Simulation calculation system for transmission track and beam spot characteristics of near-earth space electron beam

Abstract

The invention provides a near-earth space electron beam transmission track and beam spot characteristic analog calculation system, which comprises a parameter input module used for receiving parameters input by a user, mapping the parameters into parameters required by calculation, generating particle initial phase space coordinates and calculating particle three-dimensional velocity vectors meeting emittance requirements; the magnetic field modeling module is used for simulating a near-earth orbit magnetic field environment; the track and beam spot calculation module is used for circularly calculating and continuously updating the momentum and position of the particles, simulating the motion track of the particles, calculating the size of the beam spots and finally generating the density distribution of the beam spots; and the result output and visualization module is used for outputting a calculation result. The method has the advantages that parameter coverage is comprehensive; the simulation precision is high; the output result is complete; the operation is convenient and the compatibility is strong; the calculation efficiency is high.

Description

Technical Field

[0001] This application belongs to the field of electron beam transmission simulation technology, specifically relating to a simulation calculation system for the transmission trajectory and beam spot characteristics of near-Earth space electron beams. Background Technology

[0002] Research on electron beam propagation in the space environment is of great significance in specific application fields such as space science, environmental simulation and aerospace engineering, and particle physics experiments. The complex geomagnetic field in near-Earth space significantly affects the trajectory of charged particles, causing electron beam deflection, cyclotron oscillation, and changes in beam spot morphology. Analyzing the propagation characteristics of electron beams is a key technical step, and its results directly affect the rationality of equipment design and mission planning.

[0003] Traditional beam transport simulation software rarely provides detailed modeling specifically for electron beams launched from near-Earth orbit that travel over long distances (up to hundreds of kilometers) in real geomagnetic fields (including dipole fields and more precise IGRF models).

[0004] The existing technology has the following shortcomings: (1) Limited parameter coverage: Most existing software only supports electron beam simulation with energy below 1MeV, which cannot meet the analysis requirements of 5MeV high-energy electron beam; and the upper limit of beam current intensity is mostly 5nA, which cannot be adapted to the strong beam current scenario of 10mA, thus limiting the applicable scenarios.

[0005] (2) The space environment simulation is limited: it can only simulate a uniform magnetic field environment and cannot reproduce the real dipole magnetic field and IGRF (International Geomagnetic Reference Field) of near-Earth orbit, resulting in large errors in the transmission trajectory calculation, with deviations from the actual on-orbit situation reaching more than 20%.

[0006] (3) Incomplete output results: It can only output the center trajectory of the electron beam, lacking key parameters such as the envelope trajectory and beam spot density distribution; and it cannot customize the output of beam spot images at arbitrary transmission distances, making it difficult to support the precise design of the beam spot action area by subsequent equipment.

[0007] (4) High operating threshold and poor compatibility: The interface is mostly command line interaction, which requires professional personnel to write scripts, making the operation complex and the device compatibility insufficient.

[0008] Although the industry has attempted to improve the software by adding magnetic field models and expanding the parameter range, the coordination problem of "accurate simulation of multiple magnetic field environments", "full parameter coverage" and "low-threshold operation" has not yet been solved due to limitations in algorithm efficiency and interface design. There is an urgent need to develop a dedicated system that can accurately simulate the transmission behavior of electron beams in the near-Earth space geomagnetic field and provide comprehensive analysis results and a user-friendly operating experience. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a system capable of accurately simulating the propagation trajectory, beam spot evolution, and key parameter changes of an electron beam in the near-Earth space geomagnetic field. This system supports a wide range of input parameters, has a user-friendly graphical interface, and can output intuitive trajectory diagrams, beam spot density distribution maps, and quantified data.

[0010] To achieve the above objectives, this application proposes a simulation calculation system for the transmission trajectory and beam spot characteristics of near-Earth space electron beams, the system comprising: The parameter input module is used to receive parameters input by the user, map the user input parameters to the parameters required for calculation, use macro particles to replace actual particles, generate the initial phase space coordinates of the particles, and calculate the three-dimensional velocity vector of the particles that meet the emission requirements. The magnetic field modeling module has multiple built-in magnetic field models for simulating near-Earth orbit magnetic field environments. The trajectory and beam spot calculation module is used to solve the Lorentz equations of motion using the Boris algorithm, continuously updating particle momentum and position through iterative calculations to simulate particle trajectories. Simultaneously, it calculates the beam spot size for arbitrary transmission distances based on the RMS algorithm and generates a beam spot density distribution through particle density statistics. The results output and visualization module is used to output the electron beam center trajectory coordinates, envelope trajectory parameters, beam spot size, density distribution matrix, electron position distribution map, electron beam trajectory map, RMS lateral dimension during transmission, RMS deflection angle during transmission, and electron beam plane density distribution at the target distance.

[0011] As an improvement to the above system, the user input parameters include: Initial electron beam energy, initial beam spot size, initial position, beam intensity, macroparticle coefficient, target distance, initial RMS emittance, direction of propagation of relative magnetic field, date, magnetic field model, and initial beam distribution type; The initial location includes longitude, latitude, and altitude; The initial RMS emissivity includes an upper limit and a lower limit for emissivity; The transmission direction of the relative magnetic field includes the azimuth angle and the direction of the initial beam axis relative magnetic field.

[0012] As an improvement to the above system, the step of mapping user-input parameters to parameters required for calculation includes: Initial position mapping: Convert the input latitude and longitude into radians; superimpose the input altitude onto the Earth's average radius to obtain the particle's geocentric distance; map the coordinates to a three-dimensional Cartesian coordinate system according to the Earth's spherical geometry; obtain the final three-dimensional position vector, which serves as the particle's initial position; Transmission direction mapping: The three-dimensional direction of the IGRF magnetic field is determined based on the transmission position. Using the magnetic field direction as the first rotation axis, the azimuth angle of the input initial beam axis relative to the magnetic field is rotated around the magnetic field direction. Then, using the direction perpendicular to the magnetic field as the second rotation axis, the azimuth angle of the input initial beam axis relative to the magnetic field is rotated. The direction vector after the two rotations is the three-dimensional direction of ECEF.

