19results about How to "Shorten convergence time" patented technology

This invention provides a fast Doppler localization method based on opportunistic signals. The method includes: establishing instantaneous Doppler frequency shift measurements for each satellite based on the receiver's initial position and velocity information, the wavelength of the opportunistic signal, and the position and velocity information of each satellite; establishing an instantaneous Doppler localization equation based on the relationship between the instantaneous Doppler frequency shift measurements for each satellite and the receiver's position information; determining regularization parameters using the variance expansion factor method; obtaining constraint parameters based on the regularization parameters and the stable functional; and solving the instantaneous Doppler localization equation under the condition of minimizing the constraint parameters to obtain the instantaneous Doppler localization result based on the opportunistic signal. The localization result includes the receiver's final position information, final velocity information, and frequency offset.

The embodiment of the invention discloses a routing method and device for data transmission, equipment, a medium and a program product, which are used for simplifying the data routing complexity, reducing the calculation overhead and improving the data routing performance in a high dynamic environment. The method comprises the steps that a data transmission notification is received, the data transmission notification comprises a source node and a destination node for data transmission, one of the source node and the destination node is a ground network element, and the other one is a satellite-borne network element; according to pre-maintained satellite-ground topological information and inter-satellite topological information, routing information of each routing segment in a plurality of routing segments between the ground network element and the satellite-borne network element which are configured in advance is calculated, and the satellite-ground topological information comprises a connection relation between a gateway station and a satellite node; the inter-satellite topological information comprises a connection relationship among a plurality of satellite nodes; and performing routing configuration according to the routing information of each routing segment.

The application relates to the technical field of chip design, in particular to an AI-based DDR5 memory interface chip adaptive method and system.The method comprises the following steps: obtaining a configuration instruction stream, extracting current and historical configuration instructions, and predicting target pins, switching time and target modes.Before the switching time, target electrical parameters are calculated according to pin electrical characteristics, historical switching records and channel load characteristics.During the switching, the target parameters are loaded and the temporary impedance matching network is switched in.After the switching, a fine-tuning increment is generated according to the deviation of the signal quality and the threshold value, and the updated electrical parameters are obtained by superposition, and the matching network is disconnected after the updated electrical parameters are written back to the register.According to the timing error and the fine-tuning increment generated by multiple switching, the weight parameters are updated and deployed back to the chip.Through AI prediction and the dynamic switching and fine-tuning of the temporary impedance matching network, seamless, stable and low-delay mode switching of the memory interface chip in the reconfigurable computing system is realized.

The application discloses a projection figure distortion prediction compensation correction method of a laser scanning projection system, and comprises the following steps: carrying out laser scanning projection system projection; acquiring projection figure distortion data, establishing a neural network training data set and a test data set; establishing an initial topological structure, weights and thresholds of a BP neural network; optimizing the weights and thresholds of the BP neural network based on a particle swarm optimization algorithm; carrying out deviation compensation on the coordinate positions of each projection point according to the training output of the PSO-BP neural network; selecting feature points in each part of a to-be-projected plane; projecting the feature points by using a distortion deviation correction projector based on the PSO-BP neural network; acquiring feature point projection coordinate values; subtracting the ideal projection point coordinates without distortion from the corrected feature point projection coordinates; judging whether the difference value meets an error interval; if yes, it is determined that the projection coordinate value distortion error has been corrected and compensated; if no, returning to step three for feedback type multiple iterations until the difference value meets the error interval.

The application belongs to the technical field of wireless communication, and specifically provides a wideband analog adaptive cancellation circuit based on digital accumulation loop iteration, which is used to solve the problem of reduced iteration depth and decreased cancellation ratio of the existing analog adaptive cancellation circuit due to the incomplete digital integral function. The application comprises an analog adaptive cancellation module and a digital enhancement module, taking the analog adaptive interference cancellation module as the main body, using the digital enhancement module (digital circuit) to process the direct current component, and realizing the accumulation function when updating the weight, i.e. completely realizing the digital integral function, which significantly increases the iteration depth of the system and improves the cancellation ratio of the system. Meanwhile, the algorithm of the digital enhancement module can be adjusted to determine the convergence condition of the system, and the convergence time is reduced under the condition of meeting the demand cancellation ratio. Furthermore, the analog phase shifter is used to change the optimal cancellation frequency of the system, increase the working bandwidth of the system, and relax the convergence condition of the system.

