Simulation method and system for spatial non-uniform distribution of satellite Internet multi-wavelength users

Abstract

The present invention provides a simulation method and system for the spatial non-uniform distribution of satellite Internet multi-wavelength users, including: constructing a user spatial non-uniform distribution model based on distribution location, user strength, user distribution model, and global population and route distribution constraints; dividing the world into multiple grids by longitude and latitude, establishing a mapping relationship between each grid and the satellite service wavelength, setting different user strengths and user distribution models that can be carried by different cells in each grid, and associating the user strength of each cell with the global population distribution; configuring different colors and depths according to different user strengths, combining longitude and latitude coordinates, and constructing a global user distribution heat map; simulating based on the above constructed model and simulation scene to obtain simulation results. During simulation, the present invention can set different wavelength numbers, wavelength sizes, survey areas, number of users in a wavelength, and user distribution in a cell to provide user distribution support for low-orbit satellite communication technology.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a simulation method and system for spatial non-uniform distribution of satellite Internet multi-wavelength users. Background Art

[0002] At present, the International Telecommunication Standardization Organization and major research institutions have planned global seamless high-speed coverage as one of the research priorities of 6G, focusing on solving high-speed communication problems in scenarios with wide distribution, high economic value and limited number of users, such as oceans, mountains, deserts, river basins, swamps, and permafrost zones. Low-orbit satellite networks are the most effective solution to the above problems. In recent years, 3GPP has standardized non-terrestrial networks NTN, making low-orbit satellite networks have better performance. The introduction of new technologies such as phased array antennas and ground 5G air interfaces has provided low-orbit satellite Internet with agile, high-speed, mobile and reliable communication capabilities. For remote high-value target area scenarios, there are a large number of timely transmitted services. Because of its remote location, only low-orbit satellite Internet can be considered, and at the same time, real-time data transmission must be met, which requires further optimization of the link connection process of low-orbit satellite Internet. At present, the processing delay of low-orbit satellite Internet is relatively low, but the signal transmission delay of satellite links cannot be ignored. Therefore, to improve the real-time performance of low-orbit satellite Internet services, the key is to reduce the number of signaling interactions for service connections. Optimizing the traditional four-step access to a two-step access process has become the current research focus. The existing technology reflects the distribution of users in different regions by establishing a spatial distribution model of global users, provides most user resources, reduces the connection trial and confirmation process, reduces the low-orbit satellite connection process, and optimizes the two-step access. The existing idea of ​​establishing a user distribution model is mainly based on the spatial constraints of a specific cell. According to the characteristics of the service, Poisson distribution, uniform distribution, Gaussian distribution, etc. are selected as the probability distribution of users. The ground cell is small, and this method can correctly reflect the distribution probability of users in time. However, it is not applicable in the low-orbit satellite Internet scenario. Satellite cells are much larger than ground cells. The constraints of users on spatial distribution under the global large field of view will greatly constrain the randomness of user distribution. In addition, the number of users in each satellite cell varies greatly. When configured according to the same arrival intensity, it will lead to insufficient resources in cells with high user density or redundant resources in cells with low user density. When designing a communication system, the probability distribution of user intensity such as small space is used for business modeling, which cannot accurately reflect the characteristics of non-uniform spatial distribution of users under the global field of view. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides a simulation method and system for the spatial non-uniform distribution of multi-wavelength users of satellite Internet, so as to eliminate or improve one or more defects existing in the prior art, and solve the problem that the existing user distribution model is constructed with a single restriction condition, does not take into account the actual global population distribution, flight routes and other restrictions, and cannot be applied to low-orbit satellite Internet scenarios.

[0004] On the one hand, the present invention provides a simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users, characterized in that the method comprises the following steps:

[0005] Obtain the distribution location of each user in the world, and associate it with the distribution location in the form of longitude and latitude coordinates; set inspection points and inspection areas to verify the user distribution in the inspection areas; perform level normalization on the global user traffic density, and set a user strength level classification method for the inspection area; obtain a user distribution model based on business characteristics, and the user distribution model includes Poisson distribution, Gaussian distribution and uniform distribution;

[0006] The survey area is set as a global area; the global area is converted into a rectangular global plane survey area by using the Mercator projection method, and the global plane survey area is divided into a plurality of grids by the first constraint condition; the global plane survey area adopts the user intensity level division method, and the corresponding distribution in the user distribution model is selected according to the service;

[0007] Re-formulate the user intensity level division method of the global plane inspection area through the second constraint condition, and divide the global plane inspection area into multiple time periods based on the globally distributed aircraft routes; the global plane inspection area selects the corresponding distribution in the user distribution model according to the business;

[0008] Acquire multiple users surveyed by the satellite at the same time, and associate and transform the distribution position, user strength and user distribution model of each user in the corresponding space according to preset rules;

[0009] Through the above four steps, a user spatial non-uniform distribution model is constructed based on the distribution position, the user strength and the user distribution model;

[0010] The ground coverage beam position of the low-orbit satellite Internet is set according to the constraint parameters through the third constraint condition; the constraint parameters include orbital altitude, number of orbital planes, orbital inclination set and number of satellites; the beam position is composed of multiple service beam positions; the size of the service beam position and the number of users included in the service beam position are used as constraint conditions, and a preset clustering algorithm is used to match the most suitable associated service beam to the users in each grid of the global plane survey area, and determine the coverage range of the service beam;

[0011] Obtain the mapping relationship of the distribution position in the space corresponding to each grid, obtain the user strength level of each grid according to the corresponding distribution position, and set different user strength upper limits and value fluctuations for each grid through the fourth constraint condition; configure corresponding user distribution for each satellite wave position according to the service initiated by the user;

[0012] Acquire the user spatial non-uniform distribution model to obtain the user strength and distribution position in the corresponding investigation area; express the user strength in color, and calculate the kernel density value according to the distribution position and the corresponding color to obtain a global user distribution heat map;

[0013] The number and size of the wave positions, the number of users in different wave positions, the survey points and the user distribution are set to simulate the spatial non-uniform distribution of satellite Internet multi-wave position users.

