48 results about "Critical regions" patented technology

The invention belongs to the technical field of sound source processing, and provides a sound source positioning and detecting method and device. The method comprises the following steps: acquiring delay estimation of microphones I and II and delay estimation of microphones I and III based on a three-path linear uniform microphone array; based on the delay estimation sum, respectively carrying out positioning calculation under near-field and far-field conditions; according to the sound source distance under the near-field condition, comparing the sound source distance with a distance judgment threshold value, and determining a far-field / near-field output sound source position; and carrying out feature extraction on the signals of any microphone array, and carrying out event classification based on a pre-trained convolutional neural network. According to the method, far / near field model selection is carried out according to the distance judgment threshold, large deviation generated by a single model in a critical region is avoided, continuous and stable positioning from short distance to long distance is achieved, time classification can be achieved while position calculation is carried out, and integrated output is achieved.

The invention belongs to the technical field of hydraulic test and intelligent control, and particularly discloses a hydraulic component test system integrating data acquisition and automatic data analysis functions, which comprises a physical hardware test platform, a test control software platform and a digital twinborn model of a tested component, and realizes real-time interaction through an OPC UA protocol. Model parameters are corrected on line by means of measured data, and steady state, transient state and disturbance working condition tests are automatically executed based on a working condition library; and dynamically optimizing a test sequence in a test process, skipping a redundant working condition or encrypting a critical region, and synchronously generating a structured performance report. The accuracy and consistency of test results are improved, working condition simulation signals are automatically generated, performance data are collected in real time, a comprehensive performance report is generated, manual operation is reduced, the test process optimization unit can skip redundant working conditions and refine failure boundary positioning, and the test time and cost are effectively saved.

The invention provides a dynamic disaster assessment method and system based on iron tower data, and relates to the technical field of GIS data processing, and the method comprises the steps: obtaining the iron tower state data of a target region, collecting multi-dimensional basic information, determining a plurality of critical regions based on the target region, carrying out the classification according to disaster levels, adding disaster level identifiers, and carrying out the clustering analysis. The method comprises the following steps: drawing up a disaster aggregation map, carrying out risk correction on the disaster aggregation map through iron tower state data and multi-dimensional basic information, determining a disaster influence level and identifying a key risk area, and carrying out resource allocation management in the disaster aggregation map according to the disaster influence level and the key risk area. The technical problems of low resource allocation decision accuracy and low rescue efficiency caused by inaccurate disaster assessment results in the prior art are solved. The technical effects of improving disaster assessment accuracy, ensuring scientificity and pertinence of resource allocation work and further improving rescue efficiency are achieved.

The invention relates to a method for determining at least one safety-critical peak region (R1, R2, R3) on the basis of an indicator value (Z) for statistical characterization of event descriptions detected by at least one vehicle in a location-related manner, wherein those inflow- and outflow-free road sections of which the section length is greater than or equal to a predetermined minimum section length are identified on the basis of a road map or navigation map. Identified road sections which adjoin one another are connected to form in each case a section chain (10) with a starting point (11) and an end point (12). Event descriptions comprising a geoposition (P1, P2) and at least one parameter of a vehicle environment model are detected by vehicles and transferred to an analysis system. The analysis system in each case assigns a nearest road network location (O1, O2) of a section chain (10) to a geoposition (P1, P2) of an event description and describes it by a longitudinal distance (L, L1, L2) in relation to the starting point (11) of the section chain (10). For at least one section chain (10), a profile of a density value (p) of event descriptions that is related to the longitudinal distance (L, L1, L2) from the starting point (11) is determined by means of a kernel density estimator of a predetermined bandwidth. The profile of the density value (p) is transformed, using local, bandwidth-related, Z standardization, into a Z profile (Z1, Z2) of a Z value (Z) which is mean-free in relation to bandwidth. In each case a region of road network locations (O1, O2) along the motorway-like road (100) which are connected along a section chain (10) between a peak beginning (Lmin) and a peak end (Lmax) and at which the Z value (Z) exceeds a predetermined threshold value (θ) is identified as a safety-critical peak region (R1, R2, R3).

