397 results about "Paired Data" patented technology

The invention discloses a marine ecological disaster early warning method based on multi-source heterogeneous big data fusion. The marine ecological disaster early warning method comprises the steps that remote sensing image data and actual measurement environment data of a target sea area are acquired respectively; sequentially performing boundary segmentation and feature fusion on the remote sensing image data based on the image segmentation network to obtain multispectral feature data; performing space-time alignment on the multispectral feature data and the environmental data to obtain a space-time alignment data set; mapping each sample pair data in the time-space alignment data set into graph node data, and analyzing a connection relationship between any two adjacent graph node data to construct an adjacency relationship matrix; performing neighborhood aggregation processing on the adjacency relation matrix to generate a fusion feature vector; and inputting the fusion feature vector into a pre-constructed marine disaster prediction model to obtain a multi-dimensional prediction result, and carrying out ecological disaster real-time early warning based on the multi-dimensional prediction result. According to the method, the real-time performance of early warning of the marine ecological disasters is improved.

The invention relates to the technical field of information retrieval and generation, in particular to a retrieval enhancement method based on multi-modal data fusion and modal perception. According to the method, firstly, a dual-channel architecture is adopted to perform feature extraction and coding on a text and an image respectively, and mutually independent embedded representation spaces are constructed, so that high-quality collaboration and matching of cross-modal representation are realized; and a pseudo-pairing generation mechanism is introduced to effectively mine and reconstruct the existing non-paired data in the knowledge base. And designing a query modal perception and dynamic weighting mechanism for accurately controlling the fusion proportion of the image-text bimodal information in the retrieval stage so as to match the modal demand difference of different query contents. And further executing aggregation retrieval and reordering of the cross-modal information by using dynamic weighted fusion retrieval to generate a candidate set of multi-modal responses. According to the method, accurate matching and dynamic weight adjustment of the image-text content are realized, and the accuracy and expression integrity of the generated content are improved.

The invention provides a space projection error analysis-combined 4D millimeter wave radar and visual camera automatic calibration method. The method comprises the steps of constructing a sensor internal reference model; designing a composite multifunctional calibration board and a three-dimensional adjustable holder, performing rigid geometrical relationship modeling, and automatically associating physics-features-coordinates; collecting multi-frame synchronization frame pair data, and performing time sequence fine grit alignment and interpolation compensation to obtain a space-time alignment frame pair sequence; extracting cross-modal spatial constraints of the features and automatically screening and matching point pairs to obtain a high-confidence point pair set; performing point-point constraint and point-point error, point-plane error and pixel reprojection error minimization to obtain a standardized error term; and constructing an overall objective function, and performing spatial constraint and time deviation estimation to obtain corrected external parameters and synchronization deviation. According to the invention, through a full-automatic standardized process, the probability of artificial participation and error occurrence is greatly reduced; and multi-modal and multi-scene high-precision calibration is supported, and the flexibility and robustness are greatly improved.

The invention relates to a knowledge-intensive visual question and answer automatic data generation method and device, and the method comprises the steps: constructing an original visual data set containing the professional knowledge of a target domain according to a static image, a video stream and multimedia content; extracting a representative frame sequence, converting the audio information into text information, and extracting character information in the static image to construct a structured visual instance database; according to the prompt text meeting the preset professional depth condition, establishing a three-level prompt system containing domain knowledge, an evaluation standard and a generation specification; generating a corresponding visual question and answer pair data set according to the dynamic cooperation of the main agent and the domain expert agent; generating a multi-agent quality evaluation system according to the quality evaluation result; and designing a difficulty grading mechanism according to the negative example sample. According to the method, the professionality, the accuracy and the diversity of the visual question and answer data are remarkably improved, and reliable data support is provided for training and evaluation of a multi-modal large model.

