A Method and System for Parsing and Evaluating Multi-Source Heterogeneous Data Streams Based on Large Language Model Semantic Understanding

Abstract

This invention relates to the field of computer system technology based on specific computational models, and discloses a method and system for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of large language models. The method includes: extracting a logical dependency matrix representing the logical subordination relationship between data nodes in the multi-source heterogeneous data stream; generating a mask constraint tensor based on the logical dependency matrix to guide the feature fusion path of the large language model; injecting the mask constraint tensor into the self-attention computation layer of the large language model, using the tensor to suppress the distribution of attention weights, forcing feature extraction to converge within the logical constraint subspace, and generating a semantic parsing vector; and outputting the evaluation result based on the semantic parsing vector. This invention eliminates the feature dilution of core nodes by unstructured noise through numerical truncation of irrelevant weights in the computational path, avoids the illusion of association generated by semantic fusion, and improves the logical determinism of heterogeneous data parsing.

Description

Technical Field

[0001] This invention relates to a method and system for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of large language models, belonging to the technical field of computer systems based on specific computational models. Background Technology

[0002] Currently, when processing multi-source heterogeneous data streams containing structured, semi-structured, and unstructured text, semantic parsing is typically performed using sequence modeling based on large language models. Large language models capture the correlation features in the data sequence through a low-level self-attention mechanism, converting data nodes from different data sources into a unified weighted representation, thus achieving semantic fusion of heterogeneous data. The low-level self-attention mechanism of large language models captures long-distance dependencies by calculating the correlation weights between nodes in the input sequence. When performing cross-source data fusion, the computational model typically calculates the dot product weights of the query vector and key vector globally. However, when heterogeneous data streams involve engineering drawing parameters and log data with strong physical dependencies or hard geometric constraints, redundant feature vectors in the unstructured data stream dilute the sparse and decisive logically constrained node features in the structured data stream through unrestricted attention paths.

[0003] To address these issues, industry experts typically attempt to increase the model's parameter size or perform large-scale data cleaning at the input. However, analysis reveals that these methods do not alter the internal degree-of-freedom allocation mechanism of the computational model. Instead, the increased number of parameters may lead to more invalid association paths in the feature space, making it impossible to establish a physical truncation mechanism at the operator computation level that matches the original data topology. For example, Chinese invention patent CN120523966B discloses a knowledge graph construction method and system based on large language model technology, using a multimodal semantic alignment model for cross-modal feature fusion, relying on temporal evolution and knowledge... The existing technology for conflict detection implicitly relies on idealized preconditions: the correlation between heterogeneous data is established on a flexible probabilistic semantic distribution and the operating environment is stable. However, when faced with the real industrial manufacturing conditions where this invention is located, which involve high concurrency, hard geometric assembly constraints, and unstructured noise, it relies entirely on soft attention distribution and post-event graph reconstruction control methods. This results in a fundamental constraint mismatch with the physical boundaries of the actual scene. It fails to establish a hard constraint intervention mechanism that conforms to the entity topology from the bottom layer of the computational operator. As a result, during the feature alignment stage, key absolute logical nodes are easily diluted by multi-source concurrent redundant noise, creating a correlation illusion that violates objective engineering facts.

[0004] Therefore, how to perform controlled intervention on the attention path of the computational model based on the original topological constraints of multi-source heterogeneous data, so as to avoid cross-modal feature contamination while maintaining the depth of semantic understanding, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of this invention is as follows: A method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of large language models, comprising the following steps: Step 101: Obtain multi-source heterogeneous data streams and extract logical dependency matrices representing the logical subordinate relationships between data nodes in the multi-source heterogeneous data streams; wherein, the logical subordinate relationships are determined by calculating the topological hierarchy weights between data nodes in the multi-source heterogeneous data streams. Step 102: Based on the logical dependency matrix, generate a mask constraint tensor to guide the feature fusion path of the large language model; wherein, the value of each element in the mask constraint tensor is mapped from the logical subordination weight between each data node in the logical dependency matrix, and the value of each element is positively correlated with the logical subordination weight. Step 103: Dynamically inject the mask constraint tensor into the self-attention computation layer of the large language model. Use the mask constraint tensor to perform numerical suppression on the attention weight distribution of the large language model when performing feature relevance operations. By performing numerical truncation on the weight allocation path in the logically unrelated region defined by the logical dependency matrix, the feature extraction process is forced to converge in the logical constraint subspace that conforms to the definition of the logical dependency matrix, so as to eliminate the feature dilution of structured data nodes by unstructured noise features and generate semantic parsing vectors that represent the association characteristics of multi-source heterogeneous data. Step 104: Input the semantic parsing vector into the top-level evaluation model, perform multi-dimensional quantitative evaluation of manufacturability, physical connectivity and logical consistency of multi-source heterogeneous data streams, and output the corresponding quantitative evaluation results.

