Semantic analysis method for industry standard

Abstract

This invention relates to the field of semantic retrieval technology and discloses a semantic analysis method for industrial standards, comprising: a processor parsing the hierarchical structure features of an industrial standard document and identifying the terminology definition section; obtaining the feature vectors of keywords in a general context and the local constraint vectors of the corresponding definition text; calculating the numerical difference between the two to obtain a semantic shift feature vector; monitoring the words to be analyzed in the main text, determining calibration weights based on word frequency distribution when matching keywords, adjusting the semantic shift feature vectors using the calibration weights and reprojecting the original vectors of the words to be analyzed to generate a calibrated target semantic vector. This invention constructs a semantic shift factor under local context constraints, eliminates semantic isotropic bias within a closed system, achieves accurate fitting between the semantic vector and the technical connotation of the document, and solves the context drift problem of general models in professional closed domains.

Description

Technical Field

[0001] This invention belongs to the field of semantic retrieval technology, and in particular relates to a semantic analysis method for industrial standards. Background Technology

[0002] In the current process of industrial digitalization, extracting the semantic features of standard clauses and building an index database serves as a means to verify the compliance of clauses and supports the automated management of large-scale industrial standard libraries. Industrial standards have specific contextual constraints, and different industry systems have different definitions of terms with the same name. The semantic vectors extracted by using a general pre-trained model are limited by the prior distribution of general corpus and cannot generate local spatial constraints for the terminology definition chapters within a specific document, resulting in feature expressions deviating from the original definitions of the standard document.

[0003] To address the limitations of constructing indexes for large-scale text databases, improvements to the underlying semantic mapping logic and vector space construction methods are crucial. For example, Chinese invention patent application CN119088766A discloses a method, apparatus, medium, and electronic device for semantic vectorization of standard document content. This approach achieves vectorization through content graphing and entity feature attributes. However, this technical approach relies excessively on the overall entity association topology between standard documents, neglecting the strong local semantic constraints imposed by the defined chapters within standard documents on the main text content. It fails to identify and extract the semantic offset attributes of keywords in closed contexts relative to general contexts, resulting in isotropic bias in the generated semantic vectors when faced with cross-industry homonymous terms due to prior interference from the general corpus space. To reduce contextual bias, industries typically use domain model fine-tuning or add posterior filtering rules. However, fine-tuning faces limitations in the scale of professional corpora and struggles to handle definition conflicts between documents. Posterior filtering, on the other hand, ignores the feature distribution relationships within the vector space, leading to a contradiction between the semantic logic and the inaccuracy of the standard connotation during the retrieval process.

[0004] Therefore, how to extract the definition constraints within a standard document and calibrate the semantic feature space accordingly, so that the semantic representation is aligned with the specific connotation of the document, becomes the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a semantic analysis method for industry standards, comprising the following steps: Step S101: The processor parses the hierarchical structure features of the industry standard document, identifies the terminology definition section in the industry standard document using preset layout rules, and obtains the logical start and end boundaries of the terminology definition section in the industry standard document. Step S102: Read the keywords recorded in the terminology definition section, call the pre-trained language model to obtain the general semantic feature vector of the keywords in the context of the general corpus, and extract the original definition text of the corresponding keywords in the terminology definition section. Then, perform semantic feature encoding on the original definition text to generate a local constraint feature vector that reflects the specific constraints of the industry standard document. Step S103: Calculate the numerical difference between the local constraint feature vector and the general semantic feature vector in the feature space to obtain the semantic offset feature vector for the industry standard document. The semantic offset feature vector is used to characterize the semantic offset attribute and distortion degree of keywords in the closed context of the industry standard document relative to the general context. Step S104: Monitor the words to be analyzed in the main text of the industrial standard document. When the words to be analyzed are matched with keywords, determine the calibration weight coefficient for the words to be analyzed based on the contextual word frequency distribution features and semantic density of the words to be analyzed in the main text. After adjusting the semantic offset feature vector using the calibration weight coefficient, reproject the original feature vector of the words to be analyzed in the pre-trained language model to generate the calibrated target semantic vector. Then, perform semantic analysis on the industrial standard document based on the target semantic vector.

