15results about How to "Add data source" patented technology

The invention discloses a three-dimensional geologic model establishment method, a storage medium and equipment, and relates to the field of three-dimensional geologic body modeling, and the method comprises the steps: carrying out the discretization sampling of continuous geologic feature data, and dividing the sampling data into a training set and a test set; constructing a lithology category prediction model which comprises an encoder and a decoder, and training and testing the lithology category prediction model by using the training set and the test set; generating a regular grid point array by using a DEM model of a research area, and predicting lithology category labels of regular grid points by using the trained lithology category prediction model; a regular grid point is used as an angular point of a voxel grid, the step length of a regular grid point array is used as the size of the voxel grid, a regular grid voxel model is constructed, and a gradient function is set according to lithology category labels of the angular point of the voxel grid to simulate lithology composition of each voxel unit. The model is more in line with the basic rule of stratigraphic distribution, and the precision of the model is improved.

The invention is suitable for the technical field of inquiry, and particularly relates to a method and system for carrying out artificial intelligence inquiry on pet diseases, and the method comprises the steps: S100, creating a distributed architecture composed of a cloud end and terminal equipment, and deploying a pre-training language model and a knowledge base in the cloud end; s200, pet case data in different formats are collected, the case data at least comprise symptom description, diagnosis results and treatment schemes, the pet case data are preprocessed and segmented into a plurality of single items, semantic extraction is carried out on the single items to obtain key information, and a preset multi-modal fusion model is utilized to carry out multi-modal fusion on the key information; and fusing the single items in different formats, and configuring a corresponding relationship among all the single items. The multi-dimensional knowledge graph is generated, the accuracy and comprehensiveness of diagnosis are greatly improved, the pre-training language model and the knowledge base are integrated, so that the distributed architecture can continuously self-evolve, and the accuracy of diagnosis is gradually improved.

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 discloses a software-defined science and education resource dynamic collaborative recommendation method and system based on a knowledge graph. The method comprises the following steps: constructing an artificial intelligence knowledge graph; obtaining science and education resource data of a science and education institution, extracting knowledge entities in the science and education resource data, and connecting the knowledge entities to the artificial intelligence knowledge graph to obtain a science and education resource knowledge graph; constructing a user preference network model containing graph updating and time sequence feature fusion, and extracting local resource features / user features to obtain local resource features and user features of each client; constructing a local knowledge graph based on the science and education resource knowledge graph, and performing model training by taking the local resource features of each client as initial features until the model converges; and inputting the users and the resources into the graph neural network for prediction, and recommending the resources with high scores to each user. According to the invention, the data source and the coverage range of the recommendation system are greatly expanded, and multi-source science and education resource recommendation in a real sense is realized.

The invention discloses a children autism risk assessment method and system based on multi-modal collaborative reasoning and a vertical domain large model, and the method comprises the steps: collecting the audio data of a to-be-assessed child and a parent in a parent-child interaction link, and collecting the video data through collection equipment; analyzing and marking the collected audio data, and generating a sounding record and a transcription text with a timestamp; performing real-time analysis and labeling on the collected video data, and generating sight line, expression and action recognition records with timestamps; performing independent analysis on the preprocessed multi-modal data, performing feature fusion and collaborative analysis on the multi-modal data such as audio, text and video by using a multi-modal collaborative reasoning method, and generating a scientific data index for autism risk assessment; training a vertical domain large model special for autism risk assessment; and inputting the generated data indexes into a vertical domain large model, and outputting targeted assessment suggestions by the large model in combination with daily information of the children to be assessed obtained by interviews and conversations with parents.

