Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A claim settlement anti-fraud method, an apparatus, a computer device, and a storage medium are provided. The method includes generating a sub-graph of doctor and patient, a sub-graph of doctor and medical advice, and a fused large graph according to medical data. A patient relationship network with several community close loops is generated by mapping the sub-graph of doctor and patient according to the fuses large graph. A similarity between any two vertexes in the patient relationship network are computed. An average similarity of each community close loop is computed. The insurance fraud actions are confirmed based on the average similarity.

Abstract: 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.

Abstract: 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.

Abstract: 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 det

Abstract: 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.

Abstract: 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.

Abstract: 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 probabilit

Abstract: 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.

Abstract: Provided is a method for training a topic classifier: obtaining a training sample and a test sample, wherein the training sample is obtained by manually labeling after a corresponding topic model having been trained based on text data; extracting features of the training sample and of the test sample respectively using a preset algorithm, computing optimal model parameters of a logistic regression model by an iterative algorithm based on the features of the training sample, to train and get a logistic regression model containing the optimal model parameters; and drawing a ROC curve based on the features of the test sample and the logistic regression model containing the optimal model parameters, evaluating the logistic regression model containing the optimal model parameters based on the area AUC under the ROC curve, to train and get a first topic classifier. It further discloses a device and computer-readable storage medium thereof.

Abstract: 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 probabilit

Abstract: 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 det

Abstract: A method for processing real estate information comprises steps of: acquiring a target geographical location corresponding to real estate information to be appraised; acquiring all configuration information within a preset range around the target geographical location; determining a score corresponding to each piece of configuration information according to a preset scoring standard; obtaining a standardized eigenvalue by projecting the score determined according to a distance between each piece of configuration information and the target geographical location; and determining an appraised price of a real estate corresponding to the target geographical location by using a real estate price appraisal model according to the standardized eigenvalue

Abstract: 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 specif

Abstract: 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 specif