An economic data analysis method, device and medium based on AI Agent

Abstract

The present application discloses an economic data analysis method, device and medium based on AI Agent, which is used to involve the field of artificial intelligence. The method includes: determining several economic data collection methods to be applied, and standardizing and grouping several economic data collection methods; defining system instructions; wherein, the system instructions include: economic data collection method call instructions, economic data analysis model call instructions; constructing and training the economic data analysis model, and configuring the triggering and parsing logic corresponding to the system instructions in the economic data analysis model; when it is necessary to analyze actual economic data, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method for data collection and analysis. Through the above method, the present application realizes the intelligent analysis and processing of economic data using artificial intelligence technology, thereby improving the accuracy and efficiency of data analysis.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to an economic data analysis method, device, and medium based on AI Agent. Background Art

[0002] With the rapid development of the global economy and the continuous advancement of information technology, economic data has shown explosive growth, with its scale and complexity increasing. Against this backdrop, traditional manual analysis methods, due to their slow processing speed and proneness to errors, are no longer able to meet the needs of modern economic data analysis.

[0003] To address this issue, existing computer-assisted analysis tools have emerged. These tools, through automation, have improved the efficiency of data analysis to a certain extent. However, these tools still exhibit numerous shortcomings in practical application. First, existing computer-assisted analysis tools are limited in their data acquisition methods, often processing data from specific sources or formats. They are ineffective when processing diverse and heterogeneous economic data. This limits the breadth and depth of data analysis, resulting in analytical results that may not fully reflect economic phenomena. Second, these tools have limited data processing capabilities. Faced with massive amounts of economic data, they are often unable to effectively screen, integrate, and optimize them, resulting in low data quality and further affecting the accuracy of analytical results. Furthermore, while computer-assisted analysis tools can improve analytical efficiency to a certain extent, due to the limitations of their algorithms and models, the results are often inaccurate and may even be misleading. This is clearly unacceptable when precise insights into economic dynamics are required.

[0004] In summary, existing computer-assisted analytical tools are no longer able to meet the demand for comprehensive, accurate, and efficient analysis of economic data. Therefore, how to leverage artificial intelligence (AI) technologies, particularly deep learning and big data analytics, to intelligently analyze and process economic data and improve the accuracy and efficiency of data analysis has become a pressing challenge in the current economic and technological landscape. Summary of the Invention

[0005] The embodiments of the present application provide an AI Agent-based economic data analysis method, device, and medium to solve the following technical problem: how to use artificial intelligence technology to realize intelligent analysis and processing of economic data to improve the accuracy and efficiency of data analysis.

[0006] In the first aspect, an embodiment of the present application provides an economic data analysis method based on AI Agent, characterized in that the method includes: determining several economic data collection methods to be applied, and standardizing and grouping the several economic data collection methods; defining system instructions; wherein the system instructions include: economic data collection method call instructions, economic data analysis model call instructions; constructing and training an economic data analysis model, and configuring the triggering and parsing logic corresponding to the system instructions in the economic data analysis model; when it is necessary to analyze actual economic data, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method for data collection and analysis.

[0007] In one implementation of the present application, a number of economic data collection methods are standardized and aggregated, specifically including: defining a unique identification ID and a method function description for the economic data collection method; wherein the unique identification ID is used to distinguish different data acquisition methods, and the method function description is used to illustrate the specific function and scope of application of the method; the economic data collection method and the corresponding unique identification ID and method function description are stored in an economic data collection method library.

[0008] In one implementation of the present application, system instructions are defined, specifically including: determining the type and quantity of system instructions to be defined based on preset data analysis requirements; assigning a unique instruction ID to each system instruction, and determining the triggering keyword or phrase of each system instruction; defining the execution operation of each system instruction; wherein the execution operation includes the data acquisition operation corresponding to the economic data collection method call instruction, and the problem analysis or data analysis operation corresponding to the economic data analysis model call instruction.

