AI intelligent customer service multi-channel interaction and data analysis system

Abstract

The invention relates to the technical field of AI intelligent customer service, in particular to an AI intelligent customer service multi-channel interaction and data analysis system which comprises a multi-channel access module, an interaction data preprocessing module, a multi-modal intention analysis module, a dynamic strategy generation module, a data capitalization management module and a visual feedback module. The multi-channel access module is used for receiving multi-source heterogeneous interaction data; the interaction data preprocessing module generates standardized interaction data; the multi-modal intention analysis module generates a multi-dimensional user portrait and a real-time session path; the dynamic strategy generation module outputs adjustment parameters of a dynamic service strategy; the data asset management module generates a data asset packet with a timestamp; and the visual feedback module is used for generating an interactive decision graph. According to the invention, the data security, the customer service intelligence level and the decision optimization capability are improved, and finally an efficient, accurate and safe intelligent customer service system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of AI intelligent customer service, and particularly to an AI intelligent customer service multi-channel interaction and data analysis system. Background Art

[0002] With the rapid development of artificial intelligence and big data analysis technologies, intelligent customer service systems have become important tools for enterprises to provide efficient customer services. Currently, customer service interaction channels are becoming increasingly diverse, including web pages, APPs, social media, Internet of Things devices, etc. A large amount of multi-source heterogeneous interaction data generated by these channels contains multi-modal information such as text, voice, and images. Therefore, how to efficiently integrate multi-channel data, accurately understand user intentions, optimize service strategies, and ensure data security and traceability are the key challenges faced by current intelligent customer service systems. At the same time, intelligent customer service not only needs to accurately understand user needs but also needs to have the ability to dynamically adjust strategies to adapt to changing business scenarios and improve user experience and operation efficiency.

[0003] Current intelligent customer service systems still have technical bottlenecks in many aspects. First, existing customer service systems are difficult to efficiently integrate multi-source heterogeneous data. There are significant differences in data formats and transmission methods among different channels, resulting in poor data consistency and affecting the accuracy of subsequent analysis. Second, in terms of user intention understanding, existing methods usually rely on a single modality and cannot fully utilize multi-modal information such as voice and images, resulting in insufficient intention recognition accuracy. In addition, the existing service strategy generation methods are relatively static and difficult to dynamically adjust according to user portraits and real-time conversation paths, resulting in a lack of flexibility in customer service responses. On the other hand, insufficient data security and traceability are also a major problem. Traditional customer service systems lack a blockchain evidence storage mechanism, and data is easily tampered with, affecting data credibility. In addition, the customer service system lacks visual decision support, and it is difficult for managers to intuitively analyze the implementation of strategies, resulting in a lack of data support for customer service optimization and reducing operation efficiency.

[0004] In view of the above problems, the present invention provides an AI intelligent customer service multi-channel interaction and data analysis system, which can intuitively analyze the implementation of strategies, realize the efficient, accurate, and secure operation of the intelligent customer service system, and effectively improve user experience and customer service response efficiency. Summary of the Invention

[0005] The present invention provides an AI intelligent customer service multi-channel interaction and data analysis system.

[0006] The AI intelligent customer service multi-channel interaction and data analysis system includes a multi-channel access module, an interaction data preprocessing module, a multi-modal intention analysis module, a dynamic strategy generation module, a data asset management module, and a visual feedback module, wherein;

[0007] The multi-channel access module receives multi-source heterogeneous interaction data from web pages, APPs, social media, and IoT devices;

[0008] The interaction data preprocessing module decouples features and aligns time and space for the accessed multi-source heterogeneous interaction data to generate standardized interaction data;

[0009] The multi-modal intention analysis module performs cross-modal feature fusion based on the standardized interaction data to generate multi-dimensional user portraits and real-time session paths;

[0010] The dynamic policy generation module generates dynamic service policies based on the multi-dimensional user portraits and real-time session paths and outputs adjustment parameters of the dynamic service policies;

[0011] The data asset management module implements blockchain evidence storage for the interaction process data to generate timestamped data asset packages;

[0012] The visualization feedback module performs associated mapping on the data asset packages and the dynamic service policies to generate an interactive decision-making map.

[0013] Optionally, the multi-channel access module includes:

[0014] Multi-protocol adaptation configuration: Configure adaptation protocols for different channels to establish communication links, including web page channels (configure HTTP / HTTPS protocol parsers), APP channels (configure TCP long connection channels), social media channels (configure OAuth2.0 authorization interfaces), and IoT device channels (configure OAuth2.0 authorization interfaces);

[0015] Asynchronous data reception: Use an asynchronous mechanism to receive multi-source heterogeneous interaction data in parallel;

[0016] Format standardization preprocessing: Convert multi-source heterogeneous interaction data in different formats into a unified intermediate data format, and use UTF-8 encoding to uniformly convert web page data (JSON), APP data (binary stream), social media data (XML messages), and IoT device custom protocol data into key-value pair sequences, add Base64 encoding identification headers to unstructured media data, and encapsulate streaming data with data frames marked with time windows;

[0017] Metadata tag injection: Attach four-dimensional metadata tags to each multi-source heterogeneous interaction data;

[0018] Data buffering and priority scheduling: Store the marked multi-source heterogeneous interaction data in the distributed message queue Kafka and classify it according to priorities, including high-priority partitions (real-time voice streams, IoT alarm data), medium-priority partitions (text interaction data), and low-priority partitions (historical log data);

[0019] Trusted transmission startup: Use TLS encrypted channel for data transmission.

