49 results about "Probability curve" patented technology

The invention discloses a microwave radio frequency link multi-index comprehensive measurement method, and relates to the technical field of radio frequency communication testing, and the method comprises the following steps: S001, building a peak power prediction model, generating a peak power probability curve based on the statistical distribution characteristics of a plurality of carrier signals, and forming a peak early warning vector containing an estimated time point and an amplitude value; and S002, executing synchronous sampling scheduling according to the peak early warning vector, and by adjusting a sampling trigger threshold and a time window parameter, enabling a sampling window to aim at peak power occurrence time and cover a time interval of amplitude limiting protection. According to the method, through peak value prediction, accurate sampling, multi-dimensional difference and distortion identification, efficient capture and dynamic regulation and control of amplitude limiting abnormity are realized, a prediction-identification-adjustment-optimization closed-loop process is constructed, the monitoring integrity and adaptive capacity of a microwave radio frequency link to sudden distortion are remarkably improved, and stable operation of a system is guaranteed.

The invention discloses a wind power generation abnormal data analysis method and system, and relates to the technical field of data analysis, and the method comprises the following steps: constructing a wind direction change rate enhanced perception model, analyzing the potential omen of wind direction abrupt change based on an ultra-short time scale wind direction change trend curve and a wind speed fluctuation coupling index collected at multiple measurement points, and determining the wind direction abrupt change. Generating a risk early warning label of wind wheel pointing deviation; and based on the risk early warning label, executing a high-frequency yaw disturbance prediction mechanism, and predicting an inflow angle continuous offset window caused by yaw response lag by using a nonlinear time sequence evolution trend and a short-term wind direction reversal probability curve. The wind direction sudden change early warning is realized through multi-measuring-point wind direction enhanced perception and wind speed coupling analysis, the response precision is improved and the energy consumption is reduced in combination with predictive yaw compensation and torque balance, the yaw parameters are dynamically optimized by using adaptive closed loop and reinforcement learning, the inflow angle is kept stable for a long time, and the power generation efficiency and the structural safety are improved.

The invention belongs to the technical field of building construction safety control, and particularly discloses a building construction site dangerous behavior recognition method and system based on machine learning, and the method comprises the steps: obtaining a single-view continuous video stream of a construction site, segmenting the single-view continuous video stream into a time sequence video frame sequence, and extracting an initial spatial feature sequence through a spatial feature encoder; a pre-trained virtual visual angle projection and feature compensation module encodes the visual angle implicit vector and maps the visual angle implicit vector to a visual angle invariant feature space, and a sequential context is combined to compensate occlusion missing features to generate an enhanced feature sequence; a time sequence memory alignment module captures long-term and short-term time sequence dependence and outputs time sequence consistency characteristics; and finally, outputting a current dangerous behavior classification result through the classification prediction head, and outputting a future dangerous intention probability curve through the time sequence prediction head. The method does not need an additional camera, can accurately cope with a shielding scene, predicts the danger in advance, and improves the construction safety monitoring efficiency and reliability.

The invention discloses a containment failure probability curve fitting method and system based on a random forest, and relates to the technical field of nuclear power safety. Comprising the following steps: acquiring key parameters influencing ultimate bearing capacity of a containment, determining a distribution interval of the key parameters, and establishing a key parameter table; performing finite element calculation on the basis of the key parameter table, obtaining ultimate bearing capacity of the containment vessel under different parameter combinations and temperature loads, and establishing a database; performing normalization preprocessing on the database to form a training data set; establishing a random forest model and performing regression model training by using the training data set; inputting actual design parameters of the containment and the temperature required to be evaluated into the trained model to obtain an ultimate bearing capacity predicted value of the containment; and fitting of a failure probability curve is carried out based on the central limit theorem. According to the method, complex probabilistic safety analysis is converted into a standardized calculation task, and the implementation threshold of nuclear power plant design verification and safety review is greatly reduced.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention provides an accident potential road section dynamic identification method and platform based on multi-dimensional data fusion, and relates to the technical field of traffic data identification, and the method comprises the steps: carrying out the directed graph conversion of a target road section region, and determining a road network topology; performing partition traffic trajectory generation of a normal condition on the called multi-dimensional road network data, performing single-vehicle trajectory and traffic flow disturbance mode judgment in combination with a real-time traffic trajectory, and generating a hidden danger recognition instruction; a hidden danger road section is positioned in the road network topology, combined inversion and state transition probability analysis based on random variables are executed, and a hidden danger probability curve is generated; and carrying out hidden danger disturbance propagation analysis in the road network topology, and determining hidden danger identification information to carry out traffic hidden danger early warning management. According to the method and the device, the technical problem that the accuracy of the traffic hidden danger is influenced in the prior art can be solved, the dynamic hidden danger identification and prediction capability based on the real-time traffic flow data and the road network topology information is realized, and the technical effect of accurately identifying the hidden danger space-time distribution is achieved.

