969 results about "Simulated data" patented technology

System and method optimize fluid dynamics modeling using a physics informed neural network (FINN) and a deep neural network (DNN). The DNN is trained to create a data driven mapping for invariants and Reynolds stress tensors associated with the fluid field based on simulation data. The FINN is trained using Navier Stokes and continuity equation based losses, using Reynolds stress tensor correction terms derived from the mapping and from invariants and tensors derived from estimated flow field variables. The FINN inputs include boundary conditions, and time-space coordinates for sampling points in the fluid flow field which describe time evolution of the turbulence problem depending on geometric bounds for the flow field.

The invention discloses a carbon emission intensity inversion and traceability method and system based on numerical simulation and a reduced-order model. The method comprises the following steps: constructing a full-size three-dimensional map; numerical simulation of pollutant diffusion is carried out according to map information and known environment conditions; reducing the order of the analog data through an intrinsic orthogonal decomposition method; establishing a carbon emission simulation database under the multi-working-condition variable environment based on the reduced-order model; the carbon dioxide concentration and meteorological data are measured in real time and on site, a simulation database is used as a supplement, and the intensity of a known emission source and the intensity and position of an unknown emission source are calculated by adopting a source item estimation method, a particle swarm algorithm and the like. According to the invention, full-size reduction of a limited area and high-precision small-scale carbon emission monitoring can be realized, and effective emission source tracing, intensity analysis, potential emission source prediction and the like can be carried out according to a small amount of field actual measurement data.

Disclosed are a rapid inversion method and apparatus for icing thickness of a power transmission line, and a related device. The method comprises: acquiring meteorological data of the place where the power transmission line is located, wherein target meteorological factors in the meteorological data are determined in advance by means of association analysis, and the meteorological data includes meteorological monitoring data and / or meteorological forecast data; and inputting the meteorological data into a trained rapid inversion model to obtain an icing thickness inversion value, wherein the model is obtained by training by using the meteorological monitoring data as a training sample and icing thickness data as a sample label, and the icing thickness data comprises icing thickness monitoring data and icing thickness simulation data obtained by means of numerical simulation calculation. Abundant icing thickness simulation data is obtained by means of numerical simulation calculation, solving the problem of insufficient icing thickness data, meeting the high-precision training requirements of deep learning, and improving the generalization ability of the model; and using the trained rapid inversion model to implement rapid inversion of the icing thickness of the power transmission line achieves relatively high accuracy.

Provided herein is a tunnel electromagnetic joint scanning detection method and a system thereof. It introduces a new tunnel electromagnetic detection system called TEJS, realizes three-dimensional joint inversion of multi-component, time domain and frequency domain signals, and forms tunnel joint scanning imaging. This method adopts the mode of surface transmission, underground reception, multi-source transmission and multi-component reception. Based on an observation system, a large number of stochastic models are constructed and numerically simulated, and a large number of training data sets are constructed by using the simulated data to complete the training of UNet model. This model can realize real-time and fast imaging of the position of the low-resistance anomalous body in three-dimensional space. An algorithm forms a dual checking mechanism through surface imaging and underground imaging, which constrains the three-dimensional spatial position of anomalous body together to prevent misjudgment.

The invention discloses a PINNs high Reynolds number flow field solving method based on regulatory factor optimization, and the scheme comprises the steps: firstly building a physical model, on one hand, building a geometric model according to the actual condition of a reactor core rod bundle channel, and on the other hand, building a fluid mechanics model containing continuity, momentum conservation and energy conservation equations; a data set is obtained, and experimental data is preprocessed and then integrated with simulated data through experimental measurement and numerical simulation; and then training a physical information neural network, designing a neural network model of specific input and output, introducing a regulatory factor to balance the difference between a physical equation and data, constructing a comprehensive loss function containing internal data points, boundary data points and equation loss, and training the network by using a training data set until convergence. And finally, solving a flow field by using the trained neural network, outputting predicted values of a velocity field, a pressure field and a temperature field, and solving the high Reynolds number flow field of the reactor core.

The invention provides a rolling bearing fault diagnosis method driven by simulation data, and the method comprises the steps: constructing a five-degree-of-freedom dynamical model of a rolling bearing according to the failure forms of different fault types of the bearing, so as to represent the dynamic response of a bearing system in a virtual space; the method comprises the following steps: collecting vibration signals under healthy and different fault conditions in a real environment through a bearing fault simulation experiment table, and establishing a data set; the feature distribution difference between the simulation data and the measurement data is considered, and four optimization targets are constructed and are respectively used for difference feature learning and domain invariant feature learning; a Transform model is used as a feature extractor, and fault diagnosis is carried out under a cross-domain condition in combination with a mixed loss function. Compared with the prior art, the model established by the invention can perform effective diagnosis under cross-domain and sparse training data conditions, solves the problems of data feature distribution difference and sparse target domain samples in engineering practice, and has certain robustness.

