186 results about "Similarity model" patented technology

The invention belongs to the technical field of machine room safety monitoring, and discloses an image review method fusing semantic understanding and visual recognition, which comprises the following steps: calculating visual / semantic feature dynamic credibility in real time through an exponential weighted moving average algorithm in combination with environment interference and equipment state parameters; resNet50 is adopted to extract visual features in global and local branches, and a BERT model is adopted to encode semantic features; correcting the feature correlation degree according to the scene, calculating a dynamic weight, carrying out heterogeneous calibration through an attention mechanism, and calling priority rules such as'physical security features are higher than behavior features' for arbitration during conflicts; the method is advantaged in that low-credibility feature interference fusion is avoided, abnormity identification accuracy in a complex scene of a machine room is improved, multi-modal data cooperation demands are adapted, scene labels are marked based on time, work orders and historical data dimensions, an exclusive sub-model is constructed for a high-density scene through DBSCAN density clustering, and low-frequency scene parameters are migrated to a similar model.

A method for delineating an ecological corridor based on a maximum similarity model determines ecosystem type according to the determined ecological source, and assigns a habitat suitability index; and according to a maximum similarity matrix between different ecosystems, assigns the habitat suitability index to peripheral pixels of the selected ecological source, to obtain a similar value of the habitat suitability index. The habitat suitability index is corrected according to the surface curvature data and population density data, and resistance surface data is calculated according to the corrected value. Based on the resistance surface data, an ecological corridor is delineated comprehensively to find one or more paths corresponding to a minimum cost distance as ecological corridors. The influence of ecological environment on ecological corridor establishment is considered more comprehensively, so that the ecological corridor is delineated more accurately, and applied into practice to maximum extent.

The invention discloses a railway tunnel full-life-cycle model test system and test method considering train vibration, and relates to the technical field of railway tunnel engineering. The model test system adopts modular design and comprises a box frame module, a constant-pressure loading module, an excitation system module, a seepage system module and a monitoring system module. The test method comprises the steps of physical similar model laying, constant-pressure loading control, tunnel excavation and supporting, vibration loading and data monitoring analysis. By accurately simulating an initial stress field and train vibration loads in the construction and operation stages of the tunnel, dynamic monitoring of stress strain, deformation and damage processes of the tunnel surrounding rock and the lining structure in the whole life cycle is achieved. The device has the advantages of compact structure, modular design, high loading precision and stability, real simulation and the like, and a scientific basis can be provided for the fields of performance evaluation, structure optimization, reinforcement design and the like in the whole life cycle of the railway tunnel.

The invention discloses a slope physical similarity model device and a slope internal crack evolution identification method, and relates to the technical field of open pit coal mine slope ecological protection, and the slope physical similarity model device comprises a similar material simulation system, a monitoring system and an artificial rainfall system. Through cooperation of the similar material simulation system, the monitoring system and the artificial rainfall system, surface water seepage field change and instability process simulation caused by slope rock-soil mass fracture evolution under different main control factors can be artificially controlled, and temperature, rainfall, stress, fracture development and other characteristic changes in the whole process can be monitored in real time; the method can deeply explore the internal relationship with slope instability, obtain the slope instability failure path, failure source and key block, determine the main control factor threshold of each stage, reveal the seepage and instability mechanism of the open pit coal mine slope under the freeze-thaw cycle, and improve the stability of the open pit coal mine slope. And a theoretical basis is provided for establishment of multi-source criteria of seasonal freeze-thaw landslide of the high and cold opencast coal mine and early-and-middle-stage early warning.

The invention provides a large-model multi-label classification method, system and device based on ReAct and a vector library, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a multi-level label mapping tool according to each label data table corresponding to a preset multi-level label system; and constructing a label retrieval tool based on a pre-trained text similarity model and a preset label vector database. Constructing a ReAct tool chain based on a preset label dynamic adjustment tool, a multi-level label mapping tool and a label retrieval tool; the preset label dynamic adjustment tool is constructed on the basis of cue words and a large language model and is used for updating candidate labels according to the to-be-labeled text corpus, the candidate labels output by the label retrieval tool and label rules; and based on a preset ReAct cue word project, the large language model and the ReAct tool chain, constructing a large model multi-label classification module so as to input the to-be-labeled text corpus from the user terminal into the large model multi-label classification module for multi-label classification.

