12 results about "Anger" patented technology

The invention discloses an outbound system interaction method based on voice recognition, and relates to the technical field of outbound system interaction, and the method comprises the steps: obtaining callback data and combining an anger grade coefficient, calculating emotion preferential money of an anger customer, informing the customer in the form of coupons, and issuing the coupons; according to the method, the regional emotion coefficient and the emotion data are combined, the emotion perception index is scientifically calculated by utilizing a formula, the customer emotion can be objectively distinguished into satisfaction or anger, and the anger emotion is finely recognized; the angry level coefficient is calculated through the voice feature data, and the severity of the angry emotion of the client is quantified; the emotion preferential fund is dynamically calculated based on the recovery data and the anger level coefficient, and is automatically issued through voice and information in the form of coupons, so that real-time response and compensation to the emotion of the customer are realized, the anger emotion of the customer is effectively recovered, the customer satisfaction is remarkably improved, and the customer experience is improved. And therefore, the practicability and competitiveness of the outbound system in marketing and customer service scenes can be enhanced.

The invention relates to the technical field of emotion recognition, and particularly provides an emotion recognition method and device based on a large model, a medium, equipment and a product, and the method can comprise the steps: classifying to-be-recognized audio data, and obtaining user audio; generating a model input instruction based on the user audio and the related information of the user audio; wherein the related information comprises scene information, context information and task information related to the user audio; inputting the model input instruction into a pre-trained target large model, and outputting an emotion category of the user; wherein the emotion types include anger, anger, irony, gentle or happy. Some embodiments of the present application can improve the precision and effect of emotion recognition.

This application relates to the field of psychophysiological monitoring and data analysis technology, specifically an artificial intelligence-based dynamic stress and emotion analysis method and system. The method includes the following steps: collecting students' physiological data and body movement data while they are performing hazardous tasks; analyzing the physiological data and body movement data based on a preset algorithm to determine if the students have negative emotions, including anxiety, tension, fear, anger, and lethargy; issuing an early warning signal when negative emotions are detected and initiating a response procedure to notify relevant personnel, including users, rescue personnel, and students; generating a health feedback report and sending it to users, including teachers, instructors, and project leaders. This application has the capability to efficiently and in real-time monitor and analyze the psychological stress and emotional state of large groups in complex and high-risk work environments, particularly in educational practice training and emergency situations.

This invention belongs to the field of pharmaceutical technology, specifically relating to the application of N-methyltyramine in the preparation of drugs for improving anger and irritability. The CAS number of the N-methyltyramine (NMT) is 370-98-9. Experimental results show that NMT can directly target and positively regulate the γ-aminobutyric acid type A receptor (GABA). a NMT activates the downstream AC5-cAMP-PKA signaling pathway, thereby improving abnormal emotional states such as anger and depression. Through network pharmacology, molecular docking, micro-thermophoresis experiments, animal behavioral experiments, and cell experiments, NMT significantly improves the behavior of model animals. Therefore, this invention reveals for the first time that NMT improves abnormal emotional states such as anger and depression through GABA. a The mechanism by which R-related signaling pathways play a role in mood regulation can be used to prepare drugs for treating mood disorders such as anxiety, depression, and post-traumatic stress disorder, and has good application prospects.

The invention discloses a driver anger state early warning method and system based on vehicle movement, and the method comprises the following steps: obtaining the historical driving factors of anger mood migration, the historical driving factors including the headstock distance and the trajectory deviation; constructing a Gaussian mixture hidden Markov model based on a Gaussian mixture model and a continuous hidden Markov model, and training the Gaussian mixture hidden Markov model based on the vehicle head distance and the trajectory deviation to obtain an angry state recognition model; and monitoring the driver by using the anger state recognition model, and when the anger level is monitored to be higher than a preset threshold value, intervening the driving behavior of the driver. The method not only can improve the rationality and applicability of the angry state recognition model, but also provides an adaptive decision basis based on the driver emotional state for the driver model used by the traffic vehicle in automatic driving simulation, and further optimizes the man-machine interaction experience of the automatic driving vehicle.

The present application belongs to the technical field of audit information analysis, and discloses a fraud identification method, system and device based on non-verbal cues, the fraud identification method comprising: obtaining non-verbal cue features of interviewees during an interview, including pitch, loudness, fundamental frequency jitter, amplitude perturbation, harmonic-to-noise ratio and spectral difference features of the first and second harmonics of sound; eyebrow inner and outer side lifting, eyelid tightening, gaze direction, nose lifting, cheek lifting, cheek muscle dimple, mouth corner lifting, mouth corner vertical downward, chin lifting, lip tightening, lip separation, lower jaw drooping features, and fear and anger emotion features of face; analyzing the features of each channel respectively, and outputting the fraud risk level of the interviewee at the current time according to the deviation degree of each feature relative to the baseline threshold. Based on the above method, the audit personnel can quickly and accurately identify the fraud risk of the interviewee in the audit inquiry.