The invention relates to the technical field of geophysical electromagnetic data exploration, in particular to a fast Bayesian inversion method based on machine learning. Acquiring aviation electromagnetic model data for electromagnetic forward modeling; establishing a candidate forward modeling model for training according to the aviation electromagnetic model information, and obtaining electromagnetic response data obtained by forward modeling; preprocessing the aviation electromagnetic model data and the electromagnetic response data; constructing an optimized neural network, taking the preprocessed aviation electromagnetic model data as input, taking the electromagnetic response data as output, and training the neural network; according to the trained neural network, obtaining a weight and a bias coefficient used for fitting forward modeling calculation, and obtaining an optimized forward modeling fitting matrix; and obtaining forward modeling data according to the optimized forward modeling fitting matrix, establishing a Bayesian inversion model, and performing Bayesian inversion according to the forward modeling data. According to the method, the fitting matrix after machine learning is used for replacing a forward modeling calculation process, and the efficiency of variable-dimension Bayesian inversion can be effectively improved.

The invention relates to an intelligent inversion method and system for well drilling overflow shaft gas distribution based on a self-encoder, and belongs to the technical field of oil gas and geothermal development drilling and completion engineering. Comprising the following steps: step 1, accurately solving multiphase flow parameters of a shaft; 2, in combination with the drilling working condition and the geological condition of a specific overflow high-risk well section, based on a Monte Carlo sampling method, eight parameters are changed, uniform sampling is carried out, and a high-precision simulation data set is formed; step 3, constructing a neural network model for gas cut state inversion based on an auto-encoder neural network; 4, training is carried out; 5, standardizing one-dimensional time sequence parameters in the monitoring data; and inputting into a trained neural network model for gas cut state inversion based on an auto-encoder neural network to obtain distribution data of the overflow gas in the shaft in a time period in which the time sequence parameter at the current moment is located. According to the invention, the real-time rapid inversion of the gas distribution in the parallel cylinder during the drilling overflow parallel control period is realized.

The invention belongs to the technical field of finite element simulation optimization, and provides a hot upsetting process multi-target collaborative optimization method and system based on finite element simulation, and the method comprises the steps: firstly constructing a hot upsetting process multi-field coupling finite element model, and then deploying a sensor network in hot upsetting equipment to collect actual process data. Actual data and simulation data are matched through a space-time registration algorithm, then a parameter optimization model is constructed based on a dual-channel depth deterministic strategy gradient algorithm, and a technological parameter solution set is obtained through calculation; and finally, a multi-objective optimization function is defined according to a preset optimization index, and optimal hot upsetting process parameters are screened out in combination with an entropy weight method and a reference point-based non-dominated genetic algorithm. According to the method, high-precision simulation, real-time monitoring and multi-target collaborative optimization of the hot upsetting process are achieved, and the production efficiency and the product quality of the hot upsetting process are remarkably improved.

The invention relates to the technical field of automobile data updating, in particular to a data updating method of automobile diagnosis equipment and the automobile diagnosis equipment. The method comprises the following steps: acquiring a historical running log of a vehicle; vehicle owner driving behavior multi-point sampling is carried out on the vehicle historical operation log, and different scene behavior characteristic evolution is carried out, so that different scene driving behavior models of a vehicle owner are obtained; extracting vehicle time sequence state parameters based on the vehicle historical operation log; performing multi-scene operation simulation on the vehicle time sequence state parameters according to different scene driving behavior models of the vehicle owner, and generating vehicle state operation simulation data of each scene; and carrying out future time period vehicle state rolling prediction and fault time window probability calculation on the vehicle state operation simulation data of each scene to obtain a vehicle abnormal fault part positioning point and a fault time window probability curve. According to the invention, the data updating efficiency and group accuracy of the automobile diagnosis equipment are realized.

