An event-driven business intelligence body dynamic construction method and device

Abstract

The present invention discloses an event-driven business intelligent body dynamic construction method and device, which belongs to the field of intelligent application technology and is used to solve the technical problems that the intelligent transformation cost of traditional business systems is high and the logic of manually constructed business intelligent bodies is rigid and cannot respond swiftly to changes in business needs and environment. The method includes: monitoring business events occurring in the business system, obtaining corresponding business event information and extracting upstream business context information corresponding to the business event; based on the business event and upstream business context information, identifying the downstream business of the business event in the business system and obtaining business information of the downstream business; based on the business event information and the business information of the downstream business, constructing a business intelligent body that executes downstream business actions and storing it in a business intelligent body center; when a new business event is monitored, matching the corresponding business intelligent body in the business intelligent body center so that the business intelligent body executes the downstream business action triggered by the business event.

Description

Technical Field

[0001] The present invention relates to the field of intelligent application technology, and in particular to a method and device for dynamically constructing an event-driven business intelligent body. Background Art

[0002] In this new era of development characterized by the deep integration of the digital and real economies, large-scale pre-trained model technologies based on deep learning are evolving at an exponential rate, providing a core driver for the intelligent transformation of enterprise-level application systems. Common systemic flaws in traditional business systems, such as rigid rules, delayed responses, and blind spots in decision-making, have become increasingly prominent in a dynamic market environment and driven by demand for personalized services, becoming a key constraint on enterprises' digital transformation.

[0003] Current mainstream business agent-building solutions have significant technical limitations: The first type, based on hard-coded implementations of agent-based development frameworks, can automate basic processes through technologies like BPMN engines and rule engines, but requires developers to predefine all decision logic and execution paths. Furthermore, when business rules change, the code modifications required are significant, leading to an exponential increase in system maintenance costs. The second type, based on visual configuration solutions based on large model platforms, shortens the development cycle through low-code drag-and-drop components. However, their built-in predefined templates and fixed decision tree structures are still essentially static rule assembly models, resulting in significant system response delays and exposing fundamental flaws in dynamic adaptability.

[0004] Both of these methods rely on manual pre-construction, resulting in large business intelligence volumes and high construction costs. In addition, the logic of the manually constructed business intelligence bodies is rigid and cannot respond swiftly to changes in business needs and environment. Summary of the Invention

[0005] The embodiments of the present invention provide an event-driven business intelligent body dynamic construction method and device for solving the following technical problems: the cost of intelligent transformation of traditional business systems is high, and the logic of manually constructed business intelligent bodies is rigid and cannot respond swiftly to changes in business needs and environment.

[0006] The embodiment of the present invention adopts the following technical solutions:

[0007] In one aspect, an embodiment of the present invention provides a method for dynamically constructing an event-driven business agent, the method comprising:

[0008] Monitor business events occurring in the business system, obtain corresponding business event information and extract upstream business context information corresponding to the business event;

[0009] According to the business event and the upstream business context information, identifying the downstream business of the business event in the business system, and obtaining business information of the downstream business;

[0010] Based on the business event information and the business information of the downstream business, a business agent for executing the downstream business action is constructed and stored in a business agent center; wherein the business agent includes at least one key attribute of user role, target model, business prompt word, and business tool service;

[0011] When a new business event is monitored, a corresponding business agent is matched in the business agent center so that the business agent executes the downstream business action triggered by the business event.

[0012] In a feasible implementation, monitoring a business event occurring in a business system, obtaining corresponding business event information, and extracting upstream business context information corresponding to the business event specifically includes:

[0013] Monitor business events occurring in the current business system based on a polling mechanism;

[0014] Obtaining business event information of the business event; wherein the business event information includes at least event type, event summary, event status, and event source;

[0015] Understand the semantics of the business event information through natural language processing (NLP) and knowledge graph technology, and identify the upstream business associated with the business event based on the semantics;

[0016] The business information of the upstream business is extracted to obtain the upstream business context information; wherein the upstream business context information at least includes the business function, business operation, business entity and operation user role of the upstream business.

[0017] In a feasible implementation, based on the business event and the upstream business context information, identifying the downstream business of the business event in the business system and obtaining business information of the downstream business specifically includes:

[0018] Pre-creating a domain knowledge graph between business events in the business system; wherein the domain knowledge graph at least includes business entities, business actions, business services, and business event relationships;

[0019] According to the business event and the upstream business context information, searching the domain knowledge graph for downstream businesses affected by the business event;

[0020] Extracting business information of the downstream business; wherein the business information of the downstream business includes at least a business action, a user role of the business action, a business service corresponding to the business action, and a business entity corresponding to the business action.

