A natural language data processing system for online customer service interactions

Abstract

The application relates to the technical field of customer service system text semantic processing, and discloses a natural language data processing system for online customer service interaction, which comprises a context sequence discretization shunting module, a pivot feature matrix transformation topology module, a time sequence state transfer arbitration module and an intention divergence state control module. The shunting module extracts a discrete word element feature vector, the topology module converts the feature vector into a sparse adjacency matrix to establish an intention state topology directed acyclic graph, the arbitration module determines a semantic divergence coefficient according to the intention state topology directed acyclic graph, and the control module converges a state transition track through topology pruning and matrix replacement when the divergence coefficient reaches 0.68. The application decouples the conversation processing complexity from the interaction round, maintains a sparse feature space form, avoids queue accumulation, and effectively improves the data processing stability.

Description

Technical Field

[0001] This invention belongs to the field of text semantic processing technology for customer service systems, and particularly relates to a natural language data processing system for online customer service interaction. Background Technology

[0002] Currently, multi-turn interaction architectures are commonly used, primarily relying on the concatenation and splicing of historical context text sequences. This involves mechanically superimposing multiple rounds of historical dialogue content to construct a high-dimensional dependency window, extracting the feature vector of the current turn and matching intent. While this approach maintains basic intent recognition stability in low-frequency, stable interactions, it suffers from shortcomings at the software level, such as in control methods. It is difficult to flexibly adapt to non-ideal industrial environments. For example, Chinese invention patent application CN114428844A discloses a dialogue management method, system, device, and storage medium. The system relies on a multi-level finite state machine structure, switching between dialogue nodes based on preset jump conditions to control the process. However, such control methods implicitly rely on static preset premises where the state is completely determined. When faced with complex user text expressions and random switching between multiple topics, the actual dynamic boundary conditions are fundamentally mismatched with the fixed jump logic of the state machine. Due to the lack of semantic context logic perception and adaptive capabilities, the state machine is prone to logical deadlock when abnormal disturbances beyond the preset topology occur in the online interaction.

[0003] In high-concurrency, continuous-response scenarios, interactive text streams are prone to typos and polyphonic character distortions. Traditional splicing windows struggle to filter out such abnormal noise. As the number of session rounds increases, the accumulated disordered strings cause the information entropy in the feature space to diverge, leading to an expansion of text sequence length. Weakly related terms in long sequences overlap, causing abnormal deviations in state transition trajectories and resulting in variations in the semantic focus of the session. Due to the explosive increase in the dimensionality of the feature space, processor computational resource consumption rises continuously, causing response latency during high-concurrency peaks. To control overhead, conventional techniques attempt to increase the number of processor cores or adopt sliding window hard truncation strategies, but increasing computing power cannot suppress high concurrency. The exploding trend of the feature space itself can easily trigger resonance and accumulation in the core processing queue. Using sliding window hard truncation to forcibly abandon interactive content outside the specified previous rounds severs the causal relationship between multiple rounds of conversation, causing the system to lose its intent tracking and matching ability when faced with random topic switching or discontinuous expression. Solving these defects requires breaking the mechanical stacking and splicing mode of the entire historical text, achieving adaptive sparsity constraints on the text feature space while maintaining the causal relationship of the context, and establishing an effective strategy reorganization loop when sudden high concurrency overload or feature distortion occurs, so as to achieve dynamic narrowing of the intent calculation optimization boundary and ensure the stability of data processing during high concurrency and dense interaction.

[0004] Therefore, the technical problem to be solved by this invention is how to reorganize the multi-round interactive text flow to construct a sparse and convergent distributed semantic topology graph, and dynamically schedule computing resources to ensure the temporal convergence of feature extraction when complex distortion noise and high concurrency overload occur. Summary of the Invention

[0005] This invention aims to solve the problems of nonlinear explosion in the feature space complexity of text streams in continuous multi-round high-frequency sessions, as the number of session rounds increases, and processor queue accumulation and deadlock under sudden large concurrency peaks.

[0006] In this technical solution, a natural language data processing system for online customer service interaction includes: The context sequence discretization and splitting module is used to access interactive text strings, extract discrete word feature vectors, and read historical core intent states from the context state cache space. The hub feature matrix transformation topology module is used to divide the multi-turn conversation text stream into blocks based on the co-occurrence distribution decay rate among the components of the discrete word feature vector, and to convert the historical core intent state and discrete word feature vector into a sparse adjacency matrix to establish an intent state topology directed acyclic graph. The temporal state transition arbitration module is used to extract the discrete variance of non-zero eigenvalues ​​in the sparse adjacency matrix and solve the eigenvalue dispersion, thereby determining the semantic divergence coefficient of the intended state topological directed acyclic graph. The intent divergence state control module is used to start the topology pruning program when the semantic divergence coefficient reaches 0.68, stripping non-adjacent semantic state nodes to filter discrete word feature vectors, and calling the basic stemming transition matrix to replace the prior semantic space transition matrix, so that the state transition trajectory is stabilized within the preset convergence range.

[0007] Preferably, the extraction of discrete word feature vectors in the context sequence discretization and splitting module includes: importing the interactive text string into the word segmentation channel of the context sequence discretization and splitting module, and segmenting the discrete word sequence using the maximum matching operator; assigning index numbers to each discrete word in the discrete word sequence based on the vocabulary dictionary to construct an initial numerical vector; scaling the initial numerical vector to generate discrete word feature vectors, and storing them in the context state cache space.

[0008] Preferably, the intention state topological directed acyclic graph in the hub feature matrix transformation topology module includes: calculating the joint co-occurrence frequency of each component in the discrete word feature vector within a multi-round conversation interaction time window, and obtaining the physical word span distance between two components on the text chain; calculating the ratio of the joint co-occurrence frequency to the physical word span distance to output the co-occurrence distribution decay rate, and when the co-occurrence distribution decay rate is greater than 0.5, establishing the corresponding two components as topological association nodes; taking the historical core intention state as the starting node, the discrete word feature vector as the current node, and filling the sparse adjacency matrix according to the directed transition probability between topological association nodes to generate the intention state topological directed acyclic graph.

