Commercial intelligence pushing and monitoring method and system based on intelligent agent

Abstract

The invention relates to a business intelligence pushing and monitoring method and system based on an intelligent agent, and the method comprises the following steps: building an autonomous program based on a large language model, and guiding and constraining the behavior of the autonomous program through a cue word project; the autonomous program obtains personalized data uploaded by a user according to a commercial intelligence demand of the user, and obtains external associated data through an MCP service; the autonomous program processes the personalized data and the external associated data to generate a structured commercial intelligence report; pushing the structured business intelligence report to a specified user side; and collecting feedback information of a user on the structured business intelligence report, and iteratively optimizing the autonomous program based on the feedback information. According to the invention, the transformation from information matching to cognitive enhancement is realized, and highly personalized accurate intelligence service is achieved.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a method and system for business intelligence push and monitoring based on intelligent agents. Background Technology

[0002] Driven by the digital wave, enterprises' demand for business intelligence continues to rise. Business intelligence can transform external real-time data into actionable insights, helping enterprises avoid intuitive decisions, providing data support for management, and revealing emerging risks and business opportunities such as policy changes and negative public opinion. This enables enterprises to react quickly to convert business opportunities or reduce losses. Therefore, business intelligence technology has important application necessity in areas such as corporate strategic decision-making, market forecasting, competitive analysis, and risk management.

[0003] Currently, mainstream business intelligence technologies are mainly divided into two categories. One category involves periodically capturing and matching information from designated sources using preset keywords. The other category uses machine learning models to predict and provide early warnings about market trends and sales data. However, existing technologies have many shortcomings. Keyword-based business intelligence technologies suffer from high information noise, a lack of semantic understanding, and the inability to identify information gaps due to pre-set conditions. Machine learning-based business intelligence technologies are limited to specific scenarios, rely on high-quality training data, and have a "black box" decision-making process that lacks interpretability. Both types of technologies also suffer from cumbersome initial preparation for users, an inability to flexibly adapt to users' real-time intelligence needs, a lack of generalization ability to identify deep-seated user intentions, and difficulty in performing historical intelligence correlation analysis to build a complete intelligence chain. These shortcomings are the core problems that this invention patent can effectively solve. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a business intelligence push and monitoring system based on intelligent agents, which realizes the transformation from "information matching" to "cognitive enhancement". It has the effects of analyzing the deep meaning and logic of intelligence, constructing a complete business clue chain, generating personalized reports, and automatically iterating complex rules without manual maintenance.

[0005] The above-mentioned objective of this invention is achieved through the following technical solutions: A business intelligence push and monitoring method based on intelligent agents includes the following steps: An autonomous program is built based on a large language model, and its behavior is guided and constrained through prompt word engineering. The autonomous program obtains personalized information uploaded by users based on their business intelligence needs and acquires external related data through the MCP service. The autonomous program processes the personalized data and the external related data to generate a structured business intelligence report; The structured business intelligence report is pushed to the designated user terminal; Collect user feedback on the structured business intelligence report, and iteratively optimize the autonomous program based on the feedback.

[0006] The above technical solution organically combines the cognitive capabilities of large language models, personalized data acquisition, external data acquisition, report generation, and feedback iteration to achieve a transformation from "information push" to "cognitive enhancement." It supports the construction of a complete intelligence chain through historical intelligence correlation analysis and optimizes the analysis logic through feedback iteration, reducing the initial preparation work for users and achieving the convenience of "ready to use." It provides users with accurate business intelligence based on facts and logic.

[0007] As a further technical solution of the present invention: an autonomous program is built based on a large language model, and the behavior of the autonomous program is constrained through prompt word engineering. The autonomous program has the capabilities of demand understanding, analysis planning, and information integration processing, and is used to adapt to the full-process requirements of business intelligence analysis. The constraints of the prompt word project include the use of preset prompt word templates; The large language model upon which the autonomous program is based can have adjustable context length, model temperature, and probability threshold parameters.