[0020] Optionally, the interaction data preprocessing module includes:

[0021] Multi-source data cleaning: Noise filtering and missing value filling are performed on the received multi-source heterogeneous interactive data. For text data, regular expression matching is used to remove illegal characters, and for voice data, spectral subtraction is used to eliminate environmental noise.

[0022] Cross-modal spatiotemporal alignment: Dynamic Time Warping (DTW) algorithm is used to align multi-source heterogeneous interaction data;

[0023] Feature decoupling and reconstruction: Multimodal feature separation is performed on multi-source heterogeneous interaction data after spatiotemporal alignment, specifically including:

[0024] (1) Text feature extraction: using word vector projection matrix W txt Map word sequences to semantic space;

[0025] (2) Speech feature decoupling: using Mel filter banks to separate phoneme features and emotional features;

[0026] Unified tensor encapsulation: reconstruct the decoupled features into a standardized three-dimensional tensor T;

[0027] Abnormal interaction detection: Isolation forest algorithm is used to identify abnormal data points;

[0028] Normalized data output: Finally, normalized interaction data is output.

[0029] Optionally, the multimodal intent analysis module includes:

[0030] Cross-modal attention alignment: Construct a multimodal graph attention network and calculate the feature association weight α between modalities ij ;

[0031] Spatiotemporal feature fusion: Use 3D convolution gating mechanism to fuse spatiotemporal correlation features and generate spatiotemporal fusion feature tensors

[0032] Multi-granularity intent recognition: Outputs a set of intent labels through a hierarchical classification model;

[0033] Dynamic construction of user portraits: Update the user portrait feature matrix based on temporal interaction features;

[0034] Conversation path generation: Construct a weighted conversation transfer graph G = (V, E, W), where V = {v1, ..., v m} is a set of interaction event types, E is a set of edges, representing the transition between events, and W is the edge weight matrix;

[0035] Real-time path optimization: applying dynamic programming algorithm to solve the optimal session path π * (s t )。

[0036] Optionally, the dynamic policy generation module includes:

[0037] Intelligent policy inference engine: dynamically generating personalized service policies by combining deep reinforcement learning and rule inference based on multi-dimensional user portraits and real-time session paths;

[0038] Dynamic parameter adjustment and optimization: responsible for optimizing the execution effect of service policies in real time, and dynamically adjusting policy parameters by monitoring user feedback, session completion rate, and response timeliness.

[0039] Optionally, the intelligent policy inference engine includes:

[0040] Multi-source state representation construction: fusing multi-dimensional user portraits U and real-time session path G=(V, E, W) into state vector S t ;

[0041] Hybrid reward function design: defining the immediate reward function R of deep reinforcement learning t ;

[0042] Dual-channel policy network construction: jointly generating policies using policy network π θ (a|s) and rule inference engine;

[0043] Policy security verification: ensuring that the policy complies with business constraints through formal verification;

[0044] Real-time policy deployment: generating the final service policy set P t and injecting it into the execution engine.

[0045] Optionally, the dynamic parameter adjustment and optimization includes:

[0046] Multi-dimensional performance index monitoring: real-time collecting policy execution metrics, including user feedback index, response timeliness deviation, and policy volatility;

[0047] Policy performance evaluation model: constructing the policy value function Q(θ) to evaluate the benefits of policy parameter adjustment, and evaluating policy performance through the composite reward function R t ′;

[0048] Parameter space exploration strategy: using Bayesian optimization to search for policy parameters;

[0049] Policy gradient online update: updating policy parameters based on the proximal policy optimization (PPO) algorithm;

[0050] Multi - strategy effect comparison and verification: Execute the multi - armed bandit test to verify the effect of policy parameter update;

[0051] Safety margin constraint guarantee: Implement parameter safety constraints through the Lagrange multiplier method;

[0052] Parameter hot update deployment: Adopt a double - buffer mechanism to achieve lossless parameter update. Load the updated policy parameter θ new ′ into the shadow policy pool. When switching to the new parameter pool during the policy execution gap, the old parameters are retained for 300s as a rollback backup.

[0053] Optionally, the data asset management module includes:

[0054] Interactive data hash calculation: Calculate the hash value of the standardized interactive data to ensure data integrity and anti - tampering;

[0055] Data timestamp generation: Assign an immutable timestamp T to the standardized interactive data Z ;

[0056] Data asset package construction: Package the standardized interactive data, hash value, and timestamp into a data asset package P Z ;

[0057] Blockchain transaction construction: Construct the data asset package P Z into a blockchain transaction

[0058] Blockchain on - chain storage: Submit the transaction to the blockchain network and broadcast it to multiple nodes;

[0059] Data asset retrieval and verification: When it is necessary to verify the data asset, the transaction can be queried through the blockchain and the data integrity can be verified.