The invention relates to the technical field of large language models, in particular to a complex multi-field guided teaching question and answer generation method, device and equipment and a computer storage medium. According to the complex multi-field guided teaching question and answer generation method, aiming at the problem that the model is single in application field, the corresponding multi-round question and answer pair data set is processed by using a multi-field data source, and the model is finely adjusted based on the multi-round question and answer pair data set, so that the problem that the related field is single is solved, and the applicability and practicability of the teaching question and answer model are enhanced; aiming at the problem that the model lacks guiding ability, the input of the teaching question and answer model is further processed through thinking chain-based question extraction and disassembly and prompt engineering and context learning mechanism-based processing, so that the model question guiding ability is improved; in order to solve the problem that teaching effect evaluation indexes are single, the invention provides a new evaluation system, and a plurality of evaluation indexes can more comprehensively and accurately evaluate and optimize model teaching effects.

The invention discloses a severe weather image sharpening method based on continuous learning, and the method comprises the steps: 1, carrying out the preprocessing of an image data set under various severe weather conditions, including image pairing and multi-task division; 2, dividing the image restoration network into two parts of feature projection and output projection, designing a degradation sensing module for extracting degradation specific information output by the feature projection under different weather conditions, and establishing background supervision and degradation supervision by adopting a multi-teacher model for guiding learning of a student model; and 3, establishing respective optimization targets for the student model and the teacher model, inputting pairing data of different weather removal tasks, and optimizing the student network by a multilateral distillation normal form, thereby obtaining an optimal model with integrated image severe weather removal capability. According to the method, different types of severe weather images continuously acquired in a real scene can be dealt with, and the implementation effect of an image restoration network in a resource-limited scene is improved.

The invention discloses a cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning, and the method specifically comprises the following steps: introducing a difficult sample feature mining method, dynamically adjusting the loss weight of a sample according to the change of similarity, and building a dynamic difficult sample triple loss model; the method comprises the following steps: dynamically adjusting a confidence threshold value of a sample by adopting an index moving average weighting method, iteratively training and screening an unlabeled sample, namely a pseudo label, establishing a pseudo label self-training mechanism of a dynamic threshold value, mining and utilizing non-paired data, and solving the problem of high manual labeling cost; a reference image most similar to a query image is found through image retrieval, and the offset of a query position is predicted. Experiments on CVUSA and CVACT data sets show that as the distance threshold increases, the accuracy of the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning presents a stable rising trend, and the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning is superior to other methods under the same threshold condition.

The invention discloses a protein generation model optimization method based on deep learning, and relates to the technical field of protein generation. The optimization method comprises the following steps: generating a candidate protein electron density distribution diagram based on the structural characteristics of a target spot by adopting a pre-trained diffusion model; converting the candidate protein electron density distribution diagram into a corresponding amino acid sequence; determining a functional index value corresponding to the amino acid sequence, and constructing a feedback data set; setting a reward threshold value, and marking the feedback data set as a positive sample data set and a negative sample data set according to the reward threshold value; constructing a utility function taking the reward threshold as a reference point, and respectively calculating utility values of the positive sample data set and the negative sample data set; and performing iterative optimization on the diffusion model according to the utility value. By adopting the technology provided by the invention, the dependence on preference paired data of large-scale and high-quality protein sequences can be avoided, and the performance of the protein generation model can be effectively and continuously improved.

The invention discloses a time sequence remote sensing composite under-cloud reconstruction method and system based on observation modeling and a radiation reconstruction network, and belongs to the technical field of remote sensing, and the method comprises the steps: carrying out the mask processing of a composite cloud time sequence remote sensing image based on observation, and obtaining a real degraded sequence after mask; inputting the real degraded sequence into the trained radiation reconstruction network to obtain a cloudless surface reflectance sequence, and completing composite under-cloud reconstruction; wherein the training of the radiation reconstruction network comprises the following steps: constructing a simulated degradation-cloudless pairing data set; building a radiation reconstruction network; and training the constructed radiation reconstruction network by using the constructed simulated degradation-cloudless pairing data set to obtain a trained radiation reconstruction network. According to the method, joint processing of the space-time spectrum data is realized, the method can be effectively applied to joint processing of thin cloud radiation deviation correction and thick cloud information loss reconstruction, and finally reconstruction of the surface reflectance sequence under the composite cloud is realized.