[0006] Preferably, step 101 involves extracting the logical dependency matrix, including: capturing multi-source concurrent data streams containing geometric structure description files; parsing the tree-like topology in the geometric structure description files and extracting the assembly constraint weights between entity nodes to construct a global logical dependency matrix; wherein, the multi-source concurrent data streams are asynchronously scheduled and stored through a high-speed cache unit to ensure that the throughput of the multi-source concurrent data streams during the parsing phase is not less than 100MB / s.

[0007] Preferably, generating the mask constraint tensor in step 102 includes: normalizing the off-diagonal elements in the logical dependency matrix; setting negative infinity mask values ​​for the tensor coordinates that are logically mutually exclusive or unrelated, in order to construct a mask matrix that matches the computational sequence dimension of the large language model.

[0008] Preferably, the calculation rule for the attention weight distribution in step 103 follows the following formula: Where Q is the query vector, K is the key vector, and V is the value vector. Let M be the dimension of the key vector, and M be the prior constraint bias term obtained from the mask matrix. The prior constraint bias term is used to perform numerical masking on the attention weight allocation between logically unrelated node pairs before performing the normalized exponent operation.

[0009] Preferably, before step 101, the method further includes: performing data cleaning using a rule engine, correcting numerical missing values ​​using a mean-filling algorithm; performing deduplication processing on text data in multi-source heterogeneous data streams based on cosine similarity, and filtering out abnormal data exceeding 3 standard deviations using a preset step size.

[0010] Preferably, step 104 involves performing a multi-dimensional quantitative evaluation, including: extracting process constraint features corresponding to the multi-source heterogeneous data stream from the semantic parsing vector, calculating manufacturability index scores, logical connectivity index scores, and semantic consistency index scores; and comparing each index score with a preset quality benchmark value to determine the quality level of the multi-source heterogeneous data stream.

[0011] Preferably, it also includes: feeding back the quality level and scores of each indicator to the parameter fine-tuning interface of the large language model in real time, and using the backpropagation mechanism to adjust the weight bias of the self-attention layer in the large language model in order to improve the output accuracy of the subsequent semantic parsing vector.

[0012] Preferably, step 101, the multi-source heterogeneous data stream includes structured database records, CSV data, semi-structured JSON data, XML messages, and unstructured engineering logs. The mask constraint tensor reduces the computational redundancy of the large language model when performing multi-source feature aggregation by suppressing the feature flow paths between unrelated entity nodes, so that the system's response latency is less than 50ms when processing high-dimensional heterogeneous feature spaces.

[0013] Preferably, it also includes: updating the filtering threshold of the data cleaning in step 101 in real time based on the dynamic evolution trend of the quality level, and constructing a closed-loop logical adjustment path from data parsing to evaluation output.

[0014] A multi-source heterogeneous data stream parsing and evaluation system based on large language model semantic understanding includes: The data acquisition module is used to acquire multi-source heterogeneous data streams; The matrix extraction module is used to extract the logical dependency matrix that represents the logical subordination relationship between data nodes in a multi-source heterogeneous data stream. Specifically, it determines the spatial location constraints or logical topological associations defined by the logical dependency matrix by calculating the topological hierarchy weights between data nodes in the multi-source heterogeneous data stream. The tensor generation module is used to generate mask constraint tensors to guide the feature fusion path of a large language model based on the logical dependency weights between data nodes in the logical dependency matrix. The mask injection module is used to inject the mask constraint tensor into the self-attention computation layer of the large language model, and to perform numerical suppression on the attention weight distribution of the large language model using the mask constraint tensor, thereby forcing the feature extraction process to converge within the logical constraint subspace that conforms to the definition of the logical dependency matrix, so as to generate semantic parsing vectors. The comprehensive evaluation module is used to input semantic parsing vectors into the top-level evaluation model, analyze the process constraint features contained in the semantic parsing vectors, perform multi-dimensional quantitative evaluation for multi-source heterogeneous data streams, and output the evaluation results.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In the parsing of multi-source heterogeneous data streams, for the heterogeneous physical attributes and hard logical boundaries presented by data from different sources, a topological adjacency matrix generated through preprocessing is used to construct a prior constraint tensor with directional guidance capability at the feature extraction level of the computational model. This enables the model to perform hidden layer truncation based on the physical belonging relationship of the data when calculating the correlation weights between nodes, thereby avoiding the dilution or contamination of structured precise logical nodes by unstructured noise features, improving the logical determinism of multi-source heterogeneous data in the semantic fusion process, and solving the feature drift problem of traditional global self-attention mechanism when processing highly constrained industrial data.

