241 results about "Information density" patented technology

The invention discloses an image-text content auditing method and system based on a multi-modal large model, and the method achieves the deep semantic understanding and cross-modal integration of unstructured data through the cooperative work of all modules. A semantic association graph is generated through a multi-modal large model, the association recognition precision of the system on situations and information of images, texts, audios and videos is improved, and the problem that an existing system is inaccurate in semantic understanding is solved; the multi-modal data integration module realizes unified alignment of cross-modal data and layered mapping of information density, situation correlation and semantic consistency, and constructs an image-text association vector space, so that data splitting in an auditing process is eliminated; the content abnormity quantification module is combined with the intelligent control module, multi-level auditing decision is carried out through quantitative calculation and comparison of an auditing weight value Hqwz and a content abnormity index Cxzs, manual rechecking is automatically triggered, automatic issuing is directly carried out, the auditing efficiency is effectively improved, and the manual auditing burden is reduced.

The invention provides a page structure optimization method and system for a presentation file. The method comprises the following steps: identifying visual elements in a presentation page, and carrying out logical relationship analysis on the visual elements to obtain logical relationship information of the presentation page; mapping the visual structure information into a page three-dimensional implicit field to obtain three-dimensional voxels corresponding to the visual elements; calculating a visual focus thermodynamic diagram in the page of the presentation file, and distributing a three-dimensional voxel corresponding to the target visual element to a visual sensitive area; on the basis of the position relation of the visual elements and the first typesetting information, combining an information density energy function to predict a global optimal layout, and optimizing a narrative path in the presentation file according to logic relation information and user narrative preference to obtain third typesetting information, so that the spatial arrangement relation of the visual elements conforms to the narrative logic of the user preference; and executing secondary typesetting on the PowerPoint page based on the third typesetting information to optimize the page structure of the PowerPoint page and improve the page typesetting efficiency.

The invention belongs to the field of combination of a large model and a knowledge graph, and particularly relates to a large model and knowledge graph combined reasoning method for false news detection, which breaks through the limitation of a traditional detection method in the aspects of knowledge instantaneity and reasoning controllability through heterogeneous architecture design. Based on multi-dimensional features such as timeliness, propagation mode and information density of news propagation, a double-engine-driven intelligent reasoning system is constructed, on one hand, important entity extraction and semantic understanding are carried out through a large language model, and on the other hand, structured fact verification is provided through a dynamic knowledge graph. And the inherent factual illusion problem of a large language model is effectively solved. Compared with a traditional knowledge iteration mode depending on single model parameter updating, the method supports an incremental updating strategy based on knowledge graph nodes.

The invention discloses a Token-based visual task generation method, and belongs to the technical field of intelligent task automation, and the method comprises the steps: S1, cross-modal alignment; s2, performing visual Token processing; s3, constructing a task description Token sequence: constructing the task description Token sequence based on a predefined visual task template library according to requirements of a user or a specific application scene; s4, checking task feasibility; s5, task priority scheduling; s6, training a task generation model; s7, dynamic task allocation; and S8, optimizing the model. According to the method, the long sequence processing capability is optimized through a hierarchical merging strategy, the compactness of feature expression is realized while space position information is reserved, linear projection and enhanced position coding are combined to form a visual Token sequence with strong representation capability, local detail features are contained, a global context relationship is kept, and the method is suitable for the visual Token sequence with high representation capability. High-information-density feature input is provided for subsequent task processing, and the processing precision of various visual algorithms is effectively improved.

The invention relates to an intelligent interview scoring system capable of explaining decisions based on a large language model, and the system comprises a multi-mode resume analysis and feature coding unit, a resume feature adaptive matching unit, an interactive scoring and knowledge enhancement unit, and an answer quality evaluation unit. Text, image and audio features are extracted through a cross-modal attention mechanism of a multi-modal large language model, resume features are encoded into dynamic word vectors, and entity-level feature vectors are extracted; the post description text is encoded into a demand feature vector by a resume feature adaptive matching unit; calculating semantic similarity between the resume entity feature vector and the demand feature vector; the interactive scoring and knowledge enhancement unit dynamically retrieves knowledge fragments to generate a preliminary evaluation report containing a scoring basis; and the answer quality evaluation unit fuses the information density, the fluency and the integrating degree to generate a final score. And the whole-process intelligence from demand analysis to final decision making is realized.

