673 results about "System dynamics" patented technology

The invention discloses a city updating intelligent expert system architecture and method based on a large language model, and relates to the technical field of city planning and construction. The knowledge base management module supports efficient retrieval and application of professional domain knowledge; the expert agent group comprises a user-defined core expert and a system dynamic supplement expert; the expert team collaborative decision-making module is used for executing multiple rounds of hierarchical collaborative decision-making processes; the meta-agent module is responsible for project feature analysis and expert team dynamic configuration; the auxiliary agent module comprises a host agent which is responsible for guiding the decision making process; the recorder agent generates a decision summary report; the verification agent is responsible for verifying and evaluating the professional ability of the expert agent; and the scoring agent evaluates the decision quality. According to the method, the problems of dispersed professional knowledge, low cross-domain expert cooperation efficiency, high decision-making cost and the like in the existing city updating decision-making process are solved, and the decision-making efficiency and quality of city updating early-stage planning are comprehensively improved.

The invention relates to the technical field of robot control, in particular to a force control method and system based on an impedance model for a rope-driven robot. Comprising the steps of system dynamics modeling and parameter identification, real-time axial compression sensing and model updating, Cartesian space impedance model design, adaptive impedance parameter adjustment, inner ring position control based on feedback linearization, force error compensation and expected trajectory generation, real-time control circulation and anti-interference processing and experimental verification and iterative optimization. According to the method, through real-time axial compression sensing and equivalent Young modulus dynamic updating, the problem of model mismatch caused by material rigidity sudden change in the phase change process of the hyperelastic material is solved. Specifically, a traditional method depends on static material parameter calibration, and real-time compensation of the nonlinear characteristic of the material is achieved through online estimation of the axial compression amount of the main beam and iterative correction of the Young modulus.

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.

The invention discloses a method and system for driving SOAR automatic disposal based on system dynamic risk assessment. According to the invention, a closed-loop protection system including data acquisition and preprocessing, dynamic risk assessment, disposal decision triggering, SOAR automatic disposal execution, system total risk value calculation, system-level risk determination, asset risk weighted upgrading, manual intervention triggering, manual disposal feedback and alarm level overall improvement is constructed; and accurate identification and efficient response of network security threats are realized. According to the method, the total risk value of the system is calculated through a multi-dimensional dynamic risk assessment model in combination with an asset value weight algorithm, and real-time upgrading of the asset risk level is realized through a risk threshold dynamic adjustment mechanism, so that a two-level response mechanism (automatic disposal-manual takeover) is realized, and the automation efficiency and the manual reliability are balanced. According to the invention, precise and efficient disposal of network security threats and intelligence of security protection are realized, and the method can be widely applied to core service systems of various industries.

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 belongs to the related technical field of mobile robots, and discloses a rolling-walking composite motion control method and device for a double-wheel-foot robot, and the method comprises the steps: (1) simplifying the rolling motion of wheels of the double-wheel-foot robot into the sliding motion of a sliding block; (2) constructing a system kinetic equation of the double-wheel-foot robot based on the simplified kinetic model; constructing a constraint equation based on the contact state of the two legs and the ground; discretizing the system kinetic equation, the cost function and the constraint equation, and converting model prediction control problems corresponding to the system kinetic equation, the cost function and the constraint equation into nonlinear problems for numerical solution so as to complete leg motion planning; (3) calculating the rotation speed of the wheel based on the leg motion planning result; and (4) based on the rotation speed of the wheels and the leg motion planning result, a whole-body control algorithm is adopted to calculate and obtain moment instructions of leg joints and the wheels. According to the invention, the state space dimension is reduced, and the wheel-ground contact constraint is simplified.

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 relates to the technical field of power system control, solves the problems of network attack, limited bandwidth and transmission delay of a power system in a signal transmission process and conservative DET parameter selection and controller design, and particularly relates to a power system dynamic event triggered load frequency control method under Dos attack and random delay. Comprising obtaining a continuous time system parameter matrix; setting an event triggering threshold function; state parameters facing Dos attack and random time delay conditions are set; constructing a state feedback controller; and collaborative design optimization is carried out by applying a GA optimization algorithm. Compared with a traditional control method, the method has the advantages that on the premise of ensuring the stability of the system, by optimizing event triggering conditions, the communication requirement and bandwidth occupation are remarkably reduced, the utilization efficiency of communication resources is improved, network congestion and delay possibly caused by frequent communication are reduced, and the service life of the system is prolonged. And the operation efficiency of the whole power system is improved.