[0016] 2. By extracting the parsing middleware results containing database structure specifications or auxiliary design model feature maps and converting them into a mask matrix that matches the dimensions of the computation sequence, negative infinity mask values ​​are set for tensor coordinates that are topologically mutually exclusive or have no spatial subordination relationship during the feature alignment calculation process. This forces the feature extraction process to converge only in the subspace that conforms to the objective business logic. This dynamic injection of the underlying operator logic of the computation model based on prior engineering constraints enables the system to effectively avoid the association illusion caused by cross-modal data fusion without having to multiply the model parameter scale, ensuring that the evaluation results conform to the objective manufacturability and logical connectivity of the physical world.

[0017] 3. By leveraging the physical blocking of invalid feature flow paths by prior constraint tensors, the system, when processing large-scale heterogeneous data streams, only allocates attention weights and aggregates features for node pairs that conform to logical associations or assembly subordination relationships. This avoids computational redundancy caused by global traversal at the algorithm level, reduces the invalid computing power overhead when the system processes complex and intricate industrial feature networks, improves the response speed and processing throughput of the computing model to high-dimensional heterogeneous data spaces, and achieves precise allocation of computing resources to high-value core constraint nodes. Attached Figure Description

[0018] Figure 1 This is a flowchart of the semantic understanding process for parsing and evaluating multi-source heterogeneous data streams in this invention. Figure 2 This is a diagram of the module collaboration and closed-loop feedback architecture of the data flow parsing and evaluation system of the present invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] 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.

[0021] A method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of large language models includes the following steps: Step 101: Obtain multi-source heterogeneous data streams and extract logical dependency matrices representing the logical subordinate relationships between data nodes in the multi-source heterogeneous data streams; wherein, the logical subordinate relationships are determined by calculating the topological hierarchy weights between data nodes in the multi-source heterogeneous data streams. Step 102: Based on the logical dependency matrix, generate a mask constraint tensor to guide the feature fusion path of the large language model; wherein, the value of each element in the mask constraint tensor is mapped from the logical subordination weight between each data node in the logical dependency matrix, and the value of each element is positively correlated with the logical subordination weight. Step 103: Dynamically inject the mask constraint tensor into the self-attention computation layer of the large language model. Use the mask constraint tensor to perform numerical suppression on the attention weight distribution of the large language model when performing feature relevance operations. By performing numerical truncation on the weight allocation path in the logically unrelated region defined by the logical dependency matrix, the feature extraction process is forced to converge in the logical constraint subspace that conforms to the definition of the logical dependency matrix, so as to eliminate the feature dilution of structured data nodes by unstructured noise features and generate semantic parsing vectors that represent the association characteristics of multi-source heterogeneous data. Step 104: Input the semantic parsing vector into the top-level evaluation model, perform multi-dimensional quantitative evaluation of manufacturability, physical connectivity and logical consistency of multi-source heterogeneous data streams, and output the corresponding quantitative evaluation results.

[0022] Preferably, step 101 involves extracting the logical dependency matrix, including: capturing multi-source concurrent data streams containing geometric structure description files; parsing the tree-like topology in the geometric structure description files and extracting the assembly constraint weights between entity nodes to construct a global logical dependency matrix; wherein, the multi-source concurrent data streams are asynchronously scheduled and stored through a high-speed cache unit to ensure that the throughput of the multi-source concurrent data streams during the parsing phase is not less than 100MB / s.

[0023] Preferably, generating the mask constraint tensor in step 102 includes: normalizing the off-diagonal elements in the logical dependency matrix; setting negative infinity mask values ​​for the tensor coordinates that are logically mutually exclusive or unrelated, in order to construct a mask matrix that matches the computational sequence dimension of the large language model.

[0024] Preferably, the calculation rule for the attention weight distribution in step 103 follows the following formula: Where Q is the query vector, K is the key vector, and V is the value vector. Let M be the dimension of the key vector, and M be the prior constraint bias term obtained from the mask matrix. The prior constraint bias term is used to perform numerical masking on the attention weight allocation between logically unrelated node pairs before performing the normalized exponent operation.

[0025] Preferably, before step 101, the method further includes: performing data cleaning using a rule engine, correcting numerical missing values ​​using a mean-filling algorithm; performing deduplication processing on text data in multi-source heterogeneous data streams based on cosine similarity, and filtering out abnormal data exceeding 3 standard deviations using a preset step size.

[0026] Preferably, step 104 involves performing a multi-dimensional quantitative evaluation, including: extracting process constraint features corresponding to the multi-source heterogeneous data stream from the semantic parsing vector, calculating manufacturability index scores, logical connectivity index scores, and semantic consistency index scores; and comparing each index score with a preset quality benchmark value to determine the quality level of the multi-source heterogeneous data stream.

[0027] Preferably, it also includes: feeding back the quality level and scores of each indicator to the parameter fine-tuning interface of the large language model in real time, and using the backpropagation mechanism to adjust the weight bias of the self-attention layer in the large language model in order to improve the output accuracy of the subsequent semantic parsing vector.

