis task.

[0103] In some embodiments, taking user A as the target user, after displaying a first conversation interface for user A to converse with the intelligent assistant, user A can enter a first message describing a data analysis task in the first conversation interface. For example, a message input box can be displayed in the first conversation interface, in which user A can enter the first message. Subsequently, after receiving a click operation on a send control by user A, the first message sent by user A can be displayed in the first conversation interface. The intelligent assistant can understand the first message entered by user A and analyze the data included in the data analysis interface based on the first message entered by user A to obtain a first data analysis result.

[0104] It should be noted that the intelligent assistant provided in the embodiment of the present application can support contextual content understanding, so the user can have multiple rounds of dialogue interactions with the intelligent assistant, that is, the above-mentioned first message can be a multi-round dialogue message. In this way, the intelligent assistant can have a deeper understanding of the user's business needs, and then generate data analysis results that are more in line with the user's needs. For example, assuming that the user needs to analyze competitor data and conduct horizontal comparisons, the intelligent assistant can generate a complete competitor data analysis report based on the data analysis task sent by the user.

[0105] In other embodiments, see Figure 5 , Figure 5 This is a flow chart of the artificial intelligence-based data analysis method provided in the embodiment of the present application. Figure 5 As shown, when executing Figure 4 Before step 103 shown, you can also perform Figure 5 Steps 105 and 106 shown will be combined Figure 5 The steps shown are explained.

[0106] In step 105, the data included in the data analysis interface is identified to obtain the type of the data.

[0107] In some embodiments, in order to fully understand the business needs of the target object (such as user A), the intelligent assistant can also guide user A to ask questions before analyzing the data included in the data analysis interface. For example, the intelligent assistant can first identify the data included in the data analysis interface to obtain the type of data. For example, suppose that after the intelligent assistant identifies the data included in the current data analysis interface, it determines that the data included in the current data analysis interface is game data.

[0108] In step 106, a guidance message matching the type sent by the intelligent assistant is displayed in the first conversation interface.

[0109] Here, the guiding message can be used to guide the target object to ask questions.

[0110] In some embodiments, after the intelligent assistant identifies the data included in the current data analysis interface and obtains the type of the data, it can also generate a guidance message that matches the identified type. For example, assuming that the intelligent assistant identifies that the data included in the current data analysis interface is game data, it can generate a guidance message that matches the game data. In this way, it can guide users to ask professional questions to fully understand the user's business needs.

[0111] In step 104, the first data analysis result sent by the intelligent assistant is displayed in the first conversation interface.

[0112] Here, the first data analysis result may be obtained by the intelligent assistant analyzing the data included in the data analysis interface based on the first message.

[0113] In some embodiments, step 104 can be implemented in the following manner: controlling the intelligent assistant to call a machine learning model based on the first message to analyze the data included in the data analysis interface to obtain a first data analysis result; obtaining strategies and tools corresponding to the first data analysis result; and displaying the first data analysis result, strategies, and tools sent by the intelligent assistant in the first conversation interface.

[0114] For example, taking the machine learning model as a large language model, after receiving the first message input by the target object (such as user A), the intelligent assistant can call the large language model based on the first message to analyze the data included in the current data analysis interface to obtain a first data analysis result. For example, the intelligent assistant can call the API provided by the large language model based on the first message to analyze the data included in the current data analysis interface, that is, with the help of the processing power of the large language model, analyze the data included in the current data analysis interface to obtain the first data analysis result. Then, the intelligent assistant can further obtain the strategies and tools corresponding to the first data analysis result. Finally, the first data analysis result, strategy and tool sent by the intelligent assistant can be displayed in the first conversation interface. In this way, the user can use natural language to ask questions, and the intelligent assistant can understand the user's questions and provide corresponding data analysis results and corresponding strategies and tools to help the user perform subsequent processing. Moreover, with the help of the processing power of the large language model, predictive analysis, data interpretation, trend discovery and in-depth insights can be provided. In addition, the technical solution provided in the embodiment of the present application provides an intuitive operation interface and the intelligent assistant can guide users to perform complex data analysis without the need for users to have specialized data analysis skills, thereby lowering the user threshold.

[0115] It should be noted that the aforementioned APIs refer to predefined functions that enable applications and developers to access a set of routines based on certain software or hardware without requiring access to the source code or understanding the details of the internal workings. In the technical solution provided in the embodiments of this application, the intelligent assistant can call the API provided by the large language model and analyze the data included in the data analysis interface with the help of the computing power of the large language model.

[0116] In some embodiments, the first data analysis result can be obtained by the intelligent assistant calling a machine learning model (such as a large language model) based on the first message to analyze all the data included in the data analysis interface. Figure 4 After step 104 shown, you can also perform Figure 6 Step 105 shown will combine Figure 6 The steps shown are explained.

[0117] In step 107, in response to receiving a negative feedback operation from the target object regarding the first data analysis result, the parameters of the machine learning model are adjusted.

