55 results about "Systems theory" patented technology

The invention discloses an optical performance-oriented key geometric error identification method for a large aspheric element multi-axis numerical control machine tool. According to the method, a tool setting point is used as a key node to establish a surface shape error model, geometric errors and surface shape errors of a machine tool are accurately associated, multi-error coupling analysis is simplified, and targeted compensation, online monitoring and real-time correction are achieved. On the basis, a machine tool space error model is constructed in combination with a multi-body system theory, a mapping relation between a surface shape error and wavefront aberration is established by utilizing a ray tracing theory, the wavefront aberration is fitted through a Zernike polynomial, and the contribution degree of each geometric error to the aberration is quantified by means of principal component analysis, so that a key geometric error is identified and compensation is implemented. According to the method, the machining error and the final optical performance are directly associated, and the machining precision and the performance quality of the large aspheric element can be improved. The method is suitable for the manufacturing field of large complex optical elements machined by a multi-axis numerical control machine tool.

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 predetermined time expansion state observer generation method and an observation error convergence method, which are applied to the technical field of aerospace, and comprise the following steps: constructing a controller for a second-order nonlinear system according to an all-drive system theory; converting the second-order nonlinear system according to the controller to obtain an all-drive system based on the main controller; wherein the main controller is a controller for controlling the all-wheel-drive system; designing a predetermined time expansion state observer based on the total disturbance and observation error of the all-wheel-drive system; wherein the predetermined time expansion state observer is an observer which limits the upper bound of convergence time of the observer based on a predetermined time parameter. Compared with most existing extended state observers which can only ensure asymptotic convergence and finite time or fixed time convergence, the observer generated based on the predetermined time extended state observer generation method can ensure that the observation error converges within the predetermined time, so that the converged predetermined time is more stable; and the user experience is improved.

The invention discloses a distributed consistency control method suitable for a multi-unmanned trolley system, and designs a control strategy based on a preset time disturbance observer for the problem that communication topology dynamic change and external disturbance interference are difficult to consider at the same time. In system modeling, each trolley is modeled as a second-order nonlinear system, and external disturbance is generated by a heterogeneous external source system. In the design of the controller, a disturbance observer with a preset time convergence characteristic is introduced, and strict convergence of disturbance estimation errors can be realized in finite time. Control input is fused with three parts of neighbor state difference feedback, leader tracking and disturbance compensation, and it is ensured that the system can still achieve a consistent target in a complex dynamic environment. By constructing a combined Lyapunov function and combining a Markov jump system theory, it is proved that a system state error and a disturbance estimation error converge to zero after a preset time. A simulation result verifies the effectiveness and stability of the method under communication switching and non-ideal disturbance interference.

The invention provides a reinforced concrete support design optimization method and system, and the method comprises the steps: carrying out the multi-dimensional analysis of a load expected to be borne by a building, so as to determine a key stress point in a building structure; on the basis of the key stress points, behavior characteristics of the reinforced concrete material under different building structure layers are simulated, and a multi-layer mechanical model is generated; establishing an intelligent decision process by using a genetic algorithm, particle swarm optimization and a reinforcement learning framework on the basis of data obtained by a multi-level mechanical model and monitoring a building structure so as to generate a reinforced concrete support design scheme; and establishing a comprehensive evaluation system by using a Bayesian network and a grey system theory, quantifying the reinforced concrete support design scheme to obtain feedback information, optimizing the reinforced concrete support design scheme by using the feedback information, and generating an optimized reinforced concrete support design scheme. The scientificity and rationality of the design scheme of the reinforced concrete support are improved.

The invention provides a smoke analysis AI system based on a quantum field theory tensor network, the algorithm is provided with a multi-camera fusion module, a quantum state coding module, a dynamic Hamiltonian construction module, a tensor network evolution module and a chaos detection module, and by monitoring a smoke diffusion Lyapunov index in real time, the sudden change from smoke to fire behavior is warned in advance; according to the algorithm, a quantum multi-body system theory and computer vision are combined, smoke diffusion is modeled into a phase change process of a quantum spin chain, high-dimensional quantum state information is compressed by using a tensor network, and a mutation behavior of a chaotic edge is detected in combination with a Lyapunov exponent. The method solves the problem of early fire hazard false alarm under the complex operation condition of the transformer substation in a traditional method, improves the initiative and accuracy of monitoring and prevention of the extremely early smoke fire hazard of the transformer substation, provides more reliable guarantee for the safe operation of the transformer substation, and has wide application prospects and important practical significance.

