10546 results about "Evaluation result" patented technology

On a mobile communications device, visiting a link from a messaging application or web browser may result in an undesired action, such as visiting a phishing site, downloading malware, causing unwanted charges, using too much battery, or the device being exploited. In an implementation, a mobile application intercepts a request including an identifier associated with an action to be performed by another application on the device and evaluates the identifier to determine when the request should be permitted, blocked, or conditionally permitted. The client may use local data or make a request to a server to evaluate the identifier. In an implementation, server communications are optimized to minimize latency by caching evaluation results on the device, proactively priming the device's DNS cache, optimizing when DNS lookups are performed, and adapting evaluation policy based on factors such as the source of the request, and the currently active network connection.

System and method for performance-based routing in an SDN. An SDN controller is configured to adaptively determine data transmission routes based on real-time route performance evaluation provided by a virtual router. The virtual router includes a route detection component to attain route performance data and a performance evaluation component to evaluate the route performance based on predetermined criteria provided by the SDN controller. The evaluation result is sent to the SDN controller and used to intelligently determine a superior route selection and route usage for a specific application program. According to the determination, the SDN controller updates the flow table associated with the virtual router for subsequent data transmission.

Systems and methods providing a route optimization mechanism for transmitting data traffic across different autonomous systems based on real-time route performance detection. Regarding a request for routing data between a source node that is coupled to a first autonomous system and a destination node located in a second autonomous system, each of a plurality of edge nodes in the first autonomous system operates to detect and evaluate real-time route performance. The evaluation results are compared and used to select an edge node and an associated link for transporting data between the source node and the destination node. The route optimization mechanism can be adopted in an SDN-based or other virtual network autonomous system.

Methods and systems for forming a team of experts and evaluating a portfolio of patent documents have been provided. The methods comprise the step of determining a bias of an expert, and then providing evaluations of patent documents assigned to the expert according to a selected evaluation method, which is bias corrected to reduce or compensate for the bias of the expert. A system for evaluating a patent portfolio comprises means for assigning the patent documents to said experts and monitoring progress of the evaluation process of each expert, and means for performing evaluations of the assigned patent documents by each expert. The system is further enhanced with the means for determining the bias of the expert, and with a 10 visualization unit for visualizing the results of the patent portfolio evaluation by color coding the evaluation results, wherein patent documents having different values are represented by different colors of a visible part of the light spectrum. A corresponding computer program product, a computer memory and a web site for providing access to the system and management of activities of the experts are also described.

A fully computerized musical performance teaching system comprises a computer apparatus having a keyboard and a display and is connectable to an electronic musical instrument. An application program provides the computer with various functions such as of judging student's skills, selecting music pieces for practice, training the student in performance and evaluating the student's progress through the teaching course. The judging function is to judge the student's performance skill from the student's responses to the subjects which the computer presents. The selecting function is to select a piece of music for practice according to the judgment results and the student's wishes and also to select a tutoring manner from among several prepared manners. The training function is to plan a practice schedule to meet the student's performance skill and wishes and to let the student practice with proper music pieces. The evaluation function is to evaluate and mark the student's performance, and reflect the evaluation results and marked points in the progressing rate and the presented subjects and also in adjusting the judgment results.

The invention embodiment provides a personal credit data processing method and a device; the method comprises the following steps: collecting original data used for evaluating users personal credit; converting and processing original data of each user so as to form standardization personal data; forming a personal credit evaluation model according to the standardization personal data; evaluating personal credit of the user according to the personal credit evaluation model. The method and device collect the user original data and convert and process the data, thus forming the personal credit evaluation model, so the user personal credit can be quantified and automatically evaluated, thus providing a personal credit evaluation platform; the user personal credit can be learned before mass services and transactions, and an evaluation result is more objective and accurate.

The invention discloses a robustness code summary generation method based on a self-attention mechanism. The method comprises the steps that firstly, high-quality codes and description corpus pairs (query description texts and reply code texts) in a programming community are extracted; then redundant information of the codes and description corpus pairs is filtered out; then the query descriptiontexts corresponding to the codes are converted into declarative statements; lastly, a code summary of a sequential model based on the self-attention mechanism is generated. The robustness code summarygeneration method based on the self-attention mechanism has the advantages that the redundant information and noise content can be effectively removed, the automatic evaluation and artificial evaluation accuracy of the generated summary are both improved, and the evaluation result is superior to that of an existing baseline method.