[0118] In some embodiments, taking the target object as user A as an example, feedback controls may also be displayed below the first data analysis result sent by the intelligent assistant, such as positive feedback controls (such as a "like" button), negative feedback controls (such as a "thumbs-down" button), and regeneration controls, etc. When a trigger operation for a negative feedback control by user A is received, for example, assuming that user A clicks the "thumbs-down" button displayed below the first data analysis result, indicating that user A is not satisfied with the first data analysis result currently generated by the intelligent assistant, the parameters of the large language model may be adjusted to enable the large language model to generate different data analysis results. That is to say, in the technical solution provided in the embodiment of the present application, after receiving feedback from the user on the first data analysis result output by the intelligent assistant, the background service may adjust the parameters of the large language model to optimize the result output.

[0119] In other embodiments, continuing with the above example, when receiving a click operation from user A for regenerating a control, the intelligent assistant can call the large language model to analyze the data included in the current data analysis interface from other dimensions to generate different first data analysis results, so that the different first data analysis results sent by the intelligent assistant can be displayed in the first conversation interface.

[0120] It should be noted that user A can also annotate the first data analysis results sent by the intelligent assistant. After receiving the annotations input by user A, the parameters of the large language model can be adjusted accordingly based on the annotations input by user A. That is, the large language model provided in the embodiment of the present application can perform self-learning based on user feedback, thereby optimizing the result output.

[0121] In other embodiments, continuing with the above example, before displaying the data analysis interface provided by the data analysis platform, the following processing can also be performed: configuring an intelligent assistant on the data analysis platform, and storing the data information included in the data analysis interface provided by the data analysis platform into the preset information of the intelligent assistant, and setting the auxiliary interface of the intelligent assistant so that the machine learning model can recognize the interface parameters.

[0122] For example, taking the machine learning model as a large language model, an intelligent assistant can be added to the data analysis platform. At the same time, the data included in the data analysis interface provided by the data analysis platform can be initialized. For example, the data information included in the data analysis interface can be stored in the preset information of the intelligent assistant, so that the intelligent assistant can be scoped to make the intelligent assistant more intelligent. In addition, some information of the intelligent assistant (such as game characters, product type data, analysis templates, etc.) and auxiliary interfaces can be preset, so that the large language model can recognize the interface parameters, automatically guide users to ask questions and obtain the final complete data analysis results.

[0123] In some embodiments, before displaying the data analysis interface provided by the data analysis platform, the following processing may also be performed: the machine learning model is processed by means of retrieval enhancement generation and a programming framework (such as LangChain) so that the application programming interface provided by the machine learning model can be linked to the knowledge base, wherein the programming framework is a framework for developing machine learning model applications. For example, LangChain is a framework for developing large language model applications, allowing developers engaged in artificial intelligence to combine large language models with external computing and data sources. The framework is currently provided in the form of Python or JavaScript packages. The core functions of LangChain are mainly the following two: 1) it can connect large language models with external data sources; 2) it allows large language models to interact with the environment and use tools through agents.

[0124] For example, take the machine learning model as a large language model, such as Figure 10 As shown, RAG technology and LangChain can be used to connect the interface provided by the large language model to the knowledge base. The process of processing the large language model using RAG technology and LangChain primarily involves three steps: creating external data, retrieving relevant information, and enhancing the large language model's prompts. Specifically, the new data preceding the large language model's original training dataset is called external data. It can come from multiple sources, such as APIs, databases, or document repositories, and can exist in various formats, such as files, database records, or long texts. Another AI technique, called embedded language modeling, converts data into a digital representation and stores it in a vector database. This process creates a knowledge base that can be understood by generative AI models. Next, a relevance search is performed. For example, user queries can be converted into vector representations and matched against the vector database. Relevance can be calculated and established using mathematical vector calculations and representations. The RAG model can then enhance user input (or prompts) by contextualizing the retrieved relevant data. This step allows for effective communication with the large language model using prompt engineering techniques. Enhanced prompts allow the large language model to generate accurate answers to user queries. In this way, by using RAG technology and LangChain to process the large language model, the interface provided by the large language model can be linked to the knowledge base, so that the data can be accurately analyzed based on the questions entered by the user.

[0125] In other embodiments, the intelligent assistant can also analyze the files uploaded by the target object. After displaying the first conversation interface, the following processing can also be performed: in response to the file upload operation triggered by the target object based on the first conversation interface, the file sent by the target object is displayed in the first conversation interface; and the third data analysis result sent by the intelligent assistant is displayed in the first conversation interface, wherein the third data analysis result is obtained by the intelligent assistant analyzing the file sent by the target object.

