CN118689974B - An economic data analysis method, device and medium based on AI Agent - Google Patents
An economic data analysis method, device and medium based on AI AgentInfo
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Abstract
The application discloses an economic data analysis method, equipment and medium based on AI Agent, which are used for relating to the field of artificial intelligence. The method comprises the steps of determining a plurality of economic data collection methods to be applied, carrying out standardized collection on the economic data collection methods, and defining a system instruction, wherein the system instruction comprises an economic data collection method calling instruction and an economic data analysis model calling instruction, constructing and training an economic data analysis model, configuring triggering and analyzing logic corresponding to the system instruction in the economic data analysis model, and inputting a user input problem into the economic data analysis model under the condition that actual economic data needs to be analyzed so as to trigger the system instruction to call the economic data analysis model and the corresponding economic data collection method to carry out data collection and analysis. According to the method, intelligent analysis and processing of economic data by using an artificial intelligence technology are realized, and the accuracy and efficiency of data analysis are improved.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to an economic data analysis method, equipment and medium based on AI Agent.
Background
With the rapid development of global economy and the continuous progress of information technology, economic data presents explosive growth, and the scale and complexity of the economic data are increasingly improved. Under such a background, the conventional manual analysis method has the defects of low processing speed, easy error and the like, and can not meet the requirements of modern economic data analysis.
To address this problem, existing computer-aided analysis tools have evolved that improve the efficiency of data analysis to some extent through automated techniques. However, these tools still exhibit a number of disadvantages in practical applications. First, the existing computer-aided analysis tools are single in data acquisition mode, and often only can process data with specific sources or formats, and the processing effect is poor for multi-element and heterogeneous economic data. This limits the breadth and depth of the data analysis so that the analysis results may not fully reflect the economic phenomenon. Second, these tools have limited data processing capabilities. In the face of massive economic data, they often cannot be effectively screened, integrated and optimized, so that the quality of the data is low, and the accuracy of analysis results is further affected. Furthermore, although computer-aided analysis tools can improve analysis efficiency to some extent, the analysis results tend to be inaccurate and even misleading due to limitations in algorithms and models thereof. This is obviously unacceptable where accurate insight into economic dynamics is required.
In summary, the existing computer-aided analysis tools have failed to meet the need for comprehensive, accurate, and efficient analysis of economic data. Therefore, how to utilize artificial intelligence technology, especially deep learning and big data analysis technology, to realize intelligent analysis and processing of economic data, and to improve accuracy and efficiency of data analysis has become the urgent problem to be solved in the current economic technology field.
Disclosure of Invention
The embodiment of the application provides an economic data analysis method, equipment and medium based on AI Agent, which are used for solving the following technical problems of realizing intelligent analysis and processing of economic data by using an artificial intelligence technology so as to improve the accuracy and efficiency of data analysis.
The embodiment of the application provides an economic data analysis method based on an AI Agent, which is characterized by comprising the steps of determining a plurality of economic data collection methods to be applied, carrying out standardized collection on the economic data collection methods, defining a system instruction, wherein the system instruction comprises an economic data collection method calling instruction and an economic data analysis model calling instruction, constructing and training the economic data analysis model, configuring triggering and analyzing logic corresponding to the system instruction in the economic data analysis model, and inputting a user input problem 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 carry out data collection and analysis under the condition that actual economic data are required to be analyzed.
In one implementation mode of the application, the standardized collection of a plurality of economic data collection methods specifically comprises defining unique Identification (ID) and method function description of the economic data collection methods, wherein the unique Identification (ID) is used for distinguishing different data acquisition methods, the method function description is used for explaining specific functions and application ranges of the methods, and the economic data collection methods and the corresponding unique Identification (ID) and method function description are stored in an economic data collection method library.
In one implementation mode of the application, the system instructions are defined, and the method specifically comprises the steps of determining the type and the number of the needed system instructions to be defined based on preset data analysis requirements, distributing unique instruction IDs to the system instructions, determining trigger keywords or phrases of the system instructions, and defining execution operations of the system instructions, wherein the execution operations comprise data acquisition operations corresponding to economic data collection method calling instructions and problem analysis or data analysis operations corresponding to economic data analysis model calling instructions.