[0028] Preferably, step 101, the multi-source heterogeneous data stream includes structured database records, CSV data, semi-structured JSON data, XML messages, and unstructured engineering logs. The mask constraint tensor reduces the computational redundancy of the large language model when performing multi-source feature aggregation by suppressing the feature flow paths between unrelated entity nodes, so that the system's response latency is less than 50ms when processing high-dimensional heterogeneous feature spaces.

[0029] Preferably, it also includes: updating the filtering threshold of the data cleaning in step 101 in real time based on the dynamic evolution trend of the quality level, and constructing a closed-loop logical adjustment path from data parsing to evaluation output.

[0030] A multi-source heterogeneous data stream parsing and evaluation system based on large language model semantic understanding includes: The data acquisition module is used to acquire multi-source heterogeneous data streams; The matrix extraction module is used to extract the logical dependency matrix that represents the logical subordination relationship between data nodes in a multi-source heterogeneous data stream. Specifically, it determines the spatial location constraints or logical topological associations defined by the logical dependency matrix by calculating the topological hierarchy weights between data nodes in the multi-source heterogeneous data stream. The tensor generation module is used to generate mask constraint tensors to guide the feature fusion path of a large language model based on the logical dependency weights between data nodes in the logical dependency matrix. The mask injection module is used to inject the mask constraint tensor into the self-attention computation layer of the large language model, and to perform numerical suppression on the attention weight distribution of the large language model using the mask constraint tensor, thereby forcing the feature extraction process to converge within the logical constraint subspace that conforms to the definition of the logical dependency matrix, so as to generate semantic parsing vectors. The comprehensive evaluation module is used to input semantic parsing vectors into the top-level evaluation model, analyze the process constraint features contained in the semantic parsing vectors, perform multi-dimensional quantitative evaluation for multi-source heterogeneous data streams, and output the evaluation results.

[0031] Example 1: In an industrial manufacturing parsing scenario involving concurrent processing of multi-source heterogeneous data streams including geometric structure description files, semi-structured extensible markup language messages, and unstructured engineering logs, the system fuses data node features with strong physical hierarchies at a throughput of at least 100MB / s. A global self-attention calculation mechanism allocates global attention weights, diluting redundant feature vectors in the unstructured engineering logs with decisive logically constrained node features in the structured data streams. Addressing the conflict between the large language model parameter degrees of freedom and objective physical boundaries, the system constructs a physical intervention mechanism at the operator computation level, ensuring that the semantic fusion of cross-source data is controlled by prior constraints. The system utilizes a high-speed cache unit to asynchronously schedule and store multi-source concurrent data streams, parses the tree-like topology in the geometric structure description files, calculates the topological hierarchy weights between data nodes in the multi-source heterogeneous data streams, extracts assembly constraint weights between entity nodes, and constructs a representation. The logical dependency matrix, which defines the logical subordination relationships and spatial constraints among data nodes, uses the following quantification criteria for the shortest topological distance: For part entities from geometric description files, the distance is the Euclidean distance between the centroids of two entities, with a base unit of 0.1 mm; for data from structured databases or messages, the distance is the total number of nodes traversed in the logical tree structure multiplied by 10 step units; for unstructured engineering logs, the distance is the time difference between the timestamps of two records. The system forcibly maps the original values ​​of these different physical dimensions to the maximum observation value within the current 1000 sampling periods, thus unifying the dimensions of heterogeneous data weights. It also normalizes the off-diagonal elements in the logical dependency matrix, parses the tree topology of the geometric description file, identifies the assembly path of entity nodes, and calculates the data node... and Shortest topological distance The initial logical subordination weights are calculated based on the negative exponential function of the base of the natural logarithm. The system uses a string matching algorithm to associate the features of unstructured engineering log text with geometric entity nodes that share the same semantic identifier. The specific operation process is as follows: extract the three-dimensional centroid coordinate values ​​of each part entity from the geometric structure description file; when a string identifier that matches the part entity number is found in the unstructured engineering log text, the system initializes the center point of the semantic parsing vector of that log to the three-dimensional centroid coordinate of that part entity.