[0009] In one implementation of the present application, before constructing and training the economic data analysis model, the method also includes: constructing a training data set and a verification data set, the training data set includes a number of economic data analysis question samples, and the verification data set includes a number of economic data analysis answer samples, and the economic data analysis question samples and the economic data analysis answer samples correspond one to one, specifically including: constructing an economic data analysis question text; wherein the economic data analysis question text includes at least any one of the following: economic data query questions, economic data analysis questions, and economic data prediction questions; constructing an economic data analysis answer text; wherein the economic data analysis answer text includes at least any one of the following: the required economic data collection method identification ID, economic data analysis results, and economic data prediction results.

[0010] In one implementation of the present application, the triggering and parsing logic corresponding to the system instruction is configured in the economic data analysis model, specifically including: setting an instruction parsing module in the economic data analysis model; wherein the instruction parsing module is used to identify the triggering keywords or phrases in the user input questions, and determine the system instructions to be executed based on the triggering keywords or phrases; configure the execution logic of each system instruction, and define the format and processing method of the input and output data.

[0011] In one implementation of the present application, a user input question is input into an economic data analysis model to trigger a system instruction to call the economic data analysis model and the corresponding economic data collection method to perform data collection and analysis, specifically including: receiving the economic data analysis question input by the user; passing the user input question to the instruction parsing module in the economic data analysis model, so that the instruction parsing module recognizes the triggering keywords or phrases in the user input question and determines the system instruction to be executed; according to the determined system instruction, calling the corresponding economic data collection method to obtain relevant data, and passing the data to the economic data analysis model for analysis to generate analysis results.

[0012] In one implementation of the present application, the method also includes: configuring the data source, specifically including: determining the type of data source that needs to be accessed; wherein the data source types include: government public data, financial institution data, and third-party economic data service platform data; based on the data source type, configuring the data connection parameters of the corresponding data source; wherein the data connection parameters include: data source address, data access interface, and data authentication information; setting a data synchronization strategy to ensure that the data used by the economic data analysis model is the latest data.

[0013] In one implementation of the present application, the method further includes: selecting a suitable chart type for data visualization based on the analysis results generated by the economic data analysis model; and integrating the visualization results and related analysis instructions into a report for user reference.

[0014] In the second aspect, an embodiment of the present application also provides an economic data analysis device based on AI Agent, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: determine several economic data collection methods to be applied, and standardize and aggregate several economic data collection methods; define system instructions; wherein the system instructions include: economic data collection method call instructions, economic data analysis model call instructions; construct and train the economic data analysis model, and configure the triggering and parsing logic corresponding to the system instructions in the economic data analysis model; when it is necessary to analyze actual economic data, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method for data collection and analysis.

[0015] On the third aspect, the embodiment of the present application also provides a non-volatile computer storage medium for economic data analysis based on AI Agent, which stores computer executable instructions, and is characterized in that the computer executable instructions are set to: determine several economic data collection methods to be applied, and standardize and aggregate several economic data collection methods; define system instructions; wherein, the system instructions include: economic data collection method call instructions, economic data analysis model call instructions; construct and train the economic data analysis model, and configure the triggering and parsing logic corresponding to the system instructions in the economic data analysis model; when it is necessary to analyze actual economic data, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method for data collection and analysis.

[0016] The AI ​​Agent-based economic data analysis method, device, and medium provided in the embodiments of the present application have the following beneficial effects:

[0017] 1. Improve the efficiency and accuracy of economic data analysis: AI agents automate data collection and analysis, significantly reducing manual operation time and error rates. Standardized economic data collection methods ensure the accuracy and consistency of data acquisition, further improving the reliability of analysis.

[0018] 2. Enhanced flexibility and scalability of data analysis: By defining system instructions, including instructions for invoking economic data collection methods and economic data analysis models, the system can flexibly call corresponding data collection methods and analysis models based on different problems. This design not only meets diverse data analysis needs but also facilitates the future expansion of new data collection methods and analysis models.

