177 results about "Semantic difference" patented technology

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention discloses a retrieval enhancement question and answer generation method and system based on a large language model in combination with a multi-agent cooperation mechanism. The method comprises the steps of S1, text preprocessing and semantic difference enhancement and amplification; s2, checking and complementing user questions; s3, evaluating problem complexity and selecting a generator; s4, text retrieval and answer generation; the system comprises an information enhancement processing module, a query understanding optimization module, a question complexity intelligent evaluation module and a collaborative answer generation module, and is used for realizing the method. According to the method, a multi-agent collaborative RAG framework of an information enhancement agent, an interaction analysis agent and a problem complexity evaluation agent is introduced, and semantic difference enhancement and amplification, query integrity verification and complementation and a dynamic generation strategy based on problem complexity are performed after hierarchical document partitioning are combined; and the performance of the system in the aspects of semantic distinguishing capability, query understanding precision, generation efficiency and reliability is comprehensively improved.

The invention discloses a multi-modal heterogeneous medical equipment data fusion and decision support method and device, and aims to solve the problems that the fusion precision is low due to space-time semantic difference of medical equipment multi-modal heterogeneous data (equipment operation parameters, clinical records, fault signals and the like), equipment management decisions depend on experience, and standards are not uniform. According to the method, breakthrough is achieved through three-level data alignment of'time-space-semantics', hierarchical fusion of'data level-feature level-decision level ', three-level decision driven by a knowledge graph and dynamic feedback optimization: time alignment uses a dynamic time warping algorithm, space alignment depends on a unified data dictionary, and semantic alignment introduces an attention mechanism; the feature level fusion quantifies the feature support degree based on the D-S evidence theory; the decision-making layer constructs a'rule-case-prediction 'three-level system, and combines cosine similarity retrieval and information entropy quantification uncertainty. The method and device can support medical equipment maintenance, clinical diagnosis and treatment and other scenes, and the medical service standardization level and the equipment management efficiency are improved.

The invention relates to the technical field of artificial intelligence, and provides a training method and device for a voice recognition large model, a medium and equipment, and the method comprises the steps: obtaining a training data set; inputting the training data set into the initial large model, and performing identification processing on the audio sample through a streaming identification branch in the initial large model to obtain a first candidate text set; according to the text consistency between the target candidate text in the first candidate text set and the real labeled text, determining whether to activate a non-streaming recognition branch to generate a second candidate text set; and performing iterative training on the initial large model according to the semantic difference between each second candidate text and the real labeled text and the text length difference between each second candidate text and the target candidate text to obtain a trained united streaming and non-streaming speech recognition large model. According to the method and the device, the problem of subtitle jumping caused by secondary identification length change can be relieved while the identification accuracy is improved.

The invention discloses an instruction checking method and device based on semantic matching, terminal equipment and a storage medium, and belongs to the technical field of instruction checking. The method comprises the steps that a current scheduling instruction and an original scheduling instruction are input into an instruction checking model, according to the method, the instruction checking model is used for respectively capturing semantic information of the current scheduling instruction and the original scheduling instruction, so that a plurality of different semantic embedding vectors are obtained, the semantic deviation degree of the current instruction and the original instruction is obtained by comparing the plurality of groups of semantic embedding vectors of the current instruction and the original instruction, and different checking prompt information is generated according to different deviation conditions; and a dispatcher is prompted to correct the instruction with semantic deviation in time. Therefore, the semantic level of the instruction can be deeply understood, the semantic difference between the instructions can be accurately recognized, the accuracy of instruction checking is improved, and the problem that the accuracy of instruction checking is low due to the fact that the semantics of the instruction cannot be deeply understood in the prior art can be solved.

Provided are a model distillation method and apparatus. The method comprises: acquiring a fixed training dataset, wherein the fixed training dataset comprises a plurality of training text sequences; guiding, by means of a teacher model, a student model to generate target response text sequences on the basis of the training text sequences, so as to correct semantic differences between response text sequences output by the student model and response text sequences output by the teacher model; on the basis of the plurality of training text sequences and a target response text sequence corresponding to each of the plurality of training text sequences, obtaining a target training dataset; and on the basis of the target training dataset and the teacher model, performing distillation training on the student model. In the present application, semantic differences between outputs of the teacher model and outputs of the student model are marked and evaluated one by one at a sequence level, and errors are identified and corrected, such that the student model is guided to generate more reliable and diversified samples, thereby improving the training effect of the student model.