This invention discloses an LSTM-driven dynamic prediction method and system for the operational resilience of integrated utility tunnels, relating to the field of deep learning. The method includes: acquiring vibration acceleration time histories, extracting peak acceleration and vibration energy accumulation time history curves, and generating a dynamic angle sequence; correcting the baseline vulnerability curve to obtain a dynamic damage probability curve, substituting the peak acceleration and vibration energy accumulation values ​​of the cross-section into the curve, and outputting a time-varying damage index sequence; identifying critical cross-sections with abrupt changes in the damage index, calculating the average damage index using the critical cross-section as a dividing point, and determining functional bottleneck sections; obtaining the average damage index and the redundancy of subsequent alternative paths, calculating the overall functional degradation rate of the utility tunnel, and obtaining the functional recovery multiple; calculating the initial recovery time, inputting it into a pre-trained dynamic time-series prediction model, and outputting the corrected final recovery time. This invention solves the problem of low prediction accuracy and high misjudgment rate in the prediction of the operational resilience of integrated utility tunnels due to the reliance on static experience for rough estimation in existing technologies.

This invention proposes an intelligent prediction method for performance degradation and durability limit state assessment of reinforced concrete structures, comprising: establishing a probabilistic model of random variables for environmental and structural parameters; constructing an intelligent prediction model for chloride ion diffusion by fusing convolutional neural networks, and using Monte Carlo sampling analysis to analyze the probability distribution of the initial corrosion time of the reinforcing bars to predict the depassivation time of the reinforcing bars; establishing a time-varying corrosion model of the reinforcing bars to obtain the change of the corrosion loss rate of the reinforcing bars over time; further, using an incremental static analysis method, establishing a multi-scale finite element model of the reinforced concrete structure under different corrosion rate conditions through random sampling; obtaining the critical load value under the limit state to obtain the failure probability curve under different corrosion degrees; and finally, by defining a bearing capacity reduction coefficient, obtaining the bearing capacity failure time of the reinforced concrete structure to achieve the durability limit state assessment of the reinforced concrete structure under corrosion.

The invention relates to the technical field of geological disaster risk evaluation, in particular to a mountain coal mine goaf subsidence area power transmission and distribution tower risk evaluation method, system and device and a storage medium. Obtaining multi-dimensional geological environment data of the evaluation area, extracting the multi-dimensional geological environment data corresponding to the sample points and the marked collapse sample data as training features, generating a risk prediction model through training, and carrying out risk level identification on each space unit in the evaluation area through the risk prediction model, forming a regional risk distribution result; the method comprises the following steps: acquiring an accumulated vertical displacement value at a tower position, establishing a probability distribution relationship between the accumulated vertical displacement value and a tower damage grade by combining a tower damage state sample investigated on site, calculating tower equivalent damage probabilities under different accumulated vertical displacement values, and fitting according to the equivalent damage probabilities to generate a damage probability curve; and substituting the accumulated vertical displacement value of each tower into the damage probability curve to calculate a damage probability value.

The application provides an accident hidden danger road section dynamic identification method and platform based on multi-dimensional data fusion, relates to the technical field of traffic data identification, and comprises the following steps: converting a target road section area into a directed graph to determine a road network topology; generating a normal situation partition traffic trajectory through multi-dimensional road network data, judging a single vehicle trajectory and a traffic disturbance mode in combination with a real-time traffic trajectory, and generating a hidden danger identification instruction; positioning a hidden danger road section in the road network topology, performing combined inversion and state transition probability analysis based on random variables, and generating a hidden danger probability curve; performing hidden danger disturbance propagation analysis in the road network topology, determining hidden danger identification information, and performing traffic hidden danger early warning management. The application can solve the technical problem of affecting the accuracy of traffic hidden dangers in the prior art, realize dynamic hidden danger identification and prediction capability based on real-time traffic flow data and road network topology information, and achieve the technical effect of accurately identifying the space-time distribution of hidden dangers.