[0060] Optionally, the visualization feedback module includes:

[0061] Data asset and policy data fusion: Extract key information from the data asset package and the dynamic service policy to construct a fusion feature matrix M;

[0062] Mapping multi - dimensional data to a graph structure: Use a graph neural network (GNN) to map the data matrix into a weighted decision graph G′=(V′,E ″ ,W′), where V′ is the set of decision nodes, representing different customer service decision points, and E ″ is the edge between decision nodes, representing the policy execution path, and W′ is the weight matrix of the edge, measuring the correlation degree of the decision path;

[0063] Decision path weight calculation: Calculate the weight between any two decision nodes vi , v j The influence weight w ij ' between them;

[0064] Decision graph generation: Construct a visual decision graph according to the calculated G' = (V', E ″ , W').

[0065] Advantages of the present invention:

[0066] In the present invention, through multi-channel access, interactive data preprocessing, multi-modal intention analysis, dynamic policy generation, data asset management, and visual feedback, the efficient, accurate, and secure operation of the intelligent customer service system is realized. It can seamlessly integrate various interactive channels such as web pages, APPs, social media, and IoT devices, and ensure the consistency and reliability of data through standardized data preprocessing. By using cross-modal feature fusion to construct a multi-dimensional user portrait, the user intention can be accurately identified, and combined with reinforcement learning and rule reasoning, the dynamic optimization of personalized service strategies can be achieved. In addition, through priority scheduling and reliable transmission, the priority processing of high-real-time tasks is guaranteed, while reducing the impact of data traffic and improving the stability and response efficiency of the system.

[0067] In the present invention, by fusing the multi-dimensional user portrait and the real-time conversation path for policy reasoning, and combining reinforcement learning and rule reasoning to form a dual-channel policy network, the intelligence and controllability of policy decision-making are ensured. By using a mixed reward function to balance user satisfaction, task completion, and system response efficiency, and through parameter space exploration and PPO online gradient update for dynamic parameter optimization, the system is ensured to adapt to different business environments. In addition, through multi-armed bandit testing and security boundary constraint mechanisms, the security and stability of policy updates are ensured, and at the same time, a parameter hot update mechanism is adopted to ensure that the system does not interrupt during the policy upgrade process, further improving the adaptive ability and service quality of the intelligent customer service system.

[0068] In the present invention, by using blockchain technology to deposit the data of the interaction process, a timestamped data asset package is generated to ensure the security, immutability, and traceability of the data. At the same time, by parsing the complex relationships between data through graph neural networks, an interactive decision graph with weights is constructed to intuitively display the customer service decision path, support dynamic query and optimization, enabling managers to adjust strategies in real time and optimize the customer service process. Description of the Drawings

[0069] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the embodiments or the description of the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0070] Figure 1 Schematic diagram of system function modules according to an embodiment of the present invention;

[0071] Figure 2 Schematic diagram of a multimodal intent analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0073] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0074] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0075] like Figure 1 - Figure 2 As shown, the AI intelligent customer service multi-channel interaction and data analysis system includes a multi-channel access module, an interaction data pre-processing module, a multimodal intent analysis module, a dynamic strategy generation module, a data asset management module, and a visual feedback module, among which;

[0076] The multi-channel access module receives multi-source heterogeneous interaction data from web pages, apps, social media, and IoT devices;

[0077] The interaction data preprocessing module performs feature decoupling and spatiotemporal alignment on the incoming multi-source heterogeneous interaction data to generate standardized interaction data;

[0078] The multimodal intent analysis module performs cross-modal feature fusion based on standardized interaction data to generate multi-dimensional user portraits and real-time conversation paths;

[0079] The dynamic policy generation module generates dynamic service policies based on multi-dimensional user portraits and real-time session paths, and outputs the adjustment parameters of the dynamic service policies;

[0080] The data asset management module implements blockchain evidence storage for the interactive process data and generates timestamped data asset packages;

[0081] The visualization feedback module performs an association mapping between the data asset packages and the dynamic service policies to generate an interactive decision-making graph;

[0082] Through the above, the efficient, accurate and secure operation of the intelligent customer service system is realized. It can seamlessly integrate multiple interactive channels, improve the consistency and reliability of data processing, construct accurate user portraits through multi-modal analysis, improve the personalization and intelligence level of customer service responses, dynamically adjust service policies, enhance the system adaptability and user experience, use blockchain technology to ensure the trustworthy evidence storage of data, improve the security and traceability of data assets, and through the visual decision-making graph, improve the intuitiveness of data analysis and operation optimization capabilities, thereby constructing an efficient, intelligent and secure customer service system.

[0083] The multi-channel access module includes:

[0084] Multi-protocol adaptation configuration: Configure adaptation protocols for different channels and establish communication links, including web channels (configure HTTP / HTTPS protocol parsers), APP channels (configure TCP long connection channels), social media channels (configure OAuth2.0 authorization interfaces), and IoT device channels (configure OAuth2.0 authorization interfaces);

[0085] Asynchronous data reception: Adopt an asynchronous mechanism to receive multi-source heterogeneous interactive data in parallel, specifically including:

[0086] (1) Web: Real-time capture of page operation event streams through WebSocket interfaces;

[0087] (2) APP: Obtain buried point logs and touch track data from the SDK;

[0088] (3) Social media: Invoke the event subscription interface to obtain user private messages / comment streams;

[0089] (4) IoT devices: Listen to the sensor data reporting queue;