[0006] Preferably, step S102 specifically includes: step S1021, the processor parses the text topology relationship of the term definition chapter, extracts the parent-child membership level between terms, and assigns initial weights to keywords at different depths according to the parent-child membership level; step S1022, the processor uses the initial weights to perform scale normalization processing on the general semantic feature vector and the local constraint feature vector, calculates the difference in projection length between the two in the feature space, thereby eliminating the distribution deviation of the pre-trained language model at different context depths.

[0007] Preferably, step S101 specifically includes: step S1011: the processor traverses the text metadata of the industry standard document and matches the directory index nodes containing definitions, terms, vocabulary meanings or standard explanation keywords using preset regular expressions; step S1012: based on the matched directory index nodes, the processor locates the physical page of the terminology definition section and, by identifying the indentation features of each line of text on the physical page and specific guiding symbols, removes non-definition auxiliary text from the terminology definition section.

[0008] Preferably, the semantic analysis includes the following steps: Step S401: The processor calculates the cosine similarity between the target semantic vector and the standard term vector in the preset industrial ontology library to determine the attribute classification of the term to be analyzed in the technical field; Step S402: When the cosine similarity exceeds the similarity threshold of 0.85, the term to be analyzed is marked as a key feature anchor point for compliance detection, and the physical position offset of the term to be analyzed in the industrial standard document is associated with the retrieval index of the industrial standard document.

[0009] Preferably, step S401 specifically includes: step S4011: the processor monitors the similarity gradient change in the semantic analysis process in real time, and starts the second-order feature calibration logic when the similarity gradient change exceeds the preset slope threshold; step S4012: the second-order feature calibration logic extracts the contextual association features of the words to be analyzed in the main text sentence, and performs feature space resampling on the target semantic vector according to the contextual association features to eliminate semantic crosstalk caused by neighboring words.

[0010] Preferably, after obtaining the semantic offset feature vector in step S103, the method further includes: the processor storing the semantic offset feature vector in a preset cache library and establishing a mapping relationship between the semantic offset feature vector and the document identification code of the industry standard document; when the processor processes related documents belonging to the same standard system as the industry standard document, it calls the semantic offset feature vector mapped in the cache library to complete the closed context semantic calibration across documents.

[0011] Preferably, after step S104, the method further includes: the processor calculating the overlap of technical requirements between different industry standard documents based on the target semantic vector; identifying document pairs to be verified with a technical requirement overlap of more than 90%; extracting and outputting conflicting text segments in the document pairs to be verified that have logically mutually exclusive definitions for the same keyword, so as to provide standardized compliance suggestions.

[0012] Preferably, the process of generating local constraint feature vectors includes: the processor using the attention mechanism weights in the pre-trained language model to identify the core feature words in the original definition text that modify and restrict the meaning of keywords; performing mask filtering on the original definition text according to the weight distribution of the core feature words, retaining feature components with semantic relevance higher than 0.75, thereby extracting local constraint feature vectors.

[0013] Preferably, the processor calculates the hardware computing resource load rate during the real-time reprojection of the original feature vector. When the hardware computing resource load rate exceeds a preset threshold of 80%, the sampling dimension of the reprojection process is automatically reduced, and the original feature vector is calibrated in a simplified linear translation mode to ensure the real-time response performance of the industry-standard document semantic analysis process.

[0014] Compared to existing technologies, the semantic analysis method of this invention for industry standards has the following advantages: 1. In the semantic analysis of industrial standards, by extracting the semantic features of the terminology definition section in the industrial standard document, a semantic shift factor under local context constraints is constructed to eliminate the isotropic deviation of the general semantic space in a specific closed logical system, and to achieve accurate fitting of the semantic vector with the specific technical connotation of the document. Thus, without changing the underlying pre-trained model architecture, the problem of context drift of the general model in the professional closed domain is solved.