The application discloses a multi-modal driver anger emotion regulation method based on hearing and olfaction, and comprises the following steps: performing global acoustic feature extraction on a driver voice signal to obtain global feature information; performing local spectrum feature extraction on the driver voice signal to obtain local feature information; fusing the global feature information and the local feature information, and performing emotion classification on the fused feature information to obtain an emotion classification result; preparing an audio and an odor; and regulating an angry emotion in the emotion classification result by playing the audio and releasing the odor. The application has high emotion recognition accuracy, wide application range and good emotion regulation effect.

The invention relates to a three-dimensional display emotion management device based on multi-mode emotion perception, and solves the problems of single display dimension and weak perception ability of existing emotion feedback equipment. According to the technical scheme, a multi-mode sensing layer is integrated with a millimeter wave radar (penetrating clothes to detect breath / heartbeat), a camera (analyzing micro expressions), a microphone (recognizing voice and intonation) and a wearable equipment communication interface, and non-contact physiological signal fusion collection is achieved; the three-dimensional display core adopts a double-layer LED array or a spherical flexible OLED screen to generate more than ten 3D emotion particle flow maps (such as anger-lava eddy current red light and calm-deep sea ripple blue light), and is also suitable for geometries such as spheres, cubes, cuboids, cylinders, cones, prisms and pyramids; the emotion management closed loop enhances cognition through a real-time emotion mirror image, a'forest breathing 'guide mode is automatically triggered when anxiety continues for 10 minutes, and a built-in memory records emotion data at an interval of 5 minutes and outputs a time sequence analysis report.

The application discloses a driver psychological monitoring method and system based on electroencephalogram signals, real-time acquisition of the driver's head electroencephalogram signals through a multi-channel electroencephalogram device, extraction of multi-dimensional features after pretreatment, and accurate identification of emotional states such as tension, fatigue and anger by using a support vector machine model. The system adopts a dynamic threshold determination mechanism, combines sliding time window statistical analysis and composite rule verification, and adaptively adjusts the emotional abnormality standard, thereby significantly reducing the influence of environmental interference. Based on the real-time monitoring result, a multi-level safety intervention strategy is triggered, and the response is gradually performed from voice warning, speed limitation to vehicle forced takeover, so that the driving autonomy and safety are balanced. The application has high precision, strong robustness and scene adaptability, can effectively reduce the traffic accident risk caused by abnormal driver psychology, and provides an innovative solution for the development of intelligent traffic management and active safety technology.

The invention discloses a video figure abnormal behavior detection method based on emotion enhancement, which innovatively introduces an emotion feature as a core supplement, and provides deep semantic interpretation for abnormal detection by analyzing an emotion motivation (such as anger, panic and attack tendency) behind a behavior. The emotion features provide high-level semantic constraints for the motion features (flow features) and the appearance features (RGB features), and the motion features and the appearance features provide direct and morphological abnormal clues; through the multi-level feature information fusion, the video anomaly detection return method based on emotion enhancement can make accurate judgment for different types of abnormal behaviors (such as irregular actions, emergencies, abnormal crowd behaviors and the like) in a complex video scene, so that the overall anomaly detection performance is effectively improved.

A device for modeling anger based on combined emotional and physical stress relates to the technical field of experimental animal modeling devices and includes a control system, an experimental stand, and experimental cages. Several experimental cages are suspended from the experimental stand, connected to a swing mechanism and driven to swing back and forth by the swing mechanism. An intelligent striking mechanism is provided within the experimental cages, integrating a striking block, a ventilator, a water sprayer, and an air blower. The present invention can disrupt the living habits of mice and rats in various ways, making them irritable through vibrations, striking, air blowing, water spraying, and sleep disruption. An anger model in mice and rats is successfully prepared under multiple pressures, saving manpower and time, reducing operations, reducing human interference, and providing a unified and objective modeling method with accurate experimental results. This device is of reference value for research on the pathogenesis of anger and new drug development, and is worthy of further development and promotion.

The invention discloses a writing sentiment analysis device based on friction nanometer power generation sensing and artificial intelligence, and relates to the crossing field of electronic sensing technology and computer artificial intelligence sentiment analysis. According to the device, writing kinetic parameters such as pressure, speed and pause duration are passively collected through the friction nanometer power generation sensing layer below the writing board, and the invasive problem of traditional emotion monitoring equipment is solved. The device adopts a dual-channel LSTM neural network architecture, a space channel to analyze a parameter distribution mode, a time channel to recognize a dynamic evolution rule, and accurate recognition of sentiment states such as anger, anxiety, calm and pleasure is realized through feature fusion. The system structurally comprises a signal acquisition module, an AI analysis module and a visual output module, and can realize real-time emotion feedback within 200ms. The method is suitable for scenes such as psychological assessment and education tutoring, has the advantages of privacy protection, low cost and the like, and can realize non-intrusive sentiment analysis through natural writing behaviors.