[0090] Format standardization preprocessing: Convert multi-source heterogeneous interaction data in different formats into a unified intermediate data format. Use UTF-8 encoding to uniformly convert web data (JSON), APP data (binary stream), social media data (XML message), and IoT device custom protocol data into a key-value pair sequence. Add a Base64 encoding identification header to unstructured media data, and encapsulate streaming data using data frames with time window markers;

[0091] Metadata tag injection: Attach four-dimensional metadata tags to each multi-source heterogeneous interaction data, specifically including:

[0092] (1) Data source tag: L s =(C,D f );

[0093] Among them, C represents the channel type, and D f is the device fingerprint information;

[0094] (2) Timeliness tag: L t =(T g ,TTL);

[0095] Among them, T g represents the data generation timestamp, and TTL is the data's effective survival period;

[0096] (3) Security level tag: L sec ={E1,E2,...,E n};

[0097] Among them, E i represents different security encryption levels;

[0098] (4) Session continuity tag:

[0099] Among them, is used to associate the interaction sequences of the same user across channels;

[0100] Data buffering and priority scheduling: Store the marked multi-source heterogeneous interaction data in the distributed message queue Kafka, and classify it according to priority, including high-priority partitions (real-time voice stream, IoT alarm data), medium-priority partitions (text interaction data), and low-priority partitions (historical log data);

[0101] Trusted transmission startup: Use a TLS encrypted channel for data transmission;

[0102] Through the above, the efficient integration, precise parsing, and secure transmission of heterogeneous interaction data are ensured. This module can flexibly adapt to various channels such as web pages, APPs, social media, and IoT devices, achieve unified management of cross-platform data, and improve the reliability of subsequent analysis through data standardization. The priority scheduling mechanism effectively optimizes data traffic, avoids system congestion, and at the same time, the simplified trusted transmission strategy reduces the computational burden while ensuring the security of key data, thereby enhancing the real-time performance and stability of the system.

[0103] The interactive data preprocessing module includes:

[0104] Multi-source data cleaning: Filter noise and fill in missing values for the received multi-source heterogeneous interaction data. Among them, for text data, regular expression matching is used to remove illegal characters, and for voice data, spectral subtraction is used to eliminate environmental noise, expressed as:

[0105]

[0106] where X(f) is the original spectrum of the voice signal, N(f) is the estimated noise spectrum, λ is the noise reduction coefficient, with a value range of 0.8 ≤ λ ≤ 1.2, β is the protection factor, with a value of 0.05, and E[|N(f)| 2 is the expected value of the noise power spectrum;

[0107]

[0108] where N(f) is the spectrum of the noise signal, |N(f)| 2 represents the power spectrum of the noise signal, M represents the number of frames used to estimate the noise, and N m (f) represents the noise spectrum of the m-th frame;

[0109] Cross-modal spatio-temporal alignment: The dynamic time warping (DTW) algorithm is used to align multi-source heterogeneous interaction data, and the alignment cost function is expressed as:

[0110]

[0111] where D(i,j) is the cumulative alignment distance, is the feature vector of data source A at time i, is the feature vector of data source B at time j;

[0112] Feature decoupling and reconstruction: Perform multi-modal feature separation on the multi-source heterogeneous interaction data after spatio-temporal alignment, specifically including:

[0113] (1) Text feature extraction: Use the word vector projection matrix W txt to map the word sequence to the semantic space, expressed as:

[0114]

[0115] Among them, W txt ∈R d×V is the word vector projection matrix, V is the vocabulary size, d is the word embedding dimension, w k is the k-th word in the word sequence, h txt is the text feature representation vector, representing the overall semantic representation of the input text sequence, and n is the length of the text sequence;

[0116] (2) Speech feature decoupling: Use the Mel filter bank to separate the phoneme features and emotional features, expressed as:

[0117] F mel = log(M·S STFT );

[0118] Among them, M is the Mel-scale triangular filter bank matrix, S STFT is the short-time Fourier transform (STFT) spectrum of the speech signal, and F mel is the Mel spectrum feature matrix;

[0119] Unified tensor encapsulation: Reconstruct the decoupled features into a standardized three-dimensional tensor T, expressed as:

[0120] T∈R B×T×D ;

[0121] Among them, B is the batch dimension, T is the time step, and D is the fused feature dimension;

[0122] D = d txt + d audio + d image ;

[0123] Among them, d txt , d audio , d image respectively represent the feature dimensions of text, speech, and image data;

[0124] Anomaly interaction detection: Use the Isolation Forest algorithm to identify anomaly data points, expressed as:

[0125]

[0126] Among them, h(x) is the path length of the data point x in the isolation tree, and c(l) is the normalization factor;

[0127]

[0128] Among them, H(l) is the l-th harmonic number, H(l - 1) is the (l - 1)-th harmonic number, l is the total number of data points, and k is the summation index variable;

[0129] Standardized data output: Finally, standardized interactive data is output;

[0130] Through the above content, noise and abnormal data can be effectively removed, data quality can be improved, the time dimensions of different modality data can be aligned using dynamic time warping to ensure information synchronization, the key information of multiple modalities such as text and speech can be extracted using feature separation and reconstruction methods to enhance the data expression ability, and finally structured standardized interactive data is generated to ensure data consistency and availability.

