A simulation method and system for simulating an aircraft flight path

Abstract

This invention relates to the field of simulation model technology, specifically to a simulation method and system for simulating aircraft flight paths, comprising the following steps: acquiring electromagnetic fields and flight path parameters through a multi-frequency electromagnetic sensor array; dividing the space into a grid to form an electromagnetic distribution dataset; using the analytic hierarchy process (AHP) to assess the sensitivity of flight phases and generate dynamic electromagnetic thresholds; correcting flight path nodes based on spatial electromagnetic values; detecting instances of corrected nodes exceeding limits; combining candidate position coordinates to satisfy safety and maneuver constraints; and connecting a safe flight path sequence through weighted scoring to generate a simulated aircraft flight path coordinate sequence. This invention integrates real-time acquisition of multi-frequency electromagnetic field strength with spatial partitioning processing, automatically identifies key electromagnetically sensitive areas in flight path planning, quantifies environmental and task changes using hierarchical weighting, dynamically monitors electromagnetic exposure risks, coordinates spatial coordinates with safety threshold detection, balances path continuity and maneuver range, and improves environmental adaptability and task matching.

Description

Technical Field

[0001] This invention relates to the field of simulation modeling technology, and in particular to a simulation method and system for simulating aircraft flight paths. Background Technology

[0002] The field of simulation model technology involves digital modeling of the behavior of systems in physics, chemistry, biology, and engineering. It simulates and reproduces the system operation process through mathematical methods. Its core aspects include modeling the system state, abstracting the behavioral laws, defining the input-output relationship, and the evolution of the time process. It has wide applications in many industries such as aerospace, weaponry, and transportation, and can assist in research and development, prediction, training, and other scenarios.

[0003] Traditional methods for simulating aircraft flight paths involve modeling and reproducing the flight path of an aircraft in space. Based on a pre-defined sequence of waypoints, the Euclidean spatial distance between each waypoint is calculated. Then, the current position coordinates of the aircraft are calculated sequentially according to the given flight speed and time step. Mathematical interpolation operations, such as linear interpolation or spline interpolation, are used to generate a continuous flight path. The flight segments are then sequentially spliced ​​together to form a complete flight path, which is used to simulate the dynamic flight process of an aircraft in three-dimensional space.

[0004] Traditional methods rely solely on preset paths and conventional speeds to extrapolate flight safety, neglecting the impact of changes in the external electromagnetic environment on flight safety. They are unable to identify and respond to high electromagnetic risk areas in real time. When the flight path passes through areas of environmental electromagnetic interference, it is easy to miss path safety assessments. The lack of targeted protection for mission and environmental sensitivities leads to insufficient reliability of the flight path in dynamic and complex scenarios, limited risk avoidance capabilities, and a tendency for flight simulation to deviate from actual needs. This makes it difficult to meet the requirements of application scenarios with frequent electromagnetic disturbances or high safety requirements. Summary of the Invention

[0005] To address the shortcomings of traditional methods that rely solely on preset paths and conventional speeds, neglecting the impact of external electromagnetic environment changes on flight safety, failing to identify and respond to high electromagnetic risk areas in real time, and easily overlooking path safety assessments when the flight path passes through areas of environmental electromagnetic interference, this invention provides a simulation method for simulating aircraft flight paths, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a method for simulating aircraft flight paths, comprising the following steps: S1: Electromagnetic field intensity data is acquired through a multi-frequency electromagnetic sensor array, mission type identification and flight phase parameters are collected, spatial coordinate parameters and velocity vector parameters of track nodes are monitored, and electromagnetic field intensity data is partitioned using a spatial grid partitioning algorithm to form an electromagnetic distribution dataset. S2: Call the electromagnetic distribution dataset to obtain the mission type identifier and flight phase parameters. Input the dataset into the analytic hierarchy process (AHP) to calculate the electromagnetic susceptibility level threshold. Quantitatively evaluate the electromagnetic susceptibility of different flight phases through a multi-level weight allocation mechanism to generate a dynamic electromagnetic threshold parameter set. S3: Call the dynamic electromagnetic threshold parameter set, extract spatial coordinate parameters, monitor the electromagnetic intensity value corresponding to the coordinate parameters in the electromagnetic distribution dataset, perform numerical comparison and judgment, perform correction calculation on the spatial coordinate parameters, and generate corrected track node coordinates. S4: Detect the electromagnetic exposure values ​​in the corrected track node coordinates to exceed the limit, extract the candidate position coordinate parameters, compare the electromagnetic intensity values ​​of the candidate positions with the safety threshold, and verify whether the distance between adjacent nodes meets the aircraft maneuver range constraints to form a safe track candidate sequence.

[0006] As a further aspect of the present invention, the multi-level weight allocation mechanism is coupled with the flight phase to achieve dynamic avoidance of electromagnetic risk areas and self-matching trajectory adjustment, thereby improving the robustness and real-time performance of trajectory generation in complex mission scenarios. The electromagnetic distribution dataset includes grid cell electromagnetic values, spatial partition indexes, and data sampling density. The dynamic electromagnetic threshold parameter set includes low-frequency thresholds, mid-frequency thresholds, and high-frequency thresholds. The corrected track node coordinates include adjusted longitude, adjusted latitude, and adjusted altitude. The safe track candidate sequence includes an optional node set, inter-node connectivity, and path feasibility identifiers.

[0007] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Collects electromagnetic field intensity data through a multi-frequency electromagnetic sensor array, monitors the acquisition task type identifier, obtains flight phase parameters, records the spatial coordinate parameters and velocity vector parameters of the track node, summarizes them according to the time label, correlates the corresponding relationship of parameters under the same acquisition time and spatial coordinates, and generates a multi-dimensional spatiotemporal dataset. S102: Based on the multivariate spatiotemporal dataset, extract the spatial coordinates and velocity vectors of each data set, segment the coordinates corresponding to the electromagnetic field strength parameters, use spatial coordinate partitioning rules to determine the spatial grid boundary, and assign the differentiated coordinate records to the corresponding grid areas to obtain the spatial node partitioning results; S103: Based on the spatial node division results, the electromagnetic field intensity values ​​of the grid area are statistically analyzed. For grids with differences, the data of the same area are aggregated and integrated to form an electromagnetic distribution dataset.

[0008] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Call the electromagnetic distribution dataset, extract the task type identifier and classify it accordingly, filter it in combination with flight phase parameters, and generate a set of electromagnetic feature parameters by logically grouping the feature items related to the task in the electromagnetic distribution data and merging the same type of parameter items by numerical correspondence. S202: Based on the set of electromagnetic characteristic parameters, according to the analytic hierarchy process and combined with the identification items in the flight phase, data-level weights are assigned, and the electromagnetic characteristic parameters and the flight phase attribute values ​​are integrated by weighted calculation to obtain the sensitivity weighted value. The sensitivity weighted value is obtained by weighting and fusing electromagnetic characteristic parameters and flight phase attributes using the analytic hierarchy process (AHP), and is a comprehensive value reflecting the degree of sensitivity to changes in the electromagnetic environment. S203: Call the aforementioned sensitivity weighted values, apply specific division criteria to set boundaries for the real-time distribution of parameters during the flight phase, distinguish and compare each set of weighted results with the electromagnetic sensitivity level standard interval, and generate a dynamic electromagnetic threshold parameter set.

