15 results about "Recursive model" patented technology

The invention relates to multi-dimensional path geometry tracking for autonomous systems and applications. In various examples, a recursive model may be used to efficiently track and / or predict geometries associated with one or more paths in an environment. For example, the disclosed systems and methods may use Kalman filters to track and predict control points corresponding to Bezier curves (e.g., 2D and / or 3D Bezier curves). The Bezier curve may represent a geometry associated with one or more lanes of the travel surface. In some cases, multiple Bezier curves may be used to represent the geometry of the lane, and multiple Kalman filters may be used to track and predict control points for each Bezier curve. For example, edges of a lane may be represented using a first Bezier curve, and control points of the first Bezier curve may be tracked and predicted using multiple Kalman filters (e.g., one Kalman filter is used for each x, y, or z dimension for a 3D Bezier curve).

The invention relates to the technical field of space, in particular to an interference measurement constellation error sensitivity analysis method based on a proxy model. The method comprises the following steps: setting a space interference measurement constellation orbit coordinate system and related parameters; constructing a spacecraft orbit recursion model in the interference measurement constellation; constructing a spatial interference measurement constellation configuration stability index model, a 0-order cutting point of the agent model, a first-order item of the agent model, a second-order item of the agent model and an interference measurement constellation error sensitivity agent model; and giving an initial deviation based on an interference measurement constellation error sensitivity agent model, predicting a configuration stability index evolution condition, and analyzing the interference measurement constellation error sensitivity based on the predicted configuration stability index evolution. By adopting the interference measurement constellation error sensitivity analysis method based on the proxy model, the calculation precision and the calculation efficiency of error sensitivity analysis are effectively improved, the calculation mode is simple, the robustness is good, and the order is convenient to expand.

The invention relates to a building heat conduction parameter identification method and device, computer equipment and a storage medium, and relates to the technical field of virtual power plants. The method comprises the following steps: acquiring an analytic recursive model of a second-order equivalent heat conduction parameter model of a target temperature control device; the analytic recursive model comprises a heat conduction parameter to be identified; respectively obtaining a size parameter and a state measurement data set of the building space and a current regulation and control parameter of the virtual power plant; determining a target value range of the heat conduction parameter according to the size parameter and the current regulation and control parameter; and determining a target value of the heat conduction parameter according to the state measurement data set, the analytic recursion model, the target value range and a preset termination condition. By adopting the method, the identification accuracy of the building heat conduction parameters can be improved.

This invention discloses an efficient modeling method for electromagnetic transient simulation of modular multilevel converter battery energy storage systems, belonging to the field of power system simulation technology. The method includes: determining multiple operating modes of a submodule in a modular multilevel converter battery energy storage system, and for each operating mode, writing the state equation of the submodule within one cycle; performing state averaging modeling on multiple state equations using a periodic averaging operator to obtain a state-space averaged circuit simulation model of the submodule; and processing the state-space averaged circuit simulation model using a discrete difference method to obtain a state-space averaged discrete analytical recursive model of the submodule. This technical solution can reflect the high-frequency dynamic characteristics affected by the switching process, simulate the unbalanced operating state of the submodule, and improve the simulation speed.

This invention discloses a dynamic prediction method for tunnel displacement fields based on a physical information neural network, belonging to the field of tunnel engineering technology, and serving real-time deformation prediction during tunnel excavation. The invention first uses historical excavation data to generate timestamped displacement and strain field sequences, and trains a PINN physical inference model and a PLSTM temporal recursive model respectively. As the actual excavation progresses, the overall displacement and strain are rapidly calculated based on the geological parameters of the current step, and then input into the temporal recursive model to predict the displacement field of the next excavation step. Simultaneously, the mean square value and second derivative of each parameter along the excavation step sequence are analyzed to automatically identify floating and unstable parameters, and four sets of composite geological scenarios are constructed accordingly. These scenarios drive the prediction of the corresponding displacement field set output by the pipeline in parallel, forming a displacement envelope. This invention achieves physically reasonable and risk-quantified dynamic prediction of displacement fields during the excavation process, providing a quantitative basis for early warning and support decisions.

