A satellite data multi-beam switching method and system for luxury cruise ships

Abstract

The present invention relates to the technical field of wireless communication networks, and provides a satellite data multi-beam switching method and system for luxury cruise ships, the method comprising: obtaining data requests and location information of each user, wherein the users include luxury cruise ships; clustering each user according to the location information of each user to obtain multiple user sets; determining the total flow of data requests of each current user set; obtaining the beam boundary of each beam; allocating the beams of users in each user set according to the beam boundary of each beam, on the premise that all users are covered by the beam and with the goal of traffic load balancing; and performing beam switching according to the beam allocation result. The present invention clusters each user according to the location information of each user to obtain multiple user sets, and then performs beam switching based on the user set as a unit, thereby avoiding the need for frequent network switching for many users in the luxury cruise ship, reducing the risk of network interruption and data loss, and improving the communication experience of users on the luxury cruise ship.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication networks, and in particular to a satellite data multi-beam switching method and system for luxury cruise ships. Background Art

[0002] Most data transmission needs in urban areas are often met through base stations, while for special users such as remote areas, ships at sea and airplanes in the air, data transmission often requires the use of satellites.

[0003] With current satellite transmission technology, when the network signal is poor, the decision on whether to perform beam switching is made based on indicators such as signal strength, signal-to-noise ratio or bit error rate. The decision is then made to see whether the network signal has returned to normal. If so, the beam switching is completed. If not, the beam switching is continued until the signal returns to normal. This method allows users to feel that the network signal has deteriorated, giving them an unstable network experience.

[0004] Furthermore, cruise ships, especially luxury cruise ships, often have a wide range of activities and are greatly affected by factors such as the marine environment and weather conditions. Beam switching based on indicators such as signal strength may result in frequent switching, causing the communication quality to fluctuate greatly between different beams. In addition, due to the large number of tourists on board, the network transmission requirements are far different from those of ordinary ships, which may lead to more frequent beam switching and increase the risk of network interruption and data loss. Summary of the invention

[0005] In order to solve the technical problems existing in the prior art that current luxury cruise ships often have a wide range of activities and are greatly affected by factors such as the marine environment and weather conditions, and beam switching based on indicators such as signal strength may cause frequent switching, resulting in large fluctuations in communication quality between different beams, and a large number of tourists, and a huge gap in network transmission requirements compared to ordinary ships, which may lead to more frequent beam switching and increase the risk of network interruption and data loss, the present invention provides a satellite data multi-beam switching method and system for luxury cruise ships.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect

[0008] An embodiment of the present invention provides a satellite data multi-beam switching method for a luxury cruise ship, comprising:

[0009] S1: Obtaining data requests and location information of each user, wherein the user includes a luxury cruise ship;

[0010] S2: Cluster each user according to their location information to obtain multiple user sets;

[0011] S3: Determine the total data request flow of each user set at present;

[0012] S4: Obtain the beam boundaries of each beam;

[0013] S5: Allocate beams to users in each user set based on the beam boundaries of each beam, on the premise that all users are covered by the beam and with traffic load balancing as the goal;

[0014] S6: Perform beam switching according to the beam allocation result.

[0015] The S2 is specifically:

[0016] Taking the total number of beams as the number of classifications, each user is clustered through the improved K-Means algorithm to obtain multiple user sets.

[0017] The S2 specifically includes:

[0018] S201: Converting the user's location information into satellite angular position information;

[0019] S202: Constructing feature vectors for each user , x represents the horizontal coordinate in the satellite angular domain, y represents the ordinate in the satellite angular domain, s Indicates data traffic;

[0020] S203: Initialization K Cluster centers:

[0021]

[0022] in, Indicates l The characteristic vector of the cluster centers, represents the abscissa in the satellite angular domain of the cluster center, represents the vertical coordinate of the satellite angular domain of the cluster center, represents the data flow of the cluster center, represents the number of clusters, x max Indicates the maximum value of the horizontal coordinate in the satellite angle domain, x min Indicates the minimum value of the horizontal coordinate in the satellite angle domain, y max Indicates the maximum value of the vertical coordinate in the satellite angle domain, y min Indicates the minimum value of the vertical coordinate in the satellite angle domain, s max Indicates the maximum data flow rate. smin Indicates the minimum value of data flow;