[0008] Through the above technical solutions, the preset prompt word templates can effectively guide and constrain the behavior of autonomous programs, improve the interpretability of autonomous program behavior, avoid the "black box" of decision-making, and at the same time, the core parameters of the large language model are adjustable, which can flexibly adapt to the intelligence analysis needs of different users, ensure the accuracy and relevance of the output results of autonomous programs, and solve the problems of lack of interpretability and poor adaptability of traditional technologies.

[0009] As a further technical solution of the present invention: when the autonomous program obtains personalized information uploaded by the user based on the user's business intelligence needs, it includes: The autonomous program vectorizes internal enterprise data, business intelligence needs, historical intelligence data, and user feedback information, and stores them in a hybrid storage scheme that combines vector knowledge base and knowledge graph.

[0010] The above technical solution vectorizes specific types of personalized data and uses a hybrid storage scheme that combines vector knowledge base and knowledge graph. This not only supports efficient semantic retrieval but also clearly expresses complex entity relationships, providing a solid data foundation for the association analysis of intelligent agents and solving the problem that traditional technologies cannot deeply associate historical intelligence.

[0011] As a further technical solution of the present invention: when the autonomous program obtains external related data through the MCP service, it includes: The MCP service invoked is a standardized protocol interface layer that encapsulates heterogeneous external data sources and supports data retrieval using natural language commands; The MCP service uses a timestamp-based incremental retrieval method to obtain data and records the maximum timestamp of this retrieval.

[0012] Through the above technical solutions, the standardized protocol interface of the MCP service realizes the unified encapsulation of heterogeneous external data sources. Combined with the incremental retrieval and timestamp recording functions based on timestamps, it not only allows intelligent agents to conveniently call data through natural language commands, but also reduces information noise, avoids intelligence omissions, and expands the perception range and action capabilities of intelligent agents.

[0013] As a further technical solution of the present invention: when the autonomous program processes the personalized data and the external related data to generate a structured business intelligence report... The autonomous program invokes data processing tools to perform correlation filtering, comprehensive reasoning, and in-depth analysis on personalized data and external related data. The autonomous program invokes visualization tools to generate a structured business intelligence report that includes contextual interpretation and logical connections.

[0014] Through the above technical solutions, the autonomous program uses data processing tools to achieve precise data screening and in-depth analysis, avoiding the fragmentation defects of traditional processing; combined with visualization tools, it generates structured reports with contextual interpretation, logical connections, and diverse forms such as text and charts, which are accurately adapted to the user's industry background and display needs. This not only breaks through the limitations of traditional reports that pile up data and lack logic, but also solves the pain points of single format and unintuitive presentation, significantly improving the reference value of intelligence decision-making.

[0015] As a further technical solution of the present invention: when pushing the structured business intelligence report to a designated user terminal, it includes: Based on the user's preset subscription conditions, determine the urgency of the intelligence and the type of content; Select the appropriate push platform to push the report to the designated user terminal in a form that can be directly viewed.

[0016] By using the above technical solutions, the intelligence attributes are determined according to the user's preset subscription conditions and an appropriate push carrier is selected to ensure the timeliness and relevance of intelligence delivery. This allows users to conveniently receive intelligence that meets their own needs, significantly improving the user experience and solving the problems of low accuracy and insufficient convenience of traditional push methods.

[0017] As a further technical solution of the present invention: the structured business intelligence report is pushed to a designated user terminal according to the user's preset subscription conditions, and the user terminal includes instant messaging tools and system notification channels.

[0018] The above technical solution clearly defines the user end to include instant messaging tools and system notification channels, achieving multi-channel coverage of intelligence, adapting to the receiving habits of different users, ensuring that users can quickly and conveniently obtain structured business intelligence reports, and avoiding intelligence delays caused by a single receiving channel.

