88results about How to "Guaranteed convergence" patented technology

The invention provides a robust multi-modal image segmentation method and system based on instance perception query, and the method comprises the steps: obtaining original image data and text description data, and generating training data through semantic analysis and causal graph construction; extracting image features and text features by adopting a multi-layer coding network, and establishing a feature dependency relationship through causal reasoning; generating instance clustering features based on attention calculation, and obtaining fusion features in combination with a multi-scale fusion strategy; and calculating query confidence and generating candidate segmentation masks, and outputting a final segmentation result through post-processing optimization. According to the method, by introducing causal reasoning and instance awareness query mechanisms, the cross-modal feature alignment precision and the robustness of the segmentation result are improved, and the method has better segmentation performance and generalization ability in a complex scene.

The invention discloses a fixed-wing unmanned aerial vehicle predetermined time cooperative path following formation control method, and relates to an unmanned aerial vehicle technology. A multi-unmanned aerial vehicle cooperative path following control strategy based on preset time stability, neural network adaptive control and preset performance control is provided; according to the method, the multiple fixed-wing unmanned aerial vehicles can realize preset time convergence of path following errors and preset time preset performance convergence of control signal tracking errors under the conditions of external disturbance and actuator faults, so that a high-precision and quick-response cooperative path following formation control task is completed; and meanwhile, the control precision and robustness are improved.

The invention relates to an edge data processing method of an information security monitoring and early warning AI platform and a related device. The method comprises the following steps: carrying out standardization processing on original security alarm data collected by a plurality of edge computing devices to obtain standardized feature data; constructing a target function comprising a processing efficiency item and a resource consumption item based on the standardized feature data, and decoupling the target function into a plurality of sub-optimization problems of edge computing nodes by using a Lagrange multiplier to obtain an initial task allocation scheme; performing local linearization processing and convex optimization decomposition on the initial task allocation scheme to obtain an optimal task decomposition scheme; constructing a state transition probability matrix according to the optimal task decomposition scheme, and generating a dynamic task scheduling strategy; and distributing the dynamic task scheduling strategy to each edge computing node, and performing time sequence analysis and spatial clustering on multi-source data to obtain a security situation assessment result. According to the invention, the utilization efficiency of edge computing resources is improved.

The invention discloses an underwater vehicle tracking control method with state constraint under input saturation. The method comprises the following steps: firstly, constructing an AUV control system containing model uncertainty and unknown interference based on a kinematics model and a dynamics model of the AUV, and obtaining a reference trajectory of a tracking target; establishing an error model of the AUV control system in combination with the error conversion function, and obtaining a design variable related to the position tracking error; meanwhile, deducing a design variable related to a speed tracking error; constructing a fixed time integral sliding mode lumped disturbance observer based on a design variable related to a position tracking error; and finally, combining a fixed time integral sliding mode lumped disturbance observer and design variables related to a speed tracking error to construct a trajectory tracking control system, and designing a fixed time convergence sliding mode controller by using a continuous input signal and a position and speed adaptive law. According to the invention, the tracking error is stabilized to zero in a fixed time; and continuous control input is realized.

The invention relates to the technical field of scheme decision, and discloses a river channel clearing scheme decision method, device and equipment based on bottom mud elution, and the method comprises the steps: carrying out the data collection and standardization preprocessing of the river channel space distribution, section morphology, hydrological parameters and bottom mud pollution characteristics of a river network system, and obtaining a river channel network characteristic data set; carrying out sediment pollutant migration and diffusion analysis, and calculating a pollution propagation relationship between nodes to obtain a river sediment pollution propagation network model; carrying out river sediment pollution coupling degree calculation to obtain river sediment elution risk distribution data; and inputting the river sediment elution risk distribution data into the P center problem network optimization model, and generating a river network dredging scheme through pollution diffusion risk minimization and dredging resource utilization efficiency maximization analysis. According to the invention, a plurality of factors of the river network system are incorporated into the decision-making process, a systematized and normalized clearing scheme decision-making method is formed, and the scientificity and efficiency of river regulation are improved.