[0014] In some embodiments of the present invention, the global user traffic density is normalized and a user intensity level division method is set for the inspection area, further comprising:

[0015] The level of the user traffic density is set according to Gaussian distribution, and the probability of too little population density and / or too much population density is relatively lower. The user intensity of each investigated area is set to 6 different levels.

[0016] In some embodiments of the present invention, dividing the global plane investigation area into a plurality of grids by a first constraint condition further comprises:

[0017] When dividing the grid, make sure that there are as many land areas or ocean areas as possible in each grid, and reduce the number of grids with similar land areas and ocean areas. That is, the grid division must satisfy the following formula:

[0018]

[0019] Where N represents a function that calculates the quantity; l i _land indicates land area; l i _sea indicates the ocean area; Indicates the preset threshold.

[0020] In some embodiments of the present invention, the user intensity level division method of the global plane survey area is re-formulated by the second constraint condition, and the global plane survey area is divided into multiple time periods based on the globally distributed aircraft routes, and further includes:

[0021] Select multiple location points with the largest user strength, match the grid areas where the location points are located, merge the first and second levels with the lowest user strength in the user strength level division method, and decompose the sixth level with the highest user strength into two levels to obtain a new user strength level division method.

[0022] In some embodiments of the present invention, the distribution position, user strength and user distribution model of each user in the corresponding space are respectively associated and transformed according to a preset rule, further comprising:

[0023] When the distribution position is associated with the transformation, if the area of ​​the preset inspection area is larger than the area of ​​the grid area after the superposition of each user, the grid area after the superposition of each user is selected for inspection; if the area of ​​the preset inspection area is smaller than the area of ​​the grid area after the superposition of each user, the preset inspection area is selected for inspection;

[0024] When the user strength is associated and transformed, the level with the largest value among the single business user strengths is selected as the user strength for classification.

[0025] In some embodiments of the present invention, a preset clustering algorithm is used to match the most suitable associated service beam to the users in each grid of the global plane survey area. The preset clustering algorithm is a K-means clustering algorithm, and the objective function calculation formula of the K-means clustering algorithm is:

[0026]

[0027] Wherein, J(c) represents the clustering degree of the objective function; k represents the number of clusters; x represents the members of the cluster; c i Represents the cluster center of the i-th category.

[0028] In some embodiments of the present invention, different user strength upper limits and value fluctuations are set for each grid through a fourth constraint condition, and the fourth constraint condition includes the following:

[0029] The user intensity of each satellite beam position is relatively independent and is affected by the global population distribution;

[0030] The user strength in the service beam position of a single satellite beam position should be smaller than the user strength of the satellite beam position, and the sum of the user strengths in the service beam position is equal to the user strength of a single satellite beam position;

[0031] The number of service slots within each satellite slot is not necessarily the same.

[0032] In some embodiments of the present invention, according to the service initiated by the user, a corresponding user distribution is configured for each satellite position, further comprising:

[0033] When the user distribution obeys uniform distribution, that is, the corresponding investigation area obeys the probability density function of the corresponding two-dimensional random variable, the expression is:

[0034]

[0035] in, represents the corresponding two-dimensional random variable; A represents the evenly distributed intensity; Indicates the satellite wave position; Indicates the corresponding inspection area;

[0036] When the user distribution obeys Gaussian distribution, that is, the corresponding investigation area obeys the probability density function of the corresponding two-dimensional random variable, the expression is:

[0037]

[0038] in, represents the corresponding two-dimensional random variable; represents the user location coordinates in the investigation area; (μ1, μ2) represents the central tendency location coordinates of the normal distribution of all users in the investigation area; variance and Describes the degree of dispersion of users in the normal distribution on the x-axis and y-axis respectively.

[0039] In some embodiments of the present invention, calculating the kernel density value according to the distribution position and the corresponding color further includes:

[0040] Set the domain radius of the heat concentration point and calculate the number of elements whose distance is less than or equal to the domain radius. The kernel density calculation formula for any point in the corresponding space is:

[0041]

[0042] Among them, D p represents the kernel density of point p; T r represents the radius of the domain; n represents the number of elements; K represents the kernel function; f i represents the element in the i-th domain; R(p,f i ) represents the distance between the pixel and the element in the i-th domain.

[0043] The present invention also provides a simulation system for the spatial non-uniform distribution of satellite Internet multi-wavelength users, characterized in that the system is used to perform the steps of the simulation method for the spatial non-uniform distribution of satellite Internet multi-wavelength users as described in any one of the above items, and the system includes the following modules:

[0044] A user model and spatial distribution module, used to provide a user spatial non-uniform distribution model obtained as claimed in claim 1 for simulation, and configure user strength, user quantity and user distribution at different distribution locations according to the service;

[0045] The global and local scenario modules are used to set the global or local view range for the simulation, set the survey area under the corresponding view range, and configure the corresponding satellite wave position and its service wave position;

[0046] A simulation control module, used to control the activation process of each module in the system;

[0047] Data recording and analysis module, used to record and analyze simulation results

[0048] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods mentioned above.

