Abstract: A method of predicting traffic volume, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence technology, in particular to big data and deep learning technologies The method includes: generating, for a plurality of traffic regions, a function relation graph and a volume relation graph; generating a volume feature of a target traffic region among the plurality of traffic regions, according to a historical volume information of the target traffic region; generating a volume and function relation feature for the target traffic region, based on the function relation graph and the volume relation graph; and predicting a volume of the target traffic region according to the volume feature and the volume and function relation feature.

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: This disclosure discloses a resident area prediction method, apparatus, device and storage medium, involving artificial intelligence technology, big data, deep learning and multi-task learning. The specific implementation plan is: acquiring a resident area data of a target user, and the resident area data including the resident area of the target user and the corresponding resident time; obtaining an association relationship between the resident areas of the target user by inputting the resident area data into an area relationship model, and the area relationship model is used to reflect a position relationship between the areas; determining a time-sequence relationship between the areas visited by the target user, according to the association relationship, the resident time and the visiting POI data; predicting a target resident area of the target user, according to the time-sequence relationship and the basic attribute information of the target user.

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: The present disclosure discloses a method, an apparatus, a device, and a storage medium for determining a point of interest area, and relates to the field of automatic driving. The implementation solution is that the method is applied to an electronic device, and includes: receiving a point of interest area determination request input by a first user, the point of interest area determination request including a target area coverage; and acquiring grid data of at least one block within the target area coverage in response to the point of interest area determination request; acquiring, for each block, positioning data of a second user within each preset time period and number of parent points of interest; clustering corresponding grid data according to the positioning data, the grid data and the number of the parent points of interest; determining at least one POI area in each block according to a clustering result.

Abstract: A method, device, and medium for recommending a region of interest are provided. The method includes: acquiring access data, the access data including correlation information between any two regions in a region group, in which a correlation between any two regions in the region group is acquired based on a region pair formed by the any two regions in the region group where sample users are located and the number of access times corresponding to the region pair, and in which the region group is acquired based on division of map data, and the map data includes boundary information of an entity in a real world; determining a region where a first user is currently located; and recommending region information of the region of interest for the first user based on the access data and the region where the first user is currently located.

Abstract: A method of determining a regional land usage property, an electronic device and a storage medium, which relate to a field of an information technology, in particular to a field of a deep learning. The method includes: acquiring a human interaction information between a plurality of regions at a specified time; updating an initial representation vector of each of the regions according to the human interaction information, so as to obtain an embedding representation vector of each of the regions; selecting a target region from the regions, and selecting a plurality of static neighbor regions within a preset range around the target region; generating a feature map of the target region according to the embedding representation vector of the target region and the embedding representation vectors of the plurality of static neighbor regions; and predicting a land usage property of the target region by using the feature map.

Abstract: A method of predicting traffic volume, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence technology, in particular to big data and deep learning technologies The method includes: generating, for a plurality of traffic regions, a function relation graph and a volume relation graph; generating a volume feature of a target traffic region among the plurality of traffic regions, according to a historical volume information of the target traffic region; generating a volume and function relation feature for the target traffic region, based on the function relation graph and the volume relation graph; and predicting a volume of the target traffic region according to the volume feature and the volume and function relation feature.

Abstract: A method and apparatus for generating a table description text, a device, and a storage medium are provided. An implementation of the method includes: acquiring a to-be-described table, and analyzing the to-be-described table to obtain a set of metalanguage of the to-be-described table, and finally generating a description text of the to-be-described table based on the metalanguage in the set of metalanguage.

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 land usage property identification method, apparatus, electronic device and storage medium are disclosed. The method includes: acquiring point-of-interest (POI) data and area-of-interest (AOI) data; dividing a target area to be identified according to road network information, and obtaining at least one block in the target area; associating acquired POI data to a corresponding target block in the at least one block; obtaining a first weight set corresponding to a corresponding category of each POI data in the target block; obtaining a second weight set corresponding to a corresponding area of each AOI data in the target block; obtaining a land usage property weight set according to the first weight set, the second weight set and a preset land usage classification standard; and identifying a land usage property of the target block according to a target weight in the land usage property weight set.

Abstract: A traffic pattern prediction method, device, server, system, and a computer-readable medium are provided.

Abstract: A method, device, and medium for recommending a region of interest are provided. The method includes: acquiring access data, the access data including correlation information between any two regions in a region group, in which a correlation between any two regions in the region group is acquired based on a region pair formed by the any two regions in the region group where sample users are located and the number of access times corresponding to the region pair, and in which the region group is acquired based on division of map data, and the map data includes boundary information of an entity in a real world; determining a region where a first user is currently located; and recommending region information of the region of interest for the first user based on the access data and the region where the first user is currently located.

Abstract: The present application proposes a quantitative analysis method for a user decision-making behavior and an apparatus, which relate to the fields of big data calculation and artificial intelligence in computer technology. At least one quantified decision factor related to making a target decision by a user is inputted into a machine learning model; the machine learning model further analyzes the decision factor; and finally a prediction result of making the target decision by the user is determined according to an output of the machine learning model. Therefore, it is possible to analyze the decision factor for making the target decision by the user to obtain the prediction result of making the target decision, thus enriching analysis needs for the user decision-making behavior.

Abstract: This disclosure discloses a resident area prediction method, apparatus, device and storage medium, involving artificial intelligence technology, big data, deep learning and multi-task learning. The specific implementation plan is: acquiring a resident area data of a target user, and the resident area data including the resident area of the target user and the corresponding resident time; obtaining an association relationship between the resident areas of the target user by inputting the resident area data into an area relationship model, and the area relationship model is used to reflect a position relationship between the areas; determining a time-sequence relationship between the areas visited by the target user, according to the association relationship, the resident time and the visiting POI data; predicting a target resident area of the target user, according to the time-sequence relationship and the basic attribute information of the target user.

Abstract: The present disclosure discloses a method, an apparatus, a device, and a storage medium for determining a point of interest area, and relates to the field of automatic driving. The implementation solution is that the method is applied to an electronic device, and includes: receiving a point of interest area determination request input by a first user, the point of interest area determination request including a target area coverage; and acquiring grid data of at least one block within the target area coverage in response to the point of interest area determination request; acquiring, for each block, positioning data of a second user within each preset time period and number of parent points of interest; clustering corresponding grid data according to the positioning data, the grid data and the number of the parent points of interest; determining at least one POI area in each block according to a clustering result.