The invention belongs to the technical field of industrial Internet of Things data processing, and particularly discloses a data protocol conversion method and system based on the industrial Internet of Things, and the method comprises the steps: extracting a feature vector through a protocol feature recognition model according to an original data stream of target equipment, and obtaining a matching result of a dynamic protocol fingerprint database; according to a matching result of the dynamic protocol fingerprint database, judging whether a protocol learning mode is triggered to obtain a newly added fingerprint and an analysis rule; according to the analyzed structured source data and the domain ontology library, semantic annotation is carried out through a semantic understanding engine, and semantic data with physical quantity tags are obtained; and according to the real-time resource state parameters, a conversion strategy is dynamically selected, an unloading point is decided and calculated, and a lightweight and full-amount depth conversion instruction is obtained. The invention aims to solve the problem of low protocol conversion efficiency caused by protocol isomerism, semantic difference and resource constraint in the industrial Internet of Things in the prior art.

The invention discloses an Internet of Things real-time task distribution system based on AI scheduling, and the system comprises a data collection module which is used for generating a task input vector, and constructing a node resource vector set; the semantic modeling module is used for inputting the task input vector into an improved StructBERT model to construct a task semantic vector; the intention analysis module is used for constructing a historical task intention vector library and a semantic conflict graph; the comparison judgment module is used for executing task conflict judgment and scheduling priority adjustment operation according to the task aggregation degree and the semantic difference vector; and the scheduling execution module is used for executing a scheduling calculation operation, generating a task allocation result and updating the historical task intention vector library and the semantic conflict graph. The method has the advantages of high task semantic understanding precision, high conflict judgment efficiency and high scheduling decision intelligent degree, and is suitable for a real-time task allocation scene in a multi-node heterogeneous Internet of Things environment.

The invention discloses a semantic unification and anomaly traceability analysis method and system for heterogeneous program logs, and the method comprises the steps: firstly collecting original logs in various formats, and packaging the original logs into standard objects in a unified manner; thirdly, identifying fields through a rule and a semantic model, uniformly mapping and labeling semantic tags, eliminating semantic differences, and constructing log data with uniform semantics; automatically constructing a cross-system call chain based on the request identifier and the like; secondly, extracting log features and comparing the log features with a normal behavior model to carry out anomaly detection, and generating an abnormal event; combining time, calling and dependency to construct an anomaly propagation path, and reversely backtracking and positioning an anomaly source; abnormal priority scores are calculated according to the propagation range, the influence degree and the like, and sorting and visual display are carried out; through unified semantic modeling and automatic association analysis of heterogeneous logs, accurate tracing and intelligent positioning of cross-service link anomalies are realized, the operation and maintenance efficiency is effectively improved, and the dependence on artificial experience is reduced.

The invention discloses a binary vulnerability data set expansion method and system based on cross-modal alignment, provides a hierarchical semantic fusion alignment framework, and remarkably improves the precision and expandability of binary vulnerability detection through a heterogeneous modal semantic bridging and transfer learning mechanism. A multi-modal semantic mapping channel is constructed by utilizing natural language interpretation and combining program structured analysis and constant anchor point coding, so that the semantic difference between a binary code and a source code is effectively bridged; a hierarchical attention mechanism enhances the fine-grained perception capability of cross-modal matching; furthermore, a migration framework driven by a vulnerability detection task is provided, binary vulnerability detection is mapped to a source code feature space through cross-modal alignment, a binary vulnerability data set is rapidly expanded by utilizing rich source code vulnerability data, and the data scale bottleneck is broken through.

The invention discloses a transnational trust modeling method fusing quantum state mapping and language phase difference, comprising the following steps: 1) estimating the site of each country based on the voting data of the united countries, and calculating the trust weight between countries through a weighting function in combination with the political ideal point distance and voting consistency of each country to obtain the initial quantized value of transnational trust; 2) mapping the initial trust value to a quantum state on a Bloch ball, applying phase perturbation according to semantic difference reported by a core country media, and simulating trust interaction between countries under discourse difference in combination with a quantum game mechanism so as to obtain a trust value mapped back to a classical space; and 3) carrying out iterative updating on the trust value by adopting a social influence model, dynamically describing an evolution process of the trust relationship between the countries, and outputting a time sequence change result of the trust network between the countries. According to the method, by fusing the international voting behavior and the transnational utterance difference, dynamic and quantifiable modeling of the inter-country trust relationship is realized, and an innovative methodological support is provided for international relationship research, transnational strategic game analysis and global risk early warning.