The invention discloses a test question management method based on multi-modal adaptive similarity learning, and relates to the technical field of data management, and the method comprises the steps: collecting an original test question set, generating an evolutionary test question set through a preset large language model, generating a variable test question set through a data enhancement technology, and carrying out the fusion processing of the test question set to form a test question ecological library, the diversity and coverage of test question resources are enriched; the test question ecological library is input into a preset semantic similarity model and a preset syntactic similarity model to obtain a semantic similarity score and a syntactic similarity score of each test question, so that the similarity of the test questions can be comprehensively measured, and the screening accuracy is improved; the multi-modal similarity score is obtained according to the semantic similarity score and the syntactic similarity score to be compared with the preset threshold value, then the test questions are imported into the auditing module or the question bank storage module according to the comparison result, intelligent distribution of the test questions is facilitated, and the timeliness and quality of the question bank are ensured.

The invention belongs to the technical field of coal field fire evolutionary analysis, and particularly provides a visual coal field fire evolutionary process analog simulation test device and method. The test device comprises a test bench, a physical similarity model, a fiber bragg grating temperature and stress integrated monitoring system, a multi-gas monitoring system, an image and video monitoring system and a computer analysis system. A plurality of opening frames are sequentially fixed to form test box bodies, the physical similarity model is arranged in the test box bodies, the inclination angle of the test box bodies is adjusted through an inclination angle adjusting mechanism, and the gas supply system and the ignition system are connected between the test box bodies. The fiber bragg grating temperature and stress integrated monitoring system, the multi-gas monitoring system and the image and video monitoring system are respectively in signal connection with the computer analysis system. According to the invention, visualization of coal field fire evolution processes under different conditions can be realized, and real-time monitoring of change rules of a temperature field, a seepage field and a fracture field in the coal field fire evolution processes under different conditions can be realized.

The invention discloses a multi-parameter distortion compensation method for a fractured rock mass seepage similar model test, and relates to the technical field of rock mass engineering physical model tests. The method comprises the following steps of: firstly, determining two constraint conditions that the fluid property is invariable and the gravitational acceleration is invariable, correspondingly selecting a basic physical quantity and extracting a core criterion; then analyzing three types of distortion sources of geometric dimensions, dielectric materials and boundary conditions, and establishing a distortion function according to result parameters of seepage flow velocity, seepage flow and seepage pressure; secondly, parameters difficult to control are selected as distortion items, parameters easy to regulate and control are selected as compensation items, the distortion items are substituted into a distortion function to obtain a distortion ratio, the optimal distortion ratio of the compensation items is solved based on a compensation balance formula, and the parameters are adjusted; and finally verifying the interval range of the actually measured distortion ratio. According to the method, the multi-source distortion coupling influence is accurately quantified, the compensation pertinence and precision are improved, operation is easy and convenient, universality is high, and reliable support is provided for underground engineering seepage research.

The invention belongs to the field of software engineering, and particularly relates to a code translation method based on a tree instruction large language model, which can be used for converting a source programming language into a target programming language. According to the method, key tree structure information is extracted from a linearized AST, and a similarity model is trained to align a tree structure with a code structure, so that a more accurate instruction data set is constructed, and the understanding ability of a large language model to an AST structure is improved; through combination with a large language model, human intervention is greatly reduced, and the code translation cost is greatly reduced; an additional function evaluation scheme is added, so that the friendliness of a translation result to a user result is improved; in addition, the method has high expandability, different large language models can be easily switched and used, the user experience can be improved, and the professional skill threshold needed by use is lowered.