The invention discloses an AI digital human interactive response method based on a large language model, and relates to the technical field of digital human interaction, and the method comprises the steps: analyzing collected user voice data and visual data through a natural language processing method, generating a cross-modal feature vector, carrying out the cross-modal association analysis of the cross-modal feature vector, and carrying out the cross-modal association analysis of the cross-modal feature vector. Generating a semantic association topological graph; calculating a vertex coordinate and a joint activity threshold value of the semantic association topological graph through high-digital human correlation, inputting the vertex coordinate and the joint activity threshold value into a constructed coordinate index database to execute attention weight calibration, and outputting a multi-dimensional association graph; and performing information density analysis based on the multi-dimensional association map, generating an information density gradient vector field, and dividing a high-density core region and a low-density edge region, the high-density core region generating a semantic core coding tensor, and the low-density edge region generating an edge feature package. According to the method, the cross-modal fusion vector is converted into the cross-modal feature vector, so that the modeling of the cross-modal association relationship is realized.

The application discloses a feature editing method for large model content security, which compares and analyzes the sparse coding features of a chat assistant constructed based on a large language model under positive user input and negative user input, extracts the internal response differences of the model to different semantic directions, and the mechanism can automatically and accurately identify the key feature dimensions highly related to the semantic direction of the target attribute. The model activation is mapped to a sparse feature space by using a sparse autoencoder, and each dimension of the feature has independent and interpretable semantic meaning. By injecting a feature guide vector in the space, the interference of the control process on the text grammar, fluency and information density is significantly reduced. The sparse representation mechanism is introduced to structure the intermediate activation features in the reasoning process of the large language model and to intervene in a targeted manner, so that the reply of the chat assistant to the user input conforms to the preset safety specification, and the safety and controllability of the chat assistant in the interaction with the user are improved.

The invention provides a multi-modal sentiment analysis method for contrast enhancement non-isomorphic modal fusion, and relates to the technical field of sentiment analys.According to the method, a visual modal feature and an audio modal feature are fused to make up for the difference of information density with text modalities, and joint contrast learning is designed to make up for the difference of information density with text modalities. The loss in the samples, the loss between the samples and the contrast learning in the samples are obtained through the contrast learning in the samples and the contrast learning between the samples, so that the consistency between the modes and the contrast learning between the samples are enhanced, the independence and the uniqueness of each mode feature are guaranteed, and information loss caused by excessive fusion is avoided. According to the method, complementarity and difference between modes are considered, the difference of information density between the visual and audio modes and the text mode is made up, and the performance and generalization ability of the sentiment analysis model are improved.

The invention provides an intelligent teaching system based on an AI robot coach, and relates to the field of intelligent teaching. Comprising a hardware equipment module, a software platform module, a safety control module, a hierarchical intervention module, a physiological monitoring module and a cognitive load regulation module, the hardware equipment module collects vehicle state, environment and student physiological data in real time through a multi-mode sensor; the software platform module generates a teaching strategy based on a deep reinforcement learning model and analyzes a student behavior mode; the safety control module triggers an automatic braking mechanism through the obstacle distance and the vehicle speed; the grading intervention module is divided into three-level intervention strategies of an L1 unit, an L2 unit and an L3 unit according to the operation error type, and voice prompt and braking intervention are dynamically adjusted; the physiological monitoring module non-inductively evaluates a tension index through micro-expression, grip strength and voice data; the cognitive load adjusting module dynamically adjusts the teaching content difficulty and the information density in combination with the steering frequency, the accelerator and brake alternating times and the tension index.