[0023] S204: Calculate the distance between the feature vector of the current user and each cluster center:

[0024]

[0025] in, d l Represents the feature vector of the current user to l The distance between cluster centers, represents the feature vector of the current user, x represents the horizontal coordinate in the satellite angular domain, y represents the ordinate in the satellite angular domain, s Indicates data traffic. β Represents the weight coefficient of the position coordinate, 1- β Indicates the weight coefficient of data traffic;

[0026] S205: Classify the current user into a category with the smallest distance to the cluster center;

[0027] S206: Calculate the centroid of the current cluster and update the classification center through the centroid;

[0028] S207: Continue to select the next user until all users are classified.

[0029] The S3 is specifically:

[0030] The urgency of data requests is introduced to determine the normalized total flow of data requests for each current user set.

[0031] The S3 specifically includes:

[0032] S301: Standardize the data requests of each user:

[0033]

[0034] in, s i* After standardization, i The data traffic of each data request is s i It means the first i The data traffic of each data request is n Indicates the total number of data;

[0035] S302: treating data requests whose waiting time has reached 80% of the expiration time as urgent requests, treating data requests whose waiting time has not reached 80% of the expiration time as ordinary requests, and assigning weight coefficients to the urgent requests and the ordinary requests;

[0036] S303: Determine the normalized total flow of data requests of each current user set according to the weight coefficients of the emergency request and the ordinary request:

[0037]

[0038] in, S j Indicates j Normalized total data traffic for a set of users, Indicates h Emergency request data flow, Indicates j The total number of emergency requests in the user set, oh represents the weight coefficient of the emergency request, Indicates h The data flow of a normal request is Indicates j The total number of common requests in a user set, 1- oh Indicates the weight coefficient of common requests.

[0039] The S5 specifically includes:

[0040] S501: constructing a beam allocation model;

[0041] S502: Setting constraints of the beam allocation model, where the constraints include beam coverage constraints, and the beam coverage constraints are used to ensure that all users are covered by beams;

[0042] S503: With the goal of maximizing the traffic load balancing degree, the beams of the users in each user set are allocated through the beam allocation model.

[0043] The constraints also include:

[0044] Beam uniqueness constraint, used to ensure that any user is served by only one beam;

[0045] Beam service constraints, used to ensure that there are no beams with zero service;

[0046] The beam maximum capacity constraint is used to ensure that the total data traffic of the beam service is less than the maximum capacity.

[0047] The flow load balancing degree is calculated as follows:

[0048]

[0049] in, s Indicates the degree of traffic load balancing. B k Indicates k The total data traffic on the beams is K Indicates the total number of beams;

[0050] Among them, k Total data traffic on beams B k Specifically:

[0051]

[0052] in, m k Indicates that it is assigned to k The total number of user sets on beams, S j Indicates j Normalized total data traffic for a set of users.

[0053] The beam allocation model is solved by using an improved particle swarm optimization algorithm.

[0054] Second aspect

[0055] An embodiment of the present invention provides a satellite data multi-beam switching system for a luxury cruise ship, comprising:

[0056] processor;

[0057] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the satellite data multi-beam switching method for a luxury cruise ship as described in the first aspect is implemented.

[0058] The third aspect

[0059] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the satellite data multi-beam switching method for a luxury cruise ship as described in the first aspect is implemented.

[0060] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0061] (1) In the present invention, each user is clustered according to their location information to obtain multiple user sets, and then beam switching is performed based on the user sets, thereby avoiding the need for frequent network switching for many users on the luxury cruise ship, reducing the risk of network interruption and data loss, and improving the communication experience of users on the luxury cruise ship.

