883 results about "Normal density" patented technology

The invention is suitable for the technical field of life evaluation, and provides an enhanced high-voltage circuit breaker life evaluation method, which comprises the following steps: constructing a dynamic evolution model of a contact material wear rate; constructing a nozzle degradation dynamic prediction model; constructing residual life probability distribution of the insulating material; according to the dynamic evolution model of the wear rate of the contact material, the dynamic prediction model of nozzle degradation and the residual life probability distribution of the insulating material, constructing a comprehensive life evaluation index; inputting the comprehensive life evaluation index into a preset layered prediction architecture for evaluation; wherein the first layer generates basic life distribution through a Bayesian network, the second layer outputs posterior life distribution correction parameters through a convolutional neural network, and the third layer optimizes posterior distribution through a KL divergence minimization algorithm to obtain a probability density function and a confidence interval of residual electrical life; according to the method, the error of life evaluation can be reduced on the basis of complex working conditions.

A control method for fire-fighting false alarm of an energy storage system comprises the following steps of: calculating a fluctuation period, a derivative and a probability density function by using fast Fourier transform and the like by acquiring data streams such as current, voltage, power and heat energy of the energy storage system, determining a system state and predicting a related trend. And predicting power by using an autoregressive moving average algorithm and a support vector machine, and setting a threshold value according to a probability density function to judge abnormity. For heat energy data, through derivative calculation and signal analysis, states are identified and early warning is carried out in combination with models such as a deep convolutional adversarial neural network and a support vector machine. The normal range of monitoring data is determined by means of the long-short-term memory neural network, the change trend and physical quantity relation of abnormal data are analyzed and predicted through a cubic exponential smoothing method and Gaussian process regression, potential risks are judged through weighted fusion and a self-adaptive Simpson algorithm, fire-fighting false alarms are effectively reduced, fire risks are accurately recognized, and the safety of an energy storage system is guaranteed.

The invention relates to the technical field of structural damage identification, in particular to an unsupervised structural damage identification method based on CAE-LSTM. The method comprises the following steps: training a CAE-LSTM model by using a training set to obtain a trained model, and reconstructing unknown data including health data and damage data by using the trained model to obtain a reconstruction error of the health data and a reconstruction error of the damage data; determining a damage sensitivity factor of the acceleration response signal of each batch by combining a probability density function of health data, a probability density function of damage data and a reconstruction error in the acceleration response signal of each batch of the undamaged structure so as to determine a damage threshold and screen a damage position; acquiring a damage factor according to the health state data and the damage state data of the damage position sensor; and judging the damage degree based on the size of the damage factor. According to the invention, the accuracy and reliability of the structural damage identification result are improved.

The invention discloses a computer numerical simulation method, system and equipment for a composite material, particularly relates to the technical field of analog simulation, and is used for solving the problem of simulation distortion caused by neglecting the randomness of a microstructure in the prior art. The method comprises the following steps: acquiring microstructure image data of a composite material, extracting fiber arrangement direction and pore distribution parameters, and generating a fiber direction probability density function and a pore topological relation; constructing a non-uniform grid model constrained by a local coordinate system according to the fiber direction probability density function; identifying a weak connection area based on a pore topological relation and marking nodes; applying dynamic decoupling constraint to the weak connection area, wherein the dynamic decoupling constraint comprises initial rigidity and rigidity attenuation rate; applying an external load to the model and calculating a real-time stress coefficient of the weak connection area; dynamically adjusting the constraint parameter through threshold judgment and iterative correction until the stress coefficient is converged; and finally, outputting a corrected macroscopic mechanical property result.

The invention discloses a PINN-based high-precision hydrodynamic numerical simulation method and system, and the method comprises the steps: firstly building a computational domain, setting reasonable geometric parameters and boundary conditions, and constructing a dimensionless Navier-Stokes control equation set; then designing a deep neural network architecture with space-time coordinate input and flow field variable output, and adopting a loss function combining physical constraint and data driving; the core innovation lies in providing a timing sequence sensing RAR-D adaptive sampling strategy, dividing a time domain into a plurality of time frames, performing residual error evaluation in each frame, constructing a probability density function related to residual errors, and balancing priority sampling and overall coverage of a high residual error region; adam and L-BFGS optimizers are adopted to carry out network training, the weight of a loss function is dynamically adjusted, and a sampling point set is periodically updated; and finally, the solution precision is verified through multi-dimensional flow field visualization analysis. Therefore, the prediction precision of the complex flow field is effectively improved, and the calculation efficiency is remarkably improved.