[0049] The beneficial effects of the present invention are at least:

[0050] The present invention provides a simulation method and system for the spatial non-uniform distribution of satellite Internet multi-wavelength users. The method comprises the following steps: constructing a user spatial non-uniform distribution model based on user distribution positions, user strengths, user distribution models, and global population and route distribution constraints; dividing the world into a plurality of grids according to longitude and latitude, establishing a mapping relationship between each grid and a satellite service wavelength, setting different user strengths and user distribution models that can be carried in different cells of each grid, and associating the user strength of each cell with the global population distribution; configuring different colors and depths according to different user strengths, and constructing a global user distribution heat map in combination with longitude and latitude coordinates; and performing simulation based on the above-constructed model and simulation scenario to obtain a simulation result. The constructed user spatial non-uniform distribution model is based on the existing model, takes into account the actual global population distribution, route distribution and other constraints, and considers the appropriate expression of different user density intensities. The user intensity upper limit is constructed according to the world population distribution. Combined with the user data set constructed in the global perspective, different numbers of wavebands, waveband sizes, survey areas, number of users in the waveband, and user distribution in the cell can be set during simulation to provide user distribution support for low-orbit satellite communication technology, accurately verify the adaptability of low-orbit satellite Internet protocol design, and improve the accuracy and efficiency of low-orbit satellite Internet simulation verification.

[0051] Furthermore, considering the need to provide a more intuitive display of user distribution during low-orbit satellite Internet simulation and demonstration, a global user distribution heat map is constructed, and the user distribution is characterized by different colors, different depths, different locations and different user intensities.

[0052] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0053] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0055] Figure 1 The present invention is a flow chart of a simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users in one embodiment of the present invention.

[0056] Figure 2 This is an example diagram of a locally normalized user model in one embodiment of the present invention.

[0057] Figure 3 This is a structural diagram of a simulation system for spatially non-uniform distribution of satellite Internet multi-wavelength users in one embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0059] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0060] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0061] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0062] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0063] It should be emphasized here that the step marks mentioned below are not intended to limit the order of the steps, but it should be understood that the steps can be executed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be executed simultaneously.

[0064] In order to solve the problem that the existing user distribution model is constructed with a single restriction condition, does not consider the actual global population distribution, flight routes and other restrictions, and cannot be applied to low-orbit satellite Internet scenarios, the present invention provides a simulation method for the spatial non-uniform distribution of satellite Internet multi-wavelength users, such as Figure 1 As shown, the method includes the following steps S101 to S109:

[0065] Step S101: Obtain the distribution positions of users around the world, and associate them with the distribution positions in the form of longitude and latitude coordinates; set inspection points and inspection areas to verify the user distribution in the inspection areas; normalize the global user traffic density and set the user strength level classification method for the inspection area; obtain a user distribution model based on business characteristics, which includes Poisson distribution, Gaussian distribution and uniform distribution.

[0066] Step S102: Set the survey area as a global area; use the Mercator projection method to convert the global area into a rectangular global plane survey area, and divide the global plane survey area into multiple grids through the first constraint condition; the global plane survey area adopts the user intensity level division method in step S101, and selects the corresponding distribution in the user distribution model according to the business.

[0067] Step S103: re-formulate the user intensity level division method of the global plane survey area through the second constraint condition, and divide the global plane survey area into multiple time periods based on the globally distributed aircraft routes; the global plane survey area selects the corresponding distribution in the user distribution model according to the business.

[0068] Step S104: Acquire multiple users surveyed by the satellite at the same time, and associate and transform the distribution position, user strength and user distribution model of each user in the space according to a preset rule.

[0069] Step S105: through the above four steps, a user spatial non-uniform distribution model is constructed based on user distribution positions, user strengths and user distribution models.

[0070] Step S106: Setting the ground coverage beam position of the low-orbit satellite Internet through the third constraint condition according to the constraint parameters; wherein the constraint parameters include orbit altitude, number of orbit planes, orbit inclination set and number of satellites; the beam position is composed of multiple service beam positions; the size of the service beam position and the number of users included in the service beam position are used as constraint conditions, and a preset clustering algorithm is used to match the users in each grid of the global plane survey area with the most suitable associated service beam to determine the coverage range of the service beam.

[0071] Step S107: Obtain the distribution position mapping relationship in the space corresponding to each grid, obtain the user strength level of each grid according to the corresponding distribution position, set different user strength upper limits and value fluctuations for each grid through the fourth constraint condition; configure corresponding user distribution for each satellite wave position according to the service initiated by the user.

[0072] Step S108: Obtain a user spatial non-uniform distribution model to obtain the user strength and distribution position in the corresponding inspection area; express the user strength using color, and calculate the kernel density value according to the distribution position and the corresponding color to obtain a global user distribution heat map.

[0073] Step S109: setting the number and size of wavebands, the number of users in different wavebands, the survey area and user distribution to simulate the spatial non-uniform distribution of satellite Internet multi-waveband users.

[0074] In step S101, the distribution location of each user in the world is obtained, and the distribution location of each user is recorded as The longitude and latitude coordinates (X, Y) are used for association. For example, a user is assumed to be Then the distribution location of the user can be expressed as

[0075] Set the inspection point and inspection area, and mark the coordinates of the inspection point (X r ,Y r ), the location of the investigation area is recorded as The inspection points and inspection areas are set to inspect the consistency between different user distributions and the actual situation, and to determine the spatial location and user distribution for simulation. The four vertices of the inspection area are set as (x a ,y a ), (x b ,y b ), (x a ,y b ), (x b ,y a ), the corresponding investigation area is recorded as When the inspection point is set to fall within the inspection area, then (X r ,Y r ):xa <X r <x b ;y a <Y r <y b .

[0076] User strength is recorded as The user traffic density is normalized by level from a global perspective.

[0077] In some embodiments, the density level can be set according to the Gaussian distribution, that is, the probability of the situation where the population density is too low and / or the population density is too high is relatively low, and the user intensity of each inspection area is set to 6 different levels, denoted as Exemplarily, as the levels of Q1 to Q6 increase, the corresponding user strengths increase in sequence.

[0078] Obtain a user distribution model based on business characteristics and record the user distribution model as The model is based on the distribution of user-initiated requests for different services, and mainly includes Poisson distribution, Gaussian distribution, uniform distribution, etc.