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

The embodiment of the invention provides an AI-based vehicle intelligent interaction method and system, and belongs to the field of artificial intelligence interaction. The method comprises the following steps: dynamically judging a cognitive load level in a current driving scene through collaborative perception of a driver state and an environment state; determining a target man-machine interaction strategy of the vehicle based on the cognitive load level; wherein the target man-machine interaction strategy comprises a voice output mode, information density of screen display content and a feedback prompt mode; determining a corresponding trigger rule based on a switching relationship between the current man-machine interaction strategy and the target man-machine interaction strategy; and responding to a trigger signal of the determined trigger rule, executing man-machine interaction strategy switching, and executing vehicle interaction based on the switched target man-machine interaction strategy. According to the scheme of the invention, closed-loop adaptive control of a human-vehicle interaction strategy on a cognitive state is realized.

The invention relates to the technical field of voice signal processing, and discloses a voice segmentation intelligent editing system based on deep learning. The system comprises a voice feature extraction module, a segmentation boundary detection module, a semantic content analysis module, an editing strategy generation module and a real-time quality evaluation module. The voice feature extraction module collects multi-dimensional voice features and timestamp information, and verifies feature integrity and timeliness; the segmentation boundary detection module identifies voice pause intervals and semantic turning nodes and divides segmentation units and boundary types; a semantic content analysis module extracts text content and emotion features of each segment, and analyzes semantic topic relevance and information density; an editing strategy generation module formulates a segmentation retention rule and a sequence adjustment scheme, and matches user preferences and scene demands; the real-time quality evaluation module monitors voice fluency and information integrity in the editing process and analyzes splicing errors and user feedback. According to the system, intelligent processing of the whole voice editing process is realized.

The invention provides an efficient annotation-driven hierarchical fault positioning method, which comprises the following steps of: guiding a large language model to intelligently analyze and generate semantic annotations of a text based on a positioning algorithm of large model annotations, and performing fine-grained software fault positioning from a file level to a function level and then to a position level by using the annotation-based hierarchical positioning method. And an efficient hierarchical progressive sorting and screening algorithm is used for ensuring the software fault positioning efficiency. According to the method, the defect positioning efficiency can be ensured while the positioning accuracy is ensured. The positioning algorithm based on large model annotation strategically balances the information density, provides enough context clues, and improves the model understanding ability; according to the annotation-based hierarchical positioning method, positioning is divided into file / function / position hierarchies, and annotations are added by using a large model cue word project in sequence, so that the positioning accuracy is improved; and an efficient hierarchical progressive sorting and screening algorithm is provided, so that the optimal balance between the performance and the cost is realized.

The invention discloses a large model content security-oriented feature editing method, which comprises the following steps of: comparing and analyzing sparse coding features of a chat assistant constructed on the basis of a large language model under positive user input and negative user input, and extracting internal response differences of the model in different semantic directions; the mechanism can automatically and accurately identify key feature dimensions highly related to the semantic direction of the target attribute. A sparse auto-encoder is utilized to activate and map the model to a sparse feature space, and each dimension of feature has an independent and interpretable semantic meaning. By injecting a feature guide vector into the space, the interference of a control process on text grammar, fluency and information density is remarkably reduced. A sparse representation mechanism is introduced, structural modeling and targeted intervention are carried out on intermediate activation features in a big language model reasoning process, replies input by a chat assistant to a user are guided to conform to a preset safety specification, and the safety and controllability of the chat assistant in interaction with the user are improved.