The invention relates to a fractured coal and rock mass strength intelligent prediction method based on image recognition, and belongs to the technical field of rock mass mechanics prediction. The method comprises the following steps: (1) carrying out an indoor test according to different fracture parameters and lithology, and establishing an initial training data set; (2) introducing numerical simulation data, supplementing laboratory data through the simulation data, integrating the data into a data set, and constructing a sample database of coal and rock mass fractures and strength; (3) training a machine learning model by using the sample database, wherein the machine learning model is trained by adopting a gradient lifting decision tree model; (4) testing the precision of the machine learning model by using test set data; (5) acquiring a working face image; and (6) predicting the strength of the to-be-measured coal rock mass. According to the method, a traditional rock strength measurement method is improved, an accurate prediction model can be constructed by combining the nonlinear relationship between the geometrical characteristics and the strength of the coal-rock mass fractures with different lithology, and efficient prediction is realized.

The invention relates to the technical field of flood storage digital analysis, in particular to a flood diversion gate regulation and control management method and system for digital flood storage analysis. The method comprises the following steps: constructing a flood storage area terrain model; acquiring historical flood storage hydrological data of the flood storage area; according to the flood storage area terrain model, carrying out flood storage mode space hydrological characteristic coupling analysis on the historical flood storage hydrological data, and generating flood storage mode space hydrological characteristic data; constructing a flood storage water power generalization model based on the flood storage area terrain model and the flood storage mode space hydrological characteristic data; performing flood characteristic simulation analysis in the flood storage polder based on the flood storage water power generalization model to generate flood characteristic simulation data in the flood storage polder; analyzing simulated land planning data of an internal region based on the internal flood characteristic simulation data of the flood storage polder; and designing an intelligent regulation and control decision of the flood-dividing sluice based on the simulated land planning data of the internal region and the flood characteristic simulation data of the flood storage internal region. According to the method, accurate flood characteristic analysis in the flood storage polder is realized.

The invention discloses a nondestructive testing method and system and a data processing method, and relates to the technical field of nondestructive testing. Comprising the steps of obtaining initial detection data of a detected component; establishing at least one reference model according to the initial detection data, and obtaining simulation data corresponding to each reference model; determining target data according to the initial detection data and simulation data; and obtaining subsequent detection data, and comparing the subsequent detection data with the target data to form an evaluation result. According to the invention, high and low frequency mixed excitation signals are adopted, so that the penetrating power and resolution of detection are improved, surface defects and buried defects can be detected at the same time, and the comprehensiveness and accuracy of detection are remarkably enhanced; a picked complex response signal is decoupled by using an orthogonal phase locking method, a magnetic field signal and an electric field signal are effectively separated, the signal-to-noise ratio and the detection precision are improved, and accurate discrimination of defect types, depths and orientations is realized by establishing a reference model and acquiring analog data.

The invention discloses a low-altitude atmosphere three-dimensional wind field inversion method based on wind profile radar vertical observation, relates to the technical field of three-dimensional wind field inversion, and aims to solve the problem of poor inversion effect caused by inaccurate model parameters in the wind field inversion process. The difference between simulation data and actual observation data is evaluated from multiple angles, a comprehensive optimization basis is provided, parameter calculation optimization is performed by adopting a particle swarm optimization method, the method has the characteristics of high convergence speed and high global search capability, is suitable for optimization of complex problems, and is used for confirming model requirements according to a wind field inversion rule. The model is selected by combining the evaluation result of the model library, it is ensured that the selected model has high pertinence and applicability, the prediction precision and reliability of the model are improved, model initialization is conducted through wind field fusion data, data information of multiple sources is fully fused, and the comprehensiveness and accuracy of the data are improved.

An earth and rockfill dam termite nest detection method based on MSCCEAUNet and physical constraint combined driving comprises the following steps that a miscellaneous geologic model of an earth and rockfill dam structure with a termite nest is simulated, and corresponding b-scan data and underground dielectric constant data are generated through a finite difference method FDTD to serve as a simulation data set; 2, constructing an MSCCEAUNet network with multi-scale cascade convolution (MSC) and an efficient channel attention mechanism (ECA), and inputting the complex geologic model data generated by simulation in the step 1 into the MSCCEAUNet network for training; according to the method, the MSCCEAUNet is trained again by adding Gaussian noise and random medium disturbance data with different signal-to-noise ratios, the structure of the MSCCEAUNet is optimized according to the training condition of the model, and the anti-jamming capability and robustness of the model are improved; a physical residual equation meeting physical information constraints of Maxwell equations of the ground penetrating radar is used as a physical loss function to be fused into an MSCCEAUNet framework, and data and physics combined drive inversion is achieved.

