67 results about "Behavioral risk" patented technology

The invention discloses a curve driving collision early warning method and system based on multi-risk field fusion, and the method comprises the steps: 1, collecting vehicle dynamics data in real time, and carrying out the short-time prediction; step 2, geometric modeling and rasterization of a road; 3, constructing a geometric risk field; 4, constructing a kinetic energy risk field; 5, constructing a behavior risk field; step 6, constructing a comprehensive risk field; step 7, collision risk judgment; and step 8, generating an active intervention strategy. Collaborative perception and risk prediction of vehicles, roads and driving behaviors are realized through multi-risk field fusion, and potential collision hidden dangers of curve driving are identified in advance; a vehicle dynamics model and risk correction based on road adhesion are introduced to realize accurate risk assessment of the vehicle; multi-time window trajectory prediction and behavior modeling are adopted, so that the recognition capability of complex driving behaviors is improved; a closed-loop system from risk prediction and decision making to active control is realized, and the safety and stability of the vehicle driving in the curve scene are effectively improved.

The invention relates to the technical field of block chain technology and asset transfer, in particular to a multi-risk interception and artificial intelligence error correction method in RWA asset cross-chain transfer, comprising the following steps: step 1, cross-chain transfer initialization and asset mapping verification; step 2, multi-level dynamic risk interception; step 3, an artificial intelligence error correction mechanism; step 4, asset right confirmation and log auditing after cross-chain completion; according to the method, technical risks (such as contract vulnerabilities), data risks (such as information inconsistency) and behavior risks (such as abnormal transactions) in RWA asset cross-chain transfer are covered through a multi-level interception system of "pre-transaction-in-process-contract layer" in combination with AI dynamic learning ability, the risk identification rate is greatly improved compared with a traditional method, and the risk identification efficiency is greatly improved. An artificial intelligence error correction mechanism is used for automatically classifying risk types and generating strategies, so that the average error correction time is shortened, and the error correction success rate is improved.

The invention relates to the technical field of agent testing, and particularly provides an agent dynamic behavior safety testing method and system based on a risk conduction quantification model, and the method comprises the steps: collecting agent behavior data which comprises a plurality of pieces of risk operation information and a risk association relationship; constructing a behavior risk association map by taking the risk operation as a node and the association relationship as an edge, labeling a risk attribute and an aging factor for the node, and labeling risk conduction intensity for the edge; constructing a risk rule base based on the risk labeling result and a preset risk judgment logic; inputting the behavior data, the behavior risk association map and the rule base into a trained quantitative model, and calculating a risk conduction probability, an influence range and a critical node; monitoring a behavior track in real time, suspending a task when a multi-dimensional risk threshold is triggered, backtracking an inference chain and generating an intervention record; and fusing the multi-dimensional data to generate a test report. According to the method, the problems of correlation risk missed judgment and response lag in the traditional test are solved, and the pertinence and reliability of the safety test are improved.

The invention relates to the technical field of medical data analysis and rehabilitation, in particular to a cerebral apoplexy patient rehabilitation exercise analysis system based on medical big data, which comprises a data acquisition module used for acquiring electromyographic signals, exercise data and behavior data of a patient to form a data set; the data processing and fusion module is used for preprocessing the electromyographic signals in the data set, normalizing motion and behavior data and fusing the motion and behavior data into a multi-modal feature vector; the hierarchical analysis module is used for performing motion intention recognition according to the preprocessed electromyographic signals and outputting an intention classification result, recognizing the motion intention and predicting a rehabilitation effect through a double-flow Transform encoder, and evaluating a behavior risk level in combination with a risk factor formula; the optimization module is used for dynamically adjusting the mirror image treatment intensity according to the risk level and optimizing the prediction model by adopting incremental learning; and the interaction module is used for generating a thermodynamic diagram superposition rehabilitation curve in the X-Y coordinate system, and marking doctor intervention suggestions to complete visual interaction.