[0009] The specific segmentation standard refers to the logical segmentation of data based on the numerical range, temporal characteristics, or sensitivity level of flight phase parameters to match the electromagnetic sensitivity level standard.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: After obtaining the dynamic electromagnetic threshold parameter set, extract the spatial coordinate parameters, and sequentially retrieve the corresponding electromagnetic intensity value in the electromagnetic distribution dataset for each set of spatial coordinate parameters. Record the correspondence between coordinates and electromagnetic intensity, integrate them according to the spatial distribution law, and generate spatial electromagnetic intensity data. S302: Based on the spatial electromagnetic intensity data, the electromagnetic intensity values ​​of each set of spatial coordinate parameters are compared item by item with multiple thresholds in the dynamic electromagnetic threshold parameter set according to the judgment conditions of different time periods and spatial locations, and the items that are satisfied and not satisfied are counted to obtain the threshold comparison judgment result. S303: Based on the threshold comparison and determination results, for spatial coordinate parameters that are determined not to meet the threshold requirements, adjust the spatial position according to the threshold parameters in the dynamic electromagnetic threshold parameter set, correct the coordinate distribution, and generate corrected track node coordinates.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the electromagnetic exposure values ​​of the corrected track node coordinates, detect whether the electromagnetic exposure level of the node exceeds the electromagnetic exposure safety threshold, divide the positions that exceed the threshold into parameter categories, classify them into candidate coordinates, and obtain the set of coordinate parameters of the candidate positions that exceed the limit. S402: Call the set of candidate position coordinate parameters for exceeding the limit, extract the electromagnetic intensity values ​​of the candidate coordinate points, compare them item by item with the set electromagnetic intensity safety threshold, filter the parameters that are not greater than the threshold, and summarize to obtain the set of position parameters that comply with the threshold.

[0012] The electromagnetic intensity safety threshold refers to the maximum permissible electromagnetic exposure level that ensures the aircraft and personnel are protected from electromagnetic radiation hazards.

[0013] S403: Based on the threshold compliant position parameter set, sequentially verify the distance parameters of each pair of adjacent nodes to ensure that the distance between nodes is within the range of the aircraft's maneuverability, filter out node combinations that meet the requirements, and generate a safe trajectory candidate sequence.

[0014] As a further aspect of the present invention, the method further includes step S5: S5: Call the coordinate parameters in the candidate safe flight path sequence, input the candidate safe flight path sequence parameters into the weighted scoring algorithm for scoring calculation, connect the selected node coordinates according to the time sequence, and generate a simulated aircraft flight path coordinate sequence; The simulated aircraft track coordinate sequence includes sorted node coordinates, node connection order, and track continuity.

[0015] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Call the three-dimensional coordinate parameters and timestamps of the nodes in the candidate safe track sequence, arrange them in chronological order to construct a node coordinate set, integrate the node time index and spatial position, perform node distribution value calculation, and generate candidate path node position distribution values; S502: Based on the distribution values ​​of the candidate path node positions, the node coordinate parameters and the track safety factor are weighted, the weighting factor is input into the weighted scoring algorithm, the node safety score is calculated by combining the weight coefficients and the weighted sum is obtained to obtain the total weight score of the track node. S503: Based on the total weight score of the track nodes, extract the candidate track sequence with high score, connect the coordinates of adjacent nodes in the sequence in sequence according to the time index, splice the continuous coordinate chain and arrange it into a time-ordered flight path to generate a simulated aircraft track coordinate sequence.

[0016] As a further aspect of the present invention, the candidate path node position distribution value is a value calculated based on the distribution density, relative distance and spatial clustering degree of the node's three-dimensional coordinates in the time series; The total weighted score of the track nodes is the comprehensive safety score of the track, which is calculated by weighting the node position distribution value with multiple track safety factors and then using a weighted scoring algorithm. The weighted scoring algorithm combines node spatial coordinates, electromagnetic exposure values, and track safety factors, and generates track scores by weighting and mini-maximum normalization methods.

[0017] A simulation system for simulating aircraft flight paths, comprising: The electromagnetic environment perception module acquires electromagnetic field intensity data through a multi-frequency electromagnetic sensor array, monitors mission type identifiers, flight phase parameters, track node spatial coordinate parameters, and velocity vector parameters, and uses a spatial grid partitioning algorithm to process the data into partitions, generating an electromagnetic distribution dataset, which is then transmitted to the sensitivity assessment module. The sensitivity assessment module calls the electromagnetic distribution dataset to obtain the mission type identifier and flight phase parameters, calculates the electromagnetic sensitivity level threshold using the analytic hierarchy process, quantifies the electromagnetic sensitivity of different flight phases through a multi-level weighting mechanism, generates a dynamic electromagnetic threshold parameter set, and passes it to the trajectory optimization module. The trajectory optimization module calls the dynamic electromagnetic threshold parameter set, extracts spatial coordinate parameters, monitors the electromagnetic intensity values ​​corresponding to the coordinate parameters in the electromagnetic distribution dataset, performs numerical comparison and judgment, performs correction calculations on the spatial coordinate parameters, generates corrected trajectory node coordinates, and transmits them to the safety verification module. The safety verification module detects whether the electromagnetic exposure values ​​in the corrected track node coordinates exceed the limit, extracts candidate position coordinate parameters, compares the electromagnetic intensity values ​​of the candidate positions with the safety threshold, verifies whether the distance between adjacent nodes meets the aircraft's maneuver range constraints, obtains a safe track candidate sequence, and transmits it to the trajectory generation module. The trajectory generation module calls the coordinate parameters in the candidate safe flight path sequence, inputs the node parameters to the weighted scoring algorithm for scoring calculation, connects the selected node coordinates according to the time sequence, and generates a simulated aircraft flight path coordinate sequence.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by combining real-time acquisition of multi-frequency electromagnetic field intensity with spatial partitioning processing, the trajectory planning can automatically identify key electromagnetically sensitive areas according to different tasks and stages. Through a hierarchical weighting mechanism, it can quantify responses to various environmental and task changes, improve the dynamic monitoring of electromagnetic exposure risks in the flight path, and promptly correct node coordinates to avoid high-risk areas. It can simultaneously detect and screen spatial coordinates and safety thresholds, taking into account both trajectory continuity and aircraft maneuverability, making the trajectory simulation process more in line with the needs of complex environments, and effectively improving the environmental adaptability and task matching of the trajectory planning. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0022] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0023] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0024] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0025] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0026] Please see Figure 1 This invention provides a method for simulating aircraft flight paths, comprising the following steps: S1: Electromagnetic field intensity data is acquired through a multi-frequency electromagnetic sensor array, mission type identification and flight phase parameters are collected, spatial coordinate parameters and velocity vector parameters of track nodes are monitored, and electromagnetic field intensity data is partitioned using a spatial grid partitioning algorithm to form an electromagnetic distribution dataset. S2: Call the electromagnetic distribution dataset to obtain the mission type identifier and flight phase parameters. Input the dataset into the analytic hierarchy process (AHP) to calculate the electromagnetic susceptibility level threshold. Quantitatively evaluate the electromagnetic susceptibility of different flight phases through a multi-level weight allocation mechanism to generate a dynamic electromagnetic threshold parameter set. S3: Call the dynamic electromagnetic threshold parameter set, extract spatial coordinate parameters, monitor the electromagnetic intensity value corresponding to the coordinate parameters in the electromagnetic distribution dataset, perform numerical comparison and judgment, perform correction calculation on the spatial coordinate parameters, and generate corrected track node coordinates. S4: Detect and correct cases where electromagnetic exposure values ​​in the coordinates of track nodes exceed limits, extract candidate position coordinate parameters, compare the electromagnetic intensity values ​​of candidate positions with safety thresholds, and verify whether the distance between adjacent nodes meets the aircraft's maneuvering range constraints, thus forming a safe track candidate sequence. S5: Call the coordinate parameters in the candidate safe flight path sequence, input the candidate safe flight path sequence parameters into the weighted scoring algorithm for scoring calculation, connect the selected node coordinates according to the time series, and generate a simulated aircraft flight path coordinate sequence; The electromagnetic distribution dataset includes grid cell electromagnetic values, spatial partition index, and data sampling density. The dynamic electromagnetic threshold parameter set includes low-frequency threshold, mid-frequency threshold, and high-frequency threshold. The corrected track node coordinates include adjusted longitude, adjusted latitude, and adjusted altitude. The safe track candidate sequence includes an optional node set, inter-node connectivity, and path feasibility identifier. The simulated aircraft track coordinate sequence includes sorted node coordinates, node connection order, and track continuity.