[0131] The multi-modal intention analysis module includes:

[0132] Cross-modal attention alignment: Construct a multi-modal graph attention network to calculate the feature correlation weight α between modalities ij , expressed as:

[0133]

[0134] where h i , h j represent the feature vectors of text, speech, and image modalities respectively, W is a learnable weight matrix, a is an attention vector, σ is the LeakyReLU activation function, and α ij is the influence weight of modality i on j, is the neighbor set of modality i;

[0135] Spatio-temporal feature fusion: Adopt a three-dimensional convolutional gating mechanism to fuse spatio-temporal correlation features to generate a spatio-temporal fusion feature tensor expressed as:

[0136]

[0137] where, is a learnable 3D convolutional kernel, T is a three-dimensional tensor, δ is the Sigmoid activation function, and ⊙ is the element-wise multiplication (Hadamard product);

[0138] Multi-granularity intention recognition: Output an intention label set through a hierarchical classification model, expressed as:

[0139]

[0140] where y k represents the k-th type of intention label, u k , u j are classification weight vectors, b k , b j are bias terms, K is the total number of intention categories, and P(y k |x) is the probability that the input feature x belongs to the k-th type of intention label;

[0141] Dynamic Construction of User Portrait: Update the user portrait feature matrix based on temporal interaction features, expressed as:

[0142]

[0143] Where U t is the user portrait representation at the current time step t, U t-1 is the user portrait representation at the previous time step t - 1, γ is the time decay factor, and the value range is 0.9 ≤ γ ≤ 0.99. is the user portrait feature mapping function;

[0144] Φ(x) = [Demographic; Behavior; Preference];

[0145] Where Demographic is demographic information, Behavior is behavioral pattern features, Preference is user interest preference features, and d u is the number of user portrait feature dimensions;

[0146] Session Path Generation: Construct a weighted session transition graph G = (V, E, W), where V = {v1,..., v m} is the set of interaction event types, E is the set of edges representing the transitions between events, and W is the edge weight matrix;

[0147] The update rule of the weight is expressed as:

[0148]

[0149] Where μ is the historical weight decay rate, and the value is μ = 0.85. is the indicator function for the event v i migrating to v j , is the session transition weight at the current time step t, is the session transition weight at the previous time step t - 1;

[0150] Real - time Path Optimization: Apply the dynamic programming algorithm to solve the optimal session path π * (s t ), expressed as:

[0151] π*(s t ) = argmax a∈A (R(s t , a) + δ′∑ s′ P(s′|s t , a)V(s′));

[0152] Where π * (st ) is the optimal policy, s t is the current session state, a is the optional action, R(s t , a) is the immediate reward function, δ is the discount factor, P(s′|s t , a) is the probability of transferring from state s t to s′ through action a;

[0153] Through the above, efficient and accurate user intention parsing is achieved, which can integrate multi-modal information such as text, voice, and images, improve the accuracy of intention recognition, optimize the user profile in combination with temporal information, ensure the dynamic adaptability of personalized recommendations, optimize the conversation path based on reinforcement learning, make the interaction process more in line with the user behavior pattern, thereby improving the intelligent level of the system's response and the user experience, and finally achieving efficient, intelligent, and accurate customer service decision support.

[0154] The dynamic policy generation module includes:

[0155] Intelligent policy inference engine: Based on the multi-dimensional user profile and the real-time conversation path, a personalized service policy is dynamically generated by combining deep reinforcement learning and rule inference;

[0156] Dynamic parameter adjustment and optimization: Responsible for real-time optimizing the execution effect of the service policy, and dynamically adjusting the policy parameters by monitoring user feedback, session completion rate, and response timeliness.

[0157] The intelligent policy inference engine includes:

[0158] Construction of multi-source state representation: Fuse the multi-dimensional user profile U and the real-time conversation path G=(V, E, W) into the state vector S t , expressed as:

[0159]

[0160] Among them, is the vector concatenation operation, is to flatten the graph structure G into a vector, where m is the number of nodes, d s =d u +m 2 is the dimension of the state vector, and LayerNorm is the layer normalization operation to ensure the consistency of different modal features in the numerical scale;

[0161] Design of the mixed reward function: Define the immediate reward function R of deep reinforcement learning t , expressed as:

[0162] R t =λ1R sat +λ2R comp -λ3Rdelay ;

[0163] Among them, the emotional score reward, α is the gain coefficient, and the emotional score is calculated from user feedback, the task completion reward, where τ save is the time-saving threshold, R delay = max(0, t resp - t SL ) is the response delay penalty, t SL is the service level agreement time limit, and λ1, λ2, and λ3 are weight coefficients;

[0164] Construction of the dual-channel policy network: The policy network π θ (a|s) is combined with the rule inference engine to generate a policy, expressed as:

[0165] π hybrid = β'·π θ (a|s)+(1 - β')·π rule (a|s);

[0166] π θ = softmax(W4·ReLU(W3S t ));

[0167]

[0168] Among them, π θ is the neural network policy based on PPO (Proximal Policy Optimization), and π rule is the rule inference policy, is the preset rule set, β' is the mixing weight, and π hybrid is the mixed policy;

[0169] Policy security verification: Ensure that the policy conforms to business constraints through formal verification, expressed as:

[0170]

[0171] Among them, is the set of critical business states, such as VIP customers, urgent matters, is the linear temporal logic (LTL) constraint formula, that is means "high-priority requests must be transferred to manual within 3 steps";