The invention provides a natural image matting method and system based on text and boundary information aggregation, and relates to the technical field of image processing. Splicing the original color image and the corresponding ternary image, extracting initial features, and obtaining enhanced fusion features through multi-scale Laplacian high-frequency extraction and cosine similarity weighted fusion; based on the enhanced fusion feature and the ternary image, generating a gated ternary fusion feature fused with priori knowledge through a trans-attention mechanism; global coding modeling is carried out on the gated three-value fusion features to obtain deep features; text prompt and multi-scale boundary information are introduced based on deep features, adaptive up-sampling is guided through a cross-attention mechanism, semantic difference consistency constraint is adopted between decoding layers, consistency constraint is implemented from pixel appearance, high-level semantics and color component dimensions, and finally a transparency image is output through a prediction header to obtain an image matting result. And the fidelity and the boundary accuracy of high-frequency details in a matting result are effectively improved.

The invention provides a model distillation method and device, and the method comprises the steps: obtaining a fixed training data set which comprises a plurality of training text sequences; guiding the student model to generate a target response text sequence based on the training text sequence through the teacher model so as to correct the semantic difference between the response text sequence output by the student model and the response text sequence output by the teacher model; obtaining a target training data set based on the plurality of training text sequences and the target response text sequence corresponding to each training text sequence in the plurality of training text sequences; and based on the target training data set and the teacher model, carrying out distillation training on the student model. According to the method, the semantic difference between the output of the teacher model and the output of the student model is marked and evaluated one by one at the sequence level, errors are recognized and corrected, the student model is guided to generate more reliable and diversified samples, and the training effect of the student model is improved.

The invention relates to the technical field of data management, in particular to an interactive manual data management method and system.The interactive manual data management method comprises the following steps that on the basis of semantic tag content, field attribute changes and structure segmentation nodes are recognized, a practical application scene and a user operation track are combined, a path structure is optimized, path priorities are analyzed, and key path members are recognized; and tracking a field structure change, and generating node chain coding information. According to the method, automatic mapping of a manual content structure is achieved through semantic difference judgment, path sorting is dynamically adjusted in combination with user behavior tracks, path screening and grouping are completed based on behavior sequence characteristics through priority judgment, influence factors under collaborative editing and chain type reference scenes are refined through field structure change parameters, and the user experience is improved. A traceable and differentiable data circulation mode is formed, data retrieval and version management have flexible expansion capacity, and the multi-dimensional management and control requirements of high concurrency, multiple changes and complex reference chains in a manual scene are met.

The invention relates to the field of intelligent medical treatment, and discloses a hospital integrated platform system based on artificial intelligence, which comprises the steps of extracting current patient identification information, identifying a surgical patient in an anesthesia state in real time, and automatically associating a preoperative electronic medical record with an intraoperative anesthesia plan; based on an artificial intelligence semantic recognition model, performing image character extraction and semantic understanding on parameter contents set in real time in a respirator equipment screen; constructing a cross-system risk factor graph according to the semantic difference between the set parameters and the system identification, and performing risk linkage scoring on the operation behavior in the operation in real time; calling a parameter evolution path under the previous similar operation mode, and carrying out analogy modeling by combining the anesthesia depth of the current patient, the physiological data and the intraoperative stage; and when it is confirmed that strategy level abnormity exists in the setting, the early warning level and the display form are dynamically adjusted based on the operation equipment type, the setting personnel role and the load intensity of the current stage. The system has the advantage of improving the intelligent level of a hospital.