The invention belongs to the technical field of underwater target detection, and relates to a multi-target detection method and system based on sparse reconstruction and expectation maximization clustering, and the method comprises the steps: carrying out the preprocessing of echoes received by a linear array in a detection region, and obtaining K independent sample vectors meeting the zero-mean multivariate complex Gaussian distribution; the method comprises the following steps: carrying out uniform and equal-interval sampling on a space sector of a detection area to obtain K theta alternative angles, constructing a K theta-dimensional airspace sparse vector based on the K theta alternative angles, establishing a sparse model for judging whether a target exists in an independent sample vector, and obtaining a joint probability density function; a joint method of expectation maximization clustering based on a sparse model is adopted, and classification and estimation results of parameters in the multi-target scene are obtained through loop iteration; and obtaining an adaptive detector based on likelihood ratio test according to classification and estimation results, and realizing accurate multi-target detection. The method can automatically adapt to different target conditions, and the accuracy and reliability of detection are improved.

The invention relates to an equipment residual life prediction method based on a physical-data model, and the method comprises the steps: building a degradation model based on a physical degradation mechanism and a standard Wiener process model; obtaining fixed parameters in the degradation model by using a maximum likelihood estimation method in combination with a nonlinear regression method; acquiring an equipment state observation value in real time, and updating random parameters in the degradation model by using a weight optimization particle filter algorithm; and utilizing the degradation model, the fixed parameters in the degradation model and the random parameters in the degradation model to obtain a probability density function of the residual life of the equipment under a random failure threshold value, and performing numerical integration on the probability density function of the residual life of the equipment to obtain an expected value of the residual life of the equipment, and outputting the expected value as the residual life of the equipment. Compared with the prior art, high-precision and high-reliability residual life prediction is realized by fusing a physical mechanism and a data driving method.

The invention discloses a data exception monitoring method and device, equipment and a storage medium. The method comprises the steps of obtaining to-be-monitored data; inputting the to-be-monitored data into a preset multivariate Gaussian distribution model to enable the multivariate Gaussian distribution model to calculate a probability density function value of the to-be-monitored data according to a preset mean value and a covariance matrix, and outputting the probability density function value of the to-be-monitored data; the probability density function value is compared with a preset abnormal threshold value, when the probability density function value is smaller than the abnormal threshold value, it is judged that the to-be-monitored data is abnormal data, and otherwise, it is judged that the to-be-monitored data is normal data; and when it is determined that the to-be-monitored data is abnormal data, generating corresponding data abnormity early warning information. According to the invention, abnormity monitoring and early warning of mass data of the power grid can be realized.

The invention discloses a two-stage distribution robust unit combination optimization method considering inertia and wind power dual uncertainty. The method comprises the following steps: acquiring load prediction data of each synchronous generator set in a power system and a wind power plant power prediction value; considering inertia and wind power dual uncertainty, selecting an inertia prediction error probability density function and a wind power prediction error probability distribution function, and obtaining wind power and inertia multi-discrete scenes and corresponding scene probabilities by using Latin extraction scene generation and K-means scene clustering; deducing a dynamic frequency constraint containing an inertia error of the system in a frequency response process; building a two-stage distribution robust unit combinatorial optimization model containing dynamic frequency constraint; an auxiliary variable is introduced to linearize absolute value constraints in the confidence set, and Camp is adopted; the CG algorithm divides the optimization model into a main problem and a sub-problem for iterative solution. According to the method, the uncertainty of inertia prediction and the uncertainty of wind power are considered, so that the unit combination model has higher frequency safety and economical efficiency.

The invention provides a trajectory design method and system in an unmanned aerial vehicle hidden and sensing integrated network, and the method comprises the steps: firstly deducing a multi-listener cooperative receiving signal expression and a probability density function in the sensing integrated network with multiple cooperative listeners; then deriving a minimum detection error rate expression of multi-listener collaborative detection and an optimal power division ratio expression meeting covert communication constraints based on a maximum likelihood principle; and establishing an unmanned aerial vehicle track and scheduling optimization problem of anti-cooperative detection in the unmanned aerial vehicle hidden and inductive integrated network according to the deduced formula. And finally, iteratively solving the problem based on convex approximation and an ellipsoid method to obtain an optimal unmanned aerial vehicle trajectory and scheduling. According to the method provided by the invention, the covert communication requirement in the unmanned aerial vehicle communication perception integrated network can be well met, and the communication safety in the future unmanned aerial vehicle Internet of Things is improved.