The invention provides a solid waste recycling full life cycle environmental impact evaluation method, which belongs to the technical field of solid waste recycling, and evaluates environmental impact by constructing a system boundary and time dynamic list including solid waste collection, transportation, pretreatment, recycling and product use stages and applying a time weighted characterization model. A time discount rate is introduced to quantify future environmental influence, a multi-scene influence prediction function is adopted to construct a simulation matrix, and an environmental influence time distribution curve is calculated. A four-dimensional index system of resource recovery efficiency, energy substitution efficiency, pollutant emission reduction and environmental impact mitigation is established, a system dynamics and Envi rPred ictor deep learning model is combined to predict a solid waste long-term environmental behavior, an environmental impact comprehensive evaluation matrix is constructed, characterization factors are dynamically adjusted, an iterative optimization mechanism is formed, and the environmental impact mitigation efficiency is improved. Scientific evaluation on the solid waste recycling full life cycle environment influence is realized.

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 steering engine accurate control method and system based on an optimization control algorithm, and the system comprises a data collection module which collects key working parameters of a steering engine in real time through a plurality of sensors, and carries out the data processing, thereby forming a state vector; the self-adaptive filtering module is used for carrying out self-adaptive filtering fusion processing on the data acquired by the sensor and dynamically estimating the attitude angle and the corresponding angular velocity of the aircraft; the sliding mode control module constructs a sliding mode surface and a control rate of a steering engine system according to the dynamic error of the system, calculates control input of each axis of the aircraft according to the attitude angle real-time state error, and generates a sliding mode control signal; the driving execution module is used for converting the control signal into a voltage or PWM signal and inputting the voltage or PWM signal into a steering engine, so as to change thrust and realize adjustment and control of a posture or a position; and the real-time feedback module monitors the current state of the steering engine in real time to realize dynamic correction and real-time optimization. The adaptive filtering and sliding mode control method provided by the invention is higher in control precision, quicker in system response and higher in adaptability to complex environments, and can effectively improve the application performance and stability of a steering engine execution system in the fields of industrial automation, aerospace, intelligent manufacturing and the like.

The invention relates to the technical field of intelligent risk analysis, in particular to an intelligent risk analysis system of a financial system. The system comprises a data flow analysis module, a relational graph construction module, an abnormal mode detection module, a security boundary monitoring module, a staged strategy planning module, a scene simulation evaluation module, a decision support module and a comprehensive risk management module. A graph database and a graph convolutional network are utilized to deeply analyze a financial entity relationship, reveal a risk association network, improve abnormal behavior recognition through a K-mean value and an isolated forest algorithm, enhance security boundary monitoring through a random forest algorithm, enable risk threshold adjustment to be more dynamic, combine a decision tree with a genetic algorithm, optimize a risk management strategy, and improve risk management efficiency. The system dynamics and proxy model technology in scene simulation evaluation strengthens risk prediction and increases foresight, principal component analysis and risk matrix evaluation are applied in a comprehensive risk management module, a quantitative analysis tool is provided, and more systematic and comprehensive risk management is realized.

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 the technical field of process optimization control, in particular to a single crystal furnace crystal pulling process control method and system based on an intelligent optimization algorithm. Identifying to obtain equipment operation parameters and process state variables; obtaining a state estimation value of the controlled medium based on the equipment operation parameter and the process state variable; determining target operation parameters of the controlled equipment according to the technological process and the equipment operation parameters; on the basis of the target operation parameters, expected changes of process state variables are estimated in combination with system dynamic response characteristics, and predicted state variables are obtained; calculating a medium distribution parameter according to the equipment operation parameter, the process state variable and the state estimation value; generating a state prediction value of the controlled medium based on the medium distribution parameter, the target operating parameter and the predicted state variable; and integrating the state estimation value and the state prediction value, and outputting a control instruction for adjusting the execution structure according to a preset adjustment rule. The closed-loop regulation of the controlled medium is realized through the control instruction.

