247 results about "Occurrence probability" patented technology

The invention relates to a renal transplantation post-operation rejection risk monitoring and early warning method and a renal transplantation post-operation rejection risk monitoring and early warning system. The method comprises the steps of collecting recipient nursing monitoring data, laboratory indexes and transplanted kidney ultrasonic blood flow parameters in a follow-up visit period, performing timestamp alignment, deletion processing and standardization on multi-source data, extracting features to construct a time sequence feature sequence, inputting the time sequence feature sequence into a pre-training risk prediction model, outputting the rejection reaction occurrence probability of the next period, and forming a risk trend. Calculating a nursing sensitive index contribution weight based on the model contribution information, and screening a target nursing monitoring index; and establishing an individualized baseline model to obtain a baseline value and an allowable fluctuation interval, extracting characteristics such as deviation amplitude, direction, rate, fluctuation and continuous deviation duration and the like, and performing individualized calibration on the occurrence probability to obtain a calibration risk score. When the threshold value is not reached and the trend is not triggered, generating a nursing monitoring suggestion of the next period; and pushing early warning and generating grading intervention suggestions when a threshold value is reached or a trend is triggered.

The invention relates to the technical field of resource scheduling, in particular to a digital-intelligent non-accompanying service management scheduling system, which comprises a service baseline construction module for instantiating a service item into a standard plan event flow; the state portrait generation module is used for collecting service object data flow, establishing a dynamic time sequence signal by applying a recurrent neural network model, and quantifying the occurrence probability of a future abnormal event through multi-step prediction; when the triggering is abnormal, executing causal two-way collision positioning reasons by applying the knowledge graph; the task load calculation module is used for integrating the plan event flow into a plan task component, and generating a worker resource list in combination with the predictive risk score and the worker real-time load and space reachability analysis containing the fatigue index; the system further comprises a task distribution module, the optimal staff is determined and a closed-loop scheduling instruction is generated by minimizing a composite cost function containing predictive load cost, and digital intelligent unattended service management from prediction to scheduling is realized.

The invention discloses a heat pump system state anomaly detection method based on a depth auto-encoder, and the method comprises the steps: collecting compressor data, and carrying out the standardization processing to construct a multi-dimensional time sequence; a spatial-temporal feature extraction depth auto-encoder with a thermodynamic coupling attention mechanism is constructed, coupling attention is utilized to calculate physical parameter coupling strength weights to extract spatial features, time features are extracted in combination with a long and short-term memory network, and normal state data are reconstructed and predicted through a decoder after fusion; residual vectors of predicted normal state data and original data are calculated, and a weighted mahalanobis distance is calculated by using a covariance matrix to generate an abnormal score; and constructing a sliding probability distribution model based on historical normal data, calculating a current score occurrence probability, and comparing the current score occurrence probability with a preset threshold to output an anomaly detection result. According to the method, a multi-physical parameter space coupling relationship and a time evolution rule are captured through a thermodynamic coupling attention mechanism, and the anomaly detection accuracy and robustness are improved.

The invention discloses a risk assessment method for a supercritical carbon dioxide conveying pipeline and related equipment, and the method comprises the steps: constructing a fault tree model for a failure event of the pipeline; obtaining a consequence event corresponding to the failure event, and constructing a consequence model according to the consequence event; based on the fault tree model and the consequence model, obtaining a bow-tie model of the supercritical carbon dioxide conveying pipeline; obtaining a plurality of groups of evaluation data of the plurality of evaluation policies for the basic event in the bow-tie model, and obtaining a failure probability corresponding to the basic event based on the plurality of groups of evaluation data; and obtaining an occurrence probability of a leakage consequence corresponding to the basic event based on the failure probability, and performing hazard assessment on the leakage consequence based on the occurrence probability to obtain an assessment result. According to the method, the occurrence frequency of consequences in various risk scenes is accurately calculated, the problem that the risk in the actual conveying process cannot be accurately reflected is avoided, and the design and implementation of subsequent risk management decisions and optimization measures can be effectively supported.