[0013] As an improvement to the above system, the use of macroparticles to replace actual particles includes: The number of particles is reduced by dividing the actual number of particles tracked by the macro-particle coefficient.

[0014] As an improvement to the above system, the initial phase space coordinates of the generated particles include: Construct orthogonal bases e1, e2, and e3 for the beam axis, where e3 is the beam axis direction, e1 is any unit vector perpendicular to e3, and e2 is obtained by multiplying e3 by e1, ensuring the right-hand rule. Generate a lateral position distribution. If it is a uniform distribution, then random sampling is performed uniformly within the circular cross-section; if it is a Gaussian distribution, then lateral offsets are generated according to a normal probability distribution.

[0015] As an improvement to the above system, the calculation of the particle three-dimensional velocity vector that satisfies the emission requirement includes: The initial RMS emission value is used as the upper and lower limits of the emission value to form the template interval for emission determination; An initial control factor for the lateral deflection angle is randomly generated to adjust the degree of particle deflection. Based on the current initial control factor, the particle lateral deflection angle is generated according to the set ratio. For each macroparticle, the angle deflection along the e1 and e2 directions is generated. The angular deflections in the directions of e1 and e2 are converted into directional offsets in three-dimensional space, then superimposed onto the main beam axis and normalized to generate the true three-dimensional initial velocity direction of the particle, and iterative calculation begins. The transverse RMS emissivity and longitudinal RMS emissivity are calculated using the RMS emissivity formula based on the second moment. If the emission is within the template range, the iteration terminates; if the emission is too small, the control factor is increased; if the emission is too large, the control factor is decreased. The emission is recalculated based on the adjusted control factor until the emission falls into the template range or the maximum number of iterations is reached. The final iteration result is used as the three-dimensional velocity vector of the particle that meets the emission requirements.

[0016] As an improvement to the above system, the Boris algorithm is used to solve the Lorentz equations of motion, and the particle momentum and position are continuously updated through iterative calculations to simulate the particle's trajectory. Simultaneously, the beam spot size for any transmission distance is calculated based on the RMS algorithm, and a beam spot density distribution is generated through particle density statistics, including: In each calculation of particle momentum and position, the positions and velocities of all particles are summed and averaged to calculate the dynamic beam centroid of the current time step, which serves as the reference benchmark for the current beam. A local coordinate system is constructed with the calculated dynamic beam centroid as the origin. Under this coordinate system, the RMS beam size and RMS deflection angle are calculated in real time. Only the RMS beam size and RMS deflection angle are stored in the system, and all particle trajectory data are not saved. When the forward projection distance of the beam centroid reaches the set transmission distance, the particle trajectory advancement is stopped, and the positions of all particles at this time are projected onto a plane perpendicular to the beam's average velocity direction. The density is accumulated on the grid, and a two-dimensional beam spot density distribution map is output.

[0017] As an improvement to the above system, the updating of particle momentum and position further includes: After updating the momentum and position of all particles in each time step, the projected distance of the beam centroid in the initial beam direction is calculated. If the projected distance is less than the set target distance, the calculation continues to the next time step; if the target distance is reached or exceeded, the trajectory calculation loop is stopped, and beam spot statistics are performed. After the loop stops, release any excess pre-allocated space in the storage space where the computation data is stored.

[0018] As an improvement to the above system, the system pre-allocates a set number of arrays at startup to store particle trajectory and state data; when calculating particle momentum and position in a loop, the system checks whether the current number of recorded steps has reached the set ratio value of the current array in each loop. If the set ratio value is reached, the system expands the array space by a new set number of arrays at once.

[0019] As an improvement to the above system, the system further includes: The system compatibility adapter module is used to encapsulate the MATLAB Runtime environment.

[0020] Compared with existing technologies, the advantages of this application are: 1. Comprehensive parameter coverage: electron energy covers 50keV-5MeV, beam current intensity 1-10mA, and transmission distance 1-100km, meeting the simulation needs of multiple scenarios in near-Earth space, and expanding the parameter range by 2-3 times compared with existing software; 2. High simulation accuracy: By using the IGRF magnetic field model and Boris algorithm, the near-Earth space magnetic field environment is more realistically reflected, so that the physical phenomena such as deflection, cyclotron and transverse oscillation of the electron beam can be accurately simulated. The trajectory calculation error is less than 5% and the beam spot size calculation error is less than 3%, which is far better than the 20% error level of existing software. 3. Complete output results: Simultaneously outputs transmission trajectory, RMS lateral dimension, RMS deflection angle, and beam spot image, providing full-dimensional data support for equipment design; 4. Easy to operate and highly compatible: The MATLAB-based graphical user interface integrates parameter setting, calculation execution, and result visualization; no script writing is required, and beginners can complete parameter setting within 5 minutes, greatly reducing the learning curve and improving work efficiency; it is compatible with Windows 7 / 10 / 11 systems and older devices. 5. High computational efficiency: Optimized by parallel computing, the simulation of 500,000 electrons over a 100km transmission distance takes only 15-20 minutes, which is 30% more efficient than existing software. Attached Figure Description

[0021] Figure 1 The diagram shown is a flowchart of the system operation. Figure 2 The image shown is a schematic diagram of the system's main interface. Figure 3 The image shows the parameter settings in the input.txt file; Figure 4 The image shows the loading of system GUI parameters; Figure 5 The image shown is a diagram of the system interface after the calculation. Figure 6 The output data shown is the system output result; Figure 7 The image shown is the output image from the figures folder; the text information in this image is for illustrative purposes only and has no actual reference value. Figure 8 The image shown is a description of the system output image content. Detailed Implementation

[0022] The technical solution of this application will be described in detail below with reference to the accompanying drawings.

