Customer service demand intelligent pre-judgment active response method and system

Abstract

A customer service demand intelligent anticipation active response method and system relates to the technical field of intelligent customer service and artificial intelligence, constructs a data acquisition-model anticipation-response execution-closed loop optimization-operation and maintenance guarantee full-link technology collaborative system, and innovatively proposes a five-in-one framework of center-perception-execution-evolution-escort. The data layer adopts a multi-mode implicit signal acquisition and edge and cloud collaborative architecture to realize full-contact data comprehensive acquisition, real-time processing and secure transmission; the model layer improves cross-scene adaptability and pre-judgment accuracy through a dynamic adaptive federated learning and large model deep reasoning fusion system; the response layer drives a hierarchical response engine based on the priority of emotion fusion, and realizes humanized and precise active response; the optimization layer constructs a full-link closed-loop system and pushes the system to continuously iterate; the operation and maintenance guarantee layer provides intelligent monitoring and exception handling capacity and guarantees stable operation of the system.

Description

Technical Field

[0001] This invention relates to the fields of intelligent customer service and artificial intelligence technology, specifically to a method and system for intelligently predicting and proactively responding to customer service needs. Background Technology

[0002] Existing customer service demand prediction and response solutions still suffer from numerous technical shortcomings, making it difficult to form an efficient, accurate, and secure end-to-end service capability. When customers request services through customer service, their needs are unpredictable, and customer service reception assignments are often based on random principles such as workload and rotation. If the overall skill level of the customer service personnel assigned to handle these customers cannot meet their needs, it can easily lead to customer complaints and a poor service experience. Technically, current solutions generally only collect explicit data such as dialogue text and historical work orders for demand prediction, ignoring implicit signals such as user behavior patterns, device operating status, and environmental data. This results in limited demand coverage and an inability to capture implicit needs not explicitly stated by customers. Furthermore, most solutions use a purely cloud-based data collection and processing architecture, leading to high data transmission latency and making real-time prediction difficult. Uploading all raw sensitive data to the cloud also poses a serious risk of privacy leaks and does not comply with data compliance requirements. Existing prediction models mostly use centralized training methods, which are limited by data silos between different business lines and regions within an enterprise, preventing the full utilization of multi-scenario data to improve model generalization capabilities. Fixed model parameters make it difficult to adapt to the different needs of different business lines such as finance and home appliances. Furthermore, traditional Natural Language Processing (NLP) technologies can only achieve basic semantic recognition, making it difficult to analyze the specific needs corresponding to ambiguous expressions and lacking the ability to reason about the deeper demands behind those needs, resulting in insufficient accuracy and relevance in predictions. Existing solutions do not form a complete closed-loop system of "prediction-response-feedback-optimization," with customer feedback data only used for product optimization and not used to iterate prediction models and response strategies. The lack of a multi-dimensional quantitative evaluation mechanism for response effectiveness makes it impossible to accurately locate problems such as prediction bias and inappropriate response, hindering the continuous improvement of the overall system service capabilities.

[0003] Existing technologies cannot solve core issues such as comprehensive and secure data collection, cross-scenario model adaptation and accurate reasoning, personalized and humanized responses, and continuous system optimization and iteration. A solution for intelligent prediction and proactive response to customer service needs with full-link technology collaboration capabilities is needed to break through existing technological bottlenecks and improve customer service quality and operational efficiency. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing technologies, the present invention aims to provide a method and system for intelligent prediction and proactive response to customer service needs. By constructing a full-link technology collaboration system encompassing "data collection - model prediction - response execution - closed-loop optimization - operation and maintenance support," and integrating multi-dimensional innovative technologies, this invention solves problems such as incomplete data collection, poor model adaptability, low response accuracy, and lagging optimization iteration in existing solutions. It achieves accurate and real-time prediction of both explicit and implicit customer needs, as well as personalized and humanized proactive responses, while ensuring data security and compliance, and improving system stability and continuous optimization capabilities.