The invention provides an industrial part abnormal target detection method and system. According to the model, a YOLOv10 framework is used as a basic framework to construct a lightweight model, and the detection performance and efficiency are improved through three-stage optimization. Firstly, a multi-head self-attention mechanism is introduced to reconstruct a feature space, and cross-modal feature interaction is promoted by using CSP structure balance calculation efficiency and feature representation capability and combining a channel grouping shuffling strategy. Secondly, designing a context guide perception module in a feature fusion stage, enhancing multi-scale feature expression through a parallel multi-branch architecture and a spatial self-calibration mechanism, and enhancing up-sampling information density in cooperation with a dynamic interpolation fusion module; the detection head adopts a parameter sharing group convolution structure, and the calculation amount is reduced through a convolution kernel parameter sharing and feature decoupling mechanism. The method effectively solves key problems in industrial part detection, and is of great significance to industrial part automatic anomaly detection scenes on an industrial production line.

Disclosed in the present invention is a standardized process algorithm for an electronic drawing of an interior design and for improving an AI recognition rate. The standardized process algorithm comprises: on the basis of an actual size and an aspect ratio, which are included in vector data of an electronic drawing, calculating a scaling ratio of the vector data of the electronic drawing that best matches the size of an A4 standard template; selecting preset vector information parameters on the basis of the magnitude of the scaling ratio and the degree of richness of vector information, and performing data type conversion by means of central template matching, so as to convert an image of the vector data into pixel images; performing image information metric calculation on data of the pixel images obtained by means of conversion and taking same as the degree of image information richness, and classifying the pixel images into a high-information-density image and a low-information-density image in view of the degree of richness of vector information; adaptively selecting an interpolation algorithm for the low-information-density image by means of a self-defined similarity evaluation function to perform scaling processing; performing cropping processing on the high-information-density image by means of an overlapping sliding window method; and converting, in a unified manner, the processed images into an input size required by an AI recognition model.

The invention provides an agent behavior planning method, electronic equipment, a medium and a program product. The agent behavior planning method comprises the following steps: constructing an environment map based on acquired environment data; the information entropy is used as a measurement method of uncertainty, and the uncertainty is quantized by analyzing the information density and known information of each region in the environment map; the agent plans behaviors based on the information entropy. Real-time perception and behavior optimization of a complex environment are realized by dynamically constructing an environment map and performing behavior selection driven by information entropy.

The invention discloses a multi-stream chain type perception enhanced multi-modal aspect level sentiment analysis method, which is called MCPE model for short, and relates to the technical field of multi-modal sentiment analysis. According to the method, the problem of low accuracy of fine-grained sentiment analysis caused by information density difference in modals and information imbalance between modals in the prior art is solved. The method comprises the steps of obtaining a to-be-analyzed multi-modal text-image pair, inputting the text-image pair into a trained MCPE model, and obtaining aspect words and emotional polarities thereof; the MCPE model comprises a feature extraction module, a chain enhancement module, a multi-stream interaction module and a classifier; feature extraction adopts BART to extract text features and Faster R-CNN to extract image features; the chained enhancement module suppresses image and text noise and enhances fine-grained semantics through an IFE-TFE double-chain architecture; the multi-stream interaction module adopts bidirectional cross-modal attention to realize dynamic complementary fusion of text reasoning and visual evidence; and the classifier outputs an analysis result based on the fusion feature without an external tool. The method is suitable for sentiment analysis of social media comments.

The invention is suitable for the technical field of intelligent content generation and personalized recommendation control, and provides an AIGC-based retail client interaction content generation and push system, which comprises an information acquisition module, a track recognition module, a behavior association judgment module, a correction parameter generation module and a content complexity regulation and control module. According to the method, the user behavior track and the wearable equipment data are fused, and a content complexity level correction mechanism based on the combined driving of the fixation time trend and the physiological state change trend is constructed. Different from an existing mode of setting a recommendation strategy only according to a behavior label or a single-dimensional feature, the method introduces a final correction parameter to dynamically adjust the complexity level of the original content, so that the generated content better fits the current attention intention and cognitive state of the user in the aspects of expression level, information density and logic structure.