[0009] Preferably, the semantic divergence coefficient of the directed acyclic graph of the intention state topology in the temporal state transition arbitration module includes: calculating the average value of all non-zero eigenvalues ​​in the sparse adjacency matrix; calculating the discrete variance of each non-zero eigenvalue relative to the average value, and obtaining the ratio of the discrete variance to the total dimension of the sparse adjacency matrix to determine the eigenvalue dispersion; and outputting the eigenvalue dispersion as the semantic divergence coefficient to monotonically reflect the topic drift intensity of multi-turn conversations.

[0010] Preferably, the process of initiating the topology pruning procedure and stripping non-adjacent semantic state nodes in the intent divergence state control module includes: issuing a pruning instruction to the hub feature matrix transformation topology module when the semantic divergence coefficient reaches 0.68; blocking the graph extension of the intention state topology directed acyclic graph to unconnected branches, and forcibly stripping historical non-adjacent semantic state nodes with a topological distance greater than 2 from the current node; and calling the basic stemming transition matrix to replace the prior semantic space transition matrix, so that the state transition trajectory of the intention state topology directed acyclic graph is stabilized within a preset convergence range.

[0011] Preferably, it also includes a session queue backlog amplitude monitoring module and a text processing computing power degradation scheduling module; the session queue backlog amplitude monitoring module is connected to the intent divergence state control module, and is used to collect the transient waiting latency and data packet loss rate when the processor is overloaded. Based on the product of the data packet loss rate and the baseline processing capacity, and linearly superimposed with the transient waiting latency, the session queue backlog amplitude parameter is calculated and output using the following formula: ,in, For the session queue backlog amplitude parameter; For transient wait delay, Packet loss rate; The baseline processing capacity of the processor; the text processing computing power degradation scheduling module is connected to the session queue backlog amplitude monitoring module and the timing state transition arbitration module, respectively. When the session queue backlog amplitude parameter exceeds 1.2, it includes: enabling the computing power yielding degradation scheduler to disable the branch search of edge intents; limiting the search range of the static intent mapping matrix to the top 3 core business branches with the highest priority, narrowing the optimization boundary to converge intent recognition instructions.

[0012] Preferably, limiting the search scope of the static intent mapping matrix to the top 3 core business branches with the highest priority includes: obtaining the baseline priority of each core business branch in the static intent mapping matrix and filtering out the top 3 core business branches with the highest priority; limiting the optimization boundary to the top 3 core business branches with the highest priority and locking the current dialogue intent through heuristic filtering rules.

[0013] Preferably, the system also includes a linkage hedging channel: when the backlog amplitude parameter of the conversation queue drops below 1.0 and the semantic divergence coefficient drops below 0.4, the branch search of the closed edge intent is resumed, and the intent divergence state control module restores the basic stemming transition matrix to the prior semantic space transition matrix.

[0014] Preferably, the system also includes a cache update channel: after locking the current dialogue intent, the current dialogue intent is written as a new historical core intent state into the context state cache space to overwrite the original historical core intent state.

[0015] Preferably, the system also includes a status output channel: converting the locked current dialogue intent into a standardized intent recognition command, and sending the standardized intent recognition command to the business response node of the online customer service interaction system through the network interface to drive the corresponding interaction channel to make a text reply.

[0016] Compared with existing technologies, the natural language data processing system for online customer service interaction of the present invention has the following advantages: 1. In the natural language data processing of online customer service interaction, the context sequence discretization and diversion module collects the text stream and converts it into character vectors. The intent divergence state control module calculates the co-occurrence space damping ratio between components. By comparing the damping ratio with the monotonic truncation threshold, weak association branches are cut off, and the continuous context structure is reorganized into a directed acyclic state topology graph composed of discrete semantic cells and bridging hub words. The temporal state transition arbitration module uses the activated semantic cells and the attached bridging hub words as pre-joint constraint parameters to input the intent state space matrix. This mechanism decouples the session processing complexity from the historical interaction rounds, maintains the sparse form of the high-dimensional feature space, and avoids the accumulation of processing queues caused by the mechanical splicing of redundant strings.

[0017] 2. The intent divergence state control module and the intent divergence state control module generate multi-directional linkage. By extracting the discrete variance of non-zero eigenvalues ​​in the sparse adjacency matrix and solving the eigenvalue dispersion, the semantic divergence coefficient of the directed acyclic state topology graph is tracked in real time. When encountering typos or sudden polyphonic characters that cause the semantic divergence coefficient to reach the safety boundary value, the topology pruning program is automatically triggered and non-adjacent semantic cells are forcibly stripped. The input character vector is dimensionality reduced and filtered, and the basic stemming transition matrix is ​​used to replace the prior semantic space transition matrix to force convergence of the state migration trajectory. This process solves the problem of processor calculation state space divergence caused by intent transient drift.

[0018] 3. The session queue backlog amplitude monitoring module and the text processing computing power degradation scheduling module form a link under sudden overload conditions. By collecting the transient waiting latency and data packet loss rate when the processor processes real-time text data transformation, the session queue backlog amplitude parameter is calculated and output based on the ratio of the two to represent the degree of response yield. When the parameter exceeds the safety threshold, the text processing computing power degradation scheduling module forcibly restricts the search range of the static intent mapping matrix to the top 3 core business branches with the highest priority. The heuristic filtering rule of actively narrowing the optimization boundary ensures the timing convergence of intent recognition instructions, thereby balancing the processor's computing power dissipation and avoiding thread deadlock failures under overload conditions. Attached Figure Description

[0019] Figure 1 This invention relates to discrete semantic topology construction and feedback pruning control flow graph; Figure 2 This is a diagram of the collaborative architecture for concurrent call processing power degradation and dynamic convergence in this invention. Detailed Implementation

[0020] The technical solutions in the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] A natural language data processing system for online customer service interaction includes: The context sequence discretization and splitting module is used to access interactive text strings, extract discrete word feature vectors, and read historical core intent states from the context state cache space. The hub feature matrix transformation topology module is used to divide the multi-turn conversation text stream into blocks based on the co-occurrence distribution decay rate among the components of the discrete word feature vector, and to convert the historical core intent state and discrete word feature vector into a sparse adjacency matrix to establish an intent state topology directed acyclic graph. The temporal state transition arbitration module is used to extract the discrete variance of non-zero eigenvalues ​​in the sparse adjacency matrix and solve the eigenvalue dispersion, thereby determining the semantic divergence coefficient of the intended state topological directed acyclic graph. The intent divergence state control module is used to start the topology pruning program when the semantic divergence coefficient reaches 0.68, stripping non-adjacent semantic state nodes to filter discrete word feature vectors, and calling the basic stemming transition matrix to replace the prior semantic space transition matrix, so that the state transition trajectory is stabilized within the preset convergence range.

