48 results about "Risk behavior" patented technology

The invention provides a risk behavior perception method and device based on deep learning, and the method comprises the steps: firstly collecting multi-source behavior data containing a network access record, a terminal operation log and an application program interface calling sequence in a preset time window of a target user, carrying out the spatial-temporal feature extraction of the multi-source behavior data, generating a behavior track feature vector, and carrying out the feature extraction of the behavior track feature vector; the method comprises the following steps: acquiring a network access record, analyzing the network access record through a protocol to obtain a network behavior feature vector, inputting the network behavior feature vector and the network behavior feature vector into a pre-trained risk assessment neural network for joint coding to obtain behavior risk probability distribution, determining a risk level according to a risk category exceeding a preset threshold, generating an early warning signal, and finally triggering a real-time alarm module according to the early warning signal. The risk description text and the risk relief strategy are sent to the safety management terminal, multi-source data comprehensive analysis is achieved, the risk is accurately evaluated, early warning is timely and effective, and the risk perception ability is improved.

The present application relates to the technical field of big data processing, and discloses a risk behavior identification method and apparatus, a storage medium, and a computer device. The method comprises: acquiring access traffic data of sensitive data, and on the basis of service categories to which access users corresponding to the access traffic data belong, classifying the access traffic data to obtain sensitive data access data of a plurality of service categories; on the basis of the sensitive data access data of each service category, updating model parameters of a risk assessment model of the service category; by means of the risk assessment model of the service category and on the basis of the sensitive data access data of the service category, calculating a risk behavior assessment value of each access user under the service category; and on the basis of the risk behavior assessment value of each access user under the service category, obtaining a risk behavior identification result of each access user. The method can improve the adaptability, the practicability and the assessment accuracy of risk behavior assessment in service scenarios, and provides visual assessment results.

The invention discloses a financial fraud behavior intelligent tracking and early warning system, and the system comprises an obtaining module which is used for obtaining a risk behavior in a financial interaction event, and a plurality of interaction objects and a plurality of interaction behavior signals corresponding to the risk behavior; the construction module is used for constructing a risk data chain based on the plurality of interaction objects and the plurality of interaction behavior signals; the first determination module is used for determining risk information of each risk node in the risk data chain; the judgment module is used for judging whether financial fraud behaviors exist or not according to the risk information of each risk node; and the tracking and early warning module is used for carrying out intelligent tracking and early warning on the financial fraudulent behavior according to the risk data chain when the financial fraudulent behavior is determined to exist. The risk information of the risk node is locally determined based on the risk data link, whether financial fraud behaviors exist or not is judged from the whole risk data link, and accurate tracking and early warning are achieved.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention discloses a risk behavior analysis method, a large model training method and device, equipment, a storage medium and a program product, and relates to the technical field of network security. The method comprises the steps that risk behavior analysis is achieved through association analysis according to abnormal behaviors obtained through behavior baseline recognition by means of a trained large model, and the problems of low efficiency and low accuracy of manual analysis are avoided; moreover, risk behavior analysis is carried out by using a large model in combination with target historical data, so that the accuracy and efficiency of risk behavior analysis are improved.

The invention provides a remote monitoring method and system applied to children's watches, and relates to the technical field of children's watches, time axis synchronous alignment is carried out on collected multi-source heterogeneous data through an interpolation method, data time sequence chaos caused by sampling rate differences is prevented, and then the data time sequence chaos caused by sampling rate differences is prevented. Through dynamic coupling calculation of a light intensity mutation gradient and a heart rate stress coefficient, the cooperative influence of environment change on the physiological state of the child is quantified in real time, and the physiological state of the child is determined by further combining power spectrum centroid frequency and physiological phase difference analysis of the motion rhythm and logarithmic correlation of acoustic envelope frequency and GPS speed. In addition, an acoustic propagation delay compensation mechanism is introduced, a two-factor dynamic equation model is constructed through a time delay cross term, and a dynamic risk threshold generation mechanism based on a noise energy ratio is combined, so that the dynamic risk threshold of the motion intensity, the rhythm characteristics and the acoustic scene is effectively distinguished. Effective remote monitoring on child safety in different scenes is realized.