The invention discloses a hydroelectric generating set operation fault data enhancement method and device, and the method comprises the steps: firstly collecting a set vibration signal, selecting a time-frequency transformation method to convert a one-dimensional vibration signal into a two-dimensional time-frequency image, enhancing the feature dimension of the signal, constructing a diffusion feature migration model, gradually disturbing the data distribution to Gaussian noise through forward diffusion, and carrying out the recognition of the Gaussian noise. The method comprises the following steps of: performing inverse denoising to generate simulation data highly similar to a real fault sample, realizing relevance learning and migration sharing of fault features among different working conditions in combination with an adversarial feature migration architecture, and finally evaluating an enhancement effect by calculating similarity among samples, and inputting enhanced data into a fault diagnosis model to verify precision improvement. Through the combination of time-frequency transformation and a diffusion model, sample scarcity and working condition barriers are broken through, a remarkable effect is shown in the aspects of expanding the fault sample scale and enriching the sample dimension, the similarity of generated data and a real sample is improved, the diagnosis precision is improved, and the model generalization ability is remarkably enhanced.

The invention discloses a numerical simulation-based roadway surrounding rock deformation monitoring method and device. The method comprises the following steps of: constructing a three-dimensional geologic model of a roadway area; based on the three-dimensional geologic model, establishing a numerical model of heat conduction, stress action and seepage multi-physics field coupling, and based on elastic-plastic characteristics of rocks in the roadway area, simulating simulation data of surrounding rocks in the roadway area under the multi-physics field action by the numerical model; acquiring real-time monitoring data of various types of sensors arranged in a risk monitoring area of surrounding rock deformation corresponding to the simulation data; denoising, filtering and abnormal value processing are carried out on the real-time monitoring data, and then multi-source data fusion is carried out to calibrate the numerical model; and predicting the deformation trend of the surrounding rock based on the calibrated target numerical model and the real-time monitoring data. Therefore, by means of geological modeling, numerical model simulation, sensor arrangement optimization, numerical model optimization and surrounding rock deformation trend prediction, roadway surrounding rock monitoring precision is improved, and safe operation of a roadway is guaranteed.

The invention discloses the technical field of constructional engineering and intelligent control, and relates to a concrete hydration heat self-adaptive control method and system based on digital twinning. The method comprises the following steps: S1, constructing a concrete finite element model, simulating a hydration heat process, and outputting simulation data of temperature and temperature stress; s2, based on the analog data, optimizing the arrangement scheme of the cooling water pipe and the sensor through a composite convolutional neural network; s3, arranging and collecting real-time temperature and temperature stress data according to the scheme; s4, in combination with simulation and real-time data, predicting temperature change by adopting a BP neural network and a long-short term memory network, and constructing a dynamic prediction model; s5, based on the prediction result and the real-time data, constructing a WebGL digital twinborn platform, visualizing the data and correcting a prediction model; and S6, dynamically adjusting parameters of the cooling water pipe according to a correction result to realize hydration heat self-adaptive control. The invention further provides a system for realizing the method, the concrete temperature and the temperature stress can be accurately controlled, and the engineering structure safety and the quality control efficiency are improved.

The invention relates to the technical field of image analysis, and discloses a multi-modal emotion recognition method and system based on hypergraph diffusion and evidence fusion, a terminal and a storage medium, and the method comprises the steps: carrying out the random shielding of a data set through a randomly generated mask, thereby simulating the random data missing condition, and carrying out inverse sampling on the preprocessed simulation data by using a trained conditional diffusion model to obtain a training set in which missing modals are complemented so as to train an emotion classification network, and finally carrying out emotion recognition. According to the method, through dual-channel evidence fusion, uncertainty is estimated at a feature source level and a discrimination level at the same time, so that adaptive evidence fusion is realized, the condition of performance reduction caused by modal loss is reduced, potential features of the lost modal are explicitly recovered in a feature space, and the accuracy of final emotion recognition is improved.

The invention relates to the technical field of engineering intelligent algorithms, in particular to a jet grouting pile forming quality and construction optimal parameter prediction method and system, and the method comprises the following steps: building a data sample set based on similar historical engineering cases; the construction stratum soil bodies of the jet grouting piles are classified to obtain different stratum soil body categories, and construction process sensitive parameters are obtained; performing an orthogonal test to obtain an orthogonal test result so as to establish a jet grouting pile construction numerical simulation model to obtain a simulation data sample set; a jet grouting pile strength prediction model is constructed, and a jet grouting pile forming quality prediction result is obtained; establishing constraint conditions, and optimizing the pile-forming quality prediction result of the jet grouting pile according to an optimization algorithm to obtain an optimal solution set; and in combination with actual jet grouting pile construction conditions, optimal construction parameters are obtained according to an optimal solution screening method. According to the method, the optimal construction parameters of the jet grouting pile project can be rapidly and efficiently obtained, so that the construction scheme is optimized, and the pile forming quality of the jet grouting pile is guaranteed.