The invention provides a process monitoring improvement method and device for a dynamic measurement function of a system, and the method comprises the steps: constructing a kernel mode dynamic measurement function assembly which comprises a strategy library and a reference library; when a measurement task is started, traversing all process lists in the system through a kernel mode dynamic measurement function component, and detecting whether a main execution file corresponding to a current process is in a measurement strategy list or not; traversing the vma list of the current process, checking whether each vma stores a reference value in a reference library or not, measuring the Hash value of the vma and comparing the Hash value with the reference value, if yes, continuing to traverse the next vma, and if not, making a decision according to a measurement strategy; and traversing the next process of the process list until the process list is traversed. According to the method, the strategy is issued for the process main body, and the whole process is used as a measurement target, so that the problems of measurement omission and incapability of proving the actual credibility of the monitored process are solved.

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 provides a finite time cooperative tracking control method based on an adaptive RBF neural network, and belongs to the technical field of networked multi-agent control strategies. Constructing a dynamic mathematical model of a high-order all-drive uncertain nonlinear multi-agent system comprising a virtual leader and a follower; according to a cooperative tracking control target and uncertain items in the system, constructing an error dynamic system comprising an adaptive RBF neural network estimation compensation item; designing a finite time integral sliding mode surface based on a high-order all-drive system theory; designing linearization parameters of a finite time integral sliding mode surface based on a pole assignment method according to system dynamic performance and finite time convergence requirements, and obtaining a finite time integral sliding mode controller; and designing a self-adaptive updating law of an upper bound of an RBF neural network weight and an approximate error, and substituting the network weight and the approximate error into a finite time integral sliding mode controller to realize cooperative tracking control of finite time.

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 discloses a VSG-DFIG subsynchronous oscillation adaptive suppression method based on a Hamiltonian model, and belongs to the technical field of subsynchronous oscillation suppression of a power system. The method aims at solving the problem that an existing method for suppressing subsynchronous oscillation existing in grid-connected operation of the doubly-fed induction generator is poor in suppression effect. Comprising the following steps: constructing a port controlled Hamiltonian dissipation model; constructing a closed-loop system dynamic equation based on interconnection and damping distribution under strict passivity; an expected energy function in a unit closed-loop form is constructed, a DFIG unit feedback control law based on a Hamiltonian model is obtained, and further deformation is carried out to obtain a deformed DFIG unit feedback control law; the port controlled Hamiltonian dissipation model is corrected, and a corrected port controlled Hamiltonian dissipation model is obtained; constructing a dynamic equation of the anti-interference adaptive observer, and establishing an error dynamic equation; and constructing a Lyapunov function, and designing an estimation error feedback control law according to the stability condition of the Lyapunov function. The subsynchronous oscillation suppression circuit is used for suppressing subsynchronous oscillation.

The invention discloses a dynamic learning method and system for an intelligent controller, and the method specifically comprises the steps: building a system dynamic characteristic model for the intelligent controller through employing a dynamic model building method based on the real-time data of an industrial process; based on the system dynamic characteristic model, a parameter optimization objective function is set for the intelligent controller, and the parameter optimization objective function is used for adjusting the balance among control precision, strategy flexibility and real-time indexes; performing global optimization on the parameter optimization objective function by adopting a particle swarm optimization algorithm, and dynamically adjusting the particle speed and position in the optimization process through a dynamic learning mechanism to obtain an optimization result; and dynamically adjusting the control parameters of the intelligent controller based on the optimization result. According to the invention, the adaptability and flexibility of the intelligent controller are improved, rapid response and accurate control are realized, and the performance of the intelligent controller in complex industrial process control is improved.

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 relates to a surface topography prediction method based on a ball-end cutter model and a variable step size iterative algorithm, which is based on the ball-end cutter model, combines and applies a dynamic variable time step size strategy, accurately divides cutting and non-cutting tracks, and remarkably improves the simulation efficiency and precision. Through multi-coordinate system dynamic modeling and a specific function, the eccentricity of the ball-end milling cutter is compensated, the abrasion is accurately modeled, dynamic physical coupling is combined, the actual machining state of the ball-end milling cutter is accurately simulated, the simulation precision and authenticity are improved, and the high-precision requirement of aerospace parts can be met. Meanwhile, through modular design, a complex curved surface tool path is accurately simulated, and through adjustment of evaluation parameter threshold values, the method can be expanded to surface topography prediction of various difficult-to-machine materials, process parameters are rapidly iterated, and the process optimization period is greatly shortened.