The invention discloses a wind-solar-water-storage complementary system short-term risk scheduling method considering uncertainty, and the method comprises the steps: converging historical physical operation data and multi-subject behavior data, and constructing a training data set and a system parameter set; based on the training data set, constructing a combined robust radius and wind-solar combined scene containing behavior risk quantification; based on the system parameter set and the training data set, constructing a dynamic risk scheduling unit fusing multi-dimensional risks; solving a candidate short-term scheduling scheme by combining a joint robust radius and a dynamic risk scheduling unit and adopting a behavior risk-oriented Bayesian optimization method; and performing multi-subject consensus evaluation on the candidate short-term scheduling scheme, determining a final execution short-term scheduling scheme, and performing uplink execution. According to the method, the problem of separation of physical risks and behavior risks is solved, and the behavior acceptability of the scheme is improved.

The invention relates to the technical field of rehabilitation instruments, in particular to a whole-body joint rehabilitation training device and a control method thereof. The method comprises the following steps: acquiring joint angles, IMU data and electromyographic signals when a user executes a plurality of standard actions; according to the joint angle, determining a joint motion range risk level of the target joint; according to the joint angle, the IMU data and the electromyographic signal, determining a compensatory behavior risk level and a cooperative efficiency score of a target joint; determining a training mode according to the risk level of the compensatory behavior, wherein the training mode at least comprises a general mode and a mode for the compensatory behavior; determining training parameters according to the joint motion range risk level, wherein the training parameters comprise resistance; adjusting the training parameters according to the collaborative efficiency score; and outputting the training mode and the adjusted training parameters. According to the method, the rehabilitation efficiency can be improved, and compensatory behaviors during training are reduced.

A system and method for quantifying the impact of investor behavioral persona on investment outcomes. A Behavioral Risk Index (BRI) is calculated based on investor behavioral factors including investor type, behavioral biases, and financial literacy. The system simulates behavior-impacted investment returns by mapping exit and re-entry points of investor groups sharing a common behavior persona during historical market events. The simulated behavior-impacted investment outcomes is quantified by comparing to buy-and-hold strategy. Personalized visualizations, including the visualization of the BRI, behavioral factor breakdowns and performance comparisons, are displayed to assist financial professionals and investors in identifying behavioral risk exposure and demonstrating the value of behavioral coaching.

The invention relates to the technical field of campus safety protection, in particular to a method and system for preventing campus bullying by using a laser radar imaging technology, and the method comprises the following steps: obtaining 3D point cloud data of an environment in a monitoring region preset in a campus, and collecting sound data in the monitoring region; processing the 3D point cloud data, generating a three-dimensional environment image and extracting a first behavior feature; processing the sound data, and extracting a second behavior feature; performing multi-modal fusion on the first behavior feature and the second behavior feature to generate a unified behavior feature vector; based on the behavior feature vector, identifying whether a bullying behavior exists in the current scene, and calculating a corresponding behavior risk score; according to the method and the device, whether the preset alarm threshold value is reached or not is judged, and if yes, corresponding alarm and early warning operation is triggered, so that non-intrusive active monitoring of non-appearance privacy leakage of the campus secret corner is realized, and the problems of privacy limitation and blind areas of traditional video monitoring are solved.

The invention provides a user permission dynamic adjustment method and device, equipment and a medium, and the method predicts a target permission required by a target service based on a permission prediction model and a target service context vector when a service change instruction is detected. And on the basis of the permission prediction model and the target user behavior sequence, the real-time permission demand of the user and the confidence coefficient corresponding to the user behavior risk are predicted, so that the permission prediction accuracy is improved, and the data leakage risk is reduced. Based on the current permission of the target user and the target permission set, the user does not need to perform manual application and approval, and the approval of the incremental permission is automatically generated, so that the permission adjustment operation is simplified, and the permission adjustment efficiency and the adjustment accuracy are improved. The method can be applied to authority approval nodes in the financial business field or the medical pension field, so that the authority approval efficiency and accuracy are improved.

The invention discloses an unmanned aerial vehicle detection and tracking adaptive low-altitude economic road picketing and logistics method, and relates to the technical field of unmanned aerial vehicles. In the aspect of road picketing, road vehicle videos are collected through a combined sensor system carried by an unmanned aerial vehicle, and a driver behavior danger coefficient and a driving danger coefficient are calculated through information processing; vehicle danger levels are evaluated based on the two and corresponding management and control measures are taken, such as conventional monitoring, key tracking, emergency early warning and the like of different levels. In the aspect of logistics application, order receiving and processing, path planning and unmanned aerial vehicle scheduling, flight distribution and goods signing and returning are included, and specific operation and judgment methods of all links of logistics are covered.

