108 results about "Behavioral risk" patented technology

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention relates to the technical field of behavior risk early warning, and provides a risk early warning method and system for student behavior track and action analysis, and the method comprises the following steps: respectively collecting video data and sensing data of student behaviors; preprocessing the video data and the sensing data to obtain student behavior multi-source data; performing target detection and target tracking on the student behavior multi-source data to generate a student movement track; based on the student movement track, behavior characteristics of the student are extracted, abnormal behavior recognition is carried out on the behavior characteristics, and an abnormal behavior recognition result is obtained; performing risk level evaluation on the abnormal behavior recognition result to obtain an abnormal behavior risk level; and according to the abnormal behavior risk level, triggering early warning information of a corresponding level, processing the early warning information, generating a final early warning result, and pushing the final early warning result to a manager. According to the invention, omnibearing monitoring, timely identification and rapid early warning of abnormal behaviors of students are realized, and the efficiency of campus safety management is improved.

The invention discloses a driving behavior risk assessment method and system, and relates to the technical field of intelligent traffic and driving safety, and the method comprises the steps: collecting the operation parameter data of a vehicle, carrying out the feature extraction of the operation parameter data, extracting feature parameters to form a feature vector, and calculating the Mahalanobis distance score of the feature vector; according to the Mahalanobis distance score of the feature vector, carrying out risk grade division by adopting an improved hierarchical fuzzy inference system to obtain a risk grade score; and establishing a driver portrait model according to the risk level score, and when the risk level score exceeds a personalized risk threshold, outputting a driving risk behavior improvement suggestion to complete driving behavior risk assessment. According to the invention, a refined grading basis is provided for individual risk monitoring of the driver, dynamic monitoring and real-time intervention of driver behaviors are realized, traffic accidents can be reduced, and driving safety is improved.

The invention discloses an information management method and system for a smart ward, a medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that physiological index data and behavior characteristic data of a target patient are collected, and a characteristic data sequence is constructed; analyzing each piece of data in the feature data sequence, and determining a current characterization behavior and a behavior change sequence of the target patient; obtaining individual information of a patient, and inputting the individual information of the patient, the current characterization behavior and the behavior change sequence into a behavior evaluation model to obtain a risk characterization behavior and a behavior risk score; determining an early warning level and an early warning response time limit based on the behavior risk score; when the early warning level is higher than a preset early warning threshold value, generating a task execution list according to the early warning response time limit and the risk characterization behavior, and sending the task execution list to the medical care terminal; and updating the behavior risk score according to an early warning response result returned by the medical terminal. By implementing the application, patient state monitoring of the smart ward can be optimized, and potential safety hazards can be avoided to a greater extent.

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 invention discloses an AI-based unsafe behavior identification method and system, and the method comprises the steps: employing a lightweight online modeling method of incremental learning based on the current state information of a worker, and integrating a situation element to dynamically update an individual behavior model; heterogeneous situation data are collected and classified, and key situation features are extracted and dimensionality reduction is carried out; constructing a behavior risk analysis-oriented situational feature system, and performing weight distribution according to the correlation between each situational element and the unsafe behavior to obtain a situational feature weight; in an individual behavior model updating process, according to a change trend of employee behaviors and an identification feedback result, carrying out real-time evaluation on importance of each situation element, dynamically adjusting weights of situation features, and adaptively optimizing a feature fusion strategy; and detecting an abnormal behavior by adopting an abnormal behavior detection method of a self-adaptive threshold to obtain an unsafe behavior identification result. The method can effectively improve the real-time recognition accuracy of unsafe behaviors in the industrial environment, and reduces the false alarm rate.

The invention relates to the technical field of financial risk management, in particular to a big data-driven financial risk early warning system, which comprises a data detection module, a behavior risk rating module, a dynamic risk monitoring module, a behavior pattern clustering module and a financial risk early warning module. According to the method, the sensitivity and accuracy of risk detection are improved by analyzing small deviation between transaction behaviors and historical data, the dynamic risk monitoring function can realize quick response to abnormal transactions by continuously tracking the fund flow direction and the transaction frequency, and the risk detection accuracy is improved by analyzing the relationship between the fund flow direction and past risk events. According to the method, normal financial activities and potential risk behaviors can be effectively distinguished, an enterprise can take prevention measures before the risk actually occurs, and the clustering analysis of the risk behaviors can help to more accurately position a risk source, so that the measures are taken in a targeted manner, financial resources are optimized, and the risk prevention and control capability and decision-making efficiency are enhanced.

