Multi-platform intelligent customer service marketing system, method, medium, terminal and program product

Abstract

This application provides an intelligent customer service marketing system, method, medium, terminal, and program product applicable to multiple platforms. The system includes: a platform access module for accessing different public platforms based on a protocol adapter and creating corresponding enterprise customer service applications on each accessed public platform; a data processing module for processing message data interacted by users on the enterprise customer service applications of each accessed public platform to form adaptive input data; a retrieval and generation module for generating corresponding response content by retrieving from a multimodal knowledge base based on a large language model and the converted adaptive input data; and a business execution module for responding to positive content from user responses by calling the corresponding enterprise business system interface based on the model context protocol service mechanism to execute corresponding business operations. This application enables unified access across multiple platforms, intelligent decision-making, and deep integration with enterprise business systems.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to an intelligent customer service marketing system, method, medium, terminal and program product applicable to multiple platforms. Background Technology

[0002] With the continuous development of artificial intelligence and natural language processing technologies, intelligent customer service and marketing robots have been widely used in enterprise customer service, sales conversion, user operation and other scenarios.

[0003] However, existing marketing bots can only run on a single platform. If cross-platform deployment is required, repeated development and adaptation are necessary for each platform, which is time-consuming and labor-intensive. Furthermore, traditional customer service systems require manual configuration of fixed scripts and cannot be dynamically adjusted according to user needs. To meet user needs, human customer service representatives need to be trained, which increases costs.

[0004] Therefore, it is necessary to provide an intelligent customer service marketing system, method, medium, terminal, and program product applicable to multiple platforms to solve the above-mentioned problems existing in the prior art. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this application is to provide an intelligent customer service marketing system, method, medium, terminal and program product applicable to multiple platforms, so as to solve the technical problems of poor cross-platform compatibility and insufficient interactive intelligence of intelligent marketing robots in the prior art.

[0006] To achieve the above and other related objectives, the first aspect of this application provides an intelligent customer service marketing system applicable to multiple platforms, comprising:

[0007] The platform access module is used to access different public platforms based on the protocol adapter, and to create corresponding enterprise customer service applications on each public platform accessed.

[0008] The data processing module is used to process the message data interacted by users on the enterprise customer service application of the various public platforms they access, so as to form adaptive input data.

[0009] The retrieval and generation module is used to retrieve corresponding response content from the multimodal knowledge base based on the large language model and the transformed adaptive input data.

[0010] The business execution module is used to respond to the user's positive response to the response content and, based on the model context protocol service mechanism, call the corresponding enterprise business system interface to execute the corresponding business operation.

[0011] In some embodiments of the first aspect of this application, the data processing module includes: a format conversion unit, configured to convert the raw message data interacted by the user on the enterprise customer service application of the various public platforms accessed into a standard data format based on a unified data model conversion mechanism, and generate a corresponding dialogue ID for the message data in the standard data format; a middleware unit, configured to write the message data in the standard data format containing the dialogue ID into a message queue; and a context management unit, configured to supplement context information for each message data in the standard data format in the message queue based on the dialogue ID and a dynamic context window strategy, so as to form adapted input data.

[0012] In some embodiments of the first aspect of this application, the multimodal knowledge base includes a vector database and a knowledge graph, wherein the vector database is used to store vectorized unstructured enterprise business data; and the knowledge graph is used to store structured enterprise business data.

[0013] In some embodiments of the first aspect of this application, a context-aware module is also included, which is used to trigger a value-added service recommendation mechanism based on the user's business scenario and geographical location information.

[0014] In some embodiments of the first aspect of this application, a full-link visualization management module is also included, which is used to track the status of each business process in the entire link in real time based on an event-driven architecture, so as to manage each business process in the entire link.

[0015] In some embodiments of the first aspect of this application, an analysis and optimization module is also included, which is used to analyze historical data based on machine learning algorithms to generate report analysis results and to optimize based on the report analysis results.

[0016] To achieve the above and other related objectives, a second aspect of this application provides an intelligent customer service marketing method applicable to multiple platforms, comprising:

[0017] Based on the protocol adapter, access different public platforms and create corresponding enterprise customer service applications on each public platform.

[0018] The message data that users interact with on the enterprise customer service application of the various public platforms they access is processed to form adaptive input data.