The invention discloses a construction site-oriented non-face person dynamic tracking method and system, and the method comprises the steps: collecting the information data of entering persons at a construction site, comparing the information data with a construction face list, and screening suspected non-face persons; establishing a heterogeneous graph based on the information data of the suspected non-face-to-face persons and information data in a face-to-face person database, and performing local similar node aggregation based on the heterogeneous graph to identify target non-face-to-face persons; obtaining a movement track of the target non-face person, and calculating a deviation degree based on the movement track of the non-face person to obtain a track deviation degree index of the target non-face person; constructing a behavior causal graph model based on the trajectory deviation index, and judging the behavior state of the target non-face person based on the behavior causal graph model to generate a behavior risk judgment result; and generating a risk level label based on the behavior risk discrimination result.

This invention discloses a driver state monitoring and safety early warning method based on millimeter-wave radar, comprising: acquiring driver physiological micro-motion signals and driver macro-behavioral signals respectively through millimeter-wave radar to obtain driving environment information in real time; denoising the driver physiological micro-motion signals and extracting features to obtain driver physiological feature signals; constructing a time-frequency spectrum using driver macro-behavioral signals; determining the driver physiological arousal index and driver emotional state risk value based on the physiological feature signals; obtaining the driver dynamic behavior risk value based on the time-frequency spectrum; calculating an environmental complexity index based on driving environment information; constructing a driver state risk field model based on the driver physiological arousal index, driver emotional state risk value, and driver dynamic behavior risk value; calculating the driver state risk value using the driver state risk field model and environmental complexity index; and providing graded early warning based on the driver state risk value.

The present application relates to the field of artificial intelligence and stomatology technology, and discloses a dynamic risk stratification monitoring system for potential malignant diseases in the oral cavity, which comprises a multi-modal data acquisition module, an intelligent risk assessment module, a risk grade division module and a dynamic monitoring module. The present application collects multi-modal data of the patient, including the patient's clinical behavior data, pathological data and basic identity information, carries out multi-dimensional analysis on the collected data to obtain behavior risk factors, pathological risk factors and basic risk factors, weights and sums the three factors to obtain a comprehensive risk coefficient, divides the current risk level of the patient based on the comprehensive risk coefficient, including low risk, medium risk and high risk, and draws a dynamic risk change curve of the patient based on the comprehensive risk coefficient, and determines whether to issue a warning according to the slope of the curve to attract the attention of medical staff to the current patient.

The invention is applicable to the technical field of clinical examination and auxiliary diagnosis, and provides an extreme behavior risk auxiliary diagnosis biomarker combination, a kit and a detection method, the biomarker combination is composed of IL-4, IL-12 and IFN gamma, and the extreme behavior risk can be objectively identified by detecting the expression level of the combination in peripheral blood of a subject. The matched kit comprises specific antibodies aiming at the three markers, a standard substance, a buffer solution and other reagents, and according to the standardized detection method, through peripheral blood sample treatment, incubation, fluorescent staining, flow detection and result analysis, precise evaluation is achieved in combination with a clear judgment threshold value. According to the method, the defect that suicide risk assessment depends on a subjective scale and doctor experience in the prior art is overcome, the operation is convenient, the result is objective, the combined detection AUC of the suicide risk assessment, the subjective scale and the doctor experience can reach 0.726, a reliable extreme behavior risk auxiliary diagnosis means is provided for clinic, and the method is particularly suitable for risk identification of depression patients and has important clinical application value and conversion prospect.

This invention proposes a comprehensive ancient tree health monitoring method and system based on IoT and AI technologies. The method includes: collecting physiological data of ancient trees through sensors and preprocessing it; extracting disturbance indicators from the millimeter-wave radar point cloud frame data through edge computing to represent the intensity of behavioral disturbances at the current time step; initializing the ancient tree health state variable as the current health state and inputting it into a pre-constructed dynamic recursive model to obtain the predicted ancient tree health state variable; sampling the millimeter-wave radar point cloud frame data once per second and performing pre-clustering processing; constructing a graph neural network based on the target point cloud set to encode and extract the disturbance activation degree representing the current behavioral risk; correcting the disturbance indicator according to the disturbance activation degree and outputting the disturbance amount; calculating a comprehensive risk score and dynamically generating corresponding health response actions based on the comprehensive risk score.

