17 results about "Risk behavior" patented technology

This invention relates to the field of artificial intelligence technology, specifically to an AI-based medical information consultation method and apparatus. The method includes: acquiring multimodal medical consultation input from a user; preprocessing and extracting features from the multimodal input, and fusing them to generate a unified multimodal feature representation; based on the feature representation, performing topic indexing in a pre-constructed medical knowledge graph, and extracting a set of highly relevant medical knowledge using unsupervised clustering analysis and a dynamic threshold filtering mechanism; inputting the filtered medical knowledge set and multimodal features into a medical large-scale language model for logical reasoning, and running a multi-turn dialogue intent tracking algorithm during the reasoning process to complete missing entities; intercepting high-risk behaviors and correcting compliance issues in the preliminary response generated by the large-scale language model, and outputting a safe and professional medical information consultation response. This invention significantly improves the cross-modal perception capability, logical reasoning accuracy, and clinical application legitimacy of medical AI.

The invention discloses a behavior early warning method, device and equipment, a medium and a product, and relates to the technical field of intelligent supervision. The method comprises the following steps: acquiring multi-modal behavior data and scene feature information of a supervised target, wherein the multi-modal behavior data comprises positioning track data, physiological data and supervision behavior data; according to a pre-training weight model and the scene feature information, determining a dynamic weight matrix of a supervision scene to which the supervised target belongs; and according to the dynamic weight matrix, the multi-modal behavior data and a pre-training behavior analysis model, determining a risk behavior assessment result of the supervised target and performing early warning. Scene feature information is converted into quantifiable weight parameters, and then multi-modal behavior data fusing positioning, physiology and supervision behavior data are combined to predict and evaluate risk behaviors, so that an evaluation result is obtained. The risk focuses of different supervised targets can be finely distinguished, and evaluation is carried out through multi-modal data, so that the evaluation result is more accurate and reliable.

This invention discloses a method, system, and electronic device for personnel operation assessment based on video temporal joint analysis, relating to the field of power safety technology. This application involves acquiring video from the work site and extracting frames; extracting the sequence of key human points from each frame; dynamically defining risk areas for the head, torso, and hands based on these key points; calculating hand movement speed, distance from the risk area, and movement consistency characteristics; triggering refined behavior recognition when hand movement characteristics indicate a tendency to enter the risk area; extracting multi-scale temporal features before and after the trigger; and judging specific risk behaviors using a pre-trained action recognition model. This invention, through a two-level mechanism of "trend triggering + refined recognition," achieves early warning and accurate identification of risk behaviors such as removing helmets and gloves, effectively improving the real-time performance and preventative capabilities of work safety monitoring. This application realizes a shift from post-event identification to pre-event warning, resulting in better safety monitoring performance.

The invention relates to the technical field of deep learning, and discloses a behavior monitoring and risk early warning system for obstetrical patients, which comprises a data sensing acquisition module, a data analysis processing module, a human body area modeling module, a behavior early warning module, an edge calculation module and a comprehensive judgment module. According to the system, pixel-level segmentation and key point regression are performed on patient image data by constructing a mask key point-continuous time attention fusion network, a continuous time attention mechanism inspired by a neuron loop is introduced, dynamic modeling and weight distribution are performed on continuous frame attitude features, and a continuous frame attitude fusion model is constructed. Therefore, the continuous time evolution trend of the posture change of the obstetrical patient is described. And in combination with human body area modeling and behavior early warning strategies, intelligent identification and early warning of risk behaviors such as postpartum dysphoria, abnormal turning over and falling down are realized. The accuracy and stability of behavior monitoring are improved, and the method is suitable for intelligent safety management in an obstetrical monitoring scene.

The invention discloses a video monitoring personnel risk behavior identification method and system based on deep learning, and belongs to the technical field of risk behavior identification, and the method comprises the steps: obtaining a plurality of monitoring videos, and carrying out the preprocessing of the monitoring videos, and obtaining a target data set; constructing a risk behavior recognition network, and training and testing the risk behavior recognition network according to the target data set; obtaining a target video frame set, and processing the target video frame set according to the risk behavior recognition network to obtain a risk behavior recognition result corresponding to the target video frame set; the multi-modal features and the spatial-temporal features are integrated through the fusion of the enhanced fusion module and the spatial-temporal feature extraction module, and the recognition capability of subtle actions and complex scenes can be improved, so that the recognition precision of the risk behavior recognition network is enhanced.

