52 results about "Polynomial chaos" patented technology

The invention discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

The invention discloses a static risk assessment method and equipment for a power distribution network system, and belongs to the technical field of power systems. The method comprises the following steps: carrying out standardization processing on an uncertainty input variable in a power distribution network system to obtain a standard input variable; polynomial chaos expansion is carried out on the output of the power distribution network system, sparse processing is carried out on a primary function of polynomial chaos expansion, numerical integration is carried out on a standard input variable to calculate a polynomial chaos expansion coefficient, and an obtained polynomial chaos expansion formula is used as a high-dimensional agent model of the output of the power distribution network system; and performing static risk assessment on the power distribution network system based on the high-dimensional agent model. According to the method, sparse processing is carried out on the polynomial basis function, the dimension of the basis function is controlled, and then the polynomial coefficient is calculated through numerical integration, so that the overall calculation efficiency is remarkably improved under the same precision requirement, and the requirement of carrying out real-time evaluation on the static risk of a novel intelligent power distribution network system is met.

The invention provides an urban power distribution network probabilistic load flow calculation method for large-scale access of demand side resources, and belongs to the technical field of power system power distribution network analysis and optimization. The method comprises the following steps: constructing a dynamic Copula model in combination with space-time sequence analysis, introducing a Markov chain, and carrying out multi-dimensional uncertainty coupling modeling; a probabilistic power flow-multi-objective optimization model is constructed, and energy router cooperative control is carried out based on distributed model predictive control; sparse expansion is carried out by adopting error feedback adaptive sampling and sparse Bayesian learning in combination with polynomial chaos expansion, and probability power flow is calculated through OpenDSS; establishing a three-phase probabilistic power flow model and a DG-EV-DR collaborative probability model; and evaluating the extreme scene risk, and visualizing the result through a digital twin platform. According to the method, the accuracy and practicability of distribution network probabilistic load flow calculation in a demand side resource large-scale access scene are effectively improved, and a risk quantification basis is provided for power grid dispatching.

According to the power system optimization scheduling method and device, the storage medium and the computer equipment provided by the invention, when power system optimization scheduling is carried out, the initial collaborative optimization model is firstly constructed based on the provincial power grid operation constraint and the local power grid operation constraint; then constructing a feasible region of the adjustable capability of the geographic power grid based on a vertex search method so as to eliminate internal variables of the geographic power grid, and constructing a cost analytic function of the adjustable capability of the geographic power grid by adopting a polynomial chaos expansion method so as to accurately represent the cost of the adjustable capability of the geographic power grid; and then, the feasible region and the cost analytic function are adopted to reconstruct the power grid collaborative optimization model, and a target collaborative optimization model is generated, so that when the target collaborative optimization model is utilized to carry out optimal scheduling on the power system, information exchange only needs to be carried out on a geographic power grid and a provincial power grid scheduling mechanism once, and therefore, the data privacy of the geographic power grid is effectively protected; and meanwhile, the adjustable capability of the flexible load of the ground-level power grid can be effectively utilized, and the consumption of high-proportion new energy can be assisted.

The invention discloses a data-driven polynomial chaos expansion-based weak link identification method for a trans-regional electricity-hydrogen comprehensive energy system. The method comprises the following steps of 1) constructing a hydrogen energy trans-regional complementary electricity-hydrogen comprehensive energy system; 2) establishing an optimal energy flow calculation model of the electro-hydrogen comprehensive energy system; 3) establishing a random variable orthogonal polynomial basis based on the multi-order moment of the system random factor data set; 4) establishing a DPCE proxy model of random output response; 5) solving a weighting coefficient of the model by using a least square method, and completing the construction of the DPCE proxy model; 6) calculating an optimal energy flow state value of the system through the DPCE proxy model; and 7) calculating system risk indexes, and identifying weak links of the electro-hydrogen comprehensive energy system based on risk index sorting. According to the method, the optimal energy flow calculation agent model of the system is constructed through the DPCE method, the problems of system operation state diversification and energy flow coupling complexity are effectively solved, and weak links of the electro-hydrogen comprehensive energy system are accurately identified.