[0005] A customer service demand intelligent prediction and proactive response system includes a data layer, a model layer, a response layer, an optimization layer, and an operation and maintenance support layer. Each layer achieves end-to-end collaboration through data interaction. Specifically: (I) Data Layer: Multimodal Implicit Signal Acquisition and Edge-Cloud Collaborative Architecture The core of the data layer addresses the issues of comprehensive, real-time, and secure data acquisition, providing a comprehensive and reliable data foundation for subsequent model predictions. Specifically, it includes: 1. Multimodal Implicit Signal Acquisition Module: Deployed at edge nodes, this module collects real-time data from all customer touchpoints, including textual semantic data, behavioral trajectory data, device status data, and environmental data. The behavioral interaction data utilizes real-time WebSocket acquisition combined with sliding window analysis technology to improve the accuracy of capturing implicit sentiment and demand signals.

[0006] 2. Edge-Cloud Collaborative Processing Module: Edge nodes deploy a preliminary analysis module based on the widely used DistilBERT lightweight model, enabling millisecond-level data capture and triggering of urgent requests. The cloud deploys a full data integration and deep processing module; edge nodes only synchronize required feature data to the cloud, without transmitting raw sensitive data.

[0007] 3. Encrypted Synchronization and Offline Adaptation Module: Employs national-level encryption algorithms to achieve encrypted data synchronization between the edge and cloud, ensuring data transmission security. Edge nodes have built-in offline caching units that can complete basic data collection and response command caching during network interruptions, automatically synchronizing data once the network is restored.

[0008] (II) Model Layer: Dynamic Adaptive Federated Learning + Large Model Deep Inference Fusion System The core of the model layer is used to improve the accuracy and generalization ability of demand prediction, and to solve cross-scenario adaptation and privacy and security issues. Specifically, it includes: 1. Dynamic Adaptive Federated Learning Framework: This framework comprises multiple business line / regional training nodes and a cloud aggregation node. Each training node jointly trains the model without sharing the original data. The framework includes a built-in business-adaptive parameter adjustment unit, which automatically optimizes model weights and thresholds based on the specific needs of different business scenarios, such as finance. A differential privacy mechanism ensures the security of training data, and an integrated incremental learning unit enables real-time iterative updates of the model without requiring full retraining.

[0009] 2. Large-Model Deep Inference Module: Built on the Llama-3-8B-Instruct large model and integrated with domain knowledge graphs, this module possesses capabilities for fuzzy requirement parsing, deep requirement inference, and multi-round intent prediction. It employs a dynamic matching strategy of "lightweight model for rapid response + heavy-model for deep inference," with the lightweight model handling real-time preliminary prediction and the heavy-model handling in-depth parsing of complex requirements.

[0010] 3. Model Co-optimization Unit: The multi-scenario training data output by the federated learning framework provides data support for fine-tuning the large model. The inference results of the large model are fed back to the federated learning framework to optimize the model's feature weights. The integrated MAML few-shot learning algorithm enables rapid cold start of the model in scenarios with scarce data, such as new product launches.

[0011] (III) Response Layer: Prioritized Layered Response Engine Driven by Emotional Integration The core of the response layer is used to achieve accurate and human-like proactive responses, solving the problem of response being disconnected from context. Specifically, it includes: 1. Four-Dimensional Priority Assessment Unit: Constructs a four-dimensional assessment system based on urgency, customer value, scope of impact, and emotional intensity, quantifying customer sentiment through multimodal emotional feature extraction technology. Specifically, text sentiment analysis employs an optimized BERT+CNN fusion architecture, while speech sentiment analysis is based on the VGGish model, using emotional intensity as the core weight for priority, prioritizing responses to requests of the same level with higher negative sentiment.

[0012] 2. Hierarchical Emotional Adaptation Response Unit: Outputs differentiated response strategies based on priority. High-priority needs trigger emotional reassurance messages + rapid human intervention + remote assistance. Medium-priority needs push personalized solutions + intelligent guidance. Low-priority needs are addressed through targeted channels based on customer preferences. The response strategy employs a hybrid optimization scheme combining a rules engine and PPO reinforcement learning.

