35 results about "Behavioral risk" patented technology

An apparatus, system, and method are disclosed for assessing and managing behavioral risk. The present invention maintains an episode module configured to collect behavioral risk data, a risk assessment module configured to allow a user to assess behavioral risk in relation to environmental factors, and a treatment module configured to generate recommended treatment options. Beneficially, such an apparatus, system, and method takes into account the dynamic nature of behavioral risk and provides tools to aid in estimating behavioral risk, managing and communicating behavioral risk, and documenting adherence to a standard of care.

The embodiment of the invention provides a user behavior risk assessment method and device. The scheme comprises the following steps: acquiring behavior data of a to-be-evaluated user; extracting a target behavior sequence vector corresponding to the preset behavior event from the behavior data; inputting the target behavior sequence vector into a risk assessment model obtained by pre-training toobtain a behavior risk assessment value of a to-be-assessed user, wherein the risk assessment model is a model obtained by training a recurrent neural network and a logistic regression algorithm according to a preset training set, and the preset training set comprises behavior sequence vectors of a plurality of sample users and real behavior risk assessment values of the sample users. By applyingthe technical scheme provided by the embodiment of the invention, the evaluation of the user behavior risk degree can be realized.

A system includes a camera configured to generate image data and a computing device in electronic communication with the camera. The computing device includes at least one processor and is configured to receive, from the camera, one or more images representative of a location. The computing device is further configured to apply a trained classifier to the one or more images to classify the location into one of at least two risk categories, wherein the classification is based on a likelihood of a subject performing a target behavior based on presence of the subject in the location. The computing device is additionally configured to issue a risk alert responsive to the trained classifier classifying the location into a high-risk category.

Risk engines and related methods may benefit patients and their physicians. For example, patients may benefit from being able to determine their personal genetic, environmental, and behavioral risks. Moreover, physicians may be able to provide statistically-driven individual recommendations based on a risk engine's determination of such risks. A method can include selecting one or more candidate genetic variants associated with a phenotype from the scientific literature. The method can also include scoring a genetic association between the one or more candidate genetic variants and the phenotype. The method can further include selecting one or more high-scoring genetic variants. Selecting a best genetic variant within each of at least one linkage disequilibrium (LD) block can also be included in the method. The method can additionally include calculating risk associated with the best genetic variant from the at least one LD block.

The invention discloses a behavioral risk assessment method and a home security care system; the method comprises the steps of constructing a convolutional neural network model, selecting a target type to be identified and identifying and positioning, wherein the target type to be identified comprises a danger object and a nursing object category; obtaining the position coordinates of each category in the target type, predicting the movement track of the moving care object, obtaining the angle between the moving direction of the care object and the connection direction of the care object and the danger object, and recording it as [theta], recording the distance between the care object and the danger object as d; constructing a risk assessment model to calculate the behavioral risk in realtime based on the relative position information d of the care object and the danger object ,and the predicted moving direction [theta]; and setting a safety threshold, so that it will flag an alarm when the risk exceeds the safety threshold. By the behavioral risk assessment method and the home security care system the caregiver can timely care for the care object and avoid accidental injury.

The invention discloses a network user behavior risk assessment method based on AHP. The method comprises the steps that an integral user behavior risk is decomposed layer by layer through analytic hierarchy process (AHP) according to the practical application requirements and the functional characteristics, the comprehensive and general user behavior risk is decomposed into a plurality of behavior risk attributes, then the behavior risk attributes continue to be refined into behavior risk evidence which can be directly measured by software and hardware, and a comprehensive risk value is obtained finally. According to the method, for intrusion routes of APT attacks, risk assessment is conducted on network user behaviors by starting with personal online behavior data analyzing and mining, intrusion of APT attack initiators is monitored from the source, and the APT attack monitoring and defending purposes are achieved.

The invention discloses a method and a system for early warning of an improper taking elevator behavior risk. The method comprises the following steps of the step 11 of judging whether an improper taking elevator behavior exists or not, if yes, skipping to the step 12, specifically, broadcasting a voice for reminding; the step 13 of judging whether the improper taking elevator behavior is stoppedor not, if not, skipping to the step 14, specifically, turning on the voice conversation function, so that personnel on duty in the monitoring room can dissuade through voice conversation. Accordingto the method and the system, the risk caused by the improper taking elevator behavior can be early warned and controlled, accidents possibly caused by the improper taking elevator behavior can be prevented, and the consciousness of taking an elevator in a civilized and safe manner can be improved. The elevator safety management moves forward to beforehand risk early warning and risk control frombehindhand accident treatment, and the aims of handing off danger and taking preventive measures are achieved.

