635 results about "Behavioral analysis" patented technology

A computer system is configured to administer a psychological profiling test to a participant in a driver education program, or receive such profile from another source; to determine the participant's psychological profile. The system then selects a driver education program for the participant based on the profile. The system also selects a method of delivering the curriculum to the participant based on the profile. The system further selects a method of testing the participant's comprehension and retention of the curriculum material, also based on the profile. The system delivers the selected curriculum to the participant using the selected delivery method, verifies attendance or participation, and tests the participant's comprehension and retention of the curriculum material using the selected testing method.

A method for detecting the legitimacy of an application program behavior comprises the following steps: monitoring one or more system interfaces of a computer to cause the monitored system interfaces to jump to a monitoring module for execution when the monitoring system interfaces receive a call; blocking the application program of a caller and judging the validity of the caller: permitting the caller to call an interface function and continuously perform the application program of the caller if the caller is legal; and, pausing the behavior of the application program of the caller and issuing an intrusion alarm if the caller is illegal. The detection method finds out whether an executor of the application program behavior is legal or not by monitoring the system interfaces of the computer so as to intercept the program behavior executed by illegal executors and give an alarm. Compared with the existing fuzzier behavioral analysis technology, the detection method has the advantages of low false alarm rate, high recognition rate and the like, especially has good defense effect against the intrusion behavior due to overflow vulnerability, so the detection method is an effective supplement to the existing behavior analysis defense technology.

The invention relates to a high-throughput nano-litre micro-droplet forming and fixing method based on a microfluidic chip and a special chip and an application thereof. The method comprises the following steps: inlets of a continuous phase and a dispersed phase on the chip are respectively connected with two injection pumps through a teflon tube, the continuous phase and the dispersed phase are respectively infused into the chip under the driving action of the injection pumps, and continuous micro-droplets wrapped with single nematodes are formed in a T-shaped micro-droplet generation area; the generated micro-droplets enter in a droplet capture array and are captured in a circular droplet catcher in sequence; and after the micro-droplets are fully captured, the behaviors of the single nematodes in the droplet microenvironment are detected. The invention has the advantages that the microfluidic chip droplet technology is combined with the behavioral analysis to the single nematodes, a great amount of micro-droplets wrapped with the single nematodes are generated and fixed within the short time by using the characteristics of flexible combination and large-scale integration of various functional units on the chip, and the behavioral analysis to the high-throughput single nematodes on the microfluidic chip is realized for the first time.

The invention relates to a method to monitor and eliminate general unknown viruses, which comprises: inducing virus basic behaviors composted of a plurality of behavior factors through analysis and research on large amount of computer virus, determining specificity of each behavior factor and each basic virus behavior and forming a specificity-adjusting virus behaviors database which is used to judge whether a program belongs to a virus; simultaneously forming an adjustable normal program false-virus behaviors database according to false-virus behaviors generated on normal programs, wherein the database is used to judge whether a program belongs to a normal program; sorting and processing the viruses which are detected by the above method. The invention can improve the specificity of unknown virus and reduce the error report rate of behavior analysis, ensures the adjusting properties of virus behaviors specificity, increases quantity and range of unknown viruses detecting, and has distinctive advantages compared with prior computer virus monitor technique.

A configuration is realized in which driver's biological information is input and a driver's wakefulness degree is evaluated. The wakefulness degree of the driver is evaluated by applying a result of behavior analysis of at least one of an eyeball or a pupil of the driver and a wakefulness state evaluation dictionary specific for the driver. The data processing unit evaluates the wakefulness degree of the driver by using the wakefulness state evaluation dictionary specific for the driver generated as a result of learning processing based on log data of the driver's biological information. Moreover, a return time before the driver is able to start safety manual driving is estimated. A learning device used for estimation processing based on observable information is able to correlate an observable eyeball behavior of the driver and the wakefulness degree by a multidimensional factor by continuously using the learning device. By using secondary information, an index of an activity in a brain of the driver is able to be derived from a long-term fluctuation of the observable value.

The invention provides a transformer substation safety monitoring system based on a video recognition technology. The transformer substation safety monitoring system comprises a video acquisition terminal, an information management module, a behavior analysis module and a monitoring host, wherein the information management module acquires and analyzes work ticket information in real time, and sends an analysis result to the behavior analysis module; the behavior analysis module acquires a monitoring video of a target substation operation region, intelligently identifies the monitoring video, judges whether an operator has an unsafe behavior or not, and sends the obtained monitoring video, the work ticket information analysis information and a moving target action identification result to the monitoring host to be displayed and stored, so that intelligent safety monitoring of the operator in the substation operation region is realized, and the substation on-site supervision working efficiency can be effectively improved. The invention further provides a transformer substation safety monitoring method based on the video recognition technology and applying the transformer substation safety monitoring system.