[0019] 3. Optimizing the data collection and analysis process: By configuring the triggering and parsing logic corresponding to the system command in the economic data analysis model, we achieve automatic parsing of user questions and execution of commands. This automated process significantly improves the efficiency of data collection and analysis and reduces labor costs.

[0020] 4. Improve the diversity and real-time nature of data sources: By configuring multiple data sources, including government public data, financial institution data, and data from third-party economic data service platforms, and setting up data synchronization strategies, we ensure that the data used in economic data analysis models is diverse and real-time. This helps improve the comprehensiveness and timeliness of analysis.

[0021] 5. Improved User Experience: By visually displaying analysis results and integrating them into reports, users can more intuitively understand the results, improving their experience and satisfaction. This also reduces users' reliance on specialized economic knowledge, allowing more non-professionals to easily understand and utilize economic data analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A flowchart of an economic data analysis method based on AI Agent provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of the internal structure of an economic data analysis device based on an AI Agent provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] The embodiments of the present application provide an AI Agent-based economic data analysis method, device, and medium to solve the following technical problem: how to use artificial intelligence technology to realize intelligent analysis and processing of economic data to improve the accuracy and efficiency of data analysis.

[0027] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0028] Figure 1 This is a flow chart of an economic data analysis method based on AI Agent provided in the embodiment of this application. Figure 1 As shown, the economic data analysis method based on AI Agent provided in the embodiment of the present application specifically includes the following steps:

[0029] Step 101: Determine several economic data collection methods to be applied, and standardize and aggregate the several economic data collection methods.

[0030] In one embodiment of the present application, several economic data collection methods to be applied include but are not limited to API calls, file reading, database queries, etc. These methods can obtain economic data from various data sources to ensure the comprehensiveness and diversity of the data.

[0031] In one embodiment, several economic data collection methods are carefully selected and integrated to ensure comprehensive and efficient acquisition of the required economic data. These methods include, but are not limited to, API calls, file reading, and database queries. Each method is tailored to a specific data source or data acquisition scenario, ensuring comprehensiveness and diversity of the data.

[0032] First, API calls are widely used to obtain real-time economic data from various online service platforms or data providers. For example, by calling the API provided by financial information service providers, you can obtain the latest stock prices, exchange rates, interest rates, and other key economic indicators. The advantage of this method lies in its real-time and accuracy, ensuring that users obtain the latest economic data in a timely manner.

[0033] Secondly, file reading is suitable for economic data already stored in file format. These files may come from internal enterprise systems, government agencies, or third-party research institutions. By reading these files, valuable information such as historical economic data and industry reports can be obtained. This method is particularly useful when processing large amounts of historical data or conducting long-term trend analysis.

[0034] Database queries are another important method for collecting economic data. Businesses and institutions often store large amounts of economic data in databases for efficient data management and querying. By constructing complex SQL queries, economic data that meets specific criteria can be extracted from the database, providing strong support for subsequent data analysis.

[0035] Furthermore, after determining the several economic data collection methods to be applied, the several economic data collection methods are standardized and aggregated.

[0036] Specifically, the unique identification ID and method function description of the economic data collection method are defined; wherein the unique identification ID is used to distinguish different data acquisition methods, and the method function description is used to illustrate the specific function and scope of application of the method; the economic data collection method and the corresponding unique identification ID and method function description are stored in the economic data collection method library.

[0037] In one embodiment, the purpose of standardizing and grouping several economic data collection methods is to uniformly manage and call these different data collection methods, thereby improving the efficiency and scalability of the entire system.

[0038] Specifically, the standardized collection process involves defining a unique ID and functional description for each economic data collection method. The unique ID is a globally unique identifier used to accurately distinguish and locate different data collection methods within the system. This allows for quick identification and location of these methods when subsequently called or managed.

[0039] At the same time, the method function description details the specific functions and applicable scope of each data collection method. This helps users or developers understand and use these methods and ensure that they are correctly applied in appropriate scenarios.