The invention discloses a photovoltaic MPPT (Maximum Power Point Tracking) control algorithm, which relates to the technical field of photovoltaic control algorithms, utilizes the characteristics of simple structure, high convergence speed, high global optimization capability, wide applicability and the like of a gradient optimization algorithm, and improves the defects of premature convergence and sensitivity to control parameters of the gradient optimization algorithm. Meanwhile, the photovoltaic output energy efficiency and the stability of the photovoltaic output energy efficiency serve as the evaluation basis, an MPPT algorithm performance evaluation model is constructed through an analytic hierarchy process, photovoltaic MPPT performance is evaluated, and GTO performance is verified and improved.

The invention discloses an ad hoc network dynamic topology routing optimization method and system based on a Deepseek lightweight large model, and the method specifically comprises the steps: collecting the information of each node in an ad hoc network, and standardizing the node information into a feature vector format which can be analyzed by the Deepseek model; the standardized node feature vectors are input into a Deepseek model, and the topological structure change trend in a short time in the future is predicted; generating a routing path selection strategy based on a topology change trend predicted by the model, testing in an ad hoc network simulation platform, and adjusting feature weight distribution of the model according to a test result; and deploying the adjusted strategy to an actual network, collecting routing performance data in real time as a new sample input model, iteratively updating the model, and improving the accuracy of topology prediction and routing optimization. According to the method, a response can be quickly made to a new network topology, the convergence speed is increased, accurate routing selection can be realized, node energy consumption is reduced, and the life cycle of the ad hoc network is prolonged.

According to the RGB-D saliency target detection method based on mixed feature cross fusion, through a mixed feature cross fusion module, a model can fully utilize complementary information between an RGB image and a depth image, and efficient fusion of features is achieved. The module enhances the interaction between RGB and depth features through a self-attention mechanism, so that the connection between different modal features is better modeled and captured, and the problem of feature distortion under the condition of poor depth image quality is solved. Besides, the invention provides a specific training strategy and an optimization scheme, and the model can rapidly converge on a large-scale data set by adopting the combination of an MAE pre-training model and a ViT model, and obtains excellent performance in a saliency target detection task.

A mechanical arm path planning method and system based on adaptive expansion sampling belong to the technical field of path planning, and solve the problems of slow sampling rate, slow convergence speed and poor initial path quality caused by too random sampling area and unsuitable expansion step length selection when planning the path of the mechanical arm under the prior art; the adaptive expansion sampling strategy provided by the present application introduces a target bias force to correct the growth direction of the random tree based on the F-RRT* optimization algorithm, so that it is biased towards the target point, optimizes the inflection point, makes the generated path smoother, introduces the concept of target point gravity deviation, adaptively expands the strategy according to the complexity of obstacles in the environment, and expands with a dynamic step length, improves the stability and precision of the trajectory, and reduces the length of the generated path, the sampling times and the convergence time.

The invention discloses an autonomous cooperation method and system for a low-altitude emergency federated learning client, and aims to solve the problem of training efficiency and resource optimization of an unmanned aerial vehicle group in a complex environment. The method comprises the following steps: firstly, carrying out offline reinforcement learning on a pre-training strategy network through a simulation environment, and implementing a forced participation mechanism at the initial stage of deployment to accumulate real experience; secondly, each unmanned aerial vehicle encodes a local state into a message vector, exchanges and fuses with a neighbor to form a cooperative intention vector, and autonomously determines an action probability of participating in federated learning; then, in a training stage, the selected client executes local training and uploads update through differential gradient compression, and the server performs weighted aggregation according to gradient difference and a precision improvement value. And finally, performing strategy updating of centralized training and distributed execution by using the QMIX hybrid network according to the multi-target reward function, and realizing closed-loop collaborative optimization.