[0015] 2. By using the terminology definitions provided in the standard document as semantic anchors, the local center of gravity is reconstructed by calculating the tensor difference between the local semantic vector and the global reference vector. This ensures that the semantic parsing logic is controlled by the logical boundaries of the document itself, avoids interference from the prior knowledge of the general corpus on the definitions of specific industry terms, and ensures that the search results are consistent with the legal status and technical definitions of the standard document.

[0016] 3. The semantic shift factor is nonlinearly coupled with the feature density in the main text clauses. The calibration intensity is adjusted by the context sensitivity coefficient, so that the semantic offset is dynamically adjusted with the change of context relevance. This constructs a feature projection path that evolves with the context, improves the system's ability to distinguish cross-industry homonymous terms at the surface technical dimension, and eliminates semantic space collapse caused by definition conflicts. Attached Figure Description

[0017] Figure 1 This is a flowchart of the industrial standard terminology semantic offset calibration and target semantic vector generation process of this invention; Figure 2 This is a logical block diagram of the cross-document semantic offset calibration and compliance conflict analysis of the present invention. Detailed Implementation

[0018] 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.

[0019] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0020] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0021] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0022] A semantic analysis method for industry standards includes the following steps: Step S101: The processor parses the hierarchical structure features of the industry standard document, identifies the terminology definition section in the industry standard document using preset layout rules, and obtains the logical start and end boundaries of the terminology definition section in the industry standard document. Step S102: Read the keywords recorded in the terminology definition section, call the pre-trained language model to obtain the general semantic feature vector of the keywords in the context of the general corpus, and extract the original definition text of the corresponding keywords in the terminology definition section. Then, perform semantic feature encoding on the original definition text to generate a local constraint feature vector that reflects the specific constraints of the industry standard document. Step S103: Calculate the numerical difference between the local constraint feature vector and the general semantic feature vector in the feature space to obtain the semantic offset feature vector for the industry standard document. The semantic offset feature vector is used to characterize the semantic offset attribute and distortion degree of keywords in the closed context of the industry standard document relative to the general context. Step S104: Monitor the words to be analyzed in the main text of the industrial standard document. When the words to be analyzed are matched with keywords, determine the calibration weight coefficient for the words to be analyzed based on the contextual word frequency distribution features and semantic density of the words to be analyzed in the main text. After adjusting the semantic offset feature vector using the calibration weight coefficient, reproject the original feature vector of the words to be analyzed in the pre-trained language model to generate the calibrated target semantic vector. Then, perform semantic analysis on the industrial standard document based on the target semantic vector.

[0023] Preferably, step S102 specifically includes: step S1021, the processor parses the text topology relationship of the term definition chapter, extracts the parent-child membership level between terms, and assigns initial weights to keywords at different depths according to the parent-child membership level; step S1022, the processor uses the initial weights to perform scale normalization processing on the general semantic feature vector and the local constraint feature vector, calculates the difference in projection length between the two in the feature space, thereby eliminating the distribution deviation of the pre-trained language model at different context depths.

[0024] Preferably, step S101 specifically includes: step S1011: the processor traverses the text metadata of the industry standard document and matches the directory index nodes containing definitions, terms, vocabulary meanings or standard explanation keywords using preset regular expressions; step S1012: based on the matched directory index nodes, the processor locates the physical page of the terminology definition section and, by identifying the indentation features of each line of text on the physical page and specific guiding symbols, removes non-definition auxiliary text from the terminology definition section.

[0025] Preferably, the semantic analysis includes the following steps: Step S401: The processor calculates the cosine similarity between the target semantic vector and the standard term vector in the preset industrial ontology library to determine the attribute classification of the term to be analyzed in the technical field; Step S402: When the cosine similarity exceeds the similarity threshold of 0.85, the term to be analyzed is marked as a key feature anchor point for compliance detection, and the physical position offset of the term to be analyzed in the industrial standard document is associated with the retrieval index of the industrial standard document.