[0062] (2) In the present invention, all users are covered by beams, and traffic load balancing is taken as the goal. The beams of users in each user set are allocated instead of switching beams based on indicators such as signal strength. This reduces the possibility of users perceiving signal deterioration. At the same time, it helps to avoid excessive load on some beams, resulting in waste of resources and performance degradation, and reduces the possibility of congestion on some beams. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0064] Figure 1 A schematic flow chart of a satellite data multi-beam switching method for a luxury cruise ship provided by an embodiment of the present invention;

[0065] Figure 2 A schematic structural diagram of a satellite data multi-beam switching system for a luxury cruise ship provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0067] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0068] Reference Manual Attached Figure 1 , showing a flow chart of a satellite data multi-beam switching method for a luxury cruise ship provided in an embodiment of the present invention.

[0069] The embodiment of the present invention provides a satellite data multi-beam switching method for a luxury cruise ship, which can be implemented by a satellite data multi-beam switching device for a luxury cruise ship, and the satellite data multi-beam switching device for a luxury cruise ship can be a terminal or a server. The processing flow of the satellite data multi-beam switching method for a luxury cruise ship can include the following steps:

[0070] S1: Obtain data requests and location information of each user.

[0071] Among them, users include luxury cruise ships, ordinary ships, airplanes, and users in remote areas, etc.

[0072] S2: Cluster each user according to their location information to obtain multiple user sets.

[0073] It should be noted that clustering users according to location information can group users in adjacent or similar locations into the same set. This can reduce the frequency of beam switching, because users in the same set often have similar communication environments and beam switching requirements. Users in adjacent locations obtain good communication quality under the same beam, so there is no need to switch beams frequently. At the same time, after clustering users into sets, beam switching can be performed in units of sets, which can reduce the calculation and preparation time of the communication system during the switching process. Switching from one set to another set of beams may be more efficient than switching a single user, so it can reduce communication delays.

[0074] In a possible implementation, S2 specifically includes: using the total number of beams as the number of classifications, clustering each user through an improved K-Means algorithm to obtain multiple user sets.

[0075] Among them, the K-Means algorithm is a commonly used clustering algorithm that can classify data points into different categories based on the characteristics of the data. In this algorithm, each data point is considered to be a vector with specific characteristics, and the goal is to classify these vectors into K different categories so that each data point belongs to the center of the class closest to it.

[0076] The selection of the initial cluster center of the traditional K-Means clustering algorithm is often randomly selected. Different initial cluster centers may lead to different clustering results. Therefore, the convergence of the algorithm depends on the selection of the initial value, and inappropriate initial cluster centers may cause the algorithm to converge slowly or fall into a local minimum. This may affect the performance of the algorithm, especially in high-dimensional space or with complex data distribution. For this reason, the present invention proposes a new K-Means clustering algorithm to solve the problem of difficulty in selecting the initial cluster center.

[0077] In a possible implementation, S2 specifically includes sub-steps S201 to S207:

[0078] S201: Convert the user's location information into satellite angular position information.

[0079] It should be noted that converting the user's location information into satellite angular domain location information can more accurately reflect the user's position on the earth's surface, avoid distortion introduced by the earth's curvature, help improve the accuracy of the user's location, and thus improve the accuracy of clustering.

[0080] S202: Constructing feature vectors for each user , x represents the horizontal coordinate in the satellite angular domain, y represents the ordinate in the satellite angular domain, sIndicates data traffic.

[0081] S203: Initialization K Cluster centers:

[0082]

[0083] in, Indicates l The characteristic vectors of cluster centers, represents the abscissa in the satellite angular domain of the cluster center, represents the vertical coordinate of the satellite angular domain of the cluster center, represents the data flow of the cluster center, represents the number of clusters, x max Indicates the maximum value of the horizontal coordinate in the satellite angle domain, x min Indicates the minimum value of the horizontal coordinate in the satellite angle domain, y max Indicates the maximum value of the vertical coordinate in the satellite angle domain, y min Indicates the minimum value of the vertical coordinate in the satellite angle domain, s max Indicates the maximum data flow rate. s min Indicates the minimum data flow rate.

[0084] In the present invention, the formula is initialized K The cluster centers can be evenly distributed according to the maximum and minimum values ​​of the location and data traffic. K The initial positions of the cluster centers. This helps to optimize the initialization of the cluster centers, making them more representative and avoiding the undesirable situations that may be caused by random initialization.