[0019] Based on the large language model and the adapted input data, the corresponding response content is generated after searching the multimodal knowledge base.

[0020] In response to a positive response from the user, the corresponding enterprise business system interface is invoked based on the Model Context Protocol service mechanism to execute the corresponding business operation.

[0021] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method.

[0022] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to implement the method.

[0023] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the method.

[0024] As described above, the intelligent customer service marketing system, method, medium, terminal, and program products applicable to multiple platforms of this application have the following beneficial effects:

[0025] This system connects to different public platforms via protocol adapters and creates corresponding enterprise customer service applications on each platform. A data processing module processes user interactions on these applications to create adaptive input data, which is then fed into a large language model. This model retrieves responses from a multimodal knowledge base, generating appropriate responses. A business execution module responds to these responses with positive feedback, and based on the model context protocol service mechanism, invokes the corresponding enterprise business systems to perform business operations, thus constructing a complete intelligent marketing system. This application, through the collaborative operation of the platform access module, data processing module, retrieval and generation module, achieves intelligent and personalized marketing throughout the entire process, significantly improving service efficiency and user experience. Attached Figure Description

[0026] Figure 1 The diagram shown is a block diagram of an intelligent customer service marketing system applicable to multiple platforms, as described in one embodiment of this application.

[0027] Figure 2 The diagram shown is a schematic representation of an intelligent customer service marketing system applicable to multiple platforms, as described in one embodiment of this application.

[0028] Figure 3 The diagram shown is a flowchart illustrating an intelligent customer service marketing method applicable to multiple platforms in one embodiment of this application.

[0029] Figure 4 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0030] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0031] In the embodiments of this application, terms such as "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, "first XX" and "second XX" are merely used to distinguish different XXs and do not limit their order. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0032] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0033] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0034] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0035] <1> Unified Data Model (UDM): is an architecture or framework for integrating and standardizing different types of data. It aims to transform data from different sources, formats, and structures into a unified format for better data management and analysis.

[0036] <2> Distributed Conversation ID: A technology used to manage and isolate different conversations in a distributed system.

[0037] <3> Dynamic Context Window Strategy: A technique used to optimize the performance and resource utilization of dialog systems.

[0038] <4> Large Language Models (LLMs) are artificial intelligence models that have been trained on a large amount of data and have a large number of parameters. These models are usually based on deep learning architectures, such as Transformer, and are able to understand and generate natural language text. Large language models have made significant progress in the field of natural language processing (NLP) and have performed well in a variety of language-related tasks.

[0039] <5> MCP (Model Context Protocol) service mechanism: It is an open standard protocol for enhancing the ability of large language models (LLMs) to interact with external tools and data sources. Through standardized interfaces, it enables AI models to access various tools and services in a consistent manner, thereby achieving more flexible and efficient automated task processing.

[0040] <6> ERP (Enterprise Resource Planning) system: It is an integrated enterprise management software used to optimize the allocation and management of internal resources, covering multiple business areas such as production, procurement, sales, inventory, and finance.

[0041] <7> CRM (Customer Relationship Management) system: is a customer-centric management strategy and software system that aims to enhance a company's competitiveness and profitability by optimizing the interaction and relationship between the company and its customers, thereby improving customer satisfaction and loyalty.

[0042] <8> Work order management system: It is a tool used to record, track and manage internal or external service requests of an enterprise, and is commonly used in IT support, customer service, project management and other fields.

[0043] <9> Function Call Mechanism: When generating a response, the Large Language Model (LLM) identifies the external function (API, tool, or plugin) that needs to be called based on the user's input, automatically extracts the parameters and passes them to the function, and finally integrates the function's returned result into the response.

[0044] <10> Event-Driven Architecture (EDA) is a software architectural pattern that drives application behavior through the generation, propagation, and processing of events. In this architecture, system components communicate with each other via events, rather than through traditional synchronous calls or polling mechanisms. The core idea of ​​EDA is to decompose the application into a series of independent event-handling modules that interact through an event bus or message queue.

[0045] <11> Hot-swappable platform expansion: is a technology that allows hardware components to be added or removed dynamically while the system is running, without having to power off or restart the system.

