A beam scanning allocation method, apparatus, and storage medium

Abstract

This application discloses a beam scanning allocation method, apparatus, and storage medium, comprising: determining coverage areas corresponding to multiple first beams of a satellite, and multiple second beams for assisting each first beam in scanning the coverage area; determining the hopping beam period corresponding to the second beam; defining a chromosome vector and initializing the chromosome vector to obtain a chromosome population; constructing a fitness function, wherein the fitness function is related to the travel distance and the supply-demand ratio; determining the chromosome vector with the minimum fitness through iterative optimization using a genetic algorithm; and determining the coverage area scanned by each second beam in each hopping beam period based on the chromosome vector with the minimum fitness. Thus, it solves the technical problem in the prior art that relying solely on conventional beam scanning not only makes it difficult to achieve dynamic satellite resource scheduling but also makes it difficult to meet the communication needs of the coverage area, thereby affecting the quality of satellite communication services.

Description

Technical Field

[0001] This application relates to the field of satellite communication technology, and in particular to a beam scanning allocation method, apparatus, and storage medium. Background Technology

[0002] In the field of modern satellite communications, multi-beam scanning technology is commonly used to maximize the coverage capability and spectrum utilization efficiency of a single satellite. In this technology, conventional scanning beams correspond one-to-one with ground coverage areas. Through fixed beam arrangement and scanning timing, complete coverage and basic communication services for the target area are achieved.

[0003] However, traditional fixed-beam coverage suffers from rigid resource allocation. In practical applications, limited by the fixed scanning mode and resource allocation strategy of conventional beams, when hotspots with surging traffic or special scenarios requiring focused monitoring appear within the coverage area, conventional scanning beams struggle to dynamically provide additional resources or enhance scanning capabilities for these areas while maintaining full coverage. Therefore, relying solely on conventional scanning beams not only fails to achieve dynamic satellite resource scheduling but also falls short of meeting the communication demands of the coverage area, leading to a decline in satellite communication service quality.

[0004] The publication number is CN119727875A, and the title is "Satellite Beam Scheduling Method, Scheduling System, Electronic Equipment, and Medium." It includes: the satellite transmitting multiple beams; determining the operating status of the multiple beams; determining the load value of the multiple beams; and determining a target beam based on the operating status and load value of the multiple beams to provide communication services. This application improves the overall operational efficiency and benefits of satellite communication.

[0005] The publication number is CN120342462A, and the title is "A Satellite Scheduling Method, Apparatus, Device, and Storage Medium Based on Reinforcement Learning." It includes: constructing a state space comprising individual satellite identifiers of multiple satellites in a navigation satellite constellation, the elevation angles of each satellite relative to a target area, the azimuth angles of each satellite relative to the target area, and the relative distances of each satellite to the target area; constructing an action space comprising a sequence of satellite identifiers to be scheduled; designing a reward function based on reward metrics including coverage multiplicity reward and PDOP reward; training a satellite beam scheduling neural network based on the TD3 algorithm, according to the state space, action space, and reward function, to obtain a trained policy network; inputting the real-time state vector of the target area into the trained policy network to obtain real-time actions, thereby controlling all satellite beams corresponding to the real-time actions to point towards the target area. This method can improve the quality of service of satellites and increase the accuracy of reentry trajectory prediction.

[0006] There is currently no effective solution to the technical problems existing in the above-mentioned technologies, which are that relying solely on conventional scanning beams not only makes it difficult to achieve dynamic satellite resource scheduling, but also makes it difficult to meet the communication needs of the coverage area, thus affecting the quality of satellite communication services. Summary of the Invention

[0007] The embodiments of this disclosure provide a beam scanning allocation method, apparatus, and storage medium to at least solve the technical problems existing in the prior art, which are that relying solely on conventional scanning beams not only makes it difficult to achieve dynamic satellite resource scheduling but also makes it difficult to meet the communication needs of the coverage area, thereby affecting the quality of satellite communication services.