The invention discloses a probability static voltage stability analysis method and system based on a multi-fidelity agent model and a storage medium thereof, and relates to the field of power system safety and stability analysis, and the method comprises the following steps: generating a low-precision sample set and a high-precision sample set of power system source load uncertainty parameters; based on the low-precision sample set, a low-fidelity model used for estimating the static voltage stability margin is constructed through a sparse polynomial chaos expansion method; constructing a correction function based on an output difference value of the high-precision sample set and the low-fidelity model under the same input; combining a low-fidelity model with the correction function to construct a multi-fidelity agent model; and calculating and evaluating the probability static voltage stability margin of the power system by using the multi-fidelity agent model. According to the method, through cooperative use of high-precision and low-precision samples and multi-fidelity modeling, the calculation efficiency is remarkably improved while the calculation precision is ensured.

The application discloses a TBM tunneling parameter abnormal data identification method and system, and belongs to the technical field of data processing. The application constructs a polynomial chaos expansion regression model through polynomial chaos expansion, identifies abnormal data in TBM tunneling parameter data by using data clustering and polynomial chaos expansion regression error, depicts the correlation between TBM tunneling parameters through polynomial chaos expansion, compares the difference between data by using the correlation between TBM tunneling parameters, constructs a clustering objective function based on polynomial chaos expansion regression error, optimizes and solves the clustering objective function by using a Lagrange multiplier method, and obtains a TBM tunneling parameter membership degree matrix. Whether the data is abnormal is determined by using the TBM tunneling parameter membership degree matrix, and accurate identification of abnormal data of TBM tunneling parameters is realized.

The present application relates to the technical field of power system operation analysis and regulation, and specifically discloses a kind of VSC-HVDC system power flow regulation capability evaluation method, comprising the following steps: S1, the power flow regulation range mathematical model of establishing containing back-to-back flexible HVDC transmission system BTB;S2, establish new energy output uncertainty model;S3, construct power flow over-limit risk and risk adaptive power regulation mechanism;S4, generalized polynomial chaos matrix method is used to evaluate the two-way power flow regulation range;S5, output power flow regulation range evaluation results and visual index.The present application uses the above-mentioned VSC-HVDC system power flow regulation capability evaluation method, realizes the collaborative quantification of new energy randomness, operation risk and BTB regulation capacity, improves the accuracy and engineering practicability of the evaluation results, and provides a decision basis for power system planning and design, operation control.

The application discloses an unmanned distribution task allocation method based on multi-agent cooperation, and specifically comprises the following steps: step 1, obtaining distribution task data and unmanned distribution body running data; step 2, constructing a multi-agent state vector set; step 3, generating a task cost disturbance variable set and an execution time delay disturbance variable set; step 4, establishing a disturbance variable parameter coupling relationship, constructing a non-tensor polynomial chaos expansion model, and generating a disturbance propagation risk vector; step 5, constructing a parameter coupling manifold based on the expansion coefficient, and screening candidate parameters by using a DIRECT algorithm; and step 6, generating a task allocation score matrix based on the candidate parameters and the disturbance propagation risk vector, and outputting a multi-agent task matching result. The application introduces parameter coupling modeling to improve the stability of cooperative allocation.

The invention discloses an aero-engine gear residual life prediction model based on stress correction and polynomial chaos. The method comprises the following steps: realizing fault robust classification under a strong interference working condition by fusing uncertainty quantization through a double-branch deep network; based on an improved GD-YOLOv12 architecture, a lightweight self-attention mechanism is integrated, and sub-pixel positioning of micron-sized damage is achieved; constructing a surface topography-wear depth two-dimensional fusion model, and generating a damage index in combination with image correction and an Archard theory; a Walker index and a Manson-Halford factor are adopted to correct the average stress effect to establish a fatigue damage model, and material and load uncertainty is quantified through a polynomial chaos theory; and designing a dynamic decision engine driven by a risk increasing factor, and fusing multi-objective optimization to generate an optimal maintenance strategy. According to the technology, the defects of insufficient recognition precision and unquantified uncertainty of a traditional method are overcome, closed-loop management of damage recognition, accurate quantification, probability life prediction and optimization decision is achieved, the reliability of the engine is remarkably improved, and the operation and maintenance cost is reduced.

