322 results about "Occurrence probability" patented technology

The invention discloses an underground engineering geological safety dynamic risk assessment method based on multi-source data fusion, which relates to the technical field of risk assessment, and comprises the following steps: collecting multi-source heterogeneous data related to underground engineering, extracting implicit information, modeling underground engineering geological safety risk factors into a risk network, and establishing a risk network model; calculating the comprehensive importance of the nodes based on a Stacking integration algorithm, and identifying key risk factors; acquiring characteristic parameters of key risk factors by using spatio-temporal characteristics of implicit information, introducing a random walk mechanism to acquire a dynamic accident prediction chain, and performing learning representation by using a graph attention network to acquire probability distribution of an accident evolution path; and assessing the vulnerability of the connection edge in the risk network, establishing a dynamic risk assessment model based on the node importance and the edge vulnerability, and obtaining a dynamic risk value corresponding to the accident according to the accident occurrence probability and the risk mitigation factor. According to the invention, intelligent identification, dynamic evaluation and accurate early warning of risk factors are realized, and the accuracy and real-time performance of risk identification and evaluation are improved.

The invention discloses a rockburst risk early warning method based on TBM multi-source data fusion and a hybrid algorithm, and the method comprises the steps: collecting TBM tunneling parameters, geological exploration data and micro-seismic monitoring data of different rockburst levels, achieving feature complementation through multi-source data fusion, improving the completeness and reliability of early warning, constructing a hybrid neural network model based on CNN, LSTM and an attention mechanism, and carrying out the early warning of the rockburst risk. Feature weight distribution is optimized by introducing an attention mechanism, so that the model can adaptively focus key risk signals, and the applicability of model early warning is improved; and in combination with a fuzzy comprehensive evaluation algorithm and a Bayesian probability model, outputting four rockburst grades of no rockburst, slight rockburst, medium rockburst and strong rockburst and occurrence probabilities thereof, and realizing real-time dynamic early warning and probabilistic early warning of the rockburst grades. Compared with an existing method, the method has the advantages that the accuracy, timeliness and engineering applicability of rockburst early warning are remarkably improved, and real-time and accurate early warning of potential rockburst and the grade of the potential rockburst can be achieved.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method and a geological disaster early warning system, and the method comprises the following steps: S1, obtaining geological data of a target area; s2, performing quality control and interpolation processing on the geological data obtained in the S1 to obtain standardized space-time continuous data; s3, based on the standardized space-time continuous data, calculating a geologic body stress state of the target area; s4, calculating a potential instability point in the region and the probability of occurrence of the potential instability point; s5, training a disaster evolution prediction model, and outputting a disaster occurrence probability and regional distribution thereof; and S6, generating graded early warning information. According to the invention, through application of multi-source data fusion and the random forest algorithm, the occurrence probability and evolution trend of geological disasters are accurately predicted, and the accuracy and timeliness of the early warning system are significantly improved.

The invention relates to an optical module health state prediction method and device, equipment and a medium. The method comprises the steps of collecting historical operation data of an optical module; preprocessing the historical operation data to obtain target historical operation data; according to historical data of each index in a plurality of indexes in the target historical operation data, calculating an occurrence probability of an optical module fault type corresponding to the index; calculating the fault probability of the optical module according to the occurrence probability of the optical module fault type corresponding to each index in the plurality of indexes; and predicting the health state of the optical module according to the fault probability of the optical module. Therefore, according to the technical scheme, the fault probability of the optical module is calculated by integrating the multiple indexes of the optical module, and then the health state of the optical module is predicted according to the fault probability of the optical module. Therefore, the health state of the optical module is accurately predicted, a user can find the problem of the optical module in time, the risk of network service interruption is effectively reduced, and the user experience is improved.

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 belongs to the field of system reliability design analysis, and particularly relates to a common cause failure parameter-based fault analysis method, which comprises the following steps: acquiring a fault tree model of an analysis object, and fusing the fault tree model with a common cause failure parameter model; constructing a fault tree model with a common cause failure parameter model; performing common cause failure analysis by using the common cause failure parameter model; expanding the constructed fault tree model with the common cause failure parameter model into an explicit expression common cause failure fault tree model by adopting a universal parameter model expansion method; solving the extended explicit expression common cause failure fault tree model to obtain a top event occurrence probability; according to the method, the implicit common cause failure parameter model is expanded into explicit expression, the automation of the reliability analysis process of the complex system considering the common cause failure influence is realized, the modeling difficulty is reduced, and the reliability analysis working efficiency is improved.

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 discloses an automatic driving risk assessment method and system based on a dynamic Bayesian network, and the method comprises the steps: 1, recognizing a hazard event which may be caused by function missing through an HAZOP method according to each function on which an automatic driving system depends, and constructing a hazard graph model G = (V, E): enabling a root node and a child node of a node set V to represent a trigger node, the leaf node represents a hazardous event node, the trigger node causes occurrence of the hazardous event node, and the directed edge of the E comprises a directed edge of the trigger node pointing to the hazardous event node; 2, constructing a dynamic Bayesian network, and predicting the individual occurrence probability of each node in the hazard graph model in a future time period, the occurrence probability of child nodes on the basis of the occurrence of a father node, and the occurrence probability of the nodes between adjacent moments through the dynamic Bayesian network; and step 3, obtaining the risk value of the automatic driving system at the prediction moment. According to the invention, multi-dimensional quantitative evaluation of risks can be realized.