[0013] 3. Context-Aware Interaction Unit: Based on the optimized Transformer architecture DST module, a context memory is built to store historical customer interaction data, ensuring seamless response content. An integrated user behavior rhythm recognition unit analyzes operation frequency and response intervals to determine customer status and trigger responses during idle periods. Incorporating AgenticAI technology concepts, it achieves autonomous driving and seamless service processes.

[0014] (4) Optimization Layer: A pre-judgment - response - feedback - optimization full-link closed-loop system The core of the optimization layer is used to achieve continuous iterative optimization of the system and improve the full-link service capabilities, specifically including: 1. Multi-dimensional Effect Evaluation Unit: Quantify the pre-judgment accuracy and response effect through indicators such as customer satisfaction, problem-solving rate, response timeliness, and emotional change amplitude.

[0015] 2. Classification Feedback Empowerment Unit: Classify and label customer feedback data, and respectively input it back into the data layer, model layer, and response layer.

[0016] 3. Closed-loop Report and Business Feedback Unit: Automatically generate a full-link optimization report to provide data support for product defect rectification and service process optimization. Incorporate the concept of integrating sales and service, realize the联动 optimization of customer service data with marketing and product data, and enhance the enabling effect of service on business.

[0017] (5) Operation and Maintenance Guarantee Layer: An intelligent operation and maintenance and abnormal warning integrated module The core of the operation and maintenance guarantee layer is used to improve the system stability. It consists of a real-time monitoring module that monitors the running status of the model and the efficiency of the response link. An abnormal warning module that sets multi-dimensional abnormal thresholds and automatically triggers warnings and pushes fault location reports when situations such as a sudden drop in pre-judgment accuracy rate and excessive response delay occur. An automatic repair module that can automatically calibrate and repair minor faults such as model parameter drift and response channel congestion.

[0018] Based on the above system, the present invention also provides an intelligent pre-judgment and active response method for customer service requirements, including the following steps: 2]S1. Multi-modal Data Collection and Preprocessing: Real-time collect customer full-touch multi-modal data through the edge nodes of the data layer. After preliminary analysis by the lightweight model, encrypt and synchronize the demand feature data to the cloud.

[0019] S2. Intelligent Demand Pre-judgment: The model layer analyzes the feature data synchronized to the cloud based on the dynamic adaptive federated learning + large model deep reasoning fusion system, realizes fuzzy demand analysis, deep demand reasoning, and multi-round intention prediction, and outputs the demand type and pre-judgment confidence level.

[0020] S3. Response Priority Evaluation and Strategy Matching: The response layer determines the demand priority through the four-dimensional priority evaluation unit and matches the corresponding hierarchical emotion-adaptive response strategy.

[0021] S4. Active Response Execution: Based on the context-aware interaction unit, select the optimal timing to execute the active response through the customer-preferred channel.

[0022] S5. End-to-End Closed-Loop Optimization: The optimization layer collects customer feedback data, evaluates response effectiveness from multiple dimensions, and categorizes and reverse-engineers optimization at each level. The operations and maintenance layer monitors system status in real time, provides timely warnings, and handles anomalies.

[0023] The advantages of this invention compared to the prior art are: 1. More comprehensive and efficient data collection: Covers multimodal and full-touchpoint data, capturing both explicit and implicit needs; Edge-cloud collaboration + encryption mechanism, balancing real-time performance and data security; Supports offline caching, adapting to complex network environments.

[0024] 2. Accurate prediction and strong adaptability: Federated learning breaks down data silos and adapts to multiple business scenarios; large-scale model deep reasoning analyzes fuzzy / deep needs; collaborative optimization + few-sample learning enables efficient model iteration and cold start.

[0025] 3. Response experience and efficiency optimization: Four-dimensional priority allocation of resources and more accurate layered emotional adaptation response; context awareness + intelligent timing triggering reduce disturbances and improve the level of humanized service.

[0026] 4. Continuous optimization and business empowerment: full-chain closed-loop reverse optimization at each level; generating optimization reports to support business improvement; sales and service data linkage to adapt to industry development trends.