The application provides an interval event matching method and system of a content-based publish / subscribe system, through constructing a predicate disjoint set and an optimized partition model thereof, and integrating a learning index of a range query recursive model index in each partition, high-speed, stable and verifiable matching of events to a subscription set under multi-attribute interval predicates is realized; in particular, the traditional memory-intensive matching is reconstructed into a calculation-intensive process mainly based on model reasoning, while correctness and completeness are ensured, average latency is reduced and long tail delay is inhibited, and key modules such as partition and coverage optimization, model training and online reasoning, error boundary control and limited correction, and accurate back matching of residual set subscription are included.

The present application aims to provide a kind of center difference information filtering phase unwrapping method based on deep learning, comprising the following steps: A, CDIF phase unwrapping recursive model and deep neural network are constructed, the deep neural network is based on LANET interferogram semantic segmentation network;B, after interferogram is input into deep neural network processing, the semantic segmentation image based on interferogram fringe distribution is obtained;C, the merging and optimization adjustment of interferogram semantic segmentation area are carried out, and the segmentation area with appropriate size is obtained, to constitute interferogram area segmentation graph D, each segmentation area in interferogram area segmentation graph is unwrapped using CDIF phase unwrapping recursive model;E, the unwrapping result of each segmentation area is merged, and the unwrapping phase of whole interferogram is obtained.The present application can obtain more robust results in interferogram phase unwrapping experiment, and its efficiency can be accepted, can effectively process interferogram phase unwrapping problem.

In various examples, geometries associated with one or more paths in an environment may be efficiently tracked and / or predicted using recursive models. For instance, the disclosed systems and methods may use Kalman filters to track and predict control points corresponding to Bezier curves (e.g., 2D and / or 3D Bezier curves). The Bezier curves may be representative of geometries associated with one or more lanes of a driving surface. In some instances, multiple Bezier curves may be used to represent a geometry of a lane, and multiple Kalman filters may be used to track and predict control points for each Bezier curve. For instance, an edge of the lane may be represented using a first Bezier curve, and control points for the first Bezier curve may be tracked and predicted using multiple Kalman filters (e.g., for a 3D Bezier curve, one Kalman filter for each x, y, or z dimension).

The present application relates to steel pipe bolt loose state prediction technology field, especially in kind based on ultrasonic identification steel pipe bolt loose state prediction method and system. Content includes: obtaining multichannel ultrasonic reflection signal, and projecting to three-dimensional coordinate space, constructing ultrasonic energy attenuation distribution function;Based on ultrasonic energy attenuation distribution function, all multichannel ultrasonic reflection signal is mapped to four-dimensional space-time disturbance tensor field, and decoupling processing is carried out, and the disturbance energy offset rate is obtained;The disturbance energy offset rate is uniformly expressed as state vector, and a nonlinear time series recursive model is constructed to obtain the predicted state vector;Based on the predicted state vector, a double branch evaluation mechanism is introduced, state classification and pretightening force regression prediction are carried out. The traditional steel pipe bolt loose state prediction technology is not accurate for ultrasonic signal processing analysis, the high-dimensional space disturbance characteristics caused by bolt loosening cannot be obtained, which leads to low recognition accuracy and poor stability.

The invention provides an interval event matching method and system of a content-based publishing / subscribing system, and the method comprises the steps: constructing a predicate disjoint set and an optimized partition model thereof, and integrating a learning index of a range query recursive model index in each partition; high-speed, stable and verifiable matching from the event to the subscription set under the multi-attribute interval predicate is realized; in particular, traditional memory intensive matching is reconstructed into a calculation intensive process taking model reasoning as a main part, correctness and completeness are guaranteed, meanwhile, average time delay is reduced, long-tail delay is restrained, partitioning and coverage rate optimization, model training and online reasoning and error boundary control and finite correction are included, and the calculation intensive process is optimized. And key modules such as precise backspacing matching of residual set subscription.