[0032] Within a 20ms data processing loop, the correlation weights of the coordinate positions are weighted and accumulated using a decay coefficient of 0.75, thus establishing a unique topological anchor point based on physical space entities for the text features in the logical dependency matrix. The coordinate weights are synchronously updated in the logical dependency matrix to achieve cross-modal data feature space topological alignment. The logical dependency matrix is ​​an N×N matrix, where N is the total number of feature fusion data nodes. Negative infinity mask values ​​are set for tensor coordinates that are logically mutually exclusive or uncorrelated, constructing a mask matrix that matches the dimension of the large language model's computational sequence. Based on this logical dependency matrix, a mask constraint tensor guiding the feature fusion path of the large language model is generated. Each element value in this mask constraint tensor maps to the logical subordination weights between data nodes in the logical dependency matrix, and the magnitude of the element value is positively correlated with the logical subordination weight. The system dynamically injects the mask constraint tensor into the self-attention computation layer of the large language model. In this self-attention computation layer, the calculation rule for the attention weight distribution of feature correlation operations follows the formula... Where Q is the query vector, K is the key vector, and V is the value vector. Let M be the dimension of the key vector, and M be the prior constraint bias term obtained from the mask matrix. Normalization is performed on the off-diagonal elements of the logical dependency matrix to obtain scalarized association strength. Mask matrix elements are set for the x and y coordinates of logically mutually exclusive or unassociated tensors. for Before performing the normalized exponential operation, the attention allocation path values ​​are truncated. To avoid the gradient vanishing problem caused by hard truncation at the operator's underlying level, the system forcibly sets the mask constraint tensor element values ​​in logically irrelevant regions to a saturation level of -10000 mV in the underlying accumulator instruction-level processing. During the normalized exponential function operation, a static bias coefficient of 0.000001 is added to the base part to ensure that while the feature extraction process converges to the logical constraint subspace, the computation link still retains extremely weak feature background signals. This prevents parameter update stagnation when the large language model performs multi-source feature aggregation. The mask constraint tensor M is injected into the large language model. The attention computation layer, acting as a prior constraint bias term, compresses the weights of irrelevant nodes to near zero in the output formula of the attention computation layer, enabling the model feature extraction to converge within the subspace of physical objective logical constraints. Before performing the normalization exponent operation, this prior constraint bias term uses a mask constraint tensor to numerically mask the attention weight allocation between logically unrelated node pairs. The system performs numerical truncation on the weight allocation path within the logically irrelevant region defined by the logical dependency matrix, and uses a mask constraint tensor to numerically suppress the attention weight distribution of the large language model during computation, forcing the feature extraction process to converge within the logical constraint subspace defined by the logical dependency matrix.

[0033] The system performs numerical truncation on weighted paths within logically unrelated regions defined by the logical dependency matrix, eliminating the feature dilution of structured data nodes by unstructured noise features. This generates semantic parsing vectors characterizing the correlation properties of multi-source heterogeneous data. The system inputs these semantic parsing vectors into a top-level evaluation model to extract the process constraint features corresponding to the multi-source heterogeneous data streams. It calculates manufacturability, logical connectivity, and semantic consistency scores, compares each score with a preset quality benchmark, determines the quality level of the multi-source heterogeneous data streams, and outputs a multi-dimensional quantitative evaluation result including manufacturability, physical connectivity, and logical consistency. The mask constraint tensor reduces the feature flow path between unrelated entity nodes by suppressing the process flow. The computational redundancy of low-level large language model feature aggregation reduces the system's response latency to less than 50ms when processing high-dimensional heterogeneous feature spaces. The system feeds back the quality level and scores of each indicator to the parameter fine-tuning interface of the large language model in real time. It uses backpropagation to adjust the weight bias of the self-attention layer in the large language model and updates the data cleaning filtering threshold in real time based on the dynamic evolution trend of the quality level, forming a closed-loop logical adjustment path from data parsing to evaluation output. The system dynamically calibrates the normalized scaling factor λ by statistically analyzing the random edge breakage ratio δ of the input data stream. A calibration parameter is set with λ=0.15 corresponding to a structural missing ratio between 10% and 20%. An exponential moving average algorithm is used to smooth the backpropagation weight update amplitude, and the updated weight bias is calculated. ,in The weight bias before the update is given, with η being a learning rate parameter of 0.001. The composite loss gradient is calculated from the mean square error function, β is a smoothing coefficient of 0.1, and S is a time-series fluctuation trend smoothing loss factor. The network oscillation caused by local convergence in the feature space is eliminated by truncating the single jump amplitude of node weights.

[0034] Example 2: Addressing engineering scenarios where high-concurrency heterogeneous data causes large language model computational features to deviate from physical constraints, a test platform was built to verify the control mechanism of the mask constraint tensor on the self-attention computation layer. The data used in the experiment was extracted from a publicly available industrial computer-aided design assembly topology dataset, and unstructured maintenance log text output from the production line was mixed in. The system's data throughput was set to 150MB / s. To verify the system's anti-disturbance performance in a real industrial environment, the system actively superimposed 15% of random edge breaks and out-of-order labels onto the original topology sequence to reproduce network packet loss and structural incompleteness during data transmission. The system generated approximately... When generating the bundle tensor, it is necessary to determine the normalized scaling factor of the off-diagonal elements in the logical dependency matrix. The value of this parameter constrains the system balance between the strictness of topological constraints and the semantic generalization ability of the large language model. The system presets parameter decision rules and dynamically adjusts the normalized scaling factor according to the proportion of structural missing data obtained from the input data stream. When the system identifies that the proportion of structural missing data is in the range of 10% to 20%, it triggers a control instruction to lower the normalized scaling factor and increases the relative weight of the negative infinity mask value in the matrix. According to the above decision rules, after detecting the current proportion of structural missing data of 15%, the system calculates the normalized scaling factor of the output to be set to 0.15.

