246results about How to "Increase richness" patented technology

The invention relates to the technical field of data processing, and discloses a multi-granularity extraction and enhancement method, device and equipment for multi-source heterogeneous data. The method comprises the following steps: performing multi-level feature extraction on a multi-modal data set and original feature parameters to obtain a multi-modal feature set; performing correlation analysis on the multi-modal feature set to obtain a feature correlation matrix and optimized feature representation; performing cross-modal knowledge migration processing on the feature incidence matrix and the optimized feature representation to obtain migration feature parameters; performing dynamic fusion processing on the migration feature parameters to obtain target fusion features; and performing multi-task segmentation processing according to the target fusion features to obtain a plurality of sub-task sequences, performing time sequence enhancement and multi-task fusion analysis on each sub-task sequence to obtain target extraction data, and performing visual display on the target extraction data. According to the method and the device, the efficiency and the accuracy of multi-granularity extraction and enhancement for multi-source heterogeneous data are improved.

The invention discloses a method for rapidly and nondestructively detecting seed quality based on a near infrared spectrum technology, and relates to the technical field of modern agriculture, and the method comprises the following steps: using a deep learning model to carry out image recognition on a seed to be detected, determining a seed type and automatically adjusting spectrometer parameters; collecting spectral data by using the adjusted near-infrared spectrometer, and preprocessing the spectral data; obtaining the internal structure and density information of the seeds, and combining the internal structure and density information with the spectral data to form a multi-modal data set; constructing a seed vigor prediction model, and predicting the germination rate of the seeds; judging the growth trend of the seeds according to the prediction result; grading according to the growth trend of the seeds; according to the method, the seed vigor prediction model is constructed, so that high-precision prediction of the seed germination rate is realized; through classification and grading of growth trends, a scientific basis is provided for seed screening and planting management.

The invention relates to a multi-mode printed circuit board defect detection method and device based on deep learning and a storage medium, and the method comprises the steps: collecting three-dimensional point cloud data and two-dimensional image data, and carrying out the registration, alignment and preprocessing of the three-dimensional point cloud data and the two-dimensional image data; inputting the three-dimensional point cloud data into a double-view fusion module, mapping to generate an aerial view BV and a front view FV, respectively performing primary feature extraction and interpolation on the aerial view BV and the front view FV, and finally fusing to obtain multi-view fusion feature information; repeatedly inputting the multi-view fusion feature information and the two-dimensional image data into an attention module for multiple times to generate fusion features; and inputting the fusion features into a detection module, and outputting prediction results of defect types and bounding boxes. Through the target detection method based on bidirectional cross-modal feature transmission, the problem that small target defect information on a printed circuit board is easy to lose in the extraction process is solved.

The invention discloses a TEC space-time prediction method based on fusion of feature analysis and a self-attention mechanism, and the method comprises the steps: screening strong effective features through analyzing the correlation between TEC historical data and each feature parameter, calculating the attention weight between data points on each time sequence through employing a self-attention coding mechanism, and carrying out the weighted summation to obtain the attention output. In an ionosphere total electron content prediction task, firstly, for sun and geomagnetic activity characteristic parameters, the correlation between each parameter and a TEC sequence in the same time period and a TEC sequence after a day interval is evaluated, strong correlation characteristics are screened, TEC sequences of adjacent grid points are used as characteristic quantities, multiple spatio-temporal characteristic matrixes of different time scales in a global range are established, and the total electron content of the ionosphere is predicted. And encoding the input feature information according to a self-attention encoding mechanism to obtain an attention output result, and decoding in a decoding layer through a linear model to obtain a TEC predicted value at a future moment. According to the method, efficient extraction of spatio-temporal information can be completed, and higher-precision prediction of the TEC is realized through a self-attention mechanism.

The invention discloses a distributed architecture of an intelligent emergency command and dispatch system and a collaborative decision-making method. A distributed architecture comprising a basic resource layer, a support service layer and an application layer is constructed, and functions of multi-type alarm access, accurate alarm positioning, intelligent power dispatching and the like are realized by using multiple technologies. And establishing a collaborative decision-making mechanism which covers problem modeling, communication protocol design, negotiation mechanism development, dynamic adjustment and decision-making execution. The system is deployed after integration testing and security testing, personnel training, daily hardware maintenance, software monitoring, data backup and security component updating are carried out. The emergency commanding and dispatching efficiency, accuracy and collaboration are improved, the method adapts to complex emergencies, and public safety is guaranteed.