[0172] Real-time policy deployment: Generate the final service policy set P t and inject it into the execution engine, expressed as:

[0173]

[0174] Among them, a i is a policy action, is the execution probability after temperature scaling, T is the situation urgency coefficient, z i , z j are respectively the execution probabilities of policy actions a i , a j , is the execution time limit of the policy action;

[0175] Through the above content, the user portrait and the conversation path are integrated to construct an accurate state representation, improve the pertinence of policy generation, adopt a dual-channel policy network combining reinforcement learning and rule reasoning, make the system have both data-driven adaptability and rule-constrained controllability, introduce a mixed reward function, balance user satisfaction, task completion rate and response timeliness, ensure that the customer service policy is both intelligent and efficient, ensure policy security through formal verification, avoid violating business rules, and finally optimize policy execution by combining the temperature scaling method, so that the system can flexibly adapt to different business scenarios and provide accurate and personalized interaction experiences for users.

[0176] Dynamic parameter adjustment and optimization include:

[0177] Multi-dimensional performance index monitoring: Real-time collect policy execution indicators, including user feedback index, response timeliness deviation, policy volatility, expressed as:

[0178]

[0179] Among them, S i ∈[0,1] is the sentiment analysis score, C i ∈{0,1} is the conversation completion flag, α = 0.7 is the balance coefficient, controlling the weights of sentiment scoring and task completion rate, N is the number of user feedback samples, F t is the user feedback index;

[0180]

[0181] Among them, t resp is the actual response time, t SLA is the maximum allowable response time specified by the service level agreement, D t is the response timeliness deviation;

[0182]

[0183] Among them, θ τ is the policy parameter at time step τ, θ τ-1 is the policy parameter at time step τ - 1, T is the sliding window size, V t is the policy volatility;

[0184] Policy effectiveness evaluation model: Construct a policy value function Q(θ) to evaluate the benefits of policy parameter adjustment, and use a composite reward function R t ′ to evaluate the policy effectiveness, expressed as:

[0185]

[0186] where γ1 ∈ (0, 1) is the discount factor, which controls the impact of future rewards on the current policy evaluation, E′ is the expectation, and R t+k is the immediate reward at time step t + k;

[0187] Composite reward function: R t ′ = F t - ηD t - ξV t ;

[0188] where η is the timeliness penalty coefficient and ξ is the policy fluctuation penalty coefficient;

[0189] Parameter space exploration strategy: Use Bayesian optimization to search for policy parameters, expressed as:

[0190] θ new = argmax θ∈Θ (μ(θ) + κσ(θ));

[0191] where μ(θ) is the predicted mean of the Gaussian process regression, σ(θ) is the standard deviation, which measures the uncertainty of the parameters, θ new is the new policy parameter, Θ is the parameter search space, and κ is the exploration coefficient;

[0192] Policy gradient online update: Update the policy parameters based on the Proximal Policy Optimization (PPO) algorithm, expressed as:

[0193]

[0194] where θ new ′ is the updated new policy parameter, θ old is the old policy parameter currently in use,

[0195] is the learning rate, is the gradient, A t is the advantage function, that is, A t = Q(θ) - V(s), V(s) is the baseline value function of the current state s, clip is the policy clipping function, and δ′ is the clipping threshold, with a value of 0.2;

[0196] Multi-policy effect comparison and verification: Perform a multi-armed bandit test to verify the effect of policy parameter updates, expressed as:

[0197]

[0198] Among them, Q(θ new ′) is the policy value of the new parameter, and Q(θ old ) is the policy value of the old parameter. σ new ′ 2 and σ old 2 are the variances of the new and old policies respectively. n new ′ and n old are the sample numbers of the new and old policies respectively. Δ effect is the policy parameter improvement metric. When Δ effect > 2.0, accept the new parameter; otherwise, trigger the rollback mechanism to restore the old parameter;

[0199] Safety margin constraint guarantee: Implement parameter safety constraints through the Lagrange multiplier method, expressed as:

[0200]

[0201] Among them, D max = 0.5 is the maximum allowable response time deviation, and λ′ is the Lagrange multiplier, which is dynamically adjusted to ensure D t ≤D max , is the constraint optimization objective function;

[0202] Parameter hot update deployment: Implement lossless parameter update using a double-buffer mechanism. Load the updated policy parameter θ new ′ into the shadow policy pool. When switching to the new parameter pool during the policy execution gap, the old parameter is retained for 300 s as a rollback backup to ensure that the normal operation of the system is not affected during the parameter update process.