[0035] The experiment constructed a control group containing a standard global self-attention model and an experimental group using a dynamically injected mask constraint tensor model architecture. The system input a data stream with 15% structural perturbation into the control group. The control group model output an attention weight test value of 0.085 for unrelated engineering log nodes, while the attention weight test value for key structural nodes decreased to 0.213. The system extracted the corresponding semantic parsing vector and calculated a semantic consistency index score of 42.1%. Simultaneously, the system input the same data stream into the experimental group, extracted the logical dependency matrix to generate a mask constraint tensor, and then applied this mask constraint tensor... The quantity is injected into the self-attention computation layer. Before the self-attention computation layer performs normalized exponent calculation, the mask constraint tensor is used to apply numerical truncation to the feature flow path of nodes distributed in logically unrelated regions. The intermediate state tensor test data derived from the self-attention layer shows that the average attention weight of unrelated engineering log nodes in the experimental group decreased to the order of 0.0000001, and the test value of the attention weight of key structural nodes with topological connection attributes converged to 0.852. The experimental group model blocked the feature allocation path of unrelated noise feature nodes based on the mask constraint tensor.

[0036] The experiment further established out-of-range control groups with normalized scaling factors of 0.05 and 0.50, and configured gradient test environments with perturbation ratios of 5% and 25%. Test data from the experimental group showed that when the normalized scaling factor was at the absolute lower limit of 0.05, the system output a numerical penalty instruction for off-diagonal elements, resulting in a semantic consistency score of 91.2% and a logical connectivity score of 75.3%. Under the normalized scaling factor of 0.15 (the median value), the experimental group output a semantic consistency score of 96.5% and a logical connectivity score of 94.8%. When the normalized scaling factor was increased to 0.5... When the absolute upper limit of 0 is reached, the system output data exhibits nonlinear degradation. The structural noise blocking path of the mask constraint tensor fails, and the measured semantic consistency index score drops to 68.4%. Data from the gradient test environment shows that when the perturbation ratio increases from 5% to 25%, the semantic consistency index score calculated by the control group shows an exponential decay, while the semantic consistency index score output by the experimental group is consistently above 92.0%. The system adjusts the distribution of the computational tensor of the attention layer based on the mask constraint tensor to avoid the dilution of the feature vectors of structured constraint nodes by redundant unstructured feature parameters, generating quantitative evaluation data that includes manufacturability and logical connectivity.

[0037] Example 3: Industrial manufacturing analysis scenario processing of dynamically evolving multi-source heterogeneous data streams. Under this condition, the static evaluation architecture cannot directly map high-dimensional semantic parsing vectors to engineering physical quantities, and the fixed threshold parameter causes data cleaning and release deviations. The system constructs a feedforward neural network with a multi-branch topology as the top-level evaluation model. The semantic parsing vector output by the large language model is used as the input tensor of the feedforward neural network, which flows into three independent feature mapping channels. Each feature mapping channel contains two hidden layers and uses a linear rectified activation function to process the input data. The output layer of each feature mapping channel uses a sigmoid activation function to compress the data in the high-dimensional feature space to a numerical range of 0 to 1, and calculates and outputs quantitative manufacturability index scores, logical connectivity index scores, and semantic consistency index scores respectively.

[0038] The system extracts historical quality level data within a preset sliding time window, calculates the mean square error of the quality level data within the sliding time window, and when it exceeds the set fluctuation tolerance threshold, the system triggers the dynamic update logic of the filtering threshold. The update calculation of the filtering threshold follows the formula. ,in The current filter threshold is updated at the current moment. The system uses the historical filtering threshold from the previous moment, α as the time decay coefficient within the range of 0.8 to 0.95, and ΔQ as the difference between the quality level of the multi-source heterogeneous data stream at the current moment and the average quality level within the sliding time window. Based on this updated calculation result and the transient fluctuation amplitude of the data stream, the system adjusts the data release limit of the front-end data cleaning node. The system extracts the physical inspection labels from the manufacturing site feedback, associates these labels with the scores of various indicators to generate a mean square error function, and calculates and outputs the composite loss gradient representing the judgment deviation based on the mean square error function. This composite loss gradient is then propagated back along the network link to the self-attention calculation layer of the large language model via the parameter fine-tuning interface. Based on this composite loss gradient, the system corrects the weight parameters corresponding to the prior constraint bias terms, establishes a numerical transmission path from the semantic tensor to the engineering evaluation indicators through multi-branch network topology and dynamic threshold calculation rules, corrects the calculation distribution drift caused by parameter solidification through reverse gradient feedback, and outputs analytical evaluation results that match dynamic high-concurrency working conditions.