The present invention relates in general to human genetic polymorphisms and their association with human health and to methods and materials for analyzing allelic variations, and to the use of genetic polymorphisms in the diagnosis and treatment of smoking behavior and nicotine dependence. Provided herein are methods for determining risk in a subject of developing regular smoking behavior. Also provided are primers, probes, microarrays, and kits related thereto.

The present invention provides a method and system for financial risk management and administration. In the present invention, a computer implemented method and system operate to distribute selected risks, such as market risks, through a selected counterparty that can assume certain of these market risks in a financial transaction which also includes certain behavioral risks.

The invention discloses a risk early warning method of a juvenile dangerous behavior. The method comprises the following steps that: step 1, an initial examination question is selected; step 2, a tested person answers the examination question; step 3, according to an answer situation, an estimation level value is calculated; step 4, a current test information quantity is calculated and summation is carried out on the test information quantity; step 5, it is determined whether a terminal condition is satisfied or not; if so, a step 6 is turned to; if not, another examination question is selected and the step 2 is skipped to; and step6, risk coefficient is calculated, a dangerous behavior level is determined, a test report is output and the test is terminated. According to the risk early warning method provided in the invention, a dangerous behavior high risk group can be identified rapidly and effectively.

The invention relates to an operation behavior risk identification method and device, computer equipment and a storage medium. The method comprises the steps of obtaining operation data, and performing feature data extraction on the operation data to obtain first feature data; performing statistical analysis on the first feature data according to the dimensions of the risk objects to obtain secondfeature data, the second feature data including feature data of behavior features of the risk objects; according to the second feature data, determining an exception identification result of each behavior feature of each risk object; determining a risk index of each risk object and a risk characteristic of each risk object according to the abnormal identification result; and outputting a risk identification result of each risk object, wherein the risk identification result comprises a risk index of each risk object and a risk characteristic of each risk object. By adopting the method, abnormal data in the operation process can be identified, an enterprise is helped to discover operation behavior risks in time, abnormal points and risk objects are positioned, and thus the enterprise is helped to control losses.

The invention discloses a mobile phone bad use behavior risk early warning system based on a heart rate variability index, which comprises a module 1, a module 2, a module 3 and a module 4, the module 1, the module 2, the module 3 and the module 4 are connected in a wireless mode, the module 1 and the module 2 are connected through Bluetooth, the module 2 and the module 3 are connected through Bluetooth, and the module 2 and the module 4 are connected through WIFI. According to the mobile phone bad use behavior risk early warning system based on the heart rate variability index, possible physiology and emotion risks when a user uses a mobile phone can be monitored, the risk identification is based on the human body heart rate variability index and statistics, and considerable scientificity, exploratory performance and predictability are achieved; and according to the invention, the risk coefficients of users of different age groups using mobile phones in different body postures under different mobile phone using conditions can be evaluated.

The invention discloses a behavior risk intelligent early warning method and system for special personnel. The method comprises the following steps: establishing a sample data set characterized by human physiological information in a set situation; designing an SVM classifier between every two different types of samples according to the sample types contained in the sample data set; acquiring human body physiological information data of a to-be-tested person in the set situation through a sensor and preprocessing the data; respectively inputting the preprocessed data into each SVM classifier;and according to the data type output by each SVM classifier, selecting the data type with the largest number as a final behavior risk level. According to the intelligent evaluation system based on the physiological data, a complete data set can be established by collecting human physiological signals of a specific scale at a time through a plurality of preprocessing operations, re-crime risk evaluation work can be completed only by inputting short-time multi-mode bioelectricity signals of a subject, and personal privacy data is not involved.

The invention discloses a teaching method based on a dangerous driving behavior evaluation model. The teaching method includes: a driving behavior evaluation model to be disposed in a vehicle, which is used for obtaining the driving behavior video information of a driver in real time, carrying out the dangerous behavior recognition of a driving video, and setting each dangerous behavior to correspond to a preset behavior value, and summing preset behavior values corresponding to all the identified dangerous behaviors to obtain a behavior risk value; and a server, which obtains a result of the driving behavior evaluation model, sets a behavior risk value threshold value, and judges the driving behavior if an evaluation value of the result breaks through the threshold value, the judgment result comprising the driving behavior needing to be reminded and the driving behavior needing to be corrected. According to the invention, the driving behavior can be supervised in real time, and information is collected, so that targeted teaching is carried out.