[0021] In a feasible implementation manner, before constructing a business agent that executes downstream business actions based on the business event information and the business information of the downstream business, the method further includes:

[0022] Build a large model service center and store several vertical domain large models in the large model service center; the large model service center can access large model services from multiple manufacturers, multiple fields, and multiple parameter sizes. Each large model service contains at least the following self-descriptive information: model classification, field of expertise, model ranking, and semantic description;

[0023] The big model service center provides a unified execution interface for big model services, and dynamically switches the vertical domain big model to be executed based on the self-description information of the big model service.

[0024] In a feasible implementation, based on the business event information and the business information of the downstream business, a business agent that executes the downstream business action is constructed, which specifically further includes:

[0025] After obtaining the event summary in the business event information, first search the pre-built business agent center for the business agent of the downstream business that matches the business event;

[0026] If a matching business agent is found, it is directly extracted. If no matching business agent is found, a business agent capable of executing downstream business actions of the business event is constructed based on a preset program.

[0027] In a feasible implementation, a business agent capable of executing downstream business actions of the business event is constructed based on a preset program, specifically including:

[0028] Extracting large model features required for building a business intelligence entity based on the business event information and the business information of the downstream business; wherein the large model features include at least model classification and model expertise;

[0029] The vertical domain big model with the highest ranking in the matching field and classification of the big model features in the big model service center is selected as the target big model;

[0030] Generate corresponding business prompt words according to the business event information and the business information of the downstream business;

[0031] Recommend or generate business tool services based on the business service information corresponding to the business actions of the downstream business;

[0032] The user role corresponding to the business event, the target big model, the business prompt word, and the business tool service are dynamically assembled onto the business intelligent body to obtain a business intelligent body that executes downstream business actions, and the business intelligent body entity is stored in a pre-built business intelligent body center; the storage information also includes the business event information and the relationship between the business event and the downstream business.

[0033] In a feasible implementation, generating corresponding service prompt words according to the service event information and the service information of the downstream service specifically includes:

[0034] Dynamically recalling the best prompt word template from a pre-built prompt word template center based on the event summary in the business event information and the business information of the downstream business; wherein the prompt word template includes at least the following dynamic parameters: user role, business function, business operation, and business entity;

[0035] The dynamic parameters in the prompt word template are replaced with parameter values ​​extracted from the upstream service context information to obtain a service prompt word that matches the service event and the downstream service.

[0036] In a feasible implementation, recommending or generating a business tool service based on the business service information corresponding to the business action of the downstream business specifically includes:

[0037] Obtaining the business service corresponding to the business action in the business information of the downstream business, and extracting the corresponding business service information; wherein the business service information at least includes a service description, a service address, a service parameter list, and a return value;

[0038] Based on the business service information, matching recommended business tool services in a pre-built agent tool service center;

[0039] If no match is found, the corresponding business tool service is generated based on the preset script and loaded into the pre-built intelligent tool service center.

[0040] In a feasible implementation, when a new business event is monitored, a corresponding business agent is matched in the business agent center so that the business agent executes the downstream business action triggered by the business event, specifically including:

[0041] When a new business event is monitored, the business agent center matches the business agent of the corresponding downstream business based on the upstream business context information of the new business event;

[0042] Based on the downstream business organization dialogue information, a dialogue is conducted with the matching business agent, so that the business agent executes the downstream business action triggered by the business event.

[0043] On the other hand, an embodiment of the present invention also provides an event-driven business intelligence body dynamic construction device, which 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 so that the at least one processor can execute the event-driven business intelligence body dynamic construction method.

