Abstract: A method and apparatus for semanticization is provided. The method includes: ascertaining a target coordinate of a to-be-semanticized location; ascertaining, through a pre-built regional spatial index tree, a target region to which the target coordinate of the to-be-semanticized location belongs; and ascertaining semantic information of the to-be-semanticized location based on semantic information of the target region.

Abstract: The present disclosure provides a region information processing method and apparatus, and relates to the field of artificial intelligence in computer technologies. The specific implementation is: acquiring a first distance between a first region and a second region, a first object set included in the first region, and a second object set included in the second region; determining spatial dependency information between the first region and the second region according to the first distance; determining object dependency information between the first region and the second region according to the first object set and the second object set; and determining a symbiosis between the first region and the second region according to the spatial dependency information and the object dependency information.

Abstract: A method of data interaction, a data interaction apparatus, an electronic device and a non-transitory computer readable storage medium are provided, related to field of computer technologies, and in particular to the field of artificial intelligence technologies. When the method of data interaction is applied to the first data platform, the method includes: sending a data request to a second data platform based on a first resource server provided by a cloud or based on a local server; acquiring response data fed back by the second data platform based on a second resource server provided by the cloud; at least one of the first resource server and the second resource server is dynamically created by the cloud.

Abstract: Embodiments of the present disclosure provide a method, an apparatus, a device, a medium and a product for configuring a color, relates to the field of computer technology, and particularly to the data visualization technology. A specific implementation comprises: acquiring a set of chart entities in a chart; determining target color information corresponding to the chart entities in the set of the chart entities based on a preset target function and a constraint condition; and configuring colors corresponding to the chart entities in the set of the chart entities based on the target color information.

Abstract: A method for determining correlation between a drug and a target, and an electronic device are provided. The method includes: establishing a spatial molecular graph of a candidate drug and the target, the spatial molecular graph including an atomic node set and an edge set, the atomic node set including atoms in the candidate drug and atoms in the target, the edge set including at least one atom connection edge; inputting a first atom feature of the atomic node set and the spatial molecular graph into a first GAT for prediction, to obtain a second atom feature of the atomic node set; and determining a parameter value of the correlation between the candidate drug and the target in accordance with the second atom feature of the atomic node set.

Abstract: A method of training a prediction model for determining molecular binding force is provided, which relates to the field of artificial intelligence, in particular to a graph neural network in the field of deep learning. The method includes: constructing a virtual complex molecule based on a three-dimensional structure information of a first molecule and a second molecule; determining a predicted binding force and a predicted interaction matrix between the first molecule and the second molecule based on the virtual complex molecule by using the prediction model, the predicted interaction matrix indicating an element-type-based and distance-based interaction between an atom in the first molecule and an atom in the second molecule; and training the prediction model by minimizing a target loss function based on a difference between the predicted binding force and a real binding force and a difference between the predicted interaction matrix and a real interaction matrix.

Abstract: A training method for a point-of-interest recommendation model and a method for recommending a point of interest are provided.

Abstract: Provided are a training method for an air quality prediction model, a prediction method and apparatus, a device, a program, and a medium. The method includes the steps described below. A target monitoring range is divided into a plurality of regions; the air quality prediction model is pre-trained by adopting a pre-training sample and a pre-training objective function, where the pre-training sample includes measurement values; and the pre-trained air quality prediction model is trained by adopting a formal training sample and a formal training objective function, where the formal training sample includes the measurement values. The air quality prediction model is configured to predict air quality of the plurality of regions according to spatial information, historical information and environmental information.

Abstract: Provided are a training method and device for a heterogeneous generative adversarial network model, an equipment, a program and a storage medium. In the training method, measurement data of a heterogeneous station is acquired, the measurement data of the heterogeneous station is set as a training sample, and joint training is performed on the heterogeneous generative adversarial network model according to a total objective function. A generator is configured to predict environment data at a future occasion according to environment data of the heterogeneous station at a historical occasion so as to output predicted data. A discriminator is configured to be input the predicted data output by the generator and corresponding measurement data, and discriminate a similarity between the measurement data and the predicted data; a total objective function includes a first objective function of the generator and a second objective function of the discriminator.

Abstract: A method for recommending a station for a vehicle, a device, and a storage medium are provided. The method comprises: receiving, by a server, an access request from a vehicle; obtaining, based on the access request, a plurality of observation values from a plurality of stations associated with the vehicle, respectively, each observation value is based on a corresponding pre-trained recommendation model, each observation value includes factors associated with access of the vehicle to the station corresponding to the observation value; determining, an action value for the station based on the observation value and the pre-trained recommendation model for the station, the action value for the station indicates a matching degree between the access request and the station; determining a recommended station among the plurality of stations based on the action values of the plurality of stations; and sending to the vehicle an instruction of driving to the recommended station.

Abstract: A method for evaluating satisfaction with voice interaction, a device, and a storage medium are provided, which are related to a technical field of artificial intelligence, in particular, to fields of natural language processing, knowledge graph and deep learning, and can be applied to user intention understanding. The specific implementation includes: acquiring sample interaction data of a plurality of rounds of sample voice interaction behaviors; performing feature extractions on respective sample interaction data, to obtain a sample interaction feature sequence; acquiring satisfaction marks corresponding to the respective sample interaction data, to obtain a satisfaction mark sequence; and training an initial model by using a plurality of sets of sample interaction feature sequences and of satisfaction mark sequences, to obtain the model for evaluating satisfaction.

