Abstract: A method is provided that includes: obtaining first urban data of a first sample urban region; inputting the first urban data into a multi-modal foundation model to obtain respective predicted vector representations of a plurality of first data segments; obtaining a plurality of general-purpose foundation models that are pre-trained; for each general-purpose foundation model: generating a vector representation label of a first data segment of a corresponding data modality by using the general-purpose foundation model; and determining a knowledge distillation loss of the general-purpose foundation model based on the vector representation label and a predicted vector representation of the first data segment; and adjusting parameters of the multi-modal foundation model based on at least respective knowledge distillation losses of the plurality of general-purpose foundation models.

Abstract: A method of determining meteorological information, an electronic device and a storage medium are provided, which relate to a field of artificial intelligence technology, and in particular to fields of deep learning and large models. The method includes performing a feature extraction on meteorological raster data of a target region within a target time period to obtain a meteorological feature vector; inputting to-be-processed meteorological data of the target region within the target time period into a large language model to obtain a text summary including a meteorological information determination manner; performing an information enhancement processing on the meteorological feature vector by using the text summary to obtain an information enhancement result; and performing a self-attention processing on the information enhancement result to obtain a meteorological information determination result output for the to-be-processed meteorological data.

Abstract: Data query method and apparatus based on large model, an electronic device, and a storage medium are disclosed, which relates to the field of artificial intelligence, specifically in natural language processing, deep learning, and large model technologies, applicable to scenarios such as dialogue systems and information retrieval. The method includes: performing entity recognition on a query to obtain the target entity in the query; obtaining a first related content associated with the target entity from internal information, and performing data analysis on the first related content using a large language model (LLM) to obtain a data analysis result; obtaining a second related content associated with the target entity from external information, and performing data generation on the second related content using the LLM to obtain a data generation result; obtaining a query result corresponding to the query based on the data analysis result and the data generation result.

Abstract: A drug reaction prediction method, which is related to the field of artificial intelligence, specifically involving deep learning, computational biology, and chemistry, is disclosed. The drug reaction prediction method includes: obtaining a target graph based on multiple levels of entities contained in a drug to be predicted; the target graph includes an entity graph representing topological information within the entities and an interaction graph representing correlation information between the entities; performing representation extraction processing on the target graph to obtain an initial representation; obtaining a target representation based on a predetermined prompt identifier and the initial representation; and obtaining a drug reaction prediction result for the drug to be predicted based on the target representation.

Abstract: Provided is a source tracing method for traffic congestion, an electronic device and a storage medium, relating to the field of smart transportation, traffic management, traffic information processing and other technologies.

Abstract: This disclosure provides a data generation method, model training method, electronic device, and medium. The data generation method includes: obtaining urban graph data, the urban graph data including a node set, an edge set and a feature set, wherein the node set includes a central node corresponding to a predetermined urban entity, the edge set includes a neighborhood corresponding to the central node, the neighborhood includes other nodes in the node set connected to the central node via an edge, and the feature set includes features of nodes in the node set; partitioning a target region into at least two sub-regions to obtain a region partition set; obtaining a regional feature of each sub-region by aggregating features corresponding to all nodes in the sub-region; and updating a feature of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data.

Abstract: A method is provided. The method includes: obtaining an object relationship diagram; for a target object of a plurality of first objects, obtaining at least one meta-path corresponding to the target object in the object relationship diagram; for each meta-path, performing the following operations: determining a plurality of first attention weights of the target object based on inherent attribute data of the target object and inherent attribute data of each of a plurality of second objects on the meta-path; obtaining a second representation vector of the target object based on a first representation vector of the target object and the plurality of first attention weights; and obtaining a target indicator prediction result of the target object based at least on at least one second representation vector of the target object corresponding to the at least one meta-path.

Abstract: A data updating method, a model training method and related devices are provided. The method includes obtaining urban graph data in a preset region, the urban graph data including a node set including central nodes, an edge set and a feature set, the edge set including neighborhoods corresponding to the central nodes, the neighborhoods including other nodes possessing connecting edges with the central nodes, the neighborhoods corresponding to a target region, and the feature set including node features of the nodes in the node set; partitioning the target region into at least two sub-regions to obtain a region partition set; aggregating the node features corresponding to all nodes located within the same sub-region to obtain the regional features of each of the sub-regions; updating the node features of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data.

Abstract: An entity recognition method, a model training method, an electronic device, and a medium, which relate to fields of artificial intelligence, information acquiring technologies. The entity recognition method includes: extracting specified entities from a text in a source file of a webpage to be recognized, and acquiring a text encoding result for each specified entity; determining a text block formed by each specified entity in the webpage, and encoding a relative layout information between each two text blocks, to obtain a position encoding result; constructing a triple by the position encoding result for each two text blocks and the text encoding results for respective specified entities of the two text blocks; and performing a graph convolution on each triple to obtain a relation recognition result for the webpage to be recognized, where the relation recognition result indicates whether an association exists between each two text blocks in the webpage.