A system is disclosed, including a display, a processor and a memory. The memory stores instructions that, when executed by the processor, configure the system to perform operations. Active region data is generated that includes, for each of one or more active regions, active region location data and active region content. The active region data is transmitted to a display having a display area. For each active region, the active region content is displayed at an active region location of the display area based on the active region location data, the active region content being displayed at a higher spatiotemporal information density than content displayed in the display area outside of the active regions.

The invention relates to the technical field of deep learning, in particular to a forage grass yield prediction model construction method based on deep learning, which comprises the steps of constructing a multi-source data fusion module, establishing a cold start mechanism, designing a multi-modal deep learning prediction network, integrating a physical constraint mechanism and constructing a management decision support system. A seasonal attribution analysis function is realized; in the prior art, a simple data superposition or static weighted fusion scheme is generally adopted, and inherent defects of deficiency, different scales and heterogeneity of multi-source data are difficult to process, so that the fusion feature quality is poor; according to the method, firstly, a data blank is accurately filled through an intelligent algorithm based on space-time continuity, then heterogeneous data is unified to a standard grid by using a multi-scale pyramid engine, and finally, deep fusion is performed through an attention mechanism for dynamically calculating importance of each data source; the integrity, the consistency and the information density of the input data are remarkably improved, and a solid and reliable data foundation is laid for subsequent accurate prediction.

The invention discloses an RAG content generation method and system based on dynamic slicing and self-adaptive fusion, and relates to the field of natural language process.The method comprises the steps that in response to a user query instruction, semantic fusion processing is conducted on a plurality of related text blocks, and a preliminary text sequence is traversed; detecting a logic breakpoint between adjacent text blocks through a context association identifier, recalling a middle connection text block corresponding to the logic breakpoint, and inserting the middle connection text block into a sequence to generate a fusion text stream; performing adaptive information density adjustment on the fused text stream to obtain optimized context content; generating structured prompt information based on the optimized context content and the user query instruction; and calling the target large language model to generate a user query result based on the structured prompt information. The answer accuracy and logic continuity of the target large language model for user query are effectively improved, and the utilization efficiency of model computing resources is optimized.

Broadly speaking, embodiments of the present techniques provide an optically- or machine-readable fiducial marker for use in nanoscale applications and a method for automatic detection of a marker. Advantageously, the fiducial marker of the present techniques has features which make it resistant to the processing steps of electron beam lithography. Furthermore, the fiducial marker is robust when reproduced at nanoscales and has high information density.

Broadly speaking, embodiments of the present technology provide optically readable or machine readable fiducial markers for use in nanoscale applications and methods for automated detection of the markers. Advantageously, the fiducial markers of the present technology have characteristics that make them resistant to the processing steps of electron beam lithography. Furthermore, the fiducial markers are robust and have high information density when reproduced at the nanoscale.

The invention discloses a desulfurization optimization control method and system based on a multi-modal model, and relates to the technical field of industrial intelligent control, and the method comprises the steps: constructing a gas-liquid interface boundary point set, and generating a control feature vector; calculating fractal dimensions of control feature vectors by using a box counting method, constructing a disturbance point set, multiplying disturbance intensity by an expansion trend to obtain an unstable diffusion grade, matching key propagation points with nearest disturbance points by using nearest neighbor matching, and generating a control priority sequence; and defining a control unstable diffusion level, defining an optimization objective function, and solving by using constraint optimization to obtain a final control variable. According to the method, a composite strength factor is constructed through an image gradient field and a signal response ratio, a gas-liquid interface boundary point set and a control feature vector are constructed, the robustness and information density of features are improved, a main resonance frequency is recognized by combining a fractal interference field and short-time Fourier transform, key propagation points are screened by using wavelet transform and information entropy analysis, and a high-precision gas-liquid interface is obtained. The desulfurization efficiency and the stability of the reaction tower are improved.