[0027] Please see Figure 2 The specific steps of S1 are as follows: S101: Collects electromagnetic field intensity data through a multi-frequency electromagnetic sensor array, monitors the acquisition task type identifier, obtains flight phase parameters, records the spatial coordinate parameters and velocity vector parameters of the track node, summarizes them according to the time label, correlates the corresponding relationship of parameters under the same acquisition time and spatial coordinates, and generates a multi-dimensional spatiotemporal dataset. When collecting electromagnetic field strength data using a multi-frequency electromagnetic sensor array, the sensor array is first deployed inside the structure of the simulated aircraft or at the wingtip. Each sensor group needs to be assigned a corresponding frequency band, covering a frequency range of 5Hz to 500kHz. For example, the six selected sensor groups are set to the 10Hz, 50Hz, 100Hz, 1kHz, 10kHz, and 100kHz frequency bands, respectively. During takeoff, the acquisition process is initialized via commands from the main control unit. The sensor array collects the electromagnetic field strength value of the current frequency band once per second and marks the sensor number. The signal is converted into standardized voltage data by a multi-channel parallel processing circuit and recorded in the buffer unit. The flight mission type identifier is obtained through the mission ID set during mission initialization, such as "T01" for aerial survey missions and "T02" for mapping missions. The data association field is named after the mission ID. D is used as the primary key encoding; when acquiring flight phase parameters, the pitch angle, roll angle, and altitude data fed back by the airborne inertial navigation system and accelerometers are combined to determine whether the flight is in the takeoff, climb, cruise, descent, or landing phase, and corresponding thresholds are set: a pitch angle greater than 12° and an altitude gain rate exceeding 3m / s indicates the climb phase; a horizontal velocity constant ±5% and an altitude change less than 2m indicates the cruise phase; and a roll angle greater than 30° accompanied by a vertical velocity decrease exceeding 2m / s indicates the descent phase. When recording the spatial coordinate parameters of the track nodes, GPS data is extracted once per second and converted into three-dimensional coordinates (x, y, z) in the geographic coordinate system, where x is the longitude direction, y is the latitude direction, and z is the altitude. For example, at t=12s, the node coordinates are (114.25123, 30.57643, 340.2). The velocity vector consists of three-dimensional velocity components (v... x v y v z The velocity vector is calculated from the coordinates of two consecutive moments, using the following formula: ;

[0028] Taking t=12s and t=11s as examples, the node coordinates are (114.25123, 30.57643, 340.2) and (114.25118, 30.57639, 338.0) respectively. After converting to geographical distance, we get: ;

[0029] The time stamps are based on the system time and are accurate to the millisecond level. For example, 12.045s corresponds to the timestamp "2025-07-31-T12:00:12.045Z". The data summarization process is based on each record. At the sampling time, the sensor frequency band, electromagnetic intensity value, spatial coordinates, velocity vector, flight stage, and mission ID are matched at the row level to construct a spatiotemporal parameter correspondence structure array. The parameters are then bound to the time stamps for data processing, as shown below:

[0030] Table 1: Multi-source flight parameter sampling table As shown in Table 1, a complete parameter chain is established using fields such as task ID, time, and frequency band. Spatial coordinates and velocity parameters are dynamically updated. Each record corresponds to a unique time and is stored in a structure, ultimately forming a multivariate dataset containing electromagnetic spectrum data and three-dimensional spatiotemporal flight parameters. The "induction" operation in this process refers to aggregating the fields of differentiated frequency band electromagnetic data, three-dimensional coordinate data, and velocity information collected at the same time, using the timestamp as the primary key. During execution, the data fields are integrated using key-value pairs, and a complete sample data item is constructed every second.

[0031] S102: Based on a multivariate spatiotemporal dataset, extract the spatial coordinates and velocity vectors of each data set, segment the coordinates corresponding to the electromagnetic field strength parameters, use spatial coordinate partitioning rules to determine the spatial grid boundary, and assign the differentiated coordinate records to the corresponding grid areas to obtain the spatial node partitioning results; Based on a multivariate spatiotemporal dataset, after extracting the spatial coordinates and velocity vectors from each data set, it is necessary to analyze the values ​​of the coordinate vectors (x, y, z) one by one to confirm their distribution throughout the entire spatial region. Specifically, based on each record in the track data, the spatial coordinates (x, y, z) are read item by item, and the corresponding velocity vectors (v) are analyzed. x v y v zThe electromagnetic field strength parameter is also processed as a binding parameter. During processing, the program determines whether the z-axis height is greater than the specified flight altitude threshold of 200m. If it is, it is included in the valid track point set. Next, the electromagnetic field strength parameter needs to be segmented and classified. For example, the electromagnetic field strength thresholds are set as follows: <2μT is weak electromagnetic, 2-4μT is moderate electromagnetic, and >4μT is strong electromagnetic. The corresponding spatial coordinates (x, y, z) are assigned to three different spatial node sets so that the spatial distribution can be divided according to the electromagnetic level later. Spatial grid division is then performed. In the time-division process, the three-dimensional spatial boundary range needs to be set. For example, the x-axis direction is set to 114.25100 to 114.25200, with a latitude and longitude span of 0.001 degrees, approximately equal to 111m; the y-axis direction is set to 30.57600 to 30.57700; and the height z-direction is set to 300m to 400m. Then, the above three-dimensional space is divided into equally spaced grid cells, with each grid cell having a side length of 10m × 10m × 10m. This results in 11 segments in the x-direction, 11 segments in the y-direction, and 10 segments in the z-direction. A total of 1210 three-dimensional mesh elements were obtained. Then, a positioning operation was performed on each extracted (x, y, z) coordinate group to determine whether its x, y, and z coordinates fell within the boundary range of a certain mesh element. If the coordinates x ∈ [114.25110, 114.25120], y ∈ [30.57650, 30.57660], and z ∈ [330, 340], then the point was assigned to the i-th three-dimensional mesh. Points meeting the conditions had their electromagnetic intensity parameters bound to the corresponding mesh number, and a structure was generated. The record is constructed as "grid number - electromagnetic intensity value - velocity vector". During this process, it is necessary to further determine whether there is already a record in the current grid. If there is, the new electromagnetic data is added to the electromagnetic list under the grid; otherwise, a new record is created. During the execution, the coordinate assignment action is completed through floating-point interval comparison. A spatial precision Δ=0.00005 needs to be set as the judgment range to avoid inaccurate allocation caused by boundary errors. The spatial node division result is the overall division set after the track points are mapped to the specified grid according to their spatial position and electromagnetic level.