The invention provides a vehicle risk intervention method and system based on real-time multi-source data and a knowledge graph, and the method comprises the steps: collecting multi-source heterogeneous data and environment data in real time, and generating a real situation feature according to the environment data; and performing space-time alignment on the multi-source heterogeneous data to generate a standardized event stream. Constructing a dynamic knowledge graph based on the ontology definition; and carrying out risk behavior identification based on a heterogeneous graph neural network and time sequence convolutional network joint model, outputting a risk category and a risk probability, and outputting the risk category and the risk probability. And matching a corresponding intervention action in a pre-constructed intervention knowledge graph according to the risk category and the real situation feature, and issuing and executing the intervention action. And acquiring intervened data, evaluating an intervention effect through a causal inference algorithm, and feeding back and updating the joint model and the intervention knowledge graph. According to the invention, vehicle risks can be rapidly identified, graded early warning based on situations and adaptive accurate intervention can be realized, and secondary accidents and false alarm rates can be reduced.

The invention discloses a risk behavior detection and alarm method and system based on log analysis, and belongs to the technical field of big data security analysis and big models.The method comprises the steps that log collection is conducted, specifically, a multi-source log is captured in real time through a distributed agent and standardized; feature engineering: extracting time, behavior, statistics and session features and constructing feature vectors; calculating an operation interval and a non-working period mark according to the time characteristic; according to the behavior characteristics, the difference between high-frequency operation and the geographic position is counted; analyzing session duration and an operation sequence mode according to the session characteristics; reasoning by adopting a logistic regression model: outputting a risk score based on the feature vector by adopting the logistic regression model; and graded alarming: triggering alarms of different grades according to the risk score. According to the invention, accurate identification and rapid disposal of system risk behaviors can be realized.

The application discloses a multi-factor fusion dangerous behavior early warning method and system, and particularly relates to the technical field of dangerous behavior early warning, which comprises the following steps: acquiring multi-source abnormal data for a target object or a target area in the same early warning period, extracting an abnormal time, an abnormal position, an abnormal category and a change direction, and generating an abnormal event set; reading the abnormal event set, and sequentially comparing the abnormal position, the abnormal category and the change direction of each abnormal event according to the abnormal time. The abnormal event is further written into a trigger event, a support event, an exclusion event, an escalation event and a release event, and the entering qualifications of different abnormal signals are distinguished according to the events, so that weak evidence, conflicting evidence and escalation evidence are no longer directly triggered in the same position. Therefore, the application helps to realize the orderly competition, progressive release and credible escalation of multi-theme early warning in the same object or area, and relatively alleviates the problems of early warning sequence disorder and insufficient result credibility.

The invention relates to the technical field of public safety risk pre-judgment, in particular to a public safety risk pre-judgment method and system for crowd emotion recognition. The method comprises the steps of performing real-time feature monitoring on an undetermined area, collecting feature parameter values, establishing a risk prediction model, and calculating risk scores at different time stages; and performing tropism parameter marking on the specific region, and dynamically adjusting the risk prediction model according to a marking result. According to the method, real-time feature monitoring is carried out on an undetermined area, feature parameter values are collected, a risk prediction model is established, risk scores in different time stages are calculated, tropism parameter marking is carried out on a specific area, and specific area feature recognition is carried out on the specific area. And the risk prediction model is dynamically adjusted according to the marking result, the risk behavior and the non-risk behavior are specifically divided according to different scenes, and the overall pre-judgment accuracy is improved during specific risk pre-judgment.

The embodiments of the present specification describe a training method of a behavior prediction model, a risk behavior prediction method and device. According to the method of the embodiments, the type identification of a sample behavior event and the time information of the sample behavior event can be obtained when training the behavior prediction model. Then the sample behavior event is characterized in the continuous time domain, and then the behavior prediction model is trained according to the behavior event characterized in the continuous time domain, so as to optimize the prediction value of the type identification and the time output by the behavior prediction model. By characterizing the sample behavior event in the continuous time domain, the association between the behavior event and the time when it occurs is realized, so that the model can fully learn the regularity and periodicity characteristics presented by the behavior event and the time when it occurs, thereby improving the accuracy of risk behavior prediction.

The application provides a risk behavior recognition method and device, relates to the technical field of artificial intelligence, and can be applied to the technical field of finance or other technical fields. The risk behavior recognition method comprises the following steps: acquiring a video image, extracting a skeleton sequence from the video image; extracting skeleton key point information features and skeleton connection relationship features from the skeleton sequence; inputting the skeleton key point information features and the skeleton connection relationship features into a first behavior prediction model and a second behavior prediction model respectively, and obtaining a first behavior and a second behavior respectively; and obtaining a risk behavior recognition result according to the first behavior and the second behavior.