[0022] Preferably, the extraction of discrete word feature vectors in the context sequence discretization and splitting module includes: importing the interactive text string into the word segmentation channel of the context sequence discretization and splitting module, and segmenting the discrete word sequence using the maximum matching operator; assigning index numbers to each discrete word in the discrete word sequence based on the vocabulary dictionary to construct an initial numerical vector; scaling the initial numerical vector to generate discrete word feature vectors, and storing them in the context state cache space.

[0023] Preferably, the intention state topological directed acyclic graph in the hub feature matrix transformation topology module includes: calculating the joint co-occurrence frequency of each component in the discrete word feature vector within a multi-round conversation interaction time window, and obtaining the physical word span distance between two components on the text chain; calculating the ratio of the joint co-occurrence frequency to the physical word span distance to output the co-occurrence distribution decay rate, and when the co-occurrence distribution decay rate is greater than 0.5, establishing the corresponding two components as topological association nodes; taking the historical core intention state as the starting node, the discrete word feature vector as the current node, and filling the sparse adjacency matrix according to the directed transition probability between topological association nodes to generate the intention state topological directed acyclic graph.

[0024] Preferably, the semantic divergence coefficient of the directed acyclic graph of the intention state topology in the temporal state transition arbitration module includes: calculating the average value of all non-zero eigenvalues ​​in the sparse adjacency matrix; calculating the discrete variance of each non-zero eigenvalue relative to the average value, and obtaining the ratio of the discrete variance to the total dimension of the sparse adjacency matrix to determine the eigenvalue dispersion; and outputting the eigenvalue dispersion as the semantic divergence coefficient to monotonically reflect the topic drift intensity of multi-turn conversations.

[0025] Preferably, the process of initiating the topology pruning procedure and stripping non-adjacent semantic state nodes in the intent divergence state control module includes: issuing a pruning instruction to the hub feature matrix transformation topology module when the semantic divergence coefficient reaches 0.68; blocking the graph extension of the intention state topology directed acyclic graph to unconnected branches, and forcibly stripping historical non-adjacent semantic state nodes with a topological distance greater than 2 from the current node; and calling the basic stemming transition matrix to replace the prior semantic space transition matrix, so that the state transition trajectory of the intention state topology directed acyclic graph is stabilized within a preset convergence range.

[0026] Preferably, it also includes a session queue backlog amplitude monitoring module and a text processing computing power degradation scheduling module; the session queue backlog amplitude monitoring module is connected to the intent divergence state control module, and is used to collect the transient waiting latency and data packet loss rate when the processor is overloaded. Based on the product of the data packet loss rate and the baseline processing capacity, and linearly superimposed with the transient waiting latency, the session queue backlog amplitude parameter is calculated and output using the following formula: ,in, For the session queue backlog amplitude parameter; For transient wait delay, Packet loss rate; The baseline processing capacity of the processor; the text processing computing power degradation scheduling module is connected to the session queue backlog amplitude monitoring module and the timing state transition arbitration module, respectively. When the session queue backlog amplitude parameter exceeds 1.2, it includes: enabling the computing power yielding degradation scheduler to disable the branch search of edge intents; limiting the search range of the static intent mapping matrix to the top 3 core business branches with the highest priority, narrowing the optimization boundary to converge intent recognition instructions.

[0027] Preferably, limiting the search scope of the static intent mapping matrix to the top 3 core business branches with the highest priority includes: obtaining the baseline priority of each core business branch in the static intent mapping matrix and filtering out the top 3 core business branches with the highest priority; limiting the optimization boundary to the top 3 core business branches with the highest priority and locking the current dialogue intent through heuristic filtering rules.

[0028] Preferably, the system also includes a linkage hedging channel: when the backlog amplitude parameter of the conversation queue drops below 1.0 and the semantic divergence coefficient drops below 0.4, the branch search of the closed edge intent is resumed, and the intent divergence state control module restores the basic stemming transition matrix to the prior semantic space transition matrix.

[0029] Preferably, the system also includes a cache update channel: after locking the current dialogue intent, the current dialogue intent is written as a new historical core intent state into the context state cache space to overwrite the original historical core intent state.

[0030] Preferably, the system also includes a status output channel: converting the locked current dialogue intent into a standardized intent recognition command, and sending the standardized intent recognition command to the business response node of the online customer service interaction system through the network interface to drive the corresponding interaction channel to make a text reply.

[0031] Example 1: In the application scenario of a call center online customer service system with multi-turn continuous interactive text streams, the system input channel continuously receives high-dimensional semantic symbol sequences generated by multiple rounds of continuous dialogue. Based on the mixing of unstructured spoken expressions in the interactive text stream, the mechanical string concatenation method of the full historical text relied upon by traditional technical routes results in disordered divergence of information entropy in the feature space. With the increase of dialogue rounds, the back-end encoding model faces the technical dilemma of the explosion of the dimension of the text feature matrix. The feature cross-over and overlap of weakly related words in long sequences cause abnormal deviations in the state transition trajectory and changes in the semantic center of the conversation. In terms of timing, it causes transient accumulation of the processor core computing queue and response latency. Moreover, without changing the physical server hardware resources or adding hardware cache arrays, the system lacks an effective underlying feature space adaptive sparsity constraint path. Thus, at the information logic transformation level, it forms a dual technical dilemma of processor response blocking and loss of intent tracking and matching capabilities in high-concurrency multi-turn interactive scenarios.