[0044] Compared with the prior art, the event-driven business agent dynamic construction method and device provided by the embodiments of the present invention have the following beneficial effects:

[0045] The event-driven dynamic construction solution for business intelligence bodies provided by the embodiments of the present invention can directly monitor business events during business execution, dynamically extract upstream business context, identify downstream businesses affected by the business events, and construct business intelligence bodies in real time to execute the downstream business actions. Compared with the manual construction of intelligent bodies, this greatly reduces the construction cost of business system transformation and upgrading. By dynamically constructing business intelligence bodies at runtime, the flexibility of business intelligence capabilities is improved, and it can agilely adapt to business needs and environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0047] Figure 1 A flow chart of a method for dynamically constructing an event-driven business agent provided by an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of the structure of an event-driven business intelligence dynamic construction device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0050] The embodiment of the present invention provides an event-driven business intelligence dynamic construction method, such as Figure 1 As shown, the event-driven business agent dynamic construction method specifically includes steps S101-S104:

[0051] S101 : Monitor business events occurring in a business system, obtain corresponding business event information, and extract upstream business context information corresponding to the business event.

[0052] Specifically, the business events occurring in the current business system are monitored based on a polling mechanism, and then business event information of the business events is obtained, wherein the business event information includes at least event type, event summary, event status, and event source.

[0053] Furthermore, using natural language processing (NLP) and knowledge graph technology, the semantics of business event information are understood, and upstream businesses associated with the business events are identified based on the semantics. Business information of the upstream businesses is extracted to obtain upstream business context information, which includes at least the business functions, business operations, business entities, and user roles of the upstream businesses.

[0054] As a feasible implementation method, information such as the event type, event summary, event status, and event source of business events is obtained; NLP and knowledge graph technology are used to understand the semantics of business events, identify upstream business functions, business operations, business entities, and operation user roles associated with business events, and form upstream business context.

[0055] S102: Identify the downstream business of the business event in the business system according to the business event and upstream business context information, and obtain business information of the downstream business.

[0056] Specifically, a domain knowledge graph is pre-created between business events in the business system. Based on the business event and upstream business context, the domain knowledge graph is used to search for downstream businesses affected by the business event. The domain knowledge graph contains at least business entities, business actions, business services, and business event relationships.

[0057] Furthermore, business information of the downstream business is extracted; wherein the business information of the downstream business at least includes a business action, a user role of the business action, a business service corresponding to the business action, and a business entity corresponding to the business action.

[0058] As a feasible implementation method, downstream business information includes business actions, user roles of business actions, business services corresponding to business actions, and business entities corresponding to business actions. The business information of downstream businesses is obtained through graph database knowledge reasoning services.

[0059] In one embodiment, the business event is a successful payment for order 123. The upstream context information for this business event includes: business function: payment; business operation: transfer; business entity: item details included in order 123; and user role: ordering user information (user ID, delivery address, etc.). Upon receiving the event, the monitoring service or event processing service uses key identifiers in the event (such as the order ID) to proactively retrieve the data required to construct a complete upstream context by invoking the APIs of relevant business systems (the order service API to query order details, the user service API to query user information), querying shared databases, or querying caches. The downstream operations of this business event are: notifying the warehouse system to deduct inventory for the corresponding item, notifying the logistics system to generate a standard express delivery order, and notifying the customer service system to update the order status.

[0060] S103. Based on the business event information and the business information of the downstream business, a business intelligence entity that executes the downstream business actions is constructed and stored in the business intelligence entity center; wherein the business intelligence entity includes at least one key attribute of the user role, target big model, business prompt word, and business tool service.

[0061] Specifically, in order to realize the automatic construction of business intelligent bodies, the present invention pre-constructs multiple auxiliary construction centers, including a large model service center, a prompt word template center, an intelligent body tool service center and a business intelligent body center.

[0062] First, a big model service center is built, and several vertical domain big models are stored in the big model service center; the big model service center can access big model services from multiple manufacturers, multiple fields, and multiple parameter sizes. Each big model service contains at least the following self-description information: model classification, field of expertise, model ranking, and semantic description; the big model service center provides a unified execution interface for big model services, and dynamically switches the executed vertical domain big model based on the self-description information of the big model service.

[0063] Then, a prompt word template is constructed for each agent and stored in the prompt word template center. A business tool service is constructed for each agent and stored in the agent tool service center. The business agent center is used to store the business agent constructed each time a business event occurs.

[0064] After completing the above preparations, the event summary in the business event information is obtained. The pre-built business agent center is first searched for a business agent that matches the downstream business event. If a matching business agent is found, it is directly extracted. If no matching business agent is found, a business agent that can execute the downstream business action of the business event is constructed based on the pre-set program.

[0065] The specific process of building a business intelligence entity is as follows:

[0066] First, based on business event information and downstream business information, the big model features required to build the business intelligence entity are extracted. These big model features include at least the model classification and the model's domain expertise. Based on these big model features, the big model service center matches the vertical domain big model with the highest ranking that matches both the domain and classification, and selects it as the target big model.