Abstract: A method for generating an electronic report, an electronic device and a storage medium, related to the field of large data and the field of artificial intelligence, are disclosed. The method for generating an electronic report includes: establishing a template tree comprising a plurality of branches, wherein the branches comprise at least one intermediate node and bottom layer nodes comprising identification information; and calling, for respective branches, data groups corresponding to the identification information of the bottom layer nodes from a database, respectively, and displaying the called data groups at positions corresponding to the bottom layer nodes in an electronic report. Labor consumption may be reduced, and advantages of low cost, high efficiency, automation and routinization may be achieved.

Abstract: A method and an apparatus for predicting an epidemic situation, a device, a storage medium, and a program product are provided. The method may include: estimating a variable parameter of the epidemic situation in a preset area using a Markov Chain Monte Carlo (MCMC) method; acquiring a constant parameter of the epidemic situation in the preset area; constructing a transmission model based on the variable parameter and the constant parameter; and fitting, using the transmission model, to predict epidemic information of the preset area.

Abstract: Provided are an intelligent recommendation method and apparatus, a model training method and apparatus, an electronic device, and a storage medium, which relate to artificial intelligence technologies, and are applicable to the intelligent recommendation and the intelligent transportation technologies. The intelligent recommendation method includes: determining an object recommendation request; determining, according to a multi-agent strategy model and the object recommendation request, object execution actions of at least two agent objects matching the object recommendation request; determining a target object execution action according to the object execution actions; and recommending the object recommendation request to a target agent object corresponding to the target object execution action.

Abstract: A weather parameter prediction model training method, a weather parameter prediction method, an electronic device and a storage medium are provided, and relate to the technical field of artificial intelligence, such as deep learning and big data. The method includes: establishing a weather parameter prediction model according to spatial correlation information among a plurality of monitoring stations; and adjusting the weather parameter prediction model according to observation values of a weather parameter for the plurality of monitoring stations and prediction values of the weather parameter for the plurality of monitoring stations output by the weather parameter prediction model. The present disclosure can improve an accuracy of predicting weather parameters.

Abstract: Methods for monitoring an economic state and establishing an economic state monitoring model and corresponding apparatuses, and relates to the technical field of big data are disclosed. A specific implementation solution is: acquiring, from map application data, geographic location point active data in a to-be-monitored future time frame and N historical time frames before the to-be-monitored time frame respectively for a to-be-monitored region, the N being a positive integer; and inputting feature vectors of the geographic location point active data in the to-be-monitored time frame and the N historical time frames before the to-be-monitored time frame into a pre-trained economic state monitoring model, to obtain economic indicator data of the to-be-monitored region in the to-be-monitored time frame. An economic state of the to-be-monitored region in the to-be-monitored time frame in real time can be monitored, thus timely providing a reference for policy making.

Abstract: A method for information completion, an electronic device and a storage medium, related to the fields of artificial intelligence, big data, deep learning and the like, are provided. The method includes: acquiring an actual information form and an initialization information form, wherein the actual information form includes an information form which is filled out by a plurality of users and in which target information is missing, and the initialization information form is an information form in which there is target information at each target information position; performing an adjustment on the initialization information form by utilizing a similarity between the users, a low-rank constraint of the initialization information form and a difference between the initialization information form and the actual information form, to obtain an adjusted information form; and supplementing target information in the adjusted information form to a position, in the actual information form.

Abstract: A method for generating an interpretation text, an electronic device and a storage medium are provided, and relate to the technical field of artificial intelligence, natural language processing, big data and the like. The method includes: determining a target variable required for generating an interpretation text of a target chart, according to a text generation instruction; acquiring a first variable corresponding to the target variable from a node of at least one layer of a tree structure according to the target variable, wherein categories of variables in nodes of each layer of the tree structure are different, and a variable in one upper-layered node is at least associated with a variable in one lower-layered node; and generating an interpretation text of the target chart according to the first variable and the text generation instruction.

Abstract: A data management method, apparatus, a computing device, a storage medium, and a cloud platform are provided. The data management method includes: obtaining a task request, the task request indicating to retrieve stored data to execute a task; updating execution information for the data, the execution information for the data describing one or more tasks that need retrieval of the data and the execution frequency of each of the tasks; calculating, based on the updated execution information for the data and for each of a plurality of electronic storage locations, a storage-location-specific cost value of the data; and determining a target electronic storage location of the data according to the calculated cost value.

Abstract: The present application provides a model training, image processing method, device, storage medium, and program product relating to deep learning technology, which are able to screen auxiliary image data with image data for learning a target task, and further fuse the target image data and the auxiliary image data, so as to train a built and to-be-trained model with the fusion-processed fused image data. This implementation can increase the amount of data for training the model, and the data for training the model is determined is based on the target image data, which is suitable for learning the target task. Therefore, the solution provided by the present application can train an accurate target model even if the amount of target image data is not sufficient.