[0046] The existing marketing robots have the following technical problems: (1) poor cross-platform compatibility: if cross-platform is required, it is necessary to repeatedly develop and adapt on different platforms, which is time-consuming and laborious; (2) insufficient interactive intelligence: traditional marketing systems cannot dynamically adjust the scripts according to the specific customer domain characteristics; (3) slow development cycle and low efficiency: existing marketing robots need to develop dialogue logic and marketing strategies separately for different business domains (such as retail / education / medical). Each time a new domain is adapted, the model needs to be retrained or the rules need to be rewritten. Before going online, a large number of domain parameters (such as product script library, service process) need to be manually configured; (4) low utilization of real-time data: lack of intelligent linkage mechanism with external APIs; (5) serious service silo problem: marketing consultation is separated from subsequent work orders and order management systems, resulting in a disconnect in customer experience. In response to the above technical problems, this application provides an intelligent customer service marketing system, method, medium, terminal and program product applicable to multiple platforms, realizing unified access to multiple platforms, intelligent decision-making and deep integration of enterprise business systems, solving the industry pain points of traditional marketing robot platform fragmentation, insufficient intelligence and lack of business closed loop.

[0047] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 1 Detailed explanation. Figure 1 This diagram illustrates a block diagram of an intelligent customer service marketing system applicable to multiple platforms, as shown in an embodiment of the present invention. The intelligent customer service marketing system applicable to multiple platforms in this embodiment mainly includes: a platform access module 101, a data processing module 102, a retrieval and generation module 103, and a business execution module 104.

[0048] The platform access module 101 is used to access different public platforms based on the protocol adapter, and to create corresponding enterprise customer service applications on each public platform accessed.

[0049] like Figure 2 As shown, public platforms can include mainstream platforms such as WeChat Work, DingTalk, Lark, and WeChat Official Accounts. For different public platforms, a protocol adapter is designed to provide a communication interface. The corresponding protocol adapter is responsible for parsing the message format and making API calls specific to the public platform, supporting rapid integration with new platforms. Specifically, the protocol adapter adopts a plug-in architecture design, allowing the system to dynamically load or unload plugins at runtime, improving system flexibility. Furthermore, enterprise customer service applications are created on the corresponding public platforms to automate and intelligently handle business inquiries and processes. It should be understood that the protocol adapter bridges different systems, services, or applications, enabling them to exchange and communicate effectively through different communication protocols.

[0050] The data processing module 102 is used to process the message data interacted by users on the enterprise customer service application of the various public platforms they access, so as to form adaptive input data.

[0051] In some embodiments of this application, the data processing module includes: a format conversion unit, used to convert the raw message data interacted by the user on the enterprise customer service application of the various public platforms accessed into a standard data format based on a unified data model conversion mechanism, and generate a corresponding dialogue ID for the message data in the standard data format; a middleware unit, used to write the message data in the standard data format containing the dialogue ID into a message queue; and a context management unit, used to supplement context information for each message data in the standard data format in the message queue based on the dialogue ID and a dynamic context window strategy, so as to form adapted input data.

[0052] Specifically, the raw message data generated from interactions between enterprise customer service applications and users on different public platforms varies in format (e.g., JSON, XML, and custom formats) due to platform differences. To simplify the complexity of cross-platform data processing, a unified data model conversion mechanism is used to convert the raw message data from various public platforms into a standard data format, ensuring seamless data flow across platforms. Simultaneously, a distributed message queue architecture is adopted to support high-concurrency message processing, with a single node processing capacity of thousands of messages per second. Furthermore, through distributed dialogue IDs and dynamic context window strategies, intelligent maintenance of cross-platform session state is achieved, preventing context loss due to platform switching.

[0053] Through a cross-platform protocol adaptation hub composed of a platform access module and a data processing module, multiple public platforms are uniformly accessed and the differences in the original message data formats of different public platforms are eliminated. If cross-platform is required, there is no need to repeatedly develop and adapt on different public platforms, which improves cross-platform compatibility. A single system supports all mainstream platforms, saving development costs and access cycles. It supports hot-swappable platform expansion and solves the platform silo problem in marketing and customer service.

[0054] The retrieval and generation module 103 is used to generate corresponding response content by retrieving from the multimodal knowledge base based on the large language model and according to the converted adaptive input data.