[0026] Preferably, step S401 specifically includes: step S4011: the processor monitors the similarity gradient change in the semantic analysis process in real time, and starts the second-order feature calibration logic when the similarity gradient change exceeds the preset slope threshold; step S4012: the second-order feature calibration logic extracts the contextual association features of the words to be analyzed in the main text sentence, and performs feature space resampling on the target semantic vector according to the contextual association features to eliminate semantic crosstalk caused by neighboring words.

[0027] Preferably, after obtaining the semantic offset feature vector in step S103, the method further includes: the processor storing the semantic offset feature vector in a preset cache library and establishing a mapping relationship between the semantic offset feature vector and the document identification code of the industry standard document; when the processor processes related documents belonging to the same standard system as the industry standard document, it calls the semantic offset feature vector mapped in the cache library to complete the closed context semantic calibration across documents.

[0028] Preferably, after step S104, the method further includes: the processor calculating the overlap of technical requirements between different industry standard documents based on the target semantic vector; identifying document pairs to be verified with a technical requirement overlap of more than 90%; extracting and outputting conflicting text segments in the document pairs to be verified that have logically mutually exclusive definitions for the same keyword, so as to provide standardized compliance suggestions.

[0029] Preferably, the process of generating local constraint feature vectors includes: the processor using the attention mechanism weights in the pre-trained language model to identify the core feature words in the original definition text that modify and restrict the meaning of keywords; performing mask filtering on the original definition text according to the weight distribution of the core feature words, retaining feature components with semantic relevance higher than 0.75, thereby extracting local constraint feature vectors.

[0030] Preferably, the processor calculates the hardware computing resource load rate during the real-time reprojection of the original feature vector. When the hardware computing resource load rate exceeds a preset threshold of 80%, the sampling dimension of the reprojection process is automatically reduced, and the original feature vector is calibrated in a simplified linear translation mode to ensure the real-time response performance of the industry-standard document semantic analysis process.

[0031] Example 1: In a semantic retrieval scenario involving safety production standards of large multinational petrochemical enterprises, the system faces cross-retrieval needs from documents in two different fields: oil drilling and production equipment specifications and industrial instrumentation and electrical standards. When a user inputs "pressure control valve" as a search term, the general pre-trained language model maps this term to a general semantic feature vector in a high-dimensional semantic space. However, in specific petrochemical pressure vessel standards, the connotation of this term is limited by the constraints of the terminology and definition sections in the document. This leads to an isotropic deviation between the feature distribution calculated in the context of a general corpus and the technical definition within this closed logical system. Consequently, the retrieval system mistakenly recalls irrelevant clauses concerning electronic circuit overvoltage protection rather than mechanical pressure relief. The processor analyzes the hierarchical structure of the petroleum drilling and production equipment specification document, identifies the terminology definition section using preset layout rules, obtains the logical start and end boundaries of the terminology definition section in the industrial standard document, reads the keyword pressure recorded in the terminology definition section, and calls a pre-trained language model to obtain the general semantic feature vector of the keywords in the context of a general corpus. Simultaneously, the original definition text of corresponding keywords in the terminology definition section is extracted, and semantic feature encoding is performed on the original definition text to generate local constraint feature vectors that reflect the specific constraints of this industry standard document. Calculate the local constraint eigenvector With general semantic feature vector The numerical difference in the feature space yields the semantic offset feature vector for this industry standard document. Semantic offset feature vector Characterize the semantic shift of keywords in the closed context of industry standard documents relative to the general context.