[0126] For example, taking user A as the target object, the intelligent assistant can also analyze the files uploaded by user A in the first conversation interface. For example, user A can upload a local data file or a screenshot of a data page in the first conversation interface. For the local data file uploaded by user A, the intelligent assistant can understand and analyze the local data file uploaded by user A based on AI technology to generate corresponding data analysis results; and for the screenshot of the data page uploaded by user A, the intelligent assistant can first use image recognition technology to extract a chart from the screenshot of the data page, and then analyze the extracted chart to obtain the corresponding data analysis result. Subsequently, the data analysis result (i.e., the third data analysis result) obtained by the intelligent assistant for analyzing the file uploaded by user A can be displayed in the first conversation interface. In other words, in the technical solution provided in the embodiment of the present application, not only can the data included in the data analysis interface be analyzed, but also the data files uploaded by the user in real time can be analyzed, further improving the user experience.

[0127] In some embodiments, the target object can also inquire about the functions of the data analysis platform in the first conversation interface. After the first conversation interface is displayed, the following processing can also be performed: in response to the second message sending operation triggered by the target object based on the first conversation interface, the second message sent by the target object is displayed in the first conversation interface, wherein the second message is used to inquire about the functions of the data analysis platform; and the third message of the intelligent assistant's reply to the second message is displayed in the first conversation interface, wherein the third message includes an introduction to the functions of the data analysis platform.

[0128] For example, taking user A as the target user, if user A is unfamiliar with the functions of the data analysis platform, they can ask the intelligent assistant about the functions of the data analysis platform in the first conversation interface. For example, a message editing box may be displayed in the first conversation interface, and user A can enter a second message in the message editing box to inquire about the functions of the data analysis platform. After user A clicks on the send control, the second message sent by user A to inquire about the functions of the data analysis platform may be displayed in the first conversation interface. After receiving the second message from user A, the intelligent assistant can query the functions of the data analysis platform and display a third message sent by the intelligent assistant in the first conversation interface in response to the second message. The third message may include an introduction to the functions of the data analysis platform. This helps user A understand the functions of the current data analysis platform and quickly and easily locate the source of data or any questions about the current data.

[0129] In some embodiments, if the target object inquires about data unrelated to the data analysis interface in the first conversation interface, the following processing can also be performed: controlling the intelligent assistant to retrieve the knowledge base and preset information, or controlling the intelligent assistant to guide the target object to ask questions.

[0130] For example, taking user A as the target object, if user A asks for data unrelated to the current data analysis interface in the first conversation interface, the intelligent assistant can retrieve preset information such as the knowledge base and function calls (Function Calling), or the intelligent assistant can guide user A to ask questions and finally return specific data, thereby realizing the function of converting natural language to charts, and then outputting a complete data analysis report.

[0131] In some embodiments, a data analysis control (e.g., a one-click data analysis control, such as an "AI Analysis" button) may also be displayed in the data analysis interface, and the following processing may also be performed: in response to a trigger operation on the data analysis control, a second conversation interface for the target object to have a conversation with the intelligent assistant is displayed; and a second data analysis result sent by the intelligent assistant is displayed in the second conversation interface, wherein the second data analysis result is obtained by the intelligent assistant analyzing part of the data associated with the data analysis control (e.g., single chart data in the data analysis interface).

[0132] For example, taking user A as the target object, a data analysis control (e.g., an "AI Analysis" button) may also be displayed in the data analysis interface. The data analysis control may be associated with only part of the data in the data analysis interface. For example, assuming the data analysis interface includes four charts, namely Chart 1, Chart 2, Chart 3, and Chart 4, the data analysis control may be associated with Chart 3. In other words, the data analysis control can be used to analyze only part of the data in the data analysis interface. Upon receiving a click operation from user A on the data analysis control, a second conversation interface may be displayed for user A and the intelligent assistant to conduct a conversation. At this point, the intelligent assistant may analyze part of the data associated with the data analysis control (e.g., Chart 3) to obtain a second data analysis result, and display the second data analysis result sent by the intelligent assistant in the second conversation interface. In other words, the user can analyze all the data included in the current data analysis interface by clicking on the intelligent assistant, or analyze part of the data in the data analysis interface by clicking on the data analysis control. The user can choose according to their needs, further improving the user experience.

[0133] The artificial intelligence-based data analysis method provided in the embodiment of the present application adds an intelligent assistant to the data analysis platform. After the target object triggers the intelligent assistant, an intuitive conversation interface can be provided for the target object to describe using natural language. The intelligent assistant can understand the data analysis tasks proposed by the target object and analyze the data included in the data analysis interface in combination with the data analysis tasks proposed by the target object. In other words, the intelligent assistant can guide the target object to perform complex data analysis without the need for the target object to have specialized data analysis skills, thereby enhancing the data analysis capabilities of the data analysis platform and providing a more user-friendly experience.

[0134] The following describes an exemplary application of the embodiments of the present application in a practical application scenario.