The invention discloses a side slope excavation dynamic monitoring and early warning method based on digital twinning. The method comprises the following steps that terrain point cloud, rock and soil layering and construction progress are obtained and preprocessed; three-dimensional geometric twinning and physical twinning are established, finite element simulation is adopted, boundary push-back and pore pressure rebalance are completed, and a twinning base line is formed; an observation residual error and a physical conservation residual error are calculated, and a high-consistency twin and assimilation sample set is obtained through joint updating; applying a topological constraint sliding surface evolver and implementing adaptive mesh refinement and interface reconstruction of a critical zone; and constructing a displacement and osmotic pressure uncertainty conical domain based on the assimilation sample set and the critical region strengthening model, and outputting a grading early warning result. According to the method, a double-consistency assimilation and topological constraint sliding surface evolution method is adopted, high-consistency simulation and quantitative early warning of the whole slope excavation process are achieved, and the method has the advantages of being high in accuracy, high in stability and good in traceability.

The invention discloses an airborne camera anti-shake control method and system, and relates to the field of camera anti-shake control, and the method comprises the steps: building a hysteretic deformation tensor field, generating a visual hysteretic residual tensor through differential operation and lagging feature analysis, taking the visual hysteretic residual tensor as observation input, and carrying out the state estimation through a LuGre dynamic friction model. Calculating a micro bristle deformation state and an equivalent friction torque value of a blocking holder, performing visual compensation analysis based on the equivalent friction torque value of the blocking holder, judging whether the system is in a static friction zero-crossing critical region or not based on a micro bristle deformation estimated value, generating a reverse feed-forward compensation torque instruction according to the generalized all-source anti-interference torque, and determining whether the system is in a static friction zero-crossing critical region or not. The integral term of the controller is dynamically reset, vector synthesis is carried out on a reverse feed-forward compensation torque instruction and a basic instruction of a main image stabilization control loop, final driving voltage is generated and applied to a pan-tilt motor, and stepped picture lagging and jumping caused by instant release of energy in a long-focus view field are restrained.

The invention belongs to the field of deep neural networks and image classification, and particularly relates to an image classification adversarial training improvement method based on boundary sample enhancement. Comprising the following steps: step 1, preparing an image sample set; 2, designing a GAN network architecture suitable for boundary sample generation; step 3, training a GAN network suitable for boundary sample generation; and step 4, image classifier adversarial training participated by the boundary samples. The method can effectively restrain the stability of the decision boundary of the image classification model, enables the model to carry out finer feature learning in a critical region, and prevents the boundary from excessively shifting due to adversarial disturbance. And meanwhile, the model can still maintain stable and reliable feature expression when being disturbed. According to the method, the robustness of the model is improved, the damage to the precision of a natural sample is reduced, and better accuracy-robustness balance and stronger generalization ability are realized.

The embodiment of the invention provides a picture main body focusing method and device, equipment and a storage medium. The picture main body focusing method comprises the steps of obtaining a key reference object and all target main bodies in a picture; determining a focusing distance based on the first distance between each target subject and the key reference object; and taking the centers of all the target main bodies of which the first distances do not exceed the focusing distance as the focusing center of the picture. According to the method, the focusing center of the picture is intelligently judged and output by analyzing the spatial relationship between the target subject and the key reference object, and then the most critical region of interest is cut based on the focusing center. According to the method, on the premise of completely retaining key visual information, redundant backgrounds and non-related areas in the picture can be effectively eliminated, and the area of the picture is reduced as much as possible, so that the computing power is saved, and the analysis efficiency of a subsequent large model is improved.

A computer-implemented method for analyzing simulation results of a manufacturing or deformation process, comprising: obtaining simulation results, the simulation results comprising at least a geometry of a part (2) and a stress tensor (23) in the part (2) caused by a shaping process; for one or more origins (32) in a critical region (22), determining an origin line (3) by following a direction of maximum stress or a corresponding force; for each origin line (3), determining at least one line segment (31) of the origin line (3) and an origin trajectory (5) representing values of the stress or force directed along the origin line (3); and performing at least one of presenting information representing the line segment (31) and the origin trajectory (5) along the line segment (31) to a user and automatically adjusting parameters of the shaping process.

A computer-implemented method for analysing a result of a simulation of a manufacturing or deformation process, comprisesretrieving the result of the simulation, comprising at least the geometry of the part (2) and stress tensors (23) in the part (2) caused by the forming process;for one or more starting points (32) in a critical region (22), determining a cause line (3) by following the stress or a corresponding force in the direction in which it is maximal;for each cause line (3), determining at least one line section (31) of the cause line (3), and a cause trajectory (5) representing values of a stress or a force directed along the cause line (3);and performing at least one ofpresenting information representing the line section (31) and the cause trajectory (5) along the line section (31) to a user; andautomatically adapting, parameters of the forming process.