The invention discloses a user portrait grouping and behavior analysis method and system based on log data of network security equipment. The method mainly comprises the following steps: 1) extractingfeatures from data such as user logs of the network security equipment; 2) carrying out preprocessing and data analysis on the features to obtain relationships between the features and relationshipsbetween the features and user behaviors; 3) constructing a clustering model of the user groups according to the user characteristics; 4) establishing a user portrait according to the clustering model,and determining whether the user has abnormal behaviors or not according to a matching result of the user characteristics and the user portrait; the method is simple to implement and low in calculation complexity; the computing resource overhead of behavior analysis in the user log can be effectively reduced. Compared with the prior art, the invention does not need any additional mark, only needsthe data automatically recorded by the network security equipment, has the advantages of practical application, provides a user grouping method and a portrait matching method with different behaviormodes, and carries out decision-making judgment on whether the user behavior is abnormal or not.

The invention relates to a user identification system and method based on heterogeneous information network embedding algorithms. The user identification system comprises a data processing module, a joint embedding module and an evaluation analysis module. According to the invention, multi-source heterogeneous user behavior data is utilized for constructing a normal behavior model based on the thought of behavior analysis, and user identification is executed by comparing the similarity between a current behavior and the normal behavior model when behavior data of a new time period arrives. Forthe condition of identification errors, suspicious behavior sorting is given based on dot product similarity operation. The system and method can be applied to an enterprise intranet to detect potential internal threats, a more comprehensive and accurate behavior model can be obtained by combining two heterogeneous information network embedding algorithms, and the user identification accuracy isimproved by about 10%. In addition, event-level traceability clues can be provided for further analysis of the safety monitoring personnel.

A system and method for generating an agent behavioral analytics including transcribing an incoming call to produce a call transcription; and using a trained convolutional neural network (CNN) to produce behavioral labels for the agent in the incoming call for behavioral metrics, based on the call transcription. The CNN may include an embedding layer to convert words in the call transcription into vectors in a word embedding space; a convolution layer to perform a plurality of convolutions on the vectors and to generate vectors of features; a pooling layer to concatenate the vectors of features to a single vector by taking a maximum of each feature generated by the convolution layer; and a classification layer to produce grades of the agent in the incoming call for the set of attributes or behavioral metrics, based on the single vector generated by the pooling layer.

The invention discloses a method and a system, based on a geographic model and a moving track, for obtaining a characteristic crowd in real time. The method comprises the following steps: selecting a track analysis model, collecting positioning data of a mobile terminal from a base station of a region covered by the track analysis model, and performing matching filtering on the positioning data according to parameters of the track analysis model, so as to extract parameters coincident with the model. The method and the system propose an idea for setting a track analysis model according to geographical position information, such as hot spots, traffic networks and the like, and ensure that behavioral analysis based on mobile track is more close to the demands of economic production activities.

The invention provides a malicious encrypted traffic detection method and system based on behavior analysis, and the method comprises the steps: carrying out the feature analysis of network traffic data after the network traffic data is obtained, and obtaining a feature sequence based on a session; and inputting the feature sequence into a detection model to obtain a detection result score, and determining whether the network traffic is malicious encrypted traffic or not according to the detection result score. According to the method, behavior characteristic analysis is carried out on the network traffic data, so that normal encrypted traffic in the network can be effectively filtered, malicious encrypted traffic is identified, and the false alarm rate is reduced. Meanwhile, through a detection model constructed in a machine learning mode, unknown data characteristics can be deduced according to known samples, and whether the network traffic data is malicious encrypted traffic or notis accurately judged.

The invention belongs to the technical field of computer teaching. The invention particularly relates to a learning assistance method and system based on classroom emotion and behavior analysis. The system comprises a data acquisition module, a student knowledge mastering detection module, a teacher teaching content analysis module and a learning pushing module, wherein the data acquisition modulecomprises a video acquisition module and an audio acquisition module; according to the method, the classroom learning mastery degree of students can be effectively analyzed, observation, evaluation and recording of the learning quality of the students are achieved, the mastery situation of the students for knowledge points is judged in combination with classroom audio information of teachers, andthe students are assisted in perfecting a knowledge system by conducting exercise pushing practice on the knowledge points which are not mastered.

The features relate to artificial intelligence directed detection of user behavior based on complex analysis of user event data including language modeling to generate distributed representations of user behavior. Further features are described for reducing the amount of data needed to represent relationships between events such as transaction events received from card readers or point of sale systems. Machine learning features for dynamically determining an optimal set of attributes to use as the language model as well as for comparing current event data to historical event data are also included.