[0085] S204: Calculate the distance between the feature vector of the current user and each cluster center:

[0086]

[0087] in, d l Represents the feature vector of the current user to l The distance between cluster centers, represents the feature vector of the current user, x represents the horizontal coordinate in the satellite angular domain, y represents the ordinate in the satellite angular domain, s Indicates data traffic. β Represents the weight coefficient of the position coordinate, 1- β Indicates the weight coefficient of data traffic.

[0088] Among them, those skilled in the art can set the weight coefficient of the position coordinate according to the actual situation. β The present invention does not limit the size.

[0089] It should be noted that in satellite communication systems, users may have different communication requirements and data traffic. By introducing the weight coefficient of data traffic, it is possible to better adapt to the situation of different user groups. For example, for users with certain applications that require high communication quality (such as video calls), a higher weight can be given to data traffic, so that more attention can be paid to the clustering effect of these users.

[0090] In the present invention, the use of weighted Euclidean distance can more accurately measure the distance between user feature vectors. In this case, it is not just a simple location coordinate distance, but also takes into account the impact of data traffic. It helps to avoid clustering bias caused by different data traffic and improves the accuracy of clustering. The weighted Euclidean distance calculation method helps to consider the importance of different features, adapt to clustering needs in different situations, improve the accuracy and flexibility of clustering, reduce the impact of correlation between data points, and thus optimize the clustering effect of users.

[0091] S205: Classify the current user into the category with the smallest distance to the cluster center.

[0092] S206: Calculate the centroid of the current cluster and update the classification center through the centroid.

[0093] S207: Continue to select the next user until all users are classified.

[0094] In the present invention, clustering is performed according to the user's location information to obtain multiple user sets, which can optimize the efficiency of beam switching, reduce communication delays, improve the accuracy of beam switching, and facilitate the rational allocation of resources and reduce the system burden, thereby improving the performance of the satellite communication system and user experience.

[0095] S3: Determine the total data request flow of each current user set.

[0096] In a possible implementation, S3 specifically includes: introducing the urgency of the data request, and determining the normalized total flow of data requests of each current user set.

[0097] In a possible implementation, S3 specifically includes: sub-steps S301 to S303.

[0098] S301: Standardize the data requests of each user:

[0099]

[0100] in, s i* After standardization, i The data traffic of each data request is s i It means the first i The data traffic of each data request is n Indicates the total number of data.

[0101] It should be noted that standardization can make the data requests of different users have the same magnitude when calculating the total traffic, which helps to not be affected by the size of the data request when calculating the total traffic, but to focus more on the relative proportion of each user's request. This processing method more objectively reflects the user's communication needs.

[0102] S302: The data request whose waiting time has reached 80% of the expiration time is regarded as an urgent request, and the data request whose waiting time has not reached 80% of the expiration time is regarded as a common request, and weight coefficients are assigned to the urgent request and the common request.

[0103] It should be noted that data requests whose waiting time has reached 80% of the expiration time are regarded as urgent requests, and data requests whose waiting time has not reached 80% of the expiration time are regarded as ordinary requests. This can reasonably distinguish between requests with high urgency and ordinary requests based on the waiting time of the requests. This helps to prioritize urgent requests when resources are limited, and improve system efficiency and user experience.

[0104] S303: Determine the normalized total flow of data requests of each current user set according to the weight coefficients of the emergency request and the ordinary request:

[0105]

[0106] in, S j Indicates j Normalized total data traffic for a set of users, Indicates h Emergency request data flow, Indicates j The total number of emergency requests in the user set, oh represents the weight coefficient of the emergency request, Indicates h The data flow of a normal request is Indicates j The total number of common requests in a user set, 1- oh Indicates the weight coefficient of common requests.

[0107] Among them, those skilled in the art can set the weight coefficient of the emergency request according to the actual situation. oh The present invention does not limit the size.