A method for generating a test coupon specification for predicting fatigue life of a component includes determining a load condition for the component, providing a component design, and performing a strength analysis of the component design under the load condition determining a critical area of the component and a stress-related parameter of the critical area. The method includes providing a material condition of the component at least for the critical area of the component. To assist an end-user in determining which are optimal tests to be performed in order to obtain most relevant data for fatigue prediction of a specific component, the method also includes providing a material model and providing, as an input to the material model, the stress-related parameter, and the material condition. The material model generates, as an output, a test coupon specification for being tested in a testing machine.

The invention belongs to the technical field of artificial intelligence, and relates to a dynamic controllability management method and system for long-term evolution of a large model, and the method comprises the steps: S1, obtaining the current state information of the large model; s2, calculating a controllability score of the large model based on the output distribution entropy, the distribution variable quantity and the logits drift of the large model, judging whether the current state of the large model deviates from a controllability critical zone or not based on the controllability score, if the current state deviates from the controllability critical zone, entering S3, and otherwise, directly entering S4; s3, actively correcting the offset behavior of the large model in the training process through a controllable calibration head, so that the current state of the large model does not deviate from the controllable critical zone; and S4, dynamically adjusting a training strategy of the large model based on the controllability score and the task category. According to the method, the large model has the capabilities of long-term plasticity maintenance, continuous performance improvement and rapid adaptation to new tasks, and the fundamental upgrade from a static optimization model to a self-evolution model system is realized.

This invention discloses a discrete element method, system, medium, and equipment for simulating equivalent crystalline materials, relating to the field of numerical simulation of rock mechanics. The method includes: converting granite images into binary images using a binarization method to identify various minerals within the binary images; determining the size data of various minerals in the binary images based on a particle size statistical algorithm; extracting micromechanical parameters using nanoindentation experiments combined with a trial-and-error method, and constructing multiple sets of equivalent crystalline models based on the mineral size data; dividing each set of equivalent crystalline models into multiple regions using a Brazilian splitting experiment; determining the optimal magnification for each region of each set of equivalent crystalline models by magnifying from non-critical regions to critical regions using a magnification algorithm, obtaining differentiated equivalent crystalline models; and performing Brazilian splitting numerical simulation using these differentiated equivalent crystalline models to obtain simulation results.

This application discloses a numerical simulation method for wave impact suppression of structures based on SPH (Self-Pulsating Hypothesis), relating to the field of data processing technology. The method includes the following steps: determining multiple critical regions and absorbing layer particles in a preset fluid domain; calculating the critical stability of each anchor point at different times based on the position and state update of each critical particle; updating each anchor point based on the critical stability to obtain multiple final anchor points; calculating the repulsive force adjustment coefficient of each critical particle based on the repulsive force adjustment requirement of each final anchor point and the disturbance degree of the critical particles; adjusting the repulsive force of each critical particle using the repulsive force adjustment coefficient; and performing wave suppression processing on each critical particle based on the adjusted repulsive force and the absorbing layer particles. This application achieves the technical effect of completely and effectively absorbing all transmitted waves in the wave impact simulation process.

The present application relates to the technical field of safety monitoring and industrial automation, and discloses a silo safety monitoring method and system, which synchronously acquires coal pile surface point cloud, lateral pressure, vertical load and environmental data through the arrangement of a laser scanning unit, an annular pressure sensing belt, a weighing sensor and a temperature and humidity node; a high-precision three-dimensional morphology is reconstructed based on an adaptive grid refinement algorithm, and a joint representation model is constructed by fusing mechanical data; a stability discrimination function is established in combination with coal body physical parameters to identify the instability critical region; and through pattern matching with a historical stability morphology library, hierarchical early warning of abnormal working conditions such as suspended arch, bonding and blockage and generation of blockage removal strategies are realized. The present application constructs a joint representation model of coal pile three-dimensional morphology and mechanical state by fusing laser point cloud, pressure distribution and load sensing data, breaks through the limitation of traditional single-point material level measurement which cannot reflect the overall coal pile structure, and realizes high-precision measurement with a storage calculation error of less than 2%.