[0135] The embodiment of the present application provides an artificial intelligence-based data analysis method, which aims to solve the limitations of the data platform provided by related technologies in data analysis and presentation by combining front-end technology and artificial intelligence models. The technical solution provided by the embodiment of the present application can provide single-page data analysis, single-chart analysis, multi-round conversational in-depth analysis, and rapid generation of analysis reports for data files, thereby improving the efficiency of data parsing, analysis and presentation, and also enabling users to more easily use data for decision support. Specifically, the technical solution provided by the embodiment of the present application mainly includes the following contents:

[0136] 1) Integration of large AI models (e.g., large language models, corresponding to the machine learning models mentioned above) and application of scenario templates: Leverage large AI models (e.g., large language models) to process and analyze data, and use pre-designed scenario templates to match specific user needs, providing customized analysis summaries.

[0137] 2) One-click analysis and intelligent conversational operations: Users can quickly analyze data with a single click or engage in conversational interaction with the intelligent assistant. Through multiple rounds of question and answer, in-depth data analysis results are obtained, which can then recommend appropriate strategies and tools to users.

[0138] 3) Database analysis and data file processing: The technical solutions provided by the embodiments of the present application support the retrieval and analysis of specific product data from the database, and can process user-uploaded data files to generate comprehensive analysis reports;

[0139] 4) Configurability and modularity: The AI ​​module of the intelligent assistant in the technical solution provided in the embodiment of this application is configurable, allowing for separation of business logic and multi-system reuse. It also supports users to configure large language models through function calls to achieve customization for specific data analysis needs;

[0140] 5) Visual AI interface call workflow configuration: Configure the AI ​​interface call workflow through visual tools, so that non-technical users can also easily customize the data analysis process and report output to meet the needs of specific scenarios.

[0141] The following is a detailed description of the artificial intelligence-based data analysis method provided in the embodiments of the present application.

[0142] The technical solution provided in the embodiment of the present application proposes an intelligent assistant added to the data analysis platform, which combines AI large models, front-end and back-end related technologies, and aims to make up for the shortcomings of the data analysis platform provided by the related technologies in user guidance, automated analysis and intelligent interaction through a system of innovative functions. The technical solution provided in the embodiment of the present application includes functions such as one-click analysis of breadcrumb navigation data, real-time customer service support with integrated AI, direct analysis of data files, and separate analysis of charts. Through these intelligent operations, users can perform data analysis more conveniently without the need for deep background knowledge of data processing. In addition, the technical solution provided in the embodiment of the present application also utilizes the existing API interface and the optimized prompt word (Prompt) design, aiming to improve the accuracy of analysis and the aesthetics of output, while also taking into account data security and controllability factors to ensure a balance between user experience and data protection. The design of the entire solution focuses on supporting users to make data-based decisions more efficiently by reducing the user's data analysis burden, providing a richer interactive experience, and providing the quality and availability of analysis reports.

[0143] In some embodiments, see Figure 7 , Figure 7 This is a flow chart of the data analysis method based on artificial intelligence provided by the embodiment of the present application, which will be combined with Figure 7 The steps shown are explained.

[0144] In step 201 , a page of the data analysis platform is presented, and the process proceeds to step 202 or step 204 .

[0145] In step 202, a one-click data analysis control is presented on the page.

[0146] In some embodiments, as Figure 8A As shown, users can access the data analysis platform (referred to as the platform for short) through a uniform resource locator (URL). On the page 801 of the data analysis platform, there can be a one-click data analysis control 802 (i.e., a data analysis control, such as an "AI analysis" button) and an intelligent assistant 803.

[0147] In step 203 , the data is analyzed, conclusions, visualization charts, and data sources are provided, and the process proceeds to step 208 .

[0148] In some embodiments, as Figure 8BAs shown, when a user clicks on a one-click data analysis control 802 (corresponding to the above-mentioned data analysis control, such as the "AI analysis" button) on a single chart, an AI dialog box 804 (corresponding to the above-mentioned second session interface) can be automatically popped up on page 801, and the data analysis results summarized for the current chart data are presented in the AI ​​dialog box 804.

[0149] In step 204, the dialog box of the intelligent assistant is presented, and the process proceeds to step 205 or step 206.

[0150] In some embodiments, as Figure 8C As shown, when a user clicks on the intelligent assistant 803, an AI dialog box 805 (corresponding to the first conversation interface described above) may pop up on the page 801, and the data analysis results obtained by analyzing the data on this page may be presented in the AI ​​dialog box 805. Figure 8D As shown, the user can also directly ask for a complete analysis report of certain data in the pop-up AI dialog box 805. At this time, the intelligent assistant will make a multi-dimensional summary based on the relevant data already available on this platform and provide corresponding strategies and tools.

[0151] In step 205 , the data requested by the user is presented.

[0152] In step 206 , the platform functions requested by the user are presented.

[0153] In some embodiments, as Figure 8E As shown, the user can also inquire about the platform function in the pop-up AI dialog box 805, so as to easily and quickly locate the data source or some questions about the current data.

[0154] In step 207, the guidance platform function provides corresponding documents and entrances.

[0155] In step 208, the files and screenshots uploaded by the user are deeply analyzed.