[0108] It should be noted that the weight coefficient of emergency requests can be set to be greater than the weight coefficient of ordinary requests. Since emergency requests are processed first, the overall service quality of the system will be improved. This means that in an emergency, the user's communication needs can be responded to in a timely manner, reducing data transmission delays and improving user experience.

[0109] In the present invention, the normalized total flow takes into account the data request amount of different user sets and is adjusted according to the weights of emergency requests and ordinary requests. This helps to achieve traffic load balancing, that is, the total data flow allocated on each beam is relatively balanced. It avoids the problem of excessive load on some beams, resulting in waste of resources and performance degradation. At the same time, by calculating the normalized total flow, the system can allocate resources more effectively and better consider the needs of each user set when switching beams. This can improve the overall efficiency of the system, reduce waste of resources, and improve the speed and accuracy of data transmission.

[0110] S4: Obtain the beam boundaries of each beam.

[0111] It should be noted that the beam boundaries of each beam can be obtained through various methods such as pre-planning, measurement and analysis, receiver adaptation, satellite positioning services, machine learning and optimization algorithms, GIS technology, and field verification. It is usually necessary to consider a variety of factors to ensure the accuracy and effectiveness of the beam boundaries. For example, when designing a satellite multi-beam system, the coverage and boundaries of each beam can be planned in advance, which can be achieved through simulation and modeling technology, considering factors such as satellite orbits, beam directivity, and interference between beams, to determine the theoretical coverage and boundaries of each beam.

[0112] Specifically, the S4 specifically includes:

[0113] S401: Obtain satellite orbit data, satellite antenna data and satellite beam information through GIS technology;

[0114] Among them, satellite orbit data specifically includes the satellite's altitude, inclination, and eccentricity.

[0115] Among them, the satellite antenna data specifically includes the type of antenna, radiation pattern, main lobe width, side lobe level, etc.

[0116] The satellite beam information includes the beam types supported by the satellite, etc.

[0117] S402: Determine the current antenna direction of the satellite antenna according to the satellite orbit data and the satellite antenna data;

[0118] S403: According to the antenna pointing direction and the beam type, a signal coverage simulation is performed through a signal propagation model to determine the beam boundaries of each beam.

[0119] Among them, the signal propagation model can be constructed according to the signal propagation theory through MATLAB, Python, etc.

[0120] Specifically, the free space propagation model can be used as the basic model. The free space propagation model describes the propagation behavior of the signal in the absence of obstacle interference. According to the free space propagation model, the power attenuation of the signal is inversely proportional to the square of the distance. Secondly, path loss is introduced into the free space propagation model. Path loss refers to the signal attenuation caused by factors such as the propagation medium and path distance during the propagation process. Commonly used path loss models include the Friis free space propagation model and the two-line model. After that, the influence of the atmosphere on the signal is introduced into the free space propagation model. The gases, water vapor, etc. in the atmosphere absorb and scatter microwave and millimeter wave signals, which will cause signal attenuation. Common atmospheric propagation models include the troposphere model and the flat surface model.

[0121] Furthermore, in the process of constructing the signal propagation model, the influence of the earth's terrain and buildings on signal propagation is considered.

[0122] S5: According to the beam boundaries of each beam, all users are covered by the beam as a prerequisite, and traffic load balancing is taken as a goal, and beams of users in each user set are allocated.

[0123] It should be noted that ensuring that all users are covered by the beam means that all users within the communication range can obtain signal services. This helps avoid the situation where some users have "dead zones" in communication and improves the overall communication reliability and coverage.

[0124] Furthermore, evenly allocating beams can avoid overcrowding in some beams while leaving others idle, which helps optimize beam utilization, reduces the risk of performance degradation due to overloading of some beams, and improves the efficiency of the overall communication system.

[0125] In a possible implementation, S5 specifically includes sub-steps S501 to S503:

[0126] S501: Construct a beam allocation model.

[0127] S502: Setting constraints of the beam allocation model.

[0128] Constraints include beam coverage constraints, which are used to ensure that all users are covered by the beam. This is a basic requirement of the communication system, which can avoid communication "dead zones" for users and improve communication reliability and coverage.

