Semantic understanding system for user intent in complex environments

Abstract

The application relates to the technical field of semantic processing, and discloses a semantic understanding system for user intention in a complex environment, which comprises an input text serialization segmentation unit, a semantic graph topological mapping unit, a dynamic uncertainty convergence state control unit and an intention matching arbitration decision unit; the input text serialization segmentation unit reconstructs a word unit flow sequence and filters low-weight edges, the semantic graph topological mapping unit establishes a semantic topological node, the dynamic uncertainty convergence state control unit activates a search switching module when the uncertainty value is out of limit, and the intention matching arbitration decision unit outputs matching data; the application regulates graph connection weights through the entropy variation characteristics of a symbol stream, in-situ cuts off a scattered manifold and cuts off a low-probability scattered branch to make an intention feature vector quickly converge, effectively alleviates search bifurcation and index tree recalculation oscillation caused by input defects, and reduces the flow text processing delay.

Description

Technical Field

[0001] This invention belongs to the field of semantic processing technology, and in particular relates to a semantic understanding system for user intent in complex environments. Background Technology

[0002] Currently, the mainstream general technical solution is to solve the contextual semantic relationship of symbol stream sequences. It uses the spatiotemporal adjacency features of terms to construct a specific topological association probability manifold, enabling the retrieval scheduling engine to sequentially traverse index branches and complete data retrieval in a given knowledge graph topological path. However, when the above architecture is placed in real industrial interactive control conditions or heterogeneous business retrieval scenarios accompanied by high-frequency flickering of features and symbol incompleteness interference, the streaming input text frequently exhibits symbol distortion and boundary ambiguity. Existing solutions rely excessively on static hard-coded boundary matching or global probability statistical fitting. When faced with highly fragmented sequence input containing typos, its original feature space diverges towards a high-dimensional sparse manifold, thus inducing multi-directional and multi-branched index tree recalculation oscillations in the graph disambiguation process, leading to a deterioration in computational throughput and a nonlinear increase in the system's average processing latency, overflowing the hard time window of real-time scheduling.

[0003] To alleviate the aforementioned high response latency, the industry has attempted linear approaches such as increasing the parameter size of deep neural network models or introducing manual cleaning rules at the front-end inbound flow. However, these methods result in exponential memory bandwidth consumption on the computing server bus during large-scale collaborative retrieval. This makes the system prone to deadlocks due to disambiguation feedback loop oscillations and full-load deadlocks of the computing thread pool when facing high-frequency transient impacts from long-tailed mutated symbol streams. This traditional improvement path, which relies excessively on computing hardware bus bandwidth and large-scale model parameter stacking, faces bottlenecks in physical computing resource consumption. Furthermore, the underlying data architecture design and retrieval control algorithm also have limitations in terms of adaptive adjustment. For example, Chinese invention patent application CN111368027A discloses a method based on sparse... The knowledge graph query methods, devices, and computer equipment for matrices determine the minimum discrete structure by evaluating the standard deviation of the row vector length of the sparse matrix and fill the row vectors according to the byte alignment of the computation kernel to perform knowledge graph query. However, the existing technology is entirely based on a static, structurally regular, and data-complete knowledge graph pre-set. When placed under real high-noise streaming input conditions such as complex industrial interactive control or incomplete symbols, the transient feature space randomly diverges to a high-dimensional sparse manifold. If this static standard deviation discreteness evaluation and rigid filling alignment mechanism is used, it is very easy to cause frequent large-scale resets and index tree recalculation oscillations of the underlying sparse matrix structure, resulting in serious processing latency increases and computation thread deadlocks. It also lacks an adaptive control loop for self-healing of long-tailed mutated traffic.

[0004] Therefore, the technical problem to be solved by this invention is how to construct a closed-loop processing structure that uses the entropy change characteristics of the symbol stream itself to reversely regulate the connection weights of the underlying index graph, accurately truncate the scattering manifold in situ in the early stage of data flow, and integrate an adaptive degradation path with time-series integral damping characteristics and an asynchronous prefetch negative feedback flow self-healing regulation loop to achieve low-latency and high-stability intentional accurate conversion under multidimensional high-noise and high-load conditions. Summary of the Invention

[0005] This invention aims to solve the problem of high computational latency and lack of traffic self-healing and adaptive regulation caused by incomplete streaming text and word noise flickering under high noise and high load conditions, which leads to blind multiple branches of the retrieval path.

