211 results about "Behavior type" patented technology

The user is allowed to automatically generate a program by using a visual programming method and a system adopting the method for automatically generating a program wherein an object selection means 8 is used for selecting a behavioral graphical object defining the behavior of a load connected thereto, a behavior selection means 9 is used for selecting the type of the behavior of the behavioral graphical object, a relevant object selection means 10 is used for selecting relevant graphical objects related to the behavior of the behavioral graphical object and a behavioral rule setting means 11 is used for setting behavioral rules of the behavioral graphical object whereas a behavioral characteristic selection unit 3509 is used for selecting a behavior of a behavioral graphical object selected by using a behavioral object selection unit 3508, behavioral condition setting unit 3510 is used for setting conditions for the behavior of the behavioral graphical object and a relevant object selection unit 3511 is used for defining a relation.

The invention discloses a method and device for predicting advertisement click rate based on user behaviors. The method comprises the following steps of: behavior directional prediction: training a behavior directional model by analyzing the user behaviors, and predicting the click rates of the user on different types of advertisements to obtain a predicted value of the click rate; in-type sorting: sorting the advertisements of each type according to the current behavior type of the user to obtain an in-type advertisement sorting list; and comprehensive sorting: sorting the click rates of all advertisements according to the predicted value of click rate and the in-type advertisement sorting list to obtain a comprehensive sorting list. Through the method disclosed by the invention, a prediction model can be established for each type of advertisements so as to sort the advertisements more accurately according to the type characteristics of the advertisements.

A computer implemented method, apparatus, and computer usable program product for optimizing loss leader merchandizing. The process parses event data to identify physical patterns of customer behavior, wherein the event data is derived from a continuous video stream captured at a retail facility, and associates the physical patterns of customer behavior with customer profile data to form dynamic customer profiles. The process then analyzes the dynamic customer profiles to identify types of customers and presents marketing incentives for a set of loss leader items to customers of a selected customer type, wherein the set of loss leader items are selected based on the dynamic profiles.

A method and system allows a device driver to obtain the status of a device that may exhibit one of two distinct behaviors in responding to status requests that designate a plurality of event types associated with a device. Devices exhibiting the first behavior respond to the status requests by providing the status for the highest priority event type designated in the status request. Device exhibiting the second behavior respond to the status requests by providing the status for the highest priority event type that has undergone a status change. In one implementation, the device driver transmits a series of distinct status requests such that the status of all event types can be determined. In a related aspect of the invention, the device driver also analyzes the response message to determine if the device exhibits the second behavior type. When the second behavior type is detected, only a single type of status request is sent to obtain the status of all event types.

The invention discloses a method for identifying abnormal motion in a video. The method comprises the steps that an area of which the pixels have certain change is extracted from a video sequence through a foreground extraction module; a movement target detected by the foreground extraction module is judged through a pedestrian judgment module, and a rectangular box judged to be a pedestrian is kept and transmitted to a target tracking module; each identified target in the scene is continuously tracked in a multi-target way through the target tracking module; the posture of each target tracked by the target tracking module within each frame of time is judged through an abnormal behavior trigger module and behavior analysis is performed on the abnormal behavior; and the abnormal behavior is processed and computed into a video segment through a behavior identification module, and the abnormal behavior is alarmed and the behavior type of the abnormal behavior is identified. The invention also discloses a system for identifying the abnormal motion in the video. The problem that abnormal behavior (falling down) detection of massive video data cannot be performed in real time can be solved and the detection accuracy can be greatly enhanced.

The invention discloses a malicious file detection method and device. The method includes the steps of obtaining a sample file to be detected; operating the sample file, monitoring operation behaviors of the sample file and generating a log file; analyzing the log file, and carrying out malicious file detection based on a preset matching rule. The sample file is operated in a virtual machine, a monitoring program is operated in the virtual machine, the operation behaviors of the sample file are recorded to generate the log file, then the log file is matched according to an extracted feature rule, and finally malicious file detection of the sample file is achieved. By means of the method, virus analysis efficiency can be greatly improved, new samples which can not be detected by anti-virus programs in the prior art or a certain type of samples of a specific behavior type can be found timely, and accordingly detection accuracy of virus samples is improved.