In one implementation mode of the application, before constructing and training the economic data analysis model, the method further comprises constructing a training data set and a verification data set, wherein the training data set comprises a plurality of economic data analysis question samples, the verification data set comprises a plurality of economic data analysis answer samples, the economic data analysis question samples and the economic data analysis answer samples are in one-to-one correspondence, and the method specifically comprises constructing an economic data analysis question text, wherein the economic data analysis question text at least comprises any one of an economic data query question, an economic data analysis question and an economic data prediction question, and constructing an economic data analysis answer text, and the economic data analysis answer text at least comprises any one of a required economic data collection method identification ID, an economic data analysis result and an economic data prediction result.
In one implementation mode of the application, trigger and analysis logic corresponding to the system instructions is configured in an economic data analysis model, and the method specifically comprises the steps of setting an instruction analysis module in the economic data analysis model, wherein the instruction analysis module is used for identifying trigger keywords or phrases in a user input problem and determining the system instructions to be executed according to the trigger keywords or phrases, configuring execution logic of each system instruction, and defining formats and processing modes of input and output data.
In one implementation of the application, the input problem of the user 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, and the method specifically comprises the steps of receiving the economic data analysis problem input by the user; the method comprises the steps of receiving a user input problem, transmitting the user input problem to an instruction analysis module in an economic data analysis model, enabling the instruction analysis module to identify a trigger keyword or phrase in the user input problem, determining a system instruction to be executed, calling a corresponding economic data collection method to obtain related data according to the determined system instruction, and transmitting the data to the economic data analysis model for analysis so as to generate an analysis result.
The method comprises the steps of configuring a data source, and specifically comprises the steps of determining the type of the data source to be accessed, wherein the type of the data source comprises government public data, financial institution data and third-party economic data service platform data, configuring data connection parameters corresponding to the data source based on the type of the data source, wherein the data connection parameters comprise a data source address, a data access interface and data authentication information, and setting a data synchronization strategy to ensure that data used by an economic data analysis model are the latest data.
In one implementation mode of the application, the method further comprises the steps of selecting a proper chart type for data visualization display according to the analysis result generated by the economic data analysis model, and integrating the visualization result and related analysis description into a report for reference of a user.
In a second aspect, the embodiment of the application also provides economic data analysis equipment based on the AI Agent, which is characterized by comprising at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, the instructions are executed by the at least one processor so that the at least one processor can determine a plurality of economic data collection methods to be applied and normalize and integrate the plurality of economic data collection methods, and define a system instruction, the system instruction comprises an economic data collection method calling instruction and an economic data analysis model calling instruction, the economic data analysis model is constructed and trained, trigger and analysis logic corresponding to the system instruction is configured in the economic data analysis model, and if the actual economic data needs to be analyzed, a user inputs a problem 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.
In a third aspect, the embodiment of the application further provides a non-volatile computer storage medium for economic data analysis based on an AI Agent, which stores computer executable instructions, and is characterized in that the computer executable instructions are configured to determine a plurality of economic data collection methods to be applied and perform standardized collection on the plurality of economic data collection methods, and define a system instruction, wherein the system instruction comprises an economic data collection method calling instruction and an economic data analysis model calling instruction, construct and train the economic data analysis model, configure triggering and analysis logic corresponding to the system instruction in the economic data analysis model, and input a user input problem into the economic data analysis model to trigger the system instruction to call the economic data analysis model and perform data collection and analysis corresponding to the economic data collection method under the condition that actual economic data needs to be analyzed.
The economic data analysis method, the equipment and the medium based on the AI Agent provided by the embodiment of the application have the following beneficial effects:
1. The efficiency and the accuracy of economic data analysis are improved, namely, the data collection and analysis are automatically carried out through the AI Agent, so that the time of manual operation is greatly reduced, and the error rate is reduced. The standardized and collected economic data collection method ensures the accuracy and consistency of data acquisition and further improves the reliability of analysis.
2. The flexibility and the expandability of data analysis are enhanced, namely, by defining a system instruction, including an economic data collection method calling instruction and an economic data analysis model calling instruction, the system can flexibly call a corresponding data collection method and an analysis model according to different problems. The design not only meets the diversified data analysis requirements, but also facilitates future expansion of new data collection methods and analysis models.
3. Optimizing the flow of data collection and analysis, namely realizing automatic analysis of user problems and execution of instructions by configuring triggering and analysis logic corresponding to system instructions in an economic data analysis model. The flow automation mode remarkably improves the efficiency of data collection and analysis and reduces the labor cost.
4. The diversity of the data sources and the real-time property of the data are improved by configuring various types of data sources including government public data, financial institution data and third-party economic data service platform data and setting a data synchronization strategy, so that the diversity and the real-time property of the data used by the economic data analysis model are ensured. This helps to improve the comprehensiveness and timeliness of the analysis.