[0032] The system monitors the words to be analyzed in the main text of industrial standard documents. When matching words to be analyzed with keywords, it determines calibration weight coefficients based on the word frequency distribution characteristics of the words to be analyzed in the main text, and uses the calibration weight coefficients to adjust the semantic offset feature vector. Concurrently project the original feature vectors of the words to be analyzed in the pre-trained language model. Generate calibrated target semantic vector Satisfy the mapping relationship: in, For the target semantic vector, The original feature vector, The preset context sensitivity coefficient, To analyze the word frequency density of the words to be analyzed in the main text, Given the semantic offset feature vector, calculate the target semantic vector. The cosine similarity between the target term and the standard term vectors in the preset industrial ontology library is used to determine the attribute classification of the term in its technical field. When the cosine similarity exceeds a similarity threshold of 0.85, the term is marked as a key feature anchor for compliance detection. The similarity gradient change during the semantic analysis process is monitored. When the similarity gradient change exceeds a preset slope threshold, a second-order feature calibration logic is initiated to extract the contextual features of the term in the main text. Based on the contextual features, the target semantic vector is... The feature space is resampled to improve the positioning accuracy and logical consistency of semantic retrieval while maintaining computational complexity. The second-order feature calibration logic specifically identifies semantic abrupt changes by calculating the first and second-order differences of the similarity components between continuous analysis windows. When the second-order difference value exceeds a preset slope threshold, it is determined that the current semantics are affected by neighboring interference words. At this point, the resampling process is performed on the target semantic vector... Within the feature space neighborhood, feature components are re-extracted based on the probability distribution of context-related features, and the weights of each dimension are normalized and corrected using a Gaussian kernel function, thereby eliminating semantic crosstalk caused by neighboring words within the vector space. Example 2: In the experimental condition of verifying the accuracy of key term retrieval for oil drilling and production equipment specifications, the processor retrieves an industrial corpus containing 1000 petrochemical equipment standards. This corpus is pre-stacked with character substitution noise with a signal-to-noise ratio of 20dB to simulate the recognition error generated during document scanning. The experimental environment consists of a semantic computing server with a floating-point computing power of 32TFLOPS. A similarity threshold of 0.85 is set. This value is determined by a balance between the false negative rate and the false positive rate. When the similarity threshold is below 0.80, the false positive rate increases due to crosstalk from neighboring irrelevant words in the semantic space. When the similarity threshold is above 0.95, the recall rate drops below 42.5% because minor variations in the industrial standard description methods cannot match the standard term vectors. Therefore, 0.85 is used as the criterion to ensure the stability of the semantic matching process.

[0033] Setting the context sensitivity coefficient The processor reads the keyword pressure from the terminology definition section of the oil drilling and production equipment specification document, setting it to 0.12, and calculates the general semantic feature vector. The mean value of the components on the preset feature dimension is 0.425. At the same time, the original definition text of the corresponding keywords in the oil drilling and production equipment specification document is extracted and semantic feature encoding is performed to generate local constrained feature vectors. The corresponding component has a mean of 0.568. Calculate the local constraint eigenvector. With general semantic feature vector The semantic offset feature vector is obtained by the numerical difference. The modulus was measured to be 1.14, which represents the semantic shift attribute of the keyword in a specific industrial closed context relative to the general context. As control group A, the standard text containing the keyword was processed using a semantic analysis method without semantic shift calibration. The cosine similarity between the obtained target semantic vector and the standard term vector was 0.742, which was below the similarity threshold, resulting in the missed detection of the key leakage clause. The word frequency density extracted in the sample group of this invention... With a distribution characteristic of 0.045, based on the mapping relationship... After calibration, the resulting target semantic vector The cosine similarity with standard term vectors is improved to 0.915, achieving the capture of implicit semantic associations. For the target semantic vector, The original feature vector, The preset context sensitivity coefficient, To analyze the word frequency density of the words to be analyzed in the main text, This is the semantic offset feature vector.