The invention discloses an unmanned ship formation preset time control method based on a non-singular sliding mode surface, and relates to the technical field of unmanned ship formation, in particular to unmanned ship formation preset time control based on the non-singular sliding mode surface, comprising the following steps: step (1), establishing a motion three-degree-of-freedom mathematical model of the ith unmanned ship; (2) determining a formation geometric movement model of n unmanned ships; (3) obtaining tracking error dynamic states of the heading and the position of the ith under-actuated unmanned ship; (4) designing a preset time nonsingular sliding mode surface of the ith under-actuated unmanned ship; (5) designing a preset time heading controller of the ith under-actuated unmanned ship; step (6), designing a preset time surging controller of the ith under-actuated unmanned ship; and (7) performing simulation verification. The invention provides an unmanned ship formation preset time control method based on a non-singular sliding mode surface based on a non-singular sliding mode control theory. The method can realize the stability and robustness of the unmanned ship formation in a complex marine environment, ensures that the system converges to an expected state within a preset time, can effectively solve the problem of control singularity existing in a terminal sliding mode method, and ensures that a controller has relatively strong adaptability to external interference in an execution process.

The invention discloses an STAR-RIS and RSMA-based resource optimization method and system for a communication and inductance integrated system, and belongs to the field of wireless communication. The optimization method comprises the following steps: S1, constructing a system initial model; s2, setting an iteration frequency variable, and giving an initial radar detection signal covariance matrix, initial power distribution, an initial decoding sequence and a transmission matrix and a reflection matrix of an initial STAR-RIS; s3, on the premise that power distribution, a radar detection signal covariance matrix, a decoding sequence and a transmission matrix and a reflection matrix of STAR-RIS are given, optimizing the system initial model to obtain optimal power distribution, an optimal covariance matrix, an optimal decoding sequence, an optimal transmission matrix and an optimal reflection matrix; and S4, substituting the obtained optimal parameter into S3 based on a BCD method, updating an iteration frequency variable until the iteration frequency is reached, ending iteration, and obtaining an optimal solution of the total rate of the user.

The invention relates to the technical field of communication, and particularly provides a probe weight distribution method and device, a medium and a computer program product. The probe weight distribution method comprises the following steps: acquiring working condition parameters of a to-be-detected channel model, and constructing an initial objective function corresponding to the to-be-detected channel model; introducing a replica variable, and constructing a target function based on the replica variable and the initial target function; based on the objective function, respectively carrying out iterative optimization on the replica variable and the probe power weight vector; and under the condition that a preset stop criterion is met, obtaining an optimized probe power weight vector, and taking the optimized probe power weight vector as target probe weight distribution information of the to-be-detected channel model. According to the method, the original problem is decomposed and the alternative updating strategy is adopted, so that the problem of numerical instability caused by directly facing a huge Hessian matrix (Hessian Matrix) is avoided, the calculation efficiency is greatly improved, and the convergence of the algorithm is effectively ensured.

The invention discloses a flux limiter-based dynamic energy flow determination method for an electric heating comprehensive energy system. The method specifically comprises the following steps of: establishing a dynamic energy flow model of the electric heating comprehensive energy system; the thermal dynamic partial differential equation is differentiated by using a flux limiter format with the property that total variation is not increased; establishing a self-adaptive variable step size strategy; and constructing a dynamic energy flow calculation process of the electric heating comprehensive energy system, determining the dynamic energy flow of the electric heating comprehensive energy system, and alternately solving thermal, hydraulic and electric power equations so as to obtain a time sequence change track of thermal, hydraulic and electric power variables. According to the method, the partial differential equation is differentiated by using a flux limiter format with a total variation non-increasing property, so that dispersion and dissipation errors are effectively reduced, and the calculation accuracy is improved; and a self-adaptive step size control technology is used, so that the convergence of calculation is ensured, and the efficiency of dynamic energy flow calculation of the electric heating comprehensive energy system is improved.