The invention relates to the technical field of information recommendation, in particular to an AI-driven customer segmentation and personalized recommendation system which comprises a user acquisition module, a data classification module, a user division module, a training module, a preference link generation module, a retrieval link generation module, a push output module, a monitoring verification module and an analysis module. According to the method, users are subdivided into different groups and divided into a plurality of user categories, personalized link recommendation is provided for each user category, accurate user subdivision and personalized recommendation are realized by collecting and analyzing a large amount of user data, and the data processing efficiency is improved.

The invention relates to a spacecraft pose estimation method, in particular to a lightweight spacecraft pose estimation method based on a high-resolution network, and solves the technical problem that in an existing spacecraft pose estimation method, key point regression is difficult to meet precision and efficiency requirements at the same time. According to the method, the key point regression model is based on a high-resolution network, depth separable convolution is adopted in the feature extraction process, the parameter quantity and the calculation quantity are greatly reduced, meanwhile, a mixed attention mechanism is combined, the high expression ability is reserved in feature extraction, the lightweight degree is further improved, and the method is suitable for large-scale popularization and application. The balance of precision and efficiency is realized; in the feature fusion process, cavity convolution is adopted to enlarge a receptive field, global information in an image can be captured, the pixel position of an occluded key point can be more accurately deduced, then key local and global features are focused in combination with a mixed attention mechanism, the effectiveness of feature representation is further improved, and the regression precision of the key point is improved.

The invention relates to a commodity recommendation method and system, and belongs to the technical field of commodity recommendation. The commodity recommendation method comprises the following steps: collecting user information data and commodity information data, and desensitizing the user information data; constructing user features through the obtained data features to form a user portrait vector; extracting theme features of the commodity information by using a theme modeling algorithm through the commodity information data, and calculating the distribution condition of each theme feature; mining a commodity relationship between the commodity information data and the user behavior data, and establishing a time function of a change trend based on a complementary substitution relationship of commodities; and based on the user portrait, the commodity information data and the commodity relationship, establishing a recommendation score function of the recommended commodity, and generating a recommendation list of the commodity information according to recommendation scores. According to the method, matching is completed by respectively extracting the features of the user and the commodity, and the comprehensive recommendation score is given based on the relationship between the commodities, so that the accuracy and richness of commodity recommendation are improved.

The invention provides a content display method and device based on a large language model, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, in particular to the fields of generative artificial intelligence, generative search, large language models and the like. According to the implementation scheme, style description data generated based on a large language model is obtained, and the style description data comprises a first identifier used for indicating the start of the style description data, a second identifier used for indicating the end of the style description data and main body data used for describing a preset style of to-be-displayed content; sequentially identifying the first identifier and the second identifier to determine main body data from the style description data; and performing rendering based on the main body data to obtain display content with a predetermined style.

The invention discloses a construction method, system and equipment of a high-precision map and a medium. The construction method comprises the following steps: S1, generating a bird's-eye view feature map based on a static map with a standard resolution and sensor data; s2, further extracting a static feature Fsd and a dynamic environment feature Ft based on the bird's-eye view feature map; s3, based on a cross attention mechanism, fusing the static feature Fsd and the dynamic feature Ft; and S4, decoding the fused features into a high-precision map through a decoder. According to the invention, when the environment structure changes, the road information of the current frame can be reasoned by combining the high-definition map information of the previous frame with the standard map information, and the depth feature extracted by the convolutional neural network and the original standard map information are combined. The system can more accurately perceive and understand the environment around the vehicle, so that the overall performance and decision-making efficiency of the automatic driving system are improved.

The invention provides a preparation method of dark isolated endophytic fungus inoculant vegetation concrete. The preparation method of the inoculant comprises the following steps: carrying out fermentation culture on glucose, corn steep liquor dry powder and soybean cake powder, and preparing into the inoculant by using a freeze spray drying method. The method for improving the drought resistance of the plants in ecological restoration of the vegetation concrete comprises the following steps: layering the vegetation concrete: increasing the cement proportion in the base material proportion of a bottom layer, additionally adding urea, calcium chloride and urease, and inducing the urea and the calcium chloride to generate calcium carbonate precipitate through the urease to play a reinforcing role; additives such as lignocellulose, biochar and ceramsite are additionally added to the middle layer, so that the water-retaining property, porosity and fertility of soil can be improved, and the effect of enhancing plant rooting and branching is achieved; the fungicide and the plant seeds are added into the surface layer, and the fungicide can promote seed germination and plant growth.