The invention provides an opencast coal mine VR safety training dynamic difficulty regulation and control method, which comprises the steps of collecting multi-dimensional interaction behavior data of a target student in a VR training scene in real time, and encoding the multi-dimensional interaction behavior data into a behavior vector sequence; inputting the behavior vector sequence into a pre-trained prediction model, and outputting a potential risk behavior and a risk occurrence probability; when the risk occurrence probability is higher than a preset threshold value, generating a risk event continuously caused by the potential risk behavior through a risk deduction model; dynamically generating a negative guide plot based on the risk event, and implanting the negative guide plot into the VR training scene; and dynamically adjusting a subsequent generation strategy of the negative guide plot according to a response result of the target student to the negative guide plot. The method has the technical effects of deeply understanding the behavior intention of the student, early predicting the potential risk and performing dynamic intervention.

The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of events using adaptively trained artificial-intelligence processes and contextual data. For example, an apparatus may generate an input dataset based on first interaction data and contextual data associated with a prior temporal interval, and may apply an adaptively trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the adaptively trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit a portion of the generated output data to a computing system, and the computing system may be configured to generate or modify second interaction data based on the portion of the output data.

The invention relates to the technical field of steel structure construction traceability, and discloses a steel structure construction component tracking and traceability method based on the Internet of Things. The method comprises the following steps: acquiring historical violation data and normal traffic data under multiple types of traffic scenes, and forming a standardized scene data set through format specification and interference filtering processing; then, illegal features and passing features in the data are converted into a preset feature space through a feature extraction and conversion module, and scene feature vectors are generated; and constructing a violation triggering judgment model based on the feature vectors, and obtaining a cross-scene unified feature identifier by matching difference features. And further training a violation probability prediction model, and predicting the violation occurrence probability by using the cross-scene unified feature identifier and the real-time data of the target scene. And calculating a scene-level violation identification threshold according to historical violation data probability distribution, judging whether to trigger snapshot or not by combining with a real-time prediction probability, and updating model parameters regularly. According to the method, the violation snapshot accuracy and the scene applicability are improved.

The invention relates to the technical field of medical data processing, in particular to an AI-based anorectal operation auxiliary system, which comprises a patient health data analysis module, a patient health data analysis module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the patient health data analysis module is used for classifying personal information of a patient and health data associated with anorectal diseases on the basis of basic physiological parameters of the patient, past anorectal disease history, medicine allergy history and operation records; and performing correlation analysis on the anorectal disease history and the physiological parameters of the patient to generate preoperative health feature information. According to the method, the physiological parameters of the patient and the past medical history are subjected to correlation analysis, key health indexes related to the operation can be accurately extracted, potential relations in the data can be mined, and the application effect of the data is optimized. By analyzing the combination mode of different health data, the probability of occurrence of common risks in the operation can be predicted, and the accuracy of medical decision making is enhanced. In addition, key operation steps of the surgery are matched with the health state of the patient, potential operation difficulties are recognized, and the scheme is optimized in a targeted mode.

A method may obtain a failure propagation model, wherein the failure propagation model comprises: a model representation of a plurality of hardware components, a set of hardware failure probabilities; and a logic that maps signals to the hardware components. A method may receive an alert signal from the aircraft. A method may map the alert signal to the plurality of hardware components. A method may perform a root-cause diagnosis of the alert signal that has been mapped to the plurality of hardware components comprising: determining via the failure propagation model, one or more combinations of hardware failures associated with the alert signal; and determining a probability of occurrence associated with the combinations of hardware failures. A method may display a report that includes combinations of hardware failures associated with the alert signal and the probability of occurrence associated with the combinations of hardware failures.

The invention provides a dual-drive deepwater wellbore integrity failure risk evaluation method, and belongs to the technical field of deepwater drilling safety, and the dual-drive deepwater wellbore integrity failure risk evaluation method comprises the following steps: based on fault tree analysis in a bowknot model, identifying a wellbore integrity failure mode; constructing a differentiated evaluation path according to the failure mode, and quantifying a static equipment failure risk and a dynamic structure failure risk respectively; constructing a wellbore system-level failure probability model through a fault tree and Bayesian network dynamic mapping mechanism; based on event tree analysis, modeling is conducted on the safety barrier state after shaft integrity failure, accident consequences are deduced, and the occurrence probability of the accident consequences is calculated; and outputting the system-level failure probability, the key path contribution degree, the bottom event sensitivity sequence and the accident consequence probability, and solving the technical problem that the system-level failure risk is difficult to accurately predict and dynamically prevent and control because static empirical data and a dynamic mechanical mechanism are separated and cannot be coupled and updated in real time.