[0019] On the other hand, the present invention also discloses a business intelligence push and monitoring system based on intelligent agents, comprising: The intelligent agent framework module is based on a large language model and is configured with prompt word engineering units to understand users' business intelligence needs, formulate analysis plans, conduct multi-source information comprehensive reasoning, and generate structured intelligence reports. The personalized knowledge base module adopts a hybrid storage scheme that combines vector knowledge base and knowledge graph to store personalized data after vectorization, providing data support for the intelligent agent framework module. The MCP service module is used to encapsulate heterogeneous external data sources and provide standardized protocols and interfaces. It supports incremental data retrieval based on timestamps, allowing the intelligent agent framework module to call data through natural language commands. The intelligence push module is used to receive structured intelligence reports generated by the intelligent agent framework module and push them to designated user terminals according to user-preset subscription conditions. The feedback iteration module is used to collect user feedback on structured intelligence reports, write the feedback information into the personalized knowledge base module, and realize the iterative optimization of the analysis logic of the intelligent agent framework module.

[0020] Through the above technical solution, the five core modules work together to integrate the cognitive capabilities of the large language model, personalized data storage, heterogeneous data source access, precise push and feedback iteration functions, thereby realizing intelligent, personalized and forward-looking business intelligence analysis and solving the problems of traditional systems such as fragmented functions, poor adaptability and inability to meet the needs of in-depth analysis.

[0021] On the other hand, the present invention also discloses a computer device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the above-mentioned agent-based business intelligence push and monitoring method.

[0022] Through the above technical solutions, computer equipment can effectively implement business intelligence push and monitoring methods based on intelligent agents, ensuring the engineering implementation of the method and enabling users to obtain "cognitive enhancement" business intelligence through hardware carriers, enjoying the convenience and accuracy brought by correlation analysis, precise push and automatic optimization.

[0023] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned agent-based business intelligence push and monitoring method.

[0024] Through the above technical solution, the computer-readable storage medium can store the corresponding computer program, providing stable data storage support for the agent-based business intelligence push and monitoring method, ensuring that the method can be flexibly deployed and executed on different devices, and guaranteeing the wide applicability and implementation of the technical solution.

[0025] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention discloses a business intelligence push and monitoring method based on intelligent agents. By constructing an autonomous program based on a large language model and integrating prompt word engineering, personalized information, MCP service and feedback iteration mechanism, it realizes a fundamental transformation of business intelligence processing from "passive information push" to "proactive cognitive analysis and continuous optimization".

[0026] 2. This invention discloses a business intelligence push and monitoring system based on intelligent agents. By designing a system architecture in which five major modules work together, including an intelligent agent framework, a personalized knowledge base, MCP services, intelligence push and feedback iteration, the system achieves the systematic and integrated implementation of the method in engineering, ensuring the intelligent and stable operation of the entire business intelligence analysis process.

[0027] 3. This invention discloses a business intelligence push and monitoring device, which achieves hardware solidification and efficient execution of the intelligent intelligence processing technology solution by deploying the computer program that executes the method in a physical device containing a processor and memory, providing users with a reliable physical service carrier.

[0028] 4. This invention discloses a computer-readable storage medium for business intelligence push and monitoring. By storing the computer program implementing the method in the computer-readable storage medium, the technical solution is tangibly solidified and conveniently distributed, ensuring that the intelligent intelligence processing capability can be flexibly deployed and repeatedly executed in various computing environments. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of a business intelligence push and monitoring method based on intelligent agents according to the present invention.

[0030] Figure 2 for Figure 1 Composition and overall operational logic of a business intelligence push and monitoring system based on intelligent agents. Detailed Implementation

[0031] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0032] In the description of this application, it should be noted that the terms "upper," "lower," "inner," "outer," "top / bottom," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0033] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installed," "equipped with," "sleeved / connected," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Example

[0034] This invention provides a method for business intelligence push and monitoring based on intelligent agents, such as... Figure 1 The method includes: 101. Build an autonomous program based on a large language model and constrain its behavior through prompt word engineering. Specifically, build an agent framework in a local environment using Docker, deploy a local large model using Ollama, and pull a large model with adapted parameters including long context and complex reasoning capabilities based on GPU resources to build an autonomous program with the ability to understand requirements, formulate analysis plans, and synthesize information. At the same time, use a preset prompt word template written based on Jinjia2 syntax to standardize the analysis path, and configure the core parameters of the large model through the agent management backend, including context length, model temperature, and TOP-K probability threshold, to ensure the accuracy and relevance of the autonomous program's output results and avoid the "black box" problem of decision-making.