[0034] Observing the retrieval response under different gradient offset intensities, the data shows that when the semantic offset feature vector... When the modulus is between 0.5 and 1.5, the calibrated retrieval accuracy remains above 94.2%. However, when the semantic ambiguity increases due to manual modification of the definition text, resulting in semantic shift in the feature vector, the accuracy is significantly improved. When the modulus exceeds 2.2, the calibration gain saturates due to excessive distortion of the feature space reprojection, and the accuracy growth slope decreases from 0.45 to 0.08. This confirms that the calibration logic has a definite working window when dealing with industrial closed context offsets. As a partially missing control group B, after removing semantic offset feature vectors... After the calibration mechanism, the system retrieval accuracy showed a linear deterioration trend with the increase of noise level. When the signal-to-noise ratio dropped to 10dB, the accuracy dropped to 62.4%. However, the sample group of the present invention still maintained a positioning accuracy of 88.7% under the same noise level, which confirms the synergistic effect of semantic offset calibration and second-order feature calibration logic in suppressing semantic crosstalk and improving the relevance of retrieval results.

[0035] Example 3: In a scenario involving the cross-analysis of power and communication standards related to the construction of offshore platforms, the system identifies clauses concerning grounding resistance in power system lightning protection specifications. This is because the general pre-trained language model maps grounding resistance terms to original feature vectors containing electrical characteristics of civil buildings. Furthermore, the definition of this term in the specific marine power standard is constrained by the physical environment of strong corrosion and high salt spray, resulting in a semantic deviation between the general semantic distribution and the closed context in the standard document chapters. This causes the search results to be biased towards general low-voltage power distribution rather than marine engineering protection. The search then extracts the keyword "grounding resistance" from the context and calls a pre-trained model to obtain the general semantic feature vector. Furthermore, it performs semantic encoding on the original definition text of corresponding keywords within the context extraction range to generate local constrained feature vectors. The processor monitors the words to be analyzed in the standard text, and when a word to be analyzed matches a keyword, it extracts the total number of words in the current analysis window. and the number of times the words to be analyzed appear in the analysis window. According to the formula Calculate word frequency density using context sensitivity coefficient Regulation by local constraint eigenvectors With general semantic feature vector The semantic offset feature vector determined by the difference Generate target semantic vector The mapping relationship is satisfied: ,in, For the target semantic vector, The original feature vector, The context sensitivity coefficient. For word frequency density, Given the semantic offset feature vector, calculate the target semantic vector. The cosine similarity between the terminology and the vectors of standard terms in a pre-defined industrial ontology is used to determine the attribute classification of the terms to be analyzed in the field of power lightning protection. Data shows that when the context sensitivity coefficient... Set to 0.12 and word frequency density When the value is 0.045, the cosine similarity for the term "grounding resistance" increases from 0.68 before calibration to 0.89, effectively eliminating semantic shifts caused by definitional conflicts in cross-industry standards and ensuring that the semantic analysis results are physically aligned with the closed context of the industry standard document.

[0036] The processor parses the textual topology of the terminology definition section, extracts the terminology hierarchy consisting of standard numbers and sub-clause guide words, and assigns initial weights to keywords at different depths based on the terminology hierarchy. Keywords at the root node are assigned the highest initial weight of 1.0, which linearly decreases with increasing depth in steps of 0.2 until it reaches 0.4. These initial weights are then used as scaling operators on the general semantic feature vector. After completing the scale normalization process, calculate the normalized components and the local constrained eigenvectors. The difference between corresponding components is used to eliminate the distribution bias of the pre-trained language model at different context depths. The processor reads metadata from industry standard libraries including ISO, IEC, and GB, and loads directory matching feature libraries for different standard systems. The directory matching feature library contains a set of compound regular expressions consisting of scope, normative references, terms, and definitions. The processor identifies directory index nodes containing the above keywords in the document, filters non-core chapters based on the hierarchical attributes of the matched directory index nodes, and removes non-definition auxiliary text, including image captions and headers and footers, to ensure logical starting boundaries. logical termination boundary Only the substantive definition text is included. Example 4: In the deployment environment of the aero-engine maintenance standard library, the processor retrieves 50 benchmark files free of character recognition noise, reads the terminology definition sections from these files, and extracts 200 semantically aligned sample pairs. A pre-trained model is used to calculate the feature distribution shift pattern of each sample pair in a general context. The least squares method is used to fit the feature shift curve of the sample pair in the feature space. Under the condition that the feature reprojection error is less than 0.02, the context sensitivity coefficient of the current industrial field is determined. The value is 0.125, which serves as the parameter input for subsequent processing of maintenance documents containing recognition errors. The processor extracts 100 keywords, including turbine, pressure, and clearance, from the standard definition texts in 10 industry specifications. It performs semantic encoding on the standard definition texts to generate a standard term vector set and calculates the distribution variance of the standard term vectors in the feature space to determine the cosine similarity threshold.