[0006] In this technical solution, a semantic understanding system for user intent in complex environments includes: The dynamic uncertainty convergence state control unit is connected to the input text serialization segmentation unit, the semantic graph topology mapping unit, and the intent matching arbitration decision unit, respectively. The input text serialization segmentation unit is used to reconstruct the acquired input text into a word stream sequence, and uses the built-in sub-word topology graph masking module to dynamically adjust the pruning threshold according to the received uncertainty value, and filter out the associated edges with a weight lower than 0.12 in situ. Semantic graph topology mapping unit is used to parse the graph topology structure of the word stream sequence to construct the adjacency matrix and map it to a low-dimensional space to establish semantic topology nodes. The dynamic uncertainty convergence state control unit is used to calculate the uncertainty value based on the temporal window distribution of the word streaming sequence, and to smooth the calculation of the uncertainty value using the built-in discrete sliding window integral smoothing module. When the uncertainty value exceeds the running threshold value of 0.85, the static lookup table retrieval switching module is activated to truncate the feature adjustment path. The intent matching arbitration decision unit is used to retrieve the target intent for matching based on the semantic topology nodes, and outputs safe matching data within 10μs through the direct retrieval mode of the static high-frequency word list when the static table lookup retrieval switching module is active.

[0007] Preferably, the input text serialization segmentation unit uses the built-in out-of-vocabulary (OV) word recognition module to retrieve the word stream sequence and perform pattern matching with a known regular expression; when the OV word recognition module identifies an OV entity term, it outputs an uncertainty suspension control signal to lock the uncertainty value to a fixed value of 0, stops the pruning of the sub-word topology map mask module for the OV entity term, and maintains the connectivity of the original word graph of the OV entity term in the adjacency matrix.

[0008] Preferably, in the dynamic uncertainty convergence state control unit, the local discrete sliding window integral smoothing module calculates the time window distribution sliding integral smoothing value based on the time window distribution of the word streaming sequence; when the time window distribution sliding integral smoothing value meets the fixed fluctuation threshold within three consecutive calculation steps, the discrete sliding window integral smoothing module releases the migration allow control signal, allowing the dynamic uncertainty convergence state control unit to change the control state.

[0009] Preferably, the system also includes a session temporal state cache queue; the asynchronous prefetch module in the intent matching arbitration decision unit uses the remaining clock cycles of the distributed multi-core server to retrieve session history information in the session temporal state cache queue; the asynchronous prefetch module calculates the correlation value between the candidate intent node and the current session context, and asynchronously loads the predicted intent nodes with a correlation value greater than 0.85 into the cache queue.

[0010] Preferably, the intent matching arbitration decision unit is also connected to a dynamic confidence assessment module; the dynamic confidence assessment module monitors the historical prediction hit rate value of the asynchronous prefetch module and tracks the node hit status in the cache queue; when the historical prediction hit rate value decreases, the dynamic confidence assessment module reduces the prefetch window width of the asynchronous prefetch module.

[0011] Preferably, the system further includes a text feature decoupling module; the text feature decoupling module is connected to the semantic graph topology mapping unit, and is used to decouple the multi-dimensional features of the word stream sequence, extract mutually orthogonal text feature vectors, and transmit the text feature vectors to the semantic graph topology mapping unit.

[0012] Preferably, the system also includes a high-dimensional vector space index database; the high-dimensional vector space index database is connected to the intent matching arbitration decision unit, used to store known high-dimensional intent vectors and graph topology relationship states, and in response to the retrieval request of the intent matching arbitration decision unit, sends intent polysemous data that matches the semantic topology nodes to it.

[0013] Preferably, the system further includes a context state cache queue; the context state cache queue is connected to the intent matching arbitration decision unit and is used to continuously cache the historical session state stream and send session background constraint data to the intent matching arbitration decision unit.

[0014] Preferably, the input text serialization segmentation unit, the semantic graph topology mapping unit, the dynamic uncertainty convergence state control unit, and the intent matching arbitration decision unit are all deployed in a distributed computing server cluster, so as to support distributed graph parsing and multi-threaded intent matching through the hardware computing resources of the distributed computing server cluster.

[0015] Compared with existing technologies, the semantic understanding system for user intent in complex environments of this invention has the following advantages: 1. In the semantic understanding of user intent in complex environments, traditional unidirectional mapping path processing of streaming text strings faces the problem of retrieval bifurcation caused by incomplete input, resulting in frequent recalculation of the index tree. The input stream reconstruction unit captures the original text sequence and converts it into a term adjacency feature matrix. The dynamic entropy change constraint state machine, together with the historical session temporal state cache queue, calculates the entropy change deflection of transient conditional information. The embedded discrete hysteresis damper calculates the corrected entropy value. When the difference meets the damping threshold, the flip instruction triggered drives the semantic topology space mapper to orthogonally correct the node weights of the knowledge graph in situ, cut off low-probability scattering branches, and enable the intent feature vector to converge quickly, reducing the retrieval latency to within 15ms.