The invention discloses a method for building a city gas pipe network vulnerability evaluation model. The method comprises the following steps that an evaluation model is built through fuzzy hierarchy comprehensive evaluation; a vulnerability evaluation index weight is determined; a city gas pipe network system fuzzy comprehensive evaluation basic model is built. As the system vulnerability evaluation and traditional risk evaluation are similar and both need to build evaluation index systems according to evaluation purposes, a proper evaluation method is selected to build an evaluation mathematical model, the model is utilized to conduct system vulnerability calculation and vulnerability grading, the quantitative and qualitative combined system vulnerability evaluation is achieved, and a research result of the city gas vulnerability can lay the foundation for improving the level of a Chinese gas pipeline management technology, prolonging the service life of a city gas pipeline and avoiding or reducing sudden invalidation accidents. With the deepening of the vulnerability research, the vulnerability evaluation is introduced in the filed of research of the science of disasters, the point of view of the safety evaluation is made to be wider, the content of the safety evaluation is made to be fuller, and the evaluation result has more practical value.

The invention provides a driving behavior safety evaluation method based on human-vehicle-environment multiple data sources and belongs to the field of automobile safety driving research in the aspectof road transportation application. The method comprises the steps that information data of a driver, driving data of a vehicle and environmental factor data are acquired as automobile driving sourcedata; according to the automobile driving source data, related factors which influence the driving safety are analyzed, and a driving risk evaluation index system is built; through statistical analysis, an evaluation value corresponding to an evaluation index is extracted in the source data, a driving risk score of a vehicle to be evaluated is calculated and obtained, and finally the driving behavior safety grade of the vehicle is determined. The driving behavior safety evaluation method has reliable evaluation results, can prevent dangerous driving behaviors, reduces traffic accidents and improves the traffic flow.

The invention discloses a convolutional-neural-network-based reference-free three-dimensional image quality evaluation method. The method comprises the following steps: carrying out pretreatment on a 2D image; to be specific, inputting an image block obtained by pretreatment into a deep convolutional neural network, carrying out convolutional pooling processing to obtain an advanced feature of the image block, and then carrying out BP training by using the neural network based on a mass fraction of an original image so as to obtain a parameter of a base model; obtaining a corresponding basic model according to the parameter of the base model, carrying out centering on a three-dimensional image and carrying out pretreatment identical with that of the 2D image on the left image and the right image, and inputting the image blocks at the same position into the basic module simultaneously to obtain a corresponding advanced feature vector; and carrying out testing under a LIVE 3D database. On the basis of the reference-free evaluation algorithm, a good result better that the existing quality evaluation result can be obtained.

The invention discloses a dam safety comprehensive evaluation method based on depth learning, and adopts a multi-level dam safety monitoring system for comprehensive safety evaluation. The method comprises the following steps: 1, in the step of dam safety monitoring measure point grading, according to dam engineering characteristics, the monitoring system is abstracted into a tree structure; 2, in the step of monitoring measure point data preprocessing, missing values are filled and obvious anomaly values are excluded for measure point data; 3, in the step of calculating and classifying measure point threshold, calculation is conducted on a measure point selection model, the measure point threshold is determined and measure points are classified; 4, in the step of constructing a convolution network for training and evaluation, the evaluation results of a dam and the monitoring system at all levels by professional monitors are obtained as a training set and a testing set, then the convolution neural network is trained based on the training set and the testing set, and finally the safety comprehensive evaluation for the dam is made.

The invention discloses a multisource information fusion method in an evidence high-conflict environment, which comprises the following steps of: determining the mutual support degree of evidences according to evidence distances, further calculating the relative discount factor of each evidence by taking a characteristic vector corresponding to the maximum characteristic value of a evidence support degree matrix pattern as a weighting vector of the evidence, wherein the evidence with the maximum weighing coefficient is a key evidence; and discounting each evidence information, and fusing the information of multisource sensor data by using the D-S (Dempster/Shafer) rule so as to increase the accuracy and precision of a monitoring proposition judgment under an evidence high-conflict status.The method can effectively eliminate the uncertainties of multisource information, increase the accuracy of information evaluation and overcome the limitation that correct evaluation results can not be acquired by using the traditional D-S evidence theory under the evidence high-conflict status, and has the advantages of simple algorithm, high convergence rate and the like.

The invention relates to a crop growth irrigation decision-making control system comprising a crop database, a soil database, a meteorological database, an irrigation volume database, and an achievement database. Crop parameter data from the crop database, soil parameter data from the soil database, and meteorological data from the meteorological database are initialized by a crop growth mathematical model of the decision-making control system; with combination of irrigation volume data from the irrigation volume database, the crop growth mathematical model carries out state calculation; the decision-making control system makes an irrigation decision based on the irrigation decision-making model; and the irrigation decision-making effect is fed back based on daily data output provided by the achievement database; and the irrigation decision is adjusted based on the evaluation result.