An audio translation labeling and analysis method based on a multi-agent cooperation mechanism belongs to the field of information technology and artificial intelligence, and is characterized by comprising the following steps: S1, translating an audio file into a text file; s2, outputting a dialogue set of text structure arrangement for the text file through a model in which domain knowledge and language style learning are introduced; s3, extracting and structurally expressing a time expression, a place name and an abstract paragraph by adding a large language model of a prompt project, and associating an event background with a session context to obtain an information set; s4, splicing the dialogue set and the information set to obtain a document set; s5, forming a text-label pair data set by a series of texts and the corresponding labels with inclusion relations, and constructing the text-label pair data set into an instruction data set conforming to instruction fine tuning format specifications; s6, performing fine adjustment on the large language model base according to the instruction data set to obtain a fine-adjusted model; and S7, inputting the document set into the fine-tuned model, and labeling all levels of labels to obtain the document set with the labels, which is used for full-process automatic processing from audio transcription to text optimization, information extraction, labeling and analysis report generation.

The invention relates to the technical field of computer graphics and computer vision, and particularly provides a monocular relief depth estimation model construction method based on a progressive fine tuning strategy. The method comprises the following steps: constructing a pseudo real relief image data set by using a text-based image generation model; generating a depth pseudo label according to the pseudo real relief image data set; obtaining an initial relief depth estimation model based on the pseudo real relief image data set and the depth pseudo label thereof; collecting a multi-view real relief image, and generating a real relief image and depth label pairing data set in combination with three-dimensional reconstruction and geometric detail optimization; according to the method, the initial relief depth estimation model is subjected to lightweight fine adjustment by adopting low-rank adaptation LoRA, a final monocular relief depth estimation model is obtained, high-quality training data is effectively constructed in the absence of real labels, and the model generalization ability and the prediction precision are considered at the same time.

A system for training and deploying a sanitizer model of a large language model (LLM) to sanitize the harmful language text. The system generates paired data sets for training using LLM techniques. Training includes a first training phase using a paired dataset to predict the benign language text in the paired dataset based on loss of information computed between the benign language text in the paired dataset and the sanitized language text. Training also includes a second training phase using a harm model that computes a harm score of the LLM prediction by adjusting the trained sanitizing step to reduce the harm score for subsequent LLM responses.

The invention provides a method for quickly converting coordinate formats of mineral resource reserve databases in batches and related equipment. The method comprises the following steps: firstly, reading a mining area number and a registration classification number in a row coordinate record, and a character string in a mining area or mine range coordinate field and a character string in a calculation coordinate field from a coordinate management table; secondly, independently storing block coordinates in the two character strings by utilizing two one-dimensional arrays, and independently storing initial positions and inflection points of the block coordinates by utilizing two two-dimensional arrays; and finally, according to the information in the four arrays, in combination with an encoding rule of the character string, reading block coordinates from the corresponding arrays in sequence, forming a coordinate pair format, and writing the coordinate pair format into a corresponding text file, so that forward conversion from the coordinate string to the coordinate pair is realized. In addition, reverse conversion of coordinate pairs to coordinate strings is also provided. According to the invention, the coordinate pair data meeting the coordinate system transformation format requirement can be quickly realized and provided.

The invention belongs to the technical field of digital image processing, and relates to an unpaired low-illumination image enhancement method based on Fourier transform. The invention provides an innovative scheme, an unpaired training strategy is adopted, and dependence on paired data is avoided. The method comprises the following steps: firstly, acquiring a low-illumination image and an unpaired high-illumination image, then extracting illumination and reflectivity components from the low-illumination or high-illumination image by using a pre-training separation network based on a Retinex algorithm, and training an enhanced reflectivity component; then, the illumination components are sent to a cyclic consistency generative adversarial network (CycleGAN) for non-pairing training; and finally, multiplying the enhanced reflectivity by the illumination component to obtain a final enhanced image. Through combination of the pre-trained reflectivity graph and the non-pairing training, the illumination and detail recovery capability of the image is effectively improved, the adaptability and generalization capability of the model are high, a remarkable image enhancement effect can be realized without data pairing, and the method has wide practical application potential.