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 present application provides a communication method and apparatus, which can be applied to the field of communications. The method comprises: a terminal device receives first configuration information from a first access network device, the first configuration information comprising at least one of a prediction quantity, a starting condition for predicting the prediction quantity, at least one time period for predicting the prediction quantity, a trigger condition for the prediction quantity, or a trigger condition for handover, and the prediction quantity comprising one of the following abnormal events: an RLF in a serving cell, an HOF in at least one neighboring cell, an RLF in the at least one neighboring cell, a ping-pong handover in the at least one neighboring cell, or an unnecessary handover in the at least one neighboring cell; and the terminal device predicts a first prediction result on the basis of the first configuration information. In the solution, a target cell can be determined on the basis of a first prediction result, so that the probability of occurrence of an abnormal event can be reduced during mobility management, thereby improving user experience and network performance.

The invention provides a flaw detection sensitivity calibration method for a hollow axle and a storage medium thereof. The flaw detection sensitivity calibration method comprises the following steps: acquiring a space structure parameter and a reference defect parameter of a to-be-detected axle; determining an axle end and an inner hole of the axle to be detected according to the spatial structure parameters, and installing a flaw detection adapter at the axle end of the axle to be detected; controlling the probe arm to probe into the inner hole of the axle to be detected through the probe hole and rotate, and controlling the ultrasonic probe to perform ultrasonic scanning on the axle to be detected based on the scanning sensitivity to obtain scanning defect parameters of the axle to be detected; performing difference comparison on the reference defect parameter and the scanning defect parameter to obtain an error parameter; if the error parameter is in the preset error interval, the scanning sensitivity is determined as the standard sensitivity of the axle to be detected, so that the ultrasonic flaw detection sensitivity of the hollow axle can be quickly and accurately calibrated, all positions of the hollow axle can be effectively subjected to ultrasonic flaw detection, the detection quality is ensured, and the detection efficiency is improved. And the occurrence probability of error detection and missing detection is reduced.

The invention relates to the field of vulnerability mining, in particular to a vulnerability mining method based on a large model, and the method comprises the steps: constructing a label combination library for a working condition terminal, and subsequently constructing a correlation model for a label combination based on a credible log fragment corresponding to each label in the label combination; based on the occurrence probability of each label combination and combined with the constraint discrete features of the association model corresponding to the label combination, effective sub-labels are set for the label combination, a current label combination of a current log record is analyzed subsequently for a new log record, and adaptability analysis is performed on the log record according to the effective sub-labels corresponding to the current label combination, so that the log record is obtained. According to the method, potential laws of the operation process of the industrial control terminal are considered, the association model of the log records under the specific label combination is constructed, the log records are analyzed based on the effectiveness adaptability of the label combination, log fragments possibly representing that potential abnormal vulnerabilities exist in a system are rapidly marked when massive log records are faced, and the system reliability is improved. And the accuracy and efficiency of vulnerability mining are improved.

An entropy decoding method may include obtaining, from a bitstream, information about a slice type, based on an occurrence probability of a symbol, performing arithmetic decoding on a current symbol corresponding to a syntax element, when the information about the slice type indicates an I slice, determining a first scaling factor for updating the occurrence probability of the symbol by using a first function, wherein a value of the first function is determined based on a first threshold value, when the information about the slice type indicates a B or P slice, determining the first scaling factor by using a second function, wherein a value of the second function is determined based on a second threshold value, and by using the first scaling factor, updating the occurrence probability of the symbol.

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.

An equipment preservation decision support system includes a load distribution estimation unit which estimates at least one of a load which is applied to a maintenance object in accordance with time and a load which is applied to equipment constituting the maintenance object in accordance with time. A failure probability estimation unit predicts, on the basis of the estimated load, a failure occurrence probability. A risk and cost calculation unit estimates a risk when operation of the maintenance object is hindered on the basis of the predicted failure occurrence probability and estimates a cost required for maintenance of the maintenance object on the basis of the failure occurrence probability. With this system, information for performing maintenance at a suitable timing for each piece of equipment is obtained.

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 provides an illegal behavior full-scene feature recognition method and system in port intelligent monitoring, and relates to the technical field of intelligent monitoring. The method comprises the following steps: firstly, carrying out gridding division on a target port, processing operation information and meteorological data in a future first time interval, predicting violation behaviors with relatively high occurrence probability in the first time interval based on a machine learning model, then determining a simulation violation rule in advance based on the violation behaviors with the relatively high occurrence probability, and carrying out simulation on the violation rule according to the simulation violation rule. The method comprises the following steps: acquiring a plurality of sensing data, determining a first sensing data set, setting different weight information for sensing data with different importance degrees in the first sensing data set, finally predicting a plurality of pieces of first violation information based on the first sensing data, and verifying through a pre-determined violation simulation rule to determine target violation information. According to the technical scheme, multi-source data are analyzed and applied, and fine-grained prediction of illegal behaviors can be carried out in advance.

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.