5. The user experience is improved, the analysis result is visually displayed and integrated into a report form, so that the user can more intuitively understand the analysis result, and the user experience and satisfaction degree are improved. Meanwhile, the dependence of the user on professional economic knowledge is reduced, so that more non-professional persons can easily understand and utilize the economic data analysis result.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flowchart of an economic data analysis method based on AI Agent provided by an embodiment of the application;
fig. 2 is a schematic diagram of an internal structure of an economic data analysis device based on AI Agent according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application provides an economic data analysis method, equipment and medium based on AI Agent, which are used for solving the following technical problems of realizing intelligent analysis and processing of economic data by using an artificial intelligence technology so as to improve the accuracy and efficiency of data analysis.
The following describes the technical scheme provided by the embodiment of the application in detail through the attached drawings.
Fig. 1 is a flowchart of an economic data analysis method based on AI Agent according to an embodiment of the present application. As shown in fig. 1, the economic data analysis method based on AI Agent provided by the embodiment of the application specifically includes the following steps:
Step 101, determining a plurality of economic data collection methods to be applied, and carrying out standardized collection on the plurality of economic data collection methods.
In one embodiment of the application, several economic data collection methods to be applied include, but are not limited to, API calls, file reads, database queries, and the like. The method can acquire economic data from various data sources, and ensure the comprehensiveness and diversity of the data.
In one embodiment, the several economic data collection methods to be applied are carefully selected and integrated to ensure that the required economic data can be obtained comprehensively and efficiently. These methods include, but are not limited to, API calls, file reads, database queries, etc., each directed to a particular data source or data acquisition scenario, thereby ensuring the integrity and diversity of the data.
First, API calls are widely used to obtain real-time economic data from a variety of online service platforms or data providers. For example, by calling an API provided by a financial information service provider, the latest key economic indicators such as stock price, exchange rate, interest rate and the like can be obtained. The method has the advantages of real-time performance and accuracy, and can ensure that the user can acquire the latest economic data in time.
Second, file reading is applicable to those economic data that have been stored in file form. These documents may come from an internal enterprise system, government agency, or third party research agency, or the like. By reading these files, valuable information such as historical economic data, industry reports, and the like can be obtained. This approach is particularly useful when dealing with large amounts of historical data or when conducting long-term trend analysis.
In addition, database queries are another important economic data collection method. Enterprises or institutions typically store large amounts of economic data in databases for efficient data management and querying. By constructing a complex SQL query statement, economic data meeting specific conditions can be extracted from the database, and powerful support is provided for subsequent data analysis.
Further, after determining a number of economic data collection methods to be applied, a normalized collection is performed for the number of economic data collection methods.
The method comprises the steps of defining a unique identification ID and a method function description of an economic data collection method, wherein the unique identification ID is used for distinguishing different data acquisition methods, the method function description is used for describing the specific function and the application range of the method, and the economic data collection method and the corresponding unique identification ID and the method function description are stored in an economic data collection method library.
In one embodiment, the purpose of standardizing and aggregating several economic data collection methods is to uniformly manage and invoke these different data collection methods, thereby improving the efficiency and scalability of the overall system.
Specifically, the process of normalizing the collection includes defining a unique identification ID and method function description for each economic data collection method. The unique identification ID is a globally unique identifier for accurately distinguishing and locating different data collection methods in the system. In this way, these methods can be quickly identified and located by unique identification IDs when subsequently invoked or managed.
Meanwhile, the method function description specifies the specific function and application range of each data collection method. This helps the user or developer to understand and use these methods, ensuring that they are properly applied in the appropriate scenario.
Finally, the economic data collection methods and the corresponding unique identification IDs and method function descriptions are stored in a specialized economic data collection method library. The library not only provides a unified management and storage mechanism, but also facilitates the subsequent call and maintenance work of the data collection method. Through the design, the efficiency and the accuracy of economic data collection can be greatly improved, and a solid foundation is provided for subsequent economic data analysis.
Step 102, defining a system instruction.
The system instruction defined by the application comprises an economic data collection method call instruction and an economic data analysis model call instruction.
The method comprises the steps of determining the types and the quantity of required system instructions to be defined based on preset data analysis requirements, distributing unique instruction IDs to the system instructions, determining trigger keywords or phrases of the system instructions, and defining execution operations of the system instructions, wherein the execution operations comprise data acquisition operations corresponding to economic data collection method calling instructions and problem analysis or data analysis operations corresponding to economic data analysis model calling instructions.
