560 results about "System dynamics" patented technology

The invention discloses an engine additive component service life prediction method based on modal polycondensation and expansion, and the method specifically comprises the steps: firstly, achieving the grid discretization of an initial defect structure based on a finite element method, and carrying out the local refinement of a defect region; performing modal polycondensation on the overall model to obtain a low-dimensional system model, and performing rapid calculation under the low-dimensional model to obtain modal response; establishing a conversion relation between a displacement vibration mode and a stress vibration mode, and obtaining full-field stress by utilizing modal response and based on modal extension; and solving a stress intensity factor-time history of the crack tip by adopting a stress extrapolation method, obtaining a delta K-R-n three-variable rain flow matrix by utilizing a rain flow counting method, and finally carrying out residual life evaluation through an iLAPS model. According to the method, system dynamics, fracture mechanics and modal order reduction technologies are fused, a new efficient and accurate fatigue life prediction path adapting to complex working conditions is provided for additive manufacturing components with defects, and the method has remarkable engineering application value and popularization potential.

An adaptive impedance-based multi-mobile-robot collaborative transportation control method. Each mobile robot estimates the actual pose and ideal pose of a reference point and the first and second derivatives of the ideal pose by means of two finite-time fully-distributed observers, respectively; and then, on the basis of the estimated poses of the reference point, the pose of an end-effector of a mechanical arm, and closed-chain constraints for collaborative transportation, an ideal trajectory of the end-effector of the mobile robot, and an estimated value of a pose deviation between the end-effector of the mobile robot and the reference point are obtained. An adaptive impedance system of each mobile robot is interconnected with a virtual energy tank, and the energy tank is used to guide the updating of impedance parameters, thereby ensuring the passivity of the entire collaborative adaptive impedance system. To process unknown system dynamics of mobile robots, an asymptotic tracking adaptive neural network controller is designed using a neural network, thereby asymptotically achieving an ideal adaptive impedance relationship. The operational accuracy of multi-robot collaborative transportation systems is improved while ensuring safe collaboration.

The invention provides a magnesium alloy electric drive main shell mold machining precision control method and system, and belongs to the field of precision numerical control machining. The method comprises the steps that dynamic milling force, key point temperature and position signals of all shafts are synchronously collected through a multi-source sensor; a recursive least square method is adopted to identify the dynamic rigidity and damping coefficient of the tool-workpiece system on line, and the thermal deformation drift distance is calculated in combination with a temperature signal; predicting a three-dimensional deformation error vector in real time based on the dynamic model, thermal deformation and geometric errors, and constructing a space compensation field; macro motion compensation is achieved by modifying numerical control program coordinate points, and meanwhile high-frequency micro-amplitude compensation is conducted through a spindle tail end fast tool servo system. And establishing an adaptive disturbance observer to perform online estimation on unmodeled disturbance and feed-forward compensation, and realizing online self-tuning of model parameters in combination with in-situ measurement feedback. Real-time sensing, full-band dynamic compensation and closed-loop self-adaptive control of multi-source errors are achieved, and the machining precision and long-term stability of the mold are remarkably improved.

The invention provides a new energy automobile high-voltage system dynamic risk assessment method and device based on multi-source data fusion, and is applied to the technical field of data processing. According to the method, data such as high-voltage component operation parameters, battery states, environment perception, fault history and whole vehicle control instructions are obtained. Pre-processing the first working condition label, the second working condition label and the third working condition label, analyzing a CONTROLSIGNAL field to obtain a working mode of the high-voltage system, and generating a dynamic working condition label; combining a Pearson's correlation coefficient and XGBoost feature importance, screening risk sensitive features from the two, and forming a risk related feature subset; the method comprises the following steps of: establishing an improved Bayesian network model, training a subset by using an improved Bayesian network model, establishing an exclusive risk assessment model for different working conditions, reasoning real-time data by using the model to obtain a probability value of each risk dimension, and generating a dynamic risk assessment result and an early warning signal in combination with grade standard judgment.