[0032] When the system faces high concurrency in multi-round interactions, the context sequence discretization and splitting module receives the interactive text string, uses the built-in maximum matching operator to split the word term sequence, and assigns index numbers according to the vocabulary dictionary to generate discrete word feature vectors. At the same time, it reads the historical core intent state from the context state cache space as the starting node. The hub feature matrix transformation topology module receives the discrete word feature vectors and determines the co-occurrence distribution decay rate by calculating the joint co-occurrence frequency of each component within the multi-round conversation interaction time window and the physical word span distance on the text chain. When the co-occurrence distribution decay rate is greater than 0.5, the corresponding two components are established as topological association nodes. In this way, the continuous context structure is reorganized into an intent state topological directed acyclic graph composed of discrete semantic cells and bridging hub words. The temporal state transition arbitration module reads the intent state topological directed acyclic graph and calculates the average value of all non-zero eigenvalues ​​in the sparse adjacency matrix.

[0033] The discrete variance of each non-zero eigenvalue relative to the mean is calculated, and the ratio of the discrete variance to the total dimension of the sparse adjacency matrix is ​​obtained to solve the eigenvalue dispersion. This determines the semantic divergence coefficient of the current graph to monotonically reflect the intensity of topic drift. When user input of unstructured spoken noise causes transient topic drift and the semantic divergence coefficient reaches 0.68, the intention divergence state control module intervenes in place and issues a pruning instruction to the hub feature matrix transformation topology module. This blocks the extension of the intention state topology directed acyclic graph to unconnected branches, forcibly strips historical non-adjacent semantic state nodes with a topological distance greater than 2 from the current node to filter discrete word feature vectors, and simultaneously calls the basic stemming transition matrix to replace the prior semantic space transition matrix, so that the state transition trajectory is forced to stabilize within the preset convergence range to eliminate nonlinear distortion of the feature space.

[0034] To cope with unexpected overload conditions, the text processing computing power degradation scheduling module monitors the register status of the text processing queue in the processor, collects the queue waiting latency and data feature drop rate in real time when dealing with sudden surges, and calculates the data drop rate according to the formula. Calculate the session queue backlog amplitude parameter, where, For the session queue backlog amplitude parameter, For queue waiting delay, For data feature discard rate, As a baseline processing capacity, when the queue waiting latency increases from 12ms to 45ms and the data feature discard rate increases from 2% to 8%, the calculated conversation queue backlog amplitude parameter increases from 0.4 to 1.35. When it crosses the preset elastic boundary of 1.2, the text processing computing power degradation scheduling module closes the branch search of edge intents. By actively narrowing the optimization boundary selection rule, the search range of the static intent mapping matrix is ​​limited to the top 3 core business branches with the highest priority to maintain the temporal convergence of intent recognition instructions. The temporal state transition arbitration module determines the current dialogue intent and converts it into a standardized intent recognition instruction, which is then sent to the business response node of the online customer service interaction system.

[0035] This process connects information flow and state switching between processing units through cross-module collaboration, decoupling session processing complexity from historical interaction rounds and maintaining a sparse state in the high-dimensional feature space. While avoiding kernel response blocking caused by processing queue accumulation and overlapping multi-threaded computational overhead, it eliminates interference factors that degrade intent recognition accuracy at the data structure level through topology pruning. This ensures that the interaction channel smoothly maintains the sparse form of the feature space and the temporal convergence of command output throughout the multi-round deep session. In this business branch pruning optimization, the baseline priority of each core business branch in the static intent mapping matrix is ​​quantified based on the business hit rate and real-time business importance in historical call statistics. The system pre-calculates the business branches under the business response nodes. Based on the initial weights and combined with the click frequency of user inbound calls to the branch in the past 24 hours, a sliding window weighting is applied to calculate a comprehensive score reflecting the current business popularity. This comprehensive score serves as the baseline priority. During heuristic filtering, the text processing computing power degradation scheduling module constructs a heuristic evaluation function based on the product of intent matching confidence and business branch priority. When locking the current dialogue intent, this rule sequentially calculates the cosine similarity between the current discrete word feature vector and each static standard sentence in the first three core business branches. This similarity is used as the confidence input and multiplied with the baseline priority of the corresponding branch. Finally, the specific business branch with the highest composite evaluation score is identified as the currently locked dialogue intent, thereby achieving extremely fast and accurate intent recognition within a proactively narrowed search space.

[0036] Example 2: The method claimed in this invention verifies topological convergence and feature space sparsity on an information logic evaluation platform. The information logic evaluation platform includes a central processing unit with double-precision floating-point arithmetic capabilities and utilizes a publicly available multi-turn dialogue dataset to construct a dense interactive stress test source. To recreate the noise of an online customer service interaction scenario in a call center, the test source actively injects character error noise and unstructured spoken language drift perturbation with a scalar ratio of 5% into the original interactive text string input to the input channel to establish the original input data benchmark. Regarding the key parameter required for the topology transformation module of the hub feature matrix within the system—namely, the setting of the session interaction time window—its parameter identification is... The number of concurrent interaction channels is determined by the average number of historical session rounds, and the technical trade-off in its value lies in balancing the relevance of historical context with the storage dissipation load of the feature matrix. When the session interaction time window is too wide, it leads to overload of high-dimensional matrix calculation, and when it is too narrow, it leads to the break of historical semantic links. Therefore, an inverse proportional attenuation mapping rule between the session interaction time window and the channel concurrency density is established. That is, when the number of concurrent interaction channels increases from 100 to 1000, in order to reduce the system's computational load and avoid signal aliasing, the session interaction time window tends to the lower limit of its defined range. Under the current operating conditions, the current processing setting value of the session interaction time window obtained by applying this rule is 5 sessions.