The embodiment of the invention discloses a GBIC-based multi-electric aircraft starting power generation system DBN model lightweight method, and relates to the technical field of reliability design and modeling of an equipment complex system.The GBIC-based multi-electric aircraft starting power generation system DBN model lightweight method comprises the steps that a DBN model of a starting power generation system is established, nodes in the DBN model correspond to key components and working states of the starting power generation system, and the nodes in the DBN model correspond to the key components and the working states of the starting power generation system; directed edges in the DBN model are used for describing the mutual relation between the nodes, a redundant structure in the DBN model is recognized, lightweight processing is carried out, and the fault state of the starting power generation system is recognized through the DBN model subjected to the lightweight processing. Aiming at the problems of complex reasoning calculation and low efficiency in the use process of the dynamic Bayesian analysis method of the equipment complex polymorphic system, the grey system theory and the Bayesian information criterion are introduced, the lightweight of the dynamic Bayesian network of the complex polymorphic system is realized, the redundant structure is eliminated, and the accuracy of the result is ensured.

The invention provides an urban carbon emission optimization model algorithm aiming at the urgent demand of green and low-carbon transformation of an urban industrial structure. According to the algorithm, a multi-agent model comprising a public management mechanism agent and an enterprise agent is constructed based on a complex adaptive system theory. The core lies in that an evolution path of urban carbon emission and industrial upgrading is dynamically deduced by simulating an adjacent learning effect between enterprises and a regulation and control mechanism of a public management mechanism based on a pre-decision. The enterprise agent decides the output value speed increase and the carbon emission intensity change rate according to the self state, the surrounding enterprise state and the public management mechanism predetermined target; and the government agent formulates and adjusts industry and emission reduction policies according to the overall goal of the city and enterprise pre-decision. According to the invention, through dynamic coupling of the microscopic enterprise decision and the macroscopic city system, low-cost and high-efficiency simulation and optimization of the city carbon emission path under different target scenes are realized, and a scientific quantitative analysis tool is provided for city carbon emission.

The invention relates to a method for determining a theoretical deflection angle of a single umbrella in a group umbrella system, which comprises the following steps of: calculating and analyzing the theoretical deflection angle of the axis of the single umbrella relative to the central line of a group umbrella bundle in the group umbrella system consisting of circumferentially and uniformly distributed and adjacently tangent single umbrellas according to a primary design principle of maximizing the resistance performance and the stability performance of the group umbrella system; the method can quantitatively reflect the influence of the number of umbrellas in the group umbrella system and the related design parameters of the single umbrellas on the theoretical deflection angle of the single umbrellas and the comprehensive performance of the system, and can preliminarily lay foundation for formation and gradual improvement of the theoretical design method of the group umbrella system.

The invention relates to the field of report generation, and discloses an intelligent generator for full-automatic generation of a production report, which is used for fusing a complex system theory and an information security technology and constructing a non-machine learning driven full-automatic generation system of the production report. Performing fractal cutting on original time series data and encrypting and storing the original time series data to edge nodes; constructing a cellular automaton network; generating a cellular state transition rule according to network traffic and access logs; establishing a system dynamics model; and generating a production report in combination with the access permission and the risk confidence. According to the intelligent generation method for full-automatic generation of the production report, the data processing efficiency and safety are improved, the flexibility of access control and the accuracy of risk assessment are enhanced, and powerful technical support is provided for intelligent management of a modern production system.