[0040] Finally, these economic data collection methods, along with their corresponding unique IDs and functional descriptions, are stored in a dedicated economic data collection method library. This library not only provides a unified management and storage mechanism but also facilitates the subsequent use and maintenance of data collection methods. This design significantly improves the efficiency and accuracy of economic data collection, providing a solid foundation for subsequent economic data analysis.

[0041] Step 102: Define the system command.

[0042] It should be noted that the system instructions defined in this application include: economic data collection method call instructions and economic data analysis model call instructions.

[0043] Specifically, based on the preset data analysis requirements, determine the type and quantity of system instructions to be defined; assign a unique instruction ID to each system instruction, and determine the triggering keyword or phrase of each system instruction; define the execution operation of each system instruction; wherein, the execution operation includes the data acquisition operation corresponding to the economic data collection method call instruction, and the problem analysis or data analysis operation corresponding to the economic data analysis model call instruction.

[0044] In one embodiment, the two instructions are determined based on preset data analysis requirements, aiming to meet the requirements for collecting and analyzing economic data.

[0045] First, based on the pre-defined data analysis requirements, clearly define the type and number of system instructions that need to be defined. This usually involves specific requirements for economic data collection and analysis, such as what types of data need to be collected and what aspects of data analysis need to be performed.

[0046] Next, we assign a unique command ID to each system command. This ID will serve as a unique identifier for the command, facilitating subsequent management and invocation of the command. We also determine the triggering keywords or phrases for each command, which will be used to trigger the execution of the corresponding command.

[0047] After defining the instruction ID and trigger keywords, we begin to define the detailed execution operations for each instruction. For instructions that call economic data collection methods, the execution operations primarily involve data acquisition. This includes determining the data source, selecting the appropriate data collection method (such as API calls, file reading, etc.), and setting the appropriate parameters to obtain the required economic data. This data may include stock prices, market indices, macroeconomic indicators, etc., depending on the analysis requirements.

[0048] Instructions for invoking economic data analysis models primarily perform problem analysis or data analysis. This involves selecting an appropriate data analysis model (such as regression analysis or time series analysis) and performing corresponding analysis based on the collected economic data. The results of this analysis may include trend forecasts and risk assessments, providing important insights for subsequent economic decision-making.

[0049] In order to ensure the accuracy and efficiency of instructions, aspects such as the optimization of data processing flow, the setting of exception handling mechanism and the verification of instruction execution results need to be considered when defining execution operations.

[0050] It should also be noted that an agent is an intelligent entity that can perceive the environment and respond. By defining system instructions, the AI ​​model can have functions similar to those of an agent, that is, it can receive instructions, process data, and output results.

[0051] Step 103: Build and train an economic data analysis model, and configure the triggering and parsing logic corresponding to the system instruction in the economic data analysis model.

[0052] In one embodiment of the present application, after defining the system instruction, it is necessary to build and train an economic data analysis model.

[0053] It is understandable that training an economic data analysis model requires constructing a training dataset and a validation dataset. The training dataset in the embodiment of the present application includes several economic data analysis question samples, and the validation dataset includes several economic data analysis answer samples, with the economic data analysis question samples corresponding to the economic data analysis answer samples one to one.

[0054] Specifically, an economic data analysis question text is constructed; wherein the economic data analysis question text includes at least any one of the following: an economic data query question, an economic data analysis question, and an economic data forecast question; an economic data analysis answer text is constructed; wherein the economic data analysis answer text includes at least any one of the following: the required economic data collection method identification ID, the economic data analysis results, and the economic data forecast results.

[0055] In one embodiment of the present application, after the economic data analysis model is constructed and trained, the triggering and parsing logic corresponding to the system instruction is configured in the economic data analysis model.

[0056] Specifically, an instruction parsing module is set up in the economic data analysis model; wherein, the instruction parsing module is used to identify the trigger keywords or phrases in the user input questions, and determine the system instructions that need to be executed based on the trigger keywords or phrases; configure the execution logic of each system instruction, and define the format and processing method of input and output data.