The method for automatically and intelligently extracting the data of the atlas database comprises the following steps: performing data cleaning on original data to obtain cleaned data; performing cross-modal semantic alignment word segmentation processing on the cleaning data to obtain semantic alignment word segmentation data; performing entity relationship extraction on the semantic alignment word segmentation data, analyzing an influence relationship between entities through a Granger causality test algorithm, verifying the logicality of the influence relationship, and generating entity relationship pair data containing causality intensity; performing graph construction processing of incremental graph optimization on the entity relationship pair data to obtain a dynamic optimization knowledge graph; and carrying out migration fusion of field adaptation on the dynamic optimization knowledge graph to obtain a graph database of field fusion. According to the method, the defect that causal logic is difficult to mine at present, so that the map relationship lacks the deep decision support capability is overcome.

The invention discloses a laser design system, a laser design method, electronic equipment and a medium, and belongs to the technical field of integrated optics. The laser design system comprises a first model, input parameters of which comprise set grating parameters of a grating structure of a first to-be-designed laser, and which is used for predicting and outputting a predicted output characteristic curve of the first to-be-designed laser based on the set grating parameters; the input parameters of the second model comprise a set output characteristic curve of a second laser to be designed, and the second model is used for predicting and outputting predicted grating parameters of a grating structure of the second laser to be designed based on the set output characteristic curve; the first model and the second model share the same training data set, the input training data of the first model corresponds to the output training data of the second model, and the output training data of the first model corresponds to the input training data of the second model; the training data set includes pairing data between grating parameters of a grating structure of the sample laser and an output characteristic curve of the sample laser.

The invention discloses a sparse view angle 3D-DSA reconstruction method based on a three-dimensional Poisson generative model. The method comprises the following steps: obtaining pairing data of a sparse view angle 2D-DSA projection drawing and a 3D-DSA reconstruction image; extracting features of a projection image by using a projection domain encoder, then converting features of two-dimensional projection into a three-dimensional image domain according to a geometrical relationship of the cone beam CT, and then obtaining a prior image by using an image decoder; the method comprises the following steps: constructing a three-dimensional Poisson generation model, adding noise to a three-dimensional image patch in a training stage to obtain a disturbance image, outputting a network reconstruction image by taking a prior image as a condition and the three-dimensional image patch before noise addition as a target image, and calculating loss of the output image and the target image to update network parameters; the mean square error loss and the mean square error loss of the maximum intensity projection images of the three orthogonal planes are used during loss calculation; in the sampling stage, random noise is used as input, a prior image is used as a condition, noise of a noise image is continuously denoised within a limited step length, and finally a reconstructed 3D-DSA image is obtained.

The invention belongs to the technical field of cross application of an artificial intelligence large language model and hardware design, and provides a hardware design-oriented special large model aided design method and system, and the method comprises the steps: obtaining an original code data set; the original code data set is preprocessed, and a hardware description language code database meeting the hardware design requirement is constructed and obtained; generating a multi-level code description pair data set based on the hardware language code database meeting the hardware design requirements and the constructed multi-level description mechanism; wherein the construction process of the multi-level description mechanism comprises the step of constructing a special large language model designed for hardware on the basis of a multi-level code description pair data set. The large language model can better meet the requirements of the hardware design field, the accuracy and reliability of the generated content are improved, and powerful support is provided for development of hardware design.

The invention discloses a security constraint type inverse kinematics solving method and device and a storage medium. The method comprises the following steps: firstly, forming a joint angle-tail end pose pairing data set by students through forward movement of a robot; training an improved RBF (Radial Basis Function) network by using the data set to establish initial mapping from a tail end pose to a joint angle to obtain an initial approximation model; taking the prediction output of the model as an initial population of a PSO algorithm, and updating the speed and position of particles in the initial population through iteration; designing a comprehensive cost function, introducing a joint angle constraint and an obstacle collision constraint, and evaluating the advantages and disadvantages of each particle; and when the PSO algorithm reaches the maximum number of iterations or the comprehensive cost function value is lower than a set threshold value, outputting a global optimal particle position as a final inverse kinematics solution. On the premise of ensuring the solving precision, the joint limit constraint and the obstacle avoidance constraint are explicitly processed, so that an accurate, safe and feasible inverse solution is output, and the reliability and the safety of robot operation are remarkably improved.