The invention discloses a task scheduling method and system based on big data analysis, and particularly relates to the technical field of big data analysis and intelligent scheduling, and the method comprises the steps: carrying out the modeling based on a graph structure and multi-scale time sequence features through a space-time prediction module, and precisely pre-judging resource demands and system dynamics; the man-machine cooperation module generates a scheduling strategy considering efficiency and constraint through dynamic weight balance AI calculation and artificial experience; the active scheduling module utilizes a health degree evaluation and fault propagation blocking mechanism to realize rapid isolation and resource migration of fragile nodes; the knowledge management module combines a knowledge graph and reinforcement learning, continuously optimizes a strategy library and digests rule conflicts. According to the method, resource prediction errors are remarkably reduced, task processing delay is shortened, fault recovery efficiency is improved, high reliability of key services is ensured, meanwhile, the method adapts to complex and changeable unforeseen scenes through self-learning ability, and the method can be widely applied to resource scheduling and fault-tolerant management in the fields of cloud computing, industrial Internet of Things and the like.

The invention discloses an informatization system dynamic verification method based on a zero-trust architecture, and the method comprises the steps: collecting and standardizing user operation requests of all business systems, and achieving unified resource and organization identification; a distributed encryption operation log link is constructed through an end-to-end encryption protocol, and safe synchronization and tampering prevention of an operation behavior abstract are guaranteed; extracting a multi-dimensional right decision variable, inputting the multi-dimensional right decision variable into an adaptive artificial intelligence decision model, dynamically generating a reversible permission factor set, and supporting fine-grained and rollback permission authorization and real-time automatic revocation; the log chain is encrypted and recorded in the whole process, intelligent abnormity monitoring and end-to-end traceability are combined, the consistency and compliance auditing of the global permission state are supported, and the security, traceability and intelligent self-adaptive treatment capacity of sensitive operation in the multi-organization cooperation environment are effectively improved.

The invention relates to a mechanical arm friction disturbance identification and suppression method based on a vibration signal. The method is suitable for high-precision control of a multi-degree-of-freedom flexible joint robot system. The method comprises the following steps: firstly, designing a five-order Fourier excitation track meeting a zero boundary condition to excite system dynamic response; then constructing a structural dynamics regression model based on a Newton-Euler method, extracting a minimum parameter set, and obtaining a structural item output torque; aiming at a structural modeling residual error, introducing a breadth learning system (BLS) to carry out nonlinear identification on non-structural disturbance, and carrying out global optimization on network structural parameters by adopting a particle swarm optimization (PSO) algorithm; and finally, fusing the structure item and the disturbance item to form total joint torque estimation for realizing feed-forward compensation control. The method has the advantages of being high in identification precision, high in real-time performance, high in data utilization rate, excellent in robustness and the like, nonlinear friction torque disturbance generated by the mechanical arm under the vibration interference condition can be effectively restrained, the dynamic tracking performance and control stability of a system are improved, and good engineering adaptability and popularization value are achieved.

The invention discloses a backup task scheduling method based on load prediction and an improved SAC algorithm, and relates to the technical field of data backup and intelligent scheduling. Aiming at the problems of response lagging, insufficient key task guarantee and low strategy exploration efficiency of a traditional scheduling method under a dynamic load, a task weight monitoring matrix is constructed, server performance, a network state and data importance are fused, and a task risk coefficient is calculated in combination with an exponential smoothing algorithm; carrying out sequence decomposition on a service load by adopting an Autoformer model, identifying a periodic mode through a weighted autocorrelation mechanism, and predicting a future load change trend; on this basis, an improved SAC scheduling framework is designed, a task weight guidance and temperature coefficient adaptive adjustment mechanism is introduced, and the task starting opportunity and execution duration are optimized in combination with an elastic time window. The invention provides an intelligent scheduling method with strong adaptive capability, which can autonomously optimize the task scheduling strategy according to the dynamic change of the system and effectively reduce the peak load of the server and the task backlog risk.