[0129] Optionally, the constraints also include:

[0130] Beam uniqueness constraint is used to ensure that any user is served by only one beam. Signal overlap and interference can be avoided. Each user is connected to only one beam, which simplifies system design and management and improves communication stability and reliability.

[0131] Beam service constraints are used to ensure that there are no beams with zero service. This can prevent some beams from being idle, effectively utilize beam resources, and improve resource utilization and overall communication efficiency.

[0132] The beam maximum capacity constraint is used to ensure that the total data flow of the beam service is less than the maximum capacity. This helps to avoid excessive load on some beams, which leads to performance degradation and resource waste. Limiting the maximum capacity of each beam can ensure that the overall communication system can maintain stable operation under high load conditions, avoiding network congestion and data loss.

[0133] S503: With the goal of maximizing the traffic load balancing degree, the beams of the users in each user set are allocated through a beam allocation model.

[0134] In the present invention, all users are covered by beams, and the beams of users in each user set are allocated with the goal of traffic load balancing, which helps to improve coverage, reduce beam congestion risks, optimize resource utilization, improve user experience, reduce network interruption risks, and improve system elasticity and robustness. These are all key factors to ensure the efficient and stable operation of the communication system on luxury cruise ships.

[0135] Optionally, the flow load balancing degree is calculated as follows:

[0136]

[0137] in, s Indicates the degree of traffic load balancing. B k Indicates k The total data traffic on the beams is K Indicates the total number of beams.

[0138] It should be noted that the numerator represents the square of the sum of the total data traffic on all beams. The denominator is the sum of the squares of the data traffic on all beams multiplied by the total number of beams, which can be regarded as the expected value of the square of the total traffic if the data traffic on each beam is evenly distributed. Comparing the square of the total load with the square of the balanced expected load, the result is the traffic load balance. The closer this ratio is to 1, the more balanced the load on each beam; the closer it is to 0, the load distribution is uneven, and the load on some beams is higher.

[0139] Among them, k Total data traffic on beams B k Specifically:

[0140]

[0141] in, m k Indicates that it is assigned to k The total number of user sets on beams, S j Indicates j Normalized total data traffic for a set of users.

[0142] In the present invention, the calculation method of traffic load balancing helps to balance the load of each beam in the system and ensure that the data traffic is evenly distributed among the beams. This can avoid excessive load on some beams, resulting in waste of resources or reduced service quality, while improving the performance and efficiency of the overall communication system.

[0143] In a possible implementation, the beam allocation model is solved using an improved particle swarm optimization algorithm.

[0144] Among them, Particle Swarm Optimization (PSO) is an optimization algorithm based on swarm intelligence. It is inspired by the group behavior of bird flocks or fish schools, and finds the optimal solution by simulating the collaboration and information sharing of individuals in the group.

[0145] Specifically, a particle swarm is initialized, the particle swarm includes multiple particles, and each particle represents a feasible control parameter set.

[0146] Take traffic load balancing as the fitness function of the particle swarm:

[0147]

[0148] in, d ( ) represents the fitness function, i Indicates the beam allocation method, sIndicates the degree of traffic load balancing. s ( i ) indicates the use of beam allocation i The traffic load balancing degree at that time.

[0149] It should be noted that using traffic load balancing as the fitness function makes the goal of the algorithm clearer, that is, optimizing the beam allocation method to achieve the goal of traffic load balancing. The fitness function designed in this way can effectively guide the algorithm to search for a suitable beam allocation solution.

[0150] Specifically, the beam allocation method i Specifically, it includes which beam each user set is assigned to.

[0151] Each particle updates its speed and position according to its own extreme value and group extreme value:

[0152]

[0153]

[0154] in, Indicates t +1 iteration i The speed of a particle, Indicates t The iteration i The speed of a particle, w represents the inertia weight factor, c 1 represents the individual optimal acceleration factor, c 2 represents the global optimal acceleration factor, r 1 and r 2 represents a random number between 0 and 1. Indicates t The iteration i The individual optimal position of each particle, Indicates t The global optimal position at the iteration is Indicates t +1 iteration i The position of a particle, Indicates t The first iteration i The position of a particle, h Represents the chaos coefficient.