The invention relates to the technical field of car rental management, in particular to a car rental intelligent management method and system, and the method comprises the following steps: obtaining the time difference between reservation and use, judging whether to give an early warning or not, counting the number of early warning times, adjusting the credit rating if the number exceeds the limit, judging the renewal time and the return time, and recognizing abnormal renewal. Behavior nodes are constructed, abnormal statistical behavior chain early warning numbers are aggregated, resource request priorities are judged in combination with multiple factors, and a vehicle planning table is established. In the invention, through difference calculation, a short-time frequent reservation behavior is identified, abnormal renewal is determined, deep modeling of continuous abnormity on a user behavior rule is aggregated, credit rating records and behavior chain characteristics are combined, a user resource request priority is dynamically adjusted, and interference of a high-risk behavior on resource scheduling is controlled. The early warning response capability to abnormal behaviors and the pertinence of resource management are improved, behavior risk constraint and intelligent cooperation of vehicle configuration are realized, and a vehicle rental management system with timeliness, hierarchy and adaptive characteristics is constructed.

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 provides a dynamic adjustment method based on a depression mind-correcting treatment scheme, and relates to the technical field of medical care information, and the method comprises the steps: collecting multi-dimensional information of a patient, and collecting life behavior risk data, psychological scale data and cognitive function evaluation data; comprehensively evaluating the depression level of the patient; determining the priority of a treatment therapy module according to the multi-dimensional information of the patient; generating an initial treatment scheme; calculating a daily task comprehensive weight value, and dynamically adjusting the training content of the next day; and evaluating the state of the patient in stages, recalculating the priority of each therapy module, and optimizing the therapeutic scheme. Through multi-dimensional data fusion evaluation and intelligent algorithm dynamic adjustment, a complete closed-loop feedback optimization system is constructed, precision and individuation of a depression treatment scheme are achieved, the treatment effect and patient compliance are effectively improved, the recurrence risk is reduced, and a more scientific and effective treatment scheme is provided for depression patients.

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 data security detection, in particular to a security protection system for a trusted data space, which comprises the following steps of: acquiring historical access information of a user through a behavior analysis module, analyzing operation behaviors in the historical access information, determining inertial behaviors and accidental behaviors of the user, and meanwhile, determining the security of the trusted data space; tracking and analyzing the real-time behavior data of the user, dividing the real-time behavior data of the user into conventional behaviors, emergent behaviors and abnormal behaviors, acquiring operation durations and information association values of the conventional behaviors, the emergent behaviors and the abnormal behaviors, and calculating the operation durations and the information association values to obtain the real-time behavior data of the user; determining a current behavior risk value of the user, and determining early warning signals of different levels based on the behavior risk value; according to the method, the risk degree caused by user behaviors can be measured more scientifically and accurately, and more reliable data support is provided for risk early warning.

The invention discloses a risk assessment method and apparatus, and an electronic device. The method comprises the steps of obtaining a traffic log set and an event log set which are output by a to-be-analyzed system and last for N hours; calculating a flow event index and an abnormal request index corresponding to each source IP address according to the flow log set and the event log set; and determining a behavior risk index corresponding to each source IP address according to the traffic event index and the abnormal request index corresponding to each source IP address, and evaluating the risk of the corresponding source IP address according to the behavior risk index. Through the technical scheme provided by the embodiment of the invention, the risk assessment of the source IP address is realized, the uncertainty influence on the risk assessment caused by the data quality problem and the timeliness problem of the external intelligence data is effectively made up, and the effectiveness and the accuracy of the risk assessment result of the source IP address are effectively 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.

The invention relates to the technical field of smoke control area automatic identification, and discloses a smoke control area automatic identification system, which comprises a behavior data acquisition module, a behavior analysis module, a behavior risk judgment module, a response generation module, a co-processing unit and the like. The behavior data acquisition module acquires real-time sensing data streams and divides the real-time sensing data streams into far infrared thermal sensing and visible light picture data streams; the behavior analysis module generates abnormal feature maps respectively; the behavior risk judgment module fuses the feature map based on a preset strategy matrix and determines a risk level; the response generation module matches a response strategy; and the co-processing unit generates a processing instruction after cross validation. The system further comprises a network communication gateway, a security linkage module and the like, and the functions of data protocol adaptation, processing queue optimization, event archiving and the like can be achieved. According to the invention, through multi-source data fusion and intelligent analysis, the accuracy and response efficiency of smoke control monitoring are improved, and the method is suitable for automatic identification and management of smoking behaviors in public places.