[0156] In some embodiments, as Figure 8F As shown, the user can also upload local data files or data page screenshots in the pop-up AI dialog box 805. The intelligent assistant can use image recognition technology to automatically extract charts and understand, analyze and summarize text data based on AI technology.

[0157] In step 209 , it is determined whether multiple rounds of interaction are performed. If so, the process proceeds to step 201 ; if not, the process proceeds to step 210 .

[0158] In some embodiments, as Figure 8GAs shown, the intelligent assistant provided in the embodiment of the present application supports contextual content understanding, so the user can conduct in-depth multi-round dialogue interactions in the pop-up AI dialog box 805, such as analyzing competitor data separately and making horizontal comparisons to obtain a complete competitor report.

[0159] In step 210, a feedback operation is performed.

[0160] In some embodiments, as Figure 8H As shown, feedback controls can also be displayed below the data analysis results given by the smart assistant, such as a like control 806, a dislike control 807 and a regeneration control 808. After generating the data analysis results, users can also provide feedback on the data analysis results generated by the smart assistant, such as likes, dislikes (when receiving a click operation on the dislike control 807 from the user, the AI ​​model will perform self-optimization and other tuning operations based on the quality evaluation of the answer), regeneration and sharing, etc. This is conducive to multi-person collaboration and result optimization.

[0161] Next, continue to combine Figure 9 The artificial intelligence-based data analysis method provided in the embodiments of the present application is described.

[0162] For example, see Figure 9 , Figure 9 This is a flow chart of the data analysis method based on artificial intelligence provided by the embodiment of the present application, which will be combined with Figure 9 The steps shown are explained.

[0163] In step 301, the AI ​​large model is fine-tuned.

[0164] In some embodiments, the technical solution provided by the embodiments of the present application can be based on front-end technology, NodeJS service (a JavaScript runtime environment based on the Chrome V8 engine, which uses an event-driven, non-blocking I / O model to allow JavaScript to run on the server-side development platform, making JavaScript a scripting language on par with server-side languages ​​such as PHP, Python, Perl, and Ruby. NodeJS can optimize some special use cases and provide alternative APIs to enable V8 to run better in non-browser environments), LangChain, and the interface implementation provided by large AI models (such as large language models), for example, Figure 10As shown, Retrieval Augmented Generation (RAG) technology and LangChain can be used to connect the interface provided by the AI ​​model to the knowledge base. The AI ​​model can then be fine-tuned, including prompt optimization and function call configuration. Information and auxiliary interfaces for the smart assistant can also be pre-set, including game data, product type data, and analysis templates. This allows the AI ​​model to identify interface parameters and automatically guide users to ask questions to obtain complete data analysis results.

[0165] The following explanation is made using game data as an example.

[0166] For example, see Figure 11 , Figure 11 This is a process diagram of the artificial intelligence-based data analysis method provided in the embodiment of the present application. Figure 11 As shown, when the backend service (Server) receives the user (Client) input "What features can you provide?", it can send the user's input "What features can you provide?" to the large language model, which calls the recognition scenario function for recognition and returns a response of "I can provide XXXX" to the backend service. The backend service can then return the response of "I can provide XXXX" to the user. Subsequently, when the backend service receives the user input "Take a look at the features of XX Honor," it can send the user's input "Take a look at the features of XX Honor" to the large language model, which calls the recognition scenario function for recognition, obtains the app features, and sends the app features to the backend service. The backend service can query the function list under the app features and send the queryed functions (for example, including obtaining basic app feature information and obtaining app feature overlapping information) to the large language model. The large language model can then notify the backend service to call the retrieve app feature basic information and send the retrieved data to the large language model. Finally, the large language model can return a response of "XX Honor's features are XXX" to the backend service, which then returns the response of "XX Honor's features are XXX" to the user.

[0167] In step 302, a software development kit for the AI ​​assistant is produced.

[0168] Here, a software development kit (SDK) refers to a collection of development tools used by software engineers to build application software for a specific software package, software framework, hardware platform, operating system, and so on. It facilitates application creation through compilers, debuggers, and software frameworks, and can simply provide API files for a particular programming language. SDKs may also include sample code, supporting technical notes, and other supporting documentation to clarify underlying reference materials.

[0169] In some embodiments, a NodeJS service can be deployed on the server, calling interfaces provided by large language model companies. For example, interfaces provided by large language models and data reporting interfaces can be exposed. Subsequently, an AI assistant (i.e., intelligent assistant) SDK can be created, allowing data analysis platforms to install assistant components with one click through NPM (Node Package Manager, a NodeJS package management and distribution tool that has become the unofficial standard for publishing Node module packages), enabling multi-platform reuse.

[0170] In step 303, a production assistant backend management system is created.

[0171] In some embodiments, the business party can also personalize the configuration of the AI ​​assistant. In addition, the user's evaluation of the data analysis results and operational data given by the AI ​​assistant will also be displayed here.

[0172] In step 304, self-learning mechanism optimization is performed.