[0079] Based on the distribution position set in step S101 User Strength and user distribution model Initially build a local normalized user model It can be understood as the distribution of users in an abstract space with the distribution position User Strength User Distribution Model The unified representation relationship.

[0080] like Figure 2 As shown in Figure 1, 8 examples of local normalized user models are shown. From a local perspective, we can obtain a set of constraints between user distribution, distribution position, user strength and user distribution model. Figure 2 Taking the first picture in the figure as an example, the investigation area is The four vertices of the inspection area are (x 1a ,y 1a ), (x 1b ,y 1b ), (x 1a ,y 1b ), (x 1b ,y 1a ), the distribution position of the inspection point, i.e. the user, is (x1, y1), and the user strength of the inspection area is The user traffic distribution corresponding to the user distribution model is: in, Figure 2The darker the color, the sparser the user distribution is, and the lighter the color, the denser the user distribution is. In some embodiments, red and green are used to represent user density, and the greener the color, the sparser the user distribution is, and the redder the color, the denser the user distribution is.

[0081] In step S102, based on the normalized user model, an instantiation application is performed according to the number of users in the actual area, and a universal user data set is constructed with a global perspective.

[0082] First, the survey area is set as the global area, and the Mercator projection method is used to convert the global area into a rectangular global plane survey area. The global plane survey area is recorded as Design the global plane inspection area according to the first constraint:

[0083] Constraint 1: Longitude and latitude (lo, la) are used as the longitudinal and transverse calibration lines of the global plane survey area. The longitude step range Δlo and the latitude step range Δla are determined by the area of ​​the survey area. To constrain.

[0084] Constraint 2: Divide the global plane survey area into multiple grids The length and width of each grid are consistent and are integers. The relationship between the length and width of each grid is shown in formula (1) and formula (2):

[0085] x a (l i )-x b (l i )=x a (l j )-x b (l j ); (1)

[0086] y a (l i )-y b (l i )=y a (l j )-y b (l j ); (2)

[0087] Constraint 3: Sharpen the position and scale of the grid towards the land direction, so that each grid contains as many land areas or ocean areas as possible, that is, reduce the number of grids with similar land areas and ocean areas under the global view. Experiments have shown that sharper grids can achieve certain optimization during simulation. Specifically, a single grid can be set to contain both land and ocean areas, and the grid division must meet the conditions of formula (3):

[0088]

[0089] Where N represents a function that calculates the quantity; l i _land represents the grid l i The area of ​​the inland area; i _sea represents the grid l i The size of the internal marine area; Indicates the preset threshold value of the ratio of land area to ocean area.

[0090] Under the constraints of constraints 1, 2, and 3, the design of the global plane inspection area is completed. The user intensity level division method in the local normalized user model is adopted, the highest level of user intensity Q6 is set as the highest density value of the inspection area, and the corresponding distribution in the user distribution model is selected according to the business. Thus, based on the local normalized user model, the user model of the global view is constructed.

[0091] In step S103, based on the user model of the global perspective, instantiated applications can be performed according to the number of users of the actual business, such as global shipping routes, shipping routes, IoT devices, etc., and a general user data set is constructed still under the global perspective.

[0092] The global plane survey area used in the user model of the global view is still selected The global plane inspection area is redesigned according to the second constraint:

[0093] Constraint 4: The grid division calibration line constraint refers to the above constraint 1.

[0094] Constraint 5: The specification constraints of grid division refer to the above constraint 2.

[0095] Constraint 6: Considering that aircraft routes can ignore land and sea restrictions, the land and sea ratio of the grid division is not considered here, that is, the above constraint 3 is not considered.

[0096] Constraint 7: Reformulate the user intensity level division method. Normalize the user traffic density in the global field of view, select multiple user intensity levels Q6, that is, the location points with the highest user intensity, match the grid area where they are located, merge the first level Q1 and the second level Q2 with the lowest user intensity in the original user intensity level division method, and decompose the sixth level Q6 with the highest user intensity into two levels to obtain a new user intensity level division method.

[0097] Constraint 8: Considering that the flight routes span a large area and the location of the aircraft changes greatly over time, the global routes are divided according to time, that is, the global plane survey area Divide into N t The user intensity in each grid will change in each period.

[0098] Under the constraints of constraints 4 to 8, the design of the global plane inspection area is completed. The highest level Q6 of user intensity at each moment is set as the highest density value of the corresponding grid area, and the corresponding distribution in the user distribution model is selected according to the business. Thus, based on the user model of the global vision, the route model of the global vision is constructed.

[0099] In step S104, the communication resources of a single satellite in the satellite Internet scenario will serve all users under its coverage. Therefore, when considering access capability analysis, it should be possible to provide user spatial distribution of hybrid services.

[0100] Get the local normalized user model The multiple user models examined simultaneously are denoted as When multiple user models are superimposed, it is necessary to complete the associated transformation of the distribution position, user strength and user distribution model respectively.

[0101] Correlation transformation of distribution position: If the area of ​​the preset inspection area is larger than the area of ​​the grid area after the superposition of each user, only the grid area after the superposition of each user will be inspected; if the area of ​​the preset inspection area is smaller than the area of ​​the grid area after the superposition of each user, the preset inspection area will be selected for inspection.

[0102] Correlation transformation of user strength: The level with the largest value in the user strength of a single business is selected as the user strength for division, and the user strength in each grid will be defined according to the highest level.

[0103] Correlation transformation of user distribution model: Since the initiation of each service is relatively independent, they need to be considered separately during analysis.

[0104] At this point, in step S105, the initial constraint relationship of global satellite user distribution is constructed based on the global population distribution and global route distribution, and the user spatial non-uniform distribution model is constructed based on the user distribution location, user density and the user distribution model based on business characteristics.