Systems, methods, and related technologies for determining a risk score or value are described. The risk score determination may include accessing network traffic from a network, where the network traffic is associated with a plurality of entities. One or more values associated with one or more properties associated with an entity are determined. The one or more values may be based on the network traffic. At least one of a functional risk value, a configurational risk value, or a behavioral risk value associated with the entity are determined. A risk value for the entity is determined based on the functional risk value and at least one of the configurational risk value or the behavioral risk value associated with the entity.

The embodiment of the invention relates to the technical field of transaction security, and discloses a payment fraud detection method based on behavior pattern analysis and multi-modal identity verification, and the method comprises the steps: collecting the current original behavior data of a user in real time according to a transaction request when the transaction request of the user is received; respectively encrypting the current original behavior data of the user and the transaction request in real time to obtain the encrypted current behavior data and the encrypted transaction request, and simultaneously destroying the current original behavior data of the user and the transaction request; analyzing a behavior risk degree by adopting a behavior analysis model; according to the behavior risk degree, collecting current original biological data of the user, encrypting the current original biological data of the user, destroying the current original biological data, and verifying the encrypted current biological data by adopting a target verification strategy to obtain a verification result; and determining whether payment fraud exists or not according to the verification result. According to the embodiment of the invention, the effects of payment security and privacy protection are achieved.

The invention relates to the technical field of behavior analysis, in particular to an AI-based behavior recognition monitoring system which comprises a facial expression analysis module, a behavior pattern recognition module, an abnormal behavior judgment module and a monitoring adjustment decision module. According to the method, the emotion is monitored in real time by using the AI, the facial expression change can be accurately evaluated, the emotion fluctuation can be accurately analyzed, the monitoring of the emotion state is more accurate, the behavior type and the possible abnormal condition are identified through the speed and position information of the action, the judgment speed and accuracy of the abnormal behavior are improved, and the accuracy of the abnormal behavior is improved. Therefore, safety monitoring in a complex environment is more effective, potential abnormal behaviors such as invasion or theft behaviors can be accurately identified, response speed and effectiveness of prevention measures are improved, distribution of monitoring resources can be dynamically optimized by adjusting a monitoring strategy according to a behavior risk level, and safety of a user is improved. Therefore, the response capability and the resource use efficiency of the monitoring system are improved, and the overall safety is enhanced.

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 discloses a field live-line safety protection method and system for an autonomous robot, and belongs to the technical field of intelligent live-line detection. The method comprises the following steps: based on a preset adaptive access technology, in a simulation environment, training a virtual simulation model by using a constructed deep reinforcement learning algorithm to complete a specified action so as to train an action algorithm of the virtual simulation model, and transplanting the trained action algorithm to an autonomous robot; based on a preset comprehensive evaluation model, identifying the monitoring data, determining behaviors of the autonomous robot, and generating a sensing result; and generating an electronic map of risk score quantitative distribution based on a perception result, collecting fine-grained action information of the autonomous robot based on the electronic map, and performing active behavior risk early warning based on the fine-grained action information. According to the invention, the performance detection work of the combined transformer can be carried out under the electrified condition, and the power failure loss is reduced.

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.

The invention provides a driving risk assessment method and device and a driving risk early warning device, and belongs to the technical field of intelligent driving, and the method comprises the steps: obtaining an environment risk factor, a behavior risk factor and a driving scene type of a to-be-assessed traffic vehicle, constructing an external environment field according to the environment risk factor, constructing an internal behavior field according to the behavior risk factors, and constructing a comprehensive driving risk field according to the external environment field and the internal behavior field; performing driving risk potential energy analysis according to the comprehensive driving risk field and the driving scene type to obtain a relative driving safety index; and determining a safety threshold value and a danger threshold value according to the behavior risk factor, and performing comparison and grading on the relative driving safety index, the safety threshold value and the danger threshold value to obtain a driving risk grading evaluation result. According to the method, the comprehensive driving risk field is constructed through the external environment field and the internal behavior field, the influence of environment factors, vehicle factors and drivers on driving risks is comprehensively quantified, and the driving behavior risks can be accurately evaluated.

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.