[0155] It should be noted that the particle swarm algorithm has a strong global search capability and can effectively search for a better solution in the solution space. By utilizing the group behavior and individual experience of particles, the algorithm can help find a beam allocation method with a higher degree of traffic load balancing.

[0156] Among them, the chaos coefficient h Specifically:

[0157]

[0158] in, h 0 represents the initial chaos coefficient.

[0159] It should be noted that as the number of iterations increases, the chaos coefficient gradually decreases, which can make the particle swarm converge more stably to the optimal solution in the later stages of the iteration. In the early stages of the algorithm, a larger chaos coefficient helps particles search the solution space more extensively, increasing the probability of global search; and in the later stages of the iteration, a gradually decreasing chaos coefficient helps particles search for local optimal solutions more carefully.

[0160] When the number of iterations reaches the maximum number of iterations, the beam allocation method represented by the particle with the highest fitness is output as the optimal beam allocation method.

[0161] S6: Perform beam switching according to the beam allocation result.

[0162] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0163] (1) In the present invention, each user is clustered according to their location information to obtain multiple user sets, and then beam switching is performed based on the user sets, thereby avoiding the need for frequent network switching for many users on the luxury cruise ship, reducing the risk of network interruption and data loss, and improving the communication experience of users on the luxury cruise ship.

[0164] (2) In the present invention, all users are covered by beams, and traffic load balancing is taken as the goal. The beams of users in each user set are allocated instead of switching beams based on indicators such as signal strength. This reduces the possibility of users perceiving signal deterioration. At the same time, it helps to avoid excessive load on some beams, resulting in waste of resources and performance degradation, and reduces the possibility of congestion on some beams.

[0165] Reference Manual Attached Figure 2 , showing a schematic structural diagram of a satellite data multi-beam switching system for a luxury cruise ship provided by the present invention.

[0166] The present invention also provides a satellite data multi-beam switching system 20 for a luxury cruise ship, which is applied to the satellite data multi-beam switching method for the luxury cruise ship, comprising:

[0167] Processor 201.

[0168] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the satellite data multi-beam switching method for a luxury cruise ship in the method embodiment is implemented.

[0169] The satellite data multi-beam switching system 20 for luxury cruise ships provided by the present invention can execute the above-mentioned satellite data multi-beam switching method for luxury cruise ships and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0170] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0171] (1) In the present invention, each user is clustered according to their location information to obtain multiple user sets, and then beam switching is performed based on the user sets, thereby avoiding the need for frequent network switching for many users on the luxury cruise ship, reducing the risk of network interruption and data loss, and improving the communication experience of users on the luxury cruise ship.

[0172] (2) In the present invention, all users are covered by beams, and traffic load balancing is taken as the goal. The beams of users in each user set are allocated instead of switching beams based on indicators such as signal strength. This reduces the possibility of users perceiving signal deterioration. At the same time, it helps to avoid excessive load on some beams, resulting in waste of resources and performance degradation, and reduces the possibility of congestion on some beams.

[0173] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0174] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0175] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0176] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0177] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements a satellite data multi-beam switching method for a luxury cruise ship as described in a method embodiment.

[0178] A computer-readable storage medium provided by the present invention can implement the steps and effects of the satellite data multi-beam switching method for luxury cruise ships in the above method embodiment. To avoid repetition, the present invention will not go into details.