The invention discloses a medical instruction data generation and use method based on a multi-modal knowledge graph. Firstly, medical images and text information in a public data set are collected, and high-quality image-text pair data are screened out from the medical images and the text information; secondly, performing entity recognition on the medical text and retrieving related triple knowledge from the multi-modal knowledge graph to form knowledge enhanced medical image-text data; secondly, inviting a medical expert to label real scene question and answer pairs based on the knowledge-enhanced medical image-text data, and taking the real scene question and answer pairs as a seed instruction data set to guide a large-scale visual language model to generate diversified instruction question and answer data; and finally, using the medical image understanding and question-answering ability of the supervised fine-tuning training model, and performing reinforcement learning supervised by a knowledge graph triple path. According to the method, the problem of scarcity of medical instruction data can be effectively relieved, so that the trained model has higher accuracy and interpretability in a medical question and answer task.

The invention discloses a face privacy protection-oriented generated image detection method and a face privacy protection-oriented generated image detection system. The method comprises the following steps of: firstly, preparing face and text pairing data, and finely adjusting a diffusion model; secondly, on the basis of the diffusion model after fine tuning, potential vectors are extracted and clustered, and text prompts and center vectors obtained through clustering form a dictionary; and finally, based on the obtained dictionary, obtaining a pseudo image and a label through the fine-tuned diffusion model, and outputting a detection result through a classifier. According to the method, potential spatial clustering and conditional diffusion generation are combined, privacy protection and data diversity are taken into consideration, and the security and generalization ability of forged face image detection are remarkably improved.

The invention discloses a multi-modal cross-domain question and answer data construction method, device and equipment, and the method comprises the steps: carrying out the syntactic analysis of a question text in obtained general domain image-text question and answer data, generating a question template, and constructing a general question and answer template library based on the question template; performing feature extraction on the to-be-processed target domain image data to obtain image description information; generating a target domain question text by combining the general question and answer template library and the image description information; and inputting the target domain question text and the target domain image data into a multi-modal question and answer model to generate an answer text, and taking the answer text and the target domain question text as target domain image question and answer pair data. According to the method, efficient, flexible and accurate generation of question and answer data can be realized for different fields.

A system for transforming electrocardiogram images for use in one or more machine learning models, the system including at least a processor configured to receive paired data including a plurality of ECG images correlated to a plurality of standardized images, wherein each ECG image of the plurality of ECG images is correlated with each standardized image of the plurality of standardized images, train an ECG transformation model as a function of the paired data, wherein training the transformation model includes adjusting one or more parameter values of the ECG transformation model as a function of a comparison between at least one predicted standardized image and at least one standardized image of the plurality of standardized images, receive non-conforming data, generate standardized data as a function of the ECG transformation model and the non-conforming data and train an ECG machine learning model as a function of the standardized data.

A method, an apparatus, and a non-transitory computer-readable storage medium for video compression using a generative adversarial network (GAN) are provided. The method includes obtaining, by a generator of the GAN, a reconstructed target frame based on a reference frame and a raw target frame to be reconstructed; concatenating, by a transformer-based discriminator of the GAN, the reference frame, the raw target frame and the reconstructed target frame to obtain a paired data; determining, by the transformer-based discriminator of the GAN, whether the paired data is real or fake to guide reconstruction of the raw target frame; and determining a generator loss and a transformer-based discriminator loss, and performing gradient back propagation and updating network parameters of the GAN based on the generator loss and the transformer-based discriminator loss.

The present invention discloses a method, system and device for estimating the suspended sediment concentration of a river, which relates to the field of retrieving the suspended sediment concentration of a river in an ultra-large value range, and includes: obtaining a measured data set of historical river suspended sediment concentration; obtaining the water body reflectance of images of multiple satellites; generating a matching sample pair data set and dividing it into training samples and validation samples; constructing a band combination mainly based on the required bands as the bands reflecting the characteristics of the suspended sediment concentration in high-concentration water bodies, and obtaining the sensitivity index RAN; constructing an inversion model using the training samples and the sensitivity index RAN; obtaining the river range; using a cloud computing platform to analyze the image sets of multiple satellites using the river range, the sensitivity index RAN and the inversion model to obtain the spatial distribution information of the river suspended sediment concentration; obtaining the spatial distribution information of the river suspended sediment concentration. The present invention can accurately evaluate the suspended sediment concentration of a river.