[0035] 102. The autonomous program acquires and preprocesses personalized user data, while simultaneously accessing external related data from the data warehouse via the MCP service. Specifically, based on user business intelligence needs, the autonomous program acquires personalized data uploaded by users (including internal company information, business intelligence requirements, historical intelligence data, and user feedback). After segmenting and vectorizing this data using an embedding model, it stores it in a hybrid storage scheme combining a vector knowledge base and a knowledge graph, providing data support for subsequent correlation analysis. On the other hand, it acquires external related data through the MCP service. This external related data is uniformly stored in the data warehouse, which is divided into structured storage areas (such as policy databases), semi-structured storage areas (such as JSON data returned by API interfaces), and unstructured storage areas (such as news articles and industry reports), respectively carrying heterogeneous data resources such as news, policy databases, and API interfaces. The MCP service uses Protocol Buffers3 to define standard interfaces, encapsulating heterogeneous external data sources in the data warehouse. Autonomous programs can directly call the service through natural language commands. The MCP service uses a watermark semantic timestamp-based incremental retrieval method to accurately extract new / updated data from the data warehouse and record the maximum timestamp of this retrieval, effectively reducing information noise caused by duplicate data and avoiding the omission of key intelligence.

[0036] 103. The autonomous program preprocesses and performs correlation analysis on dual-source data to generate structured business intelligence reports. Specifically, the autonomous program first performs preprocessing operations such as correlation screening, deduplication, and noise reduction on the acquired personalized data and external related data. Then, it calls data processing or visualization tools such as numerical calculation and chart generation to conduct contextual interpretation and correlation logic analysis in conjunction with historical intelligence, constructing a complete intelligence chain. Finally, it generates structured business intelligence reports in the form of text interpretation, data tables, chart BI displays, or PDF files, which are deeply adapted to the user's industry background and presentation needs.

[0037] 104. Based on user-preset subscription conditions, push structured reports to designated user terminals. Specifically, based on user-preset subscription conditions, first determine the urgency and content type of the intelligence, then select an appropriate push platform to push the structured business intelligence report to the designated user terminal in a directly viewable format. The user terminals include instant messaging tools such as system in-system messages, DingTalk, Lark, and WeChat Work, as well as system notification channels, achieving multi-channel intelligence coverage, adapting to different users' receiving habits, and avoiding intelligence delays caused by a single receiving channel.

[0038] 105. Collect user feedback and iteratively optimize the analysis logic of the autonomous program. Specifically, this involves collecting feedback information such as quality evaluations and suggestions for adjusting requirements of structured business intelligence reports through user interaction portals. After classifying and organizing the feedback information, it is written into a personalized knowledge base. Based on this feedback information, the analysis logic and data filtering rules of the autonomous program are automatically iterated and optimized, eliminating the need for users to manually maintain complex monitoring rules and achieving "out-of-the-box" convenience.

