16 results about "Observability" patented technology

The invention relates to the technical field of artificial intelligence, and provides an observable container cluster fault operation and maintenance method, device, equipment and medium, on one hand, a micro-service decoupling architecture is adopted to construct an observable container cluster fault operation and maintenance system, decoupling among functional layers can be realized, the functional layers cooperatively execute tasks, and the operation efficiency is improved; the expansibility and the task execution efficiency of the system are improved; on one hand, feature construction is carried out according to a correlation analysis result between observability index data, the internal state of the system can be predicted through external visible information, the constructed features have obvious correlation, and the accuracy of subsequent fault prediction can be improved; and on the other hand, fault prediction is carried out according to the dynamic threshold value and the fault prediction model with the time sequence, the self-healing strategy is automatically executed, more accurate fault prediction can be carried out in combination with an artificial intelligence means and a dynamic threshold value mechanism, and meanwhile, the automatic self-healing strategy is provided, so that the system stability is improved.

The invention provides an atmospheric data analysis method based on observability analysis, belongs to the field of aircraft navigation, guidance and control, and aims at solving the problem that the precision of traditional atmospheric data measurement is reduced under the condition of a complex flow field or a sensor fault. In the prior art, due to lack of strict theoretical support, estimation errors and convergence problems may occur in a transonic stage. According to the method, an atmospheric data state space model is established through flight dynamics, and atmospheric data estimation is carried out by adopting an extended Kalman filter based on the model; in the estimation process, observability analysis is executed to verify the reliability of an estimation result; and finally, optimizing the system model according to an analysis result. According to the method, the dependence of an existing method on an empirical model is reduced, the convergence and the stability of an estimation result are ensured, and the adaptability and the robustness of a system in a complex environment are remarkably improved.

The present disclosure provides a kind of inertial motion measurement double-axis rotation excitation enhancement method and system based on observability analysis.First, the system state space model of introducing double-axis rotation mechanism is constructed, the normalized observability analytical expression of sensor error under zero speed condition is derived based on Gramian matrix eigenvalue decomposition;Accordingly, the attitude condition of error observability maximization is determined, and the optimal rotation excitation strategy including a series of optimal observation positions is set;In the process of inertial navigation positioning assisted by zero speed update ZUPT, in non-zero speed interval, based on the optimal rotation excitation strategy, the IMU is sequentially guided to each optimal observation position by controlling double-axis rotation mechanism, to solve the problem of low error state observability caused by single carrier attitude in zero speed correction.The present application can realize full state high-precision estimation of three-axis accelerometer bias and gyroscope zero bias, effectively improve the precision and robustness of autonomous motion measurement in complex environment.

This invention relates to an adaptive, lightweight, online calibration method for extrinsic parameters of a vehicle-mounted integrated navigation system. The method includes: Step 1, augmenting the navigation system extrinsic parameters to a Kalman filter to construct a system error model; Step 2, obtaining the observability analysis results of the extrinsic parameters through observability matrix analysis; Step 3, grouping the extrinsic parameters according to the required maneuvering mode to achieve piecewise decoupled calibration and Kalman filter dimensionality reduction; Step 4, establishing corresponding state equations and measurement equations through the system error model, and then constructing an adaptive feedback factor; Step 5, using the adaptive feedback factor to reflect the accuracy of error estimation, determining whether to provide feedback to the extrinsic parameters and the magnitude of the feedback. This invention solves the technical problems in the prior art, such as the signal being easily affected by external factors, leading to reduced navigation accuracy, and the system extrinsic parameter calibration relying on the vehicle performing specific continuous maneuvers.

The invention relates to the field of dynamic control of a power system, discloses a multi-agent reinforcement learning-based multi-machine wide-area damping cooperative control system and method, and aims to solve the problems that the low-frequency oscillation risk of a power grid is aggravated under the high proportion of new energy, the cooperative optimization of a traditional controller is insufficient and the like. An installation site and a feedback signal are determined in combination with participation factor analysis and observability analysis, and a multi-agent depth deterministic strategy gradient algorithm is adopted to optimize parameters of a doubly-fed fan and a synchronous machine controller; through training of the steps of initialization, interaction, judgment, decision making, learning and the like, the intelligent agent can quickly generate an optimal control strategy. The method can effectively improve system damping, rapidly suppress power fluctuation and guarantee stable operation of a power grid.