[0032] S103: Based on the spatial node division results, the electromagnetic field intensity values ​​of the grid area are statistically analyzed. For grids with differences, the data of the same area are aggregated and integrated to form an electromagnetic distribution dataset. Based on the spatial node division results, it is necessary to statistically analyze the numerical distribution of electromagnetic field intensity in each grid region. The specific process is as follows: First, traverse each recorded grid number and extract the list of electromagnetic intensity values ​​bound to that grid. For example, if the list of electromagnetic intensity values ​​under grid number G1 is [3.1, 3.5, 3.3, 2.9], then perform numerical statistical operations on it, calculating the maximum value, minimum value, mean, and standard deviation respectively. The formula for calculating the mean is: ;

[0033] in Let n be the electromagnetic intensity value recorded each time, and n be the number of records in the grid. If there are 4 records in G1, then calculate: ;

[0034] Similarly, the maximum value is 3.5 μT, the minimum value is 2.9 μT, and the standard deviation is obtained using conventional calculation methods. ;

[0035] For grid regions with multiple records and significant distribution differences, such as a grid where the difference between the maximum and minimum values ​​exceeds 1.5 μT, the grid is marked as a "high-difference area." Its records undergo interval aggregation processing, which involves regrouping the records according to their electromagnetic intensity levels, such as the aforementioned weak electromagnetic group [1.8, 1.9], medium electromagnetic group [3.0, 3.2], and strong electromagnetic group [4.2, 4.3]. The mean of each group is calculated, and this mean represents the typical intensity value of that electromagnetic intensity level within the current grid. Finally, the electromagnetic data from the grid intervals are integrated to form a spatially distributed structured dataset of electromagnetic intensity. Each record includes the following fields: grid number, typical electromagnetic intensity level, mean electromagnetic intensity, maximum difference value, and intensity variability label, completing this stage of the data integration process.

[0036] Please see Figure 3 The specific steps of S2 are as follows: S201: Call the electromagnetic distribution dataset, extract the task type identifier and classify it accordingly, filter it in combination with flight phase parameters, and generate a set of electromagnetic feature parameters by logically grouping the feature items related to the task in the electromagnetic distribution data and merging the same type of parameter items by numerical correspondence. The electromagnetic distribution dataset is accessed by first reading the grid number, electromagnetic intensity value, and velocity vector value corresponding to each record. These are then mapped one-to-one with the task type identifier. For example, if the task type identifier is "T01," the set of grid numbers labeled in that task needs to be filtered. By jointly screening the "task type" and "flight phase" fields in the electromagnetic data structure, the record group with the flight phase parameter "cruise" is obtained. The electromagnetic data is then bound to the flight phase to form a binary parameter group. Logical grouping is then performed according to the feature items. During execution, the electromagnetic intensity value is divided into numerical ranges. For example, less than 2μT is classified as "weak electromagnetic group," 2~4μT as "medium electromagnetic group," and greater than 4μT as "strong electromagnetic group." Flight phase information is further extracted from the records in each electromagnetic level group. For example, if the electromagnetic intensity in record R01 is 3.5μT and the flight phase is "climb," it is classified into the "medium electromagnetic-climb group," and a merging operation is performed. Records grouped together are merged into a single feature parameter vector. For example, group G02 contains records R02, R07, and R11 with electromagnetic values ​​of 2.8 μT, 3.1 μT, and 3.4 μT respectively, all belonging to the "cruising" flight phase. These are then merged into a vector [2.8, 3.1, 3.4], corresponding to the group name "Medium Electromagnetic - Cruise Group". For each electromagnetic feature parameter vector, its mean, variance, and number of records are recorded as statistical features of that group, forming a feature vector. Set F = {(Medium Electromagnetic - Climbing Group, [3.1, 3.4], Group Mean = 3.25 μT, Number of Records = 2), (Strong Electromagnetic - Descending Group, [4.6, 4.9], Group Mean = 4.75 μT, Number of Records = 2)...}, where the statistical characteristics are the arithmetic mean of the recorded electromagnetic values ​​and the variance is the average of the squared deviations of the records from the mean. For example, if the group "Strong Electromagnetic - Cruise Group" contains records [4.1, 4.3, 4.6] μT, then the mean is:

[0037] (4.1+4.3+4.6) / 3=4.33μT;

[0038] The variance is:

[0039] [(4.1-4.33)²+(4.3-4.33)²+(4.6-4.33)²] / 3=0.043μT²;

[0040] The resulting feature set is the basic parameter set for subsequent fusion calculations.

[0041] S202: Based on the set of electromagnetic characteristic parameters, according to the analytic hierarchy process, combined with the identification items in the flight phase, data-level weights are assigned, and the electromagnetic characteristic parameters and the flight phase attribute values ​​are integrated by weighted calculation to obtain the sensitivity weighted value. The sensitivity-weighted value is obtained by weighting and fusing electromagnetic characteristic parameters and flight phase attributes using the analytic hierarchy process (AHP), and is a comprehensive value reflecting the system's sensitivity to changes in the electromagnetic environment.

[0042] Based on the set of electromagnetic characteristic parameters, the electromagnetic intensity value and corresponding flight phase attribute value of each characteristic item are extracted. A manually set hierarchical structure is used to assign weights; for example, if "cruise" is the primary flight phase, its weight... Secondary stages, such as "climb" and "descent," have weights of [weight missing]. Then, characteristic weights are assigned to different electromagnetic levels. For example, "weak electromagnetic" is assigned a value of 0.2, "medium electromagnetic" is assigned a value of 0.3, and "strong electromagnetic" is assigned a value of 0.5. Simultaneously, the sensitivity weighting value is calculated by combining the aforementioned average characteristic parameters with the flight phase values. For example, if the electromagnetic characteristic term p = 3.3 μT and the flight phase parameter q = 0.6 (0.6 for the cruise phase), the sensitivity weighting value is calculated using the formula: ;

[0043] in, Represents the sensitivity-weighted value. This represents the allocation hierarchy weight corresponding to the i-th group of electromagnetic characteristic parameters. This represents the value of the i-th group of electromagnetic characteristic parameters. This represents the flight phase attribute value associated with the i-th group of electromagnetic characteristic terms. The correction weights represent the electromagnetic characteristic parameters of the j-th group. This represents the value of the electromagnetic characteristic parameter after merging the j-th group. This represents the attribute parameter value of the j-th group of flight phases. Represents the segment weight of the k-th flight phase. The value of the electromagnetic characteristic parameter representing the actual distribution of the k-th segment. The parameter values ​​for the k-th segment represent the flight phase parameters, i is the index number of the current group in the parameter set, n is the number of electromagnetic feature groups assigned weights, j is the number of correction weight parameter groups, m is the total number of correction weight groups, k is the index number of the flight phase weight parameters, and h is the total number of flight phase segments.