The invention discloses a method for a multi-modal cloud fusion service platform, and relates to the technical field of cloud service platforms, and the method comprises the following steps: S1, connecting a data source of a user through an API interface, collecting data of different modals, and obtaining a user fusion service demand and an application scene; and S2, preprocessing the collected data, establishing a cloud storage database, providing a multi-modal cloud storage service, establishing a data index and the like. Different modal data are collected in multiple modes, data sources are expanded, data richness and diversity are improved, data quality is improved through data preprocessing, a reliable data basis is provided for follow-up analysis, data are conveniently and rapidly retrieved and queried in multiple data indexing modes, data storage and management efficiency is improved, and the method is suitable for large-scale popularization and application. And user portraits and clustering analysis are established, so that user demands can be better known, personalized multi-modal services are provided for different user groups, and user satisfaction is improved.

The invention belongs to a data processing method, and provides a data enhancement method, a data enhancement system, electronic equipment and a computer storage medium aiming at the technical problems that a manual annotation mode is difficult to cope with continuously changing business scenes and the timeliness and the flexibility of corresponding model training cannot be ensured when atlas construction and relation extraction technologies are utilized at present. And marking entities and relationships in the initial sample, performing classification arrangement to obtain an entity dictionary, replacing the entities of the text in the marked sample, and marking the replaced entities to obtain a first augmented sample. Constructing a relationship template and a sentence pattern template, re-expressing the text in the replaced first augmented sample through the sentence pattern template, replacing the entity and relationship of the text in the re-expressed first augmented sample in combination with the entity dictionary to obtain a second augmented sample, and then summarizing and fusing the first augmented sample and the second augmented sample to obtain a second augmented sample; and enhanced data is obtained.

The invention relates to the technical field of image reconstruction, in particular to an MRI (Magnetic Resonance Imaging) image super-resolution reconstruction method and system based on an adaptive Fourier operator, and the method comprises the steps: obtaining target shallow layer features of at least one to-be-reconstructed MRI image and inputting the target shallow layer features into a Transform set, and performing iterative processing on the basis of the space mixing region and the channel mixing region of each Transform extraction block in the Transform set to obtain a target deep feature of at least one to-be-reconstructed MRI image, and performing up-sampling on the target deep feature and the target shallow feature based on transposition convolution to obtain a target super-resolution MRI image. According to the embodiment of the invention, the feature processing in the frequency domain and the spatial domain can be carried out in the spatial mixing region of the Transform extraction block by using the adaptive Fourier operator and the convolutional neural network, so that the frequency domain information of the MRI image is effectively extracted and integrated, and the combination of the frequency domain information and the spatial domain information is further realized. Richness of feature extraction results and resolution of reconstructed images are improved, and actual requirements of medical application are better met.

The invention provides a video generation method and system and a model training method and system. According to the method, after video description information and N semantic extension instructions are obtained, semantic extraction is carried out on the video description information and the N semantic extension instructions based on a semantic feature generation network in a pre-trained video generation model to obtain target semantic features; and performing video generation based on the target semantic features through a video generation network in the video generation model to obtain and output a target video conforming to the semantics of the video description information. In the method, the semantic feature generation network and the video generation network are jointly trained in the training process of the video generation model, so that the semantic feature generation network can generate target semantic features which better meet the requirements of the video generation network; the video generation network can generate the high-quality target video more accurately according to the target semantic features, so that the overall performance of the video generation model and the generation effect of the target video are improved.

The invention relates to the technical field of data processing, and provides a natural language processing method and device, electronic equipment and a readable storage medium. The method comprises the steps that to-be-processed data and a preset prompt are input into a trained natural language processing model for splicing processing, and extended data corresponding to the to-be-processed data are obtained; processing the extended data corresponding to the to-be-processed data through the large language model to obtain generated data corresponding to the to-be-processed data; performing splicing processing on the to-be-processed data and the corresponding generated data to obtain to-be-processed spliced data; performing retrieval processing on the to-be-processed spliced data to obtain retrieval result data corresponding to the to-be-processed spliced data; determining embedded data corresponding to the to-be-processed spliced data according to the to-be-processed spliced data and the retrieval result data corresponding to the to-be-processed spliced data; and processing the embedded data corresponding to the to-be-processed spliced data and the to-be-processed data through the large language model to obtain target data corresponding to the to-be-processed data.