This paper presents a sparse grid polynomial-based orbital game situation analysis method, belonging to the field of aerospace orbital adversarial and intelligent decision analysis technology. Addressing the problems of lack of systematic representation of multi-source uncertainties, low computational efficiency of traditional quantification methods, and difficulty in achieving multi-objective equilibrium optimization in existing high-orbit multi-to-multi dynamic adversarial scenarios, this paper constructs a POMDP multi-to-multi dynamic game model incorporating uncertainties in high-orbit dynamics and control execution, strategy behavior, and communication delay. A surrogate model is established using sparse grid sampling and polynomial chaotic expansion, and global sensitivity analysis is performed. Based on this, a multi-objective optimization model is constructed, and the Pareto optimal policy set is solved. This achieves game strategy generation that balances performance, robustness, and economy, and is applicable to spacecraft strategy optimization and space situation analysis in high-orbit multi-to-multi dynamic adversarial missions.

The invention discloses a cross-regional support capability-flexibility boundary quantification method and system. The method comprises the following steps: representing a flexibility variable of a power system in a target region based on a generalized polynomial chaos approximation principle; constructing an opportunity constraint-containing support model aiming at maximizing the flexibility supply of the unit and minimizing the operation cost; converting the support model containing the opportunity constraint into a deterministic support model by combining a Galerkin projection technology and the represented flexibility variable; traversing external transmission nodes in the target area, and calculating a flexibility insufficient expected value of the power system in the area based on a determinacy support model; and fitting a cross-regional support capability-flexibility boundary curve of the target region by combining the support power of the system and the flexibility insufficient expected value thereof.

The invention discloses an unbalanced power distribution network risk analysis method based on a multistage sparse polynomial, and belongs to the technical field of power distribution network risk analysis. The method comprises the steps of establishing a power distribution network input-output model; approximating the input and output model of the power distribution network as a linear regression model; calculating an ANCOVA index and a P value of the linear regression model, and sparsifying the input and output model of the power distribution network to obtain a preliminary sparsifying input and output model of the power distribution network; restraining a high-order cross term of the preliminary rarefaction power distribution network input and output model according to a preset truncation rule, shrinking a coefficient of the preliminary rarefaction power distribution network input and output model according to the optimized polynomial chaos expansion coefficient, and taking a preset residual error akaike information criterion as a model balance criterion to obtain a final rarefaction power distribution network input and output model; and obtaining a power distribution network risk analysis result according to the final sparse power distribution network input and output model. The method can provide a quantitative basis for operation and control of the power distribution network.

The present application relates to a kind of robustness design optimization methods of data-driven compressor blade, using the way of middle camber line superposition thickness distribution to construct blade, using NURBS curve to parameterize middle camber line of blade.4-point 3-order data-driven non-embedded polynomial chaos method is used to quantify the uncertainty of sparse sampling data, and four blade configuration modes are obtained.Latin hypercube method is used to sample blade design space, and sampling set is used to train Gaussian process regression model under each blade configuration mode;GPR proxy model at each blade configuration mode is obtained respectively.After training, multi-objective optimization algorithm NSGA II is used to optimize search with the statistical mean and standard deviation of blade total pressure loss coefficient as target;Robust compressor blade with better performance and greatly reduced sensitivity to input uncertainty is obtained.

The present invention discloses a data processing method, device, medium, and product based on polynomial chaotic expansion. The method obtains several sample points of a random variable as a sample data set; performs polynomial chaotic expansion using orthogonal polynomial basis functions and solves for the expansion coefficients in the polynomial chaotic expansion; trains several generative models to learn the distribution of the absolute value of the normalized Galerkin projection inner product term; updates the probability density function according to the generative model, obtains several new sample points of the random variable based on the updated probability density function, and incorporates the new sample points into the sample data set; re-solves for the expansion coefficients in the polynomial chaotic expansion until an iterative termination condition is met, and generates a polynomial chaotic expansion proxy model based on the last solved expansion coefficients. The present invention can improve the fitting accuracy of the proxy model, effectively reduce the amount of computation, and improve data processing efficiency.