[0105] In step S106, the constellation configuration constraint parameters of the low-orbit satellite Internet are selected to design the specifications of the ground coverage wave position of the low-orbit satellite Internet through the third constraint condition, wherein the constraint parameters mainly include the orbit height h orb , orbital plane number P orb、Orbital inclination set F orb 、Number of satellites Sat , the third constraint includes the following:

[0106] Constraint 9: The total area of ​​all satellite beam positions should be larger than the surface area of ​​the Earth.

[0107] Constraint 10: The total area of ​​the service slots within a single satellite slot should be greater than the area of ​​a single satellite slot. Specifically, the slot of a particular satellite is denoted as Divide it into multiple service waves Constraint 10 is expressed as arrive The area of ​​the j service wave positions and Should be larger than the area of ​​a single satellite position

[0108] Constraint 11: Set the coverage area of ​​each satellite The size is the same, and the service slots within each satellite slot The size and number of can be adjusted according to the user distribution.

[0109] Under the constraints of constraints 9, 10 and 11, the design of the specifications of the ground coverage wave position of the low-orbit satellite Internet is completed.

[0110] Based on the above-mentioned design of the ground coverage beam, the service beam that meets the user distribution constraints is further designed. The size of the service beam and the number of users included are used as constraints, and the goal is to achieve the most uniform service beam load in each satellite beam. The preset clustering algorithm is used to match the service beam that is most suitable for the users in each grid, thereby determining the coverage of the service beam.

[0111] In some embodiments, the preset clustering algorithm adopts the K-means clustering algorithm (K-means algorithm), and its objective function is shown in formula (4):

[0112]

[0113] Among them, J(c) represents the clustering degree of the objective function, the lower the value, the better; k represents the number of clusters; x represents the members of the cluster; c i represents the i-th cluster center, and the number of cluster centers is consistent with the number of service wave positions j.

[0114] In some embodiments, the gradient calculation formula of the objective function of the K-means clustering algorithm is shown in formula (5):

[0115]

[0116] Where J(c) represents the clustering degree of the objective function; c represents the cluster head (cluster center); k represents the number of clusters; x represents the members of the cluster; l(x) represents the number of cluster members. Formula (5) is used to calculate the value of each cluster center as the new center value, and iteratively update to obtain the optimal cluster head for each service beam position.

[0117] In the present invention, the specific process of the K-means clustering algorithm includes the following steps:

[0118] Step S1061: Initialize the number j of service beams as the number of clusters, and randomly select the initial center of each cluster.

[0119] Step S1062: In the nth iteration of the objective function based on the optimal service wave position distribution, select any possible user node, calculate the distance from the user node to the k centers respectively, and regard the user node as a branch of the cluster with the shortest distance to the center.

[0120] Step S1063: Calculate the average value of the distances of all branches of a single cluster through one traversal, and update the center value of the cluster according to the average value.

[0121] Step S1064: After the cluster centers of all clusters are updated using the above iteration method, if the positions of the cluster centers remain unchanged, the iteration ends; otherwise, the iteration continues.

[0122] Therefore, according to the cluster center distribution result obtained by clustering iteration, the population density and the service location are accurately associated with each other by taking the satellite location and the service location within the satellite location as the center.

[0123] In step S107, after completing the above design, the spatial position mapping relationship of each grid after the wave position mapping can be obtained. Each grid is defined as a service wave position of a satellite. Each grid can be further divided into multiple sub-grids, which are defined as multiple cells within a service wave position. According to the distribution position of the grid, the user strength level Q of each grid can be obtained. i For example, suppose the investigation area is There are i satellite positions Each satellite wave There are j service slots This results in the following user level strength constraint relationship:

[0124] At the satellite position level, i satellite positions The user strength set can be expressed as:

[0125]

[0126] At the service wavelength level, i satellite wavelengths Each service wave The user strength set is:

[0127] Satellite position Service Wavelength User strength set:

[0128] Satellite position Service Wavelength User strength set: ......

[0130] Satellite position Service Wavelength User strength set:

[0131] Constraint 12: The user intensity of satellite beam positions is relatively independent and is affected by the global population distribution.

[0132] Constraint 13: The user strength in the service beam of a single satellite beam should be smaller than the user strength of the satellite beam, and the sum of the user strengths in the service beam should be equal to the user strength of a single satellite beam.

[0133] Constraint 14: The number of service slots in each satellite slot is not necessarily the same.

[0134] Under the constraint requirements of the user level strength constraint relationship, an investigation area is obtained as The area can satisfy satellite beams with different user strengths and service beams with different user strengths within satellite beams.

[0135] According to the services initiated by users, corresponding user distribution is configured for each satellite beam position.

[0136] In some embodiments, when the user distribution obeys a uniform distribution, that is, the corresponding inspection area obeys the probability density function of the corresponding two-dimensional random variable, the expression is shown in formula (6):

[0137]

[0138] in, represents the corresponding two-dimensional random variable; A represents the evenly distributed intensity; Indicates the satellite wave position; Indicates the corresponding inspection area.

[0139] When the user distribution obeys the Gaussian distribution, that is, the corresponding investigation area obeys the probability density function of the corresponding two-dimensional random variable, the expression is shown in formula (7):

[0140]

[0141] And user distribution The parameters are (μ1,μ2), The two-dimensional normal distribution with ρ = 0 is denoted by

[0142] In formula (7), represents the corresponding two-dimensional random variable; represents the user location coordinates in the investigation area; (μ1, μ2) represents the central tendency location coordinates of the normal distribution of all users in the investigation area; variance and Describes the degree of dispersion of users in the normal distribution on the x-axis and y-axis respectively.