The application discloses a kind of walking tape deviation correction method, device, equipment and computer readable storage medium, applied to industrial equipment field, the polar piece position data sent by the first sensor working in composite critical region is mirrored and copied and flips to obtain mirror polar piece position data;The diaphragm position data sent by the second sensor working in diaphragm critical region is mirrored and copied and flips to obtain mirror diaphragm position data;In switching region, using the mirror flipping data copied and polar piece position data or diaphragm position data in real time transmission are weighted average to calculate feedback value, to realize the smooth switching of sensor.It avoids the problem that the feedback value of the sensor data calculated in the process of switching the sensor in the prior art may change suddenly, resulting in excessive swing of the deviation corrector and poor deviation correction effect.

This application relates to the field of UAV control technology, providing a method, system, device, and storage medium for generating visual environments based on neural radiation fields, solving the problem of poor visual environment generation effects. The method includes: semantically segmenting a corrected image to generate image data with semantic labels, which are used to label the categories of objects in an outdoor scene; updating a neural radiation field model based on the semantically labeled image data and mapping the semantic labels to the voxel feature space corresponding to the neural radiation field model to construct a semantic point cloud; identifying non-critical regions in multi-view images, performing redundant voxel clipping on the non-critical regions to generate clipped semantic point clouds; and using the clipped semantic point clouds to drive the neural radiation field model to generate arbitrary viewpoint visuals of an outdoor scene. This technical solution achieves lightweight semantic modeling and efficient free-viewpoint rendering of outdoor visual environments, improving the efficiency and quality of generating new perspectives in complex environments.

The present application relates to the technical field of marine disaster risk assessment, and particularly relates to a storm surge risk assessment method for a seawater photovoltaic field area, comprising the following steps: S1: identifying a critical region in the field area where a bearing stable state is converted into a bearing attenuation state, and generating a corresponding support state transition unit; S2: determining the transmission direction and the expansion boundary of the support state transition unit between array regions, and generating a corresponding imbalance transmission unit; S3: dividing the overall risk level based on the transmission range and the expansion degree, and generating a corresponding storm surge risk assessment result. According to the present application, by combining support state transition identification, imbalance transmission path analysis and risk space partitioning, the storm surge risk of the seawater photovoltaic field area is changed from static determination to dynamic evolution analysis, thereby improving the accuracy and spatial directivity of risk assessment.

This invention provides a method for intelligent monitoring and early warning of water droplet impact in substations during rainy weather, relating to the field of intelligent operation and maintenance and condition monitoring of substations. The method includes: acquiring dual-optical-path video streams; constructing a cross-spectral neural radiation field network; using joint high-dimensional features as input to output volume density and radiation color vectors; calculating the equivalent water film thickness from these two vectors to generate an equivalent water film thickness distribution field; mapping this field to a three-dimensional model, subdividing the mesh according to the thickness gradient modulus, and solving for the electric field to obtain the electric field distortion tensor and calibrate the high-discharge-risk critical region; generating a Lagrange particle set, solving the dynamic equations through smooth particle hydrodynamics, and simulating the water film evolution to obtain the probability of conductive water bridge connection; inputting the distribution field, distortion tensor, and connection probability into a multi-modal impedance reconstructor to generate an electrochemical impedance spectrum to obtain the dynamic insulation margin attenuation rate; and triggering an early warning based on the determined conditions. This invention significantly improves the timeliness and accuracy of early warning for external insulation faults during rainy weather.

The invention discloses an AK-MCS reliability analysis method based on a dynamic critical reinforcement learning function, and belongs to the field of mechanical equipment structure reliability analysis. According to the method, a DC learning function is provided, and the coupling influence of a sample point prediction value and a prediction variance on the failure probability estimation precision is comprehensively considered through an adaptive weight mechanism. Preferentially selecting sample points with large prediction variance in the exploration stage to quickly improve the global approximation capability of the model; in the refinement stage, a learning function is converted to be dominant by a mean item, the selection weight of sample points near a limit state curved surface is dynamically increased, local fine description of a critical region is enhanced, and a global relative uncertainty index is introduced to realize smooth transition from global exploration to local refinement. According to the method, the problems of excessive iteration and local optimization of a traditional learning function are effectively avoided, the construction efficiency of the Kriging proxy model and the robustness of failure probability estimation are remarkably improved, and the method is suitable for efficient reliability analysis of a complex mechanical equipment structure.