[0044] Then calculate the first fusion value: ;

[0045] Its weighted value is: ;

[0046] These weighted terms are recorded separately, summed, and then compared with the merged electromagnetic parameters. With flight phase attributes The absolute difference multiplied by the correction weight For example, if the electromagnetic intensity r = 4.6 μT and the flight phase attribute s = 0.75 for the "Strong Electromagnetic-Climbing Group", then... If the correction weight of this group is The corresponding item is Finally, the absolute value of the difference between the numerator terms is calculated and then divided by the weighted sum of the multiple segments. For example, the (u, v) parameter pairs corresponding to the three flight segments "takeoff", "cruise", and "landing" are (3.2, 0.5), (4.6, 0.6), and (2.7, 0.3) respectively, with their weights being respectively... , , Then the denominator is calculated as follows: ;

[0047] If the numerator value is: ;

[0048] The final sensitivity weighted value is: ;

[0049] The results indicate that the combined sensitivity of the combination of differentiated electromagnetic levels and flight phases is low under the selected mission type.

[0050] The advantage of the formula lies in the introduction of merging bias. The term can measure the inconsistency between electromagnetic response and flight behavior, and by setting weights at the flight phase level, the overall distribution is harmonized and allocated, further clarifying the sensitivity of the weighted terms, thereby improving the accuracy of the correspondence between the parameter set and actual flight behavior.

[0051] S203: Call the sensitivity weighted values, apply specific division standards to set boundaries for the real-time distribution of parameters during the flight phase, distinguish and compare each set of weighted results with the electromagnetic sensitivity level standard interval, and generate a dynamic electromagnetic threshold parameter set. Specific segmentation refers to the logical segmentation of data based on the numerical range, temporal characteristics, or sensitivity level of flight phase parameters in order to match electromagnetic sensitivity level standards.

[0052] After calling the sensitivity weighted values, the real-time distribution data of the flight phase parameters first needs to be analyzed. For example, a numerical parameter mapping table is established for the timestamps and phase types (such as cruise 0.6, landing 0.3) in the flight phase. Then, a segmentation operation based on the division criteria is introduced. Taking time series intervals or flight altitude division as references, the following segmentation criteria are set: the parameter range for the flight phase is 0.0~0.4 for "takeoff", 0.4~0.7 for "cruise", and 0.7~1.0 for "landing". Each segment is compared with the corresponding sensitivity weighted value. Further, according to the electromagnetic sensitivity level standard (weak <0.02, medium 0.02~0.05, strong >0.05), a threshold matching judgment is set for each segment. For example, in the above example, the sensitivity weighted value of the cruise segment is 0.0197, which is judged as "weakly sensitive". This result is bound to the corresponding time period label, and finally a structured electromagnetic threshold parameter set is generated. The recorded fields are as follows: flight segment number, phase parameter range, weighted sensitivity value, and sensitivity level.

[0053] Table 2: Sensitivity Parameters During Flight Phases As shown in Table 2, the sensitivity weighting values ​​and classification levels corresponding to the flight phases are listed, which are convenient for subsequent mission scheduling and phase avoidance processing.

[0054] Please see Figure 4 The specific steps of S3 are as follows: S301: After obtaining the dynamic electromagnetic threshold parameter set, extract the spatial coordinate parameters, and sequentially retrieve the corresponding electromagnetic intensity value in the electromagnetic distribution dataset for each set of spatial coordinate parameters. Record the correspondence between coordinates and electromagnetic intensity, integrate them according to the spatial distribution pattern, and generate spatial electromagnetic intensity data. After obtaining the dynamic electromagnetic threshold parameter set, the threshold definition structure of each item in the parameter set is first parsed, and the spatial coordinate parameter set bound to the corresponding spatial region is extracted in turn. The extraction process reads each track node sequentially according to a pre-established sequence, and each set of spatial coordinate parameters... These all represent the spatial location of monitoring equipment or vehicles at different points in time, such as the coordinates of a certain node recorded in the flight track. The corresponding sampling time is Then, the electromagnetic distribution dataset was accessed. This dataset is constructed in a three-dimensional raster format, with each cell containing spatial location information. Its corresponding electromagnetic intensity value Retrieve target spatial points The nearest grid point is Then its electromagnetic strength is obtained as After the retrieval is completed, the spatial coordinates are paired with their electromagnetic intensity values ​​and recorded to obtain, as shown below. The coordinate data and electromagnetic intensity are sorted according to their original time series to construct a three-dimensional spatial point series. The data are then categorized based on their distribution trends in three-dimensional space, such as in low-altitude regions. The range of internal electromagnetic intensity fluctuations is generally as follows: In the hollow region for In the high-altitude region The fluctuation range increased to We construct and integrate a spatial electromagnetic intensity dataset to generate a target three-dimensional electromagnetic field distribution structure.

[0055] S302: Based on spatial electromagnetic intensity data, the electromagnetic intensity values ​​of each set of spatial coordinate parameters are compared item by item with multiple thresholds in the dynamic electromagnetic threshold parameter set according to the judgment conditions of different time periods and spatial locations. The items that are satisfied and not satisfied are counted to obtain the threshold comparison judgment result. Based on spatial electromagnetic intensity data, for each set of coordinates Extract its corresponding electromagnetic intensity value Then, based on the principle of differentiated time period division, the entire flight path was first divided into time axis segments. Sub-period Within each sub-period, the coordinate points are grouped according to their spatial distribution. For example, the coordinate points included in the first sub-period are... For each spatial location Compare multiple threshold values ​​in the dynamic electromagnetic threshold parameter set item by item. For example, setting the threshold set as The judgment condition is: if This is recorded as low electromagnetic intensity. Medium intensity This indicates high electromagnetic intensity, and further if This is the overload region, for any Perform classification judgment, for example Then it satisfies Classified as medium intensity, the number of coordinate points occupied by each category is counted during the classification process to obtain the ratio of those meeting and not meeting the threshold conditions in the spatial region at each time period, forming a judgment record with the following structure: There are 23 coordinate points within the section, of which... There are 18 conditions, which are satisfied. The conditions are 10, more than There are two components, with proportions of 78.3%, 43.5%, and 8.7% respectively. The proportions are determined based on the actual threshold configuration standard to see if they meet the requirements. If the proportion meeting a certain threshold condition is less than the preset lower threshold... If so, then record the sub-period and the items that are not satisfied.