[0027] 5. Stable and efficient system operation and maintenance: Real-time monitoring and anomaly warning enable quick problem location; minor faults are automatically repaired, improving system availability and reducing manual operation and maintenance costs. Attached Figure Description

[0028] Figure 1 This is a diagram of the overall system architecture of the present invention.

[0029] Figure 2 This is a flowchart of the multimodal data acquisition process of the present invention.

[0030] Figure 3 This is a diagram of the training architecture of the federated learning model of this invention.

[0031] Figure 4 This is a logic diagram for predicting the requirements of this invention.

[0032] Figure 5 This is a flowchart illustrating the layered proactive response execution process of the present invention.

[0033] Figure 6 This is a schematic diagram of the end-to-end closed-loop optimization of the present invention. Detailed Implementation

[0034] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings. Many specific details are set forth in the following description to provide a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0036] Implementation, for example Figures 1-6 As shown, to achieve the above objectives, this invention innovatively proposes a five-in-one full-link collaborative architecture of "central hub-perception-execution-evolution-escort". This architecture uses the model layer as the intelligent central hub, the data layer as the global perception end, the response layer as the precise execution end, the optimization layer as the continuous evolution end, and the operation and maintenance support layer as the security escort end. Data is comprehensively aggregated from the perception end to the central hub for in-depth demand analysis. The execution end triggers personalized responses based on the analysis results. The evolution end and the escort end synchronously capture operational status and customer feedback, providing reverse support for dynamic optimization of the central hub and perception end, thus constructing a closed-loop, adaptively upgraded intelligent service system.

[0037] The system deployment in this embodiment requires the completion of hardware and software environment setup first. The edge nodes adopt industrial-grade edge computing gateways (CPU model: Intel Core i5-12400, memory 16GB, storage 128GB SSD) and are deployed on the client terminal side. One edge gateway is configured for every 1000 client terminals to ensure the real-time data collection. The cloud server adopts a distributed cloud server cluster (CPU model: Intel Xeon Platinum 8375C, single node memory 64GB, total cluster storage 10TB) and is deployed on the public cloud platform Alibaba Cloud, supporting elastic expansion to adapt to the data processing needs of multiple business lines. Each business line is configured with an independent training node (CPU: Intel Xeon Gold 6330, GPU: NVIDIA A100 40GB, memory 128GB), and the financial business line is deployed with 2 training nodes each. The cloud aggregation node shares hardware resources with the cloud server. In terms of software environment, the edge nodes are equipped with Ubuntu 22.04 LTS operating system and Python 3.9 environment, and deploy the DistilBERT lightweight model (version: transformers 4.30.2) and WebSocket server (version: websockets 11.0.3). The cloud uses CentOS 7.9 operating system, builds Hadoop 3.3.4 distributed storage framework and Spark 3.4.1 computing engine, and installs Llama-3-8B-Instruct large model (quantization version: 4-bit) and federated learning framework based on FedML 0.8.10 in Python 3.9 environment. The encryption component integrates the national cryptographic SM4 encryption algorithm library (version: gmssl 3.1.1), the differential privacy module uses TensorFlowPrivacy 0.8.0 and the privacy budget ε is set to 1.5. The edge nodes use SQLite 3.42.0 to store local cached data, and the cloud uses MySQL 8.0 to store requirement feature data, model parameters and business data, and MongoDB 6.0 to store customer interaction history and feedback data.

[0038] The implementation of the data layer begins with the multimodal implicit signal acquisition at edge nodes. Acquisition modules deployed on edge gateways capture real-time data from all customer touchpoints via WebSocket. Textual semantic data comes from input content in system chat windows and online business consultation interfaces. Behavioral trajectory data covers user click paths, page dwell time, and operation frequency. Implicit sentiment and demand signals are extracted using sliding window analysis technology (window duration set to 30 seconds, step size 10 seconds). Device status data includes the network connection status, battery level, and RAM usage of terminal devices. Environmental data collects information such as the network environment (5G / 4G / Wi-Fi), timestamps, etc., of the device's location. After acquisition, the DistilBERT lightweight model on the edge nodes performs preliminary analysis, filtering out feature data containing potential demands. Only this type of data is synchronized to the cloud using the national cryptographic SM4 encryption algorithm, while the original sensitive data is stored locally to prevent privacy leaks. Simultaneously, the offline caching unit built into the edge nodes continuously collects basic data and caches response commands during network interruptions, automatically completing data synchronization once the network is restored.