The application relates to the technical field of agent testing, and particularly provides an agent dynamic behavior safety testing method and system based on a risk transmission quantitative model, which comprises the following steps: collecting agent behavior data, containing a plurality of risk operation information and risk correlation relations; constructing a behavior risk correlation graph taking risk operations as nodes and correlation relations as edges, marking risk attributes and time limit factors on the nodes, and marking risk transmission strength on the edges; constructing a risk rule library based on the risk marking results and a preset risk judgment logic; inputting the behavior data, the behavior risk correlation graph and the rule library into a trained quantitative model to calculate risk transmission probability, influence range and critical nodes; real-time monitoring of behavior trajectories, task suspension, backtracking reasoning chain and intervention record generation when a multi-dimensional risk threshold is triggered; and test report generation by fusing multi-dimensional data. The application solves the problems of missed judgment and response lag in traditional testing, and improves the pertinence and reliability of safety testing.

The embodiment of the invention provides a behavior risk prediction method, electronic equipment, a storage medium and a program product. The behavior risk prediction method comprises the steps of obtaining a plurality of static tags and a plurality of pieces of behavior text data of a target object; fusing the behavior text data and a plurality of static tags into a behavior input vector, and encoding the static tags into tag vectors; extracting semantics of each behavior input vector and sorting according to time to obtain a semantic vector sequence; weighting semantic vectors in the semantic vector sequence in each time window in the plurality of time windows, and aggregating weighting results of the time windows to obtain behavior risk representation of the target object; and predicting a risk score of the target object based on the behavior risk representation and the plurality of tag vectors. According to the method, static and dynamic data are fused, short-term burst and long-term accumulation risks are considered through semantic extraction and multi-time window weighting, the dynamism, comprehensiveness and accuracy of risk prediction are improved, and reliable support is provided for risk management and control.

The invention belongs to the technical field of medical health monitoring and behavior intervention, and discloses a pre-school autism sleep personalized intervention system based on AI behavior analysis, which comprises a behavior acquisition unit, an environment monitoring unit, a living habit management unit, a fusion analysis unit, an intervention execution unit, a self-adaptive optimization unit and a family interaction unit, according to the system, body movement, heart rate, skin electricity and sleeping posture information of children and environment parameters such as illumination, noise and temperature and humidity of a bedroom are synchronously collected, diet and exercise balance indexes are combined for fusion analysis, and behavior risk indexes are generated; a personalized intervention scheme is automatically generated according to the risk level, and intervention is implemented through light, sound and visual guidance modes. The system dynamically adjusts analysis parameters according to an intervention result, realizes closed-loop correlation analysis and continuous regulation and control of sleep behaviors, environment changes and parent feedback, and is suitable for individualized sleep management of children with autism.

The invention relates to a method for analyzing the influence of subjective workload on driving behavior risk, and the method comprises the steps: obtaining the multi-dimensional basic data of a to-be-detected target, constructing a risk driving behavior index system based on the original data of driving behaviors, carrying out the normalization and weighting of risk driving behavior indexes, and obtaining a risk driving behavior result. A driving adaptability evaluation decision matrix is constructed based on driving adaptability data; a comprehensive driving adaptability level is obtained through analysis; subjective workload evaluation indexes are processed; subjective workload grades are divided according to a comprehensive efficiency value; and constructing a collaborative analysis framework comprising a generalized linear hybrid model sub-module and a support vector machine sub-module, solving the SHAP value of each subjective workload evaluation dimension to quantify the feature contribution degree, and outputting an analysis result. The method reveals the degree of influence of subjective workload on the driving behavior risk, the key influence dimension and the nonlinear rule, and can effectively improve the pertinence and effectiveness of driving safety related products and strategies.

The invention discloses a multi-modal data fusion-based personnel risk assessment method. The multi-modal data fusion-based personnel risk assessment method comprises the steps of obtaining positioning data, video data and physiological data of a to-be-assessed person; according to the video data and the physiological data, respectively determining a behavior risk probability and a physiological risk probability of the to-be-assessed person; according to the positioning data, determining an area where a to-be-assessed person is located and a positioning risk probability; according to the determined area where the to-be-assessed person is located, dynamic weight values of the positioning risk probability, the physiological risk probability and the behavior risk probability of the to-be-assessed person are determined respectively; and according to the determined dynamic weight values of the positioning risk probability, the physiological risk probability and the behavior risk probability, calculating the risk value of the to-be-assessed person.