The invention relates to a medical and health big model-oriented security evaluation method, which comprises the following steps of: obtaining security information related to a medical and health big model, and identifying potential security risk vulnerability types; dividing potential security risk vulnerability types into common dimension data and Prompt dimension data according to a security assessment target; constructing a data set according to a division result; using the constructed data set and through the reference model, carrying out security evaluation for the medical and health large model; an evaluation result of safety evaluation is analyzed, the severity and influence of the evaluation result are evaluated, and improvement suggestions are put forward; and monitoring the medical and health big model according to the improvement suggestion, updating the prompt data set, and re-evaluating the medical and health big model. The method is reasonable in design, the prompt dimension is used for more comprehensive evaluation, the data richness is improved, the data set coverage range is wide, the evaluation credibility is enhanced, the model is used for evaluation, the subjective prejudice of manual evaluation is reduced, and the objectivity is enhanced.

The embodiment of the invention discloses a training method of an expression driving model, an expression driving method and a corresponding device. According to the main technical scheme, first training data comprising a plurality of first training samples are obtained, the first training samples at least comprise audio samples, expression control data corresponding to the audio samples and expression parameter truth values, and the expression control data at least comprise time sequence control data; the time sequence control data comprises an action type sequence corresponding to at least one time window in a time sequence of the audio sample; and training an expression driving model based on the first training data: taking the audio sample and an expression control parameter corresponding to the audio sample as input of the expression driving model, and obtaining an expression parameter sequence output by the expression driving model. According to the invention, the fine control effect of the facial expression can be improved, and the expression change is richer and more vivid.

The invention discloses a virtual simulation data intelligent generation and enhancement method based on deep learning. The method comprises the following steps: step 1, collecting and converging multi-source heterogeneous simulation data; 2, simulation data preprocessing and feature extraction; step 3, constructing and configuring a depth generation model; step 4, iterative training and optimization of the depth generation model; step 5, intelligently generating and synthesizing new simulation data; step 6, generating data quality verification and enhancement feedback; step 7, carrying out controllability adjustment and directional enhancement on the generated data; and 8, model online learning and data generation closed-loop optimization are carried out. The method can automatically learn the internal law and distribution of the simulation data, efficiently generate highly-vivid and diversified new simulation data meeting specific requirements, and deeply enhance the existing limited data, thereby significantly reducing the simulation data acquisition cost, improving the data quality and richness, and improving the simulation data acquisition efficiency. And powerful data support is provided for development and test of a virtual simulation system and simulation-based AI model training.

The embodiment of the invention provides a content display method and device, equipment and a storage medium. In response to an exit operation on the object display page, displaying a multimedia content page, and displaying a search keyword and a search control associated with the search keyword in the multimedia content page; in response to a trigger operation on the search control, displaying a search result page; wherein a plurality of target objects associated with the search keyword are displayed in the search result page, and the target objects comprise one or more objects in the object display page. According to the content display method provided by the embodiment of the invention, when the interface exits from the object display page, the search keyword and the search control associated with the search keyword are displayed in the displayed multimedia content page to guide a user to trigger the search control, so that the search result page containing the multiple target objects is displayed; the richness of content display can be improved, so that the browsing experience of the content is further improved.

The invention discloses a fine recognition method, device and system for a farmland protection forest, and a medium. The method comprises the following steps: acquiring a prototype network model; performing enhancement processing on the original sample by adopting a data enhancement algorithm to obtain a training sample; integrating the convolution attention module into the prototype network model, and training by adopting a training sample to obtain a farmland protection forest recognition model; migrating the farmland protection forest recognition model to a target domain by adopting a fine tuning migration learning method to obtain a fine recognition model for performing farmland protection forest recognition on the target domain; and inputting the remote sensing image data of the target domain into the fine recognition model, and performing fine recognition on the farmland protection forest according to an output result of the fine recognition model. By adopting the technical scheme, the farmland protection forest can be accurately recognized based on the remote sensing image data, the recognition efficiency of the farmland protection forest is obviously improved while the recognition precision is improved, and favorable data support is provided for agricultural development.

The invention relates to the technical field of biology, in particular to a method for solid state fermentation of bagasse by using white rot fungi, which comprises the following steps: (1) activation and propagation of strains: pouring the white rot fungi into a PDA culture medium for activation and propagation; (2) raw material preparation: air-drying fresh bagasse, adding water to adjust the water content of the bagasse to 60-70%, and adjusting the pH to 4-6 with glacial acetic acid; and (3) preparing the bagasse feed, namely mixing the white rot fungi subjected to expanding culture according to 1-5% of the weight of an air-dried substance with bagasse of which the initial pH is adjusted to be 4-6, adding 1-5% of soluble sugar, and fermenting for 10-45 days to obtain the fermented bagasse feed. According to the white rot fungus fermented bagasse prepared by the method disclosed by the invention, the hemicellulose content of the bagasse can be reduced to 26.62%-22.45%, the lignin content can be reduced to 10.67%-8.66%, the crude protein content of the bagasse can be increased to 1.61%-2.12%, the lactic acid content can be increased to 26.57 mg / L-181.69 mg / L, and high-quality feed application of the bagasse can be realized.

