395 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 discloses a building construction safety risk assessment method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a neural network model, and recognizing an unsafe behavior mode of a worker; receiving a data stream from a construction site in real time, dynamically adjusting risk assessment parameters according to a current construction state and environment change, and predicting potential safety risks; according to the prediction result, generating corresponding prevention measure suggestions; key indexes and abnormal conditions are displayed through a monitoring interface, and a detailed analysis report is generated; according to the invention, through dynamic assessment and timely response to the safety risk of the construction site, the risk assessment parameters can be rapidly adjusted according to the environmental change, the potential risk can be predicted, and the prevention measures and suggestions can be provided, so that the occurrence probability of safety accidents is effectively reduced, and the construction efficiency is improved.

The invention discloses a user abnormal behavior monitoring method and device, equipment, a medium and a product. Relates to the technical field of deep learning, network security and financial science and technology, and comprises the following steps: obtaining logs generated by a user within a set time, dividing and clustering according to a target time period to obtain a clustering log set, analyzing the clustering log set to obtain a corresponding behavior event label set, and combining and inputting the two into a pre-trained long and short-term memory model to obtain a behavior event label set; and obtaining at least one target behavior operation predicted for each piece of target mark data and a corresponding target behavior occurrence probability, and carrying out abnormal behavior monitoring on the target user according to a matched abnormal monitoring rule based on the target behavior operation and the occurrence probability thereof. Through fusion of the deep learning model and clustering analysis, the monitoring accuracy is improved, the manpower and time cost is reduced through an automatic data processing mode, the monitoring efficiency and the real-time performance are improved, and thus the reliability of user abnormal behavior recognition and safety evaluation is improved.

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 discloses a converter station important event diagnosis method and system based on correlation causality. According to the method, historical messages and equipment information of the converter station are collected and preprocessed, and SER event modeling is carried out; recording sequence events in the database, namely SER events occurring according to a time sequence, and mining association rules according to event levels by utilizing an Apriori method; constructing an event task atlas based on an SER model in combination with an association rule event set and a converter station sequential control operation task process; converting the event atlas into a causal model by utilizing a causal framework based on a graph model; and carrying out real-time analysis on data in the converter station message system by utilizing a causal atlas model, and judging the occurrence probability of an emergency event after a non-emergency event occurs. According to the method, the important events of the converter station are diagnosed based on the correlation causal method, and the method is small in data demand and high in diagnosis accuracy.

The invention discloses a debris flow geological disaster monitoring and early warning method, and belongs to the field of intelligent early warning, and the method comprises the following steps: carrying out the preprocessing of debris flow disaster impact factor data collected by a multi-source heterogeneous sensor, and obtaining a fusion data set under a unified spatial-temporal scale; performing abnormal value detection and data filling on the fused data set to obtain a high-quality data set; performing feature extraction on the high-quality data set by adopting a multi-scale convolutional neural network to obtain multi-scale features; establishing a debris flow disaster occurrence probability prediction model; and inputting the multi-scale features into the debris flow disaster occurrence probability prediction model to obtain debris flow geological disaster early warning. According to the invention, accurate early warning of debris flow geological disasters is realized, and powerful support is provided for disaster prevention and reduction.

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 provides a deep learning-based method and device for evaluating the influence of an avalanche disaster on a road, and relates to the technical field of disaster coping, and the method comprises the steps: obtaining the historical avalanche monitoring data of a monitoring region; establishing an avalanche disaster influence prediction model according to the topographic parameters and the physical parameters of the snow body before avalanche; according to the avalanche disaster influence characteristics, the road positioning data and the post-avalanche road influence parameters, establishing an avalanche disaster road influence prediction model; acquiring current data of the monitoring area; obtaining current avalanche disaster influence characteristics according to the avalanche disaster influence prediction model; and predicting the influence of the avalanche disaster on each road section according to the avalanche disaster road influence prediction model. According to the method, key parameters of an avalanche occurrence area can be acquired in real time, and the occurrence probability of an avalanche disaster and the influence degree of the avalanche disaster on a road can be accurately predicted and early warned by means of a deep learning algorithm, so that the safety and traffic capacity of the road are effectively improved.

The invention relates to the technical field of potential safety hazard identification, in particular to an AI-based method and system for identifying potential safety hazards of hoisting machinery, which comprises the following steps of: collecting operation data of the hoisting machinery, including position, speed and load capacity, integrating meteorological conditions and information of near-field obstacles, and combining real-time transmission data to obtain a real-time transmission result of the hoisting machinery; and generating comprehensive real-time data. According to the method, comprehensive assessment and early warning of potential risks are achieved by integrating the operation data and the environment variables of the hoisting machinery, the operation environment can be simulated more accurately, occurrence of risk conditions can be predicted more accurately, the operation strategy is adjusted in time through dynamic risk scoring, the early warning accuracy and the operation safety are effectively improved, and the safety of the hoisting machinery is improved. The state definition and the probability model which are updated in real time can continuously adapt to environment changes, it is ensured that the operation strategy of the hoisting machinery is always kept in the optimal state, the accident occurrence probability is reduced, and meanwhile economic losses caused by equipment faults or misoperation are reduced.