The invention discloses a micro-grid linearization model order reduction method, system, equipment and medium, and belongs to the field of micro-grid stability analysis and control. Models of all elements in a micro-grid are obtained, a nonlinear differential algebraic equation set is established, a stable equilibrium point of the nonlinear differential algebraic equation set is solved, linearization processing is carried out, and a linearized differential algebraic equation set is obtained; solving a rightmost characteristic value by adopting a characteristic value algorithm, if the system is not stable, performing leftward translation transformation on the system to stabilize the system, generating a Lyapunov equation according to the translated system, solving a controllable and observable Gramb matrix by adopting a Krylov subspace algorithm, and performing order reduction on the translated system by adopting a balanced truncation method to obtain a stable system; according to the method, an existing balance truncation method is improved, the precision, the stability, the controllability and the observability of a system after order reduction are guaranteed, meanwhile, the Grubrum matrix can be efficiently solved, and the method is made to be suitable for an unstable system.

The invention relates to the field of power system simulation, in particular to an inertia active support demand evaluation method for a new energy power system, which comprises the following steps: S1, constructing a power grid model; s2, calculating a system impedance matrix and a short-circuit ratio; s3, constructing an observability Gramer matrix; s4, guiding an analytical solution of the Lyapunov equation; and S5, evaluating the inertia active support demand considering the space difference. The method breaks through the space-time decoupling limitation of a traditional method, forms a multi-scale analysis system covering electromagnetic transient-electromechanical transient-medium and long term dynamic, is especially suitable for a complex power grid scene containing high-proportion new energy access, and provides full-process quantitative decision support for system planning and operation control.

This invention belongs to the aerospace field and relates to a method for estimating the inter-satellite arm length of three satellites based on capacitive Kalman filtering. The method involves: for the primary satellite and two secondary satellites in a three-satellite gravitational wave detection formation, employing formation-specific nonlinear dynamics modeling, estimating the relative states between the primary and secondary satellites based on capacitive Kalman filtering, and thus obtaining the arm length information between the satellites to achieve three-satellite inter-satellite arm length estimation. This invention effectively suppresses the numerical divergence problem caused by matrix ill-conditioning in traditional Kalman filtering by manipulating the square root of the covariance matrix instead of the original matrix. The capacitive rule achieves third-order accuracy approximation while requiring only a linearly increasing number of sampling points, significantly outperforming computationally expensive methods such as particle filtering. This method is particularly suitable for scenarios with strong nonlinearity and weak observability in satellite dynamics models.

The invention discloses a rotary table closed-loop hierarchical control method based on multi-mode sensing real-time synchronization, and belongs to the field of intelligent control, and the control method specifically comprises the following steps: I, synchronously collecting various operation data of a rotary table system through various sensors, and carrying out the deterministic transmission and alignment of the various operation data; iI, after time alignment, preprocessing each operation data and extracting each feature, and dynamically evaluating the credibility of each operation data under the current working condition of the turntable system; iII, based on an evaluation result, adaptively fusing and reconstructing various operation data, constructing a prediction compensation model between the sensing layer and the control layer, and predicting and generating a reverse compensation amount; the observability and precision of the operation state of the rotary table are remarkably improved, sensing and execution information complementation is achieved, the continuous operation capacity of the system under the hidden fault condition is effectively improved, the oscillation and lag problems are reduced, and the method can rapidly adapt to load change, working condition switching and long-term performance drifting.

The invention belongs to the field of aerospace, and relates to a three-satellite inter-satellite arm length estimation method based on cubature Kalman filtering, which comprises the following steps of: aiming at a master satellite and two slave satellites of a three-satellite gravitational wave detection formation, adopting formation relative nonlinear dynamics modeling, estimating a relative state between the master satellite and the slave satellites based on cubature Kalman filtering, and calculating the three-satellite inter-satellite arm length. And thus, arm length information between satellites is obtained, and inter-satellite arm length estimation of the three satellites is realized. According to the method, the problem of numerical divergence caused by matrix morbidity in traditional Kalman filtering is effectively suppressed through operation on the square root of the covariance matrix instead of an original matrix, the volume rule only needs the number of linearly-increasing sampling points while achieving third-order precision approximation, the method is remarkably superior to particle filtering and other high-calculation-cost methods, and the method is suitable for large-scale popularization and application. The method is especially suitable for scenes with strong nonlinearity and weak observability in a satellite dynamics model.