The invention discloses a power system operation reserve quantification method, system and device based on photovoltaic probability prediction and a medium. The method comprises the following steps: calculating an Euclidean distance between a photovoltaic predicted value of a point to be decided and a historical photovoltaic predicted value, and searching a photovoltaic power generation historical data set similar to the point to be decided; performing quantile regression based on the similar historical data set to obtain quantiles corresponding to a plurality of tail end quantile levels, fitting a probability density function of photovoltaic prediction deviation by using a Gaussian mixture model, and further calculating a risk loss expectation of a point to be decided; and constructing a standby cost function, considering the reliability constraint of the standby, taking the sum of the minimum standby reserved cost and the risk loss expected cost as a target function, and finally optimizing to obtain the standby capacity of the power system. According to the method, the change of the reserve price along with the capacity is considered, the photovoltaic probability prediction information can be fully utilized, and a more economical and reliable power system reserve quantification result is obtained.

The invention discloses a real-time quality monitoring method based on a production process parameter dynamic optimization model, relates to the technical field of intelligent quality monitoring, and solves the technical problems of empirical parameter adjustment and insufficient pertinence of exception handling. The method captures time sequence association of process parameters, process variables and quality indexes through a dynamic optimization model, quantifies uncertainty in combination with a probability density function, avoids limitation of a fixed threshold value, realizes early warning accuracy through deviation degree grading, reduces false alarm and missing alarm, screens core influence parameters based on model feature importance, and improves early warning accuracy. Quantitative adjustment suggestions are generated in combination with historical cases and inversion calculation, and empirical operation is replaced; multiple parameters are ranked and adjusted according to influence degrees, coupling interference is avoided, a single-point problem and a linkage problem are distinguished through parameter association chain analysis, a processing flow is formulated in a targeted mode, the single-point problem focuses on local repair and rapid recovery, the linkage problem focuses on cutting off a conduction chain and radically treating the source, and invalid intervention is reduced.

The invention discloses an IGBT residual life prediction method based on variational Bayes and a mixed Wiener process, and belongs to the technical field of IGBT residual life prediction, and the method comprises the following steps: S1, giving out the definition of the mixed Wiener process, and building a degradation model to describe the degradation track of an IGBT; s2, performing off-line parameter estimation based on a variational Bayesian algorithm by using a prior sample; s3, selecting an optimal model in the degradation models according to an automatic correlation determination method; s4, selecting a prior sample which is most similar to the test sample at the current moment; s5, updating the mean value and variance of the weight vector of the test sample on line; and S6, solving a probability density function of the residual life of the mixed Wiener process in the sense of the first arrival time, and estimating the residual life value of the IGBT at each moment. According to the IGBT residual life prediction method based on the variational Bayes and the mixed Wiener process, the degradation mode can be automatically determined, the fitting precision of an IGBT degradation track is improved, the requirement for specialty is reduced, and the universality of the model is improved.

The invention discloses a new energy power grid probability harmonic load flow calculation method based on improved semi-invariant and Gram-Charlier series expansion. The new energy power grid probability harmonic load flow calculation method comprises the following steps: establishing a random variable probability model, a wind-light output model and a wind-light joint distribution model; secondly, considering the output correlation between wind and light fields, converting the wind and light combined distribution model based on a Copula function into independent variables by utilizing Rosenblatt conversion for calculation, then calculating each-order semi-invariant of load, photovoltaic output and wind power output according to the expected values of node voltage and branch power flow and a sensitivity matrix, and finally calculating the output correlation between the wind and light fields according to each order semi-invariant of the load, the photovoltaic output and the wind power output. The method comprises the following steps of: obtaining a state variable, further solving each-order semi-invariant form of the state variable and the branch power flow, and finally, obtaining a probability density function and a probability distribution function of harmonic voltage of each node by combining Gram-Charlier series expansion.