[0016] 2. The streaming interactive environment has the potential risk of feedback loop deadlock caused by sudden background noise flicker, which is often accompanied by server thread pool exhaustion. The discrete hysteresis damper contained in the dynamic entropy change constraint state machine implements integral damping on the modified entropy value according to the timing characteristics. The state flip pulse is released only when the threshold is met for three consecutive steps. When the modified entropy value continuously exceeds the running boundary value of 0.85, the dimension reduction hard switching module is activated, cutting off the parameter adjustment path of the semantic topology space mapper and switching to the static high-frequency vocabulary direct matching mode, so that the intent arbitration decision unit directly outputs a deterministic safety scheduling instruction within 10μs.

[0017] 3. Typos in the input text introduce parasitic interference components into the adjacency matrix, increasing the obstacle to long-tail recognition in graph analysis. The input stream reconstruction unit integrates a word topology adaptive mask, which uses the dynamic entropy change constraint state machine to dynamically and monotonically increase the modified entropy value to adjust the edge filtering threshold, and removes the connection edges with a weight lower than 0.12 in situ to filter 92% of spelling noise. In addition, the embedded out-of-vocabulary hard interception link convection sequence retrieval matches specific regular expressions. When uncommon entity terms are found, the modified entropy value is forcibly locked to 0 to exempt edge dropping operations, thus purifying the feature source while preserving the original topological connectivity of newly emerging words. Attached Figure Description

[0018] Figure 1 This is a diagram of the internal interaction structure of the semantic understanding system under complex environments according to the present invention; Figure 2 This is a timing diagram for the suspension control of unregistered entity terms in this invention. Detailed Implementation

[0019] The technical solutions of 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.

[0020] A semantic understanding system for user intent in complex environments includes: The dynamic uncertainty convergence state control unit is connected to the input text serialization segmentation unit, the semantic graph topology mapping unit, and the intent matching arbitration decision unit, respectively. The input text serialization segmentation unit is used to reconstruct the acquired input text into a word stream sequence, and uses the built-in sub-word topology graph masking module to dynamically adjust the pruning threshold according to the received uncertainty value, and filter out the associated edges with a weight lower than 0.12 in situ. Semantic graph topology mapping unit is used to parse the graph topology structure of the word stream sequence to construct the adjacency matrix and map it to a low-dimensional space to establish semantic topology nodes. The dynamic uncertainty convergence state control unit is used to calculate the uncertainty value based on the temporal window distribution of the word streaming sequence, and to smooth the calculation of the uncertainty value using the built-in discrete sliding window integral smoothing module. When the uncertainty value exceeds the running threshold value of 0.85, the static lookup table retrieval switching module is activated to truncate the feature adjustment path. The intent matching arbitration decision unit is used to retrieve the target intent for matching based on the semantic topology nodes, and outputs safe matching data within 10μs through the direct retrieval mode of the static high-frequency word list when the static table lookup retrieval switching module is active.

[0021] Preferably, the input text serialization segmentation unit uses the built-in out-of-vocabulary (OV) word recognition module to retrieve the word stream sequence and perform pattern matching with known regular expressions; when the OV word recognition module identifies an OV entity term, it outputs an uncertainty suspension control signal to lock the uncertainty value to a fixed value of 0, stops the pruning of the sub-word topology map mask module for the OV entity term, and maintains the connectivity of the original word graph of the OV entity term in the adjacency matrix.

[0022] Preferably, the discrete sliding window integral smoothing module in the dynamic uncertainty convergence state control unit calculates the time window distribution sliding integral smoothing value based on the time window distribution of the word streaming sequence; when the time window distribution sliding integral smoothing value meets the fixed fluctuation threshold within three consecutive calculation steps, the discrete sliding window integral smoothing module releases the migration allow control signal, allowing the dynamic uncertainty convergence state control unit to change the control state.

[0023] Preferably, the system also includes a session temporal state cache queue; the asynchronous prefetch module in the intent matching arbitration decision unit uses the remaining clock cycles of the distributed multi-core server to retrieve session history information in the session temporal state cache queue; the asynchronous prefetch module calculates the correlation value between the candidate intent node and the current session context, and asynchronously loads the predicted intent nodes with a correlation value greater than 0.85 into the cache queue.

[0024] Preferably, the intent matching arbitration decision unit is also connected to a dynamic confidence assessment module; the dynamic confidence assessment module monitors the historical prediction hit rate value of the asynchronous prefetch module and tracks the node hit status in the cache queue; when the historical prediction hit rate value decreases, the dynamic confidence assessment module reduces the prefetch window width of the asynchronous prefetch module.