[0055] In some embodiments of this application, the multimodal knowledge base includes a vector database and a knowledge graph, wherein the vector database is used to store vectorized unstructured enterprise business data, and the knowledge graph is used to store structured enterprise business data.

[0056] Specifically, for example, when a user sends a business inquiry request to an enterprise customer service application, a large language model based on the Transformer architecture performs deep analysis of the user's intent, achieving high intent recognition accuracy. Furthermore, by combining the enterprise's historical user behavior data, real-time conversation context, and current adaptive input data, a vector retrieval algorithm matches the most relevant answers from a vector database within a multimodal knowledge base, as well as using a knowledge graph within the multimodal knowledge base, achieving cross-modal knowledge retrieval. By employing a hybrid retrieval architecture of knowledge graph and vector database, it supports unified retrieval of structured knowledge and unstructured text, providing rich and accurate information retrieval. For different user groups and specific business scenarios, the system automatically invokes differentiated dialogue templates and domain knowledge modules, generating personalized and precise intelligent responses based on the Function Call mechanism.

[0057] By integrating a large language model with multimodal knowledge bases corresponding to various enterprises, the system dynamically generates corresponding dialogues and personalized recommendations, significantly improving user satisfaction. It also supports multi-turn dialogues and complex business scenario processing, thereby enhancing the level of intelligence.

[0058] The business execution module 104 is used to respond to the positive content of the user's response to the response content, and to call the corresponding enterprise business system interface to perform the corresponding business operation based on the model context protocol service mechanism.

[0059] Specifically, if a user explicitly expresses business intent in response to the reply generated by the search generation module 103, the system triggers the automated process of the service orchestration unit to automate the orchestration and execution of the business process. Through the Model Context Protocol (MCP) service mechanism, the system can intelligently call external APIs and internal business system interfaces to automatically complete business operations such as appointment form filling, order information generation, and inventory queries. Simultaneously, the system deeply integrates with core enterprise business systems such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and work order management through a standardized API gateway, achieving real-time bidirectional synchronization of order, inventory, and user data, ensuring real-time synchronization of business data and seamless process integration. All business operations are guaranteed by a transaction management mechanism to ensure data consistency and rollbackability. If the user's response to the reply generated by the search generation module 103 is still a simple inquiry, then personalized reply content and differentiated wording are generated for the corresponding inquiry content.

[0060] By deeply integrating with enterprise business systems through the Model Context Protocol service mechanism, the traditional "consultation-to-human" model is upgraded to a fully automated "consultation-intelligent processing-business closed loop" model, which greatly improves the business conversion rate. Furthermore, the entire process from user consultation to order generation is automated, providing real-time data synchronization and anomaly warnings without the need for manual intervention, thus achieving business closed loop integration.

[0061] In some embodiments of this application, the intelligent customer service marketing system applicable to multiple platforms also includes a context-aware module, which is used to trigger a value-added service recommendation mechanism based on the user's business scenario and geographical location information.

[0062] In existing technologies, additional services such as reservations, weather inquiries, and optimal route recommendations typically rely on redirects to third-party websites, leading to interruptions in the user's workflow and a poor user experience. To address this, this application designs a context-aware module. This module intelligently activates based on the user's current business scenario and geographical location information. By integrating external services such as high-precision map APIs, meteorological data APIs, and traffic information APIs, or based on dialogue content derived from user profiles, it provides personalized value-added services to users. For example, it plans the optimal route and recommends parking spaces for in-store users, and provides weather warnings and travel suggestions for outdoor service scenarios. Specifically, the context-aware module employs a hybrid decision-making mechanism combining rule engines and machine learning models, dynamically adjusting service recommendation strategies based on user preferences and historical behavior to provide users with value-added service recommendations.

[0063] In some embodiments of this application, the intelligent customer service marketing system applicable to multiple platforms also includes a full-link visualization management module, which is used to track the status of each business process in the entire link in real time based on an event-driven architecture, so as to manage each business process in the entire link.

[0064] Specifically, the end-to-end visual management module provides enterprises with a real-time data flow service monitoring dashboard, enabling anomaly alerts and automated handling through an event-driven architecture. It tracks the entire process from user inquiry to business completion in real time. The system employs distributed tracing technology to record the execution status and performance metrics of each business node. When anomalies are detected, such as response timeouts, business failures, or decreased user satisfaction, the system automatically triggers an alert mechanism and performs automated processing or manual intervention according to preset rules. All abnormal events are recorded in the audit log, supporting problem backtracking and system optimization, thereby enabling visual management of all business processes across the entire chain.