[0067] Furthermore, corresponding business prompts are generated based on the business event information and the business information of the downstream business. Business tool services are constructed based on the business service information corresponding to the business action of the downstream business. The user role, target model, business prompts, and business tool services corresponding to the business event are then dynamically assembled onto the business agent to obtain a business agent that executes the downstream business action. This business agent entity is then stored in a pre-built business agent center. The stored information also includes business event information and the relationship between the business event and the downstream business.

[0068] As a feasible implementation, corresponding service prompts are generated based on business event information and downstream business information. Specifically, the optimal prompt template is dynamically retrieved from a pre-built prompt template center based on the event summary in the business event information and the downstream business information. The prompt template contains at least the following dynamic parameters: user role, business function, business operation, and business entity. The dynamic parameters in the prompt template are replaced with parameter values ​​extracted from the upstream business context information to obtain a business prompt that matches the business event and the downstream business.

[0069] As a feasible implementation, business tool services are recommended or generated based on the business service information corresponding to the business actions of downstream businesses. This specifically includes obtaining the business service corresponding to the business action from the business information of the downstream business and extracting the corresponding business service information; where the business service information includes at least a service description, service address, service parameter list, and return value. Based on the business service information, the recommended business tool services are then matched in a pre-built intelligent agent tool service center. If no match is found, the corresponding business tool service is generated based on a preset script and loaded into the pre-built intelligent agent tool service center.

[0070] S104: When a new business event is monitored, a corresponding business agent is matched in the business agent center so that the business agent executes the downstream business action triggered by the business event.

[0071] Specifically, when a new business event is detected, the business agent center matches the corresponding downstream business agent based on the upstream business context information of the new business event. Then, based on the downstream business organization dialogue information, a dialogue is initiated with the matched business agent, allowing the business agent to execute the downstream business action triggered by the business event.

[0072] In one embodiment, when a new business event is detected, and the business event is: Order 124 successfully paid, the upstream business context information of the business event is extracted: Business Function: Payment; Business Operation: Transfer; Business Entity: Product Details of Order 124; User Role: Ordering User Information (User ID, Shipping Address, etc.). Based on this information, the Business Agent Center matches the corresponding Business Agent for the downstream business. Because the same type of event has occurred before, the Business Agent Center is able to match the Business Agent for the corresponding downstream business. At this point, based on the downstream business actions of the business event, dialogue information is organized and a dialogue is conducted with the matched Business Agent, enabling the Business Agent to execute the downstream business actions of the business event based on its assembled target macro model, business prompt words, and business tool services.

[0073] In addition, the embodiment of the present invention also provides an event-driven business intelligent body dynamic construction device, such as Figure 2 As shown, the event-driven business intelligence dynamic construction equipment specifically includes:

[0074] at least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0075] The memory stores instructions executable by at least one processor, so as to enable the at least one processor to perform:

[0076] Monitor business events occurring in the business system, obtain corresponding business event information and extract upstream business context information corresponding to the business event;

[0077] According to the business event and the upstream business context information, identifying the downstream business of the business event in the business system, and obtaining business information of the downstream business;

[0078] Based on the business event information and the business information of the downstream business, a business agent for executing the downstream business action is constructed and stored in a business agent center; wherein the business agent includes at least one key attribute of user role, target model, business prompt word, and business tool service;

[0079] When a new business event is monitored, a corresponding business agent is matched in the business agent center so that the business agent executes the downstream business action triggered by the business event.

[0080] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0081] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0082] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Thus, the embodiments of this specification may take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware. Furthermore, the embodiments of this specification may take 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.) containing computer-usable program code.

[0083] 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.