The invention provides a personalized semantic understanding learning method, medium and system under an AI platform, and belongs to the technical field of semantic understanding. According to the technical scheme, the personalized semantic understanding learning method comprises the steps that a personalized semantic file recording user question and answer habits and personal terms is constructed, and dual time decay values are set; a semantic understanding optimization model is established based on an attention mechanism, personalized feature vectors and input semantic vectors are fused, a concept drift detection algorithm adopting bipartite graph maximum matching and a Hungary algorithm is designed to monitor semantic habit changes in real time, and an incremental learning mechanism based on a variable sliding window is established to calculate a semantic difference matrix. A forgetting function based on a Gaussian kernel is applied to adjust the historical semantic feature weight according to the time distance and the use frequency, and a reinforcement learning feedback module is constructed to collect user satisfaction evaluation and generate reward signal optimization model parameters; and executing a self-iterative optimization process to periodically update semantic archives and models so as to continuously adapt to personalized semantic requirements of users.

The invention discloses a semantic alignment method and device for heterogeneous data and a computer program product, and the method comprises the steps: firstly carrying out semantic enhancement processing on a heterogeneous data field, and obtaining enhanced field representation information; and then coding the first heterogeneous data field and the second heterogeneous data field which belong to different data sources respectively by utilizing a pre-constructed domain language coding model with the capability of accurately understanding specific domain specific terms and business contexts to obtain semantic vectors of the first heterogeneous data field and the second heterogeneous data field so as to capture subtle semantic differences between the first heterogeneous data field and the second heterogeneous data field. Then, after the semantic similarity of the first heterogeneous data field and the second heterogeneous data field is calculated according to the semantic vectors of the first heterogeneous data field and the second heterogeneous data field, a target interpretation template is obtained through matching from an interpretation template library in combination with enhanced field representation information; the method and the device are used for generating a semantic alignment result and an interpretation of a first heterogeneous data field and a second heterogeneous data field, so that the semantic alignment efficiency and accuracy of the heterogeneous data fields can be effectively improved.

The invention provides a semantic communication method and system based on multi-modal perception, and the method comprises the steps: obtaining multi-modal information, carrying out the semantic extraction of the multi-modal information, and generating a corresponding current semantic representation; performing semantic association on the current semantic representation and historical semantic information to obtain context enhanced semantic information; determining a semantic difference degree or a semantic change trend between the current semantic representation and historical semantic information based on the context enhanced semantic information to generate a key frame semantic description; semantic processing and semantic completion are carried out on the semantic description of the key frame; and according to a set context constraint condition, performing conditional semantic restoration on the complemented semantic description of the key frame by using a multi-modal model, and outputting auxiliary decision information or prompt information based on a restoration result. According to the method, the multi-modal data transmission bandwidth requirement is reduced, and meanwhile, the robustness and continuity of semantic communication in a complex channel environment are improved.

The invention provides a supplier grading and classifying method based on multi-dimensional feature fusion, and relates to the technical field of supply chain management. The method comprises the steps of obtaining structured indexes and unstructured text data of suppliers; structural features are extracted through a multi-layer perceptron, and semantic features are extracted through Transform; a cross-modal attention mechanism is adopted, and deep fusion is carried out with the structured features as queries and the semantic features as key values to obtain interaction features; performing dynamic modeling on the historical time sequence data through bidirectional LSTM to generate time sequence comprehensive features; inputting a multi-task hierarchical classifier, combining ordinal regression and contrast learning loss optimization, and outputting categories and grades; according to the management priority, the weight is dynamically adjusted through attention bias, and grading flexible adaptation is achieved; and incremental learning fine tuning is carried out by using actual service data through closed-loop feedback. The method solves the problems that multi-source heterogeneous features are difficult to fuse, and feature space semantic distinguishing and grading standards lack dynamic adaptation, and is applied to intelligent evaluation of industrial internet suppliers.

The invention provides a social media multi-mode irony identification method and system based on consistency evaluation, and relates to the technical field of irony identification. Comprising the following steps: uniformly mapping and aligning text features and image features; splicing the text representation and the image embedding vector after image information alignment, and inputting the spliced text representation and image embedding vector into a plurality of fully connected layers which are connected in sequence to obtain a consistency weight; based on the consistency weight, performing weighted average on the text representation and the image embedding vector after image information alignment to obtain a fusion feature; inputting the fusion features into a shared attention mechanism module, extracting common feature representations of the to-be-recognized text and the to-be-recognized image, and further obtaining final feature representations; and based on the final feature representation, identifying whether the text and the image to be identified are irony. According to the irony irony identification method, weighted averaging of the multi-modal information is carried out based on the consistency weight, fine semantic differences and contradictions among different modals are effectively captured and resolved, and the accuracy and robustness of irony identification are improved.