In one embodiment, both instructions are determined based on preset data analysis requirements, aimed at meeting the collection and analysis requirements for economic data.
First, according to the preset data analysis requirement, the type and number of the system instructions are definitely required to be defined. This typically 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.
Next, each system instruction is assigned a unique instruction ID. This ID will serve as a unique identifier for the instruction, facilitating subsequent management and invocation of the instruction. At the same time, we also determine the trigger keywords or phrases for each instruction that will be used to trigger the corresponding instruction execution.
After the instruction ID and the trigger keyword are defined, the execution operation of each instruction is started to be defined in detail. For the economic data collection method call instruction, the execution operation thereof is mainly a data acquisition operation. This includes determining the source of the data, selecting the appropriate data collection method (e.g., API call, file read, etc.), and setting the corresponding parameters to obtain the desired economic data. Such data may include stock prices, market indices, macro economic indicators, etc., depending on the analysis requirements.
For the economic data analysis model call instruction, its execution operation is mainly a problem analysis or data analysis operation. This involves selecting an appropriate data analysis model (e.g., regression analysis, time series analysis, etc.) and performing a corresponding analysis based on the collected economic data. The results of the analysis may include trend predictions, risk evaluations, etc., which will provide an important basis for subsequent economic decisions.
In order to ensure the accuracy and the high efficiency of the instruction, the optimization of the data processing flow, the setting of an exception handling mechanism, the verification of the instruction execution result and the like are also considered in defining the execution operation.
It should be noted that, an Agent is an intelligent entity capable of sensing an environment and reacting, and by defining a system instruction, an AI model can have functions similar to that of the Agent, that is, can receive the instruction, process data, and output a result.
And 103, constructing and training an economic data analysis model, and configuring triggering and analyzing logic corresponding to the system instruction in the economic data analysis model.
In one embodiment of the application, after defining the system instructions, it is necessary to build and train an economic data analysis model.
It will be appreciated that training the economic data analysis model requires construction of a training data set and a validation data set. The training data set in the embodiment of the application comprises a plurality of economic data analysis question samples, the verification data set comprises a plurality of economic data analysis answer samples, and the economic data analysis question samples are in one-to-one correspondence with the economic data analysis answer samples.
The method comprises the steps of constructing an economic data analysis question text, constructing an economic data analysis answer text, and constructing the economic data analysis answer text, wherein the economic data analysis question text at least comprises any one of an economic data query question, an economic data analysis question and an economic data prediction question, and the economic data analysis answer text at least comprises any one of a required economic data collection method identification ID, an economic data analysis result and an economic data prediction result.
In one embodiment of the application, after the economic data analysis model is constructed and trained, the triggering and parsing logic corresponding to the system instructions is configured in the economic data analysis model.
The method comprises the steps of setting an instruction analysis module in an economic data analysis model, wherein the instruction analysis module is used for identifying trigger keywords or phrases in a user input problem, determining system instructions to be executed according to the trigger keywords or phrases, configuring execution logic of each system instruction, and defining formats and processing modes of input data and output data.
In one embodiment, the process of configuring the triggering and parsing logic corresponding to the system instructions in the economic data analysis model ensures that the model is able to accurately identify and execute the system instructions triggered by the user by keywords or phrases.
First, a special instruction parsing module is set in the economic data analysis model. The core function of this module is to identify the triggering keywords or phrases in the user input question. When a user enters a question, the instruction parsing module scans the text of the question for content that matches a predefined trigger keyword or phrase.
Once a matching trigger keyword or phrase is identified, the instruction parsing module determines the corresponding system instructions that need to be executed. For example, if the user question contains a phrase such as "query for GDP data over the last year," the instruction parsing module may identify "query" and "GDP data" as trigger keywords, thereby determining that the economic data collection method call instruction needs to be executed.
Next, the execution logic of each system instruction is configured. This includes defining the specific steps of instruction execution, the manner in which the required resources are invoked, and exception handling mechanisms, etc. For economic data collection method call instructions, execution logic may involve calling a particular API interface or retrieving data from a specified data source. For economic data analysis model call instructions, execution logic may include loading a pre-trained analysis model and analyzing the collected data input model.
In addition, the format and processing mode of the input and output data are required to be defined. This ensures that the model is able to properly parse the user input and output the analysis results in a standardized format. For example, for input data, we may need to specify a particular data format or perform data cleansing and preprocessing. For output data, we may need to convert it into an easily understood chart, report, or other visual form.