[0143] In step S108, when analyzing the access performance of low-orbit satellite Internet, it is necessary to use user distribution as simulation input, and different user distributions have a great impact on simulation performance. However, since user distribution involves many mathematical rules, it is difficult for general users to configure accurately, and the user distribution status cannot be displayed intuitively. Therefore, the present invention combines the user spatial non-uniform distribution model constructed above with the user distribution formed according to the geographical distribution example, and uses a heat map to characterize it. Specifically:

[0144] Obtain the user space non-uniform distribution model to obtain the satellite wave positions in the corresponding investigation area and the service wave positions in each satellite wave position, determine the user intensity corresponding to each investigation area, and determine the user distribution corresponding to each investigation area. Convert the user density set into a set expressed by color, combine the location parameters with the color distribution, calculate the kernel density value, and generate a heat map of the user space non-uniform distribution model. The heat map can represent the distribution density of users and provide prior support for simulation scenario design.

[0145] In some embodiments, the domain radius of the heat concentration point is set, and the number of elements whose distance is less than or equal to the domain radius is calculated. The kernel density calculation formula for any point in the corresponding space is shown in formula (8):

[0146]

[0147] Among them, D p represents the kernel density of point p; T r represents the domain radius; n represents the number of elements; K represents the kernel function; f i represents the element in the i-th domain; R(p,f i ) represents the distance between the pixel and the element in the i-th domain.

[0148] In step S109, based on the designs in steps S101 to S108, different numbers of wavebands, different survey areas, different waveband sizes, different numbers of users in wavebands, and different user distributions in cells can be set to simulate the spatial non-uniform distribution of satellite Internet multi-waveband users.

[0149] The present invention also provides a simulation system for the spatial non-uniform distribution of satellite Internet multi-wavelength users, such as Figure 3 As shown, the system includes four modules: user model and spatial distribution module, global and local scene module simulation control module and data recording and analysis module.

[0150] User model and spatial distribution module: This module mainly provides a user space non-uniform distribution model for low-orbit Internet simulation and configures its parameters. Specifically, according to the user space non-uniform distribution model, the user strength and number of users at different locations are obtained according to the global view and local view. At the same time, according to the user business situation, the user distribution model is configured. The user distribution model mainly includes uniform distribution, Poisson distribution and Gaussian distribution.

[0151] Global and local scene module: This module is used to set the simulation scene according to the simulation requirements after completing the configuration of the user model and the spatial distribution module. Specifically, set the global field of view range or the local field of view range and the investigation area under the corresponding field of view range, including the location and size of the investigation area, and configure the corresponding satellite wave position and service wave position. For example, the number of satellite wave positions and the number of service wave positions in each satellite wave position can be configured in the investigation area.

[0152] The simulation control module is used to control the activation process of each module in the system. The overall process includes: first configuring the global view and local view scenarios, as well as the satellite beam position and service beam position, then configuring the user model and model parameters in the spatial distribution module in the corresponding investigation area, then loading all the scenarios and parameters, configuring different simulation strategies for simulation, and finally outputting the simulation results.

[0153] The data recording and analysis module is used to record and analyze the simulation results. Specifically, in the present invention, the target parameters of the simulation verification are the access success rate and access delay of the satellite. After the simulation is completed, the simulation results of the users with different distribution intensities, different distribution forms and different numbers of wave positions under the user number are counted and recorded based on the user number, and the simulation results are analyzed.

[0154] The main purpose of this simulation is to verify the performance difference of different user model configurations when the number of wavelets is different and the user spatial distribution is non-uniform. Therefore, in the experimental design, the number of wavelets and the user strength of each wavelet are controlled to examine the consistency between different user distributions and the actual situation.

[0155] In some embodiments, the parameters are configured as follows:

[0156] Satellite wave number configuration: 4, 9, 16;

[0157] The number of service slots in each satellite slot is configured as: 4, 7, or 14;

[0158] User quantity configuration: 100, 200, 500, 1000;

[0159] User distribution model configuration: Poisson distribution, uniform distribution, Gaussian distribution.

[0160] In terms of specific configuration, the performance change is mainly examined by changing the examination factors and keeping other factors unchanged. The examination indicator is the root mean square error (RMSE) with the reference user distribution. Among them, the examination factors include the number of wave positions, the number of users, user strength, user distribution, etc.

[0161] The user distribution forms within different wave positions are used to verify which distribution form is more relevant to the existing scenario.

[0162] In some embodiments, the user distribution configuration of the low-orbit satellite Internet experiment simulation is shown in Table 1:

[0163] Table 1

[0164]

[0165] The overall simulation process is briefly described in the simulation control module above. Now the simulation process of the satellite Internet multi-wavelength user space non-uniform distribution is further described:

[0166] At the beginning of the simulation, import the user space non-uniform distribution model and configure the parameters therein, including the distribution location and service type of users in the simulation scenario. After the configuration is completed, import the user distribution model. Set different service types for each user and set the probability of initiating different services for them. Then configure the parameters in the existing simulation, mainly configuring the number of wave positions, user strength, user distribution form within the wave position, etc. Then use the preset simulation software to simulate, record the end of the simulation, analyze the simulation results, and delete abnormal simulation data. Finally, further analyze the simulation results in combination with the expected results.

[0167] The user model and spatial distribution module of the simulation system for the spatial non-uniform distribution of satellite Internet multi-wavelength users also includes three key sub-modules: satellite constellation design and user deployment module, satellite wavelength and antenna design module, and grid division and kernel density thermal analysis module.