The invention provides a dynamic optimization based neural network flood warning device and a method. The device comprises an information processing unit, an information collection module, a GPS satellite positioning module, a GIS geographic information module, a wireless network module and a client, wherein the information collection module, the GPS satellite positioning module, the GIS geographic information module and the wireless network module are connected with the information processing unit, and the client is connected with the wireless network module. The information processing unit is used for carrying out data assimilation on matched information, assimilated data comprises historical information data and implementation information data, a network flood forecasting model is established by using the historical information data, the established network flood forecasting model is corrected by using implementation information so as to acquire a dynamic network flood forecasting model, and a disaster evaluation result is outputted by using the network flood forecasting model; and the wireless network module sends the disaster evaluation result to the client. According to the invention, mountain flood disaster analysis and judgment are carried out timely and accurately through the network flood forecasting model, thereby providing a reliable basis for making and selecting defense solutions in real time.

The invention discloses a classroom quality bi-directional evaluation system based on depth learning, which comprises a classroom terminal, a teacher terminal, an educational administration terminal,a student terminal and a cloud server. The teacher terminal collects, processes and sends classroom pictures and videos. The teacher terminal sends text information and images to the cloud server to release the job, notify the examination time and place, and receive and display the analysis results. The educational administration terminal receives and displays the basic information of each teacher's class, history teaching situation and students' learning situation; The student terminal receives and displays the notification of class assignment, the notification of examination time and place,the individual classroom performance and the teaching quality evaluation results of each teacher. Based on the received classroom pictures and frames, the cloud server uses the object detection model,face detection model and face segmentation model to analyze the attendance, the listening rate, classroom behaviors, classroom expression, classroom seating and feedback analysis results.

A transformer operating state comprehensive evaluation method based on on-line monitoring comprises the following steps that monitoring various state indexes of a transformer in an on-line mode and conducting quantitative processing; determining the weights of evaluation indexes by combining improved analytic hierarchy process and grey correlation analysis; evaluating the state of the transformer. According to the transformer operating state comprehensive evaluation method based on on-line monitoring, the state of the transformer is combined to evaluate correlation function operation and membership degree process of the indexes, a transformer state comprehensive evaluation model based on combination of combination weighting approach and a fuzzy grey element is established, various on-line detecting state information quantities can be synchronized, the operation state of the transformer can be effectively and accurately judged in a comprehensive mode, an evaluation result is intuitive, and the method is used for instructing practical overhaul arrangement work of the transformer.

The invention discloses a big data security analysis system based on massive network monitoring data. The system includes a data traffic monitoring module, a deep packet detection module, a data jointanalysis module, an anomaly detection module and a security evaluation module, wherein the data traffic monitoring module is used to monitor data traffic in real time, analyze applications, perform lossless collection on various system traffic data, and transmit the data to other modules; the deep packet detection module is used to judge the service types and application types by deeply reconstituting and analyzing the payload content of a seventh-layer packet and matching service characteristics, and performs analysis to obtain different application types; the data joint analysis module is used to perform data aggregation and study the state and association analysis to further remove redundant information in original data; the anomaly detection module is used to detect a data analysis result and judge whether the data is abnormal; and the security evaluation module is used to obtain a data evaluation result by combining the network situation based on the analysis and detection of other modules. According to the system, the real-time monitoring of network data and corresponding data security analysis can be implemented, and the data security and reliability can be improved.

An embodiment of the invention discloses a transformer state evaluation method and system. State quantity indexes are normalized to obtain the health degree of the state quantity indexes, the subjective weight of each state quantity index is determined by an analytic hierarchy process in a fuzzy comprehensive evaluation theory, an objective weight corresponding to each state quantity is obtained by an entropy weight method and used for correcting the subjective weight to obtain comprehensive weight parameters of each state quantity index, the connection degree of each state quantity index and state classification is obtained by the aid of connection degree in a set pair analysis theory, and finally, data of evaluation results of a hierarchical index set of each unit are integrated by the aid of D-S evidence theory fusion rules to obtain state evaluation results of each sub-unit. Whether a transformer needs maintenance or not can be determined according to the state evaluation results, so that blindness of planned maintenance is avoided, and human and material resources needed by maintenance for the transformer are reduced.

The invention discloses a bus road condition processing system and method based on traffic multi-source data fusion. The bus road condition processing system comprises a preprocessing module, a source data processing module, a data source evaluation module and a data fusion module. The preprocessing module is used for processing traffic multi-source data; the source data processing module is used for processing data of a non-bus data source road condition calculation result; the data source evaluation module comprises an evaluation framework used for evaluating the quality of data sources, and the credibility of multi-source data to the bus road condition calculation result can be obtained through processing of the evaluation framework; the data fusion module is used for performing weighted calculation on the multi-source data based on the data source evaluation result, and finally the required bus road condition data are obtained; a display module is used for superposing a bus road condition map and a map file obtained through processing and displaying road condition information of the whole bus road network. By synthesizing the multiple traffic data source road condition calculation results, the advantages of all the sides can be made the best of, the fusion result information used for describing the bus road conditions can be obtained, and data support is provided for bus service.