And 104, under the condition that the actual economic data needs to be analyzed, inputting the user input problem into an economic data analysis model to trigger a system instruction to call the economic data analysis model and a corresponding economic data collection method to collect and analyze data.
In one embodiment of the present application, after the triggering and parsing logic corresponding to the system command is configured in the economic data analysis model, if the actual economic data needs to be analyzed, the user input problem is input into the economic data analysis model, so as to trigger the system command to call the economic data analysis model and the corresponding economic data collection method to perform data collection and analysis.
The method comprises the steps of receiving economic data analysis problems input by a user, transmitting the user input problems to an instruction analysis module in an economic data analysis model so that the instruction analysis module can identify trigger keywords or phrases in the user input problems, determining a system instruction to be executed, calling a corresponding economic data collection method to obtain relevant data according to the determined system instruction, and transmitting the data to the economic data analysis model for analysis so as to generate analysis results.
In one embodiment of the application, a configuration data source is also required to implement AI Agent-based economic data analysis.
The method comprises the steps of determining the type of a data source to be accessed, wherein the data source type comprises government public data, financial institution data and third-party economic data service platform data, configuring data connection parameters corresponding to the data source based on the data source type, wherein the data connection parameters comprise a data source address, a data access interface and data authentication information, and setting a data synchronization strategy to ensure that data used by an economic data analysis model are the latest data.
In one embodiment of the application, after the analysis result is generated, the appropriate chart type can be selected for data visual display according to the analysis result generated by the economic data analysis model, and the visual result and the related analysis description are integrated into a report for reference of a user.
The above is a method embodiment of the present application. Based on the same inventive concept, the embodiment of the application also provides an economic data analysis device based on AI Agent, and the structure of the economic data analysis device is shown in FIG. 2.
Fig. 2 is a schematic diagram of an internal structure of an economic data analysis device based on AI Agent according to an embodiment of the present application. As shown in fig. 2, the apparatus includes:
At least one processor 201;
and a memory 202 communicatively coupled to the at least one processor;
Wherein the memory 202 stores instructions executable by the at least one processor, the instructions being executable by the at least one processor 201 to enable the at least one processor 201 to:
determining a plurality of economic data collection methods to be applied, and carrying out standardized collection on the plurality of economic data collection methods;
Defining a system instruction, wherein the system instruction comprises an economic data collection method call instruction and an economic data analysis model call instruction;
Constructing and training an economic data analysis model, and configuring triggering and analyzing logic corresponding to a system instruction in the economic data analysis model;
under the condition that the actual economic data needs to be analyzed, the user input problem 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 collect and analyze data.
Some embodiments of the application provide a non-volatile computer storage medium corresponding to the AI Agent-based economic data analysis of fig. 1, storing computer-executable instructions configured to:
determining a plurality of economic data collection methods to be applied, and carrying out standardized collection on the plurality of economic data collection methods;
Defining a system instruction, wherein the system instruction comprises an economic data collection method call instruction and an economic data analysis model call instruction;
Constructing and training an economic data analysis model, and configuring triggering and analyzing logic corresponding to a system instruction in the economic data analysis model;
under the condition that the actual economic data needs to be analyzed, the user input problem 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 collect and analyze data.
The embodiments of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments. In particular, for the internet of things device and the medium embodiment, since they are substantially similar to the method embodiment, the description is relatively simple, and the relevant points are referred to in the description of the method embodiment.