The invention discloses a user behavior risk prediction method and device, equipment and a medium. The method comprises the following steps: constructing a graph structure according to feature data of a plurality of users; fusing the feature of each node in the graph structure with the feature of the corresponding neighbor node to obtain the static node feature of the node; determining the dynamic node characteristics of each node according to the characteristics of each node in the graph structure at different time points; and determining a behavior risk prediction result of a user corresponding to each node according to a fusion feature of the static node feature and the dynamic node feature of each node in the graph structure. According to the method, the static node features representing the graph structure features and the dynamic node features representing the time dependence are fused, so that the complex relationship between the nodes is represented, the behavior change features at different time points are dynamically and adaptively extracted, and the accuracy of user behavior risk prediction is improved.

The present application relates to the technical field of rehabilitation equipment, and particularly relates to a whole-body joint rehabilitation training device and a control method thereof. The method of the present application comprises: collecting joint angles, IMU data and electromyographic signals when a user performs a plurality of standard actions; determining a joint range of motion risk level of a target joint according to the joint angles; determining a compensatory behavior risk level and a synergy efficiency score of the target joint according to the joint angles, the IMU data and the electromyographic signals; determining a training mode according to the compensatory behavior risk level, wherein the training mode at least comprises a general mode and a mode for compensatory behavior; determining a training parameter according to the joint range of motion risk level, wherein the training parameter comprises resistance; adjusting the training parameter according to the synergy efficiency score; and outputting the training mode and the adjusted training parameter. The method of the present application can improve rehabilitation efficiency and reduce compensatory behavior during training.

The invention provides an interface security protection method and device, equipment, a medium and a product, which can be applied to the technical fields of artificial intelligence, big data and financial science and technology. The method comprises the following steps: acquiring a client interface request, and extracting request data; performing message body semantic analysis on the request data, and outputting a text abnormal feature based on a message body semantic analysis result; performing parameter structure verification on the request data, and outputting structure risk features based on a parameter structure verification result; performing behavior analysis on the current behavior data of the user based on the historical behavior data of the user, and outputting behavior risk features based on a behavior analysis result; determining a service sensitivity weight coefficient based on service context information of the interface request; carrying out weighted fusion on the text abnormal features, the structure risk features, the behavior risk features and the business sensitivity weight coefficients to calculate a real-time risk score; and executing a corresponding risk handling strategy for the interface request based on the real-time risk score.

The invention discloses a behavior risk detection method, system and device based on an evidence theory. The method comprises the following steps: acquiring at least two behavior evidences related to a first user subject based on log data, wherein the behavior evidences are preset user behavior data with operation risks; probability distribution results of the first behavior evidence for at least two propositions are determined, the propositions belong to subsets of a preset set, different elements in the preset set are used for representing different risk prediction results, and the number of the elements in the preset set is larger than or equal to 2; and fusing the probability allocation results of the at least two behavior evidences for the at least two propositions to obtain a comprehensive confidence coefficient of each proposition in the at least two propositions corresponding to the first user subject, and performing risk assessment on the behavior of the first user subject based on the comprehensive confidence coefficient. Therefore, the support degree of the association combination between different behavior evidences on the risk proposition is quantified, and the accuracy of the risk detection method is improved.

A system, method, and computer-readable medium are disclosed for performing behavioral risk analysis operation. The behavior risk analysis operation includes: monitoring an entity, the monitoring observing an electronically-observable data source; observing a behavior of the entity based upon the monitoring; identifying a standardized risk factor associated with the entity; analyzing the behavior and the standardized risk factor associated with the entity; and, performing a security operation in response to the analyzing the behavior and the standardized risk factor.