[0168] Satellite constellation design and user deployment module: In the present invention, STK software is used to automatically deploy simulation scenarios for low-orbit satellite constellation design and user deployment. When designing the satellite constellation design and user deployment module, the constellation is first constructed based on parameters such as orbital altitude, inclination, number of orbital planes, number of satellites in the orbital plane, and interval. When deploying users, the deployment of multiple types of massive terminals is automatically generated mainly based on the above requirements, including the deployment of aircraft nodes, ship nodes, handheld terminals, and Internet of Things nodes in a mobile state. When targeting mobile nodes, the moving trajectory of the mobile node is designed according to the characteristics of the node, so that each mobile node model has a reasonable moving trajectory. After the design is completed, the satellite constellation design and user deployment module can export the constellation design situation and moving trajectory data as a trajectory file type that can be read by the network protocol simulation software, and supports the conversion and output of terminal coordinates in a specified coordinate system.

[0169] Satellite beam position and antenna design module: In the present invention, the beam position coverage of the satellite is designed by modeling the antenna. Specifically, when designing the antenna, a discrete modeling method is adopted to discretize the gain diagram of the antenna into multiple two-dimensional cones, and each cone piece is memorized and divided based on the spherical coordinate system. Finally, by designing the gain set of each cone, an antenna model is formed. The antenna model differentiates the gain values ​​of the non-covered direction and the covered direction, and the antenna model of the specified direction range can be obtained. When designing the user beam position, the beam shape supports two shapes: elliptical beam and rectangular beam. The number of beams can be designed to support 4, 7 and 14 beams of low-orbit satellites, and the beam size can be adjusted in a way of equal area or angle change. The beam color can be automatically changed according to the display content, and the frequency multiplexing effect is supported, for example, such as monochrome multiplexing, 4-color multiplexing, 7-color multiplexing, etc.

[0170] Grid division and kernel density thermal analysis module: In the present invention, python software is used to grid the deployed user scenarios. After the division, the thermal map is displayed based on the kernel density thermal analysis module. Specifically, when the grid is divided, according to the above-mentioned constraints, the two key factors of the number of users in the grid and the coverage area are calculated to limit each grid, and the center point of each grid is calculated by a preset clustering algorithm, and the range and size of the corresponding grid are obtained. After the gridding is completed, the number of users and the size of each grid are calculated, and the thermal value of each grid is calculated by kernel density. Finally, it is imported into the grid and distinguished by different shades of color. Grid division and thermal map representation are thus achieved to provide prior information for subsequent simulation verification.

[0171] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users.

[0172] Corresponding to the above method, the present invention also provides a device, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the device implements the steps of the method described above.

[0173] The embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned edge computing server deployment method are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0174] In summary, the present invention provides a simulation method and system for the spatial non-uniform distribution of satellite Internet multi-wavelength users, constructs a user spatial non-uniform distribution model based on user distribution positions, user strengths, user distribution models, and global population and route distribution constraints; divides the world into multiple grids according to longitude and latitude, establishes a mapping relationship between each grid and the satellite service wavelength, sets different user strengths and user distribution models that can be carried in different cells of each grid, and associates the user strength of each cell with the global population distribution; configures different colors and depths according to different user strengths, and constructs a global user distribution heat map in combination with longitude and latitude coordinates; performs simulation based on the above constructed model and simulation scenario to obtain simulation results. The constructed user spatial non-uniform distribution model is based on the existing model, takes into account the actual global population distribution, route distribution and other constraints, and considers the appropriate expression of different user density intensities. The user intensity upper limit is constructed according to the world population distribution. Combined with the user data set constructed in the global perspective, different numbers of wavebands, waveband sizes, survey areas, number of users in the waveband, and user distribution in the cell can be set during simulation to provide user distribution support for low-orbit satellite communication technology, accurately verify the adaptability of low-orbit satellite Internet protocol design, and improve the accuracy and efficiency of low-orbit satellite Internet simulation verification.

[0175] Furthermore, considering the need to provide a more intuitive display of user distribution during low-orbit satellite Internet simulation and demonstration, a global user distribution heat map is constructed, and the user distribution is characterized by different colors, different depths, different locations and different user intensities.

[0176] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0177] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0178] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0179] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users, characterized in that: The method comprises the following steps: Step S101: obtaining the distribution positions of users around the world, and associating them with the distribution positions in the form of longitude and latitude coordinates; setting inspection points and inspection areas to verify the user distribution in the inspection areas; performing level normalization processing on the global user traffic density, and setting a user strength level division method for the inspection area; obtaining a user distribution model based on service characteristics, wherein the user distribution model includes Poisson distribution, Gaussian distribution and uniform distribution; Step S102: setting the survey area as a global area; using the Mercator projection method to convert the global area into a rectangular global plane survey area, dividing the global plane survey area into a plurality of grids by a first constraint condition; the global plane survey area adopts the user intensity level division method, and selects the corresponding distribution in the user distribution model according to the service; wherein the first constraint condition includes using longitude and latitude as longitudinal and lateral demarcation lines for the division of the global plane survey area, the longitude step and the latitude step are constrained by the area of ​​the survey area, the length and width of the divided grid are kept consistent, and the position and scale division of the grid are sharpened toward the land direction; Step S103: Re-formulate the user intensity level division method of the global plane survey area through the second constraint condition, and divide the global plane survey area into multiple time periods based on the globally distributed aircraft routes; the global plane survey area selects the corresponding distribution in the user distribution model according to the business; wherein the second constraint condition includes using longitude and latitude as longitudinal and transverse demarcation lines for the division of the global plane survey area, the longitude step and the latitude step are constrained by the area of ​​the survey area, the length and width of the grid obtained by the division are kept consistent, multiple location points with the largest user intensity are selected, the grid area where each location point is located is matched, the first level and the second level with the lowest user intensity in the user intensity level division method are merged, the sixth level with the highest user level is decomposed into two levels, a new user intensity level division method is constructed, and the globally distributed aircraft routes are divided according to time; Step S104: obtaining multiple users surveyed by the satellite at the same time, and performing correlation transformation on the distribution position, user strength and user distribution model of each user in the space according to a preset rule; Step S105: through steps S101 to S104, constructing a user spatial non-uniform distribution model based on the distribution position, the user strength and the user distribution model; Step S106: setting the ground coverage wave position of the low-orbit satellite Internet through the third constraint condition according to the constraint parameters; the constraint parameters include orbital altitude, number of orbital planes, orbital inclination set and number of satellites; the wave position is composed of multiple service wave positions; the size of the service wave position and the number of users included in the service wave position are used as constraint conditions, and a preset clustering algorithm is used to match the user in each grid of the global plane survey area with the most suitable associated service beam to determine the coverage range of the service beam; wherein the third constraint condition includes that the total area of ​​all satellite wave positions is greater than the surface area of ​​the earth, the total area of ​​the service wave positions in a single satellite wave position is greater than the area of ​​a single satellite wave position, and the size of the coverage wave position area of ​​each satellite is set to be the same, and the service wave position in each satellite wave position is adjusted according to the user distribution; Step S107: Obtain the mapping relationship of the distribution position in the space corresponding to each grid, obtain the user strength level of each grid according to the corresponding distribution position, and set different user strength upper limits and value fluctuations for each grid through the fourth constraint condition; configure the corresponding user distribution for each satellite wave position according to the service initiated by the user; wherein the fourth constraint condition includes that the user strength of each satellite wave position is relatively independent and is affected by the global population distribution; the user strength in the service wave position of a single satellite wave position should be less than the user strength of the satellite wave position; the sum of the user strength in the service wave position is equal to the user strength of a single satellite wave position; and the number of service wave positions in each satellite wave position is not necessarily the same; Step S108: obtaining the user spatial non-uniform distribution model to obtain the user strength and distribution position in the corresponding inspection area; expressing the user strength using color, and calculating the kernel density value according to the distribution position and the corresponding color to obtain a global user distribution heat map; Step S109: setting the number and size of the wave positions, the number of users in different wave positions, the survey area and the user distribution to simulate the spatial non-uniform distribution of satellite Internet multi-wave position users.