The invention provides a finite time position tracking control method of a magnetic suspension system with output constraint, which comprises the following steps of: establishing a mathematical model of the magnetic suspension system, determining an error system, and obtaining upper and lower bounds of suspension air gap constraint and system state information; designing a fractional obstacle Lyapunov function by considering upper and lower bounds of suspension air gap constraint, and solving partial derivative to obtain a constraint processing mechanism function; designing a continuous finite time state feedback constraint controller by using system state information based on a constraint processing mechanism function by applying a finite time control technology and a homogeneous system theory; and based on the constraint processing mechanism function, when the system speed state information cannot be measured, designing a non-smooth filter, and designing a finite time output feedback controller by using the filter output information and the system output information. Unified finite time control of the magnetic suspension system under symmetric / asymmetric suspension gap constraint is realized, and the method has high tracking precision, strong anti-interference robustness and engineering practicability.

The invention discloses a stay cable analysis digital twinborn model construction method, which comprises the following steps of: constructing a stay cable space three-dimensional vibration motion partial differential equation based on a material linear elasticity hypothesis and a double-coordinate system theory, and setting boundary conditions and initial conditions of the stay cable space three-dimensional vibration motion partial differential equation; a Galerkin multi-mode truncation method is adopted to obtain a displacement function of superposition of an end displacement excitation item and a vibration mode item; deducing a three-way strong coupling ordinary differential equation set through symbolic operation, and solving by using a step-variable Runge-Kutta algorithm to obtain a stay cable analysis digital twin model with stable parameters; structural response data are obtained through the sensor network and calculated, and when the obtained cable force, flexural rigidity and damping ratio exceed threshold values, a model updating mechanism is triggered for updating; and then the updated stay cable analysis digital twin model is obtained. According to the method, the real dynamic characteristics of the stay cable can be restored with high precision, and effective support is provided for structural state evaluation and early warning.

The invention relates to the technical field of building construction, and discloses a fabricated pipeline construction progress prediction method based on deep learning. According to the method, multi-source engineering data, including structured construction parameters, unstructured construction logs and the like, of fabricated pipeline construction are collected, and a standardized feature set is generated through preprocessing; extracting feature vectors of each data source by using a depth feature extraction model, constructing a construction progress knowledge base, generating global fusion features through multi-source feature fusion, and predicting a construction process sequence and progress by combining knowledge base data and by means of a time sequence prediction algorithm. Meanwhile, analyzing sensor data by adopting wavelet transform, and correcting a process sequence by utilizing a grey system theory when the sensor data is abnormal; and constructing a construction specification knowledge graph, and verifying the consistency of the process sequence and the specification by using a graph embedding algorithm. The method can accurately predict the construction progress, responds to abnormal conditions in real time, ensures that the construction accords with specifications, and improves the construction management level of the fabricated pipeline.

The invention discloses an equivalent calculation method for quantitative evaluation of the residual life of an existing pavement and a storage medium, and the method comprises the steps: firstly, determining a bearing threshold value of a lossless pavement structure based on a dual-modulus elastic layered system theory; secondly, a reduction coefficient of typical diseases to the structural strength is quantified through finite element simulation; thirdly, establishing a mathematical relationship model of the maximum effective stress and the fatigue life; then, actual disease information is accurately obtained by combining nondestructive testing technologies such as ground penetrating radar and three-dimensional laser scanning; then, correcting the maximum allowable equivalent stress in a lossless state according to the disease data; and finally, fusing the theoretical model and the traffic load history, and calculating the remaining service life. According to the method, three-dimensional and quantitative analysis on the influence of the diseases is realized through a mechanical response equivalence principle, the accuracy, objectivity and engineering application efficiency of residual life evaluation are remarkably improved, and a reliable basis is provided for scientifically making a road maintenance decision.

The invention discloses a large-aperture space telescope flexible load vibration suppression method and device based on preset performance control. The method comprises the following steps: establishing a three-inertia system dynamic model for describing a large-aperture space telescope; converting the three-inertia system dynamic model into a three-inertia system state space equation; converting the three-inertia system state-space equation into a three-inertia generalized full-drive system model by adopting a full-drive system theory; adopting a pole assignment method to improve the amplitude margin and the phase angle margin of the three-inertia generalized full-drive system model; fitting response output of the three-inertia generalized full-drive system model after the amplitude margin and the phase angle margin are improved by adopting a BP neural network, and designing a preset performance function of a three-inertia system; the preset performance function is used as a tracking error boundary of the three-inertia system, proportional differential is used as a nonlinear controller for preset performance control, closed-loop control over the three-inertia system is achieved, and then vibration suppression of the flexible load of the large-aperture space telescope is indirectly achieved.