The application discloses a brightness adjustment method and device, computer equipment and a storage medium, and relates to the field of image processing. The method comprises the following steps: acquiring a histogram corresponding to an input image, wherein the histogram is used for representing the distribution of the number of pixels with a brightness value; performing clustering analysis on the histogram in relation to the number of pixels with the brightness value, obtaining K sub-histograms corresponding to K clustering centers, wherein the sub-histograms are used for representing the distribution of the number of pixels with the brightness value to which the number of pixels belongs to the same class, K is a positive integer; fusing K cumulative probability curves corresponding to the K sub-histograms respectively to obtain a brightness adjustment relationship; and performing brightness enhancement on the input image according to the brightness adjustment relationship. The embodiment of the application can optimize the effect of brightness enhancement.

The application discloses a track robot inspection control method and system based on the Internet of Things, relates to the technical field of robot inspection, and comprises the following steps: collecting data based on a track robot, calculating accumulated cost value by comprehensively considering track width and track slope data, dynamically planning an inspection track, and collecting vibration signals in track operation to calculate vibration compensation. The method can ensure the stability of robot operation and the track keeping ability through dynamic balance load difference calculation, can construct a comprehensive fault feature map through weighted fusion of crack fault distribution probability curve and wear trend diagram, can effectively unify multiple types of fault features, can detect cracks and wear alone, can calculate the feature mean value and standard deviation of cracks, wear and composite faults respectively according to fault points extracted from the comprehensive fault feature map, can generate fault classification probability based on a classification Gaussian model, and can enhance the accurate classification ability of multiple fault areas.

This fall probability prediction device has a computer function for outputting a numerical value calculated on the basis of an input value. The fall probability prediction device comprises a trained model that has been subjected to machine learning so as to output a fall probability curve, which represents a fall probability as a function of age, using the age of a plurality of individuals and the presence or absence of a fall for each of the plurality of individuals as explanatory variables. Upon input of the age of a subject as an input value, the fall probability prediction device outputs a fall probability curve for predicting the fall probability of the subject at a time later than the time of the input.

This application relates to the field of reservoir exploration technology, specifically to a method, device, storage medium, and electronic equipment for quantitative characterization of pore size across all scales, including: performing nuclear magnetic resonance analysis on the shale to be tested, obtaining the nuclear magnetic resonance T2 spectrum of the shale to be tested, and establishing a cumulative T2 probability curve; according to the formula r c =CT2 yields the pore size r corresponding to the T2 value with the same cumulative content. c Where C is the conversion coefficient; a cumulative pore size probability curve is established based on the pore size value and the corresponding cumulative content. This application can solve the problem of unclear pore size distribution in continental shale at all scales due to strong heterogeneity, and achieve quantitative characterization of pore size at all scales while avoiding damage to the shale under test.

The present application relates to the technical field of rock fracture modeling, and discloses a method and a processor for modeling a multi-scale fracture network of a tight reservoir. The method comprises: obtaining a strike attribute of a fracture; generating a fracture strike conforming to a preset mode according to a cumulative probability density curve of the strike; taking a fracture position interpreted from a core and an imaging logging as hard data of a fracture position in a fracture network; dividing the hard data into seed hard data and correction hard data; correcting a cumulative probability curve of fracture development intensity according to the seed hard data, and generating a fracture element set according to the corrected probability density curve; generating a discrete fracture network through similarity fusion criteria, mechanical cause fusion criteria and intersection criteria; comparing the established discrete fracture network with the correction hard data, and correcting the number of fracture elements and the cumulative probability curve. Through iterative inversion of the seed hard data and the correction hard data, a more optimal multi-scale fracture network model that is more consistent with the hard data is generated.