The embodiment of the invention provides an information sharing method and device, an information access method and device, equipment, a storage medium and a program product. In the embodiment of the invention, in the process of performing screenshot sharing on target content displayed on a first application page, access link information corresponding to the target content and at least one piece of interactive information associated with the target content can be identified and acquired, and a screenshot image is associated with the at least one piece of access link information; and sharing the to-be-shared target image to a second terminal, so that a second user can check the target content, and at least one piece of interactive information associated with the target content is displayed to the second user. The screenshot sharing function and the shared content are enriched; therefore, convenience is provided for the user to check some other contents associated with the target content, the mode of checking the target content and the content associated with the target content can be simplified, the information acquisition efficiency is improved, and the use experience of the user is improved.

This specification provides a method, device, medium, and program product for training a generative model and retrieving goods. The method includes: acquiring first sample data including first sample goods information; generating multiple first sample query messages from an initial generative model based on the first sample goods information; determining a first positive sample and a first negative sample from the multiple first sample query messages; wherein the correlation between the first positive sample and the first sample goods information is higher than the correlation between the first negative sample and the first sample goods information, and the number of historical queries corresponding to the first positive sample is greater than the number of historical queries corresponding to the first negative sample; determining a first probability that the initial generative model outputs the first positive sample based on the first sample goods information and a second probability that the initial generative model outputs the first negative sample based on the first sample goods information, and training the initial generative model with the goal of increasing the first probability and decreasing the second probability to obtain a target generative model.

The invention discloses instant pricked fish and a making technology, belongs to the technical field of instant pricked fish, and aims to solve the problems that when the instant pricked fish is eaten, the taste is single and greasy, and the eating experience of a user is poor. A first fish flesh layer, a second fish flesh layer, a third fish flesh layer, a fourth fish flesh layer, a fifth fish flesh layer, a sixth fish flesh layer, a seventh fish flesh layer, an eighth fish flesh layer and a ninth fish flesh layer are arranged in the storage pipe, a condiment sauce layer is arranged between the first fish flesh layer and the second fish flesh layer, and a fruit and vegetable layer is arranged between the second fish flesh layer and the third fish flesh layer; according to the invention, through the arrangement of the condiment sauce layer, the taste of the fish fillets is more special, through the arrangement of the fruit and vegetable layer, the taste can be increased when the fish fillets are eaten, through the arrangement of the pickled radish layer, the radish strips can be eaten when the fish fillets are eaten, and the radish strips are crisp, sour and sweet and unique in taste, so that the effect of cleansing the greasiness can be achieved; the richness of the taste when the fish meat is eaten is improved, so that the eating experience of a user is improved.

The application discloses a social message display method and device, equipment and storage medium, and belongs to the field of human-computer interaction. The method is executed by a first client, and the method comprises the following steps: receiving a first social message sent by a first account in a chat session, displaying the first social message in a first view layout; and displaying the first social message in a second view layout under the condition that a first trigger condition is met; wherein at least one of the size, the layout position of the second view layout and the first view layout and the account information of the first account is different. The display mode of the view layout of the social message is changed after the trigger condition is met, the social message in the chat session can be dynamically displayed, the display mode of the social message is enriched, and the display efficiency of the social message is improved.

The invention provides a preparation method and application of amino acid derived carbon dot nano-enzyme, and belongs to the technical field of new biomedical materials. The amino acid comprises one of histidine, proline, cysteine, tryptophan, aspartic acid, arginine, lysine, methionine and phenylalanine; the mass ratio of the amino acid to the citric acid monohydrate is (1: 1)-(1: 5). The amino acid derived carbon dots provided by the invention have free radical scavenging activity and superoxide dismutase-like properties, and show low toxicity and good biological safety at the levels of cells, nematodes and animals. Meanwhile, the amino acid derived carbon dot nano-enzyme also shows excellent anti-inflammatory activity, can down-regulate the inflammatory level of cells, reduce the damage of oxidative stress to nematodes, improve the intestinal flora structure, increase the richness and diversity of intestinal flora, relieve and treat DSS-induced mouse ulcerative colitis, and has a good application prospect. The compound can be further applied to preparation of drugs for treating inflammatory bowel diseases.