The invention discloses a GNSS anti-spoofing detection method and system based on Doppler multiple check, and belongs to the technical field of satellite navigation safety, and the method comprises the steps: obtaining and preprocessing multi-frequency GNSS observation data through adaptive protocol analysis; the historical state is updated by adopting a main frequency point anchoring strategy, and the group delay influence is isolated; single-frequency pseudo-range rate and Doppler consistency verification and multi-frequency-point Doppler Z-Score coherence cross validation are executed in parallel, and deception signals are identified from two dimensions of a physical layer and an inter-frequency relation; a historical state self-cleaning mechanism driven by a state machine is introduced, rapid resetting is achieved when the signal environment changes drastically, and misinformation is avoided; and finally outputting a global alarm according to an abnormal satellite statistical result. The method can effectively resist advanced generative deception jamming, realizes millisecond-level rapid recovery after jamming is finished, has good hardware compatibility and integration, and is suitable for scenes such as unmanned aerial vehicles, vehicle-mounted terminals and the like which have strict requirements on real-time performance and power consumption.

The application discloses a BMCA implementation method of a gPTP protocol based on FPGA, and relates to the technical field of high-precision time synchronization and network communication. In view of the problems of large software BMCA processing delay, slow multi-port convergence and excessively high logic complexity of complete hardware implementation, the application provides a simplified BMCA hardware acceleration method: core priority vector comparison logic is extracted, an independent comparison module and a timeout timer are instantiated for each port in the FPGA; an event-driven mechanism is adopted, real-time iterative updating is triggered as soon as an Announce message is received; local best master clock is automatically announced when power is turned on or a link is timed out; a global arbitrator adopts a multi-stage pipeline tree-shaped comparator, and parameters of each port are compared in parallel, so that the role of the port is quickly determined. The application greatly reduces the convergence time and CPU load under the premise of ensuring the correctness of the election, and plays the parallel processing advantage of FPGA.

The application belongs to the technical field of nonlinear system control, and discloses a method and system for introducing composite learning into nonlinear system output feedback adaptive control. The application directly reuses the existing K-filter in standard output feedback backstepping control to construct an extended prediction error, and does not need to establish an additional observer or a state estimation model (such as a series-parallel estimation model, a fuzzy observer, etc.) in parallel with the K-filter. This functional reuse design eliminates the calculation overhead of the additional dynamic system, reduces the storage requirement and real-time calculation burden of the controller, and significantly reduces the hardware implementation cost and system debugging complexity. Meanwhile, through the composite driving of the tracking error and the extended prediction error, the cumulative information is continuously introduced by using the historical memory of the regression quantity, so that the adaptive and self-learning ability of the control system is significantly enhanced, and the parameter estimation convergence and the system response performance are improved.

This invention discloses a spatial optimization scheduling method and system for medical device distribution based on the Internet of Things (IoT), relating to the field of medical device storage. The method includes collecting historical warehouse outbound data and extracting the outbound frequency and co-occurrence frequency of medical devices; obtaining the location coordinates and exit positions from the warehouse map information; constructing a comprehensive cost function, which consists of the weighted distance cost from the device to the exit and the collaborative handling cost between related devices; and solving for the minimum value of the cost function through an optimization algorithm to generate the optimal storage distribution strategy. This invention solves the problems of low picking efficiency and scattered storage of related devices caused by static layout in traditional medical warehousing, significantly shortening the handling path of frequently used devices and reducing the operational complexity of collaborative outbound scenarios.