The invention discloses a measurement data correction capability calculation method and system, electronic equipment and a storage medium. The measurement data correction capability calculation method comprises the steps of obtaining historical measurement error data; dynamically updating a measurement error variance matrix on the basis of historical measurement error data according to the measurement error data at the current moment; based on the updated measurement error variance matrix, a polynomial chaos expansion algorithm is adopted to calculate and obtain the residual error of each measurement and the corresponding probability distribution; obtaining weighted error entropy through sampling and weighting on the basis of the residual error measured by each quantity and the corresponding probability distribution; and putting the weighted error entropy into a monitoring system to realize quantitative evaluation of the measurement data correction capability. According to the method, the overall data quality is improved while the measurement redundancy is ensured, so that the estimation precision and robustness of state estimation are improved.

The application discloses a method for rapidly predicting hydrodynamic load of a sailing body under the influence of internal waves, and belongs to the technical field of hydrodynamic load prediction of the coupling of ocean internal waves and a sailing body. The method steps comprise the following steps: establishing a numerical model of the coupling of internal solitary waves and a sailing body; constructing a mapping relationship between time and spatial position, converting time-domain load data into spatial position load data; data preprocessing and division; constructing and training a polynomial chaos expansion model; calculating a prediction residual based on each load component predicted by the polynomial chaos expansion model; performing Gaussian process regression correction on the standardized prediction residual; superimposing the prediction value output by the polynomial chaos expansion model and the correction value to obtain a rapid prediction model of the hydrodynamic load of the sailing body; and load prediction and model verification. The application solves the technical problem of real-time prediction of the load of the sailing body under the influence of internal waves, and solves problems such as complicated steps, high time cost and poor timeliness.

The invention discloses a Bayesian polynomial chaos neural network proxy model method, and belongs to the technical field of sandwich board structure optimization design and proxy models. The method comprises the following steps: firstly, determining structural composition, size association and design parameters of the Y-shaped sandwich panel, constructing an automatic modeling script file, and simulating a drop hammer impact experiment process; secondly, generating a data set required by optimization of the Y-shaped sandwich panel, completing preprocessing, determining a protection performance evaluation index and an optimization target, and generating effective sample data; thirdly, training the proxy model based on the training set and the verification set; finally, multi-objective optimization design is carried out, design space is explored, a Pareto solution set is obtained, and a comprehensive optimal solution is selected. Through cooperation of the high-precision efficient proxy model and the intelligent optimization algorithm, the design period is shortened, the design space exploration range is expanded, the energy absorption protection effect is remarkably improved while the light weight of the Y-shaped sandwich panel is achieved, and reference is provided for engineering application and optimization design of the Y-shaped sandwich panel.

The invention discloses a polynomial chaos expansion wind power plant generating capacity prediction method and system fused with an active learning algorithm, and the method comprises the steps: obtaining a small number of parameter samples of a wind power plant through a Latin hypercube sampling method, generating wind rose data representing the statistical characteristics of wind climate through wind direction sector division and wind speed interval joint statistics, and carrying out the prediction of the generating capacity of the wind power plant. Therefore, a probability model of wind speed and wind direction is established, a key wind regime area with the largest contribution to prediction precision is automatically identified and calculated, a polynomial chaos expansion proxy model building module is formed, an active learning module fusing exploration and utilization criteria is introduced, and rapid and accurate prediction of the generating capacity of the wind power plant is achieved. According to the scheme, the simulation cost can be remarkably reduced, the calculation efficiency is greatly improved on the basis of ensuring the prediction precision, the output uncertainty can be effectively quantified, and efficient and reliable technical support is provided for layout optimization, operation evaluation and global sensitivity analysis of the wind power plant.

The invention relates to a clearing method and device for a unified electricity market, computer equipment, a storage medium and a computer program product. Relates to the technical field of power systems. The method comprises the following steps: constructing a tie line variable data set based on historical operation data of a provincial power market in a unified power market; obtaining the current clearing cost based on each tie line variable in combination with the current clearing condition; constructing an explicit expression of tie line variables and clearing cost based on a preset sparse polynomial chaos expansion algorithm, and reconstructing a preset hierarchical clearing model of the unified power market into a single-layer clearing model; constructing a three-dimensional tensor and a tensor feasible region based on historical clearing data and physical constraints, and performing dimension reduction processing on the current high-dimensional clearing variable feasible region in combination with a preset dynamic tensor decomposition algorithm to obtain a low-dimensional tie line variable feasible region; and performing optimization processing on the single-layer clearing model to obtain a clearing result of the unified electricity market. By adopting the method, the clearing efficiency of the clearing method of the unified electricity market can be improved.