The invention discloses a fault detection method and a fault detection device for an accelerometer of a downhole tool. Taking an accelerometer and a gyroscope as state quantities, establishing a generalized nonlinear system for the downhole tool sensor model, and performing linearization processing to obtain a generalized linear variable parameter system; based on a T-N-L observer architecture, constructing a system state estimation error equation and a residual error generation equation, establishing a fault sensitivity and interference robustness evaluation system on the basis of the system state estimation error equation and the residual error generation equation, and determining observer parameters; and the sensor obtains a system state value, an estimated value is obtained through the observer, a residual value of a current system state quantity is calculated to judge an occurrence event, and a threshold value of sensor fault occurrence under the current event is calculated to judge a fault occurrence condition. A nonlinear model is adopted, the system dynamic state is described more accurately, the model is linearized, it is guaranteed that the model and the actual working condition have the high matching degree, and meanwhile a corresponding linear variable parameter system is obtained to reduce the calculated amount.

The invention relates to a wireless power transmission system parameter identification method based on a time domain differential model, and belongs to the technical field of wireless power transmission system control. The method comprises the following steps: establishing a parameterized time domain dynamic physical model; collecting system dynamic response data; constructing an optimization target based on a time domain residual error; and performing optimal parameter estimation based on iterative optimization. According to the method, a time domain dynamic physical model of a wireless power transmission system is constructed, and unknown parameters are solved through an iterative optimization algorithm with a minimum waveform matching error as a target. According to the algorithm, in an iteration process, a physical model is called to carry out forward differential equation solution, a time domain residual error between a model solution current and a real measurement current is used to drive a parameter estimation value to converge, and finally high-precision and high-robustness wireless power transmission system parameter identification under any working frequency is realized. The problem that in the prior art, due to static stiffness and sensitivity to waveform distortion of a theoretical model, the identification precision is insufficient is effectively solved.

The invention relates to the technical field of new energy power system instability risk assessment, in particular to a new energy power system frequency instability risk assessment method based on a heterogeneous graph attention network. The method comprises the following steps: constructing a wind power-photovoltaic heterogeneous graph model according to a power grid topology; performing combined sampling on different operation modes and anticipated disturbances, and determining corresponding input feature vectors; calculating a frequency stability index value label of the sample set; expanding the training set through an active learning iteration process, and carrying out model training; and inputting the collected operation data into the trained heterogeneous graph attention model, outputting a frequency stability index value, and evaluating the system frequency instability risk in combination with the risk matrix. By adopting the frequency instability risk assessment method for the new energy power system based on the heterogeneous graph attention network, the problem of low efficiency of risk assessment in a high-dimensional uncertain scene is solved, and the heterogeneous graph attention network can reflect the influence of different types of devices at different positions and disturbance types on the dynamic frequency of the system; and the accuracy of frequency instability risk assessment is improved.

The invention discloses a subway tunnel fire behavior spreading prediction method, which comprises the steps of 1, fire source type digital modeling, 2, fire scene initialization, 3, dynamic fire source loading and ignition triggering, 4, fire behavior spreading multi-physics field coupling calculation, 5, ventilation system dynamic response, 6, visual risk thermodynamic diagram generation, and 7, fire behavior spreading prediction. Step 7, carrying out real-time early warning on the safety margin; the method has the beneficial effects that a total factor modeling capability is realized, multi-dimensional parameter input from fire source characteristics to a tunnel environment is supported, a scene range covering a typical subway fire disaster is expanded, solving is realized through a multi-physics field simultaneous equation, a fire spreading error is reduced, smoke diffusion simulation precision is improved, and a dynamic ventilation adjustment and safety early warning mechanism is realized. And more buffer time is provided for emergency response.

The invention provides a distributed driving vehicle preset performance fault-tolerant control method and system based on an adaptive neural network observer. The method comprises the following steps: constructing a vehicle dynamics model containing motor faults and unknown disturbance; in order to realize real-time estimation of faults and disturbance and break through the limitation on disturbance derivative boundaries, a self-adaptive neural network observer is designed; aiming at the problem that the control direction is uncertain, an upper-layer controller is designed based on a preset performance control theory, a Nussbaum function and an obstacle Lyapunov function are introduced, it is ensured that system dynamic errors meet preset performance constraints all the time, and accurate tracking control over the transverse force, the longitudinal force and the yawing moment of a vehicle is achieved; in order to improve the system coordination capability under the multi-motor fault working condition, an optimal torque distribution model fusing the motor torque constraint and the tire force limit is constructed, collaborative optimization control of the driving and steering system is realized through a quadratic programming algorithm, and the stability and safety of the whole vehicle in the fault state are improved.