The invention discloses a safety production intelligent monitoring method and device, and the method comprises the steps: employing a safety production large model, combining a video and operation type definition of a safety production operation process, and recognizing the operation type of safety production; obtaining a safety rule corresponding to the identified operation type, wherein the safety rule is obtained by using a safety production large model; obtaining environmental data by using a large safety production model and combining the operation type and the corresponding safety rules and environmental parameters; obtaining a risk behavior analysis result by using a multi-modal large model in combination with the operation type, the corresponding safety rule, the environment data and the video of the safety production operation process; and obtaining risk behavior suggestion measures by using a safety production large model and combining the risk behavior analysis result. According to the invention, the risk behavior can be identified efficiently and accurately, and suggested measures can be given.

The invention discloses a student driving habit risk prompting system, and relates to the technical field of intelligent traffic, and the system comprises the steps: collecting the driving habit data of a student during driving, and constructing a driving behavior feature model; comparing the driving behavior characteristic model with a safe driving standard model to obtain risk behavior characteristic data; performing risk level classification on the risk behavior feature data to obtain risk levels; and generating risk prompt information according to the risk level, and prompting the student. According to the invention, the technical problems of insufficient driving behavior evaluation precision and real-time performance in the prior art are solved, and the technical effects of monitoring and evaluating the driving behavior of the student in real time and improving the driving safety are achieved.

The invention relates to a risk behavior detection method and system based on the DLP technology, and the method comprises the following steps: building a user risk behavior monitoring model based on the DLP technology, and the user risk behavior monitoring model is used for collecting and monitoring user behavior data and IM communication software communication data; establishing a user risk behavior identification model through a machine learning technology according to user historical behavior data collected by the user risk behavior monitoring model, and monitoring daily operation behaviors of the user in real time; according to IM communication software communication data collected by the user risk behavior monitoring model, whether risk content exists in the IM communication software communication data or not is recognized through the NLP technology; and according to an output result of the user risk behavior identification model and risk content identified by the NLP technology, constructing a comprehensive risk assessment model, when a risk value output by the risk assessment model is higher than a preset value, giving a risk alarm, and automatically recording related data to provide evidence for subsequent investigation and auditing.

The invention discloses an internal control risk dynamic identification method and system based on a multi-modal model, and relates to the technical field of risk dynamic identification methods. According to the method, through an unsupervised state transition analysis mode, the hidden abnormal jump paragraph in the behavior mode can be dynamically identified, manual rule setting is not needed, and the self-adaptive capability of risk identification is enhanced; when a time lag problem exists in a multi-modal log, through operation chain backtracking and a virtual calibration mechanism, the time consistency and causal integrity of a cross-modal behavior chain are ensured, and the misjudgment probability caused by record inconsistency is remarkably reduced; a state sequence is mapped into a process topological graph by adopting a sliding matching method, free fragments which cannot be matched structurally are effectively identified, and starting and ending points of risk behaviors can be accurately positioned.

The invention discloses a correction type technique supervision and analysis system, and the key points of the technical scheme are that the system comprises the following steps: data collection and preprocessing, mode recognition and modeling, anomaly detection and evaluation, correction and feedback mechanism, personalized recommendation and service optimization, and visualization and reporting. Comprehensive optimization and fine management of the whole massage service process can be realized through the modified form manipulation supervision and analysis system, and the system identifies and predicts customer demands and potential abnormal conditions by utilizing an intelligent algorithm through accurately collecting and processing physiological and psychological data and massage manipulation information of customers. Improper techniques or risk behaviors are found and corrected in time, a personalized recommendation mechanism automatically adjusts a massage scheme according to health conditions and preferences of customers, and through a clear visual interface and a real-time report, the service quality and customer satisfaction are improved, and intelligentization, personalization and high efficiency of massage services are achieved.

The embodiment of the invention provides a risk behavior recognition method and device, equipment, a storage medium and a program product, and relates to the field of big data. The method comprises the following steps: receiving encrypted entity relationship data uploaded by a plurality of mechanisms; using a federated learning algorithm to generate fusion features of a plurality of pairs of transaction entities according to the encrypted data uploaded by the plurality of mechanisms; constructing a dynamic graph according to the fusion features of the plurality of pairs of transaction entities; dividing the dynamic graph into a plurality of sub-graphs; and identifying risk behaviors existing in each sub-graph. According to the method provided by the invention, by using the federated learning algorithm, the encrypted entity relationship data of the multiple mechanisms are fused, so that cross-mechanism risk behavior identification is effectively realized on the basis of guaranteeing the privacy of the user.