[0008] According to one aspect of the present disclosure, a beam scanning allocation method is provided, comprising: determining coverage areas corresponding to a plurality of first beams of a satellite, and a plurality of second beams for assisting each first beam in scanning the coverage area; determining a hopping beam period corresponding to a second beam, wherein within the hopping beam period, the second beam is used to switch and scan the coverage area; defining a chromosome vector and initializing the chromosome vector to obtain a chromosome population, wherein the chromosome vector represents the coverage area scanned by each second beam within each hopping beam period; constructing a fitness function, wherein the fitness function is related to the movement distance and the supply-demand ratio, wherein the movement distance represents the distance moved by the second beam during switching and scanning the coverage area within each hopping beam period, and the supply-demand ratio represents the relationship between the communication capability of the satellite and the demand for the coverage area; iteratively optimizing the chromosome population using the fitness function according to a genetic algorithm to determine the chromosome vector with the minimum fitness; and determining the coverage area scanned by each second beam within each hopping beam period based on the chromosome vector with the minimum fitness.

[0009] According to another aspect of the present disclosure, a storage medium is also provided, the storage medium including a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0010] According to another aspect of the present disclosure, a beam scanning allocation apparatus is also provided, comprising: a beam determination module for determining coverage areas corresponding to a plurality of first beams of a satellite, and a plurality of second beams for assisting each first beam in scanning the coverage area; a hopping beam period determination module for determining a hopping beam period corresponding to a second beam, wherein within the hopping beam period, the second beam is used to switch and scan the coverage area; and an initialization module for defining a chromosome vector and initializing the chromosome vector to obtain a chromosome population, wherein the chromosome vector represents the coverage area scanned by each second beam within each hopping beam period. The system comprises the following modules: a region; a fitness function construction module, used to construct a fitness function, which is related to the movement distance and the supply-demand ratio, where the movement distance represents the distance the second beam switches to scan the coverage area during each beam-hopping cycle, and the supply-demand ratio represents the relationship between the satellite's communication capability and the demand for coverage area; a genetic algorithm module, used to iteratively optimize the chromosome population using the fitness function to determine the chromosome vector with the minimum fitness; and a coverage area determination module, used to determine the coverage area scanned by each second beam during each beam-hopping cycle based on the chromosome vector with the minimum fitness.

[0011] According to another aspect of the present disclosure, a beam scanning allocation apparatus is also provided, comprising: a processor; and a memory connected to the processor, configured to provide the processor with instructions for processing the following steps: determining coverage areas corresponding to a plurality of first beams of a satellite, and a plurality of second beams for assisting each first beam in scanning the coverage areas; determining a hopping beam period corresponding to a second beam, wherein within the hopping beam period, the second beam is used to switch and scan the coverage area; defining a chromosome vector and initializing the chromosome vector to obtain a chromosome population, wherein the chromosome vector represents the coverage area scanned by each second beam within each hopping beam period; constructing a fitness function, wherein the fitness function is related to a movement distance and a supply-demand ratio, wherein the movement distance represents the distance moved by the second beam during switching and scanning the coverage area within each hopping beam period, and the supply-demand ratio represents the relationship between the communication capability of the satellite and the demand for the coverage area; iteratively optimizing the chromosome population using the fitness function according to a genetic algorithm to determine the chromosome vector with the minimum fitness; and determining the coverage area scanned by each second beam within each hopping beam period based on the chromosome vector with the minimum fitness.

[0012] To address the lack of flexible scheduling of satellite resources, this application provides a beam scanning allocation method. First, coverage areas corresponding to multiple first beams of a satellite are determined, along with multiple second beams to assist each first beam in scanning its coverage area. Then, since the second beams switch the coverage area they scan, a beam-hopping period corresponding to each second beam is determined.

[0013] Furthermore, chromosome vectors are defined, and a chromosome population is obtained by initializing the chromosome vectors. Then, based on a genetic algorithm and a fitness function related to movement distance and supply-demand ratio, the chromosome population is iteratively optimized to determine the chromosome vector with the minimum fitness.