[0037] When the confidence interval of the sample distribution covers 95% of the baseline sample points, the similarity threshold is fixed at 0.85, and semantic offset feature vectors are monitored. The magnitude fluctuation in the semantic offset feature vector When the magnitude fluctuation exceeds 15%, the local constraint feature vector within the analysis window is re-acquired. To update the context sensitivity coefficient Maintaining the target semantic vector during standard document processing at different stages Numerical stability of the target semantic vector The following mapping relationship is satisfied: ,in, For the target semantic vector, The original feature vector, The context sensitivity coefficient. For word frequency density, This is the semantic offset feature vector.

[0038] Example 5: In a semantic analysis scenario involving high-precision aerospace standard parts, the processor retrieves 20 industry benchmark documents free of character recognition noise to construct a calibration dataset. It reads the definition text of corresponding keywords and tolerances in the calibration dataset and extracts 500 feature sampling points. The pre-trained model is used to calculate the distance distribution of the feature sampling points in the feature space. A kernel function is used to fit the probability density curve of the distance distribution. When the second derivative of the probability density curve is in the range of 0 to 0.005, the similarity judgment threshold in the current context is determined to be 0.88. This provides a discrimination criterion for subsequent retrieval of unstructured clauses.

[0039] The processor establishes a weight matrix generation procedure for the aerospace standard parts domain, extracts context vectors of 50 keywords, including tolerances, clearances, and wear, from the calibration dataset, determines the contextual contribution of keywords at different document levels through linear projection, and monitors the fluctuation of feature projection residuals during semantic analysis. When the feature projection residuals exceed a preset fluctuation threshold of 0.05, the processor adjusts the context sensitivity coefficient through iterative operations with a step size of 0.01. To bring the feature projection residuals to below 0.015, so that the target semantic vector... Satisfy the mapping relationship: ,in, For the target semantic vector, The original feature vector, The context sensitivity coefficient. For word frequency density, As a semantic offset feature vector, this calibration process maintains the consistency of semantic retrieval results. The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in these embodiments can be combined with each other. This application is not limited to the specific implementation methods described above; these specific implementation methods are merely illustrative and not restrictive. Those skilled in the art, under the guidance of this application, can make many other modifications without departing from the spirit and scope of this invention, all of which fall within the protection scope of this application.

Claims

1. A semantic analysis method for industry standards, characterized in that, Includes the following steps: Step S101: The processor parses the hierarchical structure features of the industry standard document, identifies the terminology definition section in the industry standard document using preset layout rules, and obtains the logical start and end boundaries of the terminology definition section in the industry standard document. Step S102: Read the keywords recorded in the terminology definition section, call the pre-trained language model to obtain the general semantic feature vector of the keywords in the context of the general corpus, and extract the original definition text of the corresponding keywords in the terminology definition section. Then, perform semantic feature encoding on the original definition text to generate a local constraint feature vector that reflects the specific constraints of the industry standard document. Step S103: Calculate the numerical difference between the local constraint feature vector and the general semantic feature vector in the feature space to obtain the semantic offset feature vector for the industry standard document. The semantic offset feature vector is used to characterize the semantic offset attribute and distortion degree of keywords in the closed context of the industry standard document relative to the general context. Step S104: Monitor the words to be analyzed in the main text of the industrial standard document. When the words to be analyzed are matched with keywords, determine the calibration weight coefficient for the words to be analyzed based on the contextual word frequency distribution features and semantic density of the words to be analyzed in the main text. After adjusting the semantic offset feature vector using the calibration weight coefficient, reproject the original feature vector of the words to be analyzed in the pre-trained language model to generate the calibrated target semantic vector. Then, perform semantic analysis on the industrial standard document based on the target semantic vector.