[0037] In the aforementioned multi-round interactive operation, the test scheme divides the parameter working windows of three independent test gradients based on the monotonic truncation threshold of the co-occurrence distribution decay rate. The first working window sets the monotonic truncation threshold at the absolute lower limit of 0.50, the second working window at the median of 0.70, and the third working window at the absolute upper limit of 0.90. When the topic drift of the test source increases from a low-density, regular business flow to a high-intensity, sudden transient drift, the hub feature matrix transformation topology module converts the received discrete word feature vectors into co-occurrence frequencies and calculates the sparse adjacency matrix. The discrete variance of the non-zero eigenvalues ​​of the topology matrix generated under the first working window is measured to be low, medium, or high. The drift gradients for the three topics are 0.05, 0.18, and 0.36, respectively, corresponding to semantic divergence coefficients of 0.24, 0.40, and 0.68 output by the temporal state transition arbitration module. However, due to the monotonic truncation threshold being at the absolute upper limit of 0.90, the discrete variance of the non-zero eigenvalues ​​of the matrix measured under the same drift gradient changes to 0.01, 0.03, and 0.04 due to the large-area semantic cell truncation, resulting in the calculated semantic divergence coefficients remaining within a fixed range. This, through the numerical evolution of the feature matrix spectral distribution, confirms the nonlinear response law and boundary validity of the monotonic truncation threshold in the range of 0.50 to 0.90.

[0038] To ensure the consistency and rigor of the terminology system throughout the technical solution, the co-occurrence distribution decay rate is equivalent in both physical mechanism and logical function to the co-occurrence space damping ratio mentioned in the foregoing invention. Both are used to characterize the damping effect of the correlation attenuation of different word components in a multi-turn conversational text stream as the physical span lengthens. Similarly, the monotonic truncation threshold set in this invention is selected as 0.50 under the baseline working condition. This value serves as the discrete criterion boundary for nonlinear filtering of the co-occurrence space damping ratio. When the calculated co-occurrence distribution decay rate, i.e., the co-occurrence space damping ratio, is greater than this monotonic truncation threshold, the corresponding semantic cell is allowed to be retained and activated. Otherwise, a monotonic hard truncation is triggered to block the extension of weakly correlated branches, thereby effectively resolving the terminological ambiguity between different expressions at the logical level and ensuring the complete closed loop of the causal deduction chain of the distributed semantic topology control architecture.

[0039] In the stress comparison test under the condition of a sudden flood peak, the raw data shows that when the queue waiting latency caused by the large volume of text surges increases from 12ms to 40ms and the data feature discard rate increases from 2% to 9%, the processor core computing queue of the control group experiences transient accumulation, and its response latency exhibits a non-linear exponential increase. Ultimately, due to memory overload, the interaction channel experiences a deadlock failure. The sample group of this invention, due to the formula... Real-time calculation of session queue backlog amplitude parameters, where, For the session queue backlog amplitude parameter, This is the dimensionless value of the queue waiting delay. The data feature discard rate is a dimensionless value. Using the dimensionless processing capacity as a baseline, it was measured that when the input operating parameters caused the session queue backlog amplitude parameter to increase regularly from 0.40 and cross the preset elastic boundary inflection point of 1.20 to reach 1.40, the intention divergence state control module issued a pruning instruction to the hub feature matrix transformation topology module. This blocked the extension of the directed acyclic graph of the intention state topology to unconnected branches, causing the hub feature matrix transformation topology module to cut off weakly correlated branches. The processed text feature matrix dimension converged from the original 512 and stabilized at 64. The corresponding processor latency smoothed out from 45ms and dropped back to 14ms. This performance inflection point corresponds to the computing power yielding and degradation scheduler's control over sudden overload. Regarding stability, based on the data verification from the aforementioned multi-dimensional comparative experiments, the sample group of this invention maintained an average intent recognition accuracy of 96.4% throughout the entire process of multiple rounds of continuous deep interaction under injected character noise and high-concurrency overload disturbances, and the system's global average response latency remained within 15ms. In contrast, the control group's intent recognition accuracy degraded to 62.1% under the same interference conditions, accompanied by frequent computational deadlocks. This set of quantitative comparison facts confirms that the distributed semantic topology control architecture, composed of the context sequence discretization and diversion module, the hub feature matrix transformation topology module, the temporal state transition arbitration module, and the intent divergence state control module, maintains high reproducibility and temporal convergence without changing the processor's hardware clock speed.

[0040] Example 3: This example combines Figures 1 to 2 A description of a natural language data processing system for online customer service interaction, such as... Figure 1 As shown, the input channel receives the interactive text string and inputs it into the context sequence discretization and distribution module. Simultaneously, there is a logical interaction between the context state cache space and the context sequence discretization and distribution module to read historical core intent states. After completing the input of the text string and feature extraction, the context sequence discretization and distribution module outputs discrete word feature vectors and historical core intent states to the hub feature matrix transformation topology module. The hub feature matrix transformation topology module divides the data into blocks based on the co-occurrence distribution decay rate and establishes an intent state topological directed acyclic graph. The generated sparse adjacency matrix and topological directed acyclic graph are output to the right to the temporal state transition arbitration module. The temporal state transition arbitration module extracts discrete variance and calculates the dispersion to determine the semantic divergence coefficient, and outputs the semantic divergence coefficient to the intent divergence state control module. When the system determination coefficient reaches 0.68, the intent divergence state control module outputs control flow in the opposite direction to the upper left, performing the stripping of non-adjacent semantic state nodes and calling the topological pruning and convergence operation of the basic stemming transition matrix. This state control action is then fed back to the hub feature matrix transformation topology module in situ.

[0041] like Figure 2As shown, the input channel receives the interactive text string and is connected to the central processing unit (CPU) of the system evaluation platform. The CPU of the system evaluation platform deploys a context state cache space, a context sequence discretization and splitting module, a hub feature matrix transformation topology module, an intent divergence state control module, a temporal state transition arbitration module, a session queue backlog amplitude monitoring module, a text processing computing power degradation scheduling module, and a system-built-in logical channel architecture including a linkage hedging channel, a cache update channel, and a state output channel. The context sequence discretization and splitting module is connected to the input channel, has a bidirectional connection to the context state cache space, a rightward connection to the hub feature matrix transformation topology module, and a downward unidirectional control connection to the intent divergence state control module. The hub feature matrix transformation topology module is downward connected to the temporal state transition arbitration module. The graph divergence state control module interacts with the timing state transition arbitration module to the right and is connected downwards to the session queue backlog amplitude monitoring module. The session queue backlog amplitude monitoring module is connected to the text processing computing power degradation scheduling module to the right. The text processing computing power degradation scheduling module is connected upwards and in reverse to the hub feature matrix transformation topology module. The timing state transition arbitration module is connected downwards to the system's built-in logical channel architecture via data flow. The system's built-in logical channel architecture performs internal logical adjustments through internally deployed linkage hedging channels, cache update channels, and state output channels. It is connected upwards to the context state cache space via signal interaction, and its output end sends standardized intent recognition instructions to the business response nodes of the online customer service interaction system through the network interface to drive the corresponding interaction channels to perform text replies.