[0023] The simulation system for near-Earth space electron beam propagation trajectory and beam spot characteristics provided in this application is based on the single-particle orbit method and the Boris algorithm. Through a core workflow of "parameter input - magnetic field modeling - trajectory calculation - result output," it achieves accurate simulation of near-Earth orbit electron beam propagation. The system includes: 1. Parameter Input Module The parameter input module provides a graphical user interface (GUI) that supports user input of the following parameters: Initial electron beam energy (50keV-5MeV); Initial beam spot size (0.5-5cm); Initial position (LLA coordinate system): The initial position [φ, λ, h] represents the latitude, longitude, and altitude in the LLA (WGS-84) geodetic coordinate system, respectively; the latitude range is [-90°, 90°]; the longitude range is [-180°, 180°]; and the altitude range is [300-2000km], specifically referring to the altitude above the Earth's surface. Beam current intensity (1-10mA); Macroparticle coefficient: If 500,000 electrons are simulated, and 5,000 macroparticles are actually tracked, then the macroparticle weight coefficient should be set to 100, that is, 1 macroparticle = 100 real particles. Transmission target distance (1-100km); Initial RMS emittance ([min max] mm.mrad); The direction of transmission of the relative magnetic field (pitchB gyroB): represents the azimuth angle and the direction of the initial beam axis relative to the magnetic field, respectively. pitchB = 0 / 90 / 180, representing the parallel / perpendicular / antiparallel magnetic field, respectively. Date: Uses yyyy-mm-dd format for calculating the IGRF14 magnetic field. Based on the definition of the IGRF14 magnetic field, the possible value range is set from 2025-01-01 to 2029-12-31. Magnetic field models: uniform magnetic field / dipole magnetic field / IGRF magnetic field; Initial beam distribution type: uniform distribution / Gaussian distribution.

[0024] In addition, the above input parameters can be imported in batches through the "input.txt" file, which can check the integrity of the parameters in the file and avoid duplicate input or format errors.

[0025] Space electron beam simulation is essentially a particle dynamics problem under a three-dimensional time-varying magnetic field, involving complex coordinate systems and geometric relationships. Furthermore, the two sets of definitions for the beam direction and the magnetic field direction are difficult to unify. Experts in the field of space physics are more accustomed to using physical descriptive quantities. Traditional tools require users to handle these mathematical transformations themselves, which is complex, error-prone, and technically challenging, hindering direct use by non-specialist computing personnel.

[0026] In the parameter input module, this application constructs an "automatic mapping mechanism from user-oriented physical description to computation-oriented mathematical model," forming an engineering-graded initialization layer for space electron beam simulation. The parameter input module is designed with an interface layer that automatically maps intuitive, physics-oriented physical input parameters to the complex parameters required by the underlying dynamic numerical model, ensuring the physical correctness and mathematical consistency of the initialization and minimizing the geometric knowledge requirements for the user. The specific implementation process includes: (1) Initial location mapping: geographic coordinates (latitude, longitude, altitude) → three-dimensional geocentric coordinates (ECEF) Electronic trajectory calculation, IGRF magnetic field calculation, and Boris propulsion position update all require a Cartesian coordinate system; user-inputted LLA cannot be directly used for any physical calculations. In this system, users only need to input three intuitive physical quantities: longitude, latitude, and altitude. The parameter input module automatically converts these into three-dimensional geocentric coordinates suitable for numerical trajectory propulsion. During this process, users do not need to understand geocentric coordinate systems or perform any coordinate transformation calculations.

[0027] The specific steps are as follows: The parameter input module first converts the latitude and longitude input by the user into radians; it then superimposes the input "altitude" onto the average radius of the Earth to obtain the distance from the Earth's center to the particle; based on the geometry of the Earth's sphere, it maps this point to a three-dimensional rectangular coordinate system; and finally obtains the three-dimensional position vector, which is used as the particle's starting position.

[0028] (2) Emission direction mapping: emission direction relative to the magnetic field pitchB / gyroB → three-dimensional direction vector During the calculation process, the space electron beam is often specified by the "relative magnetic field direction" to indicate the emission angle, which needs to correspond to the direction of the geomagnetic field. Boris propulsion requires the direction of the three-dimensional unit vector in ECEF coordinates, which is used to construct the beam axis basis vector, construct the initial velocity direction, and calculate the trajectory coupled with the magnetic field.

[0029] Based on the above considerations, the specific steps for processing the transmission direction mapping parameters are as follows: First, the three-dimensional direction of the IGRF magnetic field is determined according to the transmission position. Then, with the magnetic field direction as the first rotation axis, GyroB is rotated around the magnetic field direction. Next, with the direction perpendicular to the magnetic field as the second rotation axis, PitchB is rotated. The direction vector after the two rotations is then converted into the three-dimensional direction of ECEF.

[0030] (3) Beam distribution generation The parameter input module offers two initial beam distribution options: "uniform" and "Gaussian." Based on the user's selection, it automatically generates a large number of initial phase space coordinates for particles that conform to statistical laws. No manual parameter tuning by the user is required, ensuring that the statistical properties of the final particle swarm meet physical requirements.

[0031] The specific steps include: The initial particle distribution (Gaussian / uniform) must be defined in a plane perpendicular to the beam axis. First, construct an orthogonal basis (e1, e2, e3) for the beam axis, where e3 is the beam axis direction, e1 is any unit vector perpendicular to e3, and e2 is obtained by multiplying e3 by e1, ensuring the right-hand rule. Then, generate the lateral position distribution. If the user selects a uniform distribution, uniform random sampling is performed within the circular cross-section; if the user selects a Gaussian distribution, a lateral offset is generated according to a normal probability distribution. The offset is mapped as: Initial 3D position = Emission point ECEF + dx·e1 + dy·e2. The lateral position determines the beam spot physical properties and is the starting point for all subsequent beam characteristic diagnostics. It can be used for subsequent RMS beam spot calculations and divergence angle calculations.

[0032] Preprocessing the input parameters has the following beneficial effects: (1) Lowering the threshold: Space physics experts do not need to delve into the details of the coordinate system implemented by the software, and can directly use familiar physical parameters to operate.

[0033] (2) Improve reliability: Automated mapping avoids human error and ensures the physical correctness of the initial conditions.

[0034] (3) Improve efficiency: Users only need to focus on the physical target, rather than the tedious and error-prone initialization coding work.