[0039] During model layer implementation, the dynamic adaptive federated learning framework initiates training nodes for each business line. The training nodes for the financial business line train their models locally based on their own business data, protected by a differential privacy mechanism, without transmitting raw data to the cloud aggregation node. The framework's built-in business adaptive parameter adjustment unit adjusts model weights towards transaction-related features based on scenario requirements such as order inquiries and after-sales rights protection. For financial scenarios such as account inquiries and wealth management consultations, it optimizes threshold settings for fund security-related features. After local training is complete, each node encrypts and uploads its model parameters to the cloud aggregation node. The aggregation node uses the FedAvg algorithm to fuse the parameters to generate a global model, which is then distributed to each training node for updates. Simultaneously, the integrated incremental learning unit receives new data in real time and updates the model parameters, eliminating the need for full retraining. The large-scale deep inference module is based on the Llama-3-8B-Instruct large model, integrating knowledge graphs and financial business rule graphs. It adopts a strategy of "lightweight model for fast response + heavy model for deep inference". The lightweight model (DistilBERT) completes the initial prediction within 50 milliseconds. For such ambiguous statements, the heavy model calls the domain knowledge graph for deep inference to identify potential after-sales return and exchange needs of users. At the same time, the model co-optimization unit uses multi-scenario training data output by federated learning for fine-tuning of the large model. The inference results of the large model are fed back to the federated learning framework to optimize feature weights. For data-scarce scenarios such as new product launches, the MAML few-shot learning algorithm (supporting fast training with 5-10 samples) is used to achieve model cold start.

[0040] When implementing the response layer, the four-dimensional priority evaluation unit first evaluates the demand type and confidence level output by the model layer. The urgency of the demand is graded according to whether the problem affects the current transaction and whether there is an account security risk. Customer value is determined based on the user's historical consumption amount and membership level. The scope of impact refers to the number of other users that the problem may involve. The emotional intensity is quantified by analyzing text sentiment through an optimized BERT+CNN fusion architecture and recognizing voice emotion through the VGGish model. The weight of emotional intensity is set at 40%. Demands of the same level with negative emotion scores higher than 80 points (out of 100) are given priority in response. Based on the evaluation results, the hierarchical emotional adaptation response unit matches differentiated strategies. High-priority needs are immediately triggered with emotional reassurance messages, and a dedicated human customer service representative is assigned to quickly intervene and provide remote assistance. Medium-priority needs are pushed personalized solutions through the customer's preferred channels, along with intelligent guidance messages to help users solve problems. Low-priority needs are targeted to users during their idle time (when the response interval is ≥5 minutes and there are no continuous clicks, as analyzed by the behavior rhythm recognition unit). The response strategy is continuously optimized through a rule engine combined with PPO reinforcement learning. The context-aware interaction unit builds a context memory bank based on the optimized Transformer architecture DST module to store users' historical consultation records and processing results, ensuring seamless connection between subsequent responses and historical interactions. It incorporates the AgenticAI technology concept to achieve autonomous driving of the service process, such as automatically guiding users through subsequent return and exchange operations after they inquire about product after-sales service.

[0041] The optimization layer is implemented with a closed-loop end-to-end approach. A multi-dimensional performance evaluation unit quantifies prediction accuracy and response effectiveness using metrics such as customer satisfaction ratings (1-5 points), problem resolution rate (number of successfully resolved requests / total requests), response timeliness (time from prediction completion to initiating a response), and emotional change magnitude (difference in emotional scores before and after the response). The categorized feedback empowerment unit classifies customer "satisfied" and "unsatisfied" ratings and feedback information annotated by human customer service. Data-driven feedback is input into the data layer to optimize collection dimensions; model-driven feedback is input into the model layer to adjust parameters; and response-driven feedback is input into the response layer to optimize strategies. The closed-loop reporting and business feedback unit automatically generates a monthly end-to-end optimization report, clearly defining metrics such as prediction accuracy and response adaptation rate. This provides data support for platform product defect rectification and financial institution service process optimization. It also enables the linkage between customer service data and marketing data, such as synchronizing product pain points reported by users in after-sales inquiries to the marketing department to optimize promotional scripts and adapt to the trend of intelligent customer service becoming agent-based and integrated.