[0039] Reference Figure 2 This invention also provides a business intelligence push and monitoring system based on intelligent agents. The system includes an intelligent agent framework module built upon a large language model and prompt word engineering units (Jinja2 syntax templates). Internally, it integrates components such as long-term memory and tools, and possesses retrieval augmented generation (RAG) capabilities to achieve intelligent retrieval of personalized knowledge bases. Specifically, the long-term memory component stores historical interaction records, the tools component performs tasks such as data filtering and visualization, and the RAG capability ensures accurate retrieval of relevant information from the knowledge base. The core functions of this module are understanding user needs, formulating analysis plans, performing comprehensive reasoning, and generating structured intelligence reports. The personalized knowledge base module employs a hybrid storage scheme combining vector knowledge bases and knowledge graphs to store personalized data segmented and vectorized using an embedding model, providing data support for correlation analysis to the intelligent agent framework module. The MCP service module encapsulates heterogeneous external data sources such as news and policy databases, providing the Protocol Buffers3 standardized protocol and interface, supporting watermark semantics-based timestamp-based incremental retrieval, allowing the intelligent agent framework module to call data via natural language commands. The intelligence push module receives structured intelligence reports generated by the intelligent agent framework module, determines intelligence attributes based on user-preset subscription conditions, selects an appropriate push carrier, and pushes the reports to designated user terminals such as system in-app messages or instant messaging tools. The feedback iteration module collects user feedback on structured intelligence reports, categorizes and organizes the feedback information, and writes it into the personalized knowledge base module, enabling automatic iterative optimization of the intelligent agent framework module's analysis logic and data filtering rules.

[0040] Reference Figure 2The system comprises users, core functional modules (feedback iteration module, personalized knowledge base module, intelligent agent framework module, MCP service module, and intelligence push module), and a data warehouse. The intelligent agent framework module integrates a large language model and prompt word engineering units. The data warehouse contains structured, semi-structured, and unstructured storage units, serving as an external data support carrier. The system's operational logic is as follows: Users first submit business intelligence requests and compose prompt words, simultaneously uploading personalized information. This information is distributed to the corresponding modules via a task queue (Kafka). The personalized information is pushed to the personalized knowledge base module, which uses a hybrid storage scheme combining vector knowledge bases and knowledge graphs. The request is then forwarded to the prompt word engineering unit of the intelligent agent framework module. Simultaneously, the MCP service module uses a standard interface defined by Protocol Buffers3 to retrieve external data from the data warehouse using a watermark-based, timestamp-based incremental retrieval method. This data is then provided to the large language model of the intelligent agent framework module. The large language model, combined with the data support provided by the personalized knowledge base module, completes analysis and reasoning, generating a structured business intelligence report, which is then forwarded to the intelligence push module via the task queue (Kafka). Subsequently, the intelligence push module receives the report and pushes it to the user according to the user's preset subscription conditions; the intelligence quality information fed back by the user is transmitted to the feedback iteration module via the task queue Kafka, and the feedback iteration module writes the feedback into the personalized knowledge base module.

[0041] The present invention also provides a computer device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the above-mentioned agent-based business intelligence push and monitoring method. The computer device executes the computer program in the memory through the processor, transforming the software-level method logic such as Docker framework building, Ollama large model deployment, data processing and push into actual hardware-level operations, ensuring the engineering implementation of the technical solution and enabling users to obtain "cognitive enhancement" business intelligence through hardware carriers.

[0042] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-mentioned agent-based business intelligence push and monitoring method. The storage medium can stably store the corresponding computer program, providing support for the flexible deployment and cross-device execution of the method, ensuring that the intelligent intelligence processing technology solution can be widely applied in various computing environments, and realizing the tangible solidification and convenient distribution of the technology solution.

[0043] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0044] The implementation principle of this invention is as follows: An intelligent closed-loop system is constructed, driven by a large language model as the cognitive core and a dual-flow of data and feedback. Its core logic is: through a constrained intelligent agent that analyzes needs and schedules resources, it fuses and deeply analyzes internal and external dual-source data, delivers the generated structured intelligence accurately on demand, and continuously optimizes the system's intelligence using user feedback, thereby achieving an evolution from "information push" to "cognitive enhancement." Specifically: First, an intelligent agent based on a large language model and constrained by cue word engineering is responsible for understanding user needs and planning analysis tasks. Next, the intelligent agent simultaneously schedules an internal personalized knowledge base and external MCP services to achieve unified acquisition and semantic integration of multi-source data, including structured and unstructured data. Then, the intelligent agent calls analysis tools to correlate, reason, and interpret the data, generating a structured report with contextual logic. Subsequently, the system accurately pushes the report through adapted channels according to user-preset conditions. Ultimately, user feedback is collected by the system and used to optimize the knowledge base and agent analysis logic, forming a complete closed loop of "demand-data-analysis-delivery-feedback-optimization", enabling the system to continuously improve its personalized intelligence service capabilities.