[0014] This allows for the determination of the coverage area scanned by each second beam within each beam-hopping cycle, enabling dynamic allocation of the second beams used to assist the first beam in scanning the coverage area. Furthermore, it solves the technical problem in existing technologies where relying solely on conventional scanning beams not only makes dynamic satellite resource scheduling difficult but also fails to meet the communication needs of the coverage area, thus affecting the quality of satellite communication services. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of this disclosure and form part of this application, illustrate exemplary embodiments of the present disclosure and are used to explain the disclosure, but do not constitute an undue limitation thereof. In the drawings:

[0016] Figure 1 This is a schematic diagram of the scanning coverage area of ​​the first beam and the second beam according to Embodiment 1 of this disclosure;

[0017] Figure 2 This is a hardware structure block diagram of a satellite according to Embodiment 1 of this disclosure;

[0018] Figure 3 This is a schematic flowchart of the beam scanning allocation method according to Embodiment 1 of this disclosure;

[0019] Figure 4 This is a schematic diagram of the second beam scanning coverage area within one hopping beam cycle according to Embodiment 1 of this disclosure;

[0020] Figure 5 This is a schematic diagram of the second beam scanning coverage area during another hopping beam cycle, according to Embodiment 1 of this disclosure;

[0021] Figure 6 This is a schematic diagram of the beam scanning and allocation device according to Embodiment 2 of this disclosure; and

[0022] Figure 7 This is a schematic diagram of the beam scanning and allocation device according to Embodiment 3 of this disclosure. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] Example 1

[0026] According to this embodiment, a method embodiment for beam scanning allocation is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 A schematic diagram of the scanning coverage area of ​​the first beam and the second beam according to Embodiment 1 of this disclosure is shown. (Refer to...) Figure 1 As shown, the first beam of satellite 10 Corresponding to the coverage area First beam Used for covering areas It performs scanning and provides satellite communication services. However, due to a portion of the coverage area... The increased communication demands exceeded the first beam. The communication capability is required, therefore a second beam is needed. Assisting the first beam Coverage areas with increased scanning communication needs .in, Indicates the first beam Quantity, It also indicates the coverage area. Quantity, Indicates the second beam The quantity. And, .

[0028] Figure 2 Further shown Figure 1 A schematic diagram of the hardware architecture of Zhongwei Satellite-10. (Reference) Figure 2 As shown, satellite 10 includes an integrated electronic system, which includes a processor, a memory, a bus management module, and a communication interface. The memory is connected to the processor, allowing the processor to access the memory, read program instructions stored in the memory, read data from the memory, or write data to the memory. The bus management module is connected to the processor and also to a bus such as a CAN bus. Thus, the processor can communicate with onboard peripherals connected to the bus through the bus managed by the bus management module. Furthermore, the processor also communicates with devices such as cameras, star sensors, telemetry and command transponders, and data transmission equipment via the communication interface. Those skilled in the art will understand that… Figure 2 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, a satellite may also include components that are larger than... Figure 2 The more or fewer components shown, or having the same Figure 2 The different configurations shown.

[0029] It is worth noting that the aforementioned spaceborne peripherals connected to the CAN bus can be one or multiple. These spaceborne peripherals include, but are not limited to, GNSS modules, fiber optic gyroscopes, and high-torque flywheels. Further details will not be elaborated upon here.

[0030] It should be noted that, Figure 2 One or more processors and / or other data processing circuits shown herein may generally be referred to as "data processing circuitry". This data processing circuitry may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be integrated, in whole or in part, into any other element in a computing device. As involved in embodiments of this disclosure, the data processing circuitry serves as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0031] Figure 2The memory shown can be used to store software programs and modules of application software, such as the program instruction / data storage device corresponding to the beam scanning allocation method in the embodiments of this disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the beam scanning allocation method of the application described above. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0032] It should be noted here that, in some optional embodiments, the above... Figure 2 The device shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 2 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned devices.