[0025] Preferably, the system further includes a text feature decoupling module; the text feature decoupling module is connected to the semantic graph topology mapping unit, and is used to decouple the multi-dimensional features of the word stream sequence, extract mutually orthogonal text feature vectors, and transmit the text feature vectors to the semantic graph topology mapping unit.

[0026] Preferably, the system also includes a high-dimensional vector space index database; the high-dimensional vector space index database is connected to the intent matching arbitration decision unit, used to store known high-dimensional intent vectors and graph topology relationship states, and in response to the retrieval request of the intent matching arbitration decision unit, sends intent polysemous data that matches the semantic topology nodes to it.

[0027] Preferably, the system further includes a context state cache queue; the context state cache queue is connected to the intent matching arbitration decision unit and is used to continuously cache the historical session state stream and send session background constraint data to the intent matching arbitration decision unit.

[0028] Preferably, the input text serialization segmentation unit, the semantic graph topology mapping unit, the dynamic uncertainty convergence state control unit, and the intent matching arbitration decision unit are all deployed in a distributed computing server cluster, so as to support distributed graph parsing and multi-threaded intent matching through the hardware computing resources of the distributed computing server cluster. Example 1

[0029] In multimodal interaction scenarios within complex industrial environments, users retrieve device B pose data via voice input. Due to metal friction and high-frequency pulse background noise interference generated by device operation, the character sequence acquired at the input end exhibits phoneme segmentation errors, presenting as a discrete instruction device B pose sequence. This invention's system utilizes an input stream reconstruction unit to transform this sequence into a word stream sequence and constructs a 3x3 adjacency feature matrix in system memory. The semantic graph topology mapping unit parses this matrix, establishing semantic nodes through the graph topology structure. The constructed adjacency matrix reflects the association probability between each word. When the dynamic uncertainty convergence state control unit determines that the uncertainty value of the current input stream exceeds a preset safety threshold, it activates the retrieval switching module. Through the semantic graph topology mapping unit, it corrects the local node weights of the knowledge graph, filters out association edges with weights below 0.12 in situ, cuts low-probability scattering branches, and enables rapid convergence of the intent feature vector. It evaluates the transient fluctuations and historical context inertia of the text stream temporal window distribution. The sub-word topology map masking module receives the corrected entropy value. The pruning threshold is calculated using a monotonically increasing function as the control variable. Using dynamic tracking of uncertainty values, under low-noise baseline conditions, the pruning threshold The initial lower limit parameter is 0.12; when the uncertainty value increases due to noise interference, the pruning threshold is... Synchronous monotonically increasing, the sub-word topology map masking module uses the pruning threshold generated in real time. Filtering edges associated with adjacent feature matrices, removing those with weights below the pruning threshold in situ. Redundant related edges are eliminated, and the divergent paths of high-dimensional sparse manifolds are truncated in the early stages of data flow. The system calibrates parameters using a pre-collected set of one thousand standard industrial field historical short text instructions, calculates the statistical variance of the first-order Shannon information entropy between adjacent text slices within a sliding time window containing fifty consecutive time steps, and experimental data show that the transient sensitivity coefficient... Weights in the range of 1.10 to 1.40 and with historical convergence. Within the range of 0.35 to 0.50, the calculated uncertainty value exhibits a noisy convergence boundary. The value is 1.25, so select... The corrected entropy value calculated within the sampling period is 0.42. The sum of squared residuals fitted to the standard semantic deviation trend reaches a minimum.

[0030] In this process, the discrete hysteresis damper calculates the corrected semantic entropy value in real time based on the input noise intensity. Specifically, the transient information entropy of the input stream is set to... The convergence factor of the conversation history is Correcting the entropy value The calculation follows the formula: ,in, This is the transient sensitivity coefficient, with a value of 1.25. The historical convergence weight coefficient is set to 0.42. Specifically, the discrete sliding window integral smoothing module performs time-series smoothing on the corrected entropy values ​​of each discrete time step obtained above to eliminate numerical glitches caused by sudden transient high-frequency noise. In actual discretization calculations, this module maintains a memory array containing a fixed number of... A circular queue of computational steps serves as a discrete sliding window, sequentially storing the current time step and its preceding steps. The corrected entropy value for each historical step is used, and the final uncertainty value after smoothing is output by calling the following sliding integral smoothing formula: ,in, To smoothly calculate the uncertainty value of the output at the current moment, For the first time in history The corrected entropy value of the step size, These are the smoothing weight coefficients corresponding to the step size; smoothing weight coefficients It decays monotonically along the historical direction over time in an arithmetic sequence and satisfies normalization constraints. This effectively suppresses random word spelling noise through the discrete integral filtering algorithm within the sliding window, based on the damping hysteresis characteristics of the system in response to the temporal fluctuations of streaming text.