Claims

1. A satellite data multi-beam switching method for luxury cruise ships, characterized in that: include: S1: Obtaining data requests and location information of each user, wherein the user includes a luxury cruise ship; S2: Cluster each user according to their location information to obtain multiple user sets; S3: Determine the total data request flow of each user set at present; S4: Obtain the beam boundaries of each beam; S5: Allocate beams to users in each user set based on the beam boundaries of each beam, on the premise that all users are covered by the beam and with traffic load balancing as the goal; S6: Perform beam switching according to the beam allocation result; The S2 specifically includes: S201: Converting the user's location information into satellite angular position information; S202: Constructing feature vectors for each user , x represents the horizontal coordinate in the satellite angular domain, y represents the ordinate in the satellite angular domain, s Indicates data traffic; S203: Initialization K Cluster centers: in, Indicates l The characteristic vector of the cluster centers, represents the abscissa in the satellite angular domain of the cluster center, represents the vertical coordinate of the satellite angular domain of the cluster center, represents the data flow of the cluster center, represents the number of clusters, x max Indicates the maximum value of the horizontal coordinate in the satellite angle domain, x min Indicates the minimum value of the horizontal coordinate in the satellite angle domain, y max Indicates the maximum value of the vertical coordinate in the satellite angle domain, y min Indicates the minimum value of the vertical coordinate in the satellite angle domain, s max Indicates the maximum data flow rate. s min Indicates the minimum value of data flow; S204: Calculate the distance between the feature vector of the current user and each cluster center: in, d l Represents the feature vector of the current user to l The distance between cluster centers, represents the feature vector of the current user, x represents the horizontal coordinate in the satellite angular domain, y represents the ordinate in the satellite angular domain, s Indicates data traffic. β Represents the weight coefficient of the position coordinate, 1- β Indicates the weight coefficient of data traffic; S205: Classify the current user into a category with the smallest distance to the cluster center; S206: Calculate the centroid of the current cluster and update the classification center through the centroid; S207: Continue to select the next user until all users are classified.

2. The satellite data multi-beam switching method for luxury cruise ships according to claim 1, characterized in that: The S3 is specifically: The urgency of data requests is introduced to determine the normalized total flow of data requests for each current user set.

3. The satellite data multi-beam switching method for luxury cruise ships according to claim 2, characterized in that: The S3 specifically includes: S301: Standardize the data requests of each user: in, s i* After standardization, i The data traffic of each data request is s i It means the first i The data traffic of each data request is n Indicates the total number of data; S302: treating data requests whose waiting time has reached 80% of the expiration time as urgent requests, treating data requests whose waiting time has not reached 80% of the expiration time as ordinary requests, and assigning weight coefficients to the urgent requests and the ordinary requests; S303: Determine the normalized total flow of data requests of each current user set according to the weight coefficients of the emergency request and the ordinary request: in, S j Indicates j Normalized total data traffic for a set of users, Indicates h Emergency request data flow, Indicates j The total number of emergency requests in the user set, ω represents the weight coefficient of the emergency request, Indicates h The data flow of a normal request is Indicates j The total number of common requests in a user set, 1- ω Indicates the weight coefficient of common requests.

4. The satellite data multi-beam switching method for a luxury cruise ship according to claim 1, characterized in that: The S5 specifically includes: S501: constructing a beam allocation model; S502: Setting constraints of the beam allocation model, where the constraints include beam coverage constraints, and the beam coverage constraints are used to ensure that all users are covered by beams; S503: With the goal of maximizing the traffic load balancing degree, the beams of the users in each user set are allocated through the beam allocation model.

5. The satellite data multi-beam switching method for luxury cruise ships according to claim 4, characterized in that: The constraints also include: Beam uniqueness constraint, used to ensure that any user is served by only one beam; Beam service constraints, used to ensure that there are no beams with zero service; The beam maximum capacity constraint is used to ensure that the total data traffic of the beam service is less than the maximum capacity.

6. The satellite data multi-beam switching method for luxury cruise ships according to claim 4, characterized in that: The flow load balancing degree is calculated as follows: in, σ Indicates the degree of traffic load balancing. B k Indicates k The total data traffic on the beams is K Indicates the total number of beams; Among them, k Total data traffic on beams B k Specifically: in, m k Indicates that it is assigned to k The total number of user sets on beams, S j Indicates j Normalized total data traffic for a set of users.

7. The satellite data multi-beam switching method for luxury cruise ships according to claim 4, characterized in that: The beam allocation model is solved by using an improved particle swarm optimization algorithm.

8. A satellite data multi-beam switching system for luxury cruise ships, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the satellite data multi-beam switching method for a luxury cruise ship as described in any one of claims 1 to 3 is implemented.

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