[0035] Real electron beams contain a massive number of electrons (e.g., trillions), making direct full-particle simulation computationally infeasible. A key engineering problem is how to reduce the problem size to a level acceptable to computational capabilities while preserving statistical significance and reflecting the collective space charge effect.

[0036] The parameter input module introduces a configurable "macro-particle" model, allowing users to flexibly balance computational accuracy and computational resources through "macro-particle weight" coefficients. The specific implementation process includes: The "macroparticle coefficient" parameter (magic) is provided in input.txt and the GUI. After obtaining the macroparticle coefficient, the theoretical total number of electrons is first calculated based on the current intensity. Then, the actual number of tracked particles is reduced to the number of macroparticles using N = floor(N / magic). Each macroparticle represents magic real electrons.

[0037] Although this application reduces the number of particles during calculation, it ensures that the statistical properties on the beam cross section are preserved by controlling the initial distribution (uniform / Gaussian).

[0038] The technical effects brought about by macro-particle coefficients include: (1) Make simulation feasible: transform the uncomputable massive particle problem into a computable macro particle problem.

[0039] (2) Resource controllability: Users can control the system load by adjusting a parameter based on available memory and computing time, making it possible to perform large-scale electron beam transmission simulation on ordinary workstations.

[0040] The lateral emittance of an electron beam is one of the most important indicators of beam quality, reflecting the combined extent of beam spread in both lateral position and divergence space. Lower emittance indicates a thinner, more collimated beam with higher quality; conversely, increased emittance suggests defocusing or coupling, leading to increased lateral divergence. The initial emittance parameters of the electron beam directly affect the accuracy and physical plausibility of transmission simulations. Since emittance is a statistical measure, manually adjusting these parameters relies on expert experience, is cumbersome, and makes it difficult to guarantee consistent results. The parameter input module implements an adaptive emittance control mechanism based on iterative feedback, automatically ensuring that the initial beam quality meets physical requirements. The specific implementation process includes: (1) Based on the user-input parameters: initial RMS emissivity ([min max] mm.mrad), the upper and lower limits of emissivity are read to form the template interval for emissivity determination; (2) Randomly generate an initial control factor for the lateral deflection angle (corresponding to the angular offset of the particle relative to the beam axis) to adjust the degree of particle deflection; (3) Based on the current initial control factor, generate the particle lateral deflection angle proportionally. For each macro particle, automatically generate a small angle deflection along the e1 and e2 directions. (4) The deflection angle is superimposed on the beam axis direction to obtain the actual initial direction. The system automatically converts the above deflection angle into a small directional offset in three-dimensional space, then superimposes it on the main beam axis direction and normalizes it to generate the true three-dimensional initial velocity direction of the particle; (5) Calculate the RMS emission of each particle based on "position-angle". The system uses the true emission formula (second moment) to calculate: transverse RMS emission (x-x') and longitudinal RMS emission (y-y'), where x and y come from "transverse position" and x' and y' come from "deflection angle direction". This step of calculation truly reflects the divergence of the particle swarm phase space; (6) Determine whether the user's emission target range is met. The system determines: if the emission is within the template range, it is successful and the iteration can be terminated; if the emission is too small, it indicates insufficient angle deflection, so the control factor is increased; if the emission is too large, it indicates excessive divergence, so the control factor is decreased. The system can adjust the control factor by a fixed ratio to gradually approach the target range.

[0041] (7) Adjust the control factor and enter the next iteration cycle. The system executes the following in a loop: generate a new deflection angle, calculate the emittance, determine whether it is within the range, and adjust the control factor if it is not satisfied. This continues until the emittance falls into the template range or the maximum number of iterations is reached (to ensure computational stability).

[0042] (8) The initial velocity for trajectory propulsion is finally determined. The system writes the three-dimensional velocity vector that finally meets the emission requirements into the particle data structure as the input of the Boris algorithm and the initial condition for subsequent trajectory propulsion.

[0043] The technical benefits of adaptive reactivity control mechanisms include: (1) Automated adjustment: freeing users from tedious parameter adjustment work.

[0044] (2) Ensure physical rationality: Ensure that the initial quality of the beam is within the range of physical rationality to improve the credibility of the simulation results.

[0045] (3) Improve the consistency of results: Simulations conducted by different users at different times are comparable, ensuring the consistency of research results.

[0046] 2. Magnetic Field Modeling Module The magnetic field modeling module has three built-in magnetic field models to meet the simulation requirements of near-Earth orbit environments: Uniform magnetic field model: based on formula ( The magnetic field strength is constant, with a default value of 3.12 × 10⁻⁶. -5 T). Used to simplify environmental simulation; Dipole magnetic field model: based on formula (R is the Earth's radius of 6371 km, and x, y, z are spatial coordinates) (The distance from the electron to the Earth's center). Used to reconstruct the characteristics of the Earth's dipole magnetic field; IGRF magnetic field model: based on formula V is expanded from spherical harmonics. , ( θ is the geocentric distance, where θ is the geocentric co-latitude, and longitude is expressed in terms of longitude. The Earth's reference radius is expressed as... express( The Gaussian coefficients, typically 6371.2 km, are represented by g and h and obtained by fitting observational data. The associated Legendre function is denoted by P, and the order of the spherical harmonic function is denoted by N, typically 13. This is used to accurately simulate the actual near-Earth magnetic field distribution.

[0047] High-precision magnetic field models such as IGRF involve complex calculations of higher-order spherical harmonic functions and reading external coefficient files. Repeating these operations at each step of particle tracking incurs significant computational overhead, making long-term simulations based on precise magnetic field models difficult to perform on conventional computing devices. The magnetic field modeling module employs a global variable caching and lazy loading mechanism, significantly reducing repetitive calculations and file I / O operations. The specific implementation process includes: (1) Global variable declaration: Declare global variables in the magnetic field calculation function to store the magnetic field coefficients.

[0048] (2) Lazy loading strategy: When the magnetic field calculation function is called for the first time, check whether the global variables have been initialized. If they have not been initialized, load or calculate the coefficient data from the file and save it to the global variables.