[0042] The real-time monitoring module of the operations and maintenance layer continuously monitors key indicators such as model operating status (prediction accuracy, inference latency) and response link efficiency (response triggering time, channel accessibility). The anomaly warning module sets multi-dimensional thresholds; when the prediction accuracy drops sharply by more than 10% or the response latency exceeds 500 milliseconds, it automatically triggers an warning and pushes a fault location report to the operations and maintenance backend. The automatic repair module addresses model parameter drift by calling historically optimal parameters for calibration. For response channel congestion, it automatically allocates backup channel resources to ensure stable system operation, improve availability, and reduce manual operations and maintenance costs.

[0043] The intelligent prediction and proactive response method for customer service needs in this embodiment is executed according to the following steps: First, multimodal data collection and preprocessing: edge nodes collect multimodal data from all customer touchpoints in real time, and after preliminary analysis by the DistilBERT lightweight model, the encrypted synchronous demand feature data is sent to the cloud; Second, intelligent demand prediction: the federated learning framework and the large model deep inference module work together to parse fuzzy demands, infer deep-seated needs, and output demand types and prediction confidence levels; Third, response priority evaluation and strategy matching: the four-dimensional priority evaluation unit determines the demand priority and matches the corresponding hierarchical sentiment-adaptive response strategy; Fourth, proactive response execution: the context-aware interaction unit selects the customer's idle time and executes the response through preferred channels; Fifth, end-to-end closed-loop optimization: the optimization layer collects feedback data and evaluates the effect from multiple dimensions, which in turn empowers the optimization of each level, and the operation and maintenance support layer monitors and handles anomalies in real time, forming a complete service closed loop.

Claims

1. A customer service demand intelligent prediction and proactive response system, characterized in that, It includes a data layer, a model layer, a response layer, an optimization layer, and an operation and maintenance support layer. By constructing a full-link technical collaboration system that integrates multimodal data acquisition, federated learning, and large model fusion inference technologies, it achieves accurate real-time prediction of customers' explicit and implicit needs and personalized and proactive responses, while ensuring data security and compliance, improving system stability and continuous optimization capabilities, and effectively addressing existing technical shortcomings.

2. The intelligent prediction and proactive response system for customer service needs according to claim 1, characterized in that, The data layer adopts a multimodal implicit signal acquisition and edge-cloud collaborative architecture, including a multimodal implicit signal acquisition module, an edge-cloud collaborative processing module, and an encrypted synchronization and offline adaptation module. The multimodal implicit signal acquisition module is deployed on edge nodes and acquires full-touchpoint data such as text semantics, behavior trajectory, device status, and environment through WebSocket real-time acquisition and sliding window analysis technology. The edge nodes deploy the DistilBERT lightweight model for preliminary analysis, synchronizing only the required feature data to the cloud, while retaining the original sensitive data locally. Data encryption synchronization is achieved using national cryptographic-level encryption algorithms, and the edge nodes have built-in offline caching units to ensure basic data acquisition and response command caching when the network is interrupted, and automatic synchronization after the network is restored.

3. The intelligent prediction and proactive response system for customer service needs according to claim 1, characterized in that, The model layer is a fusion system of dynamic adaptive federated learning and large model deep inference, including a dynamic adaptive federated learning framework, a large model deep inference module, and a model collaborative optimization unit. The federated learning framework includes multiple business line / region training nodes and cloud aggregation nodes. Each node does not share the original data. It adapts to different business scenarios through a business adaptive parameter adjustment unit and introduces a differential privacy mechanism and incremental learning unit to ensure data security and real-time model iteration. The large model deep inference module is based on the Llama-3-8B-Instruct large model, integrates domain knowledge graphs, and adopts a "lightweight model for fast response + heavy model for deep inference" strategy to resolve fuzzy requirements and deep demands. The model co-optimization unit fine-tunes the large model using federated learning output data, and the inference results of the large model inversely optimize the feature weights of the federated learning model. It integrates the MAML few-shot learning algorithm to achieve rapid cold start of the model in data-scarce scenarios.