The application provides a kind of invariant set control and guaranteed cost control method and system of multiple equilibrium point switching generalized system, belongs to the field of hybrid system control. In order to solve the problem that traditional switching generalized system is difficult to directly apply to different equilibrium points of actual engineering application, and the traditional guaranteed cost control method is invalid due to the switching characteristics of multiple equilibrium points. The application considers the more general case of multiple equilibrium points, and studies the invariant set control strategy method of multiple equilibrium point switching generalized system, so as to realize more accurate control of the behavior of complex switching system. On the other hand, for the guaranteed cost control problem in practical application, the application designs a multiple equilibrium point switching generalized guaranteed cost controller to ensure that the cost of the system can have an upper bound. This innovation provides strong technical support for the practical application of switching generalized system theory.

The present invention discloses a pulse control method for a flame nozzle of a wide and thick plate hot working furnace, comprising the following steps: based on the distributed parameter system theory and based on the temperature transfer process of the subspace in the furnace of the wide and thick plate hot working furnace, constructing a distributed parameter system based on the time-space transformation of the temperature in the furnace of the wide and thick plate hot working furnace; combining the wide and thick plate hot working heating curve, obtaining a distributed parameter error model between the temperature change function of each subspace of the wide and thick plate hot working furnace and the target temperature; constructing a pulse control scheme for the flame nozzle of the wide and thick plate hot working furnace to achieve stable control of the distributed parameter system error model; based on Lyapunov stability theory, solving and obtaining sufficient conditions for achieving asymptotic stable control of the distributed parameter system error model; performing numerical simulation, and solving the pulse control parameters based on the obtained sufficient conditions.

The invention provides a gantry dual-drive control method and system based on an all-drive system theory, and relates to the technical field of control, and the method comprises the steps: obtaining system state data, reference input data and control performance index data of an all-drive system model; based on the control performance index data, determining control parameters by solving a Silverster matrix equation; and executing control operation based on the control parameters, the all-wheel-drive system model, the system state data and the reference input data to obtain target control data for controlling a target system so as to improve the control performance and the motion precision of the control system under various uncertainties and disturbance effects.

The invention provides a power system load frequency control method based on a high-order all-drive system theory, and relates to the technical field of power system load frequency control. Comprising the following steps: collecting physical parameters of a power system, and establishing a power system state space equation mathematical model according to the physical parameters of the power system; verifying the controllability of the power system according to the state-space equation model, and obtaining a system controllability matrix; based on the controllability matrix, converting a state-space equation model of the power system into a high-order all-drive system model; providing a power system state observer and a spoofing attack observer according to the high-order all-drive system model; and providing a power system load frequency controller based on a high-order all-drive system theory according to the high-order all-drive system model and the observer. According to the method, the linear dynamic model of the electric power system more conforming to actual working conditions is constructed. Based on the high-order all-drive method, the design complexity of the power system controller can be reduced, and the stability of the power system is ensured.

The present invention discloses a deep foundation pit construction safety risk pre-control method and system based on cybernetics and dual-system theory. Based on sensor expansion, the system performs risk identification and risk monitoring on deep foundation pit construction safety, clarifies risk points, and performs pre-control judgment and pre-control rating on the identified and monitored deep foundation pit construction safety risks. Finally, based on actuators and DST expansion, the system performs pre-control decision-making and pre-control execution on the pre-control risks of step 2. The present invention first systematically identifies and monitors root risks and state risks, which helps to timely discover and prevent potential risks, thereby improving the operational stability of the project. By monitoring various state risks, the system can timely identify and respond to potential safety hazards, ensuring the safety of construction personnel and the surrounding environment, and also playing a positive role in project cost control.