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 financial credit intelligent analysis and risk management method based on artificial intelligence, belongs to the technical field of credit analysis, and obtains a risk assessment result by establishing a financial credit association analysis model and utilizing credit data entry vectors and the like. Model establishment comprises the steps of data collection and preprocessing, feature selection and representation, model training, parameter optimization and the like. And the risk management model covers risk behavior analysis, processing limitation and result feedback. Processing limitation is carried out by obtaining the uniqueness information of the loan subject, risks are evaluated according to preset risk management parameters, and a final risk management result is obtained, fed back and updated. According to the method, internal and external credit data can be efficiently processed, standardization and feature selection and representation are carried out, after risk behavior analysis is carried out, borrowing subject information is fed back and updated, and dynamic adjustment and continuous optimization are realized.

Embodiments of the invention provide a risk assessment method and apparatus, a computing device, a readable storage medium and a product. The method comprises the steps of obtaining user behavior data of a target project; and performing semantic reasoning on the user behavior data based on the project rule of the target project by using the risk analysis model, determining risk behavior data, extracting a risk behavior mode corresponding to the risk behavior data, performing risk assessment on the risk behavior data based on the risk behavior mode, and obtaining a risk assessment result. The semantic analysis and logical reasoning capabilities of the risk analysis model are utilized to deeply mine potential risks in the user behavior data, the risk behavior mode corresponding to the risk behavior data is extracted, and risk assessment is performed on the risk behavior data based on the risk behavior mode. According to the invention, deep risk research and judgment of a complex scene can be realized based on the extracted deep behavior characteristics, so that flexible judgment and timely prevention and control of the risk are realized, and the flexibility, accuracy and efficiency of risk assessment are enhanced.

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 health management, in particular to an ultrasonic image dynamic recognition-based liver, gall and kidney stone auxiliary diagnosis method, which comprises the following steps of: deploying a recommended sports diet monitoring server, collecting sports nutrition data for analysis and processing, constructing a sports nutrition mind map, and constructing a health management system; deploying an ultrasonic image scanner to acquire feature data of calculus personnel and outputting a calculus risk behavior set; mapping the obtained calculus risk behavior set to a motor nutriology mind map, and outputting a suggested mapping text to judge whether the calculus risk behavior set is unreasonable or not; collecting medical record checking data associated with a calculus person, analyzing and processing a calculus risk behavior set passing checking, and judging whether to output early warning data or not based on a contrastive analysis record; and collecting immune evaluation data of the associated calculus person in the sports diet monitoring time, deploying adaptive correction data, and performing reply processing. According to the invention, health management can be effectively implemented, so that liver, gall and kidney stones of patients can be effectively relieved.

The present invention discloses a psychological portrait system for intelligently identifying risky personnel based on FMT features, which includes the following steps: S1: feature screening, key feature collection and risk feature screening; S2: psychological portrait drawing and evaluation of high-risk personnel; S3: scale compilation, test administration and data collection; S4: data analysis, determining the portrait model through a Binarylogistic regression model; S5: obtaining the final risk level of the tester based on a fuzzy comprehensive judgment method. The psychological portrait system for intelligently identifying risky personnel based on FMT features constructs a psychological portrait of high-risk personnel by collecting individual psychological feature information of suicides, wounding persons and other individuals who have engaged in risky behaviors. According to the fitting of the psychological portraits of the tester and the high-risk personnel, a fuzzy comprehensive judgment method is used to distinguish high-risk personnel from non-risk personnel, so as to find the real high-risk population in a more comprehensive, specific and targeted manner.

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 risk behavior prediction method and system based on microvascular image change, and relates to the technical field of image processing, and the method comprises the steps: collecting a plurality of frames of preprocessed microvascular images, and marking a microvascular region in the microvascular image to generate a basic template; comparing the basic template at the T-k moment with the basic template at the T moment to generate basic template change data; performing matching score calculation according to the basic template change data to obtain a template change score; comparing the template change score with a preset change score value to generate an image change tag, the image change tag including an abnormal image change tag and a normal image change tag; and inputting the template change score of the abnormal image change label into the established risk prediction model to obtain the risk behavior, the risk prediction model based on the regression network is constructed in combination with the abnormal image change label and the historical blood vessel image data, and the risk behavior can be predicted in advance.