To provide a design support device and a design support method that take into consideration a non-destructive inspection.SOLUTION: A design support device includes: an input processing unit 10 that receives input of a CAD model related to an object to be subjected to a non-destructive inspection, and also receives input of information related to a supposed anticipated defect and a design variable in the CAD model; a detection probability processing unit 30 that calculates a detection probability curve for the supposed defect; a sensitivity analysis unit 40 that calculates sensitivity of a detection probability, which is the degree of change in the detection probability curve when the design variable is changed; and a display processing unit 50 that outputs the detection probability and the sensitivity. The input processing unit 10 also includes a check unit 20 that receives characteristic information of an inspection probe for performing the non-destructive inspection and determines, on the basis of the characteristic information of an inspection probe and the CAD model, whether or not the inspection probe can be installed.SELECTED DRAWING: Figure 1

The invention aims to provide a power grid asset wall risk prediction algorithm and system based on a KMP method. According to the method, the in-wall age limit of each type of equipment of the power grid is measured and calculated, the in-wall scale of assets is predicted, and the asset wall risk is predicted. Firstly, a K-M method is used for calculating a reference scrap probability curve, then Prophet is used for fitting external influence factors, the reference scrap probability curve is corrected, the in-wall age limit of various assets of a power grid is calculated in sequence, finally, the in-wall asset scale and value of a target year are calculated, and the future asset wall scale of the power grid is predicted. And determining the risk level of the asset wall. On the basis of considering the average life of each type of equipment of the power grid, the influence of external influence factors on the asset scrapping probability is also considered, the life distribution models are respectively established for each type of equipment, and the in-wall asset value of each type of equipment is predicted, so that the dynamic and refined prediction of asset wall risks is realized; and the risk prediction precision and timeliness of multiple types and long-life assets of the power grid assets are significantly improved.

The invention provides an intelligent lease operation system based on multi-dimensional data analysis, and the system comprises a multi-dimensional data collection module which is used for carrying out the three-dimensional modeling of a lease space, and collecting the multi-dimensional data; the data analysis processing module is used for inputting the multi-dimensional data into a pre-constructed dynamic pricing, contract risk, cash flow prediction and customer loss early warning model, and outputting an optimal listing price, a risk score, cash flow probability distribution and a loss probability curve; the intelligent decision execution module is used for automatically generating a strategy packet, and issuing the strategy packet to a corresponding execution unit after approval; and the visual interaction module is used for displaying the comprehensive operation state of each space unit in the three-dimensional rental model in real time, generating a differentiated data view, receiving user input and feeding back the user input to the intelligent decision execution module in real time. Through data driving and automatic intelligent decision making, experience-driven to data-driven transformation is realized for lease operation, so that balance between asset value maximization and operation risk minimization is achieved.

The invention belongs to the field of industrial data processing, and particularly relates to a differential machining optimization method based on deep learning. The method comprises the steps that a differential three-dimensional simulation model is constructed, and the running state and machining data are collected; carrying out adaptive wavelet packet decomposition on the preprocessed data to suppress noise; the failure probability of an output component of the CNN-LSTM hybrid network is improved; fitting a probability curve by a least square method and calculating a component influence coefficient; and determining an optimization priority according to the influence coefficient, optimizing processing parameters by using a particle swarm algorithm, and circularly verifying until the failure probability reaches the standard. The method achieves the dynamic association of machining parameters and working conditions, improves the denoising and failure prediction precision, quantifies the coupling relation of parts, optimizes and efficiently, and prolongs the operation stability and service life reliability of the differential mechanism.

The application provides a pump-driven two-phase flow loop fault diagnosis method and device based on Naive Bayes, a medium and equipment. The method comprises the following steps: acquiring real-time monitoring data of a pump-driven two-phase flow loop; converting a target characteristic parameter of the real-time monitoring data into a discrete state value based on a preset discretization rule; inputting the discrete state value into a pre-trained fault diagnosis model to generate a probability curve corresponding to each of a plurality of fault types defined by the fault diagnosis model; determining a diagnosis result of the pump-driven two-phase flow loop based on a preset multi-threshold decision rule and the probability curve; wherein the multi-threshold decision rule is used to determine a fault occurrence, and the multi-threshold decision rule comprises an absolute probability condition, a relative probability value condition and a relative threshold length condition that need to be met by the probability curve of each fault type. The application constrains the model through the multi-threshold decision rule, improves the accuracy of the fault diagnosis result, and the obtained result has interpretability.