The invention relates to the field of zero-carbon energy, and discloses a zero-carbon integrated energy system and an optimization method of the zero-carbon integrated energy system.The optimization method of the zero-carbon integrated energy system comprises the steps that a historical dependency relationship is accurately captured by constructing a multi-scale prediction model fusing fractional calculus and a grey system theory; decomposing the system into multi-scale optimization sub-problems by adopting wavelet transform, and establishing a collaborative optimization objective function system; performing global optimization solution by using a fractional gradient descent algorithm to ensure stable operation of the system; and a complete closed-loop management system is formed through an efficiency verification system and a parameter adaptive correction mechanism. According to the invention, autonomous optimization and continuous improvement of the system are realized, and the operation efficiency and stability of the zero-carbon comprehensive energy system are improved.

The invention, which belongs to the technical field of image processing and artificial intelligence, discloses a multi-level image intelligent damage assessment and prediction system for highway infrastructures, comprising a multi-scale feature level extraction module, a damage type intelligent identification module, a time sequence damage evolution prediction module and an adaptive maintenance decision module. Multi-scale geometric invariance features are extracted through topological manifold learning, robust damage identification is realized based on a Lie group transformation theory, a damage evolution prediction model is established by using a power system theory, a maintenance decision is optimized by using a game theory framework, and a closed-loop adaptive optimization system is formed. According to the invention, comprehensive assessment, accurate prediction and intelligent decision-making of damage are realized, and the intelligent level and preventive maintenance capability of health monitoring of highway infrastructures are significantly improved.

The present application relates to the field of desulfurization control, and discloses a desulfurization intelligent control system based on a machine learning algorithm, comprising a collection module, an analysis module, a prediction module and a decision module, wherein a multi-physical field parameter in a desulfurization tower is collected through multi-sensor fusion to generate a multi-physical field state tensor; the dynamics behavior of an intelligent feature extraction analysis system is utilized to generate a characteristic parameter tensor; a state space model is constructed based on a dynamic system theory to generate a multi-target intelligent prediction result. The present application establishes a closed-loop control system from holographic perception to intelligent decision-making, solves the problems of perception limitations and single targets existing in traditional desulfurization systems, and improves desulfurization efficiency and reduces energy consumption.

The invention relates to the technical field of deformation monitoring, in particular to a space-time self-learning early warning system for foundation settlement risks in the highway construction process, and the system comprises an information processing module, a weight construction module, a tensor analysis module, a risk calculation module and an early warning release module. According to the method, the distribution density is evaluated through the spatial position vector and the Gaussian kernel function, the coverage weight is generated, local deviation caused by uneven network distribution is eliminated, balanced expression of regional settlement characteristics is ensured, a lagging interval is deduced through the soil consolidation theory, and deformation direction consistency screening is executed. Environmental noise is eliminated to lock effective cumulative settlement caused by a specific process, a prediction model with self-adaptive capability is constructed in combination with a grey system theory, the dynamic growth rate of a settlement cumulative value is tracked in real time, conversion from static management and control to dynamic trend early warning is realized, the sensitivity of foundation instability precursor capture is improved, and the stability of a foundation is improved. And a scientific basis is provided for construction procedure adjustment.

The invention provides a false data injection attack method for a multi-elastic-joint robot system, and belongs to the technical field of multi-agent network attacks based on computer data processing. Firstly, a multi-elastic joint robot nonlinear dynamic mathematical model is constructed and converted into a high-order all-drive quasi-linear multi-robot system dynamic model based on an all-drive system theory; and then designing a distributed consistency controller containing a linearization item and a neighbor error related item, establishing closed-loop global dynamics and deducing a state analytical solution in combination with equivalent conversion of the model. Designing a false data injection attack strategy, and constructing an attacked system dynamic model; a closed-loop terminal state error in the presence or absence of attacks is taken as a target function, an attack optimization problem is converted into a combinatorial optimization problem, a greedy algorithm is designed by applying a sub-module optimization theory, and a suboptimal solution in polynomial time is obtained and is taken as an optimal scheme of false data injection attacks of the system. According to the method, the applicability of the complex nonlinear multi-agent system is improved, and the calculation complexity is reduced.