The invention discloses an engine state estimation and system modeling correction method based on double-layer variational inference, which relates to the field of engine state estimation and comprises a variational inference stage aiming at component performance states and kinetic model parameters; a system output prediction stage based on an observation equation; and a solving stage of performing objective function optimization through an evidence lower bound. The structure clearly presents information flow and key calculation links of the proposed algorithm in state estimation and model learning. According to the method, combined reasoning of state variables and model parameters is achieved by building a probability modeling structure, the modeling problem when system dynamics is partially or completely unknown is solved by combining a modeling method of a stochastic differential equation, and while the state variables and the model parameters are optimized, the modeling efficiency is improved. Precise inference of component states and reliable identification of fault features are achieved, the fault detection accuracy of sudden gas circuit abnormity reaches the standard, and meanwhile the performance is better in the aspect of tracking long-term performance degradation.

The invention relates to the technical field of compressors, and discloses a scroll compressor shafting dynamic balance simulation method, device and equipment and a medium, and the method comprises the following steps: carrying out fluid mechanics analysis based on a movable scroll plate model to obtain initial three-way gas force; performing dynamic analysis based on a scroll compressor model by taking the initial three-way gas force as a boundary condition to obtain a displacement result of a movable scroll plate; inputting the displacement result into the movable scroll plate model, updating the position and the motion state of the movable scroll plate, and carrying out fluid mechanics analysis again to obtain a new three-way gas force; and performing dynamic balance optimization on the scroll compressor model by taking the new three-way gas force as a boundary condition until a preset condition is met, and obtaining a target dynamic balance result. According to the technical scheme, the precision of dynamic balance simulation of the shaft system of the scroll compressor can be improved, and therefore the product reliability is improved through optimization of dynamic balance.

The invention discloses a dynamic simulation modeling method for a compressed air energy storage system based on AMESIM and application, and belongs to the technical field of compressed air energy storage.The method comprises the steps that firstly, system indexes are determined, based on the system indexes, a system technological process diagram is designed, main equipment and auxiliary equipment are selected, and design point parameters of the main equipment and the auxiliary equipment in the energy storage and release stage are determined; then, according to the equipment model selection result, sub-models of all elements in the air circulation loop and the heat storage and exchange loop are selected in the AMESIM; thirdly, calibrating and verifying each main equipment sub-model by utilizing main equipment characteristics and model selection parameters, and building a steady-state simulation model of the compressed air energy storage system; and then, constructing a dynamic simulation model based on the steady-state model, and carrying out dynamic simulation verification and evaluation. According to the method, the co-simulation model integrating thermodynamics, fluid mechanics, control theory and mechanical dynamics can be quickly built, and the method has the advantages of being high in model modeling precision, high in multidisciplinary coupling calculation capacity, high in simulation efficiency and the like.

The invention discloses a CKM-CNN-based power system transient stability assessment method, system, equipment and medium, and relates to the technical field of power system assessment, and the method comprises the steps: collecting time sequence electrical quantity data after power system fault disturbance, constructing a transient sample set, carrying out the standardization processing and marking of a sample, and carrying out the calculation of the transient stability of the power system; a transient sample set is divided through a clustering algorithm, based on a sample cluster division result, an adaptive oversampling technology is adopted to carry out directional enhancement on an unstable sample, an equalized sample space is generated, a multilayer convolutional neural network is utilized to carry out hierarchical feature extraction on the equalized sample space, a high-dimensional feature vector is generated, and according to the high-dimensional feature vector, a high-dimensional feature vector is generated. And outputting a transient stable state probability evaluation result through the classification decision-making layer. According to the method, a generalization sample set is constructed through standardization and labeling, the scarce instability sample identification capability is improved by combining two-stage clustering and ADASYN oversampling, and the system dynamic state is accurately captured by utilizing 1D-CNN hierarchical feature extraction, so that efficient and robust evaluation of the transient stability of the power system is realized.