[0042] Example 4: When a call center's online customer service interaction system continuously handles concurrent customer service text interaction streams and the input channel receives dense multi-round conversations, the mechanical string splicing of the full context history text, which is relied upon by traditional technical approaches, causes disordered dispersion of information entropy in the feature space due to the interference of unstructured spoken expressions and typos in the interactive text strings. As the number of conversation rounds increases, the back-end encoding model faces the technical problem of nonlinear explosion of the dimension of the text feature matrix. The feature cross-over and overlap of weakly related words in long sequences cause abnormal deviations in state transition trajectories and changes in the semantic center of conversation, and cause queue accumulation and response latency in the processor kernel processing queue in terms of timing. Limited by the red lines of the technical toolbox defined by the classification number of semantic processing and machine translation, the system lacks an implementation path to allocate physical server hardware resources or add hardware cache arrays, thus forming a dual technical problem of processor response blocking and loss of intent tracking and matching capabilities under concurrent interaction conditions at the information logic transformation level.

[0043] When regulating the feature space complexity under the above working conditions, the context sequence discretization and diversion module, the hub feature matrix transformation topology module, the temporal state transition arbitration module, and the intent divergence state control module construct a causal closed loop through hierarchical step-by-step control flow. The context sequence discretization and diversion module uses the built-in maximum matching operator to segment the input current interactive text string to obtain a discrete word sequence, and consults the preset word dictionary to assign a unique integer index number to each word in the discrete word sequence. The integer index number is converted into a discrete word feature vector composed of floating-point numbers. At the same time, the historical core intent states accumulated from the previous dialogue are retrieved from the context state cache space as the starting topology node of the graph.

[0044] The hub feature matrix transformation topology module receives discrete word feature vectors, calculates the co-occurrence distribution decay rate by statistically analyzing the co-occurrence frequency of the current term and historical terms within the sliding session interaction time window, and calculates the physical distance between the current term and historical terms on the text chain. When the co-occurrence distribution decay rate is greater than 0.5, the corresponding two word feature components are established as directed connections. In this way, the continuous context structure is reorganized into an intention state topology directed acyclic graph composed of discrete semantic cells and bridging hub words. In the background, the intention state topology directed acyclic graph is transformed into the corresponding sparse adjacency matrix.

[0045] The temporal state transition arbitration module retrieves the sparse adjacency matrix, extracts all non-zero eigenvalues ​​from the matrix, calculates their arithmetic mean, and simultaneously calculates the discrete variance of each non-zero eigenvalue relative to the arithmetic mean. The discrete variance is then divided by the dimension of the sparse adjacency matrix to calculate the eigenvalue dispersion, which is defined as the semantic divergence coefficient to characterize the severity of topic drift in multi-turn dialogues. In actual calculations, since the sparse adjacency matrix typically exhibits an asymmetric real matrix form in multi-turn continuous interaction scenarios, the extracted non-zero eigenvalue set may contain complex numbers. To ensure the self-consistency of algebraic operations, this invention pre-processes all non-zero eigenvalues ​​with a modulus extraction mapping before calculating the arithmetic mean and discrete variance, uniformly transforming the complex eigenvalues ​​into the arithmetic square root of the sum of the squares of their real and imaginary parts. The high-dimensional complex feature space is lossily reduced to a one-dimensional real scalar axis. Based on the scalar of all real moduli obtained after the transformation, the arithmetic mean of the corresponding values ​​is calculated. The sum of squares of the differences between the moduli of each non-zero feature value and the mean is then calculated. This sum is used as the final value of the discrete variance to ensure the continuity and closed loop of the topic drift measurement logic under the complex spectrum distribution. When the semantic divergence coefficient calculated by the time-order state transition arbitration module reaches the truncation critical point of 0.68, the intention divergence state control module issues a network pruning instruction to the hub feature matrix transformation topology module to block the extension of the topology structure to unconnected branches. This forcibly prunes non-adjacent semantic state nodes with a topological span greater than 2 from the current node and simultaneously calls the preset basic stemming transition matrix to replace the current prior semantic space transition matrix, so that the state transition trajectory is stabilized within the preset convergence range.

[0046] Through the decoupling and topology pruning procedures of the discrete semantic states described above, the processor maintains the high-dimensional feature space in a low-dimensional sparse form when dealing with dense call traffic peaks. The session processing complexity is decoupled from the logic of historical interaction rounds, avoiding the accumulation of processing queues caused by mechanical splicing of redundant strings. This enables the business response nodes of the online customer service interaction system to distribute intent recognition instructions within 15ms, with the intent recognition accuracy maintained at 96.4%. Even in environments with character noise and overload interference, the timing convergence of instruction output is maintained.

[0047] Example 5: In the offline optimization parameter search test of the customer service call center system, the system evaluation platform configured with a double-precision floating-point processor calls the following... The stress test source of scalar proportional character misspelling noise and topic random switching disturbances deploys the context sequence discretization and diversion module, the hub feature matrix transformation topology module, the temporal state transition arbitration module, and the intent divergence state control module in the system evaluation platform. By continuously inputting the original interactive text string under different channel concurrency densities, the target control boundaries of co-occurrence distribution attenuation rate, semantic divergence coefficient, and conversation queue backlog amplitude parameter are determined.