[0057] In one embodiment, configuring the triggering and parsing logic corresponding to the system instruction in the economic data analysis model ensures that the model can accurately identify and execute the system instruction triggered by the user through keywords or phrases.

[0058] First, a dedicated command parsing module is built into the economic data analysis model. The core function of this module is to identify trigger keywords or phrases in user-entered questions. When a user enters a question, the command parsing module scans the question text for matches to predefined trigger keywords or phrases.

[0059] Once a matching trigger keyword or phrase is identified, the instruction parsing module determines the corresponding system instruction to execute. For example, if a user's question contains the phrase "Query last year's GDP data," the instruction parsing module will identify "query" and "GDP data" as trigger keywords and determine that the instruction to call the economic data collection method must be executed.

[0060] Next, configure the execution logic for each system instruction. This includes defining the specific steps for executing the instruction, how to access the required resources, and exception handling mechanisms. For instructions invoking economic data collection methods, the execution logic may involve calling specific APIs or retrieving data from designated data sources. For instructions invoking economic data analysis models, the execution logic may involve loading a pre-trained analysis model and inputting the collected data into the model for analysis.

[0061] Furthermore, the formats and processing of input and output data must be defined. This ensures that the model can correctly interpret user input and output analysis results in a standardized format. For example, input data may require a specific data format or data cleaning and preprocessing. Output data may need to be converted into easily understandable charts, reports, or other visualizations.

[0062] Step 104: When actual economic data needs to be analyzed, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method for data collection and analysis.

[0063] In one embodiment of the present application, after configuring the triggering and parsing logic corresponding to the system instruction in the economic data analysis model, if it is necessary to analyze actual economic data, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method for data collection and analysis.

[0064] Specifically, an economic data analysis question input by a user is received; the user input question is passed to the instruction parsing module in the economic data analysis model, so that the instruction parsing module recognizes the trigger keywords or phrases in the user input question and determines the system instructions that need to be executed; according to the determined system instructions, the corresponding economic data collection method is called to obtain relevant data, and the data is passed to the economic data analysis model for analysis to generate analysis results.

[0065] In one embodiment of the present application, in order to implement economic data analysis based on AI Agent, it is also necessary to configure the data source.

[0066] Specifically, determine the type of data source that needs to be accessed; where data source types include: government public data, financial institution data, and third-party economic data service platform data; based on the data source type, configure the data connection parameters of the corresponding data source; where data connection parameters include: data source address, data access interface, and data authentication information; set a data synchronization strategy to ensure that the data used by the economic data analysis model is the latest data.

[0067] In one embodiment of the present application, after generating the analysis results, you can also select a suitable chart type for data visualization based on the analysis results generated by the economic data analysis model; integrate the visualization results and related analysis instructions into a report for user reference.

[0068] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides an economic data analysis device based on AI Agent, whose structure is as follows: Figure 2 shown.

[0069] Figure 2 This is a schematic diagram of the internal structure of an economic data analysis device based on AI Agent provided in an embodiment of the present application. Figure 2 As shown, the equipment includes:

[0070] at least one processor 201;

[0071] and, a memory 202 communicatively coupled to the at least one processor;

[0072] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to:

[0073] Identify several economic data collection methods to be applied and standardize the collection of several economic data collection methods;

[0074] Define system instructions; where system instructions include: economic data collection method call instructions, economic data analysis model call instructions;

[0075] Build and train an economic data analysis model, and configure the triggering and parsing logic corresponding to the system instruction in the economic data analysis model;

[0076] When actual economic data needs to be analyzed, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method for data collection and analysis.

[0077] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for economic data analysis based on AI Agent, storing computer executable instructions, wherein the computer executable instructions are set to:

[0078] Identify several economic data collection methods to be applied and standardize the collection of several economic data collection methods;

[0079] Define system instructions; where system instructions include: economic data collection method call instructions, economic data analysis model call instructions;

[0080] Build and train an economic data analysis model, and configure the triggering and parsing logic corresponding to the system instruction in the economic data analysis model;

[0081] When actual economic data needs to be analyzed, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method for data collection and analysis.