The invention discloses an unmanned aerial vehicle intelligent metasurface-assisted satellite-ground physical layer secure transmission method and system, and the method comprises the steps: constructing a signal transmission model based on a communication system, and obtaining the instantaneous signal to interference plus noise ratio of a communication terminal and the instantaneous and secure rate of the system; forming a transmission optimization problem model which jointly optimizes a satellite base station transmitting precoding matrix, a satellite-borne passive intelligent metasurface phase shift matrix, an airborne active intelligent metasurface diagonal reflection matrix and an unmanned aerial vehicle track and takes maximization of the average and safety rate of the system as a target; through analysis of a problem structure, a solution framework of hierarchical processing is provided, and a transmission signal and intelligent metasurface reflection are respectively processed by using successive convex approximation, penalty-based manifold optimization and a positive semidefinite programming algorithm in an inner layer; an unmanned aerial vehicle flight path algorithm based on depth deterministic strategy gradient is adopted at the outer layer to obtain the optimal flight path design, and the eavesdropping is effectively inhibited and the safety performance of the satellite-ground communication system is improved by cooperating with the optimization solution of the inner and outer layer problems.

The invention provides an optical imaging lens. The optical imaging lens can be used for near-infrared band imaging, the optical imaging lens comprises a lens barrel, a lens group and a spacing element group, and the lens group comprises a first lens, a second lens, a third lens and a fourth lens; the spacing element group comprises a first spacing element and a second spacing element, and the effective focal length f2 of the second lens, the center thickness CT2 of the second lens on the optical axis and the distance EP12 between the image side surface of the first spacing element and the object side surface of the second spacing element in the extension direction of the optical axis meet the condition that f2 / (CT2 + EP12) is greater than or equal to 1.03 and less than or equal to 2.88; the curvature radius R3 of the object side surface of the second lens and the outer diameter D1m of the image side surface of the first spacing element satisfy the following relation:-1.93 < = R3 / D1m < =-0.41. The optical imaging lens solves the problem of serious stray light caused by improvement of assembly stability of an optical imaging lens in the prior art.

The invention discloses a method for constructing a fusion model of a high-flow double-variable cycle engine based on reinforcement learning, and belongs to the field of aero-engine modeling. The method comprises the following steps: based on an aerodynamic thermodynamic mechanism of working of the high-through-flow double-variable-cycle engine, selecting a common working equation to realize complete machine performance matching; designing a fusion model architecture based on a Newton-Rafson algorithm and a double-delay depth deterministic strategy gradient algorithm TD3; after an input and output structure and a reward system are designed according to the working mechanism of the fusion model, system hyper-parameters are set according to reward and common working equation residual errors. The high-through-flow double-variable-cycle engine fusion model established by the method is high in convergence speed, high in precision and good in real-time performance, can break through the limitation that a traditional model can only carry out simulation at a specific point, reduces the number of iterations of the model under abrupt-change input parameters, and guarantees the convergence within a full envelope range.

The invention relates to an analytical calculation method for axial magnetic pulling force of an electric propulsion conical permanent magnet motor of an electric aircraft. A segmented equivalent method and an equivalent magnetic network method are combined, a conical motor is equivalent to a multi-segment cylindrical motor, three-dimensional modeling is converted into two-dimensional section modeling, the calculation complexity is reduced, meanwhile, the motor section change, the magnetic circuit saturation influence and the specific distribution of an air gap magnetic field are fully considered, the calculation accuracy is close to that of a finite element method, and the calculation efficiency is improved. The calculation speed is obviously higher than that of a finite element method. According to the method, the magnetic field distribution is prevented from being predicted in advance before modeling, and the modeling thought is simplified. Meanwhile, according to the method, grid subdivision is simplified, a quadrilateral grid in a standard shape is constructed as much as possible, grid direction magnetic conductance and branch magnetic conductance can be calculated through a unified formula, and modeling efficiency is improved.