[0031] When the absolute value of the difference between the corrected entropy values ​​of adjacent time steps Greater than the preset discrete disturbance threshold When the intent matching arbitration decision unit triggers retrieval path reconstruction, the system dynamically adjusts the pruning threshold through the sub-word topology map mask module. This threshold and the corrected entropy value Relationship, satisfy , As the value increases, it effectively suppresses multiple branching of the retrieval path in high-noise environments. When the modified entropy value of three consecutive time steps exceeds the safe operating limit of 0.85, the dynamic uncertainty convergence state control unit forcibly disconnects the closed-loop adjustment link and outputs a static protection command to achieve system adaptive degradation under high load conditions and ensure that intent matching is completed under controlled conditions. Example 2

[0032] When the system protected in this application runs in a test bench hardware and software computing environment consisting of multiple distributed multi-core processors, high-speed synchronous dynamic random access memory, and a distributed graph structure spatial index database, the signal input source distributes three streaming test character sequence streams with clear uncertainty gradients to the input text serialization segmentation unit to cover operating conditions with different interference intensities. The low-noise intensity comparison sample group constructs a basic interference environment by superimposing Gaussian white noise with a signal-to-noise ratio of 30dB onto the original character stream. The medium-noise intensity comparison sample group simulates a typical industrial interactive environment by mixing Gaussian white noise with a signal-to-noise ratio of 20dB into the streaming text and applying a continuous power frequency electromagnetic interference harmonic signal with a frequency of 50Hz. The high-noise intensity comparison sample group simulates a wireless interactive communication environment by superimposing Gaussian white noise with a signal-to-noise ratio of 10dB onto the streaming text and frequently doping it with random impulse noise with a duration of 5.0ms and a transient amplitude 2.5 times the root mean square value of the original signal. To provide discrete disturbance thresholds... The programmed determination method involves, during the test run preparation phase, the processor calling 1000 standard industrial field historical short text instructions pre-stored in the high-speed cache as input sources. After filtering out invalid tabs in the sequence through a spatial reconstruction operator, the statistical variance of the first-order Shannon information entropy between adjacent text slices within a sliding time window containing 50 consecutive time steps is continuously calculated. The calculated variance is then determined based on the reciprocal of the variance and the scaling constant. The target boundary mapping range is established by multiplying by 0.15. When the short-time burst fluctuation frequency of the input signal exceeds 12Hz and the spectral broadening is greater than 4.5kHz, to prevent aliasing during word segmentation sampling, the target boundary mapping range converges to the value range of 0.20 to 0.50. Based on this determination method, the discrete perturbation threshold of sample group one in this invention is... The discrete perturbation threshold of sample group two in this invention is set to an absolute lower limit of 0.20. The discrete perturbation threshold of sample group three in this invention is set to the normal median of 0.35. Set to an absolute upper limit of 0.50.

[0033] When inputting a fragmented text stream under the condition of medium noise intensity comparison sample group, the input text serialization segmentation unit in sample group two of this invention converts the streaming word sequence into a 3x3 word adjacency feature matrix. Its internal discrete hysteresis damper reads the transient information entropy of the current time step in real time at a specific sampling period node and calculates it in a specific processor; wherein the formula for calculating the corrected entropy value is as follows: ;in, To correct the entropy value, The transient information entropy at the current time step is 0.45, which is measured under this condition. The session history convergence factor is 0.32, measured under this condition. This is the transient sensitivity coefficient, set to 1.25 here. The historical convergence weight coefficient is set to 0.42 here; substituting this value into the calculation yields the current corrected entropy value. The value is 0.70. At this point, the absolute value of the difference between the corrected entropy values ​​of adjacent time steps becomes 0.38. Since this absolute value of the difference is greater than the set discrete disturbance threshold, The dynamic uncertainty convergence state control unit sends an independent state-flipping pulse logic level to the semantic graph topology mapping unit, causing the word topology map masking module to respond to the level and set the pruning threshold. Switch to about variables The monotonically increasing mapping function outputs a value of 0.15. Under this hierarchical control architecture, the system maintains a dynamic high-bit masking and underlying physical erasure coordination mechanism in parallel at the data flow level. When the dynamic pruning threshold... When the target output value of 0.15 under high-noise conditions has been switched and updated, the system ensures that the most basic, extremely low-weight edges below the global hard lower limit of 0.12 are physically removed in situ. The sub-word topology graph masking module then overlays and calls the 0.15 software masking matrix to perform low-pass filtering and cleaning of the secondary manifold scattering branches that were originally in the weight range of 0.12 to 0.15 at the algebraic level. This logically forms a closed-loop adjustment loop for parameter adaptive upgrading, prioritizing the elimination of redundant topological connection edges with weights below 0.12 in situ in the underlying adjacency feature matrix. Within a processing delay of 14.2ms, the orthogonal convergent feature vector is output to the intention matching arbitration decision unit. The sub-word topology graph masking module performs edge weight filtering operations to achieve low-pass filtering feature cleaning of the high-dimensional sparse matrix at the matrix algebraic level, filtering low-weight edges. Re-association edges remove parasitic high-frequency components introduced by misspellings and spelling noise, reducing the divergence speed of non-zero eigenvalues ​​in the adjacent feature matrix, and concentrating matrix energy into the principal component orthogonal subspace. The semantic graph topology mapping unit calculates the cleaned matrix feature vector, eliminating the index tree recalculation oscillation caused by blind multi-branching of the retrieval path, and locking the convergence direction of the feature vector to the target intent node. In contrast, the feature-deficient comparison sample group with the discrete hysteresis damper removed exhibits disordered damped oscillations with an amplitude greater than 0.55 in the intermediate corrected entropy value under the same input, and cannot provide feature input to the intent matching arbitration decision unit within the 50.0ms timeout period. Thus, the intermediate quantization difference between sample group two of this invention and the feature-deficient comparison sample group shows the positive feedback space synergy between the hysteresis damping adjustment mechanism and the topology mask pruning mechanism.