[0049] (3) Cache reuse: In all subsequent calls, the coefficient data cached in the global variable is used directly to avoid repeated file reading and coefficient calculation.

[0050] The technical benefits of global variable caching and lazy loading mechanisms include: (1) Significantly improve efficiency: Reduce the computational cost of the IGRF model by an order of magnitude, making it possible to perform particle simulations based on a precise magnetic field model on a personal computer.

[0051] (2) Improve user experience: Users do not need to wait for the long magnetic field coefficient loading process and get a smooth computing experience.

[0052] (3) Supports complex simulations: It removes performance obstacles for carrying out precise simulations of multi-parameter, long-distance, and large-scale particles.

[0053] 3. Trajectory and Bead Spot Calculation Module Trajectory Calculation: The Boris algorithm is used to solve the Lorentz equations of motion. Through iterative calculations, the electron's momentum and position are continuously updated to simulate its trajectory. The core formulas include the momentum update formula. (where q is the electron charge, m is the electron mass, and Δt is the time step), and the position update formula. (p is electron momentum, c is speed of light); Beam spot calculation: Based on the RMS (Root Mean Square) algorithm, calculate the beam spot size for any transmission distance. ), Using the reference electron x-coordinate, the beam spot density distribution is generated through particle density statistics (statistical counting of the number of electrons per unit area).

[0054] In traditional particle simulations, trajectory calculation and analysis of the physical parameters of the beam characteristics (such as beam size and deflection angle) are two separate steps. Users need to first run the trajectory calculation simulation, outputting massive amounts of particle trajectory data (TB-level), and then write complex post-processing scripts for statistical analysis. This process is extremely time-consuming, puts enormous pressure on storage, and makes it impossible to monitor the beam evolution during the calculation, greatly limiting research efficiency.

[0055] The trajectory and beam spot calculation module integrates the RMS diagnostic module into the main loop of particle dynamics calculation. At each time step, beam statistics are calculated immediately after the particle position is updated. Historical trajectories are not saved, and no offline post-processing is performed; only real-time updated results such as RMS, centroid, and deflection angle are retained. This achieves deep integration of "trajectory calculation" and "beam spot diagnosis," enabling real-time, online calculation of beam characteristics and completely eliminating the need for separate post-processing. The specific implementation process includes: (1) Real-time calculation of dynamic centroid: In each time step (for time = 1: duration), after the positions of all particles are updated, the system immediately sums and averages the positions and velocities of all particles to calculate the dynamic beam centroid (Xr(time) and Vr(time)) for the current time step. This centroid serves as the reference reference for the current beam.

[0056] Step 1: Update particle position and velocity (Boris main loop).

[0057] At each time step, the momentum direction, velocity, and position of the particle are updated based on the velocity and magnetic field of the previous moment.

[0058] After this step, the latest true positions and velocities of all particles are determined. This forms the basis for calculating RMS, center of mass, and deflection angle.

[0059] Step 2: Calculate the beam centroid (center trajectory) in real time.

[0060] Within the same time step, the system calculates the average of the three-dimensional coordinates of all particles to obtain the three-dimensional centroid position of the beam at that moment, and stores the centroid position in an array for subsequent trajectory drawing.

[0061] The center of mass is extremely important; it is the reference point for beam spot calculation. Otherwise, beam drift will be mistaken for an increase in beam spot size.

[0062] (2) Real-time diagnosis in local coordinate system: The system constructs a local coordinate system with the dynamic centroid calculated in the previous step as the origin. In this coordinate system, two key parameters, RMS lateral dimension (beam spot size) and RMS deflection angle (beam divergence), are calculated in real time.

[0063] Calculate the transverse RMS beam spot size with the centroid as the center; The system executes the following in the current time step: The lateral offset relative to the center of mass is calculated for each particle using the following formula:

[0064]

[0065] Calculate the square of the lateral offset and the average of all particles, then take the square root of the average to obtain the RMS beam spot size, as shown in the following formula:

[0066]

[0067] The system saves data in real time. , .

[0068] Real-time calculation of beam RMS deflection angle (divergence angle); The system calculates the average velocity direction of the beam simultaneously with the transverse RMS beam spot size: Calculate the average velocity vector of all particles to obtain the average velocity direction. Calculate the velocity direction of each particle and Find the included angle and calculate the root mean square (RMS) of the included angle, and the RMS lateral deflection angle. , .

[0069] This step provides real-time changes in beam divergence characteristics with distance.

[0070] (3) Data flow optimization: The calculation results are directly stored in the memory array. When the simulation calculation ends, the complete beam evolution curve (such as the change of beam spot size with transmission distance) is also generated synchronously.

[0071] Step 1: All statistics are stored in real time, but the historical trajectory of particles is not saved; The system stores only the following at each time step: Beam centroid position [ , , Beam centroid velocity RMS beam spot size , RMS deflection angle , RMS geometrical emittance[ ], along the arc length of the trajectory and forward projection distance It does not save all particle trajectories, thus greatly reducing storage requirements.

[0072] Step 2: Trigger beam spot density statistics after reaching the target distance; When the forward projection distance of the beam centroid reaches the specified transmission distance, the particle trajectory calculation is stopped, the spatial positions of all particles at the current moment are projected onto the target plane (the plane perpendicular to the beam average velocity direction), the particle density is counted on the grid, and a two-dimensional beam spot density distribution map is generated and output.

[0073] All statistical data can be immediately used for plotting and analysis, and the entire process requires no offline processing.

[0074] The technical benefits of integrating the RMS diagnostic module into the main loop of particle dynamics calculations include: (1) Efficiency improvement: The beam characteristics are obtained instantly without post-processing, reducing the analysis time from several hours or even days to "the analysis is completed as soon as the calculation is completed".

[0075] (2) Real-time monitoring: Real-time monitoring of beam evolution process (such as beam spot expansion and divergence angle change) is realized, enabling researchers to judge the simulation status in real time.

[0076] (3) Storage optimization: There is no need to store the trajectory data of each particle at every step, which greatly reduces storage overhead.