The invention discloses a non-linear non-strict feedback multi-agent system fixed time fault-tolerant control system, and belongs to the technical field of agent system cooperative control. Comprising a multi-agent system model, a coordinate transformation and command filtering module, an error compensation signal module, a backstepping recursion module, a neural network module, an adaptive law module and an adaptive fault-tolerant controller module. The method is used for compensating the influence of sensor faults on system consistency and ensuring that the system converges within fixed time, so that when the system has sensor deviation, gain and other faults, the system can still realize output consistent tracking within a predetermined time upper limit irrelevant to an initial state, the reliability and safety of the system are remarkably improved, and the system reliability and safety are improved. The technical problems of state feedback information distortion and system cooperation precision and stability reduction caused by sensor faults of the intelligent agent in the prior art are solved.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The application discloses a radar complementary sparse frequency waveform sequence set design method based on a complex circle flow surface algorithm, and specifically comprises the following steps: according to a monitored spectrum environment, delimiting a usable spectrum range and an unusable spectrum range; converting a weighted integral sidelobe level of a waveform sequence set into a quadratic function form with a waveform sequence set vector as a variable, constructing an objective function to describe the weighted integral sidelobe level of the waveform set; calculating a power spectrum of the waveform set, controlling a weighting coefficient according to an expected spectrum, performing weighted summation on the power spectrum, and converting a weighted summation expression of the power spectrum into a quadratic function form with the waveform sequence set vector as the variable; and performing weighted summation on a WISL objective function and an EFS objective function to construct a joint objective function. The CCM optimization algorithm is applied to the complementary sparse frequency waveform sequence set design problem for the first time, effectively solving the constant modulus constraint problem of the waveform sequence, and compared with the existing cyclic iteration algorithm and the interior point method.

The application belongs to the technical field of optical three-dimensional measurement, and discloses a two-division iteration sub-pixel matching method and system for phase structured light three-dimensional measurement, which comprises the following steps: obtaining a phase map; performing legality marking and pixel-level initial matching on the phase map; determining a sub-pixel search area based on the pixel-level initial matching; and performing sub-pixel matching point search based on the sub-pixel search area by using a two-division iteration method to obtain a sub-pixel matching point. The application can improve the precision of binocular matching in phase structured light three-dimensional measurement, thereby improving the precision of three-dimensional reconstruction.

The invention discloses a distributed fixed time robust formation trajectory tracking control method for a heterogeneous cluster system, and belongs to the technical field of formation control, and the method comprises the following steps: S1, building kinematics and dynamics models of a six-degree-of-freedom four-rotor unmanned plane and kinematics and dynamics models of a three-degree-of-freedom unmanned ship; s2, determining a formation tracking error and a speed tracking error of the air-sea heterogeneous cluster system; s3, generating an error conversion function; s4, determining a fixed time robust virtual control law and a distributed fixed time robust controller; s5, determining a distributed fixed time robust controller under the dynamic time triggering mechanism; and S6, completing trajectory tracking control. On the basis of dynamic event triggering, the distributed fixed-time robust H-infinity formation control method based on dynamic event triggering is determined, the updating frequency of the controller is reduced, and communication resources are saved.

This invention discloses a lightweight joint estimation method for battery state of charge (SOC) in resource-constrained MCUs, comprising: extracting the weight matrices and bias terms of each layer of a trained LSTM time-series estimation network and flattening them into a one-dimensional static constant array; constructing a custom pure C language matrix operation library independent of a third-party AI inference framework on the target microcontroller, employing a global static memory pool management strategy, and defining a unified structure containing the number of matrix rows, columns, and one-dimensional floating-point data pointers at the C language level; the target microcontroller calling the custom pure C language matrix operation library to directly read the one-dimensional static constant array to complete the forward inference calculation of each gate unit of the LSTM network and output the prior SOC observation value; and calling the custom pure C language matrix operation library to perform state equation iteration of the Kalman filter and output the SOC estimation result. This invention enables the safe and efficient operation of complex coupled algorithms on a very low computing power platform.

The invention relates to a fixed-time sliding mode anti-lock braking control method and system based on fuzzy logic and a neural network. The method comprises the steps that a dynamic model of a vehicle anti-lock braking system is established; designing a fixed time sliding mode controller, and constructing a sliding mode surface containing a terminal attractor and an approaching law; a T-S fuzzy logic system is adopted to approach a non-linear tire-road surface friction function in an ABS system on line; an approaching law of the sliding mode controller is approached online by adopting an artificial neural network; carrying out online optimization on additional control item gains introduced by neural network approximation by adopting a particle swarm optimization algorithm; and the fixed time stability of the whole closed-loop system is monitored based on the Lyapunov stability theory. According to the invention, the braking efficiency, the stability and the robustness of the ABS system are effectively improved, and the braking distance and the braking time are reduced.