The invention discloses a multi-energy microgrid dynamic balance control and energy scheduling method and system. The multi-energy micro-grid dynamic balance control and energy scheduling method comprises the following steps: establishing a dynamic electric and thermal load prediction model of a rotary hearth furnace; a multi-energy micro-grid framework containing biomass power generation, photovoltaic power generation, gas turbine combined cooling heating and power generation, flue gas waste heat power generation and energy storage is constructed; establishing a system dynamic balance model and a multi-objective optimization framework considering production process constraints; designing a hierarchical dynamic balance joint control strategy; and scheduling by adopting a multi-time scale optimization algorithm. Through multi-energy complementation, waste heat gradient utilization and intelligent optimization scheduling, economical, reliable, low-carbon and efficient operation of energy supply of the rotary hearth furnace is achieved, comprehensive energy consumption and carbon emission are remarkably reduced, and an effective solution is provided for green low-carbon transformation of a high-energy-consumption industrial process.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an intelligent agent collaborative fault test method, device and equipment and a medium. And generating a cross-cluster feature incidence matrix and inputting the cross-cluster feature incidence matrix into the fault prediction model to obtain a potential fault type. And generating a cooperative fault injection strategy according to the potential fault type, and executing fault injection by the target intelligent agent. And monitoring a self-healing process, collecting a cross-cluster recovery time index, a data consistency index and an agent cooperation degree index, analyzing to obtain a self-healing capability analysis result, and updating a strategy network of the intelligent agent based on the result to realize dynamic test and optimization of the distributed system. According to the invention, data acquisition, fault prediction, fault injection and self-healing capability evaluation are realized through intelligent agent cooperation, and the active test and self-adaptive capability of the distributed system is improved.

The invention discloses a cooperative control simulation system for an underwater robot and an umbilical cable system, belongs to the technical field of ocean engineering simulation and automatic control, and is used for analyzing system dynamic characteristics in the research and development stage of deep sea operation equipment. Comprising an environment initialization and marine environment construction module, a mother ship model construction module, an underwater robot and umbilical cable coupling module, an instruction generation module, a man-machine interaction control module and a data acquisition and storage module. According to the method, an external interference source with physical authenticity is provided for a control algorithm by combining an energy density function and simulating a composite environment modeling method of a sea level height field and an LOD dynamic grid, and a distributed model prediction control algorithm for uniformly optimizing underwater robot trajectory tracking and umbilical cable tension safety maintenance is constructed; data such as the motion state of the underwater robot and the tension of the umbilical cable can be output in real time, algorithm performance testing is completed in a virtual environment, and dependence on high-cost sea testing is not needed.

The invention relates to the technical field of energy storage system scheduling methods, and discloses an energy storage system dynamic threshold scheduling method. According to the method, power fluctuation values and load distribution data of a target system in continuous time are monitored and acquired in real time, and no less than ten time control points are selected in a scheduling period to measure operation parameters; and performing comprehensive processing to generate a dynamic threshold sequence arranged according to a time sequence as a scheduling reference. During scheduling, real-time parameters are collected to be compared with a target threshold value, a difference value is calculated to form a threshold value data sequence, and accordingly an adjustment amount is output and time sequence continuity is controlled. When a single-point threshold value is abnormal or suddenly changed in a local period, an independent adjustment process is started, a correction value is calculated by using an adjacent control point interpolation algorithm, an adjustment range is limited, and a priority judgment rule is introduced to process an abnormal point. The method can adapt to dynamic changes of the system, and the scheduling precision and stability are improved.