The invention discloses a pipeline anticorrosion decision-making method based on pipeline residual life deduction, and aims to solve the problems of passive response, low prediction precision and extensive strategy of existing pipeline anticorrosion management. According to the method, pipeline wall thickness, metal loss defect and crack data are collected through an ultrasonic sensing technology, after preprocessing and noise reduction, the residual life of the pipeline is deduced based on a Monte Carlo probability model in combination with a corrosion evolution model and a failure pressure model, and prediction results such as a failure probability curve are output; and the pipelines are divided into high risk, medium risk and low risk according to the remaining life, differentiated anti-corrosion operation and maintenance strategies are matched, and an optimal scheme is determined by combining full-life-cycle cost analysis. Passive maintenance of pipeline operation and maintenance is converted into active prediction, operation safety and economical efficiency are improved, and the method is suitable for oil gas, water supply and industrial metal pipelines.

The invention discloses a post-earthquake building semi-ruin structure collapse probability prediction method based on image recognition and deep learning. The method comprises the following steps of: (1) acquiring a building ruin image capable of representing key damage characteristics such as a concrete peeling area (S), a steel bar buckling degree (Y), a structure overall inclination angle (alpha), a component node cracking and local falling degree (F), ruin overall integrity and residual support degree (E) and the like through an unmanned aerial vehicle; (2) constructing a deep learning model CVCB-Net fusing a convolutional neural network, a visual Transform, a convolutional long-short term memory network and a Bayesian network; and (3) based on the quantized multi-dimensional damage characteristics, generating a collapse probability curve and risk distribution under different aftershock peak acceleration conditions by adopting Bayesian inference, and carrying out risk rating division. According to the method, non-contact, rapid and intelligent safety assessment of the post-earthquake semi-ruin structure can be realized, and a scientific basis is provided for emergency rescue and reinforcement decision during aftershock.

The present application relates to the field of intelligent evaluation and management of the growth cycle of medicinal plants, and provides a method for evaluating the growth cycle of medicinal plants based on AI image recognition, which comprises collecting multi-dimensional image sequences of the growth process of plants through a multispectral imaging device, synchronously recording ambient light intensity and temperature and humidity data; performing spatio-temporal registration on the image sequences to eliminate pixel offset caused by plant swinging and changes in shooting angle; using an adaptive hybrid filtering algorithm for denoising to preserve leaf edge texture details; performing multi-scale contrast enhancement on the denoised images to reconstruct high-frequency feature maps; constructing a double-branch deep network to extract leaf morphological dynamic feature sets and physiological parameter change feature sets, respectively; performing cross-modal fusion on the two feature sets to calculate a dynamic growth index matrix in combination with environmental parameters; and based on matching degree analysis of the dynamic growth index matrix and a preset growth stage model, outputting a current growth cycle stage identifier and a stage conversion probability curve.

The invention provides an air gap breakdown risk calculation and air gap interval design method based on an oscillation attenuation impulse voltage parameter domain, and the method comprises the steps: S1, collecting test data, including a gap length, an attenuation length, an oscillation dominant frequency and a corresponding 50% breakdown voltage; s2, constructing a breakdown probability curve, and extracting parameters; s3, constructing a mapping model of the U% with respect to the gap length and a mapping model of the dispersion parameter t with respect to the gap length; s4, solving a breakdown risk rate; and S5, establishing a statistical insulation cooperation model, and determining a design interval of the air gap. According to the invention, research is carried out based on the probability distribution of the actually measured oscillation attenuation impulse voltage, the breakdown risk rate calculation is more in line with the actual operation condition of the transformer substation, and the problem of design deviation caused by dependence on standard double-exponential waves in the existing method is solved.

The invention discloses a new energy electric power construction progress prediction and early warning method and system under a comprehensive working condition, and relates to the technical field of new energy electric power construction management, and the method comprises the following steps: constructing a task dependence graph according to standardized task data; calculating a working condition index according to the multi-source data, and obtaining an output rate reduction function in combination with a preset task sensitivity coefficient; according to the task dependency graph and the output rate reduction function, residual construction period distribution parameters and a completion probability curve of each task are obtained through a graph time sequence prediction model, correction is carried out through a hierarchical Bayesian earned value fusion method, a corrected prediction result is obtained, then a predicted completion time interval of milestone nodes is obtained through simulation analysis, and a prediction result is obtained. Identifying a key path set; and on the basis of the predicted completion time of the milestone node and the key path set, performing progress deviation detection to obtain a baseline plan deviation and triggering early warning. The method can solve the problem that the prior art lacks comprehensive modeling of the task dependency relationship and the external working condition factors.