The invention particularly relates to a multi-modal data processing and retrieving method. The multi-modal data processing and retrieval method comprises the following steps: respectively carrying out depth feature extraction on image data and text data to generate an image vector and a text vector; carrying out interaction on the image vector and the text vector, and capturing semantic association information among multiple modes; mapping the vectors after interaction to a unified semantic space to realize semantic alignment among different modes; dividing the unified semantic space vector into fragments, storing the fragments in distributed nodes, and establishing a vector index; and generating a query vector by using an image or text queried by a user, carrying out parallel calculation on the similarity with a storage vector, and returning a retrieval result according to the similarity. According to the multi-modal data processing and retrieval method, the problems that the semantic difference between different modal data such as images and texts is large, the retrieval efficiency is low and storage is difficult to expand are solved, the accuracy and efficiency of multi-modal data retrieval are remarkably improved, and the method has remarkable technical advantages and wide application scenes.

This invention relates to the field of software development technology and discloses a document processing method, apparatus, device, and medium. The method includes: acquiring multiple heterogeneous data sources of a software project, wherein the heterogeneous data sources include at least source code and software documents associated with the source code; constructing a semantic difference graph describing the semantic relationships between the heterogeneous data sources based on the multiple heterogeneous data sources; assessing the synchronization risk value between the software documents and the current source code based on the semantic difference graph; if the synchronization risk value exceeds a preset threshold, determining the target content in the software documents that needs to be updated synchronously based on the semantic difference graph; generating differential update information for updating the target content based on the current source code and the semantic difference graph; and triggering an update processing flow for the software documents based on the differential update information. This method can be applied to document processing scenarios in fintech and healthcare, improving the accuracy and efficiency of document processing.

A framework for a new uncertainty metric, semantic density (SD), that can quantify the confidence of large language model (LLM) responses in semantic space is described. Semantic density rebuilds the output probability distribution from a semantic perspective, and extracts an uncertainty indicator analogous to probability density. The proposed semantic density metric has the following advantages: (1) It does not need any further training or fine-tuning of the original LLM; it is an “off-the-shelf” tool that can be directly applied to any pre-trained LLMs without modifying them; (2) it does not pose any restrictions on the problem type; in particular, it works for general free-form generation tasks (3) the returned metric is response-wise, making it possible to evaluate trustworthiness of each response; and (4) it takes the fine-grained semantic differences between resposes into account, which makes uncertainty quantification more precise.

The invention discloses a query selection-based ship sign accurate detection method under a complex background. Aiming at the characteristic that the ship brand target scale is variable, a scale-sensitive foreground selector is introduced to separate foreground and background queries, only the selected foreground query is processed layer by layer in a Transform encoder, and foreground features are subjected to enhanced expression. In order to compensate for the semantic difference between the background query and the foreground query which are not subjected to self-attention calculation, a semantic reconstruction module is designed to bridge the encoder and the decoder. Additional explicit reference point supervision is provided in a decoder to enhance the validity of reference point generation. Finally, the whole model has good ship sign detection performance under a complex background.

The invention provides an identification system and method for intrinsic semantic difference learning, relates to the technical field of wine body identification and identification, and solves the problem that weak but essential differences between highly simulated adulterated wines and true wines are difficult to effectively identify from highly simulated adulterated wines. In the system, an instrument module forms unified wine body sample pair multi-source data for detected wine bodies and reference wine bodies, and a feature embedding module converts the unified wine body sample pair multi-source data into advanced semantic features and fuses the advanced semantic features; the intrinsic semantic extraction module is used for respectively extracting intrinsic semantic features reflecting a detected wine body and a reference wine body, the true and false difference syndrome extraction module is used for obtaining key difference features between the detected wine body and the reference wine body, and the causal diagram reasoning flavor extraction module is used for respectively extracting a component coupling relationship reflecting the detected wine body and the reference wine body; and finally, the true and false wine inference module identifies the true and false of the detected wine body to obtain an identification result. According to the method, multi-modal heterogeneous data are fused, true and adulterated wines can still be accurately distinguished under the condition that components are highly similar, and a more essential and reliable identification effect is achieved.