[0077] (4) Low barrier to entry: Users no longer need to write complex programs such as trajectory analysis scripts, beam density statistics scripts, and plotting scripts. All of these can be completed automatically by the system, which greatly reduces the barrier to entry and enhances usability and ease of use.

[0078] In traditional electron beam trajectory simulation systems, particle propulsion typically employs the following two methods: Using a fixed number of time steps, such as: advancing 1×10 on a pre-set trajectory. 6 The problem with this method is that particles may reach the target distance in tens of thousands of steps, and the remaining hundreds of thousands of steps are completely meaningless calculations, which greatly increases the running time; the actual position of the particle reaching the target does not strictly correspond to the "target distance" set by the user, which destroys physical consistency.

[0079] Using a fixed physical time, such as a simulation runtime of 10ms, has several drawbacks: the electron beam is deflected in a strong magnetic field, making its actual forward propagation distance difficult to predict; it cannot guarantee that the particles will arrive at the user-specified target distance precisely; and subsequent beam spot statistics cannot correspond to a precise spatial plane, severely impacting the accuracy of beam spot analysis.

[0080] The above two methods cannot simultaneously address issues such as computational efficiency, precise spatial location mapping, and stability of memory and array pre-allocation. This application uses transmission distance as the termination condition for dynamic simulation and proposes an adaptive termination strategy to ensure that the results strictly correspond to the target's physical location. The specific implementation process includes: Step 1: Update the position and velocity of all particles at each time step; The system uses the Boris algorithm to calculate the new velocity direction based on the previous velocity and the local magnetic field, advances the particle position according to the new velocity, and obtains the three-dimensional coordinates of all particles at the current time step.

[0081] This is the basis for dynamically checking "whether the target distance has been reached".

[0082] Step 2: Real-time detection of whether any particles have reached the target distance; At each time step, the system immediately reads the coordinates of all particles in the main propagation direction and determines whether they exceed the user-defined "target distance". If no particles reach the "target distance", the system continues to the next time step. If a particle reaches the "target distance", the system records the current time step, immediately stops the entire loop, and enters the beam spot statistics.

[0083] The key point of step 2 is that the process stops once any particle reaches the target distance, rather than waiting for all particles to reach the target distance. This is because in the near-Earth magnetic field, different particles arrive at the target surface at different times due to differences in energy, lateral velocity, and initial position. Waiting for all particles to arrive would require calculating a large number of meaningless time steps. Collecting particles from multiple time steps to the target surface would lead to beam aliasing. Therefore, the first arrival is chosen as the termination condition, ensuring that all particles stop at the same physical time step, thus guaranteeing statistical consistency in beam aliasing.

[0084] Step 3: Data pruning and consistency processing upon termination; When a particle is detected to have reached the target distance, the main loop is immediately exited. All arrays used to store RMS, centroid, and trajectory are automatically trimmed to the current effective step number, and beam spot density statistical calculation is performed.

[0085] At this point, all particles are at the same time step and have not proceeded to the next step, ensuring statistical physical consistency. This pruning mechanism is tightly coupled with the dynamic termination mechanism and is a necessary engineering logic.

[0086] Step 4: Enter beam spot density statistics; Dynamic termination provides a unique time step and a unique physical cross-section, making beam spot density statistics a non-aliasing process that accurately corresponds to spatial locations. Dynamic termination is a necessary prerequisite for subsequent statistical modules.

[0087] The technical benefits of an adaptive termination strategy include: (1) Improved computational efficiency: Efficiency can be improved by 40% to 70%, and it will not execute more than the necessary time steps. It will not waste a lot of computation due to overestimation of the number of running steps, and avoid meaningless trajectory advancement.

[0088] (2) Physical authenticity: It can ensure that the beam spot statistics correspond to the same physical cross section. Traditional methods (acquiring multiple time steps) will cause particles to come from different physical times, resulting in larger beam spot artifacts and "tailing" or "smearing" phenomena in the image. However, the dynamic termination proposed in this invention ensures that all particle states come from the same time step, thus guaranteeing the physical authenticity of the beam spot statistics.

[0089] (3) Low barrier to entry: Users only need to set the target distance, and the system will automatically complete step planning, termination judgment, data clipping, and spot statistics preparation, which greatly reduces the barrier to entry.

[0090] In traditional electron beam trajectory simulation systems, the number of steps required to simulate long-distance electron beam transmission is difficult to estimate accurately in advance. Pre-allocating a fixed-size array may lead to memory overflow or waste; while improper dynamic allocation can severely impact computational performance. MATLAB's dynamic array expansion is costly, requiring memory reallocation for each expansion. Expanding the array at every step would significantly slow down the computation, doubling or even more the overall simulation time.

[0091] This application employs a block-based, dynamically expanding memory management strategy, adaptively allocating memory resources according to demand during computation. The specific implementation process includes: Step 1: Block initialization; The system pre-allocates an "initial block size" upon startup. Before the simulation begins, the system pre-allocates a block of approximately 10,000 to store particle trajectories and state data. This avoids triggering dynamic allocation during the first write operation.

[0092] Step 2: Dynamic monitoring and expansion; In the main loop, the current usage is monitored. At each time step, the system checks if the current number of recorded steps is close to the upper limit of the current array block. If the array occupancy exceeds 99%, the expansion condition is triggered, and a new complete block is expanded at once (expanding by another 10,000 bits). All statistical arrays are expanded simultaneously. This avoids the cost of memory reallocation only once, prevents frequent array relocation, and ensures index consistency and data alignment.

[0093] Step 3: Optimize resource release; After dynamic termination, the array is trimmed to the actual usable length. When the transmission distance condition triggers termination, the main trajectory loop exits, and the system automatically trims all arrays to the current effective length, releasing unused resources. After trimming, there is no memory waste, the output array is completely neat, and the data sequence maintains consistency (facilitating plotting).

[0094] The technical benefits of the block-based dynamic expansion memory management strategy include: (1) Support long-distance simulation: ensure that the system can stably complete transmission simulation up to 100km.