The application provides a process state real-time correction method based on acoustic characteristics, and relates to the technical field of process control, and comprises the following steps: collecting acoustic signals generated by interaction between a processing medium and a processing unit when the processing unit is executing a processing task; performing real-time spectrum analysis on the acoustic signals to extract characteristic frequencies and energy distribution characteristics thereof which are related to the current physical state of the processing medium; determining whether the current physical state of the processing medium is in an abnormal critical region in real time based on changes of the characteristic frequencies and the energy distribution characteristics thereof relative to a preset reference; the abnormal critical region comprises a first precursor state in which the processing medium tends to be excessively dried or a second precursor state in which the processing medium tends to be excessively accumulated; and if it is determined that the processing medium is in the abnormal critical region, the physical state of the processing medium being currently processed is corrected in real time by adjusting at least one execution parameter which influences the physical state of the processing medium before the processing medium being currently processed reaches a target region.

The invention discloses a protection action curve visualization engine based on an IED (Intelligent Electronic Device) model difference atlas, which relates to the technical field of relay protection teaching, training and testing of a power system and comprises an IED model difference atlas construction module and a WebGL (Web Graphics Library) visualization engine module. According to the technical scheme, data are analyzed and a JSON equation is constructed based on a to-be-processed SCD file through an IED model difference atlas construction module, a boundary gradient vector is calculated based on the JSON equation to obtain a difference degree matrix, and a protection action curved surface is constructed and rendered in a unified parameter space based on the difference degree matrix through a WebGL visualization engine module. And a visual result of the protection action curve is displayed, so that students can understand protection action conditions and critical regions more visually, and the teaching efficiency and the practical operation conversion rate are remarkably improved.

This invention belongs to the field of deep neural networks and image classification, and specifically relates to an image classification adversarial training improvement method based on boundary sample enhancement. It includes the following steps: Step 1, image sample set preparation; Step 2, designing a GAN network architecture suitable for boundary sample generation; Step 3, training the GAN network suitable for boundary sample generation; Step 4, adversarial training of the image classifier involving boundary samples. This invention effectively constrains the decision boundary stability of the image classification model, enabling the model to perform more refined feature learning in the critical region and avoiding excessive boundary shift due to adversarial perturbations. Simultaneously, it ensures that the model maintains stable and reliable feature representation even when subjected to perturbations. This invention improves model robustness while reducing the damage to the accuracy of natural samples, achieving a better accuracy-robustness balance and stronger generalization ability.

The invention relates to the technical field of safety monitoring and industrial automation, and discloses a silo safety monitoring method and system.The method comprises the steps that a laser scanning unit, an annular pressure sensing belt, a weighing sensor and a temperature and humidity node are arranged, and coal pile surface point cloud, lateral pressure, vertical load and environment data are synchronously obtained; a high-precision three-dimensional form is reconstructed based on an adaptive mesh refinement algorithm, and mechanical data are fused to construct a joint representation model; establishing a stability discrimination function in combination with coal physical parameters, and identifying an instability critical region; through mode matching with a historical stable form library, graded early warning and blockage clearing strategy generation of abnormal working conditions such as suspension arching, bonding and blockage are realized. According to the method, the laser point cloud, the pressure distribution and the load sensing data are fused, a combined characterization model of the three-dimensional form and the mechanical state of the coal pile is constructed, the limitation that traditional single-point material level measurement cannot reflect the whole coal pile structure is broken through, and high-precision measurement with the reserve calculation error smaller than 2% is achieved.

This invention relates to the field of aero-engine technology and discloses a method for predicting the fracture speed of an integral bladed disk (IBD) based on sequential sampling and a surrogate model. Utilizing a strain prediction surrogate model for analysis and calculation improves the accuracy and efficiency of predicting the fracture speed of an IBD. Furthermore, in the construction of the strain prediction surrogate model, a sequential sampling method is used to optimize the generation logic of sample points, ensuring that sample points cluster within the critical region of interest in over-speed fracture tests. This improves the accuracy of IBD estimation and lays the foundation for building a high-quality surrogate model. This invention considers the influence of uncertain parameters on IBD behavior and predicts the fracture speed range under the influence of uncertain parameters, improving the accuracy and reliability of IBD prediction and providing a more scientific basis for the design and optimization of integral bladed disks (IBDs).