The invention relates to a quantitative analysis method for morbidity risk factors of old-age lung cancer, and belongs to the technical field of medical biological information processing. The method integrates relevant data of the investigated object, such as demographic data, smoking habits, disease history, radiation exposure and behavioral risk relevant data; aiming at the problem that the actual number of patients suffering from the lung cancer is far lower than the number of non-patients, data imbalance processing is carried out, and then the data is preprocessed and layered; a deep neural network method is used to train models of the senile hierarchical data, respective risk factors are recognized, and quantitative analysis of the risk factors of the senile lung cancer is carried out. The method has the advantages of high precision and high calculation speed, and can be used for high-speed calculation of large-scale data; the method has the advantages of quantitative analysis and high accuracy, and is simple to operate.

The invention relates to the technical field of big data, is applied to the field of intelligent security and protection, and discloses a behavior risk identification visualization method, device andequipment and a storage medium, which are used for visually displaying risk data and improving the intuitiveness and quickness of behavior risk identification. The behavior risk identification visualization method comprises the steps of collecting first behavior data, a burying point identifier and a target object identifier of a target object from a terminal of a preset burying point mechanism; performing data mining on the first behavior data according to the buried point identifier, the target object identifier and a preset feature type to obtain multi-dimensional behavior features of the target object; performing risk assessment on the multi-dimensional behavior characteristics of the target object through a preset risk identification engine to obtain a plurality of assessment results;and when it is detected that at least one evaluation result has a risk, obtaining second behavior data of the target object, and processing the first behavior data and the second behavior data through a preset two-dimensional map application to obtain a target visual interface.

The invention relates to a driving behavior risk prediction method and system. The method comprises the following steps: acquiring historical travel data; performing data cleaning on the historical travel data; performing label definition on the cleaned travel data to obtain label parameters; determining a trained risk prediction model by adopting a Fibonacci algorithm according to the label parameters; acquiring current travel data; and determining current label parameters according to the current travel data, and performing risk prediction by using the trained risk prediction model. According to the invention, the risk can be judged dynamically and timely, and the real-time performance of risk evaluation and early warning is ensured.

The invention discloses a chronic disease medicine taking behavior risk quantification analysis method and application, and the method comprises the steps: defining the risk level of a medicine takingbehavior, i.e., firstly setting the medicine taking time which is the time for suggesting a patient to take medicine in a treatment scheme, building a risk model through employing the medicine takingtime and the actual medicine taking time of the patient as parameters, and forming various applications of risk grade axis, risk quantitative calculation of medicine taking behaviors and risk quantitative analysis of the medicine taking behaviors. The problems that in the medicine taking process of a chronic disease patient, the risk brought to treatment by illegal medicine taking behaviors cannot be clearly or timely known, the actual medicine taking situation cannot be timely adjusted, correct treatment of the disease is delayed, and a treatment scheme is not personalized, humanized or reasonable enough are solved; and the method can be widely applied to the field of chronic disease treatment and supervision technology application.

The invention provides an intranet user behavior analysis method and system based on weight calculation. The method comprises the following steps: acquiring all behavior information of an intranet user in a preset time period; analyzing and matching the behavior information, labeling corresponding users and entities, and aggregating to form a plurality of user behavior sequences; based on the user behavior sequence, calculating a high-weight tag corresponding to each user by using a TF-IDF algorithm; and monitoring and analyzing the high-weight label of each user, and determining the risk behavior of the user. According to the invention, user portraying can be accurately carried out, and important behavior risks can be prevented and controlled in a targeted manner.

The invention discloses a network security state assessment method and device and a storable medium, which are specifically applied to the technical field of network security assessment, and the method comprises the following steps: obtaining current operation information, and preprocessing the current operation information to obtain a corresponding behavior feature vector and a behavior feature value; processing the behavior characteristic value by using a preset risk probability model to obtain a corresponding behavior risk probability; constructing a behavior detection model, and detecting the behavior feature vector by using the behavior detection model to obtain a corresponding detection result; fusing the detection result with the behavior risk probability, and evaluating the risk existing in the current operation information according to a preset threshold value to realize evaluation of the network security state; according to the invention, the accuracy of network security assessment can be improved while the user operation behavior is comprehensively analyzed.