The application discloses a power system dynamic observability calculation method based on a data-driven Koopman operator, belongs to the technical field of power system dynamics and observability analysis, and comprises the following steps: establishing a power system dynamic observability analysis model based on system state variables and measured variables; converting the established dynamic state observability model into an augmented Koopman canonical coordinate system; constructing an observation matrix of the system in the augmented Koopman canonical coordinate system; and calculating the dynamic observability degree of the power system by using a matrix index in the Koopman space. The method of the application uses the data-driven Koopman operator, overcomes the inaccuracy of the model while considering the nonlinear characteristics of the power system dynamic model, effectively solves the low-precision problem of the traditional linear method and the problem of excessive calculation amount of the nonlinear method, and can realize efficient and rapid analysis and calculation of the dynamic observability of the power system.

The application discloses a state observer design method of a space tether system, a rigid body tether with a tensioning effect is used as the tether; firstly, a dynamics equation of the whole tether satellite system is established by using a Lagrange energy method; then, a criterion for the observability of the system is derived by using a linearization method when the velocity term state of the system is unobservable; and a nonlinear system state observer satisfying a linear observation convergence condition is further designed under the observability condition; the obtained observer can guarantee local asymptotic stability around a linearization point. The application can guarantee that the observation error converges in a dynamically changing neighborhood of an equilibrium point, and the application can be designed separately from a controller to realize the stability of the system.

The present application relates to the technical field of atmospheric monitoring, and more particularly to a system and method for dynamic monitoring of sources and sinks of atmospheric pollutants and greenhouse gases. The system includes a unified carbon pollution monitoring network, a multi-source data adaptive fusion module, a source / sink dynamic differentiation module, a carbon pollution coupling analysis module, an emission source driving force bidirectional decoupling and tracing module, and a closed-loop verification and adaptive correction module. The present application calculates the atmospheric transmission time lag and biological process time lag based on double time lag parameters, realizes real-time discrimination of source / sink state in combination with the net flux dynamic balance criterion, quantifies the carbon pollution coupling effect through multi-factor dynamic modeling, causal reasoning and dynamic weight distribution, introduces the observability theory to construct a nonlinear dynamic system model, designs a state observer to invert the unobservable emission source driving force parameters, decouples them into internal driving force and external driving force, completes bidirectional attribution verification, and finally performs adaptive correction on the parameters of the multi-module through closed-loop feedback.

The invention relates to the technical field of water treatment intelligent control, and discloses a water plant optimization control method based on deep reinforcement learning, which solves the problems of data quality and partial observability through multi-source data fusion and data enhancement under physical constraints. A hybrid modeling method combining a mechanism model and a data-driven network is utilized, a composite error control mechanism is introduced, and a high-confidence-coefficient environment model is constructed; a progressive training normal form combining off-line conservative learning and on-line triple safety constraint fine tuning is adopted to ensure the safety and high efficiency of the learning process; reliable fusion of the model, an existing industrial system and operators is realized through hierarchical instruction adaptation, man-machine cooperation three-level decision and full-period risk assessment; finally, through a standardized interface and four-stage progressive deployment, the optimization strategy is safely applied to actual production, and the problems that an existing method is insufficient in simulator fidelity, poor in data quality, difficult in industrial fusion and high in safety risk are solved.

A method according to one approach includes: receiving observability metrics associated with a system, and quantifying the observability metrics by determining an entropy value associated with the respective observability metrics. The method further includes, comparing mutual information measures between pairs of the observability metrics in a subset of the observability metrics having respective entropy values that are in a first predetermined range. In response to determining differences between the mutual information measures of a given one of the pairs of the observability metrics are outside a second predetermined range, one of the observability metrics in the given pair is selected to maintain. Moreover, the remaining one of the observability metrics in the given pair is discarded.

This invention relates to the design of a preset-time fault-tolerant control method for a two-dimensional heterogeneous intelligent connected vehicle (ICV) platoon. The invention constructs a preset-time fault-tolerant control method for a two-dimensional heterogeneous ICV platoon, including longitudinal / lateral two-dimensional dynamic models of the platoon under actuator failure and external interference, a fault-based two-dimensional dynamic coupling vehicle spacing error strategy, a distributed preset-time collaborative fault-tolerant control strategy for the platoon, and feasibility conditions and criteria. This invention addresses the heterogeneous ICV platoon system affected by actuator failure and external interference by constructing longitudinal and lateral two-dimensional dynamic models. Next, it combines multiple theories to build an information flow topology model and constructs a fault-based two-dimensional dynamic coupling vehicle spacing error strategy. Based on control theory, a distributed preset-time collaborative fault-tolerant control strategy is formed. Finally, an internal stability criterion is established to analyze and synthesize the platoon's stability, controllability, and observability. The beneficial effect of this invention is that it solves the preset-time fault-tolerant control problem of two-dimensional heterogeneous ICV platoons, improving the safety of ICV platoons.