In various examples, geometries associated with one or more paths in an environment can be efficiently tracked and / or predicted using recursive models. For example, the disclosed systems and methods can use Kalman filters to track and predict control points corresponding to Bézier curves (e.g., 2D and / or 3D Bézier curves). The Bézier curves can represent geometries associated with one or more lanes of a road surface. In some cases, multiple Bézier curves can be used to represent a lane geometry, and multiple Kalman filters can be used to track and predict control points for each Bézier curve. For example, a lane edge can be represented using a first Bézier curve, and control points for the first Bézier curve can be tracked and predicted using multiple Kalman filters (e.g.,(for a 3D Bezier curve, a Kalman filter for each x, y or z dimension).

This invention relates to the field of remote sensing technology and discloses a remote sensing estimation method for ecological carrying capacity. The method includes: constructing a standard spatiotemporal benchmark for multi-source data fusion; using surface temperature as a nonlinear correction factor to adjust the gain of nighttime light and calculating a human activity pressure index; calculating the neighborhood thermal potential difference based on the thermodynamic gradient principle and converting it into a thermal radiation inhibition coefficient; combining water deficit and thermal radiation inhibition to correct and normalize potential productivity, obtaining a standardized effective ecological supply index; constructing a time-series recursive model with a missing compensation mechanism to calculate ecological fatigue; and finally, comprehensively calculating the ecological carrying capacity state by integrating the supply index, pressure index, and ecological fatigue. This invention solves the problems of nighttime light saturation overflow, inconsistent supply and demand dimensions, and lack of consideration for historical cumulative effects, achieving a dynamic and refined assessment of regional ecological carrying capacity.

The application discloses a dynamic self-adaptive energy consumption optimization method and device for casting, aiming at the problem that the existing energy consumption management is static and difficult to be optimized online under the constraints of process, safety, beat and maximum demand due to the multi-process coupling and working condition fluctuation of casting, the device set and metering, state acquisition and instruction issuing interface are constructed, the time sequence data is synchronously acquired and the abnormality is marked, the process coupling relation graph of three types of relations is established and the edge weight is dynamically updated, the device state recursive model is constructed, the error boundary is formed by conformal prediction to form the risk margin constraint, the conditional diffusion control instruction generation model is trained, the output is constrained by the behavior support domain, the executable control instruction sequence is generated by combining the control barrier function filtering and multi-step constraint checking, and the execution and rewriting update are issued. The application is used for the full-process collaborative energy consumption optimization control of casting production, reduces the comprehensive energy consumption and peak energy consumption, and improves the operation stability.

This invention relates to the field of parameter field cascade reconstruction technology, and discloses a method for cascade reconstruction of three-dimensional water vapor parameter fields based on sparse GNSS station network data. The method includes: 1. Collecting observation data from m GNSS stations sparsely distributed within a target area; 2. Obtaining the slant path water vapor content of all GNSS signals corresponding to each GNSS station; 3. Establishing a water vapor tomography equation set for each GNSS station, using a single GNSS station as the basic unit; 4. Obtaining the local three-dimensional water vapor parameter field of each GNSS station; 5. Dividing the entire target area into spatial units with the same spatial resolution as the local three-dimensional water vapor parameter field of each station, and calculating the spatial correlation coefficient between each spatial unit and the local three-dimensional water vapor parameter field of each station at each height level; 6. Constructing a recursive model to obtain the three-dimensional water vapor parameter field of the target area. This invention achieves high-precision reconstruction of the three-dimensional water vapor parameter field of the target area under sparse GNSS station network conditions, improving the inversion accuracy of three-dimensional water vapor parameter fields based on sparse GNSS station networks.

This invention relates to the field of computer vision technology, specifically providing a video 3D human pose estimation method based on a state-space recursive model. The method first extracts human keypoint information from video frames using a 2D pose estimator, and encodes it into frame-level features via a pose embedding module. Then, a state-space recursive network is used to achieve temporal modeling with linear time complexity. A token pruning clustering module based on state density peaks is combined to select keyframes. Representative tokens are input into the state-space recursive network for long-sequence temporal modeling with linear computational complexity. Finally, state interpolation and propagation mechanisms are used to recover the complete sequence, achieving 3D human pose reconstruction. This method has low computational complexity, strong real-time performance, and is suitable for video pose estimation tasks in edge computing and embedded devices.