The present invention provides an evaluation method, system and readable storage medium based on psychological literacy. The method includes: according to a preset cycle, and using an evaluation table to evaluate the psychological literacy of students within a specified range; event information of each student; build an extreme behavior risk prediction model, and train the extreme behavior risk prediction model to obtain the optimal extreme behavior risk prediction model; based on the event information of each student, and through the optimal behavior risk prediction model Predict and obtain the extreme behavior risk value of the corresponding student; determine whether the extreme behavior risk value reaches the first preset threshold, if so, mark the corresponding student as an early warning student, and trigger the use of an evaluation form to evaluate the early warning student in time , and determine whether to intervene in psychological counseling intervention based on the evaluation results. The invention can actively discover the psychological problems of the students, and is beneficial to the mental health development of the teenagers.

The embodiments of the present application provide a user behavior risk assessment method and device. The scheme is as follows: obtain the behavior data of the user to be evaluated; extract the target behavior sequence vector corresponding to the preset behavior event from the behavior data; input the target behavior sequence vector into the risk evaluation model obtained by pre-training to obtain the behavior risk evaluation value of the user to be evaluated , the risk assessment model is a model obtained by training a recurrent neural network and a logistic regression algorithm according to a preset training set. The preset training set includes behavior sequence vectors of multiple sample users and real behavior risk assessment values ​​of sample users. By applying the technical solutions provided by the embodiments of the present application, the assessment of the risk degree of user behavior can be realized.

The invention relates to a behavior risk detection method and device and a clustering model construction method and device. The method comprises the steps that behavior log data are input into a pre-established clustering model, prediction points and clustering results corresponding to the prediction points are obtained, and the behavior log data comprise log data generated by behaviors of a using system; calculating a deviation degree according to the prediction point, the total number of the clustering points in the clustering result and the clustering centroid in the clustering result; and determining a risk level corresponding to the behavior according to the deviation degree and a preset evaluation probability. By adopting the method, the deviation degree can be calculated by using the clustering model without depending on expert experience, and the finally determined risk level can feed back a real risk condition in an actual application scene.

The invention provides a dangerous behavior early warning method based on action rules. The method comprises the following steps: determining key monitoring personnel and acquiring at least one key event; determining at least one key management and control place corresponding to the key monitoring personnel; monitoring each key management and control place in real time, and when the key monitoring personnel appear at any key management and control place, acquiring a time risk index, a place risk index and a same personnel type risk index of the key monitoring personnel based on the key events; and predicting a behavior risk index of the key monitoring personnel according to the time risk index, the place risk index and the same personnel type risk index, and performing early warning if the behavior risk index is greater than a set threshold value. According to the method, the behavior risk index of the key personnel is analyzed and evaluated from multiple dimensions, and timely early warning of the key personnel in each key management and control range is realized.

The invention relates to an abnormal behavior risk assessment model building method and an abnormal behavior risk assessment method.The abnormal behavior risk assessment model building method provided by the embodiment of the invention comprises the steps that target data are collected, and the target data are used for assessing abnormal behaviors; preprocessing the target data to obtain a target vector, and constructing a data set according to the target vector; a neural network model is constructed, the neural network model comprises multiple layers of full-connection networks, and each full-connection layer comprises batch standardization; the neural network model is trained through the data set, an abnormal behavior risk assessment model is obtained, the abnormal behavior risk assessment model is used for monitoring abnormal behaviors, abnormal behavior risk assessment can be achieved directly through automatic data collection, the mode of manual interview and return visit is not needed any more, and manpower resources are saved.

The invention provides an evaluation method and system based on psychological attainment and a readable storage medium, and the method comprises the steps: carrying out the psychological attainment evaluation of students in a specified range according to a preset period through employing an evaluation table; acquiring event information of each student in a specified range; constructing an extreme behavior risk prediction model, and training the extreme behavior risk prediction model to obtain an optimal extreme behavior risk prediction model; based on the event information of each student, predicting an extreme behavior risk value of the corresponding student through an optimal behavior risk prediction model; determining whether the extreme behavior risk value reaches a first preset threshold value or not, if so, marking the corresponding student as an early warning student, triggering an evaluation table to evaluate the early warning student in time, and determining whether psychological tutoring intervention is involved or not according to the evaluation result. The psychological problems of trainees can be actively found, and psychological healthy development of teenagers is facilitated.

The present application relates to a driver state monitoring method, device, system and controller. The method includes: when the vehicle is in a driving state, acquiring the driver's video information, physiological information and driving information; according to the video information, obtaining the driver's behavioral risk level; according to the physiological information, obtaining the driver's physiological risk level ;According to the driving information, the driver's driving risk level is obtained; the highest risk level among the behavioral risk level, physiological risk level and driving risk level is selected as the driver's current risk level; according to the current risk level, the execution and current risk level corresponding operation. By adopting the method, the state of the driver can be monitored comprehensively and accurately.