[0034] When the system switches to extreme numerical boundary conditions and is under stress testing, in the first out-of-range comparison sample group with parameters extended outward, the discrete disturbance threshold... When the threshold is lowered to below 0.10, the system still triggers state flip pulses at high frequency even when faced with slight disturbances in the low-noise intensity comparison samples. This causes the word topology map masking module to over-prune, mistakenly erasing effective semantic nodes in the feature matrix. Consequently, the final intent matching accuracy deteriorates drastically from 98.3% to 61.5%. In the second over-range comparison sample group, the discrete disturbance threshold... Raising the threshold to 0.60, which is higher than the upper limit, when facing the high noise intensity comparison sample group, the absolute value of the difference between the corrected entropy values ​​of adjacent time steps can never cross this threshold. This causes the uncertainty convergence loop to be in a saturated passivation state for a long time without response, blocking the hard switching scheduling to the safety protection command matching path. The intention matching arbitration decision unit cannot obtain the convergence feature vector, causing the multi-threaded computing task in the distributed server to trigger a deadlock due to full load of the table thread pool within 45.8ms. The factual data of the degradation inflection point of these two sets of out-of-range comparison sample groups are connected with the stable operation results of the sample group one and sample group three of this invention, which establish the discrete disturbance threshold range of 0.20 to 0.50 as a closed working window that takes into account both feature extraction accuracy and system anti-vibration safety. Example 3

[0035] When the system operates under continuous long text interaction conditions with high loads from parallel scheduling of multi-core processors, the operation terminal in the industrial center distributes a series of incomplete control semantic text sequences with temporal correlations. The continuous high-frequency superposition of long-tailed variant words in continuous multi-turn dialogues causes the residual contextual information and temporal information to be mixed and interfered with in the three-level cache. The semantic topological position of the word adjacency feature matrix is ​​locally deflected during manifold mapping. The processing delay of subsequent node matching space reconstruction exhibits a step-like nonlinear increasing change.

[0036] To quantify the disambiguation hindering effect of historical context semantics on the current time step in a multi-turn dialogue flow, the system introduces a temporal intent dispersion index. This index applies a time-weighted attenuation to the variance distribution of intent feature vectors at each historical time step within a preset historical time window in the vector space, and sums these values ​​to obtain a quantified value of the temporal intent dispersion of the current interaction node. In the specific deployment of this technical solution, the processor reads sliding time window data containing five historical cycles from the temporary address space of high-speed dynamic random access memory, and the corresponding intent feature vectors are normalized. The variance sequence, from left to right, is 0.20, 0.20, 0.20, 0.30, and 0.40. The floating-point unit inside the processor calls the weight update vector stored in read-only memory, multiplying the variance sequence corresponding to the time nodes from farthest to near by decay coefficients of 0.10, 0.10, 0.10, 0.20, and 0.50 respectively, and then weighted summing to obtain the current discrete metric value of 0.32. The floating-point unit uses this discrete metric value of 0.32 as the independent variable to directly correspond to the session history convergence factor at the current time step. This yields the session history convergence factor at the current time node. The actual value is 0.32, which is the conversation history convergence factor under any sliding window length in the general calculation model of continuous long text interaction in the system. The dynamic update rule follows the time-weighted derivation formula as follows: ,in, The total number of sliding history cycles retrieved from the cache memory. For the first The normalized variance distribution of the intent feature vector corresponding to each historical cycle in the vector space indicates the degree of semantic dispersion in different rounds of dialogue. The decay weight coefficient corresponding to the historical time series node and satisfying Normalization constraints, weighting coefficients The weighting increases monotonically from far to near over time to achieve the decay of time weight due to historical context inertia, which can be fully extended to historical convergence calculation in any multi-round continuous dialogue scenario.