The invention belongs to the technical field of traditional Chinese medicine, and particularly relates to a personalized traditional Chinese medicine inquiry system based on a traditional Chinese medicine large model and multiple knowledge bases, which comprises the traditional Chinese medicine large model, a traditional Chinese medicine pre-training embedding model, a sorting model, a traditional Chinese medicine vector database and a traditional Chinese medicine graph database, the output end of the traditional Chinese medicine pre-training embedded model is connected with the traditional Chinese medicine vector database, and the output end of the traditional Chinese medicine pre-training embedded model is connected with the traditional Chinese medicine graph database. And the construction difficulty of the preference database of the traditional Chinese medicine large model in RLHF training is reduced. The adaptation of the pre-trained semantic embedding model to the vectorization of the traditional Chinese medicine text is improved, and the accuracy of vector matching is improved. And a traditional Chinese medicine knowledge graph data source is increased. A traditional Chinese medicine large model is trained, and a sorting model is trained and introduced, so that context selection is more accurate. Vector data and a graph database are constructed for each famous traditional Chinese medicine, and personalized question and answer logic for each famous traditional Chinese medicine is realized.

The invention relates to an SAR (Synthetic Aperture Radar) historical data recalibration method and an SAR historical data recalibration system based on cross of different satellites. The method comprises the following steps: acquiring calibrated satellite-borne SAR historical image data and to-be-calibrated satellite-borne SAR historical image data of different satellites with the same wave band, and performing preprocessing, terrain correction, longitude and latitude cutting and registration to generate a re-calibration historical data pair; through resolution resampling processing and incident angle correction, a recalibration cross data pair is constructed and input into a Gaussian mixture model, and a calibration coefficient of an SAR historical image is obtained through calculation. By collecting satellite-borne SAR historical image data of different satellites in the same wave band, the imaging resolution difference between different loads is solved; after processing such as preprocessing, topographic correction and cutting registration is performed on satellite-borne SAR recalibration data, clustering fitting is performed by adopting a Gaussian mixture model, and weighting fitting is performed on different data pairs to solve a calibration coefficient of to-be-calibrated historical data, so that the recalibration precision of the satellite-borne SAR historical data can be improved.

The invention relates to the technical field of vehicles, in particular to a time processing method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring corresponding candidate time from candidate clock sources except a local clock source under the condition that a time updating event aiming at the local clock source corresponding to a main controller is detected; wherein each candidate clock source comprises a system clock source of a target vehicle system bearing the main controller, and an external clock source of external equipment communicating with the main controller; selecting reference time from the obtained candidate time according to the priority of each candidate clock source; correcting the reference time according to a current timing format corresponding to the reference time, a local timing format of the local clock source and a timing overflow mechanism of the local clock source to obtain target time; and updating the local time of the local clock source by adopting the target time.

The invention discloses a Bluetooth earphone with a scene recognition function. The Bluetooth earphone comprises a sensing acquisition module, an analysis module and an execution module, according to the method, the extracted data is subjected to targeted analysis in four data analysis modes for each sensing mode, and the corresponding use scene is matched according to the feature parameters obtained by data analysis; the problem that an existing Bluetooth headset scene recognition technology is insufficient in recognition precision is solved. Through the setting of the first sensing mode, the second sensing mode and the third sensing mode, the Bluetooth earphone is connected with the electronic equipment, analysis data is obtained, and the data source is expanded. The problem that in scene recognition calculation, the Bluetooth headset can obtain limited data used for scene recognition is solved; through the first modification window and the second modification window, a user is allowed to customize and modify a scene recognition result and a matched adjustment function according to requirements. The misjudgment of the Bluetooth earphone in scene recognition is avoided, and the user experience is optimized.

The invention discloses a content recommendation method and device, equipment, a medium and a program product, and relates to the field of content recommendation, and the method comprises the following steps: receiving equipment connection information uploaded by first equipment, the equipment connection information being information which is collected by the first equipment and is related to second equipment when the second equipment is connected with the first equipment, the first device and the second device are connected through a short-distance wireless transmission connection mode, and a first account is logged in the first device; first content is obtained based on the device connection information, the first content is to-be-recommended content, and the first content comprises elements associated with the device connection information; and pushing the first content to a third device logged in with the first account, wherein the third device is the same as or different from the first device. Recommended contents can be determined based on equipment connection information, and the content recommendation effect is improved.