The system, the medium and the method provided by the embodiment of the application are in one-to-one correspondence, so that the system and the medium also have similar beneficial technical effects to the corresponding method, and the beneficial technical effects of the method are explained in detail above, so that the beneficial technical effects of the system and the medium are not repeated here.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (7)
1. An economic data analysis method based on AI Agent, which is characterized by comprising the following steps:
determining a plurality of economic data collection methods to be applied, and carrying out standardized collection on the economic data collection methods;
defining a system instruction, wherein the system instruction comprises an economic data collection method call instruction and an economic data analysis model call instruction;
constructing and training an economic data analysis model, and configuring triggering and analyzing logic corresponding to the system instruction in the economic data analysis model;
Under the condition that the actual economic data needs to be analyzed, inputting a user input problem into the economic data analysis model to trigger a system instruction to call the economic data analysis model and a corresponding economic data collection method to collect and analyze data;
the standardized collection of the economic data collection methods comprises the following steps:
Defining a unique identification ID of an economic data collection method and a method function description, wherein the unique identification ID is used for distinguishing different data acquisition methods, and the method function description is used for explaining the specific function and application range of the method;
storing the economic data collection method and the corresponding unique identification ID and method function description in an economic data collection method library;
Defining a system instruction, specifically including:
determining the type and the number of required system instructions to be defined based on preset data analysis requirements;
Assigning unique instruction IDs to the system instructions, and determining trigger keywords or phrases of the system instructions;
defining execution operation of each system instruction, wherein the execution operation comprises data acquisition operation corresponding to an economic data collection method calling instruction and problem analysis or data analysis operation corresponding to an economic data analysis model calling instruction;
Configuring trigger and analysis logic corresponding to the system instruction in the economic data analysis model, wherein the trigger and analysis logic specifically comprises:
the economic data analysis model is provided with an instruction analysis module, wherein the instruction analysis module is used for identifying a trigger keyword or phrase in a user input problem and determining a system instruction to be executed according to the trigger keyword or phrase;
The execution logic of each system instruction is configured, and the format and processing mode of input and output data are defined.
2. The AI Agent-based economic data analysis method of claim 1, wherein prior to constructing and training the economic data analysis model, the method further comprises:
The method comprises the steps of constructing a training data set and a verification data set, wherein the training data set comprises a plurality of economic data analysis question samples, the verification data set comprises a plurality of economic data analysis answer samples, and the economic data analysis question samples and the economic data analysis answer samples are in one-to-one correspondence, and specifically comprises the following steps:
constructing an economic data analysis problem text, wherein the economic data analysis problem text at least comprises any one of an economic data query problem, an economic data analysis problem and an economic data prediction problem;
and constructing an economic data analysis answer text, wherein the economic data analysis answer text at least comprises any one of a required economic data collection method identification ID, an economic data analysis result and an economic data prediction result.
3. The AI Agent-based economic data analysis method of claim 1, wherein a user input question is entered into the economic data analysis model to trigger a system instruction to invoke the economic data analysis model and a corresponding economic data collection method for data collection and analysis, comprising:
Receiving economic data analysis questions input by a user;
Transmitting the user input problem to an instruction analysis module in the economic data analysis model so that the instruction analysis module can identify a trigger keyword or phrase in the user input problem and determine a system instruction to be executed;
And calling a corresponding economic data collection method to acquire related data according to the determined system instruction, and transmitting the data to an economic data analysis model for analysis so as to generate an analysis result.
4. The AI Agent-based economic data analysis method of claim 1, further comprising:
the configuration data source specifically comprises:
determining the type of a data source to be accessed, wherein the data source type comprises government public data, financial institution data and third-party economic data service platform data;
configuring data connection parameters corresponding to a data source based on the type of the data source, wherein the data connection parameters comprise a data source address, a data access interface and data authentication information;
A data synchronization policy is set to ensure that the data used by the economic data analysis model is up-to-date.
5. The AI Agent-based economic data analysis method of claim 1, further comprising:
selecting a proper chart type for data visualization display according to an analysis result generated by the economic data analysis model;
The visual results and associated analytical instructions are integrated into a report for reference by the user.
6. An AI Agent-based economic data analysis apparatus, the apparatus comprising:
at least one processor;
And a memory communicatively coupled to the at least one processor;
Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform an AI Agent-based economic data analysis method as recited in any one of claims 1-5.
7. A non-volatile computer storage medium storing computer executable instructions for AI Agent-based economic data analysis, wherein the computer executable instructions, when executed, implement an AI Agent-based economic data analysis method as recited in any one of claims 1-5.
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| CN111460119A (en) * | 2020-03-27 | 2020-07-28 | 海信集团有限公司 | Intelligent question and answer method and system for economic knowledge and intelligent equipment |
| CN117555986A (en) * | 2023-11-03 | 2024-02-13 | 山东浪潮科学研究院有限公司 | Intelligent data analysis method and device based on large language model |
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| CN118093687A (en) * | 2023-10-16 | 2024-05-28 | 福建省邮电规划设计院有限公司 | Digital economic data acquisition system, method and storage medium based on big data |
| CN117743566A (en) * | 2023-12-06 | 2024-03-22 | 中国建设银行股份有限公司北京市分行 | Analysis report generation method and device, electronic equipment and storage medium |
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| CN117555986A (en) * | 2023-11-03 | 2024-02-13 | 山东浪潮科学研究院有限公司 | Intelligent data analysis method and device based on large language model |
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