[0173] In some embodiments, as Figure 12 As shown, users can make customized annotations on feedback questions. For example, when a user clicks on the "Annotation" control 1201, a pop-up window 1202 for adding new annotations can pop up. Users can make customized annotations on feedback questions in the pop-up window 1202. The AI ​​big model will also record the pros and cons of user evaluations, conduct self-learning, and ultimately optimize the answer output.

[0174] Next, continue to combine Figure 13 The artificial intelligence-based data analysis method provided in the embodiments of the present application is described.

[0175] For example, see Figure 13 , Figure 13 This is a flow chart of the data analysis method based on artificial intelligence provided by the embodiment of the present application, which will be combined with Figure 13 The steps shown are explained.

[0176] In step 401, an intelligent assistant is configured on a data analysis platform.

[0177] In some embodiments, the business party can configure relevant business logic in the assistant backend management system, such as the business page path that needs to be analyzed, data capture logic (such as data capture through request interface, page screenshot analysis data, etc.), quick operation analysis events, etc. Figure 14 As shown, the technical solution provided in the embodiment of the present application can also support workflow configuration, for example, including configuring the feature analysis report workflow of XX Honor.

[0178] In step 402, pages and data are initialized.

[0179] In some embodiments, after entering a specific configured business page path, users can initialize data and save the current page data information to the AI ​​assistant's preset information, allowing the AI ​​assistant to analyze and scope it, making it more intelligent. In addition, users can use the AI ​​assistant to conduct in-depth conversation analysis by horizontally comparing data from multiple pages.

[0180] In step 403, an analysis request initiated by a user is received.

[0181] In step 404, the data is analyzed and processed.

[0182] In step 405, the data analysis results are displayed on the front end.

[0183] In some embodiments, if the user asks a question about data that is not related to the current page, the user can also search for preset information such as knowledge base and function calling, or, for example, Figure 15 As shown, users can also be guided to ask questions and eventually return specific data, thereby realizing the function of converting natural language into charts and outputting a complete data analysis report.

[0184] In step 406, the system is optimized and the model is fine-tuned.

[0185] In some embodiments, after receiving user feedback on the data analysis results (such as likes, dislikes, comments, and regeneration, etc.), the background service can also optimize the output of the return results of the AI ​​big model.

[0186] In summary, the artificial intelligence-based data analysis method provided in the embodiments of the present application has the following beneficial effects:

[0187] 1) Improved data analysis efficiency: Users can quickly obtain data analysis results with a simple one-click operation, greatly saving the time required for data analysis and processing;

[0188] 2) Lowering the technical threshold: The introduction of AI technology enables users without technical backgrounds to easily perform complex data analysis, thereby promoting the application of data-driven decision-making;

[0189] 3) Increased interactivity: The multi-round dialogue function provided by the intelligent assistant enables users to query data and obtain analysis in natural language, improving the user experience and making data interaction more friendly and intuitive;

[0190] 4) Optimized personalized experience: Users can customize the behavior of the intelligent assistant according to different business needs, thereby obtaining highly relevant data analysis reports;

[0191] 5) Improved depth and breadth of data analysis: Using advanced artificial intelligence models (i.e., AI big models), users can obtain not only descriptive data analysis results, but also predictive and diagnostic analysis results;

[0192] 6) Enhanced data security: By embedding data security mechanisms in the technical architecture, the security and privacy of user data during the analysis process are guaranteed;

[0193] 7) Improved decision support: Accurate data analysis results provide real-time and evidence-based support for user decisions, enhancing the company's data-driven decision-making capabilities;

[0194] 8) Optimizing resource utilization: The AI ​​large model provided by the embodiments of the present application can more efficiently process and analyze large data sets compared to the data analysis methods provided by related technologies, thereby optimizing the utilization of computing resources;

[0195] 9) Promotes team collaboration: By sharing data analysis reports and dashboards generated by intelligent assistants, collaboration and communication among team members can be promoted.

[0196] In other words, the technical solutions provided by the embodiments of the present application provide significant advantages in improving the work efficiency, data processing capabilities and decision-making quality of enterprises and individual users.

[0197] The following continues to describe the exemplary structure of the artificial intelligence-based data analysis device 555 provided in the embodiment of the present application as a software module. In some embodiments, such as Figure 3 As shown, the software modules stored in the artificial intelligence-based data analysis device 555 of the memory 550 may include: a display module 5551.

[0198] Display module 5551 is used to display the data analysis interface provided by the data analysis platform, wherein the data analysis interface includes an intelligent assistant; display module 5551 is also used to display a first conversation interface for the target object to have a conversation with the intelligent assistant in response to a trigger operation on the intelligent assistant; display module 5551 is also used to display a first message sent by the target object in the first conversation interface in response to a first message sending operation triggered by the target object based on the first conversation interface, wherein the first message is used to describe the data analysis task; display module 5551 is also used to display a first data analysis result sent by the intelligent assistant in the first conversation interface, wherein the first data analysis result is obtained by the intelligent assistant analyzing the data included in the data analysis interface based on the first message.