[0039] Example 4: When a heterogeneous data processing system connects to a new production line data source, differences in the physical environment cause the preset quality benchmark value to become invalid. Before formally parsing the multi-source concurrent data stream, a standard geometric structure description file with no defects covering the historical production cycle of the target production line and a verified unstructured engineering log are extracted to form a calibration dataset. The calibration dataset is input into the top-level evaluation model, and the initial manufacturability index score, initial logical connectivity index score, and initial semantic consistency index score of each data sample are calculated respectively. The internal acquisition clock is started synchronously to record the initial index score values ​​of each item within 10,000 consecutive sampling periods. The accumulator is used to calculate the arithmetic mean of the initial index scores of each item within the time series. The output arithmetic mean is written into the underlying register and set as the quality benchmark value for evaluating the quality level of the multi-source heterogeneous data stream of this specific production line.

[0040] The discrete distribution characteristic data of the initial index scores within the sampling period are continuously extracted. The variance tensor of each initial index score relative to the quality benchmark value is calculated. Principal component analysis is used to solve for the maximum eigenvalue of this variance tensor in the multidimensional feature space. This maximum eigenvalue and the engineering margin coefficient are then substituted into the formula. Determine the state trigger boundaries, where, Here, λ represents the critical value for fluctuation tolerance, and λ is an engineering margin coefficient set to 0.35. The maximum eigenvalue is used as the benchmark for triggering the dynamic update mechanism of the data cleaning and filtering threshold. The tolerance boundary is defined based on the feature decomposition and scalar constraints of the calibration data, and the system initialization configuration state with specific spatial environment adaptability is output.

[0041] Example 5: To address the technical risk of uncontrolled parameter evolution due to the lack of an anti-oscillation mechanism in the process of mapping semantic parsing vectors to engineering physical quantities, the system initiates a model steady-state calibration procedure before performing parameter backpropagation. A smooth evolution path is constructed in the parameter fine-tuning loop to limit weight jumps. A historical baseline tensor composed of initial index scores from 10,000 consecutive sampling periods is extracted, and the observed feature tensor at the current calculation moment is extracted simultaneously. A cosine similarity algorithm is used to calculate the cosine of the angle between the historical baseline tensor and the observed feature tensor in the multi-dimensional feature space, generating a cosine similarity matrix. A first-order difference operation is then performed on this matrix, transforming the discrete similarity values ​​into a variance gradient characterizing the temporal fluctuation trend of the data stream. The system sets a statistical threshold of 0.05 for triggering parameter fine-tuning. When the variance gradient exceeds this statistical threshold for three consecutive sampling periods, the system determines that the current system state deviates from the stable envelope and triggers the parameter fine-tuning interface to update the weight bias of the self-attention calculation layer.

[0042] After triggering the parameter fine-tuning interface, the system applies the exponential moving average algorithm as a smoothing operator to convert the discrete variance gradient into a smoothing loss factor with temporal continuity. The system then uses the previously derived composite loss gradient and this smoothing loss factor, combined with the formula... Calculate the node weight update magnitude of the self-attention computation layer, where For the updated weight bias, The weight bias before the update is given, and η is a learning rate parameter set to 0.001. The composite loss gradient is calculated from the mean square error function, β is a smoothing coefficient set to 0.1, and S is a smoothing loss factor. The system imposes a hard truncation limit on the single jump amplitude of the weights calculated by the large language model by introducing a smoothing loss factor related to time-series fluctuations. The system uses this constraint mechanism to eliminate network oscillations caused by local convergence of the feature space during the operator fine-tuning stage, and outputs a smoothly evolving model parameter configuration state.

[0043] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of large language models, characterized in that, Includes the following steps: Step 101: Obtain multi-source heterogeneous data streams and extract logical dependency matrices representing the logical subordinate relationships between data nodes in the multi-source heterogeneous data streams; wherein, the logical subordinate relationships are determined by calculating the topological hierarchy weights between data nodes in the multi-source heterogeneous data streams. Step 102: Based on the logical dependency matrix, generate a mask constraint tensor to guide the feature fusion path of the large language model; wherein, the value of each element in the mask constraint tensor is mapped from the logical subordination weight between each data node in the logical dependency matrix, and the value of each element is positively correlated with the logical subordination weight. Step 103: Dynamically inject the mask constraint tensor into the self-attention computation layer of the large language model. Use the mask constraint tensor to perform numerical suppression on the attention weight distribution of the large language model when performing feature relevance operations. By performing numerical truncation on the weight allocation path in the logically unrelated region defined by the logical dependency matrix, the feature extraction process is forced to converge in the logical constraint subspace that conforms to the definition of the logical dependency matrix, so as to eliminate the feature dilution of structured data nodes by unstructured noise features and generate semantic parsing vectors that represent the association characteristics of multi-source heterogeneous data. Step 104: Input the semantic parsing vector into the top-level evaluation model, perform multi-dimensional quantitative evaluation of manufacturability, physical connectivity and logical consistency of multi-source heterogeneous data streams, and output the corresponding quantitative evaluation results.