The invention discloses a federal learning image recognition method based on model inversion and distillation. The method comprises the following steps: 1, training a local image model on each client and uploading the local image model to a federal center server; 2, the federal center server receives the local image model and performs weighted aggregation; 3, the federal center server uses a local image model to carry out depth inversion so as to construct a synthetic data set; 4, the federal center server performs primitive re-marking and knowledge distillation by using the synthesized data set; and 5, performing iterative optimization on the global image model parameters to obtain a global model with optimal parameters, and issuing the global model to each client for image recognition. According to the method, data are synthesized by adopting a depth inversion method, generated primitives with different information densities are screened through attention weights, and primitives with low information densities are re-marked by utilizing an integrated model in a subsequent knowledge distillation stage, so that the image recognition problem of single communication federal learning in a data heterogeneous scene is solved.

The embodiment of the invention provides an intelligent operation and maintenance method and system based on a large language model. According to the method, firstly, at least M multi-modal original logs generated by a micro-service cluster are collected, compression processing is conducted on the multi-modal original logs by means of a large language model, at least N multi-modal target logs are obtained, the log data volume can be greatly reduced, and the effective information density is improved; synchronously collecting system operation indexes of timestamps corresponding to the multi-modal target logs and determining an association relationship between the system operation indexes to reduce invalid diagnosis workload; a multi-type heterogeneous operation and maintenance topological graph is constructed based on the incidence relation, a current fault is accurately positioned, the problem that root causes are difficult to quickly position through traditional manual analysis of mesh topology is solved, and the root cause recognition time is shortened; and finally, a preset historical fault knowledge base is called, a target repairing script matched with the current fault is generated in combination with the large language model, repairing of the current fault is achieved, time consumed by manual intervention is shortened, and therefore the fault diagnosis efficiency is remarkably improved.

The invention relates to the technical field of data analysis, and discloses a data analysis method, system and device and a computer storage medium. The method comprises the following steps: acquiring user behavior data; calculating a hierarchical weight according to the user behavior data; performing keyword preference calculation according to the user behavior data to obtain keyword preference scores; calculating information density characteristics according to the keyword preference scores; according to the information density feature, the keyword preference score and the hierarchical weight, calculating a user demand priority; calculating a content and keyword correlation score according to the level weight, the keyword preference score and the user behavior data; and calculating a recommended content global priority according to the content and keyword correlation score and the user demand priority, and recommending the content corresponding to the keyword to the user according to the recommended content global priority. According to the method, the accuracy of data analysis can be improved, and more accurate content recommendation is further realized.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an AI-based file data content accurate and deep analysis and interpretation method, which comprises the following steps: acquiring multi-format file data and file meta-information, constructing an AI analysis network, extracting text semantic vectors, image visual features, table structure information and document layout features, and constructing a multi-dimensional semantic map. According to a user query intention, semantic extension is performed in combination with a domain knowledge base, an enhanced semantic description vector is generated through a graph attention mechanism, semantic reasoning and relation mining are performed by adopting an improved knowledge distillation Transform model, and a deep analysis conclusion is generated through a multi-hop reasoning model in combination with file data complexity, information density and user requirements. And generating a personalized interpretation report in combination with a user role and a task scene, and outputting an analysis result through a visual interface. Therefore, the problems of poor understanding ability, poor file adaptability and the like in the prior art are solved.

The invention discloses a document segmentation method for self-adaptive slices generated by large model retrieval enhancement, and relates to the technical field of large model retrieval enhancement generation, the method comprises the following steps: obtaining a to-be-segmented document, and segmenting the to-be-segmented document according to a title type to obtain at least one group of original blocks; according to the information density and the theme change degree corresponding to any original block, the optimal segmentation number of the original block is calculated; and performing segmentation processing on the original blocks according to the optimal segmentation number. According to the method, firstly, the document is segmented according to the hierarchical titles, then the information density and the theme change degree under the hierarchical titles are calculated, and the optimal segmentation size under the hierarchical titles is automatically calculated by taking the hierarchical titles as units, so that self-adaptive segmentation of the document is guided, and the effects of subsequent retrieval and task generation are improved.