The invention discloses a hydrological data prediction method based on a convolutional neural network and a physical information neural network. Comprising the following steps: 1) utilizing an HEC-RAS hydrological model to generate cross sectional area, flow and water level elevation data of a key section of a target river channel in a preset time period as training reference truth values; 2) constructing space-time coordinates corresponding to the data as an input data set; 3) space-time input is processed through a physical information neural network model based on a convolutional neural network, the convolutional neural network extracts local heterogeneity features of a spatial position of a river channel through one-dimensional convolution, and a continuity equation and a momentum equation of a Saint-Venant equation set are embedded into a training process in a residual form through physical information constraint; and 4) finally, directly outputting a hydrological parameter prediction result of the target river channel. Therefore, under the environment that the observation data is deficient or missing, the hydrological parameter prediction precision and the model generalization ability are remarkably improved by fusing the simulation data and the physical mechanism constraint.

The invention relates to the field of juicer communication, in particular to a background communication method and a background communication system for a juicer system. The method comprises the following steps: obtaining a fruit and vegetable image reserved in a juicer; performing self-adaptive juicing feature analysis on the reserved fruit and vegetable image, performing intelligent juicing parameter adjustment, and constructing a self-adaptive juicing strategy; identifying surrounding environment parameters of the juicer; according to the surrounding environment parameters of the juicer, a self-adaptive juicing strategy is carried out, environment trend influence intelligent adjustment is carried out, juicing control simulation is carried out, and juicing control simulation data are obtained; multi-period juicing effect evolution is carried out on the juicing control simulation data, abnormal state prediction is carried out, and juicer abnormal early warning signals and juicer abnormal fault prediction information are generated; acquiring the network access state of the current juicer; through efficient and rapid background communication, the juicer use experience and performance of a user are improved.

The invention discloses a cooperative control method and device, equipment, a storage medium and a product, and relates to the technical field of big data edge computation.The method comprises the steps that a first system control instruction is input into a target industrial control system and a corresponding system mathematical model through an industrial gateway, and the system mathematical model is constructed by a cloud computing platform; collecting first response data generated by the target industrial control system in response to the system control instruction and first simulation data generated by the system mathematical model; the first response data and the first simulation data are transmitted back, so that after the cloud computing platform receives the first response data and the first simulation data, parameter intelligent identification is carried out according to the first response data and the first simulation data, the system mathematical model is updated according to a parameter intelligent identification result, and a dynamics updating model is obtained; by means of the method, real-time updating of the complex system model of the industrial control system is supported, and the efficiency and effect of model building and tuning are improved.

The invention discloses a curtain wall installation operation monitoring and guiding system and method. Real-time position installation data, actual material installation data and real-time temperature data in the curtain wall installation process are collected; inputting preset position installation data, material installation data and preset temperature data into the material position monitoring model to obtain preset force interaction simulation data of the curtain wall installation operation material position; inputting the real-time position installation data, the actual material installation data and the real-time temperature data into the material position monitoring model to obtain real-time force interactive simulation data of the curtain wall installation operation material position; the preset strength interactive simulation data and the real-time strength interactive simulation data are analyzed, an analysis result of curtain wall installation is obtained, an early warning signal is sent out, and a worker adjusts curtain wall installation according to the analysis result, so that the changes of strength and installation direction in the curtain wall installation process can be monitored in real time; and curtain wall installation errors which do not conform to curtain wall installation specifications can be avoided in advance.

The invention provides a large eddy simulation method and system based on a mechanism-only model. The method comprises the following steps: acquiring a current dictionary database of a target turbulent flow field; the current dictionary database comprises multi-dimensional flow field features in a current time step; performing feature screening on the current dictionary database to obtain key flow field features of the target turbulent flow field; inputting the key flow field features into a mechanism-only model, and reconstructing to obtain the sublattice turbulence viscosity of the target turbulence field in the current time step; wherein the mechanism-only model is obtained by training an initial neural network model based on direct numerical simulation data of a sample turbulence field and a sublattice turbulence viscosity label of the sample turbulence field. According to the method, the simulation precision of small-scale turbulence vortex motion in large vortex simulation is improved, so that the problems of poor performance of a bottom layer model and low simulation calculation precision caused by calculation grid scale limitation and sub-grid stress simplification hypothesis are effectively solved.