2. The method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of a large language model according to claim 1, characterized in that, Step 101 involves extracting the logical dependency matrix, including: capturing multi-source concurrent data streams containing geometric structure description files; parsing the tree-like topology in the geometric structure description files and extracting the assembly constraint weights between entity nodes to construct a global logical dependency matrix; wherein, the multi-source concurrent data streams are asynchronously scheduled and stored through a high-speed cache unit to ensure that the throughput of the multi-source concurrent data streams during the parsing phase is not less than 100MB / s.

3. The method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of a large language model according to claim 1, characterized in that, Step 102 generates a mask constraint tensor, including: normalizing the off-diagonal elements in the logical dependency matrix; setting negative infinity mask values ​​for the tensor coordinates that are logically mutually exclusive or unrelated, in order to construct a mask matrix that matches the computational sequence dimension of the large language model.

4. The method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of a large language model according to claim 3, characterized in that, Step 103 The calculation rule for attention weight distribution follows the following formula: Where Q is the query vector, K is the key vector, and V is the value vector. Let M be the dimension of the key vector, and M be the prior constraint bias term obtained from the mask matrix. The prior constraint bias term is used to perform numerical masking on the attention weight allocation between logically unrelated node pairs before performing the normalized exponent operation.

5. The method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of a large language model according to claim 1, characterized in that, Before step 101, the method further includes: performing data cleaning using a rule engine, correcting missing numerical values ​​using a mean-filling algorithm; performing deduplication on text data in multi-source heterogeneous data streams based on cosine similarity, and filtering out abnormal data exceeding 3 standard deviations using a preset step size.

6. The method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of a large language model according to claim 1, characterized in that, Step 104 involves performing a multi-dimensional quantitative evaluation, including: extracting process constraint features corresponding to the multi-source heterogeneous data stream from the semantic parsing vector, calculating manufacturability index scores, logical connectivity index scores, and semantic consistency index scores; and comparing each index score with a preset quality benchmark value to determine the quality level of the multi-source heterogeneous data stream.

7. The method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of a large language model according to claim 6, characterized in that, Also includes: The quality level and scores of each indicator are fed back to the parameter fine-tuning interface of the large language model in real time. The weight bias of the self-attention layer in the large language model is adjusted by using the backpropagation mechanism to improve the output accuracy of the subsequent semantic parsing vector.

8. The method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of a large language model according to claim 1, characterized in that, Step 101: The multi-source heterogeneous data stream includes structured database records, CSV data, semi-structured JSON data, XML messages, and unstructured engineering logs. The mask constraint tensor reduces the computational redundancy of the large language model when performing multi-source feature aggregation by suppressing the feature flow paths between unrelated entity nodes.

9. The method for parsing and evaluating multi-source heterogeneous data streams based on semantic understanding of a large language model according to claim 7, characterized in that, Also includes: Based on the dynamic evolution trend of quality level, the filtering threshold of data cleaning in step 101 is updated in real time, and a closed-loop logical adjustment path from data parsing to evaluation output is constructed.

10. A system for parsing and evaluating multi-source heterogeneous data streams based on large language model semantic understanding, used to execute the method for parsing and evaluating multi-source heterogeneous data streams based on large language model semantic understanding as described in claim 1, characterized in that, include: The data acquisition module is used to acquire multi-source heterogeneous data streams; The matrix extraction module is used to extract the logical dependency matrix representing the logical subordination relationship between data nodes in a multi-source heterogeneous data stream; Specifically, by calculating the topological hierarchy weights between data nodes in a multi-source heterogeneous data stream, the spatial location constraints or logical topological associations defined by the logical dependency matrix are determined. The tensor generation module is used to generate mask constraint tensors to guide the feature fusion path of a large language model based on the logical dependency weights between data nodes in the logical dependency matrix. The mask injection module is used to inject the mask constraint tensor into the self-attention computation layer of the large language model, and to perform numerical suppression on the attention weight distribution of the large language model using the mask constraint tensor, thereby forcing the feature extraction process to converge within the logical constraint subspace that conforms to the definition of the logical dependency matrix, so as to generate semantic parsing vectors. The comprehensive evaluation module is used to input semantic parsing vectors into the top-level evaluation model, analyze the process constraint features contained in the semantic parsing vectors, perform multi-dimensional quantitative evaluation for multi-source heterogeneous data streams, and output the evaluation results.

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