The invention discloses a cross-organization collaborative project risk and progress coupling early warning method and system, and the method comprises the steps: obtaining risk data and progress data of a project and external environment data corresponding to the risk data, the risk data comprising a risk occurrence probability, and the progress data comprising a planned progress completion rate and an actual progress completion rate; determining the product of the risk occurrence probability and the risk influence degree as a risk dominant coefficient; determining an absolute value of a difference value between the actual progress completion rate and the planned progress completion rate as a progress dominant coefficient; when it is determined that the risk dominant coefficient is greater than or equal to the progress dominant coefficient, inputting the risk data and the external environment data into a preset risk-progress model to obtain first early warning information; and when it is determined that the progress dominant coefficient is greater than the risk dominant coefficient, inputting the progress data to a preset progress-risk model to obtain second early warning information. The project risk and progress early warning method is used for improving the accuracy of project risk and progress early warning.

The invention relates to the technical field of power system operation and control, in particular to a power system recovery capability evaluation method, device and equipment considering unconventional risks and a medium, and the method comprises the steps: determining parameters for calculating the occurrence probability of earthquake disasters of different earthquake magnitudes; calculating intensity distribution by using an earthquake intensity diffusion model; calculating transmission tower and transmission line fault probabilities to obtain a fault line set; according to the operation power flow and the maximum allowable power flow, the cascading failure situation is divided into high risk and low risk; aiming at the two conditions, respectively enumerating a first-order fault state set and a second-order fault state set to obtain a system recovery capability initial value; the value serves as input, a convergence threshold value is set, a ULBI-IISE algorithm is adopted for iterative calculation, when the difference value of the upper cut set and the lower complementary set is smaller than the threshold value, the recovery capacity and the interval are output, and the method reduces high-order fault complete enumeration and the calculation amount while guaranteeing the precision, and is suitable for rapid evaluation of emergency scenes such as earthquakes.

The invention relates to a work order generation and processing monitoring method and system, equipment and a storage medium, and the method comprises the following steps: collecting operation data of power equipment, carrying out the preprocessing, then constructing an equipment abnormality prediction model, calculating the occurrence probability of each fault type through the preprocessed data, setting an occurrence probability threshold value, and carrying out the prediction of the abnormality of the equipment; taking the fault type exceeding the threshold as an actual fault type to generate a corresponding work order; work order weights are calculated according to factors such as work order generation time, fault severity and field environment, a work order distribution model is constructed, and reasonable scheduling of maintenance resources is realized; for the completed work order, performing weighted summation through indexes such as response duration and satisfaction, and evaluating the overall processing efficiency; and judging the overall efficiency of each type of fault work order according to a set processing efficiency threshold, and if the overall efficiency does not reach the standard, dynamically adjusting the maintenance resources in the next time period by using the feedback optimization model, and updating the processing efficiency threshold, thereby continuously improving the fault response and maintenance efficiency.

The invention relates to the field of risk assessment, in particular to a nine-small-place risk identification and intelligent assessment method based on artificial intelligence. The method comprises the following steps: firstly, obtaining an evaluation weight of each risk according to the number of occurrence records of each risk of a target place and rectification duration of each risk after occurrence each time, and obtaining a dynamic evaluation value of each risk according to the change of the number of occurrence records of the same risk of all places at the same time point; according to the difference of monitoring data between the occurrence period and the non-occurrence period of each risk of each sensor of the target place, the evaluation weight of each risk of the target place is adjusted, the adjusted evaluation weight of each risk at the current moment is obtained, and then the occurrence possibility of each risk in the target place at the current moment is analyzed; and carrying out real-time early warning on each risk of the target place based on the occurrence possibility. According to the invention, the accuracy of risk prediction of nine small places can be improved.

The invention discloses a port bulk cargo intelligent collaborative transport full-process safety control method and platform, and the method comprises the steps: building a digital twin model and an associated database of a port and equipment, and collecting multi-dimensional monitoring data; the risk identification deep neural network identifies suspected risks, and through verification of a preset rule, risk types and occurrence probabilities are determined; outputting the structured risk information; dynamically optimizing the original operation plan; mapping and driving the parameter change of the digital twinborn model, and inputting the preliminary scheduling instruction sequence for simulation verification and optimization to obtain a final scheduling instruction; uploading a storage certificate; according to the management and control method and platform, multi-dimensional monitoring data are fused and subjected to risk identification through cooperation of a deep neural network and a rule engine, the identification precision, reliability and accuracy are improved, a whole-process safe closed loop is achieved, a scheduling instruction is verified through a digital twin model, the accuracy and safety of decision making are guaranteed, and the management and control efficiency is improved. And the global job cooperation efficiency and the resource utilization rate are improved through multi-target dynamic optimization.