[0065] In some embodiments of this application, the intelligent customer service marketing system applicable to multiple platforms further includes an analysis and optimization module, which is used to analyze historical data based on machine learning algorithms to generate report analysis results, and to optimize based on the report analysis results.

[0066] Specifically, after customer service and marketing services are completed, the analysis and optimization module automatically generates a complete user journey report, covering multi-dimensional information such as user behavior patterns, service quality indicators, and business conversion data. It automatically generates user behavior profiles and predictive models for each user, supporting targeted marketing and churn warnings. Through machine learning algorithms, historical data is analyzed in depth to identify user behavior patterns and business optimization opportunities. Based on the report analysis results generated by the machine learning algorithms, the system automatically updates the knowledge base, optimizes dialogue strategies, and adjusts business rules, forming a closed-loop optimization mechanism of data collection, analysis and insight, strategy optimization, and effect verification.

[0067] The end-to-end visualization management module collects and analyzes end-to-end data, providing enterprises with complete user journey insights. Furthermore, based on a continuous optimization mechanism using machine learning models, the system's performance and effectiveness are continuously improved as data accumulates over time.

[0068] Through the deep integration of the aforementioned platform access module, data processing module, retrieval and generation module, business execution module, context awareness module, full-link visual management module, and analysis and optimization module, a closed-loop intelligent system of perception-decision-execution-feedback is formed. This achieves full-process automation from user consultation to business closure. Furthermore, the system deployment is shortened through microservice architecture and containerized deployment, while providing a visual configuration interface, allowing business personnel to complete a large amount of configuration work.

[0069] It should be noted that the intelligent customer service marketing system applicable to multiple platforms in this application embodiment can be extended to multiple vertical fields such as online education, medical consultation, financial services, and government services by adjusting the knowledge base and business rules.

[0070] Figure 3 This is a flowchart illustrating an intelligent customer service marketing method applicable to multiple platforms, as provided in an embodiment of this application. Figure 3 As shown in the figure, the intelligent customer service marketing method applicable to multiple platforms in this application includes the following steps:

[0071] Step S31: Connect to different public platforms based on the protocol adapter, and create corresponding enterprise customer service applications on each public platform.

[0072] Step S32: Process the message data that the user interacts with on the enterprise customer service application of each public platform to form adaptive input data.

[0073] Step S33: Based on the large language model and the transformed adapted input data, retrieve the corresponding response content from the multimodal knowledge base.

[0074] Step S34: In response to the user's positive response to the response content, the corresponding enterprise business system interface is invoked based on the model context protocol service mechanism to perform the corresponding business operation.

[0075] It should be understood that the intelligent customer service marketing method applicable to multiple platforms in this embodiment can realize the functions of the intelligent customer service marketing system applicable to multiple platforms described above. For the sake of brevity, it will not be described again here.

[0076] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0077] Figure 4 This is a schematic block diagram of the electronic terminal provided in an embodiment of this application. Figure 4 As shown, the electronic terminal 400 includes at least one processor 401, a memory 402, at least one network interface 403, and a user interface 405. The various components in the electronic terminal 400 are coupled together via a bus system 404. It is understood that the bus system 404 is used to implement communication between these components. In addition to a data bus, the bus system 404 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 4 The general will label all buses as bus systems.

[0078] The user interface 405 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0079] It is understood that memory 402 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0080] In this embodiment of the invention, the memory 402 is used to store various types of data to support the operation of the electronic terminal 400. Examples of this data include: any executable program for operation on the electronic terminal 400, such as the operating system 4021 and application program 4022; the operating system 4021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 4022 may contain various applications, such as media players, browsers, etc., for implementing various application services. The intelligent customer service marketing method applicable to multiple platforms provided in this embodiment of the invention can be included in the application program 4022.

[0081] The methods disclosed in the above embodiments of the present invention can be applied to processor 401, or implemented by processor 401. Processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 401 or by instructions in the form of software. The processor 401 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 401 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 401 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0082] In an exemplary embodiment, the electronic terminal 400 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0083] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute... Figure 3 The method in the illustrated embodiment.