This application relates to the field of vehicle technology, and in particular to a time processing method, apparatus, computer device, and computer-readable storage medium. The method includes: upon detecting a time update event for a local clock source corresponding to a main controller, obtaining corresponding candidate times from candidate clock sources other than the local clock source; wherein each candidate clock source includes the system clock source of the target vehicle system carrying the main controller, and the external clock source of an external device communicating with the main controller; selecting a reference time from the obtained candidate times according to the priority of each candidate clock source; correcting the reference time according to the current timing format corresponding to the reference time, the local timing format of the local clock source, and the timing overflow mechanism of the local clock source to obtain a target time; and updating the local time of the local clock source using the target time.

The invention is suitable for the technical field of data sharing, and particularly relates to a multi-source heterogeneous data sharing method and system for a smart city, and the method comprises the steps: S100: determining the sources of multi-source heterogeneous data in the smart city, configuring the relationship between the sources, defining standard phrases, integrating the standard phrases and the relationship, and constructing a standard map, the method comprises the following steps: acquiring a source, positioning an internet-of-things device at the source, constructing a data pipeline, connecting one end of the data pipeline with the internet-of-things device, connecting the other end of the data pipeline with a standard map, acquiring a real-time data stream in the data pipeline, correcting the real-time data stream by using the standard map, creating a plurality of blocks in one-to-one correspondence with the source, and storing the plurality of blocks in the data pipeline. And the corrected real-time data stream is transferred into the data block. According to the method, the multi-source heterogeneous data can be graded by splitting the data pipeline, so that the data sharing effect is guaranteed, the data security is greatly enhanced, and the data leakage risk is reduced.

The invention relates to an enterprise loan willingness assessment method based on multi-dimensional public data. The method comprises the following steps: collecting multi-channel public data; performing fusion processing on the multi-channel public data to obtain sample data; screening out multi-dimensional feature data from the sample data; training a machine learning model by using the multi-dimensional feature data, performing tuning work, and establishing a loan willingness evaluation model; and inputting the multi-dimensional features of the to-be-evaluated enterprise into the loan willingness evaluation model, and outputting to obtain a loan willingness scoring result. Compared with the prior art, the enterprise loan willingness evaluation method has the advantages that the multi-dimensional enterprise loan willingness evaluation is performed by fusing the multi-source public data and utilizing the machine learning model, so that the accuracy of the evaluation result can be improved, and the purpose of comprehensively and objectively evaluating the enterprise loan willingness is achieved.

This disclosure relates to the field of model training technology, and in particular to a model training method and system. It includes: constructing a cold-start dataset for the security field, which contains multimodal security scenario data, target scenario questions, and corresponding text responses; secondly, adjusting the parameters of a basic multimodal model based on this cold-start dataset to obtain an adjusted model with preliminary security recognition capabilities; then, acquiring difficult samples, including failure samples, prediction bias samples, and adversarial generation samples selected from the test set; finally, strengthening the adjusted model based on these difficult samples, ultimately obtaining a high-performance, robust multimodal model for the security field. This solves the technical problem of poor model performance in the security field due to insufficient training data.

The application discloses a heterogeneous data fusion device, method and medium for AI large model pre-training, and relates to the technical field of artificial intelligence. The heterogeneous data fusion device comprises a collection module, a processing module and an output module, the processing module comprises an identification and analysis unit and an alignment and evaluation unit, the output module comprises a filling unit and an index unit; the identification and analysis unit is used for identifying and analyzing heterogeneous data of different modalities, obtaining each standard data object, the alignment and evaluation unit is used for unifying semantic vectors of each standard data object and calculating quality scores of pairs of multi-modal data; the filling unit is used for filling the pairs of multi-modal data to corresponding slots of a semantic driving template, and attaching labels and quality scores to target samples, and the index unit is used for encapsulating the target samples into a standard data structure and constructing a multi-dimensional index of the target samples. The device can unify standards, accurately fuse multi-modal data, and improve processing efficiency and the quality of pre-training data.