[0084] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0085] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0086] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A method for dynamically constructing an event-driven business intelligence entity, characterized in that: The method comprises: Build a large model service center and store several vertical domain large models in the large model service center; the large model service center can access large model services from multiple manufacturers, multiple fields, and multiple parameter sizes. Each large model service contains at least the following self-descriptive information: model classification, field of expertise, model ranking, and semantic description; The large model service center provides a unified execution interface for large model services and dynamically switches the vertical domain large model to be executed based on the self-description information of the large model services; Monitor business events occurring in the business system, obtain corresponding business event information and extract upstream business context information corresponding to the business event; According to the business event and the upstream business context information, identifying the downstream business of the business event in the business system, and obtaining business information of the downstream business; Based on the business event information and the business information of the downstream business, a business agent that executes the downstream business action is constructed and stored in the business agent center; wherein the business agent includes at least one key attribute of user role, target model, business prompt word, and business tool service; specifically including: After obtaining the event summary in the business event information, first search the pre-built business agent center for the business agent of the downstream business that matches the business event; If a matching business agent is found, it is directly extracted. If no matching business agent is found, a business agent capable of executing downstream business actions of the business event is constructed based on a preset program, specifically including: Extracting large model features required for building a business intelligence entity based on the business event information and the business information of the downstream business; wherein the large model features include at least model classification and model expertise; The vertical domain big model with the highest ranking in the matching field and classification of the big model features in the big model service center is selected as the target big model; Generate corresponding business prompt words according to the business event information and the business information of the downstream business; Recommend or generate business tool services based on the business service information corresponding to the business actions of the downstream business; Dynamically assembling the user role corresponding to the business event, the target macro model, the business prompt word, and the business tool service onto a business agent to obtain a business agent that executes downstream business actions, and storing the business agent entity in a pre-built business agent center; the stored information also includes the business event information and the relationship between the business event and the downstream business; When a new business event is monitored, a corresponding business agent is matched in the business agent center so that the business agent executes the downstream business action triggered by the business event.

2. The event-driven business agent dynamic construction method according to claim 1, characterized in that: Monitor business events occurring in the business system, obtain corresponding business event information, and extract upstream business context information corresponding to the business event, specifically including: Monitor business events occurring in the current business system based on a polling mechanism; Obtaining business event information of the business event; wherein the business event information includes at least event type, event summary, event status, and event source; Understand the semantics of the business event information through natural language processing (NLP) and knowledge graph technology, and identify the upstream business associated with the business event based on the semantics; The business information of the upstream business is extracted to obtain the upstream business context information; wherein the upstream business context information at least includes the business function, business operation, business entity and operation user role of the upstream business.

3. The event-driven business agent dynamic construction method according to claim 1, characterized in that: Identifying, in the business system, a downstream business of the business event according to the business event and the upstream business context information, and obtaining business information of the downstream business, specifically includes: Pre-creating a domain knowledge graph between business events in the business system; wherein the domain knowledge graph at least includes business entities, business actions, business services, and business event relationships; According to the business event and the upstream business context information, searching the domain knowledge graph for downstream businesses affected by the business event; Extracting business information of the downstream business; wherein the business information of the downstream business includes at least a business action, a user role of the business action, a business service corresponding to the business action, and a business entity corresponding to the business action.

4. The event-driven business agent dynamic construction method according to claim 1, characterized in that: Generate corresponding business prompt words according to the business event information and the business information of the downstream business, specifically including: Dynamically recalling the best prompt word template from a pre-built prompt word template center based on the event summary in the business event information and the business information of the downstream business; wherein the prompt word template includes at least the following dynamic parameters: user role, business function, business operation, and business entity; The dynamic parameters in the prompt word template are replaced with parameter values ​​extracted from the upstream service context information to obtain a service prompt word that matches the service event and the downstream service.

5. The event-driven business agent dynamic construction method according to claim 1, characterized in that: Recommend or generate business tool services based on the business service information corresponding to the business action of the downstream business, specifically including: Obtaining the business service corresponding to the business action in the business information of the downstream business, and extracting the corresponding business service information; wherein the business service information at least includes a service description, a service address, a service parameter list, and a return value; Based on the business service information, matching recommended business tool services in a pre-built agent tool service center; If no match is found, the corresponding business tool service is generated based on the preset script and loaded into the pre-built intelligent tool service center.

6. The event-driven business agent dynamic construction method according to claim 1, characterized in that: When a new business event is monitored, a corresponding business agent is matched in the business agent center so that the business agent executes the downstream business action triggered by the business event, specifically including: When a new business event is monitored, the business agent center matches the business agent of the corresponding downstream business based on the upstream business context information of the new business event; Based on the downstream business organization dialogue information, a dialogue is conducted with the matching business agent, so that the business agent executes the downstream business action triggered by the business event.

7. An event-driven business intelligence dynamic construction device, characterized in that: The device comprises: 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, so that the at least one processor can execute the event-driven business intelligence body dynamic construction method according to any one of claims 1-6.

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