The invention provides a model training method, a risk behavior identification method, a system, equipment and a medium. The training method comprises the following steps: acquiring a training data set; the training data set comprises image data and text data; based on the training data set, utilizing a multi-stage distillation training strategy to train a risk behavior identification model, and obtaining a trained risk behavior identification model; the multi-stage distillation training strategy comprises the step of carrying out distillation training on semantic representation, attention distribution and / or risk label stages of the risk behavior recognition model. According to the method, the risk behavior recognition model is trained by using the multi-stage distillation training strategy, so that the memory and computing power consumption is remarkably reduced while the multi-task performance of a small model is close to the level of a large model, and the problems of large parameter quantity and difficulty in edge deployment of a traditional view language model for recognizing risk behaviors in the prior art are solved.

The application discloses an Internet of Things-based safety tool intelligent management full-life-cycle tracing system, and relates to the technical field of warehouse management.The system comprises an Internet of Things data acquisition module, which is used for acquiring environmental parameters and tool use behavior data; a behavior feature extraction module, which is used for feature extraction to obtain a behavior feature sequence; a risk behavior mining module, which is used for statistically obtaining risk behavior features of a fault tool based on the behavior feature sequence, defining a risk behavior graph, and learning embedding vectors and attention weights of the risk behavior by using a graph convolution network; a risk level prediction module, which is used for encoding real-time risk behavior features by using the embedding vectors and the attention weights, and predicting risk levels of the encoded real-time risk behavior features by using an LSTM model; and a risk tracing update module, which is used for generating an event chain and an early warning log when the risk level is greater than or equal to a risk level threshold, and updating the risk behavior graph and the LSTM model, so that potential tool use risks can be found in advance.

The invention discloses a drug rehabilitation place management and control method based on a security and protection Internet of Things sensing platform, and relates to the technical field of intelligent management and control. Deducing the behavior trajectory of the drug addict by using a neural differential prediction model based on the video stream to obtain a behavior trajectory evolution path coordinate sequence, establishing a high-risk behavior pattern library based on historical relapse action feature rules, and marking feature fragments in combination with the behavior trajectory evolution path coordinate sequence; establishing a symbol rule base based on the historical behavior characteristic data, and generating a risk report according to the characteristic fragment, the behavior track evolution path coordinate sequence and the symbol rule base; matching an emergency disposal scheme according to the risk report, positioning the position of a high-risk person, and scheduling the nearest security resource to execute a hierarchical intervention action; according to the method, the spatial position rule, the motion mode rule and the physiological response rule are integrated through the symbol rule base, so that collaborative analysis of multi-source heterogeneous data and accurate matching of risk characteristics are realized.

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 discloses a user risk behavior early warning method and device for an operating system, a medium and a product, and relates to the technical field of computers, and the method comprises the steps: monitoring a target user behavior of a target operating system, and inputting the obtained information into a preset risk early warning network, matching the corresponding target monitoring information by sequentially utilizing each locally pre-configured target knowledge base through a preset risk early warning network, and if each piece of target monitoring information is successfully matched in the corresponding target knowledge base, determining a preset weight and a target risk coefficient corresponding to each piece of target monitoring information by utilizing the target knowledge base, and finally, determining a risk entropy value corresponding to the target user behavior according to a preset weight value and the target risk coefficient, and determining whether risk behavior early warning is performed or not based on the risk entropy value. Therefore, risk behavior early warning can be performed according to various operation information of the user, so that the damage of the risk behavior of the user to the operation system is effectively avoided.

The application provides a model training method, a risk behavior identification method, a system, a device and a medium. The training method comprises: acquiring a training data set; the training data set comprises image data and text data; a risk behavior identification model is trained based on the training data set by using a multi-stage distillation training strategy, and a trained risk behavior identification model is acquired; the multi-stage distillation training strategy comprises distillation training on a semantic representation, an attention distribution and / or a risk label stage of the risk behavior identification model. The application trains the risk behavior identification model by using the multi-stage distillation training strategy, so that the small model has a multi-task performance close to the level of a large model while significantly reducing memory and computing power consumption, thereby solving the problems of a large number of parameters of a conventional visual language model for identifying risk behaviors and difficulty in edge deployment in the prior art.

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.