[0056] S303: Based on the threshold comparison judgment result, for spatial coordinate parameters that are judged not to meet the threshold requirements, adjust the spatial position according to the threshold parameters in the dynamic electromagnetic threshold parameter set, correct the coordinate distribution, and generate corrected track node coordinates. Based on the threshold comparison results, for the coordinate points that were determined to not meet the requirements in the aforementioned comparison process... First, read the node number of its track, find the five nearest points in time, and extract the coordinate set of the neighboring points. With corresponding electromagnetic intensity set Comparing multiple points, the point with the most stable electromagnetic intensity is the one that satisfies... Points (such as setting) If adjacent points , ,but The region was identified as an abnormal jump region. Based on the correction standard in the dynamic electromagnetic threshold parameter set, the coordinate adjustment step size was set to [value missing]. On this basis Adjusted to This means performing offset correction, and the corrected point coordinates are... The electromagnetic distribution dataset is called again to calculate its new electromagnetic intensity. ,like Then it satisfies The coordinates of the track node are updated, and the corrected track dataset is finally generated. The electromagnetic field strength of the non-satisfied region is adjusted within the interval, while the structural continuity remains unchanged.

[0057] Table 3: Electromagnetic Intensity and Correction Status of Track Nodes As shown in Table 3, the original electromagnetic intensity value of track node number 47 was 5.2µT, which exceeded the preset threshold. After correction, its electromagnetic intensity decreased to 4.4µT, which met the electromagnetic threshold condition, and the coordinate correction was completed.

[0058] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the electromagnetic exposure values ​​of the corrected track node coordinates, detect whether the electromagnetic exposure level of the node exceeds the electromagnetic exposure safety threshold, divide the positions that exceed the threshold into parameter categories, classify them into candidate coordinates, and obtain the set of coordinate parameters of the candidate positions that exceed the limit. Based on the electromagnetic exposure values ​​derived from the corrected track node coordinates, it is first necessary to analyze the electromagnetic exposure values ​​for each corrected track node coordinate. Corresponding electromagnetic exposure value The data was acquired by correcting the original flight path points using an inertial navigation system. Based on real-time electromagnetic radiation power data collected along the flight path, and combined with the distance and direction of the aircraft from the source, the electromagnetic field intensity at the actual location was derived. A point-by-point sampling method was used to establish a mapping table that associates the spatial coordinates of each node with the electromagnetic field intensity corresponding to its time-domain sampling. Based on an equal time-sharing strategy within the aircraft's cruise path over t seconds, several spatial locations were selected in each segment of the path for electromagnetic data recording. For example, during a single flight, the electromagnetic power density value was acquired at a sampling frequency of once per second, resulting in the following electromagnetic exposure intensity data:

[0059] Table 4: Electromagnetic Exposure Values As shown in Table 4, the electromagnetic exposure values ​​for each node are... With respect to the set electromagnetic exposure safety threshold Compare and perform judgment operations During execution, nodes that meet the conditions are indexed and extracted, that is, from the full node set. Select a subset of electromagnetic exposure values ​​that exceed a threshold. If in the above operations It is not a point exceeding the limit, but If the condition is out of bounds, the final output will be the out-of-bounds node number and its coordinate parameters, forming a candidate set of out-of-bounds location parameters. For example, this set contains node N005 with coordinates (140, 170, 28) and an exposure value of 12.5 μW / cm², thus completing the construction process of this set.

[0060] S402: Call the set of candidate position coordinate parameters for exceeding the limit, extract the electromagnetic intensity values ​​of the candidate coordinate points, compare them with the set electromagnetic intensity safety threshold item by item, filter the parameters that are not greater than the threshold, and summarize to obtain the set of position parameters that comply with the threshold. Electromagnetic intensity safety threshold refers to the maximum permissible electromagnetic exposure level that ensures aircraft and personnel are protected from electromagnetic radiation hazards;

[0061] Call the candidate position coordinate parameter set for the out-of-limit For each candidate coordinate point, the electromagnetic intensity is resampled in a refined manner. A cubic grid is constructed with the coordinate point as the center and a radius of r=5m. A sampling point is distributed every 1m within this area, and the actual electromagnetic field intensity value of the sub-sampling point is calculated. , forming an array Then each value is compared with the electromagnetic strength safety threshold. Perform item-by-item comparison and execute operations. , will meet the conditions corresponding coordinates Labeling and extraction are performed to form a threshold-compliant location parameter set. For example, taking N005 as the center, 27 points in the cubic grid are scanned. Assuming that 16 of these points meet the condition, the spatial coordinates of these 16 points are included in the compliance parameter set. In this process, the original coordinates of the out-of-limit points are not directly retained. Instead, safer points are selected through sampling within the region, resulting in the following example of compliance point parameters:

[0062] Example compliance point set (partial): ,

[0063] Each coordinate satisfies This ensures that it is at a threshold compliance position.

[0064] S403: Based on the threshold compliant position parameter set, verify the distance parameters of each pair of adjacent nodes in turn to ensure that the distance between nodes is within the range of the aircraft's maneuverability, select the node combinations that meet the requirements, and generate a candidate sequence of safe flight paths; Based on the threshold-compliant location parameter set, the spatial distance between any two points is sequentially calculated. The verification is performed, and the calculation method is as follows: ;

[0065] The result Critical distance corresponding to the maximum maneuverability of an aircraft Make a judgment and set Then the judgment logic is executed as follows: For pairs of points that meet the conditions, an ordered connection relationship is established sequentially. This results in a sequence of multiple compliant nodes, which are stored using a graph structure. Nodes are spatial coordinates, and edge weights represent the distances between nodes. During execution, a breadth-first search strategy is used to sequentially attempt the generated contiguous path. For the starting point... To the end point The goal is to find directed paths consisting of compliant nodes, where the distance between adjacent points does not exceed 15m, thereby forming a candidate sequence of safe flight paths. For example, a point sequence exists within the set of compliant points. The distances are as follows: ; ; ;

[0066] All are less than 15m, therefore they can form a compliant flight path, and this path will eventually be... The safe flight path candidate sequences were included in the results set.

[0067] Please see Figure 6 The specific steps of S5 are as follows: S501: Call the three-dimensional coordinate parameters and timestamps of the nodes in the candidate sequence of safe flight paths, arrange them in chronological order to construct a set of node coordinates, integrate the node time index and spatial position, perform node distribution value calculation, and generate candidate path node position distribution values; When retrieving the 3D coordinate parameters and timestamps of nodes in the candidate safe flight path sequence, the spatial location data (x, y, z) and corresponding time information t of each node need to be read from the preset candidate path sequence database. During the reading process, the node numbers are extracted one by one and arranged in ascending order according to time t, resulting in a data stream of nodes arranged in the order of flight. To facilitate subsequent processing, this data stream is integrated into a matrix form to construct a node coordinate set as follows: ;

[0068] During execution, the time difference between nodes needs to be calculated. Extract and combine with the position change vector in three-dimensional space. Calculations are performed to use these as fundamental parameters for evaluating node distribution, combined with the definition of distribution density, to... The formal calculation of node distribution values, taking the inverse proportional relationship to illustrate the path density per unit spacetime, if , ,but After calculating the distribution values ​​of each group of consecutive nodes, an array is formed. Furthermore, a combination matching method is used, combining the time index with the spatial location of the node, such as matching the i-th time point with the spatial coordinates. Pair them up and assign a corresponding distribution value to each paired node. Integrated into a triplet format and recorded as A real-world flight example is a drone taking off from point A (100, 200, 300) m, moving along 5 nodes to point F. The sampling time interval is fixed at 5 seconds, but the flight speed is slightly adjusted. The distances between the nodes... Given [150, 200, 180, 160] m, substitute into the formula to calculate respectively. The values ​​are [0.00133, 0.001, 0.00111, 0.00125], and the node values ​​are the distribution values ​​of the candidate path node positions.