The invention discloses a rail emergency early warning method and device, a storage medium and computer equipment. The method comprises the following steps: constructing a dynamic knowledge graph based on historical operation and maintenance data of a track; identifying a real-time operation event of the track to obtain event entities, and event semantic relationships and event time attributes among the event entities; according to the event entity, the event semantic relationship and the event time attribute, identifying a causal path of the real-time operation event in the dynamic knowledge graph; calculating the probability of occurrence of a causal path; if the occurrence probability is greater than a preset threshold value, determining the real-time operation event as an emergency; and according to the operation reasoning result in the causal path, early warning information of an emergency is determined, and the early warning information is displayed in the whole-station line building information model of the track. According to the invention, through the dynamic knowledge graph and the building information model, real-time judgment and spatial visual early warning of the track operation risk are realized, and the reliability of track operation and maintenance is improved.

The invention provides a manual hail elimination operation method, device and equipment and a storage medium. Relates to the technical field of meteorological disaster defense. The method comprises the following steps: acquiring multi-source data including numerical forecasting and the like, and performing quality control; based on the data after quality control, building a short-term model based on numerical values and intelligent grid forecasting to obtain a first hail occurrence probability, calculating a second hail occurrence probability based on sounding data, calculating a third hail occurrence probability based on satellite data, and calculating a fourth hail occurrence probability based on radar and extrapolation forecasting; interpolating the four hail occurrence probabilities and then obtaining a comprehensive probability according to the weight; obtaining artificial influence weather resources of the operation areas, screening operation ranges, sorting and scheduling the areas meeting conditions, and issuing a scheme; and after operation, multi-source data is used for evaluating the effect, optimizing the weight and storing process data. The problems of single data source, low forecasting accuracy, unscientific operation sequence, unreasonable operation arrangement and the like are solved, and the effect of manually eliminating hail operation is improved.

The invention belongs to the field of landslide early warning and risk management and control research, and particularly discloses a rainfall threshold value dynamic updating method for landslide early warning, which comprises the following steps: according to a regional landslide record and rainfall data, counting the occurrence probability of a landslide on a region; useful information in the real-time early warning record is extracted, the useful information comprises a plurality of rainfall parameters and whether the landslide is triggered or not, then sample marking is conducted on the early warning record according to whether the landslide is triggered or not, if the landslide occurs, the occurrence probability is marked as 100%, and if not, the occurrence probability is marked as 0%; taking the occurrence probability of the landslide on the region as pre-training data, fitting the relationship between rainfall parameters and the occurrence probability of the landslide through a machine learning model according to the occurrence probability of the landslide under a certain rainfall event in the slope, and dynamically updating a rainfall threshold value of the risk slope; according to the updated rainfall threshold value, landslide early warning is issued to the risk slope, and a new early warning record is generated. The method is high in early warning accuracy, and can meet the requirements of disaster prevention and reduction under the conditions of changeable climate and frequent earthquakes at present.

The invention discloses an industrial park micro-grid toughness planning method and system based on opportunity constraint, and relates to the technical field of power grid planning, and the method comprises the steps: building a combined scene pool containing scene weight according to the extreme strength probability of risk factors in a historical scene based on the coupling and association failure of multiple risk factors; constructing a pre-disaster protection planning layer according to opportunity constraints and protection dimensions; constructing a post-disaster recovery planning layer by using a staged recovery mechanism and an emergency operation mode; constructing an optimization objective function by minimizing the protection cost and the recovery cost; constructing a dual-stage planning model by using the pre-disaster protection planning layer, the post-disaster recovery planning layer and the optimization objective function; and matching the current triggering scene and the occurrence probability according to the real-time industrial park micro-grid operation data and the combined scene pool, inputting the current triggering scene and the occurrence probability into the double-stage planning model, and generating a tough protection planning strategy. The method has the beneficial effects that the protection planning strategy gives consideration to the toughness requirement and disaster recovery requirement of the micro-grid under the extreme event.