The invention discloses a data deduplication method and system based on incremental calculation and feature clustering and electronic equipment, and the method comprises the steps: receiving multi-source heterogeneous text, image, audio and video data streams in real time, adding a timestamp to each data unit, and generating an input data set with a time attribute; performing multi-modal feature extraction on the input data set, and dynamically adjusting feature weights of the extracted multi-modal features through a sliding window model and a time decay factor to obtain an increment feature vector set with the weights; executing two-stage clustering based on the feature vector set to generate a plurality of fine-grained data clusters; and comparing the intra-cluster data of each fine-grained data cluster in pairs by adopting a composite similarity model to determine intra-cluster repeated data, and performing data deduplication on the input data set according to the intra-cluster repeated data to obtain a deduplicated input data set. According to the method, the processing efficiency of the real-time data is improved, and the storage and calculation cost is remarkably reduced.

The invention discloses a rainfall image sequence similarity search method based on a twin neural network. The rainfall image sequence similarity search method comprises the steps of obtaining a target rainfall image sequence and a target rainfall image library; constructing a rainfall image similarity model; based on the target rainfall image sequence and the target rainfall image library, performing training optimization on the constructed rainfall image similarity model to obtain a target rainfall image similarity model; and based on the target rainfall image sequence and the feature vector of the corresponding frame in the target rainfall image sequence extracted by the target rainfall image similarity model, carrying out rainfall image sequence similarity search on the target rainfall image library to obtain a rainfall image sequence similar to the target rainfall image sequence. According to the method, deep learning and a meteorological mechanism are fused, efficient similarity search of a rainfall image sequence is realized by constructing a rainfall image sequence similarity search model of a feature extraction network and twin neural network architecture, and three bottlenecks of feature robustness, time sequence modeling and calculation efficiency are broken through.

A method implements pre-trained large language model driven bug localization. The method includes receiving a report and applying a fine-tuned language model to report text from the report, to source text from a source file of a set of source files, and to commit text from a commit of a set of commits to respectively generate a report vector, a source vector, and a commit vector from the fine-tuned language model. The method further includes applying a similarity model to the report vector and the source vector to generate a report source score and includes applying the similarity model to the report vector and the commit vector to generate a report commit score. The method further includes applying a ranking model to the report source score and the report commit score to identify the source file corresponding to the report and includes presenting the source file responsive to the report.

Systems, media, and computer-implemented methods are provided for identifying similar chunks of text to tune a text similarity model, such as a text similarity model that is used to find content in response to queries. Using a masked language model, a machine learning model may be tuned on different content from that which the machine learning model was trained. The machine learning model as tuned may be used to determine vector embeddings for terms in chunks of content. Chunks may be matched to each other by finding a term in one chunk having a highest similarity score with a corresponding term in another chunk. Aggregate similarity scores may be determined between the chunks based on the term-to-term similarity scores. If an aggregate similarity score for a pair of chunks satisfies one or more conditions, a text similarity model may be tuned to identify the pair as similar.

The invention provides a hybrid filtering denoising method and device based on adaptive parameters, and relates to the technical field of three-dimensional point cloud processing. The method comprises the following steps: acquiring three-dimensional point cloud data of a target object; performing voxel segmentation processing on the three-dimensional point cloud data to obtain a plurality of voxels; according to the segmented three-dimensional point cloud data, combining a plurality of voxels by using a density similarity model to obtain a voxel set with different density distributions; based on the merged voxels, a radius filtering parameter and a statistical filtering parameter of each voxel set are adaptively determined, the radius filtering parameter is used for filtering isolated noise in the three-dimensional point cloud data, and the statistical filtering parameter is used for filtering random noise in the three-dimensional point cloud data; performing random noise removal processing on the three-dimensional point cloud data according to the statistical filtering parameters; and performing isolated noise removal processing on the three-dimensional point cloud data after random noise removal according to the radius filtering parameter.

The invention discloses a tobacco marketing abnormal address management and control method, system and device based on GIS and multi-dimensional data fusion and a medium, and belongs to the technical field of tobacco industry informatization and internet marketing. The technical problems that in the prior art, address risk control dimensionality is single, query precision is insufficient, data linkage is lacked and visual analysis capacity is weak are solved. According to the technical scheme, the method comprises the steps that abnormal address data is constructed and queried based on a multi-dimensional label; gIS visualization and correlation analysis based on a mixed similarity model: GIS positioning and range loading, spatial candidate set filtering, text similarity fine screening, mixed weighted scoring and visualization rendering and statistics are realized through the mixed similarity model of spatial index coarse screening and text algorithm fine screening, and correlation order detail form display based on GIS range triggering is carried out.