The invention discloses an air-space-ground network resource optimization method for minimizing the age of data collection information, and belongs to the field of wireless communication; the method comprises: S1, constructing an initial model; S2, setting initial parameters for iteration; S3, obtaining the optimal time allocation under the conditions of given flight trajectory, transmission power, rate allocation and segmentation ratio; S4, obtaining the optimal flight trajectory and rate allocation under the conditions of given time allocation, transmission power and segmentation ratio; S5, obtaining the optimal segmentation ratio under the conditions of given time allocation, flight trajectory, rate allocation and transmission power; S6, obtaining the optimal transmission power under the conditions of given time allocation, flight trajectory, rate allocation and segmentation ratio; S7, substituting the optimal result obtained in the front of the BCD method into S3, updating the variable of the number of iterations, repeating S3 to S6, until the initial model meets the convergence condition, the iteration is terminated, and the optimal solution of the initial model is obtained.

An automatic modeling method for damaged concrete structure belongs to the technical field of artificial intelligence and building structure health assessment, and comprises the following steps: (1) collecting point cloud information of the damaged concrete structure, and performing two-dimensional slice dimension reduction processing on the point cloud information; (2) constructing a SDS-YOLO segmentation network optimized for sparse discrete features; (3) adopting a U-Net model to detect and extract the outer contour edge line of the mask information of instance segmentation; (4) adopting a surface reconstruction method based on an implicit function to construct an entity unit; and (5) extracting internal steel information by using an LLM, and finally realizing finite element modeling. The present application utilizes the instance segmentation and edge detection technology in deep learning to extract the outer contour information of the point cloud information slice, simultaneously utilizes the large language model LLM to extract the internal steel information of the concrete structure, and finally realizes the reconstruction of the entity unit, and analyzes the fire resistance and static behavior of the damaged structure.

The invention relates to a hierarchical federal learning method oriented to a heterogeneous environment, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a hierarchical federal learning framework in a cloud edge end scene; constructing a hierarchical federated learning algorithm capable of reducing model performance loss caused by heterogeneity between nodes; constructing a virtual data set used in the edge server enhancement algorithm and a data distribution mode of the edge server; under the strict mathematical hypothesis, the boundary of the average global gradient deviation is theoretically deduced to quantify the influence of the cloud edge aggregation interval, the number of participating devices and other important factors, and the theoretical convergence boundary of the hierarchical federated learning algorithm under the influence of the heterogeneous environment is analyzed.

The invention relates to the technical field of intelligent decision making, and provides an insurance premium pricing method, device and equipment based on a random deep network model, and a computer readable storage medium. On the basis of the random depth mechanism, part of network layers can be randomly skipped in the training process, the calculation amount of forward and back propagation during model training is reduced, and therefore the calculation complexity during model training is reduced; and only a part of network layers are used in each training, so that the training time is remarkably shortened, the convergence of the model is ensured, the model is prevented from being over-fitted, and the robustness of the model to abnormal data and noise data is enhanced. In addition, a pricing model is obtained based on random depth mechanism training. On the premise of ensuring the model performance, the model training efficiency and the pricing accuracy can be further improved.

The invention relates to the technical field of electricity markets, in particular to a day-ahead-real-time rolling pricing method and system for an electricity selling company, and the method comprises the following steps: S1, constructing an evolutionary game replication dynamic equation of vehicle network interaction of the electricity selling company and an electric vehicle; s2, constructing an evolutionary game income matrix; s3, constructing a day-ahead two-stage pricing optimization model; and S4, constructing a real-time rolling optimization model. According to the method, the interactive behavior with the electric vehicle is simulated through the replication dynamic equation of the evolutionary game, and the economic benefits of the electricity selling company and the electric vehicle user under different cooperation strategies can be effectively predicted and adjusted; powerful theoretical support is provided for establishing a stable cooperation relationship through an evolutionary game income matrix; through constructing a day-ahead two-stage pricing optimization model and a real-time rolling optimization model, an effective price and load management strategy is provided for an electricity selling company in a complex and changeable spot market.