[0037] The dynamic uncertainty convergence state control unit obtains the session history convergence factor. The factual value is 0.32, and the transient information entropy input by the input text serialization segmentation unit. The actual value is 0.45, and the current corrected entropy value is calculated by calling the multiply-accumulate instruction from a specific operator register. The value is 0.70; when the system's corrected entropy value remains above the safe operating limit of 0.85 for three consecutive sampling time steps due to continuous high-power pulse interference on the external interactive communication channel, the state machine inside the dynamic uncertainty convergence control unit triggers a hard interrupt wake-up signal; the dimensionality reduction hard switching module responds to the hard interrupt and rewrites the enable state bit in the specific control bus register, disconnecting the topology adaptive pruning feedback link that was in the previous dynamic adjustment state; to address the obstacles to microsecond-level response caused by the network communication delay and bus synchronization overhead of the multi-node distributed computing server cluster, the system adopts local memory direct addressing and high-speed cache memory register direct addressing. In the bypass structure, when the enable state bit of the dimensionality reduction hard switching module is rewritten, the state machine generates a local hardware-level hard interrupt, suspending the distributed multi-threaded task and closing the network input / output communication link. The data stream is then directed to the local static flash memory storage space of the current computing node. The instruction pipeline reloading and feature path truncation highly overlap on the time axis. The local high-speed register absorbs the second-order loss of 12μs internal delay lag in parallel during the hardware-level reset cycle triggered by the hard interrupt, without occupying the external clock cycle of instruction retrieval output. This allows the intent matching arbitration decision unit to output safe matching data within 10μs. The control allocation mechanism redirects the data that would originally flow to the high-dimensional vector disambiguation computing array. The flow reset is scheduled to the core lookup table matching address space stored in static flash memory. This scheduling process generates a 12μs internal second-order latency loss during the instruction pipeline reload cycle. This 12μs internal second-order latency loss occurs entirely within the hardware-level register reset and pipeline clearing phases. On the timeline, this deeply overlaps with and is completely absorbed by the preceding distributed multi-threaded task cleanup and distributed network I / O communication link closure processes during state machine hard interrupt suspension. Therefore, this underlying structural loss is an internal friction that is not externally apparent and does not occupy the core lookup table retrieval time after the business instruction is address-ready. When the reset to address bypass channel crosses the 12μs reset cycle and declares full readiness, the core matching clock starts timing independently. This allows the intent matching arbitration decision unit to output the corresponding static safety stop control command to the external industrial end control component within 10μs after the delay loss ends. This blocks the transmission of retrieval fork oscillation to the deep thread pool, enabling the thread pool load rate of the distributed computing cluster to converge from 94.5% and stabilize at a safe idle level of 12.3% when facing strong interference. This maintains the continuity of the entire link data flow of the complex retrieval architecture under long-tailed variable multi-wave conditions without using external cleaning hardware.

[0038] Example 4: When the system faces acoustic interference in a newly deployed working environment, the input text serialization segmentation unit continuously captures a 60.0s pure ambient noise data stream in a zero-user state. The processor reads the data stream and converts it into a decibel spectral density feature vector. Within 15.5ms, it retrieves a discrete test vocabulary containing 5000 control commands to calculate the activation damping deviation value of the node. The semantic graph topology mapping unit calculates the activation damping deviation value based on a fixed scaling factor. The product of these factors is used to establish the initial node weight matrix and correct and update the pruning threshold baseline parameters, thus establishing the preceding baseline parameters; where... This is a preset fixed proportional constant with a value of 0.24.

[0039] When the system inputs a test command stream into the calibrated channel, the reconstructed initial node weight matrix acts on the connection edge filtering logic of the word stream sequence in the input text serialization segmentation unit. When the state machine receives a sudden signal where the absolute value of the difference between the corrected entropy values ​​of adjacent time steps exceeds 0.41, it drives the sub-word topology map masking module to adjust the pruning threshold of the current time step. The output value of 0.18, established by pre-calibration, is switched to intercept low-weight scattering branch data caused by environmental drift in situ. The system matching calculation latency remains at 11.5ms during 24.0h of continuous operation. The residual rate of the three-level cache of the distributed computing cluster remains within 5.2%. The intention matching arbitration decision unit remains in a state of no thread blocking and bus deadlock.