The invention provides a file-free attack detection method and device and related equipment, and relates to the technical field of communication. The method comprises the following steps: counting the occurrence probability of each byte value of a target process in a memory window at the current moment; determining an actual entropy value of the target process based on the occurrence probability of each byte value; based on the occurrence probability of each byte value in the memory window of the target process at a plurality of moments before the current moment, utilizing a time sequence prediction model to determine a prediction entropy value of the target process; based on the actual entropy value and the predicted entropy value of the target process, determining an entropy increase abnormal score of the target process; and determining whether the target process has no file attack based on the entropy increase anomaly score. Through the above technical means, the problem of low detection accuracy of no file attack in related technologies is solved.

This application discloses a method for predicting the probability of myopia occurrence in adolescents and related products. When implementing the method provided in the embodiments of this application, firstly, multidimensional prediction parameters such as race, gender, age, equivalent spherical power, and genetic characteristics are collected, and the validity of the parameters is verified. Then, a hash matching algorithm is used to retrieve a pre-built myopia risk prediction dictionary, matching corresponding data entries, thereby outputting the probability of myopia occurrence, risk level, risk interpretation, and prevention and control guidance for one and two years, supporting bilingual display, completing the entire process of quantitative prediction of myopia risk in adolescents. This application avoids human judgment bias through automated prediction, quantifies the probability of myopia occurrence and risk level in two cycles of one and two years, dynamically predicts the onset window period, and improves the limitations of traditional static prediction. Furthermore, the prediction results support bilingual presentation and have both prevention and control guidance and popular science functions.

This application provides a multi-intent prediction model training method and a multi-intent recommendation method, relating to the field of artificial intelligence technology. The multi-intent prediction model training method locates multiple target candidate intents of users to the dimension of target scene, constructs a unified sample for multiple target candidate intents in the target scene, and trains a model using the unified sample. The resulting model can predict the probability of occurrence of multiple target candidate intents in this target scene. It eliminates the need to construct samples separately for each target candidate intent and train the model separately using the constructed samples, thereby avoiding repetitive sampling operations for different intents at the same sampling time, and avoiding multiple model trainings, which can save manpower and resources and reduce costs.

The application discloses a kind of stable control system strategy adaptability risk assessment method, it is related to power system safety and stability control technical field.The method includes: processing new energy historical output data and establishing time sequence correlation model, generating partition scene set;Accordingly generate multiple risk working condition sample, determine key feature vector set by transient simulation, feature extraction and dimension reduction;Scene clustering reduction is obtained to strategy scene set containing probability weight;Enumerate fault chain sequence, execute time sequence simulation to three-layer stable control strategy;Calculate multiple risk indexes to construct multidimensional matrix;Combined with new energy power time-varying quantile fluctuation band, the risk value and occurrence probability are identified and classified using fuzzy comprehensive judgment, and the comprehensive evaluation conclusion is output.The application can accurately depict the time sequence fluctuation characteristics of new energy, realize stable control strategy full-link assessment and risk form differentiation control, and provide support for power grid safety decision-making.

The invention discloses an event prediction method and system based on a time sequence hypergraph, and belongs to the technical field of event prediction. The method comprises the following steps: acquiring multi-source heterogeneous data of a target region, defining a unified time index, and generating a time sequence feature sequence of each variable of the region through preprocessing; then, identifying a variable causal relationship in the region based on a frequency domain anti-fact condition mutual information algorithm, and fusing the variable causal relationship with a time sequence evolution relationship to construct a time sequence hypergraph structure representing the interior of the region; a dynamic filter is used for filtering the graph, and deep features of the graph are learned by means of a multi-band spectrum gating mechanism, so that internal complex causal and time sequence modes are effectively captured; and finally, performing dichotomy prediction based on the learned graph representation, and outputting the occurrence probability of future events in the region. According to the method, accurate and explainable event prediction is realized, training and prediction do not need to cross regions, data privacy and calculation efficiency are guaranteed, and stronger robustness is shown for specific data distribution change of the regions.