[0203] The data asset management module includes:

[0204] Interactive data hash calculation: Calculate the hash value of the standardized interactive data to ensure data integrity and anti-tampering, expressed as:

[0205] H Z = SHA-256(Z);

[0206] Among them, H Z is the hash value of the standardized interactive data Z, and SHA-256 is the secure hash algorithm;

[0207] Data timestamp generation: Assign an immutable timestamp T Z to the standardized interactive data to ensure the timeliness and traceability of the data, expressed as:

[0208] T Z= UnixTimestamp(CurrentTime);

[0209] Where UnixTimestamp is the Unix timestamp function and CurrentTime is the record of the current system time;

[0210] Data asset package construction: Encapsulate the standardized interaction data, hash value, and timestamp into the data asset package P Z , expressed as:

[0211] P Z = {Z, H Z , T Z , σ Z};

[0212] Where P Z is the complete data asset package, Z is the standardized interaction data, H Z is the SHA-256 hash value of the data, T Z is the Unix timestamp of the data, and σ Z is the digital signature (generated by encrypting with the private key and used to verify the data source);

[0213] Blockchain transaction construction: Construct the data asset package P Z into a blockchain transaction , expressed as:

[0214]

[0215] Where is the blockchain transaction record, Sign PrivateKey (P Z ) is to digitally sign the data asset package P Z using the private key, and PrivateKey is the private key of the data provider;

[0216] Blockchain on-chain storage: Submit the transaction to the blockchain network and broadcast it to multiple nodes to ensure the security and verifiability of data storage, expressed as:

[0217] Block t = Block(TX Z , H t-1 );

[0218] Where Block t is the newly generated block, Block is the block constructor, and H t-1 is the hash value of the previous block;

[0219] Data asset retrieval and verification: When it is necessary to verify the data asset, the transaction can be queried through the blockchain And verify data integrity, expressed as:

[0220]

[0221] Among them, D * is the data obtained by the query, is the hash value of the query data.

[0222] The visualization feedback module includes:

[0223] Data asset and policy data fusion: Extract key information from the data asset package and the dynamic service policy, and construct a fused feature matrix M, expressed as:

[0224]

[0225] Among them, M is the fused data matrix, is the vector concatenation operation, S strategy is the policy feature vector extracted from the dynamic service policy, d m = d p + d s is the fused feature dimension;

[0226] Mapping multi-dimensional data to a graph structure: Use a graph neural network (GNN) to map the data matrix to a weighted decision graph G′=(V′,E ″ ,W′), where V′ is the set of decision nodes, representing different customer service decision points, E ″ is the edge between decision nodes, representing the policy execution path, and W′ is the weight matrix of the edge, measuring the correlation degree of the decision path;

[0227] Calculating the weights of decision paths: Calculate the influence weight w i , v j between any two decision nodes v ij ′, expressed as:

[0228]

[0229] Among them, w ij ′ is the correlation weight between decision nodes v i and v j , M i , M j , M k are the data feature vectors of the corresponding nodes respectively, ∥M i - M j ∥2 is the Euclidean distance, measuring the similarity between nodes;

[0230] Decision graph generation: According to the calculated G′=(V′,E ″,W′) construct a visual decision-making graph, expressed as:

[0231]

[0232] Among them, is the finally generated interactive decision-making graph.

[0233] The present invention covers any alternatives, modifications, equivalent methods, and solutions made within the spirit and scope of the present invention. To enable the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention. However, those skilled in the art can fully understand the present invention without these detailed descriptions. Additionally, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0234] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An AI intelligent customer service multi-channel interaction and data analysis system, characterized in that, It includes multi-channel access module, interactive data pre-processing module, multimodal intent analysis module, dynamic strategy generation module, data asset management module and visual feedback module, among which; The multi-channel access module receives multi-source heterogeneous interaction data from web pages, apps, social media, and IoT devices; The interaction data preprocessing module performs feature decoupling and spatiotemporal alignment on the received multi-source heterogeneous interaction data to generate standardized interaction data; The multimodal intent analysis module performs cross-modal feature fusion based on standardized interaction data to generate multi-dimensional user portraits and real-time conversation paths; The dynamic strategy generation module generates a dynamic service strategy based on the multi-dimensional user profile and the real-time session path, and outputs the adjustment parameters of the dynamic service strategy; The data asset management module implements blockchain authentication for the interaction process data and generates a data asset package with a timestamp; The visual feedback module associates and maps data asset packages with dynamic service strategies to generate an interactive decision map.

2. The AI intelligent customer service multi-channel interaction and data analysis system according to claim 1, wherein The multi-channel access module includes: Multi-protocol adaptation configuration: Configure adaptation protocols for different channels and establish communication links, including web channels, APP channels, social media channels, and IoT device channels; Asynchronous data reception: Use asynchronous mechanism to receive multi-source heterogeneous interactive data in parallel; Format standardization preprocessing: Convert multi-source heterogeneous interactive data in different formats into a unified intermediate data format. Use UTF-8 encoding to uniformly convert web page data, app data, social media data, and IoT device custom protocol data into a sequence of key-value pairs. Add Base64 encoding identification headers to unstructured media data, and encapsulate streaming data using data frames with time window markers. Metadata tag injection: attach a four-dimensional metadata tag to each piece of multi-source heterogeneous interaction data; Data buffering and priority scheduling: Store the marked multi-source heterogeneous interaction data in the distributed message queue Kafka and classify it according to priority, including high-priority partitions, medium-priority partitions, and low-priority partitions; Trusted transmission startup: Use TLS encrypted channel for data transmission.