The invention provides a voice authentic identification method and system based on dynamic feature ablation of a frequency band critical region, and relates to the technical field of voice signal processing and multimedia content security, and the method comprises the steps: extracting a time-frequency feature map from a to-be-detected voice signal; preprocessing the extracted time-frequency characteristic pattern, including frequency band division and positioning of a critical transition region between a low-frequency region and a high-frequency region; inputting the preprocessed time-frequency feature map into a voice depth authentic identification model to obtain an authentic identification result; wherein the voice depth authentic identification model firstly performs dynamic feature ablation processing on a critical transition region in the time-frequency feature map, then performs frequency band differentiation modeling on the time-frequency feature map subjected to the dynamic feature ablation processing to obtain voice features, and finally performs classification by using the voice features. The identification result of the real voice or the forged voice is obtained; according to the method, a detection blind area caused by a frequency band boundary effect is effectively eliminated, and the detection precision of the model in a cross-dataset scene is remarkably improved.

The invention belongs to the technical field of nuclear power station reliability management, and particularly relates to a nuclear power station key area monitoring method and system. The method comprises the following steps: fully recording a bottom plane image of a key area so as to form a first video stream, and generating an event alarm according to bottom plane static residues or low-speed moving objects of the key area in the first video stream; recording an airspace image of the key area in a rolling cache mode so as to form a second video stream; and when an event alarm is received, extracting and outputting the cached data in the second video stream according to the event alarm. The system has the advantages that the millimeter-level high-speed flying object can be captured without omission, full-period traceability evidence can be provided, the core problems of mechanical delay, unreliable triggering and insufficient traceability of traditional monitoring are solved, and the reliability and performance of overall operation of the system are effectively improved.

The invention provides a construction engineering intelligent construction site monitoring method and system, and relates to the technical field of intelligent engineering, and the method comprises the steps: collecting the horizontal displacement offset, deformation rate and rate time distribution data of a supporting structure in real time, and obtaining a continuous time period monitoring data set; extracting deformation rate time distribution according to the continuous time period monitoring data set, identifying rate change, and determining a transition point of the rate from uniform distribution to abrupt change distribution; calibrating starting and ending time of a mutation time period according to the transition point, identifying a time period in which the deformation rate presents mutation distribution, and separating a mutation section from the continuous time period monitoring data set to obtain a mutation deformation subset; extracting area coordinates when the soil stress reaches a critical state from the stress field distribution to obtain a critical area coordinate set; and identifying the spatial distribution density of the coordinates of the critical area, determining the collapse risk level according to the spatial distribution density of the coordinates of the critical area, identifying the foundation pit collapse risk according to the determined collapse risk level, and obtaining an early warning signal.

This invention discloses a method and apparatus for controlling welding parameters of composite materials, belonging to the field of intelligent welding technology. The method includes: constructing a process coupling model, setting a critical positioning method, generating a parameter bifurcation map, constructing a chaotic boundary probability cloud map, associating image feature differences between different materials, and locating the critical region of parameters; acquiring a dynamic dataset, setting a critical warning method, extracting key features, and triggering an alarm once an abnormal change is detected by analyzing abnormal changes in image features; calculating a safety offset, setting a dynamic reconstruction method, introducing a time-varying damping factor, dynamically adjusting the collaborative output, and reconstructing the optimal parameter combination by combining the welding depth and interface fusion state fed back by real-time image feedback; collecting batch welding data, setting an iterative optimization method, updating the parameters of the process coupling model, dynamically expanding the critical region identification range, and correcting the bifurcation boundary conditions in real time to enhance the adaptability to parameter critical point drift.

The invention discloses an approximate critical eccentricity rate Kepler equation solving method, and belongs to the technical field of aerospace dynamics. Aiming at an elliptical or hyperbolic orbit of which the eccentricity is close to 1, the method adopts a mixed strategy of combining series expansion and Newton iteration, and comprises the following steps: acquiring orbit eccentricity and mean anomaly parameters; constructing an iteration initial value by using Lagrange series expansion; setting a tolerance error condition and the maximum number of iterations; and carrying out iterative solution by adopting a Newton iteration method. According to the method, when the eccentricity is close to the critical value 1, rapid convergence and high-precision calculation can still be achieved, the problems that a traditional method is slow in convergence and prone to divergence in a critical region are effectively solved, and reliable orbit position calculation support is provided for tasks such as earth spacecraft orbit design and deep space exploration.