The invention provides a test case processing method and device, and the method comprises the steps: recognizing the semantic difference between m modified code files and corresponding m unmodified code files, so as to obtain M to-be-tested methods, and the to-be-tested methods are methods containing the semantic difference; n candidate test cases are determined from the multiple test cases of the test case set, the N candidate test cases are used for testing the M to-be-tested methods, and the candidate test cases cover at least one to-be-tested method in the M to-be-tested methods. According to the scheme of the embodiment of the invention, the method needing to be tested can be identified, so that the test efficiency is improved.

The invention discloses a personalized image generation method and system based on maximum difference anchoring and mask attention guidance. In order to solve the problems of concept excessive generalization and background interference distortion of an existing text-to-image diffusion model in a user-defined concept and general concept combined scene, an end-to-end dynamic constraint framework is constructed. The method comprises the following steps: firstly, generating a candidate set related to a user concept through a multi-modal large model; a plurality of anchor point concepts with the maximum semantic difference are selected through K-means clustering; secondly, designing a double-stage training mechanism, wherein in the first stage, attention loss guided by an SAM mask is adopted to suppress background interference, and in the second stage, maximum differentiation anchor point constraint loss is introduced to optimize embedded distribution; and finally, a high-quality image fusing the custom concept and the general concept is generated. According to the method provided by the invention, unique features of a user-specified concept can be better reserved in the generated image, meanwhile, the deficiency or distortion of a general concept is avoided, and the consistency and the structural accuracy of the generated image are improved.

The invention discloses a pregnancy assisting process management system and method based on intelligent interaction, and relates to the technical field of data processing. The intelligent interaction-based pregnancy assisting process management method comprises the steps of S1, collecting pregnancy assisting stage data and historical symptom data of a pregnancy assisting process, performing preprocessing, generating semantic vectors in combination with natural language processing, and constructing a pregnancy assisting management database; s2, analyzing semantic meanings of the symptom words in different pregnancy-assisting stages through pregnancy-assisting stage data, and adjusting the matching precision of the symptoms and the pregnancy-assisting stages; s3, quantifying the context matching degree by analyzing the comprehensive semantic representation and stage semantic difference of the symptom words; and S4, fusing multi-dimensional stage semantics, feature data, evaluation symptoms and pregnancy assisting stage matching degrees to carry out stage classification analysis, and realizing stage attribution optimization and problem type classification. The problems that in the pregnancy assisting process, symptoms and different stages are difficult to accurately match, contextual understanding is insufficient, and the problem classification error is large are solved.

The invention relates to the technical field of material science data extraction, in particular to an end-to-end material property relation extraction method of a large language model, which comprises the steps of literature data preprocessing, dynamic data classification, context sensing retrieval, staged relation extraction and structured output. The method is characterized in that a semantic comprehension capability of a large language model (LLM) is combined with a retrieval enhancement generation (RAG) framework. According to the method, cross-domain migration can be realized without fine adjustment of the model by utilizing a collaborative architecture of a large language model and retrieval enhancement generation (RAG), the technical bottleneck that a traditional method needs repeated training is solved, a multi-query difference retrieval strategy is adopted, and the literature recall rate at10 is increased to 100% through a semantic differentiation query generation technology, so that the method is remarkably superior to a traditional single-query retrieval method, and the method has the advantages that the method is simple and convenient to operate and high in efficiency. And a staged and classified prompt framework is innovatively designed.

The application provides a product modeling design method and system, comprising the following steps: obtaining a research object, establishing a product perceptual vocabulary library and a modeling sample library; performing preliminary screening on the product perceptual vocabulary library based on a word vector; scoring product modeling samples by using a semantic difference method; screening an advantage perception intention vocabulary by using a factor analysis method; obtaining product modeling characteristic morphological elements and coding by using a morphological analysis method; establishing a mapping relationship between the characteristic morphological elements and the product perceptual intention modeling elements based on a three-layer BP neural network model, and performing finite element analysis based on the screened modeling elements; performing topological optimization on force distribution characteristics of product modeling, coupling a topologically optimized structure model and product perceptual intention modeling elements; outputting a product modeling scheme; evaluating the product modeling scheme based on an eye tracking experiment, and generating a product modeling design scheme when the evaluation is passed.