[0069] S502: Based on the distribution values ​​of candidate path node locations, the node coordinate parameters are weighted with the track safety factor, the weighting factor is input into the weighted scoring algorithm, the node safety score is calculated by combining the weight coefficients and the weighted sum is obtained to obtain the total weight score of track node. After obtaining the distribution values ​​of candidate path node locations, the corresponding node 3D coordinate parameters need to be weighted with the track safety factor, which includes the flight altitude safety factor. Flight speed variation factor Distance factor from no-fly zone For each node, its flight altitude needs to be retrieved from the environmental data. It is compared with the set flight altitude threshold range [200m, 400m]. If ,but ,otherwise The flight speed variation factor is calculated. If the velocity change of a certain node exceeds 20% of the velocity of the previous node, that is... Then determine ,otherwise The distance to the no-fly zone is the Euclidean distance between the node and the known center point of the no-fly zone. It means that if ,set up ,otherwise For example, if node i is located at (1500, 1600, 300) and the center of the no-fly zone is at (1000, 1000), then Therefore The above three security factors are weighted according to preset coefficients. , , After weighting, the node security score is calculated as follows: As mentioned above , , ,but The total weight score of track nodes is obtained by averaging or summing the node scores.

[0070] S503: Based on the total weight score of the track nodes, extract the candidate track sequence with high score, connect the coordinates of adjacent nodes in the sequence in sequence according to the time index, splice the continuous coordinate chain and arrange it into a time-ordered flight path to generate a simulated aircraft track coordinate sequence. Based on the total weighted score of track nodes, a threshold standard for candidate paths is set at 80% of the mean. If a path's score exceeds this value, it is determined to be a high-scoring track sequence. This sequence is then further extracted, and its nodes are connected pairwise according to their time indices, and the coordinates of adjacent nodes are retrieved. By connecting spatial straight lines, a continuous coordinate chain is generated. Each line segment is combined according to its time sequence to form a time-ordered coordinate sequence. For example, if the node time sequence is t=[0, 5, 10, 15, 20]s, the corresponding spatial coordinates are:

[0071] [(100, 200, 300), (220, 300, 340), (400, 320, 370), (560, 330, 400), (700, 350, 420)]m;

[0072] The flight path is sequentially pieced together and recorded as five consecutive line segments. Each line segment is represented by a straight line connecting two points, forming a chain of flight paths, which is then recorded as an array. The final output is the sequence of simulated flight track coordinates for the candidate path.

[0073] Table 5: Sample Table of Simulated Track Nodes As shown in Table 5, the coordinate parameters, corresponding time, distribution value and score of each node in the simulated flight are listed, which facilitates the generation of a complete track chain coordinate sequence.

[0074] Please see Figure 7 A simulation system for simulating aircraft flight paths, comprising: The electromagnetic environment perception module acquires electromagnetic field intensity data through a multi-frequency electromagnetic sensor array, monitors mission type identifiers, flight phase parameters, track node spatial coordinate parameters, and velocity vector parameters, and uses a spatial grid partitioning algorithm to process the data into partitions, generating an electromagnetic distribution dataset, which is then transmitted to the sensitivity assessment module. The sensitivity assessment module calls the electromagnetic distribution dataset to obtain the mission type identifier and flight phase parameters. It uses the analytic hierarchy process to calculate the electromagnetic sensitivity level threshold, and uses a multi-level weighting mechanism to quantify the electromagnetic sensitivity of different flight phases, generating a dynamic electromagnetic threshold parameter set, which is then passed to the trajectory optimization module. The trajectory optimization module calls the dynamic electromagnetic threshold parameter set, extracts spatial coordinate parameters, monitors the electromagnetic intensity values ​​corresponding to the coordinate parameters in the electromagnetic distribution dataset, performs numerical comparison and judgment, performs correction calculations on the spatial coordinate parameters, generates corrected trajectory node coordinates, and transmits them to the safety verification module. The safety verification module detects and corrects cases where electromagnetic exposure values ​​in the coordinates of track nodes exceed limits, extracts candidate position coordinate parameters, compares the electromagnetic intensity values ​​of candidate positions with safety thresholds, verifies whether the distance between adjacent nodes meets the constraints of the aircraft's maneuvering range, obtains a safe track candidate sequence, and transmits it to the trajectory generation module. The trajectory generation module calls the coordinate parameters in the candidate sequence of safe flight paths, inputs the node parameters to the weighted scoring algorithm for scoring calculation, connects the selected node coordinates according to the time sequence, and generates a simulated aircraft flight path coordinate sequence.

[0075] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for simulating aircraft flight paths, characterized in that, Includes the following steps: S1: Electromagnetic field intensity data is acquired through a multi-frequency electromagnetic sensor array, mission type identification and flight phase parameters are collected, spatial coordinate parameters and velocity vector parameters of track nodes are monitored, and electromagnetic field intensity data is partitioned using a spatial grid partitioning algorithm to form an electromagnetic distribution dataset. S2: Call the electromagnetic distribution dataset to obtain the mission type identifier and flight phase parameters. Input the dataset into the analytic hierarchy process (AHP) to calculate the electromagnetic susceptibility level threshold. Quantitatively evaluate the electromagnetic susceptibility of different flight phases through a multi-level weight allocation mechanism to generate a dynamic electromagnetic threshold parameter set. S3: Call the dynamic electromagnetic threshold parameter set, extract spatial coordinate parameters, monitor the electromagnetic intensity value corresponding to the coordinate parameters in the electromagnetic distribution dataset, perform numerical comparison and judgment, perform correction calculation on the spatial coordinate parameters, and generate corrected track node coordinates. S4: Detect the electromagnetic exposure values ​​in the corrected track node coordinates to exceed the limit, extract the candidate position coordinate parameters, compare the electromagnetic intensity values ​​of the candidate positions with the safety threshold, and verify whether the distance between adjacent nodes meets the aircraft maneuver range constraints to form a safe track candidate sequence.

2. The method for simulating aircraft flight paths according to claim 1, characterized in that, The electromagnetic distribution dataset includes grid cell electromagnetic values, spatial partition indexes, and data sampling density. The dynamic electromagnetic threshold parameter set includes low-frequency thresholds, mid-frequency thresholds, and high-frequency thresholds. The corrected track node coordinates include adjusted longitude, adjusted latitude, and adjusted altitude. The safe track candidate sequence includes an optional node set, inter-node connectivity, and path feasibility identifiers.