The invention discloses a prescription circulation management system for multi-role permission subdivision, and relates to the technical field of medical informatization, and the system comprises a prescription generation and verification module which is in butt joint with an HIS system, and automatically checks and archives an electronic prescription; the role permission dynamic adaptation module adjusts the permission based on a risk algorithm and generates an encryption token; the prescription circulation scheduling module distributes prescriptions according to authority and pushes medicine purchasing information; the abnormal behavior recognition module recognizes violation through a knowledge graph and an algorithm and generates an early warning; the medicine service module provides personalized guidance and encrypts whole-process data; through a dynamic permission adaptation mechanism, the permission boundary is adjusted in real time in combination with scene and behavior risks, the limitation of static management is broken through, and the safety and compliance of medical data are improved; meanwhile, a medical rule knowledge graph and a causal model are constructed, complex violation behaviors are accurately identified, an early warning report is generated to assist supervision, and the industry risk prevention and control capability is remarkably enhanced.

This application relates to the field of physiological function assessment technology, and in particular to a method and system for assessing swallowing function in psychiatric patients based on multidimensional physiological signals. The method includes: S1, acquiring basic information and medication information of the psychiatric patient to be assessed, and collecting multidimensional physiological signals; S2, extracting swallowing feature vectors based on these signals; S3, identifying behavioral risks in psychiatry based on swallowing event sequences and inertial measurement signals, obtaining behavioral risk labels and their confidence levels; S4, extracting extrapyramidal response representation vectors based on multidimensional physiological signals, and outputting the probability of swallowing difficulties related to extrapyramidal responses through a pre-set classification model; S5, mapping different abnormal indicators to item scores for nursing risk assessment items, obtaining a total score; S6, generating an assessment report containing item scores, total score, risk level, and key physiological evidence. This application solves the problem that existing assessments rely on subjective observation and lack objective evidence support.

The invention specifically relates to the technical field of data processing and analysis, and discloses a child eye behavior recognition and optical defocus regulation and control method, which comprises the following steps: S1, physiological behavior data acquisition: acquiring physiological behavior data; s2, child eye use risk judgment: judging a child eye use behavior risk level; s3, designing an optical defocusing scheme: calculating optical defocusing parameters; s4, behavior auxiliary intervention collaborative formulation; s5, real-time monitoring and real-time intervention: making a real-time linkage intervention strategy, and setting a behavior reminding triggering mechanism; s6, short-term and middle-term iteration and redisk: formulating an iteration adjustment strategy, and by constructing a closed-loop collaborative mechanism of data acquisition, risk judgment, multi-means intervention, effect feedback and iterative optimization and constructing a behavior-optical dynamic collaborative optimization mechanism, realizing technical precise tracking and immediate response, enhancing the pertinence of myopia prevention and control, and improving the accuracy of myopia prevention and control. And the overall energy efficiency of the prevention and control system is effectively improved.

The present invention relates to the field of behavioral risk warning technology, and proposes a risk warning method and system for analyzing student behavior trajectories and movements, comprising the following steps: separately collecting video data and sensor data of student behavior; preprocessing the video data and sensor data to obtain multi-source data of student behavior; performing target detection and target tracking on the multi-source data of student behavior to generate student motion trajectories; extracting student behavioral characteristics based on the student motion trajectories, identifying abnormal behavior on the behavioral characteristics, and obtaining abnormal behavior identification results; performing risk level assessment on the abnormal behavior identification results to obtain abnormal behavior risk levels; triggering warning information of corresponding levels based on the abnormal behavior risk levels, processing the warning information, generating final warning results, and pushing the final warning results to management personnel. The present invention achieves all-round monitoring, timely identification, and rapid warning of abnormal student behavior, thereby improving the efficiency of campus safety management.

The invention relates to the technical field of wind power plant personnel management and control, and discloses a wind power plant personnel positioning track deviation management and control method and system based on the Internet of Things, and the method comprises the steps: firstly fusing the real-time positioning data, work order task data and environment state data of personnel, and constructing a time sequence data queue rich in context information; through dynamic time sequence feature engineering, the kinematics features and the behavior mode features of the trajectory are converted into personnel positioning trajectory time sequence feature vectors; and then an unsupervised learning model is used to carry out abnormal probability scoring on the personnel positioning track time sequence feature vector, and finally, dynamic grading alarm is realized according to a quantized abnormal score. Therefore, accurate identification, quantitative evaluation and intelligent early warning of personnel behavior risks are realized through deep fusion of multi-source data and application of a time sequence data analysis model, so that the defects in the prior art are overcome.