The invention relates to the technical field of building engineering construction management, in particular to a stirring method and system for TRD. The method comprises the following steps: collecting construction parameter data in real time through a sensor and a measuring device which are arranged on TRD equipment; performing preliminary evaluation on the construction parameter data to obtain construction preliminary evaluation data; performing construction parameter optimization adjustment according to the construction preliminary evaluation data to obtain construction parameter optimization data; and performing real-time construction optimization operation according to the construction parameter optimization data, and performing real-time construction quality evaluation to obtain real-time construction quality evaluation data so as to perform real-time construction quality feedback operation. According to the method, high-risk areas or parameters (such as non-uniform stirring and insufficient grouting) in construction can be recognized in time, and the probability of occurrence of construction quality problems is reduced by adjusting in advance.

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 discloses a port disaster risk grading method, which relates to the technical field of risk grading, and further considers comprehensive correlation coefficients among all consequences in each disaster type in a data-driven manner on the basis of the prior art, so as to ensure that correlation factors among different consequences can be comprehensively considered, and improve the risk grading efficiency. Meanwhile, a first composite index and a second composite index of each disaster type are analyzed and calculated, and a third composite index is obtained on the basis of combining the first composite index and the second composite index so as to ensure that complexity factors of consequences of each disaster type are effectively considered, and finally the obtained result is incorporated into an existing calculation formula of an expected loss value, so that the calculation accuracy of the expected loss value is improved. According to the method and the device, the probability of occurrence of risk level misjudgment can be effectively reduced to the greatest extent even if the risk level of disaster types with the same coordinates is judged subsequently, so that the expected loss value which is more accurate, more comprehensive and more scientific is ensured to be obtained, and the probability of occurrence of risk level misjudgment can be effectively reduced to the greatest extent.

The embodiment of the invention discloses a kitchen ware anomaly detection method and device, equipment and a storage medium, and the method comprises the steps: selecting a first cooking mode matched with the food material information from a preset cooking database according to the food material information of the recognized to-be-cooked food material; cooking the food materials in a first cooking mode, and detecting whether a first cooking abnormal risk corresponding to the first cooking mode occurs in the cooking process; and when it is detected that the first cooking abnormal risk occurs, executing a first abnormal risk response task to process the first cooking abnormal risk. According to the kitchen ware anomaly detection method, device and equipment and the storage medium, the problem that danger occurs in the cooking process due to the fact that an existing steaming oven lacks an effective anomaly detection mechanism is solved, the occurrence probability of the danger caused by anomaly in the cooking process is reduced, and the safety of the cooking process is ensured.

The invention discloses an industrial control network security risk assessment method based on data analysis, and relates to the technical field of network security risk assessment, a risk trend coefficient Rte is calculated through multiple regression analysis, the coefficient can reflect the trend change of a potential security event, and risks possibly occurring in the future are recognized in advance. In combination with a dynamic risk assessment mechanism, by setting a dynamic threshold value Td, when the risk trend coefficient Rte exceeds the dynamic threshold value Td, the system can automatically generate an alarm and a protection measure suggestion. According to the dynamic evaluation mode, the system can automatically adjust an early warning mechanism according to real-time changes of the network, the flexibility and the response speed of dealing with sudden network attacks are enhanced, and managers can take measures in time to carry out safety protection. Through big data analysis and dynamic risk assessment, the method can timely identify potential risks and carry out real-time early warning, so that the occurrence probability of security incidents is remarkably reduced, and the dependence on manual operation is reduced.

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 discloses an IT work order risk management and control method based on combination of large and small models, and belongs to the technical field of IT work order systems. According to the method, the problem that IT work order risks cannot be effectively identified and managed in the prior art is solved, factors such as occurrence probabilities, influence degrees and solutions of different types of problems can be analyzed by adopting a mode of combining large and small models, targeted risk early warning and coping strategies are provided for IT teams, and the risk management efficiency is improved. According to the method and the system, multiple data mining technologies are combined, deep mining and understanding of the work order data are realized, and problems can be found in time and corresponding measures can be taken by monitoring whether the abnormal value in the abnormal value processing process causes that the data quality does not meet the data quality requirement or not in real time, so that the accuracy and reliability of the data are ensured.

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.