The invention discloses a method and a system for calculating energy consumption of a coupler system in the running process of a railway wagon, and relates to the technical field of crossing of railway vehicle dynamics and green energy technologies. The multi-source energy consumption dynamic coupling model comprises a car coupler gap collision area, a sliding friction area between car couplers, a car coupler-slave plate seat impact area, a car coupler-carrier wearing plate fretting wear area and a buffer damping area; based on the energy flow topology network, determining an energy transfer priority and efficiency matrix in the multi-source energy consumption dynamic coupling model; and generating an energy consumption probability density function based on a generalized polynomial chaos expansion method, and outputting a low-carbon design parameter set and total energy consumption. According to the method, high-precision calculation and low-carbon design optimization of the full-life-cycle energy consumption of the coupler system are realized, and a core theoretical support is provided for green transformation of railway freight.

The invention relates to the technical field of data processing, in particular to a multi-series short-duration rainstorm data frequency analysis method. The method comprises the following steps: obtaining rainfall original data of multiple series of short duration, and carrying out missing data interpolation and sequence extension processing, duration consistency and rainfall intensity rule check to obtain a rainstorm data set; respectively performing reliability, representativeness and consistency review and sample validity screening on the rainstorm data set; a Pearson III type probability density function model is constructed, probability distribution fitting is carried out, and a frequency distribution curve is generated; and carrying out extra heavy rain value analysis and empirical frequency adaptive line analysis adjustment, generating a corrected frequency parameter, further calculating a design heavy rain value of each design return period, and carrying out duration consistency and spatial rationality check to obtain a final design result output data set. According to the invention, through multi-dimensional data complementation, checking and optimization fitting, the system improves the accuracy, reliability and engineering applicability of short-duration rainstorm frequency analysis.

The invention relates to a wind power interval prediction method based on deep learning and Markov chain Monte Carlo. The method comprises the steps of carrying out feature extraction on historical wind power generation data by utilizing LSTM, constructing a Gaussian likelihood function based on a mean value and a variance according to hidden layer information representation, carrying out modeling processing on the Gaussian likelihood function by utilizing negative binomial distribution, optimizing hidden layer parameters of the LSTM by utilizing a loss function of a probability density function obtained through design, and obtaining hidden layer parameters of the LSTM. Obtaining a mean value and a variance of the optimized Gaussian likelihood function; a Markov chain is constructed according to the distribution characteristics of data characteristics represented by the mean value and variance of the optimized Gaussian likelihood function by adopting a Markov chain Monte Carlo algorithm to randomly generate a large number of samples, so that the stable distribution of the samples is the same as the known distribution; and taking a prediction interval with a predetermined size as a final prediction result for the distribution of the samples which most accord with the historical data characteristics. By adopting the method, the problem that a large amount of historical data cannot be effectively utilized by probability prediction can be solved.

The invention discloses an aero-engine remaining service life prediction method based on data and model hybrid drive, and relates to the field of aero-engine service life prediction, and the method comprises the steps: collecting the operation data of an aero-engine; preprocessing the data to obtain a training sample; inputting the training sample into an auto-encoder based on a time convolutional network, taking the deviation between time windows as a health index of the aero-engine, and ensuring the effectiveness of the constructed health index by using three evaluation indexes; and carrying out degradation modeling and RUL prediction by using a random effect Wiener process model with an acceleration factor to obtain an RUL predicted value of the aero-engine and a corresponding probability density function thereof. According to the hybrid driving method, the advantages of a statistical data driving method and a deep learning method are combined, and the problems that current deep learning lacks transparency and interpretability and is relatively limited in uncertainty quantification capability, and the statistical data driving method is limited in capability of processing and monitoring big data are solved.

The invention discloses an optical measurement segmental arc correlation method based on probability expression, and the method comprises the steps: obtaining an initial orbit feasible region of a segmental arc through an admissible domain, carrying out the uniform distribution discrete sampling of the admissible domain, and describing the uncertainty distribution of the initial orbit of the segmental arc through a Gaussian mixture model. The uncertainty distribution and propagation of the orbit are described in a corrected spring equinox orbital element space, and the state transition tensor of a gravity potential model of any order is applied, so that the precision and speed of orbit uncertainty propagation are improved. In combination with a Gaussian mixture model and a state transition tensor, sparse sampling points are used as component mean propagation dense sampling points of the Gaussian mixture model, and uncertainty distribution of the initial orbit of the arc section is propagated to a common moment. And giving a definition of association probability measurement, calculating the association probability measurement by utilizing a probability density function of track distribution of the segmental arc at the public moment, and carrying out association judgment on the optical measurement segmental arc based on probability expression.