The present invention provides a finite-time cooperative tracking control method based on an adaptive RBF neural network, belonging to the technical field of networked multi-agent control strategies. The method comprises the following steps: constructing a dynamic mathematical model of a high-order all-wheel drive uncertain nonlinear multi-agent system including a virtual leader and followers; constructing an error dynamic system including an adaptive RBF neural network estimation compensation term according to a cooperative tracking control target and uncertain terms in the system; designing a finite-time integral sliding mode surface based on the theory of a high-order all-wheel drive system; designing linearization parameters of the finite-time integral sliding mode surface based on a pole configuration method according to the dynamic performance of the system and the finite-time convergence requirement, and obtaining a finite-time integral sliding mode controller; designing an adaptive update law for the RBF neural network weight and the upper bound of the approximate error, introducing the network weight and the approximate error into the finite-time integral sliding mode controller, and realizing finite-time cooperative tracking control.

This invention discloses a multi-level image-based intelligent damage assessment and prediction system for highway infrastructure, belonging to the fields of image processing and artificial intelligence technology. The system includes a multi-scale feature extraction module, a damage type intelligent identification module, a temporal damage evolution prediction module, and an adaptive maintenance decision module. It extracts multi-scale geometrically invariant features through topological manifold learning, achieves robust damage identification based on Lie group transformation theory, establishes a damage evolution prediction model using dynamical system theory, optimizes maintenance decisions using a game theory framework, and forms a closed-loop adaptive optimization system. This invention achieves comprehensive damage assessment, accurate prediction, and intelligent decision-making, significantly improving the intelligent level of highway infrastructure health monitoring and preventive maintenance capabilities.

The invention discloses a pulse control method for a flame nozzle of a wide and thick plate hot working furnace, which comprises the following steps: on the basis of a distributed parameter system theory, constructing a distributed parameter system based on in-furnace temperature space-time transformation of the wide and thick plate hot working furnace based on an in-furnace subspace temperature transfer process of the wide and thick plate hot working furnace; in combination with a wide and thick plate hot working heating curve, a distribution parameter error model between the temperature change function of each subspace of the wide and thick plate hot working furnace and the target temperature is obtained; constructing a wide and thick plate hot working furnace flame nozzle pulse control scheme to realize stable control of a distributed parameter system error model; on the basis of the Lyapunov stability theory, solving to obtain sufficient conditions for realizing the asymptotic stability control of the distributed parameter system error model; numerical simulation is carried out, and pulse control parameters are solved in combination with the obtained sufficient conditions.

This invention discloses a fault-tolerant game-theoretic control method for quadrotor helicopter swarm formations. It designs a quadrotor helicopter swarm formation mode, constructs an information interaction topology graph of the helicopter swarm, and establishes fault models for the rotating and translational subsystems. An adaptive fault diagnosis observer is designed to estimate actuator faults. Using the translational subsystem as the leader and the rotating subsystem as the follower, an inner and outer loop fault-tolerant game framework based on an incentive-based Steinberg differential graph game is constructed. Through full-drive system theory, fault-tolerant game controllers for the rotating and translational subsystems are designed separately, and a linear closed-loop system of the quadrotor helicopters is constructed. Performance indices for the rotating and translational subsystems are designed, and an optimal fault-tolerant game-theoretic control strategy is designed based on differential game theory. This invention enables stable formation control of quadrotor helicopter swarms, effectively improves the fault tolerance of helicopter swarms, and enhances the stability and intelligence of the helicopter swarm system.

The invention discloses a process modeling and fault detection method and system for machine learning guided by a control theory, and the method comprises the steps: obtaining historical operation data of an industrial process, and extracting an operation dynamic state in the historical operation data through a neural network, approximation is carried out on an observer gain part of the kernel representation model to realize complete kernel representation model design; building an adjoint system of the nuclear representation model by using a Hamilton system theory, and performing guided optimization on the learning and training process of the nuclear representation model and the adjoint system thereof by using nondestructive constraint to realize the design of a regularized nuclear representation model; and a detection statistic is constructed by using the residual error of the kernel representation model and the regularization kernel representation model, and the maximum value of the detection statistic during normal operation of the industrial process is set as a fault detection threshold, so that fault detection of the industrial process is realized, and the safety of the industrial process is guaranteed. In addition, by designing an online fine tuning strategy, generalization and applicability of the model are improved, and the method has high practical value.