Systems and methods for generating simulation data based on real-world dynamic objects are provided. A method includes obtaining two- and three-dimensional data descriptive of a dynamic object in the real world. The two- and three-dimensional information can be provided as an input to a machine-learned model to receive object model parameters descriptive of a pose and shape modification with respect to a three-dimensional template object model. The parameters can represent a three-dimensional dynamic object model indicative of an object pose and an object shape for the dynamic object. The method can be repeated on sequential two- and three-dimensional information to generate a sequence of object model parameters over time. Portions of a sequence of parameters can be stored as simulation data descriptive of a simulated trajectory of a unique dynamic object. The parameters can be evaluated by an objective function to refine the parameters and train the machine-learned model.

The invention discloses an oil reservoir production dynamic prediction method fusing discrete gradient information, and belongs to the technical field of oil reservoir development and artificial intelligence crossing, and the method comprises the steps: building a heterogeneous oil reservoir oil-water two-phase flow numerical simulation data set based on a numerical simulation method; designing a double-branch network structure and extracting spatial and physical characteristics of input field data in parallel, wherein the spatial and physical characteristics comprise a main characteristic coding branch and a differential operator branch; designing a backbone network to carry out deep nonlinear modeling; an efficient pressure and saturation field prediction neural network model is constructed based on a double-branch network structure and a backbone network, in a model training stage, spatial region observation points of part of time steps are used to participate in data item loss calculation, and meanwhile, physical control equation residuals are introduced into all time steps and a whole space to serve as physical loss items; a trained efficient pressure and saturation field prediction neural network model is obtained, and high-precision prediction of a full-time-sequence pressure field and a saturation field is achieved.

The invention relates to the technical field of crane stress analysis, in particular to a crane structure parameter stress analysis method and system. The method comprises the following steps: constructing an accurate three-dimensional structure simulation model by obtaining crane device data and operation logs, and carrying out crane operation analogue simulation based on the model; performing boom structure division on the simulation data, respectively obtaining simulation data of the truss boom type crane and the telescopic boom type crane, evaluating stress coupling increment of the truss boom type crane and telescopic boom type mechanical conduction response characteristics, and comprehensively detecting dynamic mechanical response of the crane; based on the dynamic response data, identifying the breakage risk of crane components, determining the chain type degradation condition of the components, and evaluating the operation stability gradient attenuation of the crane in combination with the three-dimensional structure model; carrying out structural mechanics optimization treatment according to the stability attenuation condition; through crane stress analysis, the crane can run more stably and efficiently.

The invention relates to the technical field of aero-engines, and discloses an aero-gas turbine engine complete machine stability margin evaluation method, which comprises the following steps of: analyzing physical characteristics and working principles of all parts of a gas turbine engine to construct an engine complete machine model, and constructing an engine experiment table to carry out an experiment; judging whether the compressor enters a surge state and recording experimental data, carrying out numerical simulation based on the constructed engine complete machine model and recording simulation data, comparing the simulation data with the experimental data, verifying the accuracy of the model, if so, constructing a surge boundary, and if not, optimizing the model. The method comprises the following steps: judging whether an engine has a stall hysteresis phenomenon or not in real time, generating a normal operation signal or a stall hysteresis signal, if the stall hysteresis signal is generated, updating a surge boundary, calculating a stability margin value of the engine based on the surge boundary, and evaluating the stability margin of the whole engine.

The invention provides a power distribution network source network storage cooperative control method and system, and relates to the technical field of power distribution networks, and the method comprises the steps: collecting multi-dimensional power grid information of a power distribution network, and setting a target constraint condition according to the multi-dimensional power grid information; establishing a target optimization index and a cooperative control simulation model in cooperation with the target constraint condition, and performing cooperative control simulation on the power distribution network through the cooperative control simulation model to obtain a simulation record; a fitness evaluation strategy is introduced to analyze a plurality of simulation data sets in the simulation records, and an optimal cooperative control scheme is obtained through screening; and performing source network storage cooperative control of the power distribution network through the optimal cooperative control scheme. According to the invention, the technical problem that the power distribution network is difficult to realize effective coordination of the power generation source, the energy storage equipment and the load in the prior art can be solved, and the technical effects of improving the new energy consumption capability, reducing the operation cost, guaranteeing the operation safety of the power grid and improving the overall stability of the system are achieved.