[0199] In some embodiments, the data analysis interface also includes a data analysis control; the display module 5551 is also used to display a second conversation interface for the target object to conduct a conversation with the intelligent assistant in response to a trigger operation on the data analysis control; and is used to display the second data analysis result sent by the intelligent assistant in the second conversation interface, wherein the second data analysis result is obtained by the intelligent assistant analyzing part of the data associated with the data analysis control.

[0200] In some embodiments, the first data analysis result is obtained by the intelligent assistant calling a machine learning model to analyze the data based on the first message; the artificial intelligence-based data analysis device 555 also includes an adjustment module 5552, which is used to adjust the parameters of the machine learning model in response to receiving a negative feedback operation from the target object regarding the first data analysis result.

[0201] In some embodiments, the artificial intelligence-based data analysis device 555 also includes a configuration module 5553 and a storage module 5554, wherein the configuration module 5553 is used to configure the intelligent assistant on the data analysis platform; the storage module 5554 is used to store the data information included in the data analysis interface into the preset information of the intelligent assistant; the configuration module 5553 is also used to set the auxiliary interface of the intelligent assistant so that the machine learning model can recognize the interface parameters.

[0202] In some embodiments, the configuration module 5553 is also used to process the machine learning model by retrieval enhancement generation and programming framework, so that the application programming interface provided by the machine learning model can be linked to the knowledge base, wherein the programming framework is a framework for developing machine learning model applications.

[0203] In some embodiments, the artificial intelligence-based data analysis device 555 also includes an identification module 5555, which is used to identify the data included in the data analysis interface and obtain the type of the data; the display module 5551 is also used to display the guidance message matching the type sent by the intelligent assistant in the first conversation interface, wherein the guidance message is used to guide the target object to ask questions.

[0204] In some embodiments, the display module 5551 is further used to display the file sent by the target object in the first conversation interface in response to a file upload operation triggered by the target object based on the first conversation interface; and to display the third data analysis result sent by the intelligent assistant in the first conversation interface, wherein the third data analysis result is obtained by the intelligent assistant through analysis of the file.

[0205] In some embodiments, the display module 5551 is also used to display a second message sent by the target object in the first conversation interface in response to a second message sending operation triggered by the target object based on the first conversation interface, wherein the second message is used to inquire about the functions of the data analysis platform; and to display a third message in the first conversation interface that the intelligent assistant replies to the second message, wherein the third message includes an introduction to the functions of the data analysis platform.

[0206] In some embodiments, the artificial intelligence-based data analysis device 555 also includes a control module 5556 for controlling the intelligent assistant to retrieve the knowledge base and preset information in response to the target object asking for data unrelated to the data analysis interface in the first conversation interface, or controlling the intelligent assistant to guide the target object to ask questions.

[0207] In some embodiments, the control module 5556 is also used to control the intelligent assistant to call the machine learning model based on the first message to analyze the data included in the data analysis interface to obtain a first data analysis result; the artificial intelligence-based data analysis device 555 also includes an acquisition module 5557, which is used to obtain strategies and tools corresponding to the first data analysis result; the display module 5551 is also used to display the first data analysis results, strategies and tools sent by the intelligent assistant in the first conversation interface.

[0208] In some embodiments, the display module 5551 is further used to display a data analysis interface provided by the data analysis platform in response to receiving a uniform resource locator input by the target object, wherein the uniform resource locator is a web page address corresponding to the data analysis platform.

[0209] It should be noted that the description of the device in the embodiment of the present application is similar to the description of the method embodiment above, and has similar beneficial effects as the method embodiment, so it will not be repeated here. Figure 4 、 Figure 5 ,or Figure 6 The present invention should be understood by referring to the description of any one of the accompanying drawings.

[0210] The present invention provides a computer program product comprising a computer program or computer-executable instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer-executable instructions from the computer-readable storage medium and executes the computer-executable instructions, causing the computer device to perform the artificial intelligence-based data analysis method described above in the present invention.

[0211] The embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the processor will execute the artificial intelligence-based data analysis method provided by the embodiment of the present application, for example, Figure 4 、 Figure 5 ,or Figure 6 The data analysis method based on artificial intelligence is shown.

[0212] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface storage, optical disk, or CD-ROM; or various devices including one or any combination of the above memories.

[0213] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0214] As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0215] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent replacements, and improvements made within the spirit and scope of the present application are included in the scope of protection of the present application.

Claims

1. A data analysis method based on artificial intelligence, characterized in that: The method comprises: Displaying a data analysis interface provided by a data analysis platform, wherein the data analysis interface includes an intelligent assistant; In response to a triggering operation on the intelligent assistant, displaying a first conversation interface for a target object to have a conversation with the intelligent assistant; In response to a first message sending operation triggered by the target object based on the first conversation interface, displaying a first message sent by the target object in the first conversation interface, wherein the first message is used to describe a data analysis task; A first data analysis result sent by the intelligent assistant is displayed in the first conversation interface, wherein the first data analysis result is obtained by the intelligent assistant analyzing the data included in the data analysis interface based on the first message.

