34results about How to "Increase data dimension" patented technology

The invention discloses an information recommendation method, device, computer equipment and storage medium, which can be used to improve the pertinence of information recommendation. The method part includes: calculating a user's interest in each first application according to search data, click data and installation data; obtaining application description text corresponding to each first application; according to the TF-IDF vector of the application description text and each first application The user's contribution to the user determines the user's feature vector; according to the user's feature vector, the user is clustered to obtain a user group; according to the similarity between the target user and other users in the user group, and other users' interests in the second application The second application for the target user is determined as the application to be recommended; recommendation information is generated according to the application to be recommended, and the recommendation information is recommended to the target user.

The invention discloses a distribution automation data-based single-phase ground fault line selecting method and device for a small-current system. The distribution automation data-based single-phaseground fault line selecting method comprises the following steps: acquiring distribution automation data, and extracting the fault characteristic quantity of each distribution automation terminal; according to the fault characteristic quantity of each distribution automation terminal, constructing a fault measure membership function and a weight coefficient function of each distribution automationterminal; based on the fault measure membership function and the weight coefficient function of each distribution automation terminal, constructing a line fault membership function; calculating optimal values of line fault membership function parameters by a genetic algorithm, calculating the line fault membership of each line, judging whether a line has a fault according to the line fault membership of each line, and selecting a fault line. The distribution automation data-based single-phase ground fault line selecting method and device improve the fault line selecting accuracy, are applicable to a neutral ungrounded system and an arc suppression coil grounded system, and can identify various grounding conditions such as high-resistance grounding, arc grounding, intermittent grounding and instantaneous grounding.

The invention discloses a security sandbox system supporting the security fusion of multiple data sources, including a data layering module, a multi-tenant isolation module, an audit module, a desensitization module and a lifecycle management module. The security sandbox system is equipped with An audit module, the audit module includes a judgment module, an alarm module and a blocking module, and the judgment module, the alarm module and the blocking module are connected through a control; the invention is safe and reliable, and the safety sandbox system provides a safe The data fusion mechanism enables different companies to import data into the security sandbox, perform data fusion in the security sandbox, and expand the data dimension. The security sandbox will ensure that customer data can flow to each other within the sandbox, but cannot flow out of the system In addition, through the security sandbox, enterprises can safely share data with partners, and no party can unilaterally export and use the data in the sandbox, ensuring data security.

The invention discloses an attribute reasoning and product recommendation method based on an adaptive graph convolutional network, including: 1. Constructing heterogeneous data: user rating matrix for products, user attribute matrix, product attribute matrix, user attribute index matrix, Product attribute index matrix; 2. Perform missing value filling preprocessing on user attribute matrix and product attribute matrix; 3. Obtain synergy matrix through one-hot encoding; 4. Construct feature fusion layer according to attribute matrix and synergy matrix; 6. Construct a prediction layer for attribute reasoning and product recommendation; 7. Update the node attribute matrix according to the output of the prediction layer; 8. Repeat steps 4 to 7 until the effect of attribute reasoning and product recommendation is optimal. The invention can fully exploit the high-order structural information of the graph, the internal interaction between node attributes, and the potential association between node attributes and link relationships, thereby realizing more accurate attribute reasoning and product recommendation.

The invention relates to the field of invasive plant monitoring, in particular to a monitoring method and system for invasive plants. The invention establishes a monitoring system for invasive plants with aircraft, a control platform and a monitoring platform. Through this system, large, medium and small-scale invasive plants can be quickly and accurately identified, especially the invasive plants can be monitored before the outbreak of invasive plants, and provide With higher resolution image information, the identification algorithm based on deep convolutional neural network can identify invasive plants more quickly and accurately.

The invention relates to a network learning environment-based resource recommendation system and method. The system comprises a data collection module, a user multi-dimensional eigenvector and scoringvector extraction module, a scoring credibility vector extraction module and a learning resource recommendation module. By utilizing a cooperative filtering thought, a user group similar to a targetuser is screened out; and in combination with the similarity between the similar user group and the target user, and the credibility of user scoring, learning resources are scored and recommended, sothat the scoring of the learning resources has user pertinence and scoring objectivity, and personalized and high-quality learning resources are recommended for users. The resource recommendation system and method can be applied to an online network learning platform; and personalized and reliable learning resource recommendation services are provided for the users, so that the satisfaction of theusers to the recommended learning resources is improved.

The invention relates to an atmospheric fugitive dust pollution baseline removal method based on a distributed algorithm. The method comprises the following steps: fugitive dust concentration sensor nodes are distributed according to the area of a monitoring zone; values of fugitive dust concentration in the monitoring zone are acquired and transmitted to a cloud server; the cloud server receives the sensor data, takes the sensor data as raw data and calculates a fugitive dust pollution baseline of the monitoring zone with a multi-dimensional data fusion algorithm; the baseline in the raw data is removed by means of the fugitive dust pollution baseline of the monitoring zone, and fugitive dust discharge amount corresponding to the environmental pollution baseline in a specific time interval is obtained; the total value of the fugitive dust discharge amount in the same time interval is calculated on the basis of the raw data; the environmental pollution baseline discharge amount is removed on the basis of the total value of the fugitive dust discharge amount, and human factor discharge amount of the monitoring zone is obtained. With adoption of the method, an enterprise is only responsible for self-produced fugitive dust, and the fugitive dust calculation way is more reasonable; the problem that a detection result of single fugitive dust point is one-sided is solved, and the core of the problem can be solved directly by means of real-time remote monitoring.