2. The simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users according to claim 1 is characterized in that: Normalizing the global user traffic density and setting a user intensity level division method for the inspection area also includes: The level of the user traffic density is set according to Gaussian distribution, and the probability of too little population density and / or too much population density is relatively lower. The user intensity of each investigated area is set to 6 different levels.

3. The simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users according to claim 1 is characterized in that: Dividing the global plane investigation area into a plurality of grids by a first constraint condition further comprises: When dividing the grid, make sure that there are as many land areas or ocean areas as possible in each grid, and reduce the number of grids with similar land areas and ocean areas. That is, the grid division must satisfy the following formula: Where N represents a function that calculates the quantity; l i _land indicates land area; l i _sea indicates the ocean area; Indicates the preset threshold.

4. The simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users according to claim 1, characterized in that: The distribution position, user strength and user distribution model of each user in the corresponding space are respectively associated and transformed according to the preset rules, and also include: When the distribution position is associated with the transformation, if the area of ​​the preset inspection area is larger than the area of ​​the grid area after the superposition of each user, the grid area after the superposition of each user is selected for inspection; if the area of ​​the preset inspection area is smaller than the area of ​​the grid area after the superposition of each user, the preset inspection area is selected for inspection; When the user strength is associated and transformed, the level with the largest value among the single business user strengths is selected as the user strength for classification.

5. The simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users according to claim 1, characterized in that: A preset clustering algorithm is used to match the most suitable associated service beam to the users in each grid of the global plane survey area. The preset clustering algorithm is a K-means clustering algorithm. The objective function calculation formula of the K-means clustering algorithm is: Wherein, J(c) represents the clustering degree of the objective function; k represents the number of clusters; x represents the members of the cluster; c i Represents the cluster center of the i-th category.

6. The simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users according to claim 1, characterized in that: According to the services initiated by users, the corresponding user distribution is configured for each satellite wave position, including: When the user distribution obeys uniform distribution, that is, the corresponding investigation area obeys the probability density function of the corresponding two-dimensional random variable, the expression is: in, represents the corresponding two-dimensional random variable; A represents the evenly distributed intensity; Indicates the satellite wave position; Indicates the corresponding inspection area; When the user distribution obeys Gaussian distribution, that is, the corresponding investigation area obeys the probability density function of the corresponding two-dimensional random variable, the expression is: in, represents the corresponding two-dimensional random variable; represents the user location coordinates in the investigation area; (μ1, μ2) represents the central tendency location coordinates of the normal distribution of all users in the investigation area; variance and Describes the degree of dispersion of users in the normal distribution on the x-axis and y-axis respectively.

7. The simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users according to claim 1, characterized in that: Calculating the kernel density value according to the distribution position and the corresponding color also includes: Set the domain radius of the heat concentration point and calculate the number of elements whose distance is less than or equal to the domain radius. The kernel density calculation formula for any point in the corresponding space is: Among them, D p represents the kernel density of point p; T r represents the radius of the domain; n represents the number of elements; K represents the kernel function; f i represents the element in the i-th domain; R(p,f i ) represents the distance between the pixel and the element in the i-th domain.

8. A simulation system for spatial non-uniform distribution of satellite Internet multi-wavelength users, characterized in that: The system is used to execute the steps of the simulation method for spatial non-uniform distribution of satellite Internet multi-wavelength users as claimed in any one of claims 1 to 7, and the system includes the following modules: A user model and spatial distribution module, used to provide a user spatial non-uniform distribution model obtained as claimed in claim 1 for simulation, and configure user strength, user quantity and user distribution at different distribution locations according to the service; The global and local scenario modules are used to set the global or local view range for the simulation, set the survey area under the corresponding view range, and configure the corresponding satellite wave position and its service wave position; A simulation control module, used to control the activation process of each module in the system; Data recording and analysis module, used to record and analyze simulation results.

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