2. The semantic analysis method for industrial standards according to claim 1, characterized in that, Step S102 specifically includes: Step S1021, the processor parses the text topology relationship of the term definition chapter, extracts the parent-child membership level between terms, and assigns initial weights to keywords at different depths according to the parent-child membership level; Step S1022, the processor uses the initial weights to perform scale normalization processing on the general semantic feature vector and the local constraint feature vector, calculates the difference in projection length between the two in the feature space, thereby eliminating the distribution bias of the pre-trained language model at different context depths.

3. The semantic analysis method for industrial standards according to claim 1, characterized in that, Step S101 specifically includes: Step S1011, the processor traverses the text metadata of the industry standard document and matches the directory index nodes containing definitions, terms, vocabulary meanings or standard explanation keywords using preset regular expressions; Step S1012, based on the matched directory index nodes, the processor locates the physical page of the terminology definition section, and by identifying the indentation features of each line of text in the physical page and specific guiding symbols, the processor removes non-definition auxiliary text from the terminology definition section.

4. The semantic analysis method for industrial standards according to claim 1, characterized in that, Semantic analysis includes the following: Step S401: The processor calculates the cosine similarity between the target semantic vector and the standard term vector in the preset industrial ontology library to determine the attribute classification of the term to be analyzed in the technical field. Step S402: When the cosine similarity exceeds the similarity threshold of 0.85, the term to be analyzed is marked as a key feature anchor point for compliance detection, and the physical position offset of the term to be analyzed in the industrial standard document is associated with the retrieval index of the industrial standard document.

5. A semantic analysis method for industrial standards according to claim 4, characterized in that, Step S401 specifically includes: Step S4011: The processor monitors the similarity gradient change in the semantic analysis process in real time, and starts the second-order feature calibration logic when the similarity gradient change exceeds the preset slope threshold; Step S4012: The second-order feature calibration logic extracts the contextual features of the words to be analyzed in the main text sentence, and performs feature space resampling on the target semantic vector according to the contextual features.

6. The semantic analysis method for industrial standards according to claim 1, characterized in that, After obtaining the semantic offset feature vector in step S103, the process further includes: the processor storing the semantic offset feature vector in a preset cache library and establishing a mapping relationship between the semantic offset feature vector and the document identification code of the industry standard document; when the processor processes related documents that belong to the same standard system as the industry standard document, it calls the semantic offset feature vector mapped in the cache library to complete the closed context semantic calibration across documents.

7. A semantic analysis method for industrial standards according to claim 1, characterized in that, After step S104, the process further includes: the processor calculating the overlap of technical requirements between different industry standard documents based on the target semantic vector; identifying document pairs to be verified with a technical requirement overlap of more than 90%; extracting and outputting conflicting text segments in the document pairs to be verified that have logically mutually exclusive definitions for the same keyword, in order to provide standardized compliance suggestions.

8. A semantic analysis method for industrial standards according to claim 1, characterized in that, The process of generating local constraint feature vectors includes: the processor uses the attention mechanism weights in the pre-trained language model to identify the core feature words in the original definition text that modify and restrict the meaning of keywords; the original definition text is masked and filtered according to the weight distribution of the core feature words, and feature components with semantic relevance higher than 0.75 are retained, thereby extracting local constraint feature vectors.

9. A semantic analysis method for industrial standards according to claim 1, characterized in that, The processor calculates the hardware computing resource load rate during the real-time reprojection of the original feature vector. When the hardware computing resource load rate exceeds a preset threshold of 80%, it automatically reduces the sampling dimension of the reprojection process and switches to a simplified linear translation mode to calibrate the original feature vector, so as to ensure the real-time response performance of the industry-standard document semantic analysis process.

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