The invention discloses a color consistency control method in a bamboo kitchen ware carbonization process, and belongs to the technical field of bamboo processing. The method comprises the following steps: acquiring a multi-dimensional spectral data vector of the surface of the bamboo kitchen ware; determining a multi-dimensional change rate vector of the spectral data; respectively generating a first candidate control signal and a second candidate control signal based on the spectral data and the change rate vector; determining a system dynamic state index based on the change rate vector; generating a dynamic weighting coefficient according to the dynamic state index of the system; and performing weighted fusion on the first candidate control signal and the second candidate control signal by using a dynamic weighting coefficient to generate a final heat regulation control signal. According to the invention, the method achieves the self-adaptive optimal control of all working conditions through the construction of a dual-mode decision fusion composite control system, guarantees the quick response in a dynamic stage, guarantees the smooth convergence in a steady-state stage, and fundamentally improves the thermal inertia problem of the system.

The invention provides a double-input-signal virtual power system stabilizer optimization method, and relates to the technical field of power system stability control. Multi-source signals during VPSS operation are collected in real time and subjected to normalization processing, a one-dimensional time sequence signal is converted into a two-dimensional image matrix through a GASF and a GADF, and the time dependence and the dynamic phase relation of the signals are completely reserved; the physical variable frequency-power coupling is coded through a multi-channel GAF matrix; a lightweight convolutional neural network is adopted, optimization features are extracted from a GAF matrix, and VPSS control parameters are dynamically adjusted by combining a near-end strategy optimization algorithm with Lyapunov stability guarantee; introducing an online learning mechanism and an abnormal switching strategy; a closed-loop framework of signal acquisition, feature coding, mode extraction, parameter optimization, control output and abnormity recovery is adopted, system response is fed back in real time, parameters are adjusted to balance dynamic performance and steady-state efficiency of the system, and the VPSS3B is made to adapt to various operation conditions of access of the network-building type converter.

The invention discloses a tethered satellite system dynamics modeling method considering perturbation factors. The tethered satellite system dynamics modeling method comprises the steps that a geocentric coordinate system, an orbital coordinate system of a Kepler orbit where a tethered satellite system is located and a body coordinate system of a unit satellite are established; according to the movement of the centroid of the tethered satellite system on the orbit, the generalized coordinates are the distance from the centroid of the system to the earth center and the true anomaly of the orbit, the Lagrange function of the centroid of the system is obtained; according to the movement of the unit star relative to the centroid, the generalized coordinates are the distance between the unit star and the centroid, the relative first rotation angle and the relative second rotation angle, the relative Lagrange function of the unit star is obtained; comprehensively considering perturbation factors to obtain non-spherical earth, third gravitational force and solar pressure perturbation potential energy; establishing a system kinetic equation considering comprehensive perturbation factors; and obtaining a complete Lagrange equation of the tethered satellite system. The error problem caused by the fact that the master satellite directly serves as the mass point of the system and the influence of comprehensive perturbation on system motion is not considered is solved, and the number of the slave satellites is not limited.

The invention provides a dynamic adaptation method and system for a multi-mode power supply compatible charging pile system, and the method comprises the steps: building a multi-protocol physical layer compatible connection between a charging pile and a power supply module through a multi-communication interface layer, and receiving a data frame inputted by the charging pile; analyzing a baud rate, a check bit and an instruction set of the data frame, and identifying a charging pile protocol type and a power module protocol type; querying a matching mapping relationship between the charging pile protocol type and the power supply module protocol type in the dynamic adaptation matrix; if the matching mapping relation exists, data field conversion is executed, and a target protocol frame is generated; and if the matching mapping relation does not exist, starting a machine learning process, creating a temporary mapping rule, and effectively solving the problems of poor compatibility and high manual configuration cost of the charging pile and the power supply module in a traditional protocol mode through the modes of protocol analysis, intelligent matching and dynamic learning.

The invention relates to the field of artificial intelligence control, in particular to a world model and reinforcement learning collaborative optimization method based on constraint perception gradient projection, which comprises the following steps: constructing a world model based on Transform architecture, performing unified modeling on an environment state, system dynamics and a reward function, and training the model based on reference model data to predict an environment future state; training a reinforcement learning strategy based on a virtual track and a reward signal generated by the world model, and calculating strategy gradient information; an explicit gradient projection operator of constraint perception is designed, a strategy gradient is corrected in real time according to future constraint conditions predicted by the world model, and it is ensured that the gradient updating direction meets the safety constraint and the optimization target at the same time; and synchronously updating world model parameters and reinforcement learning strategy parameters by using the gradient after projection correction to realize collaborative optimization of the world model parameters and the reinforcement learning strategy parameters. According to the method, the key problems of local optimal trap, low convergence speed, insufficient stability and the like in the collaborative optimization process of a traditional method are solved.