[0084] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when executed on a computer, causes the computer to perform... Figure 3 The method in the illustrated embodiment.

[0085] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0086] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0087] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0089] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0091] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0092] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] In summary, this invention provides an intelligent customer service marketing system, method, medium, terminal, and program product applicable to multiple platforms. It eliminates platform differences through unified access via a cross-platform protocol hub and converts raw message data into a standard data format based on a unified data model conversion mechanism. It achieves accurate responses by combining a large language model and a multimodal knowledge base, and provides intelligent personalized recommendations based on user profiles. It implements an intelligent service orchestration mechanism based on the Model Context Protocol (MCP) service mechanism and deeply integrates with enterprise business systems. It employs a context-aware module to provide personalized value-added services to users. It also designs a full-link visual management module based on an event-driven architecture to track the entire process status in real time. Furthermore, it designs an analysis and optimization module to automatically generate complete report analysis results for continuous optimization. This completes the closed-loop business process from user consultation on multiple platforms – intelligent semantic understanding – business process automation – enterprise business system integration – value-added service provision – data analysis and optimization, constructing a complete intelligent marketing solution. The collaborative operation of the system's platform access module, data processing module, retrieval and generation module, business execution module, context awareness module, end-to-end visual management module, and analysis and optimization module enables intelligent and personalized marketing throughout the entire process, significantly improving service efficiency and user experience. Even outside of working hours when human customer service is offline, users can still receive responses to their inquiries, avoiding the loss of potential customers due to untimely customer service. Therefore, this application effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0094] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A smart customer service marketing system applicable to multiple platforms, characterized in that, include: The platform access module is used to access different public platforms based on the protocol adapter, and to create corresponding enterprise customer service applications on each public platform accessed. The protocol adapter adopts a plug-in architecture design; Supports hot-swappable platform expansion; The data processing module is used to process the message data interacted by users on the enterprise customer service application of the various public platforms they access, so as to form adaptive input data. The data processing module includes: a format conversion unit, used to convert the raw message data interacted by users on the enterprise customer service application of various public platforms into a standard data format based on a unified data model conversion mechanism, and generate a corresponding dialogue ID for the message data in the standard data format; a middleware unit, used to write the message data in the standard data format containing the dialogue ID into a message queue; and a context management unit, used to supplement context information for each message data in the standard data format in the message queue based on the dialogue ID and a dynamic context window strategy, so as to form adapted input data. The retrieval and generation module is used to retrieve corresponding response content from the multimodal knowledge base based on the large language model and the transformed adaptive input data. The business execution module is used to respond to the positive content of the user's response to the response content, and to call the corresponding enterprise business system interface to perform the corresponding business operation based on the model context protocol service mechanism; The end-to-end visualization management module is used to track the status of each business process in the end-to-end in real time based on an event-driven architecture, so as to manage each business process in the end-to-end. The context-aware module is used to trigger a value-added service recommendation mechanism based on the user's business scenario and geographical location information.

2. The intelligent customer service marketing system applicable to multiple platforms according to claim 1, characterized in that, The multimodal knowledge base includes a vector database and a knowledge graph. The vector database is used to store vectorized unstructured enterprise business data, and the knowledge graph is used to store structured enterprise business data.

3. The intelligent customer service marketing system applicable to multiple platforms according to claim 1, characterized in that, It also includes an analysis and optimization module, which is used to analyze historical data based on machine learning algorithms to generate report analysis results, and to optimize based on the report analysis results.

4. A smart customer service marketing method applicable to multiple platforms, characterized in that, An intelligent customer service marketing system applicable to multiple platforms as described in any one of claims 1 to 3, comprising: Based on the protocol adapter, access different public platforms and create corresponding enterprise customer service applications on each public platform. The message data exchanged by users on the enterprise customer service application of the various public platforms they are connected to is processed to form adaptive input data. Based on the large language model and the adapted input data, the corresponding response content is generated after searching the multimodal knowledge base. In response to a positive response from the user, the corresponding enterprise business system interface is invoked based on the Model Context Protocol service mechanism to execute the corresponding business operation.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in claim 4.

6. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to implement the method as described in claim 4.

7. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method as described in claim 4.

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