[0095] (2) Efficient use of resources: It avoids memory waste caused by pre-allocating too large arrays and also prevents computation interruption caused by insufficient memory.

[0096] (3) Stable calculation process: It provides users with a "set-up and run" experience, without having to worry about complex memory management issues.

[0097] 4. Results Output and Visualization Module Data output: Saves electron beam center trajectory coordinates, envelope trajectory parameters (maximum offset in x / y / z directions), beam spot size (RMS lateral dimension), density distribution matrix, etc. in “output.mat” format; Image output: Automatically generates electron position distribution map, electron beam trajectory map, RMS lateral dimension during transmission, RMS deflection angle during transmission, and electron beam xy plane density distribution at the target distance. Supports exporting images to PNG / JPG format.

[0098] 5. System compatibility and adaptation module By encapsulating the MATLAB Runtime environment, system dependency issues are resolved, making it compatible with Windows 7 / 10 / 11 systems; at the same time, memory usage is optimized, allowing it to run stably on Windows 7 32-bit systems (minimum configuration: 4GB RAM, 500MB storage space).

[0099] The operation process of the simulation calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics provided in this application is as follows: (1) Environment startup: Double-click the software icon in Windows system to automatically load the MATLAB Runtime environment and pop up the graphical main interface; (2) Parameter settings: Set the initial electronic parameters, initial position, transmission direction and target distance by inputting or importing the "input.txt" file through the interface; select uniform / dipole / IGRF magnetic field in the "Magnetic Field Model" drop-down menu, and select normal / uniform distribution in the "Beam Spot Distribution" drop-down menu; (3) Simulation calculation: Click the “Start Calculation” button, and the software will automatically call the magnetic field model and trajectory algorithm, and display the calculation progress on the interface in real time; (4) Result viewing: After the calculation is completed, the interface will automatically jump to the "Result Display" page, which displays the transmission trajectory, RMS lateral dimension, RMS deflection angle, beam spot image, etc.; it supports clicking the "Export Data" and "Export Image" buttons to save the results.

[0100] The simulation and calculation system for near-Earth space electron beam propagation trajectory and beam spot characteristics provided in this application has the following beneficial effects: (1) Comprehensive parameter coverage: electron energy covers 50keV-5MeV, beam current intensity 1-10mA, and transmission distance 1-100km, meeting the simulation needs of multiple scenarios in near-Earth space, and expanding the parameter range by 2-3 times compared with existing software; (2) High simulation accuracy: By using the IGRF magnetic field model and Boris algorithm, the near-Earth space magnetic field environment is more realistically reflected, so that the physical phenomena such as deflection, cyclotron and transverse oscillation of the electron beam are accurately simulated. The trajectory calculation error is less than 5%, and the beam spot size calculation error is less than 3%, which is far better than the 20% error level of existing software. (3) Complete output results: Simultaneously output transmission trajectory, RMS lateral dimension, RMS deflection angle and beam spot image, providing full-dimensional data support for equipment design; (4) Easy to operate and highly compatible: The MATLAB-based graphical user interface integrates parameter setting, calculation execution and result visualization; no script writing is required, and novices can complete parameter setting within 5 minutes, which greatly reduces the threshold of use and improves work efficiency; it is compatible with Windows 7 / 10 / 11 systems and is compatible with older devices; (5) High computational efficiency: Parallel computing optimization is adopted. The simulation of 500,000 electrons with a transmission distance of 100km only takes 15-20 minutes, which is 30% more efficient than existing software.

[0101] Taking the simulation of a 3000keV Gaussian distributed electron beam propagation in an IGRF magnetic field as an example, the execution effect of the simulation calculation system for the near-Earth space electron beam propagation trajectory and beam spot characteristics provided in this application is illustrated: (1) Parameter settings: When the user runs the main program in the MATLAB environment, the main interface of the software will pop up, such as Figure 2 As shown. Click "Load input file" to import the pre-configured input.txt file (e.g., ...). Figure 3 As shown), the interface automatically updates to display all parameter values, such as Figure 4 As shown.

[0102] (2) Model selection: Users can select the desired "magnetic field model" and "initial distribution" through the drop-down menu.

[0103] Magnetic field model: IGRF; Initial distribution: Gaussian distribution.

[0104] (3) Simulation calculation: The user clicks the "Calculate" button, such as Figure 5 As shown.

[0105] (4) Output of results: The calculation results are stored as data and images in the file output.mat and the folder figures, respectively. The output.mat data is described as follows: Figure 6 As shown. The folder "figures" is described as follows. Figure 7 and Figure 8 As shown in Figure 5.png, the average and peak current densities at the target location are displayed in both the command window and Figure_info.png. The offset at the target location is shown in Figure 5.png.

[0106] Through the above process, this invention achieves high-fidelity simulation and comprehensive analysis of the entire process of electron beam transmission in near-Earth space.

[0107] This application may also provide a computer device, including: at least one processor, memory, at least one network interface, and a user interface. The various components in this device are coupled together via a bus system. It is understood that the bus system is used to implement communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0108] The user interface can include a display, keyboard, or clicking device. Examples include a mouse, trackball, touchpad, or touchscreen.

[0109] It is understood that the memory in the embodiments disclosed in this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0110] In some implementations, the memory stores elements such as executable modules or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0111] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. Programs implementing the methods of the embodiments of this disclosure can be included in the application programs.

[0112] In the above embodiments, the processor can also invoke programs or instructions stored in memory, specifically programs or instructions stored in an application program, for the following purposes: Follow the steps described above.

[0113] The above methods can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic diagrams disclosed above. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the disclosed methods can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0114] It is understood that the embodiments described in this application can be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application, or combinations thereof.

[0115] For software implementation, the technology of this application can be implemented by executing the functional modules (e.g., procedures, functions, etc.) of this application. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0116] This application may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, it can implement the steps in the above method embodiments.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application, and should all be covered within the scope of the claims of this application.