3. The AI intelligent customer service multi-channel interaction and data analysis system according to claim 1, characterized in that, The interactive data preprocessing module includes: Multi-source data cleaning: Noise filtering and missing value filling are performed on the received multi-source heterogeneous interactive data. For text data, regular expression matching is used to remove illegal characters, and for voice data, spectral subtraction is used to eliminate environmental noise. Cross-modal spatiotemporal alignment: Dynamic time warping algorithm is used to align multi-source heterogeneous interaction data; Feature decoupling and reconstruction: Multimodal feature separation is performed on multi-source heterogeneous interaction data after spatiotemporal alignment, specifically including: (1) Text feature extraction: Use the word vector projection matrix W txt Map the word sequence to the semantic space; (2) Speech feature decoupling: using Mel filter banks to separate phoneme features and emotional features; Unified tensor encapsulation: reconstruct the decoupled features into a standardized three-dimensional tensor T; Abnormal interaction detection: Isolation forest algorithm is used to identify abnormal data points; Normalized data output: Finally, normalized interaction data is output.

4. The AI intelligent customer service multi-channel interaction and data analysis system according to claim 3, characterized in that, The multimodal intent analysis module includes: Cross-modal attention alignment: Construct a multi-modal graph attention network to calculate the feature correlation weight α between modalities ij ; Spatio-temporal feature fusion: A three-dimensional convolutional gating mechanism is used to fuse spatio-temporal correlation features to generate a spatio-temporal fusion feature tensor Multi-granularity intent recognition: Outputs a set of intent labels through a hierarchical classification model; Dynamic construction of user portraits: Update the user portrait feature matrix based on temporal interaction features; Session path generation: Construct a weighted session transition graph G = (V, E, W), where V = {v1,..., v m} is the set of interaction event types, E is the set of edges representing the transitions between events, and W is the edge weight matrix; Real-time path optimization: Applying the dynamic programming algorithm to solve for the optimal session path π * (s t )。 5. The AI intelligent customer service multi-channel interaction and data analysis system according to claim 4, characterized in that, The dynamic policy generation module includes: Intelligent policy inference engine: Based on multi-dimensional user portraits and real-time session paths, it dynamically generates personalized service policies by combining deep reinforcement learning and rule inference; Dynamic parameter adjustment and optimization: Responsible for optimizing the execution effect of service policies in real time. By monitoring user feedback, session completion rate, and response timeliness, it dynamically adjusts policy parameters.

6. The AI intelligent customer service multi-channel interaction and data analysis system according to claim 5, characterized in that, The intelligent policy inference engine includes: Multi-source state representation construction: Fuse the multi-dimensional user profile U with the real-time session path G = (V, E, W) into the state vector S t ; Hybrid Reward Function Design: Define the immediate reward function R of deep reinforcement learning t ; Construction of dual-channel policy network: The policy network π θ (a|s) is combined with the rule inference engine to generate policies; Policy security verification: Ensures that the policy complies with business constraints through formal verification; Real-time policy deployment: Generate the final service policy set P t And inject it into the execution engine.

7. The AI intelligent customer service multi-channel interaction and data analysis system according to claim 6, wherein The dynamic parameter adjustment and optimization includes: Multi-dimensional performance index monitoring: Real-time collection of policy execution metrics, including user feedback index, response timeliness deviation, and policy volatility; Policy Efficiency Evaluation Model: Construct a policy value function Q(θ) to evaluate the benefits of policy parameter adjustment, and use a composite reward function R t ′ Evaluate policy efficiency; Parameter space exploration strategy: Uses Bayesian optimization for policy parameter search; Policy gradient online update: Updates policy parameters based on the proximal policy optimization algorithm; Multi-policy effect comparison and verification: Performs multi-armed bandit tests to verify the effect of policy parameter updates; Security boundary constraint guarantee: Implements parameter security constraints through the Lagrange multiplier method; Parameter Hot Update Deployment: Implement lossless parameter update using a double-buffer mechanism. Load the updated policy parameter θ new ′ into the shadow policy pool. When switching to the new parameter pool during the policy execution gap, the old parameters are retained for 300s as a rollback backup.

8. The AI intelligent customer service multi-channel interaction and data analysis system according to claim 7, characterized in that, The data asset management module includes: Interactive data hash calculation: Calculates the hash value of standardized interactive data to ensure data integrity and anti-tampering; Data Timestamp Generation: Assign an immutable timestamp T to the standardized interaction data Z ; Data Asset Package Construction: Encapsulate the standardized interaction data, hash value, and timestamp into a data asset package P Z ; Blockchain transaction construction: constructing data asset package P Z into blockchain transaction TX Z ; Blockchain on-chain storage: Submit transaction TX Z to the blockchain network and broadcast it to multiple nodes; Data Asset Retrieval and Verification: When it is necessary to verify a data asset, the transaction TX can be queried through the blockchain Z and the data integrity can be verified.

9. The AI intelligent customer service multi-channel interaction and data analysis system according to claim 8, characterized in that, The visualization feedback module includes: Data asset and policy data fusion: Extracts key information from the data asset package and dynamic service policies to construct a fusion feature matrix M; Mapping multi-dimensional data to a graph structure: Using a graph neural network to map the fused feature matrix to a weighted decision graph G ′ =(V ′ , E ″ , W ′ ), where V ′ is the set of decision nodes, representing different customer service decision points, E ″ is the edge between decision nodes, representing the policy execution path, and W ′ is the weight matrix of the edge, measuring the correlation degree of the decision path; Decision path weight calculation: Calculate the influence weight w i between any two decision nodes v j and v ij ′ ; Decision graph generation: Construct a visual decision graph based on the calculated G ′ =(V ′ , E ″ , W ′ ).

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