The invention discloses behavior analysis and alarm embedded equipment based on a specific area and relates to the technical field of behavior analysis. The equipment comprises a camera, a video transmission module, an action capturing module, a face recognition module, an auxiliary processor, a database, a main processor, a main displayer, a second displayer and an alarm device, wherein the camera is dispersed in a to-be-monitored area and is used for monitoring the to-be-monitored area in real time and transmitting the video information to the action capturing module and the face recognitionmodule through the video transmission module. According to the behavior analysis and alarm embedded equipment based on the specific area in the invention, the camera, the face recognition module andthe action capturing module are used for carrying out video monitoring on a specific area needing to be monitored, automatically judging the person actions and the human face in the video, and judgingwhether the person has a danger coefficient or bad behaviors; and an alarm is given in time when the person is abnormal through the arrangement of the auxiliary processor; the information of a suspicious person can be displayed in time through the arrangement of the main displayer, thus bringing convenience for workers to observe.

The invention discloses an abnormal behavior analysis method based on a target trajectory. The method comprises the following steps: moving target tracking: inputting a video into a system, carrying out the target detection of the video through the system, carrying out the moving target tracking of a target in the video, obtaining the position of the moving target in each frame of image in a video sequence, and obtaining a target movement trajectory; path extraction: preprocessing the target movement track by adopting a polynomial fitting method, clustering by adopting a clustering algorithm, modeling data obtained after clustering, and extracting to obtain a scene path; behavior analysis: matching the input to-be-judged movement mode with the normal behavior model, and obtaining a behavior analysis result according to the matching condition; according to the method, the track of the moving object is obtained, and the behavior and activity of the moving object are further understood and predicted, so that the problem that judgment of the occurrence of a suspicious event mainly depends on manpower in a traditional video monitoring system is solved.

The invention discloses a driving behavior analysis method based on improved K-means, and belongs to the technical field of driving behavior analysis. According to the method, the problem that the existing K-means method is poor in scientificity and accuracy of a driving behavior clustering result is solved. In order to select an optimal initial center, the invention provides a DC algorithm, the algorithm calculates the product of the density of samples in a data set, the reciprocal of the average difference degree among the samples in the class and the difference degree among the clusters asa center index, and the initial center is determined by the center index; then the initial center obtained through the DC algorithm is used as a default parameter to be input into the K-means algorithm, so that the accuracy and stability of the K-means algorithm on the clustering result of the driving behaviors are improved, and the clustering result of the improved K-means algorithm on the driving behaviors is more scientific. Experimental results show that on the research of driving behavior clustering analysis, the improved algorithm is higher in accuracy and stronger in anti-interference capability, and the accuracy reaches 90%. The method can be applied to driving behavior analysis.

The invention discloses a teacher-student behavior analysis system based on classroom videos, which comprehensively evaluates teacher-student classroom interaction conditions and is divided into a teacher classroom teaching behavior analysis module, a teacher classroom teaching sound rhythm analysis module, a student classroom hand raising behavior analysis module and a teacher-student speech behavior interaction analysis module. Index calculation is performed on the classroom teaching video so that the data result of the classroom teacher-student behaviors can be obtained, the classroom teacher-student behavior data report can be generated and reference can be provided for evaluation of the teacher-student classroom interaction effect.

The invention discloses an internet teaching auxiliary teaching system based on the key teaching point evaluation. The system comprises a student terminal image collection device which collects a face image of an evaluated student; an information transmission device used for transmitting the face image acquired by the student terminal image acquisition device to the classroom behavior analysis processor; a class behavior analysis processor used for classifying emotions of the students when learning a plurality of knowledge points related to the key teaching points and respectively endowing the emotions with mastering probability values; an intelligent classroom device used for realizing teaching interaction with the student terminal, and automatically judging whether the corresponding knowledge points are mastered or not according to the correct and wrong conditions of all questions in the teaching interaction; and a key teaching point evaluation device which calculates the total mastery rate of the key teaching points according to the analysis and judgment results of the classroom behavior analysis processor and the intelligent classroom device. According to the system, related videos of the key teaching points can be conveniently and quickly found according to the mastery condition of the key teaching points. The course look-back is effectively carried out based on recording and playing courses.

The invention discloses a construction site pedestrian wandering detection method, device and equipment and a storage medium. The construction site pedestrian wandering detection method comprises thesteps: acquiring a frame image captured from a video stream collected by a camera installed on a construction site in real time; delimiting a pedestrian wandering detection area on the acquired frameimage; tracking each pedestrian in the detection area through deep learning in combination with a multi-target matching algorithm; calculating the track distance and the residence time of the trackedpedestrian, and identifying the pedestrian as wandering if the track distance is greater than a set distance threshold and the residence time is greater than a set time threshold; and after identifying that a person wanders, connecting an alarm to carry out early warning. According to the invention, deep learning is combined with a multi-target matching algorithm to carry out tracking calculationon each pedestrian in the detection area; therefore, whether the pedestrian wanders or not can be monitored in real time based on the behavior analysis of the pedestrian in the detection area, the precision of the detection result is improved, the tracking effect is improved, and the problem that in the prior art, a large number of missed recognition and false recognition situations occur in a method is solved.