[0048] The context sequence discretization and splitting module receives the interactive text string input from the stress test source, segments it into a discrete word sequence using the built-in maximum matching operator, assigns an index number to each discrete word in the discrete word sequence according to the vocabulary dictionary to construct an initial numerical vector, scales the initial numerical vector to generate a discrete word feature vector and stores it in the context state cache space, and reads the historical core intent state from it. The hub feature matrix transformation topology module receives the discrete word feature vector, calculates the joint co-occurrence frequency of each component in the discrete word feature vector within the multi-turn conversation interaction time window and the physical word span distance on the text chain, calculates the ratio of joint co-occurrence frequency to physical word span distance to output the co-occurrence distribution attenuation rate. When the channel concurrency density increases from 100 to 1000, the hub feature matrix transformation topology module compares the co-occurrence distribution attenuation rate with different candidate cutoff values. When the co-occurrence distribution attenuation rate is greater than 0.5, the corresponding two components are established as topology association nodes, and the historical core intent state is used as the starting node.

[0049] Using discrete word feature vectors as the current node, the sparse adjacency matrix is ​​filled according to the directed transition probabilities between topologically related nodes, reconstructing an intention state topological directed acyclic graph composed of discrete semantic cells and bridging hub words. In the specific operation of the above vector scaling, in order to avoid the excessively large discrete word index numbers causing overload of subsequent neural network feature space calculations, this invention adopts a one-dimensional scalar normalization scaling operator based on feature smoothing. Specifically, after constructing the initial numerical vector, the context sequence discretization and splitting module extracts the maximum index number value of each dimension in the vector as the global scaling benchmark denominator, and divides each component in the initial numerical vector by the benchmark denominator in turn, so that the value range of all elements in the vector is proportionally compressed to the floating-point number range between 0 and 1. Then, a preset logarithmic nonlinear smoothing function is called to perform a second-order scaling on the compressed vector to suppress the drastic fluctuations caused by long-tailed high-frequency words to the feature distribution. Finally, a discrete word feature vector with adaptive density control is output, providing a standardized data input form for its efficient storage in the context state cache space and subsequent conversion into a sparse adjacency matrix.

[0050] The temporal state transition arbitration module retrieves the sparse adjacency matrix, calculates the average of all non-zero eigenvalues ​​in the sparse adjacency matrix, calculates the discrete variance of each non-zero eigenvalue relative to the average, obtains the ratio of the discrete variance to the total dimension of the sparse adjacency matrix to determine the eigenvalue dispersion, and outputs the eigenvalue dispersion as the semantic divergence coefficient to monotonically reflect the severity of topic drift in multi-turn conversations. When the character misspelling noise in the stress test source causes a transient topic shift and the semantic divergence coefficient reaches the discrete trigger point of 0.68, the intent divergence state control module generates a conditional control flow jump, issues a pruning instruction to the hub feature matrix transformation topology module, blocks the extension of the intention state topology directed acyclic graph to unconnected branches, forcibly prunes historical non-adjacent semantic state nodes with a topological distance greater than 2 from the current node to filter discrete word feature vectors, and calls the preset basic stemming transition matrix to replace the prior semantic space transition matrix, converging the state migration trajectory, compressing the dimension of the text feature matrix from the original state of 512 to 64.

[0051] When the system evaluation platform simulates high-concurrency traffic peak conditions, the session queue backlog amplitude monitoring module monitors the register status of the text processing queue in the processor. When the processor is under overload, it collects the transient latency and packet loss rate when the processor is transforming real-time text data. The product of the packet loss rate and the baseline processing capacity is linearly superimposed with the transient latency, and the result is calculated using the formula... Calculate the session queue backlog amplitude parameter, where, For the session queue backlog amplitude parameter, For transient wait delay, For packet loss rate, As the processor's baseline processing capacity, when a large volume of test input increases the transient latency from 12ms to 45ms and the packet loss rate from 2% to 8%, the calculated session queue backlog amplitude parameter increases from 0.4 to 1.35. When the session queue backlog amplitude parameter exceeds the elastic boundary value of 1.2, the text processing computing power degradation scheduling module starts the computing power yielding degradation scheduling program to close the branch search of edge intents, obtains the baseline priority of each core business branch in the static intent mapping matrix, and selects the top 3 core business branches with the highest priority. The optimization boundary is limited to the top 3 core business branches with the highest priority. The current dialogue intent is locked through heuristic filtering rules, so that the processor's queue latency is reduced from 45ms to 14ms, ensuring the timing convergence of intent recognition instruction output.

[0052] To eliminate the dimensional conflict caused by directly adding the millisecond dimension of transient latency and the dimensionless probability of packet loss rate in the above calculation formula, and to objectively reflect the ratio of their sum to the baseline processing capacity in the text description, this invention performs explicit dimensionless normalization and weight bridging processing on the input parameters before actually executing the above formula calculation. Specifically, the transient latency in the formula needs to be pre-removed by a 1-millisecond baseline time constant to convert it into a dimensionless scalar before being substituted; at the same time, the processor's baseline processing capacity is determined at the underlying level. The system dynamic amplification factor, defined in the logic as a reciprocal form, is used to weight and scale the packet loss rate. Under this mechanism, the product term in the latter half of the formula is actually equivalent to solving the amplitude contribution of the packet loss rate under the baseline capacity constraint, and then linearly superimposed with the dimensionless delay scalar. The final output accumulated amplitude parameter effectively matches the joint ratio effect of transient delay amplitude and packet loss rate amplitude under the baseline processing capacity scale in mathematical essence, thus constructing an overload evaluation index with completely unified dimensions and logical self-consistency at the system bottom layer.

[0053] When the traffic of the stress test source decreases to the level of normal interaction, the linkage hedging channel in the system continuously monitors the operating indicators. When the backlog amplitude parameter of the conversation queue drops below 1.0 and the semantic divergence coefficient drops below 0.4, the branch search of the closed edge intent is resumed, and the intent divergence state control module restores the basic stemming transition matrix to the prior semantic space transition matrix. After locking the current dialogue intent, the cache update channel writes the current dialogue intent as the new historical core intent state into the context state cache space to overwrite the original historical core intent state. The state output channel converts the locked current dialogue intent into a standardized intent recognition instruction and sends the standardized intent recognition instruction to the business response node of the online customer service interaction system through the network interface to drive the corresponding interaction channel to receive the reply text. After experiencing the entire process of continuous deep interaction including character misspelling noise and overload interference, the test group using the method of this invention has achieved an average intent recognition accuracy of 96.4% and the system's global average response latency remains within 15ms.