A method of managing slowly changing dimensions in a dimensional database, such as a dimensional data mart, is disclosed. The method consists of providing a dimension template for each dimension undergoing slow changes, which template assigns a behavior to each column of the dimension table. When a row of dimensional data is to be delivered to the dimension table, the table is searched for a record having the key value of the row of data to be delivered. If such a record exists and if any attributes of the row to be delivered have their values changed compared with the existing record, the table is updated according to the behavior type assigned to these attributes in the dimension template, either by overwriting the existing record with the record to be delivered or by creating a new record or records.

The invention provides a Wi-Fi location fingerprint map building method and a system based on crowd-sourcing. The method comprises the steps as follows: acquiring relevant crowd-sourcing data; using a dead reckoning approach to calculate the relative motion trajectory of a user based on the crowd-sourcing data; identifying specific behaviors of the user including a plurality of behavior types through machine learning; building a behavior sequence model formed by the types of the specific behaviors in the relative motion trajectory and the relative spatial relationship between the specific behaviors; generating a point-line model of an indoor map, and matching the point-line model with the behavior sequence model through a hidden Markov model to obtain the indoor map coordinate information of the relative motion trajectory; and finally, building a Wi-Fi location fingerprint map based on the indoor location coordinate information and the crowd-sourcing data. The Wi-Fi location fingerprint map is built and updated automatically based on the crowd-sourcing data.

The invention discloses a motion history image and convolutional neural network-based behavior identification method. The method comprises the following steps of S1, obtaining an input original videoimage, and processing the input original video image through a motion history image-based behavior sequence feature extraction method; and S2, performing behavior identification on a local motion history image by adopting a deep convolutional neural network-based method to obtain a behavior type classifier, and finally outputting a behavior identification result through the behavior type classifier. The motion history image is calculated in an original video sequence, so that the to-be-processed information amount is reduced and key time-space information in behavior identification is extracted; and by taking the motion history image as an input, a deep convolutional neural network is established, then the network is trained by utilizing a stochastic gradient descent method and a Dropout policy, and finally behavior type identification is realized. The method can be effectively applied to online real-time behavior identification.

An attrition warning and control system informs an employer as to the risk of attrition for an employee. The employee is assigned a discrete attrition category. The discrete attrition category is determined from an employee satisfaction behavior. The employee satisfaction behavior may be categorized according to its behavior type. Portal logic executed by the attrition warning and control system generates employee team attrition risk reports for employee teams. The portal logic also builds project team attrition risk reports from the employee team attrition risk reports. Reporting logic executed by the attrition warning and control system delivers the discrete attrition category, employee satisfaction behavior, the employee team attrition risk reports, and the project team attrition risk reports through an authorized connection via a communication interface.

An embodiment of the invention discloses a user portrait updating method, apparatus and system. According to the embodiment of the invention, the method comprises the steps of: obtaining a current user behavior log; obtaining a corresponding first interest label according to interest identifiers carried in the user behavior log; determining the user behavior type according to the user behavior log; obtaining a user portrait updated most recently according to the interest identifiers in the user behavior log; and updating the user portrait according to the first interest label, the behavior type and a second interest label carried in the user portrait. According to the technical scheme, the user portrait can be updated in real time, the consumption of stored resources can be reduced, the calculated amount is reduced, and the processing efficiency is improved.

A recommendation method, comprising: acquiring the current behavior data of a user, the behavior data comprising the current behavior object information and the current behavior type information; according to the current behavior type information, determining the historical behavior data of any user corresponding to the current behavior type information, and determining, from the historical behavior data, the associated object information associated with the current behavior object information; and recommending the associated object information. Also provided is a recommendation device.

The present invention discloses a video big data rapid searching method and system constrained by abnormal behavior early-warning information, wherein a massive video monitoring data clue investigation range is constrained by using abnormal behavior early-warning information sent out by an intelligent monitoring camera. The method comprises: receiving abnormal behavior early-warning information of a front-end intelligent camera, and establishing an abnormal behavior library; establishing a correlation model table, wherein the correlation model table stores a correlation between a case or event type and an abnormal behavior type; and according to the correlation model table, searching the abnormal behavior library, and positioning a corresponding valuable clue of monitoring point The method and system disclosed by the technical scheme of the present invention significantly reduce the size of recording data called in the video searching process and greatly improve valuable clue discovery efficiency.