[0082] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0083] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0084] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0085] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0088] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0089] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0090] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0091] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0092] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. An economic data analysis method based on AI Agent, characterized in that: The method comprises: determining a number of economic data collection methods to be applied, and standardizing and grouping the several economic data collection methods; Define system instructions; wherein, the system instructions include: economic data collection method call instructions, economic data analysis model call instructions; Build and train an economic data analysis model, and configure the triggering and parsing logic corresponding to the system instruction in the economic data analysis model; When actual economic data needs to be analyzed, the user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method to perform data collection and analysis; Standardize and aggregate several economic data collection methods, including: Define the unique ID and method function description of the economic data collection method; the unique ID is used to distinguish different data acquisition methods, and the method function description is used to explain the specific function and scope of application of the method; Storing the economic data collection method and its corresponding unique identification ID and method function description in an economic data collection method library; Define the system directive, including: Based on the preset data analysis requirements, determine the type and quantity of system instructions to be defined; Assign a unique command ID to each system command and determine the trigger keyword or phrase for each system command; Define the execution operations of each system instruction; wherein, the execution operations include data acquisition operations corresponding to the economic data collection method call instruction, and problem analysis or data analysis operations corresponding to the economic data analysis model call instruction; Configuring the triggering and parsing logic corresponding to the system instruction in the economic data analysis model specifically includes: An instruction parsing module is provided in the economic data analysis model; wherein the instruction parsing module is used to identify trigger keywords or phrases in the user input question and determine the system instruction to be executed based on the trigger keywords or phrases; Configure the execution logic of each system instruction and define the format and processing method of input and output data.

2. The economic data analysis method based on AI Agent according to claim 1 is characterized in that: Before building and training the economic data analysis model, the method further includes: Construct a training data set and a validation data set. The training data set includes several economic data analysis question samples, and the validation data set includes several economic data analysis answer samples. The economic data analysis question samples correspond to the economic data analysis answer samples in a one-to-one manner, specifically including: Constructing an economic data analysis question text; wherein the economic data analysis question text includes at least any one of the following: an economic data query question, an economic data analysis question, or an economic data forecast question; Construct an economic data analysis answer text; wherein, the economic data analysis answer text includes at least any one of the following: the required economic data collection method identification ID, economic data analysis results, and economic data forecast results.

3. The economic data analysis method based on AI Agent according to claim 1 is characterized in that: The user input question is input into the economic data analysis model to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method to perform data collection and analysis, specifically including: Receive economic data analysis questions input by users; Passing the user input question to the instruction parsing module in the economic data analysis model so that the instruction parsing module can identify the trigger keywords or phrases in the user input question and determine the system instructions that need to be executed; According to the determined system instructions, the corresponding economic data collection method is called to obtain relevant data, and the data is passed to the economic data analysis model for analysis to generate analysis results.

4. The economic data analysis method based on AI Agent according to claim 1 is characterized in that: The method further comprises: Configure the data source, including: Determine the type of data source that needs to be accessed; where the data source types include: government public data, financial institution data, and third-party economic data service platform data; Based on the data source type, configure the data connection parameters of the corresponding data source; wherein the data connection parameters include: data source address, data access interface, and data authentication information; Set up data synchronization strategies to ensure that the data used by economic data analysis models is up to date.

5. The economic data analysis method based on AI Agent according to claim 1 is characterized in that: The method further comprises: Based on the analysis results generated by the economic data analysis model, select the appropriate chart type for data visualization; Integrate the visualization results and related analysis instructions into a report for user reference.

6. An economic data analysis device based on AI Agent, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an economic data analysis method based on an AI Agent as described in any one of claims 1 to 5.

7. A non-volatile computer storage medium for AI Agent-based economic data analysis, storing computer-executable instructions, characterized in that: When the computer-executable instructions are executed, an economic data analysis method based on AI Agent as described in any one of claims 1 to 5 is implemented.

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