The embodiment of the invention discloses a self-adaptive intelligent sensor development method and system. The method comprises the following steps that input parameters of a sensor to be developed are obtained; inputting the input parameters into the universal development platform; the method comprises the following steps: sampling input characteristics of each unit of a general development platform, and storing as standardized input in a quantized table form; partial parameter extraction is carried out on the standardized input, partial characteristic parameters are obtained through flexible block fitting, and the flexible block fitting comprises segmentation fitting, transformation fitting and post-transformation segmentation fitting; hierarchical parameter extraction is carried out on the standardized input, and hierarchical feature parameters are obtained through multiple times of convolution; matching the extracted partial feature parameters and hierarchical feature parameters with back-end processing module parameters and circuit module parameters in a database; and constructing a simulation platform by utilizing a general development platform according to each unit parameter, wherein the simulation platform is used for sensor detection or sensor system development. According to the method, the parameter matching efficiency and the system development intelligence level are remarkably improved.

This invention discloses a seamless switching positioning method integrating RTK and PPP-RTK algorithms. This method achieves seamless switching between RTK and PPP-RTK algorithms by designing a dual-state model within a Kalman filtering framework. When network connectivity is available, the system automatically uses NRTK corrections to achieve high-precision RTK positioning; when the network is interrupted, the system seamlessly switches to satellite-based PPP-RTK corrections for PPP-RTK positioning without requiring a software restart. This invention, through the design of a state transition matrix and an adaptive noise adjustment mechanism, ensures positioning continuity and accuracy stability during the switching process, solving the problem of traditional methods requiring re-initialization during switching, which leads to positioning interruptions. This significantly improves the robustness and availability of GNSS positioning systems in complex environments.

The invention discloses a cross-domain relay selection and routing forwarding method for a double-layer low-orbit constellation, which comprises the following steps of: constructing a resident relay satellite cluster, and strictly isolating inter-domain link state change signaling caused by high-speed movement of satellites in the cluster, thereby avoiding invalid topology perception of nodes of the whole network, and improving the reliability of the relay selection and routing forwarding. And the cross-domain routing signaling overhead and the convergence time are greatly reduced. On the basis, satellites in the cluster serve as cross-domain relay nodes, cross-domain routing forwarding is conducted on double-layer satellite network cross-domain routing through the resident relay satellite cluster, nodes outside the cluster can transmit cross-domain traffic to the relay cluster of the domain only through default routing, an on-satellite routing decision-making mechanism is greatly simplified, and the routing efficiency is improved. And the reliability and efficiency of whole network communication are improved.

The invention discloses an island multi-integrated energy system operation optimization method based on electricity-hydrogen energy storage sharing, and the method comprises the following steps: constructing an island electricity-hydrogen integrated energy system which comprises a plurality of integrated energy systems and a shared energy storage station; establishing an energy cooperation sharing model and a benefit distribution model of the island electricity-hydrogen comprehensive energy system; and solving the energy cooperation sharing model and the benefit distribution model by adopting an optimization algorithm to obtain an optimal scheduling strategy and a benefit distribution scheme of the shared energy storage station and each integrated energy system. According to the method, the improved adaptive ADMM algorithm is adopted to carry out distributed solution, so that the calculation efficiency is effectively improved, and the privacy of each participant is protected. According to the method, joint optimization of electric energy storage and hydrogen energy storage is comprehensively considered, and the cross-space-time energy scheduling capability of the system is improved. The strategy can improve the operation economy of the island energy system and improve the energy utilization efficiency, optimizes the energy transaction mode, and provides a new frame support for the intelligent scheduling of the ocean island energy system.

The application relates to a power distribution network operation decision method and system based on retrieval enhancement technology, which comprises the following steps: constructing a power distribution network case database and mapping the database into a joint representation space to obtain scene embedding vectors and scene-decision joint representation vectors; performing staged retrieval on a current power distribution network scene to be decided, screening high-migration-value historical cases and calculating retrieval confidence to obtain Top-K similar historical candidate decisions and corresponding continuous retrieval confidence; dynamically controlling correction strength in combination with the corresponding continuous retrieval confidence, and adaptively correcting the Top-K similar historical candidate decisions to obtain a near-optimal feasible decision initial value; performing verification, hot start solving and constraint compliance verification to obtain a final power distribution network operation decision. The technical scheme can improve the migratability of historical cases, eliminate the artificial threshold defects, reduce the solving time, and improve the power distribution network decision efficiency, reliability and generalization ability.