The invention belongs to the technical field of power systems, and discloses a power system dynamic simulation and fault prediction method based on digital twinning. The method comprises the following steps: S1, collecting multi-source heterogeneous data in a power system in real time based on a deployed sensor network, and preprocessing the multi-source heterogeneous data; wherein the multi-source heterogeneous data comprises power data and heat data; s2, calculating the preprocessed multi-source heterogeneous data based on a dynamic correlation model, and analyzing a thermal-electric relationship of the power electronic equipment through a calculation result; and S3, establishing a dynamic mapping function of a thermal-electric relationship in the digital twinborn model. According to the method, the digital twin model is adopted to fuse the thermal-electric relation effect of the heat data characteristics of the power electronic equipment and the power data characteristics of the power grid, dynamic simulation is achieved, the simulation precision is improved, and prediction deviation caused by model mismatch is reduced. Meanwhile, virtual-real linkage is achieved, and bidirectional feedback of simulation results and fault prediction is achieved.

The invention relates to a direct current furnace machine-furnace coordination system dynamic modeling method suitable for a wet condition. Based on the law of mass conservation and energy conservation, a lumped parameter method is adopted to construct a dynamic model comprising a coal pulverizing system, a coal economizer-water wall system, a water storage tank system, a superheater system and a steam turbine system; identifying parameters are optimized step by step by only utilizing unit variable load dynamic operation data and combining with the mechanism constraint that the heat absorption capacity of a working medium is monotonically increased along with the coal quantity as fired: static parameters are determined through dynamic data regression, nonlinear parameters are optimized based on a preset water-cooled wall outlet flow and dryness function structure, and the heat absorption capacity of the working medium is calculated. And finally, identifying dynamic parameters step by step by adopting an immune genetic algorithm. The dynamic characteristics and the physical consistency of the established model are verified through open-loop simulation, and the technical problems that dynamic description of the water level of the water storage tank is missing and the modeling precision is low under the wet working condition are solved.

The invention discloses a power grid mode system dynamic file configuration method and device, electronic equipment and a medium, which are used for solving the technical problems of low development efficiency, high code maintenance cost, low reuse rate and poor configuration information expansibility in the prior art. The method comprises the following steps: acquiring a metadata configuration file; constructing a field configuration mapping table according to the metadata configuration file; based on the field configuration mapping table, table column configuration, screening item configuration and form item configuration are generated respectively; and transmitting the table column configuration, the screening item configuration and the form item configuration to a front-end rendering engine of the power grid mode system, so that the front-end rendering engine performs dynamic rendering according to the table column configuration, the screening item configuration and the form item configuration.

The invention discloses a power grid transformer technical renovation and overhaul time domain interval evaluation method. The method comprises the steps of 1, establishing an evaluation basic data set through multi-source data acquisition and data preprocessing; 2, screening key influence variables, quantifying variable influence weights by adopting regression modeling, and realizing accurate quantification of the health state of the transformer in combination with a TOPSIS algorithm; 3, forming a dynamic fault rate model; 4, constructing a system dynamics-Monte Carlo cooperation model, a fault tree-Bayesian network dynamic and static diagnosis model and a long and short term memory network time sequence prediction model; 5, setting an optimization target and a constraint condition, and outputting an optimal time point and a reasonable time domain interval of technical renovation and overhaul of the transformer by adopting a multi-target optimization solution method; and 6, model evaluation precision is tested back and verified through historical data, and key parameters of the model are subjected to feedback adjustment by adopting a particle swarm optimization algorithm. The method effectively solves the problems that a traditional method is low in evaluation precision, single in target and poor in dynamic adaptability, and is convenient to popularize and use.