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.

Abstract: A POI valuation method, apparatus, device and computer storage medium are disclosed. The method comprises: obtaining information of first POIs with known values and information of second POIs with unknown values within a regional range; creating a valuation model which is configured to revaluate a first POI using values of surrounding POIs of the first POI, the surrounding POIs including other first POIs and second POIs within a predetermined range of distance from the first POI, and adjusting values of second POIs in the surrounding POIs using an error between a revaluated value of first POI and the known value of the first POI; training the valuation model until the error is minimized; obtaining the values of the second POIs from the valuation model. The solutions may reduce the requirement for manpower and improve the valuation efficiency as compared with manually valuation of POIs one by one.

Abstract: Provided is a house-renting recommendation method. The method includes: receiving a destination and a house-renting requirement input by a current user; where the house-renting requirement includes at least a commuting time and a house-renting expense; inputting, into a pre-trained recommendation model, the destination and the house-renting requirement input by the current user; outputting, through the recommendation model, a search result for the current user based on a pre-constructed house-renting knowledge graph; and recommending, in response to the search result for the current user comprising information about at least one candidate housing resource, the information about the at least one candidate housing resource to the current user.

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: Methods for predicting an economic state and establishing an economic state prediction 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 N historical time frames before a to-be-predicted future time frame respectively for a to-be-predicted region, the N being a positive integer; and inputting feature vectors of the geographic location point active data in the N historical time frames before the to-be-predicted time frame into a pre-trained economic state prediction model, to obtain economic indicator data of the to-be-predicted region in the to-be-predicted time frame. An economic state of the to-be-predicted region in the to-be-predicted time frame can be predicted, thus providing a reference for policy making in advance.

Abstract: A POI valuation method, apparatus, device and computer storage medium are disclosed. The method comprises: obtaining information of first POIs with known values and information of second POIs with unknown values within a regional range; creating a valuation model which is configured to revaluate a first POI using values of surrounding POIs of the first POI, the surrounding POIs including other first POIs and second POIs within a predetermined range of distance from the first POI, and adjusting values of second POIs in the surrounding POIs using an error between a revaluated value of first POI and the known value of the first POI; training the valuation model until the error is minimized; obtaining the values of the second POIs from the valuation model. The solutions may reduce the requirement for manpower and improve the valuation efficiency as compared with manually valuation of POIs one by one.