The invention provides a radar and optical observation segmental arc correlation method and system for space debris. A joint probability density function is obtained through a kernel density estimation algorithm. A radar observation arc section and an optical observation arc section are input. And converting the observation data into a geocentric celestial coordinate system. And acquiring system parameters of the observation equipment. Radar observation data and optical observation data are unified to the same dimension, and a distance measurement value weight and an angle measurement value weight are set. And calculating an initial orbit by the radar observation arc section. And calculating a joint objective function corresponding to the current orbit position speed. And if the convergence condition is met, outputting the minimum value of the joint objective function and the orbit position speed corresponding to the minimum value. Otherwise, updating the track position speed, and repeating until the convergence condition is met. And according to the minimum value of the joint objective function and the joint probability density function, judging whether association is carried out, and outputting confidence during association. The method is easy to implement and can be effectively applied to association of the space debris radar and the optical observation arc section.

The invention discloses a new energy joint output scene generation system based on data driving, and the system comprises a data preprocessing module which collects and preprocesses wind power and photovoltaic historical output data; the joint probability distribution model construction module obtains wind power and photovoltaic edge distribution through probability density function fitting, and performs correlation analysis on wind power and photovoltaic through a Kendall rank correlation coefficient and a tail dependency coefficient to obtain a matching error of each connection function; the Euclidean distance between each connection function and the preprocessed wind power and photovoltaic historical output data is calculated, and an optimal connection function is obtained by combining the matching error, so that a wind power and photovoltaic joint probability distribution model is established; the scene generation module uses a Markov chain to form a joint probability distribution model with time correlation, and generates a random scene set through Markov chain Monte Carlo algorithm sampling. The stability of the power system is improved, and the operation cost is reduced.

The invention discloses a small fault detection method based on a prediction error sum of squares and JS divergence, and relates to the technical field of engineering detection, the algorithm of the application replaces a traditional algorithm for calculating JS divergence based on projection data after PCA dimension reduction, and a probability density function of the prediction error sum of squares of projected data is fitted by using kernel density estimation, so that the detection accuracy is improved. And calculating the JS divergence between different data distributions. The sum of squares of prediction errors reflects parts, which cannot be well reconstructed in the PCA model, of original data points, and the parts may contain abnormal or fault information. According to the method, the fault extraction capability of the prediction error sum of squares in the JS divergence is utilized, so that tiny faults can be better detected. By introducing the time sliding window, the method can more sensitively detect the local abnormal condition causing the increase of the JS divergence value, thereby timely and accurately capturing the process change, providing the early warning about the fault occurrence, and ensuring the safety and reliability of industrial production.

The embodiment of the invention provides a method for determining a generative model. The method includes embedding a white box watermark and a black box watermark in a generative model. Black box watermarks are first embedded into probability density functions of data abstraction in different layers of the generative model. The method also includes embedding the white box watermarks into different layers for generative model output after the embedding of the black box watermarks is completed. Model data is generated by the generative model based on predetermined trigger data. The predetermined trigger data comprises a predetermined trigger text or a predetermined trigger image. An identity associated with the generative model is determined based on the model data. By using the method, white box and black box attacks can be resisted by embedding two complementary and independent watermarks, so that double-layer protection is provided for the generative model.

The invention relates to the technical field of edge computing, in particular to a method for predicting end-to-end time delay probability distribution of a wireless network, which constructs a probability density function by using an improved Gaussian mixture model and generalized Pareto distribution on the basis of data driving in a mixed density network mode. According to the variation trend of the trailing probability of the delay probability distribution of the data packets in the data set obtained through measurement, a segmentation threshold u of the main body part and the trailing part is obtained through calculation; other parameters of the probability density function are obtained through mixed density network training; and finally, for data packets with different sizes, predicting the probability that the time delay is less than (or greater than) a given value. Multiple experimental results verify that compared with the prior art, the accuracy of the distribution of the end-to-end time delay trailing probability (greater than the probability of given time delay) obtained by using the technology of the invention is significantly improved.

A method for determining the likelihood that a pixel in a video image sequence represents true motion in the image. Light from a scene is captured to yield an original image having a sequence of frames. A prototype image is created from the original image as a geometrically accurate representation of the scene which is substantially free of contamination. A tilt variance-Gaussian mixture model and / or multi-variate Gaussian model, based upon optical turbulence statistics associated with the scene, is developed. Each pixel in the original image sequence of frames is evaluated against the prototype image using a probability density function, to yield a likelihood that the respective pixel represents true motion.