The invention discloses a multi-stage attention and generative adversarial network collaborative product generation method and system, and relates to the technical field of image generation, and the method comprises the steps: obtaining a low-resolution clothing image, focusing the low-resolution clothing image on a clothing image key region through a multi-stage attention mechanism, and carrying out the image refining processing through combining text features, a high-resolution clothing image is obtained; processing the text description to obtain corresponding word features, processing the high-resolution clothing image obtained by processing the multi-stage attention generative adversarial network to obtain corresponding local image features, and inputting the word features and the local image features into a deep attention multi-modal similarity model to obtain a deep attention multi-modal similarity model; outputting and obtaining the fine-grained similarity loss of the image and the text; and optimizing a pre-constructed multi-stage attention generative adversarial network based on the adversarial loss and the fine-grained similarity loss of the image and the text, and generating a product image based on the optimized multi-stage attention generative adversarial network.

The invention discloses a knowledge graph completion method based on a semantic similarity model, and the method comprises the steps: S1, extracting an ontology network and an entity network in an existing knowledge graph, and carrying out the analysis, and obtaining a triple of the existing knowledge graph; s2, inputting the unstructured text data into a large language model, and generating a new triple in combination with Schema constraint; s3, encoding the new triad and the triad of the existing knowledge graph through a semantic similarity model, and converting the new triad and the triad into a semantic vector with a fixed length; s4, performing similarity evaluation on the new triad and the triad in the existing entity network according to the semantic vector with the fixed length; and S5, according to a similarity evaluation result, screening out a new triad of which the similarity with the triad in the existing entity network is lower than a similarity threshold, and adding the new triad to the existing knowledge graph, and the method is used for solving the problems of incomplete knowledge graph, update lag and the like in the prior art.

In a computer-implemented method and associated tangible non-transitory computer-readable medium, an image of a damaged vehicle may be analyzed to generate a repair estimate. A dataset populated with digital images of damaged vehicles and associated claim data may be used to train a deep learning neural network to learn damaged vehicle image characteristics that are predictive of claim data characteristics, and a predictive similarity model may be generated. Using the predictive similarity model, one or more similarity scores may be generated for a digital image of a newly damaged vehicle, indicating its similarity to one or more digital images of damaged vehicles with known damage level, repair time, and / or repair cost. A repair estimate may be generated for the newly damaged vehicle based on the claim data associated with images that are most similar to the image of the newly damaged vehicle.

A system and method for channel layout evaluation including: a computer processor and a channel detective service executing on the computer processor and including functionality to: receive a request to perform channel layout evaluation on a media item including a provided set of channels; perform metadata extraction on the media item to generate a metadata representation of the media item and to identify at least one channel layout discrepancy of the provided set of channels; perform layout detection using the metadata representation by executing a similarity model configured to generate a mix group comprising at least a subset of the provided set of channels, annotating a primary language of the mix group, and annotating a service type of the mix group; and updating the metadata representation with the annotated mix group, wherein the updated metadata representation is provided for streaming the media item.

The invention discloses a denture three-dimensional model feature construction and matching method based on curvature adaptive sampling, which comprises the following steps: S1, acquiring denture surface point cloud data through three-dimensional scanning, converting the data into an STL model, and comparing a historical STL model pre-stored in a folder; s2, constructing a self-adaptive sampling weight, selecting a triangular patch through replacement random sampling, and generating a sampling point; s3, carrying out normalization processing on the sampling points; s4, calculating the distribution of D2 and A3 by using the normalized sampling points, and obtaining corresponding shape features; s5, splicing the features to form a joint geometric feature vector; and S6, performing S1-S5 on the to-be-matched false tooth to obtain a matching vector, performing S2-S5 on the historical models one by one to obtain comparison vectors, calculating cosine similarity between the matching vector and each comparison vector, and outputting a Top-K similarity model. According to the method, key geometric structure feature expression is enhanced, robustness to point cloud density change, local defects and the like is high, and matching is ensured to be reliable and consistent.