The application belongs to the technical field of computers and discloses a parallel segmentation learning method and device based on clusters, equipment and a storage medium. The method comprises the following steps: obtaining a plurality of to-be-learned user terminals and user communication information of each to-be-learned user terminal; performing cluster division on each to-be-learned user terminal according to each to-be-learned user terminal and the user communication information of each to-be-learned user terminal, and determining a plurality of target user clusters; and performing cluster serial segmentation learning according to an aggregated user terminal model and each target user cluster to obtain a target user terminal model, wherein the aggregated user terminal model is obtained by performing parallel segmentation learning on each target user cluster according to the target spectrum resources of each to-be-learned user terminal. Through the above method, the overall training time delay in the segmentation learning process is effectively reduced, the efficiency of segmentation learning is improved, the negative effects caused by network heterogeneity and dynamics are inhibited, and the convergence and accuracy of existing segmentation learning technology are ensured.

The invention provides a control method based on specified time nonsingular preset performance. The control method comprises the following steps: establishing an error dynamic model; establishing a preset performance function of specified time; designing an adaptive item of envelope adjustment based on a preset performance function; designing an error conversion variable based on an envelope-adjusted adaptive item to obtain a constrained error dynamic model; and designing a controller based on the error conversion variable. According to the controller designed by the invention, the error variable of the system is sufficient for performance constraint, and the error can still be converged to a neighborhood near zero without generating singularity under the condition of controller saturation limitation and system disturbance; according to the invention, it can be ensured that the preset performance function converges to the final function value at the specified time, and the system error variable satisfies the performance constraint, that is, the error variable is always located between the upper envelope and the lower envelope; according to the method, the singularity of the controller is avoided by adding the adaptive item, and the controller enables the error conversion variable to converge.

This invention discloses a method and system for adaptive allocation of preheating power for a target drone turbojet engine based on dynamic compensation of environmental parameters. It relates to the field of aero-engine control and thermodynamic process optimization technology, and includes: collecting flight environmental parameters and calculating the mass flow rate of air at the combustion chamber inlet; constructing a multi-condition compensation matrix to determine aerodynamic reference quantities; dividing the combustion chamber into regions based on the reference quantities; establishing a thermodynamic unsteady model and generating a combustion instability sensitivity matrix; calculating the dynamic instability index and performing thermoacoustic risk classification; constructing a preheating energy transfer matrix based on the risk level; introducing an inertia factor to achieve adaptive allocation of preheating power in each region; converting the allocation strategy into injector pulse modulation control commands; and performing dynamic iterative optimization by combining a spatiotemporal evolution model of thermoacoustic parameters and wall temperature. This invention can achieve refined allocation of preheating power, effectively suppress thermoacoustic instability, and improve engine preheating uniformity and operational reliability.

The application belongs to the technical field of robot control, and particularly relates to a multi-robot arm preset performance event trigger predefinition time control method. In the preset performance control framework, the application fuses a predefinition time stability theory, designs a fixed time performance function, and eliminates strict constraints of initial errors of traditional preset performance control. A predefinition time command filter is used to replace analytical derivation of a virtual control signal in a traditional backstepping method, so as to avoid a 'complexity explosion' problem and ensure that filter errors converge within an accurately set time. A dynamic event trigger mechanism is introduced, a trigger threshold is adaptively adjusted, the update frequency of a controller and the consumption of communication resources are significantly reduced while the control performance is ensured, and RBF neural networks are used to perform online estimation on unknown functions of the system, so that effective control is realized on a non-strict feedback system in combination with an error compensation mechanism.

This application relates to an adaptive rapid positioning method and system based on GNSS synchronization status, belonging to the field of navigation and positioning technology. The method includes: entering the normal positioning process when all tracked satellite signals have completed message time synchronization; otherwise, entering the adaptive time-free positioning process, which includes first dynamically determining the minimum reliable time unit and time search interval based on the synchronization status level of the tracked satellite signals; then determining a relatively accurate local reference time as the local baseline time based on the synchronization status level and time search interval; and after correcting the signal transmission time of the tracked satellites based on the local baseline time and the minimum reliable time unit, performing rapid AGPS iterative calculation. This method enables rapid receiver positioning under a wide range of time errors.