The invention provides a hidden backscatter communication method based on multicarrier randomized continuous waves. The method comprises the following steps: acquiring multicarrier randomized continuous wave signal characteristic parameters, backscatter signal characteristic parameters and channel parameters between a transceiver and a listener and between the transceiver and each user; according to the characteristic parameters of the multicarrier randomized continuous wave signal and channel parameters between the transceiver and the listener and between the transceiver and each user, determining relative entropy between probability density functions of the listener receiving signal when the user generates a backscattering signal and when the user does not generate the backscattering signal; determining the total reachable rate between the transceiver and all users according to the multicarrier randomized continuous wave signal characteristic parameters, the backscattering signal characteristic parameters and the channel parameters; and maximizing the total reachable rate through a target optimization algorithm by taking the condition that the relative entropy meets the concealment condition as the constraint, and obtaining the optimized statistical characteristic parameters of the multicarrier randomized continuous waves. According to the invention, hidden backscatter communication can be realized.

The invention discloses an oil spill diffusion prediction method and device for realizing dispersant response, relates to the technical field of sea surface oil spill simulation, and aims to solve the problems that oil spill diffusion simulation of an existing oil spill model is not fine enough and cannot support the dispersant response. The method comprises the following steps: acquiring simulation parameters of an oil spilling area and dispersant action parameters; determining a target entrainment probability of the oil spilling area through an entrainment algorithm according to the dispersant action parameters and the simulation parameters; determining the target particle diameter of each oil particle in the oil spilling region by adopting an oil drop particle size formula and a logarithmic normal distribution probability density function according to the dispersant action parameter and the simulation parameter; determining the target position of each oil particle based on the initial position, the simulation parameter, the target entrainment probability, the target particle diameter and an oil spill model; determining a prediction result of oil spill area diffusion based on the target position; according to the oil spill diffusion prediction method provided by the invention, prediction of oil spill diffusion under response of the dispersant can be realized, and the accuracy of a prediction result is improved.

The invention discloses a Fisher information matrix-based deformation monitoring sensor optimization arrangement method. The method comprises the steps of defining observation variables and unknown parameters; establishing a relation matrix; calculating a probability density function and a quality function of the observation variable; calculating a Fisher information matrix according to the deformation reconstruction equation; the traces of the Fisher information matrix are maximized to achieve optimal arrangement of the sensors. According to the method, the relation between the Fisher information matrix and the deformation reconstruction precision is deduced from the basic principle of an inverse finite element, optimal arrangement of the sensor is completed by maximizing the trace of the Fisher information matrix represented by the overall stiffness matrix, and efficient optimal arrangement of the sensor is achieved.

The invention relates to a wind power distribution prediction method and device, electronic equipment and a medium. The method comprises the following steps: constructing multi-dimensional feature data, preprocessing the multi-dimensional feature data to obtain preprocessed multi-dimensional feature data, extracting time sequence features, spatial features and meteorological features, and constructing a multivariate condition feature vector. And further inputting the current multi-dimensional feature data into the trained diffusion generation model, and outputting a wind power probability distribution sample. And carrying out probability density function fitting based on the wind power probability distribution sample to obtain multi-dimensional joint probability distribution of the wind power. According to the method of the embodiment, the complex characteristics of the wind power are effectively captured by constructing the multi-dimensional characteristic data, the prediction accuracy is remarkably improved, the time-space characteristics of the data are fully mined through characteristic extraction, the depiction ability of the model to complex wind conditions is enhanced, and the reliable probability distribution prediction is provided by utilizing the excellent distribution learning ability of the diffusion model.

The invention discloses a box girder bridge whole system performance degradation probability evaluation method based on DBN and Gamma distribution, and belongs to the technical field of box girder bridge performance degradation probability evaluation. Determining the staying time of each component of the box girder bridge; calculating related parameters of a Gamma distribution probability density function; calculating an accumulated probability density function and an accumulated survival probability function of each component of the box girder bridge; modeling performance reduction of the box girder bridge to obtain a transition probability; calculating state probability distribution of each component; a probability degradation curve of each component and the overall structure is formed through a dynamic Bayesian network; and evaluating the influence of the component on the probability degradation curve of the affiliated component and the overall structure. By adopting the DBN and Gamma distribution-based box girder bridge whole system performance degradation probability evaluation method, the performance degradation of the box girder bridge from components to parts and from the parts to the whole can be more scientifically predicted, so that the service life of the box girder bridge can be predicted from the probability level.