[0054] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A natural language data processing system for online customer service interaction, characterized in that, include: The context sequence discretization and splitting module is used to access interactive text strings, extract discrete word feature vectors, and read historical core intent states from the context state cache space. The hub feature matrix transformation topology module is used to divide the multi-turn conversation text stream into blocks based on the co-occurrence distribution decay rate among the components of the discrete word feature vector, and to convert the historical core intent state and discrete word feature vector into a sparse adjacency matrix to establish an intent state topology directed acyclic graph. The temporal state transition arbitration module is used to extract the discrete variance of non-zero eigenvalues ​​in the sparse adjacency matrix and solve the eigenvalue dispersion, thereby determining the semantic divergence coefficient of the intended state topological directed acyclic graph. The intent divergence state control module is used to start the topology pruning program when the semantic divergence coefficient reaches 0.68, stripping non-adjacent semantic state nodes to filter discrete word feature vectors, and calling the basic stemming transition matrix to replace the prior semantic space transition matrix, so that the state transition trajectory is stabilized within the preset convergence range.

2. The natural language data processing system for online customer service interaction according to claim 1, characterized in that, The extraction of discrete word feature vectors in the context sequence discretization and splitting module includes: importing the interactive text string into the word segmentation channel of the context sequence discretization and splitting module, segmenting the discrete word sequence using the maximum matching operator; assigning index numbers to each discrete word in the discrete word sequence based on the vocabulary dictionary to construct an initial numerical vector; scaling the initial numerical vector to generate discrete word feature vectors, and storing them in the context state cache space.

3. The natural language data processing system for online customer service interaction according to claim 1, characterized in that, The topology transformation module of the hub feature matrix includes the following steps to establish a directed acyclic graph of intent state topology: calculating the joint co-occurrence frequency of each component in the discrete word feature vector within a multi-round conversation interaction time window, and obtaining the physical word span distance between two components on the text chain; calculating the ratio of joint co-occurrence frequency to physical word span distance to output the co-occurrence distribution decay rate, and when the co-occurrence distribution decay rate is greater than 0.5, establishing the corresponding two components as topological association nodes; using the historical core intent state as the starting node, the discrete word feature vector as the current node, and filling the sparse adjacency matrix according to the directed transition probability between topological association nodes to generate a directed acyclic graph of intent state topology.

4. A natural language data processing system for online customer service interaction according to claim 1, characterized in that, The semantic divergence coefficient of the directed acyclic graph of the intent state topology in the temporal state transition arbitration module includes: calculating the average value of all non-zero eigenvalues ​​in the sparse adjacency matrix; calculating the discrete variance of each non-zero eigenvalue relative to the average value, and obtaining the ratio of the discrete variance to the total dimension of the sparse adjacency matrix to determine the eigenvalue dispersion; and outputting the eigenvalue dispersion as the semantic divergence coefficient to monotonically reflect the topic drift intensity of multi-turn conversations.

5. A natural language data processing system for online customer service interaction according to claim 1, characterized in that, The process of initiating a topology pruning procedure and removing non-adjacent semantic state nodes in the intent divergence state control module includes: issuing a pruning command to the hub feature matrix transformation topology module when the semantic divergence coefficient reaches 0.68; blocking the intention state topology directed acyclic graph from extending to unconnected branches and forcibly removing historical non-adjacent semantic state nodes with a topological distance greater than 2 from the current node; and calling the basic stemming transition matrix to replace the prior semantic space transition matrix, so that the state transition trajectory of the intention state topology directed acyclic graph is stabilized within the preset convergence range.

6. A natural language data processing system for online customer service interaction according to claim 1, characterized in that, It also includes a session queue backlog amplitude monitoring module and a text processing computing power degradation scheduling module. The session queue backlog amplitude monitoring module is connected to the intent divergence state control module. It is used to collect the transient waiting latency and data packet loss rate when the processor is overloaded and transforming real-time text data. Based on the product of the data packet loss rate and the baseline processing capacity, and linearly superimposed with the transient waiting latency, the session queue backlog amplitude parameter is calculated and output using the following formula: ,in, For the session queue backlog amplitude parameter; For transient wait delay, Packet loss rate; The baseline processing capacity of the processor; the text processing computing power degradation scheduling module is connected to the session queue backlog amplitude monitoring module and the timing state transition arbitration module, respectively. When the session queue backlog amplitude parameter exceeds 1.2, it includes: enabling the computing power yielding degradation scheduler to disable the branch search of edge intents; limiting the search range of the static intent mapping matrix to the top 3 core business branches with the highest priority, narrowing the optimization boundary to converge intent recognition instructions.

7. A natural language data processing system for online customer service interaction according to claim 6, characterized in that, Limiting the search scope of the static intent mapping matrix to the top 3 core business branches with the highest priority includes: obtaining the baseline priority of each core business branch in the static intent mapping matrix and filtering out the top 3 core business branches with the highest priority; limiting the optimization boundary to the top 3 core business branches with the highest priority and locking in the current dialogue intent through heuristic filtering rules.

8. A natural language data processing system for online customer service interaction according to claim 6, characterized in that, The system also includes a linkage hedging channel: when the backlog amplitude parameter of the conversation queue drops below 1.0 and the semantic divergence coefficient drops below 0.4, the branch search of the closed edge intent is resumed, and the intent divergence state control module restores the basic stemming transition matrix to the prior semantic space transition matrix.

9. A natural language data processing system for online customer service interaction according to claim 8, characterized in that, The system also includes a cache update channel: after locking the current dialogue intent, the current dialogue intent is written as the new historical core intent state into the context state cache space to overwrite the original historical core intent state.

10. A natural language data processing system for online customer service interaction according to claim 9, characterized in that, The system also includes a status output channel: converting the locked current dialogue intent into standardized intent recognition instructions, and sending the standardized intent recognition instructions to the business response nodes of the online customer service interaction system through the network interface to drive the corresponding interaction channels to make text replies.

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