The present invention discloses a method, system and medium for fault analysis and cross-domain optimization of human-machine collaborative functional testing, which relates to the field of electronic information technology, including: constructing a fault tree, based on reverse quantitative analysis of the fault tree structure, converting the test ratio of intermediate events corresponding to test items into the test ratio of bottom events, calculating the product of the probability of occurrence of the bottom event and the test ratio of the bottom event without functional testing, and obtaining the probability of the motherboard being missed and defective; constructing a test ratio optimization model for the motherboard functional testing process, constructing the objective function of the test ratio optimization model based on the test ratio of the test items and the probability of the motherboard being missed and defective, solving the test ratio optimization model to obtain the optimal motherboard functional testing strategy, so as to test the motherboard; the cross-domain overall optimization method for functional testing improves the cross-domain overall optimization capability, and realizes cost reduction and efficiency improvement in the motherboard functional testing process.

A maintenance work assistance system (1) comprises: a basic event inference engine (20) that infers, in accordance with a pre-created causal model, a basic event (42) that will cause a failure in an industrial machine (2) and the probability of occurrence thereof; a confirmation task presentation engine (21) that, in accordance with a pre-created processing model, presents a confirmation task for confirming whether or not the basic event (42) is occurring; a history information extraction engine (22) that extracts, from a history management database (31), a history of environment-related information related to the operating environment of the industrial machine (2) serving as a target of the confirmation task, and a history of maintenance work records; and a basic event identification engine (23) that, in accordance with the pre-created processing model, identifies the basic event (42) for which there is a causal relationship therebetween with a top event (40) or an intermediate event (41).

An object of the present disclosure is to predict an occurrence probability of a violation with high accuracy.Therefore, content of the present disclosure is a violation prediction apparatus that predicts an occurrence probability of a violation, and is configured to: evaluate conversation data that includes conversation partner information for identifying a partner of a conversation and indicates a conversation content and a conversation situation, thereby obtaining a conversation content evaluation value and a conversation situation evaluation value; evaluate a relationship with the partner of the conversation based on relationship data that indicates a human relationship with a target user and on the conversation partner information, thereby obtaining a relationship evaluation value; calculate a social interaction effect based on the conversation content evaluation value, the conversation situation evaluation value, and the relationship evaluation value; calculate a time attenuation value of the social interaction effect based on a time attenuation function; and calculate an occurrence probability of a violation.

This disclosure provides a method and apparatus for predicting human-machine conflict, capable of proactively predicting human-machine conflict from multiple dimensions. By acquiring and analyzing various physiological and behavioral data of operators in historical human-machine interaction scenarios, significant characterization factors corresponding to fatigue states are screened, enabling a more accurate quantitative representation of operator status. Simultaneously, the significant characterization factors of operators are combined with environmental and machine characteristic dimensions (including the complexity of interface interaction) to establish a human-machine conflict prediction model suitable for the target scenario. This method can dynamically reflect changes in multiple factors during system operation, proactively predicting the probability of human-machine conflict occurrence, thereby improving the comprehensiveness, timeliness, and reliability of identification, avoiding lag and bias caused by single data or rules, and ultimately contributing to improving the safety and stability of complex human-machine systems.

The application relates to the technical field of artificial intelligence, in particular to a label recommendation method and device, a label recommendation model training method and a medium, wherein the method comprises the following steps: obtaining a reference label and at least two candidate labels; determining semantic similarity between the reference label and the candidate labels; determining co-occurrence probability between the reference label and the candidate labels, the co-occurrence probability being used for describing the probability that the reference label and the candidate label belong to the same label of multimedia data; and selecting a target label from the at least two candidate labels according to the semantic similarity and the co-occurrence probability. Since the semantic similarity represents the semantic similarity of the labels and the co-occurrence probability represents the probability that the labels appear in the same multimedia data, the target label obtained based on the semantic similarity and the co-occurrence probability is more consistent with the distribution assumption of the multimedia data, so that the obtained target label is more accurate.

The invention belongs to the technical field of industrial safety monitoring, and particularly relates to a risk situation monitoring model, method and system. The risk situation monitoring method comprises the steps that the occurrence state of each initial event and the operation states of preventive protection measures and remission protection measures are collected; determining the access probability of a single event evolution link according to the occurrence state of the single initial event and the running state of the preventive protection measure, and determining the access probability of a single accident evolution link according to the access probability of the single event evolution link and the running state of the remission protection measure; determining the occurrence probability of the top event according to the access probability of the plurality of event evolution links; and determining the occurrence probability of a single accident consequence according to the occurrence probability of the top event and the operation state of the remission protection measure. According to the method, the occurrence probability of a certain accident consequence under the condition is calculated by considering the condition that a plurality of initial events exist, so that the method is closer to the actual industrial operation condition.