2. The method according to claim 1, characterized in that The data analysis interface also includes a data analysis control; The method further comprises: In response to a triggering operation on the data analysis control, displaying a second conversation interface for the target object to have a conversation with the intelligent assistant; A second data analysis result sent by the intelligent assistant is displayed in the second conversation interface, wherein the second data analysis result is obtained by the intelligent assistant analyzing part of the data associated with the data analysis control.

3. The method according to claim 1, characterized in that The first data analysis result is obtained by the intelligent assistant invoking a machine learning model to analyze the data based on the first message; The method further comprises: In response to receiving a negative feedback operation from the target object regarding the first data analysis result, the parameters of the machine learning model are adjusted.

4. The method according to claim 3, characterized in that Before displaying the data analysis interface provided by the data analysis platform, the method further includes: Configuring the intelligent assistant on the data analysis platform and storing the data information included in the data analysis interface into the preset information of the intelligent assistant, and An auxiliary interface of the intelligent assistant is set to enable the machine learning model to recognize interface parameters.

5. The method according to claim 3, characterized in that The method further comprises: The machine learning model is processed by retrieval enhancement generation and programming framework so that the application programming interface provided by the machine learning model can be linked to the knowledge base, wherein the programming framework is a framework for developing the application of the machine learning model.

6. The method according to claim 1, characterized in that Before sending the first message in response to the target object triggering the first conversation interface, the method further includes: Identifying the data included in the data analysis interface to obtain the type of the data; A guidance message matching the type sent by the intelligent assistant is displayed in the first conversation interface, wherein the guidance message is used to guide the target object to ask questions.

7. The method according to claim 1, characterized in that The method further comprises: In response to a file upload operation triggered by the target object based on the first conversation interface, displaying the file sent by the target object in the first conversation interface; The third data analysis result sent by the intelligent assistant is displayed in the first conversation interface, wherein the third data analysis result is obtained by the intelligent assistant through analysis of the file.

8. The method according to claim 1, characterized in that The method further comprises: In response to a second message sending operation triggered by the target object based on the first conversation interface, displaying a second message sent by the target object in the first conversation interface, wherein the second message is used to inquire about the function of the data analysis platform; A third message that the intelligent assistant sends in reply to the second message is displayed in the first conversation interface, wherein the third message includes an introduction to the functions of the data analysis platform.

9. The method according to claim 1, characterized in that The method further comprises: In response to the target object asking for data unrelated to the data analysis interface in the first conversation interface, the intelligent assistant is controlled to retrieve the knowledge base and preset information, or the intelligent assistant is controlled to guide the target object to ask questions.

10. The method according to claim 1, characterized in that The displaying, in the first conversation interface, the first data analysis result sent by the intelligent assistant includes: Controlling the intelligent assistant to call a machine learning model based on the first message to analyze the data included in the data analysis interface to obtain a first data analysis result; Obtaining strategies and tools corresponding to the first data analysis results; The first data analysis result, the strategy, and the tool sent by the intelligent assistant are displayed in the first conversation interface.

11. The method according to any one of claims 1 to 10, characterized in that The data analysis interface provided by the display data analysis platform includes: In response to receiving the uniform resource locator input by the target object, a data analysis interface provided by the data analysis platform is displayed, wherein the uniform resource locator is a web page address corresponding to the data analysis platform.

12. A data analysis device based on artificial intelligence, characterized in that: The device comprises: A display module, configured to display a data analysis interface provided by the data analysis platform, wherein the data analysis interface includes an intelligent assistant; The display module is further configured to display a first conversation interface for a target object to have a conversation with the intelligent assistant in response to a triggering operation on the intelligent assistant; The display module is further configured to display a first message sent by the target object in the first conversation interface in response to a first message sending operation triggered by the target object based on the first conversation interface, wherein the first message is used to describe the data analysis task; The display module is further configured to display a first data analysis result sent by the intelligent assistant in the first conversation interface, wherein the first data analysis result is obtained by the intelligent assistant analyzing the data included in the data analysis interface based on the first message.

13. An electronic device, characterized in that: include: a memory for storing executable instructions; A processor, configured to implement the artificial intelligence-based data analysis method according to any one of claims 1 to 11 when executing the executable instructions stored in the memory.

14. A computer-readable storage medium storing computer-executable instructions, characterized in that: When the computer-executable instructions are executed by a processor, the artificial intelligence-based data analysis method according to any one of claims 1 to 11 is implemented.

15. A computer program product comprising a computer program or computer executable instructions, characterized in that When the computer program or computer executable instructions are executed by a processor, the artificial intelligence-based data analysis method according to any one of claims 1 to 11 is implemented.

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