[0033] Under the aforementioned operating environment, according to the first aspect of this embodiment, a beam scanning allocation method is provided, the method comprising: Figure 2 The processor implementation shown. Figure 3 A flowchart illustrating the method is shown below. (Refer to...) Figure 3 As shown, the method includes:

[0034] S302: Determine the coverage area corresponding to the multiple first beams of the satellite, and the multiple second beams used to assist each first beam in scanning the coverage area;

[0035] S304: Determine the hopping beam period corresponding to the second beam, and within the hopping beam period, the second beam is used to switch and scan the coverage area;

[0036] S306: Define the chromosome vector and initialize the chromosome vector to obtain the chromosome population, where the chromosome vector represents the coverage area scanned by each second beam in each hopping beam period;

[0037] S308: Construct a fitness function, which is related to the travel distance and the supply-demand ratio. The travel distance represents the distance the coverage area scanned by the second beam switching in each hop beam cycle is moved, and the supply-demand ratio represents the relationship between the satellite's communication capability and the demand for the coverage area.

[0038] S310: Based on the genetic algorithm, the chromosome population is iteratively optimized using a fitness function to determine the chromosome vector with the lowest fitness; and

[0039] S312: Based on the chromosome vector with the lowest fitness, determine the coverage area scanned by each second beam within each hopping beam period.

[0040] Specifically, refer to Figure 1 and Figure 3 As shown, firstly, the processor determines multiple first beams corresponding to satellite 10. Corresponding coverage area and for assisting each first beam Scan coverage area Multiple second beams .in, Indicates the first beam Quantity, It also indicates the coverage area. Quantity, Indicates the second beam The quantity. And, (Corresponding to step S302).

[0041] Then, the processor determines the second beam. Corresponding beam skipping period And in the hopping beam period Inside, the second beam Used to switch and scan coverage areas .in, Indicates the hopping beam period The quantity.

[0042] For example, Figure 4 This is a schematic diagram of the second beam scanning coverage area within one hopping beam cycle according to an embodiment of the present disclosure. Figure 5 This is a schematic diagram of the coverage area of ​​the second beam scan during another beam-hopping cycle, according to an embodiment of this disclosure. (Reference) Figure 4 As shown, in one hopping beam cycle Inside, the second beam It can assist in scanning the coverage area. Second beam It can assist in scanning the coverage area. Second beam It can assist in scanning the coverage area. .

[0043] Further, refer to Figure 5 As shown, in another hopping beam cycle Inside, the second beam It can assist in scanning the coverage area. Second beam It can assist in scanning the coverage area. Second beam It can assist in scanning the coverage area. .in, (Corresponding to step S304).

[0044] Furthermore, define chromosome vectors. and chromosome vectors The chromosome population is obtained through initialization. Among them, the chromosome vector... This indicates that in each hop beam cycle Inside, each second beam The scanned coverage area .

[0045] First, define the chromosome vector. for:

[0046] .

[0047] in, This represents a chromosome vector. This represents the genes in a chromosome vector. L represents the number of genes in the chromosome vector. Furthermore, the genes... The number is determined by the second beam Number and hop beam period The quantity determines this. That is, .

[0048] Define genes Indicates the first A second beam In the One hop beam cycle The scanned coverage area .in, By the second beam and hopping beam period The decision is made using the following formula:

[0049] .

[0050] in, Represents chromosome vectors The gene sequence number. The sequence number indicates the hopping beam period. Indicates the second beam The quantity. This indicates the sequence number of the second beam. That is, , , .

[0051] Table 1 shows the beam skipping period Inner second beam With genes The correspondence.

[0052] Table 1

[0053]

[0054] Referring to Table 1, during the hopping beam period Inside, the second beam Corresponding genes During the hopping beam cycle Inside, the second beam Corresponding genes ...; in the hopping beam period Inside, the second beam Corresponding genes .