The invention discloses an aviation propeller aerodynamic noise prediction method based on data assimilation, and belongs to the field of aviation aerodynamic acoustics. The method comprises the following steps: firstly, establishing a propeller flow field numerical simulation model and finishing initial simulation; quantifying the parameter uncertainty of the turbulence model through Latin hypercube sampling and a non-interference polynomial chaos proxy model, and determining key sensitive parameters in combination with global sensitivity analysis; then collecting wake flow velocity field and far field noise data of the wind tunnel experiment; and finally, assimilating experimental data and a CFD prediction result by using an ensemble Kalman filtering method, correcting key sensitive parameters of the turbulence model, and performing simulation again. The ensemble Kalman filtering data assimilation technology is applied to propeller turbulence model parameter correction, prediction errors of transonic velocity and a complex vortex structure area are reduced, the prediction precision of a propeller flow field and aerodynamic noise is improved, the method has the advantages of being high in precision and adaptability, the efficient and low-noise design requirements of modern aviation are met, and the method is suitable for popularization and application. The method has important engineering application value.

The invention relates to the technical field of rail vehicle dynamics, and provides a rail vehicle dynamics performance stochastic analysis method and system and a computer, and the rail vehicle dynamics performance stochastic analysis method comprises the steps: obtaining a plurality of irregularity data sample groups of a rail, and obtaining a plurality of amplitude coefficient sets; a joint probability distribution model of a plurality of amplitude coefficient sets is constructed, and track irregularity excitation is obtained; constructing a dynamic simulation model of the railway vehicle, obtaining dynamic performance indexes of the railway vehicle, and constructing a dynamic performance random model of the railway vehicle; based on the sparse polynomial chaos primary function set and a rail vehicle dynamic performance random model, obtaining a rail vehicle dynamic performance random agent model; and obtaining a vehicle dynamics performance stochastic analysis result of the railway vehicle. Through the above method, the random correlation between the irregularity states of different rails is considered, and the limitation of the analysis of the dynamic performance of the rail vehicle is reduced.

The application discloses a reliability evaluation method for an electric-hydrogen integrated energy system based on data-driven polynomial chaos expansion, and comprises the following steps: 1) constructing an orthogonal polynomial base based on multiple moments; 2) constructing a chaos polynomial surrogate model; 3) calculating the optimal load reduction of the electric-hydrogen integrated energy system according to sample values of matching points; 4) solving each coefficient of the chaos polynomial surrogate model by using a least square method; 5) acquiring current random variables and inputting the current random variables into the chaos polynomial surrogate model to calculate the optimal load reduction of the current electric-hydrogen integrated energy system; and 6) calculating a reliability index of the electric-hydrogen integrated energy system. The chaos polynomial surrogate model is constructed through historical data of random variables, a large number of mixed random variables can be efficiently processed, information such as probability characteristics of the random variables is not required, and fast and accurate reliability evaluation can be realized.

The invention belongs to the technical field of doubly-fed variable-speed pumped storage, and particularly discloses a doubly-fed pumped storage unit adjusting process uncertainty analysis method and device. The method comprises the following steps: firstly, establishing a speed regulation-excitation system state-space equation of the doubly-fed pumped storage unit considering initial rotating speed and rotor power response; setting uncertain parameters and distribution characteristics of the uncertain parameters, and substituting the uncertain parameters into the speed regulation-excitation system state-space equation to obtain a speed regulation-excitation system state-space equation under an uncertain framework; selecting an orthogonal polynomial, and performing generalized polynomial chaos expansion on the state-space equation of the speed regulation-excitation system; and finally, obtaining an approximate solution of each state vector in the state-space equation of the speed regulation-excitation system through a numerical solution method, and quantifying the output response statistical characteristics of the state-space equation of the speed regulation-excitation system. According to the method and the device, each state variable of the system can be quickly solved, so that statistical characteristics of output response of the control system are quantified.