An ICU patient abnormal behavior recognition method and system based on computer vision, relating to the technical field of computer vision, comprising: acquiring a continuous image sequence of a monitored area by a camera device; extracting motion parameters of a patient in the monitored area according to the continuous image sequence; according to the motion parameters, identifying corresponding behavior nodes according to a preset rule; according to a preset risk behavior logic rule, logically associating the identified behavior nodes in time sequence, matching a risk behavior causal chain representing a preset risk behavior; when the risk behavior causal chain is successfully matched, outputting an alarm information. By constructing the causal chain of the risk behavior, even if a key action cannot be directly observed due to obstruction, the complete risk event can still be inferred through other observable behavior nodes before and after it, such as sitting up and moving the limbs to the dangerous area, effectively reducing the false negative rate.

The invention discloses a risk behavior identification method and apparatus, an electronic device and a storage medium. The method comprises the steps of determining a to-be-processed fence region; determining a first risk behavior recognition result based on the to-be-processed object in the to-be-processed fence area through a supervised first behavior recognition model, and / or determining a second risk behavior recognition result based on the to-be-processed object in the to-be-processed fence area through an unsupervised second behavior recognition model; and based on the first risk behavior identification result and / or the second risk behavior identification result, determining whether a risk behavior occurs in the fence area to be processed. According to the method, the sensitivity, the accuracy and the adaptability of risk behavior identification can be improved, the false alarm rate is reduced, and the method has a good application effect especially for a complex dynamic scene.

The invention discloses a multi-factor fusion dangerous behavior early warning method and system, and particularly relates to the technical field of dangerous behavior early warning, and the method comprises the steps: obtaining multi-source abnormal data for a target object or a target region in the same early warning period, extracting an abnormal moment, an abnormal position, an abnormal category and a change direction, and generating an abnormal event set; and reading the abnormal event set, and comparing the abnormal position, the abnormal category and the change direction of each abnormal event in sequence according to the abnormal time. Abnormal events are further written into a trigger event, a support event, an exclusion event, an upgrade event and a release event, and entry qualifications of different abnormal signals are distinguished according to the events, so that weak evidence, conflict evidence and upgrade evidence do not directly trigger an alarm at the same position any more; therefore, orderly competition, progressive release and credible upgrading of multi-theme early warning can be realized in the same object or area, and the problems of disordered early warning sequence and insufficient result credibility are relatively relieved.

The invention discloses a construction site risk perception and dynamic management and control system integrating BIM and an intelligent safety belt, and relates to the technical field of building construction safety monitoring. Comprising a data acquisition and behavior identification module, an AI risk behavior evaluation and scoring module, a BIM model fusion and space mapping module, a three-dimensional risk thermodynamic diagram generation module, an AI behavior learning and trend prediction module and a management linkage module. According to the method, through the intelligent safety belt and the AI behavior recognition module, dangerous behaviors such as high-place operation are monitored and judged in real time, and the timeliness and initiative of safety management are greatly improved. According to the method, precise spatial mapping is realized in combination with the BIM model. According to the method, the dynamic three-dimensional risk thermodynamic diagram is generated, the three-dimensional thermodynamic diagram can be dynamically updated according to the risk behavior frequency and grade of different areas and different components, the potential safety hazard distribution and change trend are visually displayed, and a manager can visually perceive and make a judgment conveniently.

The invention discloses a risk number identification method based on adaptive learning. The method comprises the following steps: acquiring encrypted call behavior data of a suspected risk behavior number; extracting call behavior characteristics of the suspected risk behavior number from the call behavior data, expanding data statistical characteristics of the suspected risk behavior number through a statistical method, and performing data cleaning and filling preprocessing on the expanded data to obtain a data characteristic set; constructing a soft threshold objective function as a risk number behavior recognition model, calculating a target value y by taking the preprocessed data feature set data as a feature X of the objective function, and performing iterative updating and tuning on parameters of the risk number behavior recognition model in combination with a cross entropy loss function to obtain a final risk number behavior recognition model; and identifying the data feature set by using the final risk number behavior identification model, generating a suspected risk number list, and performing early warning on suspected risk numbers. According to the method, the identification effect of the risk number is effectively improved through adaptive learning of parameters and multiple iterations.

The present invention provides an image-based patient risk behavior monitoring system for, by using a patient analysis model, identifying a patient and objects, which are subjects to be monitored, in an image acquired through a camera, detecting the location and posture of the patient and an object interacting with the patient on the basis of the image, determining whether the behavior of the patient fits into the category of risk behavior on the basis of the location and posture of the patient and the interacting object, and if the behavior of the patient comes under the category of risk behavior, generating a risk behavior event and storing the type, start time, and end time of the risk behavior in a risk behavior log.