3. The method for simulating aircraft flight paths according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collects electromagnetic field intensity data through a multi-frequency electromagnetic sensor array, monitors the acquisition task type identifier, obtains flight phase parameters, records the spatial coordinate parameters and velocity vector parameters of the track node, summarizes them according to the time label, correlates the corresponding relationship of parameters under the same acquisition time and spatial coordinates, and generates a multi-dimensional spatiotemporal dataset. S102: Based on the multivariate spatiotemporal dataset, extract the spatial coordinates and velocity vectors of each data set, segment the coordinates corresponding to the electromagnetic field strength parameters, use spatial coordinate partitioning rules to determine the spatial grid boundary, and assign the differentiated coordinate records to the corresponding grid areas to obtain the spatial node partitioning results; S103: Based on the spatial node division results, the electromagnetic field intensity values ​​of the grid area are statistically analyzed. For grids with differences, the data of the same area are aggregated and integrated to form an electromagnetic distribution dataset.

4. The method for simulating aircraft flight paths according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Call the electromagnetic distribution dataset, extract the task type identifier and classify it accordingly, filter it in combination with flight phase parameters, and generate a set of electromagnetic feature parameters by logically grouping the feature items related to the task in the electromagnetic distribution data and merging the same type of parameter items by numerical correspondence. S202: Based on the set of electromagnetic characteristic parameters, according to the analytic hierarchy process and combined with the identification items in the flight phase, data-level weights are assigned, and the electromagnetic characteristic parameters and the flight phase attribute values ​​are integrated by weighted calculation to obtain the sensitivity weighted value. S203: Call the aforementioned sensitivity weighted values, apply specific division criteria to set boundaries for the real-time distribution of parameters during the flight phase, distinguish and compare each set of weighted results with the electromagnetic sensitivity level standard interval, and generate a dynamic electromagnetic threshold parameter set.

5. The method for simulating aircraft flight paths according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: After obtaining the dynamic electromagnetic threshold parameter set, extract the spatial coordinate parameters, and sequentially retrieve the corresponding electromagnetic intensity value in the electromagnetic distribution dataset for each set of spatial coordinate parameters. Record the correspondence between coordinates and electromagnetic intensity, integrate them according to the spatial distribution law, and generate spatial electromagnetic intensity data. S302: Based on the spatial electromagnetic intensity data, the electromagnetic intensity values ​​of each set of spatial coordinate parameters are compared item by item with multiple thresholds in the dynamic electromagnetic threshold parameter set according to the judgment conditions of different time periods and spatial locations, and the items that are satisfied and not satisfied are counted to obtain the threshold comparison judgment result. S303: Based on the threshold comparison and determination results, for spatial coordinate parameters that are determined not to meet the threshold requirements, adjust the spatial position according to the threshold parameters in the dynamic electromagnetic threshold parameter set, correct the coordinate distribution, and generate corrected track node coordinates.

6. The method for simulating aircraft flight paths according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Based on the electromagnetic exposure values ​​of the corrected track node coordinates, detect whether the electromagnetic exposure level of the node exceeds the electromagnetic exposure safety threshold, divide the positions that exceed the threshold into parameter categories, classify them into candidate coordinates, and obtain the set of coordinate parameters of the candidate positions that exceed the limit. S402: Call the set of candidate position coordinate parameters for exceeding the limit, extract the electromagnetic intensity values ​​of the candidate coordinate points, compare them item by item with the set electromagnetic intensity safety threshold, filter the parameters that are not greater than the threshold, and summarize to obtain the set of position parameters that comply with the threshold. S403: Based on the threshold compliant position parameter set, sequentially verify the distance parameters of each pair of adjacent nodes to ensure that the distance between nodes is within the range of the aircraft's maneuverability, filter out node combinations that meet the requirements, and generate a safe trajectory candidate sequence.

7. The method for simulating aircraft flight paths according to claim 1, characterized in that, The method also includes step S5: S5: Call the coordinate parameters in the candidate safe flight path sequence, input the candidate safe flight path sequence parameters into the weighted scoring algorithm for scoring calculation, connect the selected node coordinates according to the time sequence, and generate a simulated aircraft flight path coordinate sequence; The simulated aircraft track coordinate sequence includes sorted node coordinates, node connection order, and track continuity.

8. The method for simulating aircraft flight paths according to claim 7, characterized in that, The specific steps of S5 are as follows: S501: Call the three-dimensional coordinate parameters and timestamps of the nodes in the candidate safe track sequence, arrange them in chronological order to construct a node coordinate set, integrate the node time index and spatial position, perform node distribution value calculation, and generate candidate path node position distribution values; S502: Based on the distribution values ​​of the candidate path node positions, the node coordinate parameters and the track safety factor are weighted, the weighting factor is input into the weighted scoring algorithm, the node safety score is calculated by combining the weight coefficients and the weighted sum is obtained to obtain the total weight score of the track node. S503: Based on the total weight score of the track nodes, extract the candidate track sequence with high score, connect the coordinates of adjacent nodes in the sequence in sequence according to the time index, splice the continuous coordinate chain and arrange it into a time-ordered flight path to generate a simulated aircraft track coordinate sequence.

9. The method for simulating aircraft flight paths according to claim 8, characterized in that, The candidate path node location distribution value is a value calculated based on the distribution density, relative distance and spatial clustering degree of the node's three-dimensional coordinates in the time series; The total weighted score of the track nodes is the comprehensive safety score of the track, which is calculated by weighting the node position distribution value with multiple track safety factors and then using a weighted scoring algorithm. The weighted scoring algorithm combines node spatial coordinates, electromagnetic exposure values, and track safety factors, and generates track scores through weighting and normalization weighting calculations.

10. A simulation system for simulating aircraft flight paths, characterized in that, The system is used to implement the simulation method for simulating aircraft flight paths according to any one of claims 1-9, the system comprising: The electromagnetic environment perception module acquires electromagnetic field intensity data through a multi-frequency electromagnetic sensor array, monitors mission type identifiers, flight phase parameters, track node spatial coordinate parameters, and velocity vector parameters, and uses a spatial grid partitioning algorithm to process the data into partitions, generating an electromagnetic distribution dataset, which is then transmitted to the sensitivity assessment module. The sensitivity assessment module calls the electromagnetic distribution dataset to obtain the mission type identifier and flight phase parameters, calculates the electromagnetic sensitivity level threshold using the analytic hierarchy process, quantifies the electromagnetic sensitivity of different flight phases through a multi-level weighting mechanism, generates a dynamic electromagnetic threshold parameter set, and passes it to the trajectory optimization module. The trajectory optimization module calls the dynamic electromagnetic threshold parameter set, extracts spatial coordinate parameters, monitors the electromagnetic intensity values ​​corresponding to the coordinate parameters in the electromagnetic distribution dataset, performs numerical comparison and judgment, performs correction calculations on the spatial coordinate parameters, generates corrected trajectory node coordinates, and transmits them to the safety verification module. The safety verification module detects whether the electromagnetic exposure values ​​in the corrected track node coordinates exceed the limit, extracts candidate position coordinate parameters, compares the electromagnetic intensity values ​​of the candidate positions with the safety threshold, verifies whether the distance between adjacent nodes meets the aircraft's maneuver range constraints, obtains a safe track candidate sequence, and transmits it to the trajectory generation module. The trajectory generation module calls the coordinate parameters in the candidate safe flight path sequence, inputs the node parameters to the weighted scoring algorithm for scoring calculation, connects the selected node coordinates according to the time sequence, and generates a simulated aircraft flight path coordinate sequence.

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