Claims

1. A simulation and calculation system for the transmission trajectory and beam spot characteristics of an electron beam in near-Earth space, characterized in that, The system includes: The parameter input module is used to receive parameters input by the user, map the user input parameters to the parameters required for calculation, use macro particles to replace actual particles, generate the initial phase space coordinates of the particles, and calculate the three-dimensional velocity vector of the particles that meet the emission requirements. The magnetic field modeling module has multiple built-in magnetic field models for simulating near-Earth orbit magnetic field environments. The trajectory and beam spot calculation module is used to solve the Lorentz equations of motion using the Boris algorithm, continuously updating particle momentum and position through iterative calculations to simulate particle trajectories. Simultaneously, it calculates the beam spot size for arbitrary transmission distances based on the RMS algorithm and generates a beam spot density distribution through particle density statistics. The results output and visualization module is used to output the electron beam center trajectory coordinates, envelope trajectory parameters, beam spot size, density distribution matrix, electron position distribution map, electron beam trajectory map, RMS lateral dimension during transmission, RMS deflection angle during transmission, and electron beam plane density distribution at the target distance.

2. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 1, characterized in that, The parameters input by the user include: Initial electron beam energy, initial beam spot size, initial position, beam intensity, macroparticle coefficient, target distance, initial RMS emittance, direction of propagation of relative magnetic field, date, magnetic field model, and initial beam distribution type; The initial location includes longitude, latitude, and altitude; The initial RMS emissivity includes an upper limit and a lower limit for emissivity; The transmission direction of the relative magnetic field includes the azimuth angle and the direction of the initial beam axis relative magnetic field.

3. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 2, characterized in that, The process of mapping user-input parameters to parameters required for calculation includes: Initial position mapping: Convert the input latitude and longitude into radians; superimpose the input altitude onto the Earth's average radius to obtain the particle's geocentric distance; map the coordinates to a three-dimensional Cartesian coordinate system according to the Earth's spherical geometry; obtain the final three-dimensional position vector, which serves as the particle's initial position; Transmission direction mapping: The three-dimensional direction of the IGRF magnetic field is determined based on the transmission position. Using the magnetic field direction as the first rotation axis, the azimuth angle of the input initial beam axis relative to the magnetic field is rotated around the magnetic field direction. Then, using the direction perpendicular to the magnetic field as the second rotation axis, the azimuth angle of the input initial beam axis relative to the magnetic field is rotated. The direction vector after the two rotations is the three-dimensional direction of ECEF.

4. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 2, characterized in that, The use of macroparticles to replace actual particles includes: The number of particles is reduced by dividing the actual number of particles tracked by the macro-particle coefficient.

5. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 2, characterized in that, The initial phase space coordinates of the generated particles include: Construct orthogonal bases e1, e2, and e3 for the beam axis, where e3 is the beam axis direction, e1 is any unit vector perpendicular to e3, and e2 is obtained by multiplying e3 by e1, ensuring the right-hand rule. Generate a lateral position distribution. If it is a uniform distribution, then random sampling is performed uniformly within the circular cross-section; if it is a Gaussian distribution, then lateral offsets are generated according to a normal probability distribution.

6. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 5, characterized in that, The calculation of the particle's three-dimensional velocity vector that satisfies the emission requirement includes: The initial RMS emission value is used as the upper and lower limits of the emission value to form the template interval for emission determination; An initial control factor for the lateral deflection angle is randomly generated to adjust the degree of particle deflection. Based on the current initial control factor, the particle lateral deflection angle is generated according to the set ratio. For each macroparticle, the angle deflection along the e1 and e2 directions is generated. The angular deflections in the directions of e1 and e2 are converted into directional offsets in three-dimensional space, then superimposed onto the main beam axis and normalized to generate the true three-dimensional initial velocity direction of the particle, and iterative calculation begins. The transverse RMS emissivity and longitudinal RMS emissivity are calculated using the RMS emissivity formula based on the second moment. If the emission is within the template range, the iteration terminates; if the emission is too small, the control factor is increased; if the emission is too large, the control factor is decreased. The emission is recalculated based on the adjusted control factor until the emission falls into the template range or the maximum number of iterations is reached. The final iteration result is used as the three-dimensional velocity vector of the particle that meets the emission requirements.

7. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 1, characterized in that, The process involves using the Boris algorithm to solve the Lorentz equations of motion, continuously updating particle momentum and position through iterative calculations to simulate particle trajectories, and simultaneously calculating the beam spot size for arbitrary transmission distances using the RMS algorithm. The beam spot density distribution is then generated through particle density statistics, including: In each calculation of particle momentum and position, the positions and velocities of all particles are summed and averaged to calculate the dynamic beam centroid of the current time step, which serves as the reference benchmark for the current beam. A local coordinate system is constructed with the calculated dynamic beam centroid as the origin. Under this coordinate system, the RMS beam size and RMS deflection angle are calculated in real time. Only the RMS beam size and RMS deflection angle are stored in the system, and all particle trajectory data are not saved. When the forward projection distance of the beam centroid reaches the set transmission distance, the particle trajectory advancement is stopped, and the positions of all particles at this time are projected onto a plane perpendicular to the beam's average velocity direction. The density is accumulated on the grid, and a two-dimensional beam spot density distribution map is output.

8. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 7, characterized in that, The updating of particle momentum and position also includes: After updating the momentum and position of all particles in each time step, the projected distance of the beam centroid in the initial beam direction is calculated. If the projected distance is less than the set target distance, the calculation continues in the next time step. If the target distance is reached or exceeded, the trajectory calculation loop is stopped and beam spot statistics are performed. After the loop stops, release any excess pre-allocated space in the storage space where the computation data is stored.

9. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 1, characterized in that, The system pre-allocates a set number of arrays at startup to store particle trajectory and state data. When calculating particle momentum and position in a loop, the system checks whether the current number of recorded steps has reached the set ratio of the current array in each loop. If it has reached the set ratio, the system expands the array space by a new set number of arrays at once.

10. The simulation and calculation system for near-Earth space electron beam transmission trajectory and beam spot characteristics according to claim 1, characterized in that, The system also includes: The system compatibility adapter module is used to encapsulate the MATLAB Runtime environment.

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