The invention discloses a remote bank fuzzy question-answer accurate matching method based on deep learning. The method comprises the following steps: firstly, constructing a business semantic system (clear business class-scene-specific intention), a term and variant mapping library and a sentence pattern template containing a replaceable entity slot position; extending the original similar question pairs to generate derivative sentences and candidate similar sentences, filtering, clustering to generate cross-sentence pattern similar pairs, and selecting negative samples to control the literal overlapping rate; then constructing a twin BERT network, mixing original and enhanced samples into a training set, and training in two stages by using improved triple loss; finally, sentence vectors are generated through the trained twin network, semantic similarity is judged according to cosine similarity, question pairs which are judged to be similar through a model are subjected to secondary verification through a service rule base, and score reduction is carried out if the question pairs do not pass verification.

The present invention relates to the technical field of petroleum data statistical testing, and discloses a method for calculating oil and gas reservoir analog similarity based on statistical testing, comprising: obtaining a number of historical mixed samples and performing data cleaning on the historical mixed samples to obtain original mixed samples, each of the original mixed samples comprising original text-type geological data and original numerical geological data; converting the original text-type geological data into intermediate numerical geological data, the intermediate numerical geological data and the original numerical geological data together constituting an original full numerical sample; grouping the data fields of the original full numerical samples to obtain groupings and establishing an initial total similarity model; and assigning an optimal weight coefficient to the similarity corresponding to each group, thereby optimizing the initial total similarity model to obtain an optimal total similarity model. The present invention provides a more accurate reference for recovery rate and reserves.

The present invention discloses a slope physical similarity model device and a method for identifying the evolution of internal cracks in the slope, which relates to the technical field of ecological protection of open-pit coal mine slopes, including a similar material simulation system, a monitoring system, and an artificial rainfall system. Through the cooperation of the similar material simulation system, the monitoring system, and the artificial rainfall system, the present invention can artificially control the changes in the surface water seepage field and the simulation of the instability process caused by the evolution of cracks in the slope rock and soil under different main controlling factors, and monitor the characteristic changes such as temperature, precipitation, stress, and crack development in real time throughout the process. It can deeply explore the intrinsic relationship between them and slope instability, obtain the slope instability failure path, failure source, and key blocks, determine the main controlling factor thresholds at each stage, reveal the seepage and instability mechanism of open-pit coal mine slopes under freeze-thaw cycles, and provide a theoretical basis for the establishment of multi-source judgment criteria for seasonal freeze-thaw landslides in high-altitude open-pit coal mines and early and medium-term warnings.

The invention provides a bridge deck reduced scale model design method and experiment system in a plateau environment, and the design method comprises the steps: building a multi-factor coupling similarity criterion based on a similarity principle, physical parameters affecting the prototype structure performance of a bridge deck, and plateau environment parameters; determining a dynamic stress similarity ratio model based on a multi-factor coupling similarity criterion and the plateau environment parameters; based on the dynamic stress similarity ratio model and the section shape and size of the bridge deck prototype structure, performing scale design on the bridge deck prototype structure to obtain an initial scale model; and performing simulation experiment and simulation verification on the initial scale model based on a preset numerical simulation strategy and a preset plateau environment simulation strategy, and performing parameter optimization of the dynamic stress ratio similar model to design a target scale model. According to the scheme provided by the invention, experimental simulation of the bridge deck in the plateau environment can be efficiently and accurately carried out, and the experimental simulation cost is reduced.

The invention discloses an image restoration method, which comprises the following steps: acquiring a target image, a reference image set and a boundary intensity image, and extracting target features, a reference feature list and boundary features; based on the extracted features, performing similarity modeling by using a learnable spiral path to generate a similarity model result; based on a similarity model result, performing geometric alignment and double-domain fusion on the reference feature list to generate initial fusion features and alignment parameters; calculating an error mode pattern based on the initial fusion features; executing closed-loop feedback according to the error mode pattern, and iteratively updating to generate a final fusion feature; and decoding the final fusion feature to obtain a repaired image. Through spiral sampling of boundary perception, non-folding alignment and closed-loop feedback, the structure consistency and detail fidelity of the repaired image under non-rigid deformation are improved.