4. The intelligent prediction and proactive response system for customer service needs according to claim 1, characterized in that, The response layer is a priority-driven hierarchical response engine for emotion fusion, including a four-dimensional priority evaluation unit, a hierarchical emotion-adaptive response unit, and a context-aware interaction unit. The four-dimensional priority evaluation unit constructs an evaluation system of demand urgency, customer value, scope of influence, and emotion intensity. It analyzes text emotion through an optimized BERT+CNN fusion architecture and identifies speech emotion through a VGGish model, with emotion intensity as the core weight. The hierarchical emotional adaptation response unit outputs differentiated strategies based on priority. High-priority needs trigger emotional soothing, human intervention, and remote assistance; medium-priority needs push personalized solutions and intelligent guidance; and low-priority needs are targeted through customer preference channels. The response strategy adopts a hybrid optimization of rule engine and PPO reinforcement learning. The context-aware interaction unit builds a context memory bank based on the optimized Transformer architecture DST module, integrates a user behavior rhythm recognition unit to select idle time to trigger the response, and incorporates AgenticAI technology to achieve autonomous driving and connection of service processes.

5. The intelligent prediction and proactive response system for customer service needs according to claim 1, characterized in that, The optimization layer is a closed-loop system of prediction-response-feedback-optimization, including a multi-dimensional effect evaluation unit, a classified feedback empowerment unit, and a closed-loop report and business feedback unit; the multi-dimensional effect evaluation unit quantifies the prediction accuracy and response effect through indicators such as customer satisfaction, problem resolution rate, response timeliness, and emotional change magnitude. The classification and feedback empowerment unit categorizes and labels customer feedback data, then inputs it back into the data layer, model layer, and response layer to optimize the collection dimensions, model parameters, and response strategies. The closed-loop report and business feedback unit automatically generates a full-link optimization report to support product defect rectification and service process optimization, integrating the concept of sales and service integration to achieve linkage optimization of customer service data with marketing and product data.

6. The intelligent prediction and proactive response system for customer service needs according to claim 1, characterized in that, The operation and maintenance support layer is an integrated module for intelligent operation and maintenance and anomaly early warning, including a real-time monitoring module, an anomaly early warning module, and an automatic repair module. The real-time monitoring module continuously monitors key indicators such as model operation status and response link efficiency. The anomaly early warning module sets multi-dimensional anomaly thresholds, and automatically triggers an early warning and pushes a fault location report when situations such as a sharp drop in prediction accuracy or excessive response latency occur. The automatic repair module can automatically calibrate and repair minor faults such as model parameter drift and response channel congestion to ensure stable system operation.

7. A method for intelligently predicting and proactively responding to customer service needs, characterized in that, Includes the following steps: S1. Multimodal data acquisition and preprocessing: Real-time acquisition of multimodal data from all customer touchpoints through edge nodes of the data layer; after preliminary analysis by a lightweight model, the demand feature data is encrypted and synchronized to the cloud. S2. Intelligent Demand Prediction: The model layer is based on a dynamic adaptive federated learning + large model deep reasoning fusion system. It analyzes the feature data synchronized in the cloud to realize fuzzy demand parsing, deep demand reasoning and multi-round intent prediction, and outputs demand type and prediction confidence. S3. Response Priority Assessment and Strategy Matching: The response layer determines the priority of needs through a four-dimensional priority assessment unit and matches the corresponding hierarchical emotional adaptation response strategy. S4. Proactive Response Execution: Based on context-aware interaction units, proactive responses are executed at the optimal time through customer preference channels; S5. End-to-end closed-loop optimization: The optimization layer collects customer feedback data, evaluates the response effect from multiple dimensions, and provides reverse empowerment for optimization at each level through classification. The operation and maintenance layer monitors the system status in real time, provides timely warnings and handles anomalies.

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