[0040] 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 semantic understanding system for user intent in complex environments, characterized in that, include: The dynamic uncertainty convergence state control unit is connected to the input text serialization segmentation unit, the semantic graph topology mapping unit, and the intent matching arbitration decision unit, respectively. The input text serialization segmentation unit is used to reconstruct the acquired input text into a word stream sequence, and uses the built-in sub-word topology graph masking module to dynamically adjust the pruning threshold according to the received uncertainty value, and filter out the associated edges with a weight lower than 0.12 in situ. The semantic graph topology mapping unit is used to parse the graph topology structure of the word stream sequence to construct the adjacency matrix and map it to a low-dimensional space to establish semantic topology nodes. The dynamic uncertainty convergence state control unit is used to calculate the uncertainty value based on the temporal window distribution of the word streaming sequence, and to smooth the calculation of the uncertainty value using the built-in discrete sliding window integral smoothing module. When the uncertainty value exceeds the running threshold value of 0.85, the static lookup table retrieval switching module is activated to truncate the feature adjustment path. The intent matching arbitration decision unit is used to retrieve the target intent for matching based on the semantic topology nodes, and outputs safe matching data within 10μs through the direct retrieval mode of the static high-frequency word list when the static table lookup retrieval switching module is active.

2. The semantic understanding system for user intent in a complex environment according to claim 1, characterized in that, The input text serialization segmentation unit uses the built-in out-of-vocabulary (OV) word recognition module to retrieve the word stream sequence and perform pattern matching with known regular expressions. When the OV word recognition module identifies an OV entity term, it outputs an uncertainty suspension control signal to lock the uncertainty value to a fixed value of 0, stopping the pruning of the sub-word topology map mask module for the OV entity term and maintaining the connectivity of the original word graph of the OV entity term in the adjacency matrix.

3. The semantic understanding system for user intent in a complex environment according to claim 1, characterized in that, The local discrete sliding window integral smoothing module in the dynamic uncertainty convergence state control unit calculates the time window distribution sliding integral smoothing value based on the time window distribution of the word streaming sequence; When the time-series window distribution sliding integral smoothing value meets the fixed fluctuation threshold within three consecutive calculation steps, the discrete sliding window integral smoothing module releases the migration enable control signal, allowing the dynamic uncertainty convergence state control unit to change the control state.

4. The semantic understanding system for user intent in a complex environment according to claim 1, characterized in that, The system also includes a session temporal state cache queue; the asynchronous prefetch module in the intent matching arbitration decision unit uses the remaining clock cycles of the distributed multi-core server to retrieve session history information in the session temporal state cache queue; the asynchronous prefetch module calculates the correlation value between the candidate intent node and the current session context, and asynchronously loads the predicted intent nodes with a correlation value greater than 0.85 into the cache queue.

5. A semantic understanding system for user intent in a complex environment according to claim 4, characterized in that, The intent matching arbitration decision unit is also connected to a dynamic confidence assessment module; the dynamic confidence assessment module monitors the historical prediction hit rate of the asynchronous prefetch module and tracks the node hit status in the cache queue; when the historical prediction hit rate decreases, the dynamic confidence assessment module reduces the prefetch window width of the asynchronous prefetch module.

6. A semantic understanding system for user intent in a complex environment according to claim 1, characterized in that, The system also includes a text feature decoupling module; The text feature decoupling module is connected to the semantic graph topology mapping unit to decouple the multi-dimensional features of the word stream sequence, extract mutually orthogonal text feature vectors, and transmit the text feature vectors to the semantic graph topology mapping unit.

7. The semantic understanding system for user intent in a complex environment according to claim 1, characterized in that, The system also includes a high-dimensional vector space index database; the high-dimensional vector space index database is connected to the intent matching arbitration decision unit, and is used to store the known high-dimensional intent vectors and graph topology relationship states, and in response to the retrieval request of the intent matching arbitration decision unit, sends intent polysemous data that matches the semantic topology nodes to it.

8. A semantic understanding system for user intent in a complex environment according to claim 1, characterized in that, The system also includes a context state cache queue; the context state cache queue is connected to the intent matching arbitration decision unit and is used to continuously cache the historical session state stream and send session background constraint data to the intent matching arbitration decision unit.

9. A semantic understanding system for user intent in a complex environment according to claim 1, characterized in that, The input text serialization and segmentation unit, the semantic graph topology mapping unit, the dynamic uncertainty convergence state control unit, and the intent matching arbitration decision unit are all deployed in a distributed computing server cluster to support distributed graph parsing and multi-threaded intent matching through the hardware computing resources of the distributed computing server cluster.

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