Abstract: The present application relates to a field of big data technology, in particular to a field of data storage technology. More specifically, the present disclosure relates to a method of importing data to a database, an electronic device, and a storage medium. A specific implementation solution is: acquiring incoming data from a data source according to a database config file; the incoming data is original data directly acquired from the data source; calculating and processing the incoming data according to the database config file to obtain computational data; the computational data is obtained by integrating and calculating the incoming data; writing the incoming data and the computational data into a database.

Abstract: The present disclosure provides a method for optimizing a tag of a point of interest s(POI). The method includes: obtaining first portrait feature data of each POI in a plurality of POIs and second portrait feature data of each tag in a plurality of marked tags corresponding to the plurality of POIs; mapping the first portrait feature data of each POI and the second portrait feature data of each tag to a metric space to obtain a first feature vector of each POI and a second feature vector of each tag; and optimizing at least one marked tag corresponding to a target POI based on a vector similarity between a first feature vector of the target POI and a second feature vector of at least one tag. The present disclosure provides an apparatus for optimizing a tag of a POI, an electronic device and a computer readable medium.

Abstract: A fermentation process for preparing a postbiotic with a by-product of wheat processing includes: preparation of a material; inoculation of Paenibacillus polymyxa (P. polymyxa)+Brachybacterium paraconglomeratum (B. paraconglomeratum)+Flavobacterium pectinovorum (F. pectinovorum) as fermentation strains; and isolation and extraction of the postbiotic. Since wheat starch wastewater and wheat bran include abundant nutrient components, the wheat starch wastewater and wheat bran can be used to prepare postbiotics, which is conducive to reducing a wastewater treatment cost of a related enterprise and protecting the environment, and can turn waste into treasure and increase an added value of an agricultural product.

Abstract: An online ride-hailing information processing method and apparatus, a device, and a computer storage medium, relating to big data computing and deep learning technologies in the field of AI technologies, are disclosed.

Abstract: A method for training a click rate prediction model includes: obtaining sample feature information and a label value, in which the sample feature information includes feature information of a sample user and feature information of a target object, and the label value is configured to indicate whether the sample user interacts with the target object; obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module; obtaining a click rate prediction value of the sample user on the target object using the prediction module, according to the sample feature information and the plurality of adjacent matrixes; and training the click rate prediction model according to the label value and the click rate prediction value.

Abstract: A method for acquiring point of interest (POI) representation information, includes: acquiring a POI relationship graph, and acquiring a node relationship set of each node on the POI relationship graph based on the POI relationship graph, in which the POI relationship graph includes edges representing relationships between nodes of POIs, and the node relationship set includes relationship paths where the node reaches all second-order neighbor nodes via any one first-order neighbor node; acquiring a spatial distance matrix between the node and corresponding second-order neighbor nodes; and determining first POI representation information of the node based on the node relationship set of the node and the spatial distance matrix.

Abstract: A method for controlling a traffic light, a method and apparatus for navigating an unmanned vehicle and a method and apparatus for training a model are provided. An implementation comprises: generating a reinforced traffic light state parameter according to vehicle state representation information of an unmanned vehicle currently contained in a preset area of a target traffic light and a current traffic light state parameter of the target traffic light; and generating a traffic light control action according to the reinforced traffic light state parameter; where the reinforced traffic light state parameter is used to cause an unmanned vehicle navigation end to generate a reinforced vehicle state parameter according to a reinforced traffic light state and a current vehicle state parameter of a target unmanned vehicle, and generate an unmanned vehicle navigation action according to the reinforced vehicle state parameter.

Abstract: A method for determining a competitive relation of Points of Interest (POI), and a device are provided in the present disclosure. The specific implementation includes: determining POI representation data between two target POIs based on service-related data of the target POIs; and determining a competitive relation between the target POIs based on the POI representation data.

Abstract: A method and apparatus for mining a competition relationship between POIs. An embodiment of the method includes: acquiring a graphlet mining result obtained by mining map retrieval data of users which encompasses attribute information of retrieved target POIs, the graphlet mining result encompassing occurrence frequencies of respective preset situations, and a preset situation comprising: conforming to attribute information of POIs represented by a corresponding preset graphlet and a preset association relationship between attribute information of at least two POIs; for a first and second POI, determining an occurrence frequency of a preset situation corresponding to a preset graphlet where attribute information of the first and second POI co-occur, and generating a relationship feature of the first and second POI; and inputting the relationship feature into a pre-trained relationship prediction model to obtain a competition relationship prediction result of the first POI and the second POI.

Abstract: A method is provided.

Abstract: A technical solution relates to a big data technology in the field of artificial intelligence technologies. The technical solution includes: acquiring training data including annotation results of a risk grade of each sample region and a risk grade of a district to which each sample region belongs; and training an initial model including an encoder, a discriminator and a classifier using the training data, and obtaining the risk prediction model using the encoder and the classifier after the training process. The encoder performs a coding operation using region features of the sample regions to obtains a feature representation of each sample region; the discriminator identifies the risk grade of the district to which the sample region belongs according to the feature representation of the sample region; the classifier identifies the risk grade of the sample region according to the feature representation of the sample region.