Ping an Technology (shenzhen) Co., Ltd.

Tianbo Wu

Jing Jing Xiao

Jianzong Wang

Zhangcheng Huang

Jin Ma

Lihong Liu

Xinhui Liu

The disclosure relates to a data source-based service customizing device, method and system, and a computer readable storage medium. The data source-based service customizing device includes: a memory, a processor and the data source-based service customizing system stored on the memory and operated on the processor. The data source-based service customizing system is executed by the processor to implement the following steps: acquiring user generated contents in various predetermined data sources; recognizing the user generated contents by using a user group label recognition model generated by pre-training to recognize user group labels corresponding to the various data sources; determining group services corresponding to the various data sources according to a predetermined mapping relation between the user group labels and the group services, and sending the various data sources and the corresponding group services to a predetermined terminal to perform group service customization on the various data sources.

Data source-based service customizing device, method and system, and storage medium

A method for topic early warning includes: acquiring a self-defined keyword; calculating similarity between the self-defined keyword and each word in a corpus, and acquiring extended keywords related to the self-defined keyword from the corpus according to the similarity; selecting a target keyword from the extended keywords according to a type of the extended keywords and similarity between the extended keywords and the self-defined keyword, and adding the target keyword to a target keyword list; performing real-time monitoring according to the target keyword in the target keyword list; and performing topic early warning when it is monitored that the number of topics corresponding to the target keyword reaches a preset threshold.

Topic monitoring for early warning with extended keyword similarity

A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.

Deep learning based license plate identification method, device, equipment, and storage medium

The present disclosure publishes a vulnerable plaque identification method. The method includes: receiving an angiocarpy image sent by a terminal device; transforming the angiocarpy image in an original Cartesian coordinate system into a polar coordinate system to form a polarization image; constructing a faster RCNN architecture and accomplishing a training; inputting the polarization image into the faster RCNN architecture accomplished the training to identify the polarization image, and outputting the image with the marked vulnerable plaques; feed backing the image with the marked vulnerable plaques to the terminal device. The present disclosure also publishes an application server and a computer readable medium. The vulnerable plaque identification method, the application server, and the computer readable medium can quickly and correctly conform the position of the vulnerable plaque.

Vulnerable plaque identification method, application server thereof, and computer readable medium

A method of matching cross-platform data, comprising: receiving a data matching request sent by a terminal; obtaining a group behavior data corresponding to the first user group in the first social network platform, and learning the group behavior data to obtain a group feature distribution function; obtaining associated users of the designated root node users and corresponding behavior data in the second social network platform; learning the behavior data of the root node users, and generating the group feature distribution function after matching the root node users; performing the behavior learning to the behavior data of the associated users; calculating a maximum entropy value of the group feature distribution function after matching the associated users, and determining the associated users corresponding to the largest maximum entropy value as the matching users of the first user group; and regarding the determined matching users as current root node users, determining a next matching user until the determined matching users meet a set quantity condition, and completing a group matching.

Cross-platform data matching method and apparatus, computer device and storage medium

Disclosed is a method for identifying a user interest, including: obtaining training samples and test samples, the training samples being obtained by manually labeling after the corresponding topic models have been trained based on text data; extracting characteristics of the training samples and of the test samples, and computing optimal model parameters of a logistic regression model by an iterative algorithm based on the characteristics of the training samples; evaluating the logistic regression model based on the characteristics of the test samples and an area AUC under an ROC curve to train and obtain a first theme classifier; determining a theme to which the text data belongs using the first theme classifier, computing a score of the theme to which the text data belongs based on the logistic regression model, and computing a confidence score of the user being interested in the theme according to a second preset algorithm. Further disclosed are a device for identifying a user interest and a computer-readable storage medium.

Method and device for identifying a user interest, and computer-readable storage medium

A method for data distribution includes: receiving a request for data distribution sent from a terminal; searching for raw association network data, wherein the raw association network data includes a set of nodes and a set of edges existing between the nodes; searching for a corresponding user grade according to a user identifier carried in the request for data distribution, and determining a privacy budget parameter corresponding to the user grade; determining the distributing probability distribution corresponding to the raw association network data to be distributed according to a pre-constructed Laplace Model and the determined privacy budget parameter; selecting an arbitrary value within the distributing probability distribution as a distributing probability for each network edge of the set of edges, generating a random number between 0 to 1 for each network edge, comparing the random number to the distributing probability, and distributing the corresponding network edge when the distributing probability is greater than the random number.

Method, server and storage medium for data distribution

A method of searching a group based on a social network includes: receiving a group search request transmitted by a query terminal, wherein the group search request contains a specified query user identification (ID), a group scale and a group coritivity; retrieving a pre-generated social network graph; using the user node corresponding to the query user ID as a starting node to perform search expansion according to the social network graph, and determining a user node having the maximum number of adjacent nodes in each search expansion layer as a group member until the number of determined group members equals to the group scale in the social network graph; a first level of the expansion layer includes the user node, which is an adjacent node of the query user, and the user node included in a next level of expansion layer is an adjacent node of the group member determined by the upper level of the expansion layer; and determining that a group coritivity of the group to be searched is not less than the specified group coritivity, and returning the group to be searched as a query result to the query terminal for displaying.

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Shenzhen, China

Tianbo Wu