[0045] The embodiments described herein are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for business intelligence push and monitoring based on intelligent agents, characterized in that, Includes the following steps: An autonomous program is built based on a large language model, and its behavior is guided and constrained through prompt word engineering. The autonomous program obtains personalized information uploaded by users based on their business intelligence needs and acquires external related data through the MCP service. The autonomous program processes the personalized data and the external related data to generate a structured business intelligence report; The structured business intelligence report is pushed to the designated user terminal; Collect user feedback on the structured business intelligence report, and iteratively optimize the autonomous program based on the feedback.

2. The method according to claim 1, characterized in that, An autonomous program was built based on a large language model, and its behavior was constrained through prompt word engineering. The autonomous program has the capabilities of demand understanding, analysis planning, and information integration processing, and is used to adapt to the full-process requirements of business intelligence analysis. The constraints of the prompt word project include the use of preset prompt word templates; The large language model upon which the autonomous program is based can have adjustable context length, model temperature, and probability threshold parameters.

3. The method according to claim 1, characterized in that, When the autonomous program obtains personalized information uploaded by the user based on the user's business intelligence needs, it includes: The autonomous program vectorizes internal enterprise data, business intelligence needs, historical intelligence data, and user feedback information, and stores them in a hybrid storage scheme that combines vector knowledge base and knowledge graph.

4. The method according to claim 1, characterized in that, When the autonomous program obtains external related data through the MCP service, it includes: The MCP service invoked is a standardized protocol interface layer that encapsulates heterogeneous external data sources and supports data retrieval using natural language commands; The MCP service uses a timestamp-based incremental retrieval method to obtain data and records the maximum timestamp of this retrieval.

5. The method according to claim 1, characterized in that, When the autonomous program processes the personalized data and the externally related data to generate a structured business intelligence report, The autonomous program invokes data processing tools to perform correlation filtering, comprehensive reasoning, and in-depth analysis on personalized data and external related data. The autonomous program invokes visualization tools to generate a structured business intelligence report that includes contextual interpretation and logical connections.

6. The method according to claim 1, characterized in that, When pushing the structured business intelligence report to a designated user terminal, it includes: Based on the user's preset subscription conditions, determine the urgency of the intelligence and the type of content; Select the appropriate push platform to push the report to the designated user terminal in a form that can be directly viewed.

7. The method according to claim 1, characterized in that, When the structured business intelligence report is pushed to a designated user terminal, the user terminal includes instant messaging tools and system notification channels.

8. A business intelligence push and monitoring system based on intelligent agents, characterized in that, include: The intelligent agent framework module is based on a large language model and is configured with prompt word engineering units to understand users' business intelligence needs, formulate analysis plans, conduct multi-source information comprehensive reasoning, and generate structured intelligence reports. The personalized knowledge base module adopts a hybrid storage scheme that combines vector knowledge base and knowledge graph to store personalized data after vectorization, providing data support for the intelligent agent framework module. The MCP service module is used to encapsulate heterogeneous external data sources and provide standardized protocols and interfaces. It supports incremental data retrieval based on timestamps, allowing the intelligent agent framework module to call data through natural language commands. The intelligence push module is used to receive structured intelligence reports generated by the intelligent agent framework module and push them to designated user terminals according to user-preset subscription conditions. The feedback iteration module is used to collect user feedback on structured intelligence reports, write the feedback information into the personalized knowledge base module, and realize the iterative optimization of the analysis logic of the intelligent agent framework module.

9. A computer device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When executed by the processor, the computer program implements the agent-based business intelligence push and monitoring method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the agent-based business intelligence push and monitoring method as described in any one of claims 1-7.

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