To provide a coil structure enclosed with an active shield, and a multipurpose optimization method therefor.SOLUTION: A coil structure enclosed with an active shield includes a primary coil 01 and active shield coils. The active shield coils are reversely connected in series outside the primary coil, and include an inner shielding coil 02 having a current direction the same as that of the primary coil and an outer shielding coil 03 having a current direction opposite to that of the primary coil. A multipurpose optimization method includes a step of constructing a finite element simulation model of an active shield coil and a wireless power transmission (WPT) system, a step of constructing an optimization proxy model by a data driving method, and a step of realizing multi-objective optimization design by taking the proxy model, a polynomial chaos proxy model as an objective function of a non-dominated sorting genetic algorithm (NSGA-II).SELECTED DRAWING: Figure 1

The application belongs to the technical field of automobile engineering, and particularly relates to an AEB system parameter collaborative optimization design method and system based on multi-source uncertainty, which breaks through the limitation of single uncertainty factor optimization by identifying and quantifying multi-source uncertainty parameters, and improves the adaptability of the system to complex environments; a multi-objective optimization model is established to realize multi-objective balance; a polynomial chaos expansion model is introduced and embedded in the multi-objective optimization model for evaluation and screening to accelerate the solution, solve the problem of low efficiency of traditional Monte Carlo simulation, and then obtain optimal optimization variables, thereby improving the robustness and safety of the automatic emergency braking system in complex environments.

This invention relates to a health status prediction method based on adaptive Bayesian deep learning under multi-source uncertainty. It introduces adaptive model-free techniques into the Bayesian deep learning framework to fully leverage the capabilities of the prediction model. First, a model-free dropout method is employed to quantify cognitive uncertainty. This method can automatically learn the dropout rate and distribution type, better capturing highly nonlinear degradation characteristics. Second, an arbitrary polynomial chaos expansion (aPc) method is used to quantify stochastic uncertainty. This method avoids introducing additional subjectivity from limited samples or sparse information into the assumed distribution. Finally, a health status prediction framework based on adaptive Bayesian deep learning is proposed. Variational inference is used to construct the network loss function and conduct training, unifying the quantification of cognitive uncertainty and stochastic uncertainty in a model-free manner, thereby simultaneously performing mean and interval predictions of health status.

The present invention relates to a method and device for static risk assessment of a distribution network system, and belongs to the technical field of power systems. The method comprises: standardizing the uncertain input variables in the distribution network system to obtain standard input variables; performing polynomial chaos expansion on the output of the distribution network system, performing sparse processing on the basis functions of the polynomial chaos expansion, performing numerical integration on the standard input variables to calculate the polynomial chaos expansion coefficients, and using the obtained polynomial chaos expansion as a high-dimensional proxy model of the output of the distribution network system; and performing static risk assessment on the distribution network system based on the high-dimensional proxy model. The present invention significantly improves the overall computing efficiency under the same accuracy requirements by performing sparse processing on the polynomial basis functions, controlling the basis function dimensions, and then calculating the polynomial coefficients by numerical integration, thereby meeting the demand for real-time assessment of the static risks of the new intelligent distribution network system.

The application discloses a lattice structure process uncertainty modeling method, which comprises three parts of finite element model establishment, process uncertainty modeling and uncertainty response solving. The application establishes a lattice structure finite element model through a beam element, classifies the lattice structure according to the form of a rod piece, determines the covariance matrix of different types of rod pieces, carries out principal component analysis on the covariance matrix, determines the discrete model of different types of rod pieces, carries out truncation according to the required accuracy, determines the number of random variables, determines the polynomial chaos expansion order and corresponding calculation integral points, carries out finite element analysis on the integral points, and obtains the polynomial chaos coefficient and the mean value and standard deviation of the response. The application starts from the actual process of the lattice structure, considers the manufacturing process error of the lattice structure, and can improve the structure analysis accuracy.

The invention belongs to the technical field of automobile engineering, and particularly relates to an AEB system parameter collaborative optimization design method and system based on multi-source uncertainty, which break through the limitation of single uncertainty factor optimization by identifying and quantifying multi-source uncertainty parameters, and improve the adaptability of the system to a complex environment. By establishing a multi-objective optimization model, multi-objective balance is realized; a polynomial chaos expansion model is introduced, the polynomial chaos expansion model is embedded into a multi-target optimization model solution for evaluation, screening and accelerated solution, the problem that traditional Monte Carlo simulation is low in efficiency is solved, then an optimal optimization variable is obtained, and robustness and safety of an automatic emergency braking system in a complex environment are improved.