The invention provides a purchase project auditing method and device based on a large model, a medium and equipment. The method comprises the following steps: performing information extraction on bidding and tendering related files of a current purchase project by adopting an information extraction model, and storing the extracted information in a database; a similarity model is adopted to retrieve similar historical purchase items of the current purchase item from a file library, and the current purchase item and the similar historical purchase items are compared on at least one element; according to each item of information extracted for the current purchase project, analyzing whether the bid opening process of the current purchase project has a mixed bid problem and an accompanying bid problem; according to the information extracted from the bid winning notice in the bidding and tendering related file, the scoring condition of an expert judge in the bidding process is analyzed; and generating a full life cycle analysis report of the current purchase project. According to the invention, the processing efficiency can be improved, and comprehensive deep auditing of the purchase project is realized.

The invention relates to the technical field of building information models and intelligent design, and provides a bridge steel structure collaborative design method and system based on the BIM technology, and the method comprises the steps: obtaining a plurality of bridge steel structure BIM models from a BIM database, and design parameter vectors, structure topological graphs and collaborative design data vectors thereof; constructing a structural feature extraction model by training a BIM structure encoder model, calculating the similarity between the models, and retrieving a similar model; establishing a BIM data fusion model, and training by combining the design parameter vector of the target model and the collaborative design data vector of the similar model to obtain a collaborative design prediction model; and searching a similar model for a to-be-collaborative design model, and generating collaborative design data through a prediction model based on design parameters of the to-be-collaborative design model and collaborative data of the similar model. According to the method, intelligent collaborative optimization of bridge steel structure design can be realized, the design efficiency and accuracy are improved, and manual intervention and repeated work are reduced.

The system and methods for audience and return recommendation for targeted content items using machine learning and similarity models. The method includes receiving an input dataset comprising of an input audience or specified constraints on the targeted content items. A join operation augments the input dataset by adding individual characteristics data. A trained machine model generates an engagement score for each individual and by using a threshold on the engagement score each individual is classified into a tier category. Based on the engagement level a target audience is generated. Using a similarity model, an expanded audience is generated which increases the size of the target audience by adding similar individuals from the reference audiences. A return is computed using the individuals in the expanded audience while meeting the specified constraints. The expanded audience and return for the targeted content items are returned to the client device.

The invention relates to the technical field of aero-engines, and discloses a dynamic tester design method and system for simulating a complex rotor system, and the method comprises the steps: constructing a three-dimensional entity similar model of a dual-rotor supporting system, and analyzing the modal shape vector of each order of a one-dimensional dynamic parameterized model of the dual-rotor supporting system. Calculating the parameter modal confidence coefficient and the associated modal confidence coefficient of the similar model according to the modal shape vector of the similar model and the modal shape vector of each order of the similar model, and adjusting the similar model by taking the two modal confidence coefficients as evaluation criteria to obtain a similar model with high confidence coefficient; according to the total rigidity, the supporting rigidity, the squirrel cage rigidity and the end seal rigidity of the similar model, an oil film damper rigidity reference value is obtained through analysis, the oil film damper structure is adjusted, and therefore the oil film damper structure meeting the design requirement is output. According to the invention, the bearing environment of a real aero-engine rotor can be simulated with high fidelity, and the problem of insufficient measurement dimension in a rotor synchronous monitoring scheme of a double-rotor tester is solved.

The invention relates to the technical field of face recognition, and particularly discloses a face recognition method based on an artificial intelligence model, and the method comprises the following steps: S1, photographing a face image, generating a face model, presetting a collection point in the face model, and setting a living body attribute for the collection point; s2, screening a living body model and a non-living body model based on living body attributes, and calculating comparison data of collection points in the feature region; s3, sorting feature regions in the living body model, calculating a difference value and screening a first similar model; s4, screening a second similar model based on the comparison points; and S5, calculating similarity rates of the living body model and the second similar models, and selecting the second similar model with the maximum similarity rate. The human face recognition method based on the artificial intelligence model is used for improving the accuracy of an artificial intelligence model living body detection technology and distinguishing a real human face from an attack medium, so that the accuracy of human face recognition is improved.