The invention provides an event time sequence data processing method, device and system for recognizing user behaviors. The method comprises the steps of obtaining event operation data of a user within a preset period of time, and converting the event operation data into user event time sequence data conforming to constructed hidden Markov recognition model input data formats; inputting user event time sequence data into the constructed hidden Markov recognition models of different label types for calculating to obtain probability value data of the user event time sequence data in the hidden Markov recognition models of different label types; selecting label types corresponding to the hidden Markov recognition models with the probability value data conforming to the screening condition as the behavior type to which the recognized user s behavior belongs. By means of the event time sequence data processing method, on the basis of time sequence user s behavior data, recognized user s behaviors of the user are judged, the user operation behavior recognition result accuracy and reliability are improved, and the user s risk operation behavior recognition and control prevention effects are improved.

The invention relates to a webshell detection method and apparatus based on total access log analysis. The method includes that the total access log of a website are obtained; the total access log is subjected to characteristic analysis, the request parameters, message headers, and returned data content in the total access log are subjected to regular matching with the behavior characteristic database of the webshell, and the matched corresponding webshell files in the total access log are determined as suspected back-door files. The webshell behavior types, names, and the corresponding total access log of the suspected webshell files are sent to a log server and are alarmed. The method is suitable for detecting the scenes that hackers control the directly uploaded webshell deformed files and encrypted files, and embed the webshell files to normal files to carry out attacks, and makes up the defects of file hash comparison, common function comparison or other webshell detection methods.

The invention provides an abnormal behavior detection method based on a linear dynamic system and a deep network. An LDS dynamic system model and a TSN deep learning network are combined, the space-time feature information of actions is extracted through the TSN, then action features are connected in series through an LDS to form complete behavior features, and finally the behavior type is judgedthrough a support vector machine (SVM). According to the method, the 3D convolutional network is established on the basis of the residual network, and the 3D convolutional kernel is established in a 2D+1D form, so that the network parameter quantity is reduced, and the problem that the original 3D network cannot preload the weight is solved. A residual 3D network is introduced into the TSN structure, so that the feature extraction capability of the network is improved. The number of network layers is increased, and the fitting capability of the network is improved. According to the invention,the high-precision identification of long-sequence abnormal actions can be realized, and finally the accurate monitoring of abnormal behaviors is realized.

A robot for generating multiple emotions is provided. The robot includes a sensor unit sensing external environment information, a database unit storing a predetermined list including information on types of behavior the robot is allowed, a user's reaction corresponding to each behavior of the robot, and favorite objects of the robot, a primary emotion generation unit generating an emotion corresponding to characteristics of the sensed information when the sensed information exceeds a predetermined threshold value, and a secondary emotion generation unit generating an emotion corresponding to characteristics of the sensed information based on the information sensed by the sensor unit, the list stored in the database unit, the amount of internal power of the robot, internal information on the robot indicating the existence of an operational error and an elapse of time.

The invention discloses a pedestrian abnormal behavior identification method based on 3D convolution. The pedestrian abnormal behavior identification method comprises the steps of S1, creating a dataset containing abnormal behaviors such as fighting, dog walking and falling; S2, in combination with the latest video behavior identification scheme, constructing a 3D convolutional neural network considering both precision and speed; S3, preprocessing the images in the data set, and sending the preprocessed images into a 3D convolutional neural network to obtain a video abnormal behavior recognition model; And S4, inputting a tested pedestrian monitoring video, and outputting an abnormal behavior type. According to the identification method provided by the invention, the lightweight 2D convolutional network MobileNet idea is migrated to the 3D network, so that the calculation cost can be reduced on the basis of maintaining the identification performance; Meanwhile, a self-adaptive poolinglayer and a sparse time sampling strategy are adopted, so that a large amount of redundant information and fuzzy noise contained in continuous frames can be reduced.

The invention provides a robot interaction control method, a server and a robot. The method comprises the following steps: receiving audio and video streams of a monitored object, which is obtained by a photographing unit and is uploaded by the robot, or related sensor data; matching a corresponding behavior type from a pre-set behavior type library according to the audio and video streams or characteristic data extracted from the sensor data; determining a machine behavior instruction corresponding to the behavior type from a pre-set corresponding relation database; and sending the machine behavior instruction to the robot to drive the robot to execute the machine behavior instruction, so as to enable the robot to execute corresponding output behaviors to be taken as feedback of the monitored object. According to the method and equipment, provided by the invention, the intelligence level of the robot is greatly improved, the interaction between the robot and people is more convenient and forms are more abundant.