The invention belongs to the technical field of sandstone-type uranium ore prospecting, and particularly relates to a sandstone-type uranium ore prospecting area prediction method based on U content statistics. The method comprises the following steps: 1, collecting and sorting previous and early-stage regional geological survey, uranium ore geological survey and uranium ore drilling data, and carrying out uranium prospecting target horizon optimization; 2, performing lithology-geochemical type division on uranium prospecting target layer sand-conglomerate according to characteristics such as metagenetic alteration; 3, counting U contents of the whole area and each lithology-geochemical type sand-conglomerate, and respectively drawing a U content cumulative probability curve graph; 4, performing comparative analysis on each lithology-geochemical type sand-conglomerate and the U content cumulative probability curve of the whole area, and dividing uranium emigration and migration types; 5, determining the lower limit of U abnormity of U immigration and emigration-immigration type sand-conglomerate; and 6, predicting a uranium metallogenic prospective area according to U immigration type sand-conglomerate abnormal sample distribution and regional uranium mineralization information.

The invention relates to the technical field of private domain reaching, and discloses a middle-aged and elderly private domain user reaching method and device based on dynamic preference prediction.The method comprises the steps that original behavior data and external environment data of a target user are obtained and preprocessed, and a structured time sequence data table of the target user is obtained; inputting the structured time sequence data table into a dynamic preference prediction model for prediction to obtain a touch response probability density curve of the target user in a future preset time period; obtaining to-be-reached content metadata, and generating a reaching task list of the target user in a future preset time period in combination with the reaching response probability density curve; and extracting the reaching content and the reaching time in the reaching task list, and pushing the reaching content to the target user according to the reaching time. According to the method, the response probability curve is generated based on the behavior and environment data, the user disgusting and shielding rate is reduced, the high response probability window is matched in combination with the content metadata, and the time and the content are synthesized, so that the pushed content is accurately reached in the user active period.

The invention discloses a microseismic signal identification method and device based on multi-model probability curve combination, and the method comprises the steps: inputting a microseismic data file into a plurality of deep learning models, and obtaining seismic phase probability curves outputted by the plurality of deep learning models; determining superposition weights corresponding to the seismic phase probability curves output by the different models according to the seismic phase identification effects of the seismic phase probability curves output by the different models; carrying out weighted stacking on a plurality of seismic phase probability curves output by different models to obtain a new seismic phase probability curve; and based on the new probability curve and a set amplitude threshold value, performing seismic phase identification and pickup again by using a long-short time window ratio method to obtain a final seismic phase pickup result. According to the method, the probability curves corresponding to a plurality of models are calculated and weighted superposition combination is carried out, so that effective seismic phase enhancement and noise suppression can be realized.

This disclosure provides a method and system for assessing direct containment heating under severe nuclear power plant accidents, relating to the field of containment risk assessment technology for pressurized water reactor nuclear power plants. The method includes: identifying key influencing factors of direct containment heating; acquiring typical severe accident sequences for batch simulation analysis; sampling key influencing factors based on the results of the batch simulation analysis to form a sample library; calculating the sample library samples by selecting conservative and optimized models to obtain the corresponding peak containment pressure under direct containment heating conditions, performing quantitative analysis; obtaining the probability distribution of containment pressure values ​​based on the quantitative analysis results; and determining the containment failure probability under the quantitative analysis of direct containment heating based on the probability distribution of containment pressure values ​​and the containment failure condition probability curve. This disclosure can obtain more reasonable and accurate containment failure probability analysis results.

The present application relates to a kind of permeability model construction method and system and electronic equipment, the method includes: the core porosity data and permeability data obtained according to porosity data size range, core porosity data and permeability data are evenly divided into several pore permeability data group, and make the porosity interval of each pore permeability data group not more than first preset threshold;Judge the sample number of each pore permeability data group whether greater than second preset threshold, if yes, then the pore permeability data group is determined as high confidence group, if not, then the pore permeability data group is determined as low confidence group;Determine the permeability value corresponding to each porosity data under each preset cumulative distribution probability, obtain corresponding multiple permeability cumulative distribution probability curve, interpolation calculation is carried out according to cumulative distribution probability curve data, to obtain permeability probability distribution atlas. It can realize the permeability probabilistic prediction based on porosity data, provides theoretical basis and quantitative method for the characterization of permeability uncertainty.