The present disclosure provides a data collection method and apparatus, a computer device, and a storage medium. The method includes: determining, in response to a garbage collection request, respective first index entries from a first data table to be processed, first key data and storage location information in the first data table of a first key-value pair data corresponding to the first key data are stored in the first index entries; selecting valid target key data from the first key data according to current respective second data tables in the log-structured merge tree; reading target value data corresponding to the respective target key data in the first data table according to storage location information in the first index entries; constructing a new first data table according to the target key data and the target value data, and collecting the first data table to be processed.

The invention discloses a two-arm smart teleoperation method and system based on motion redirection and self-adaption, and relates to the technical field of robots, and the method comprises the steps: determining the posture of a human hand and the posture of a human arm according to a human body image; the robot is redirected according to the human hand posture and the human arm posture through the space attention gating map convolutional network, and the joint angle of a mechanical hand of the robot and the joint angle of a mechanical arm of the robot are correspondingly obtained; the mechanical arm is driven according to the joint angle of the mechanical arm so that the mechanical arm can grab the target object; and target force needed for grabbing the target object is determined, and the mechanical arm is driven to grab the target object according to the target force and the joint angle of the mechanical arm. According to the method, topology modeling and cross-platform action redirection between the human body and the robot are achieved through the space attention gating map convolutional network, pairing data dependence is eliminated, and therefore the high precision of action reproduction and the safety and flexibility of the grabbing process are both considered in the complex dynamic environment.

The invention belongs to the technical field of multimedia analysis, and discloses a multi-modal media tampering detection method, system and device based on multi-view comparative learning and a medium, and the method comprises the steps: obtaining a training data set which comprises a training image-training text pair and a corresponding tampering category label; a cross encoder is introduced on the basis of a vision-language model, a plurality of multi-layer sensor head structures are arranged, and three kinds of comparative learning of noise enhancement, prototype-based and multi-label tampering classification are designed to obtain an initial multi-view comparative learning framework; training the initial multi-view comparative learning framework based on the training data set to obtain a trained multi-view comparative learning framework; and based on the trained multi-view contrast learning framework, executing a tampering detection task of the to-be-detected image-text to the data. According to the technical scheme, the accuracy and robustness of multi-label classification can be improved.

Disclosed in the present application are a data processing method and apparatus, and a device and a readable storage medium. The method comprises: performing category division on M pieces of question-answer pair data on the basis of a question-answer pair category information set, so as to obtain category labels respectively corresponding to the M pieces of question-answer pair data; on the basis of the M pieces of question-answer pair data and the category labels respectively corresponding to the M pieces of question-answer pair data, generating N training sample sets and sample set category labels respectively corresponding to the N training sample sets; sorting the N training sample sets by means of learning difficulty corresponding to the sample set category labels, so as to obtain a training sample sequence; and in the training sample sequence, sequentially acquiring input training samples from the sorted training sample sets, and sequentially adjusting model parameters of an initial question-answering model by means of the sequentially acquired input training samples, until a target question-answering model is obtained after the initial question-answering model is trained and converges. By using the present application, the model precision of the target question-answering model can be improved.

The invention relates to a foreign exchange contract valuation method and device, computer equipment and a readable storage medium, and the method comprises the steps: determining a target foreign exchange transaction parameter carried by a real-time valuation request in response to the real-time valuation request generated for a foreign exchange contract; determining target hash key value pair data matched with the target foreign exchange transaction parameter from the hash key value pair data of the multi-level index data structure in the out-of-heap memory; each hash key value pair data has a corresponding data storage area; reading a plurality of foreign exchange contract data from a target data storage area corresponding to the target hash key value pair data; and performing batch calculation on the current foreign exchange data and the multiple pieces of foreign exchange contract data in a parallel mode, and determining an estimation result corresponding to each piece of foreign exchange contract data. Rapid positioning and efficient retrieval of foreign exchange contract data are realized; the problems of influence of frequent garbage collection on performance and memory leakage are avoided; and the real-time performance and the calculation efficiency of foreign exchange contract estimation are effectively improved.