[0055] During the beam skipping period Inside, the second beam Corresponding genes During the hopping beam cycle Inside, the second beam Corresponding genes ...; in the hopping beam period Inside, the second beam Corresponding genes .

[0056] And so on.

[0057] During the beam skipping period Inside, the second beam Corresponding genes During the hopping beam cycle Inside, the second beam Corresponding genes ...; in the hopping beam period Inside, the second beam Corresponding genes .

[0058] Furthermore, genes The value corresponds to the scanned coverage area. .Right now, For example, when When the value is 1, during the hopping beam period Internal, genes The corresponding second beam Scan coverage area .when The value is At that time, during the hopping beam period Internal, genes The corresponding second beam Scan coverage area .

[0059] In conclusion, when When the value is 2, during the hopping beam period Inside, the second beam Scan coverage area .when When the value is 5, during the hopping beam period Inside, the second beam Scan coverage area .when When the value is 1, during the hopping beam period Inside, the second beam Scan coverage area .

[0060] Then, for chromosome vectors Initialize and obtain the chromosome population (corresponding to step S306).

[0061] Furthermore, construct the fitness function. Among them, the fitness function With distance traveled and supply-demand ratio Related. Distance traveled. This indicates that in each hop beam cycle Inner second beam Switch the scanned coverage area The distance moved, the supply-demand ratio This indicates the communication capabilities and coverage area of ​​satellite 10. The relationship between the needs. Among them, (Corresponding to step S308).

[0062] Then, the processor uses a genetic algorithm and a fitness function to determine the optimal choice of the target. Iterative optimization of the chromosome population is performed to determine the chromosome vector with the minimum fitness. (Corresponding to step S310).

[0063] Finally, the processor selects the chromosome vector with the lowest fitness. Determine the period of each hopping beam. Inside, each second beam The scanned coverage area That is, through chromosome vectors. genes Determine the period of each hopping beam. Inside, each second beam The scanned coverage area (Corresponding to step S312).

[0064] Therefore, the method provided in this application achieves this through the first beam of satellite 10. Corresponding coverage area Used to assist the first beam The second beam and with the second beam Corresponding beam skipping period It can realize chromosome vector The definition of chromosome vector. Then, based on the chromosome vector... and distance traveled and supply-demand ratio Related fitness function By using a genetic algorithm to iteratively optimize the chromosome population, the chromosome vector with the minimum fitness can be determined. Furthermore, it can determine the chromosome vector in each hopping beam cycle. Inside, each second beam The scanned coverage area Furthermore, it is possible to dynamically allocate the second beam. Assisting the first beam Scan coverage area To ensure the quality of satellite communication services.

[0065] As described in the background section, in the field of modern satellite communications, multi-beam scanning technology is commonly used to maximize single-satellite coverage and spectrum utilization efficiency. In this technology, conventional scanning beams correspond one-to-one with ground coverage areas, achieving complete coverage and basic communication services for the target area through fixed beam arrangement and scanning timing. However, traditional fixed-beam coverage suffers from rigid resource allocation. In practical applications, limited by the fixed scanning mode and resource allocation strategy of conventional beams, when hotspots with surging traffic or special scenarios requiring focused monitoring appear within the coverage area, conventional scanning beams struggle to dynamically provide additional resources or enhance scanning capabilities for these areas while maintaining full coverage. Therefore, relying solely on conventional scanning beams makes it difficult to achieve dynamic satellite resource scheduling to meet the communication needs of the coverage area, leading to a decline in satellite communication service quality.

[0066] In view of this, this application provides a beam scanning allocation method. First, coverage areas corresponding to multiple first beams of a satellite are determined, as well as multiple second beams used to assist each first beam in scanning its coverage area. Then, since the second beams switch the coverage area they scan, the beam hopping period corresponding to the second beams is determined.

[0067] Furthermore, chromosome vectors are defined, and a chromosome population is obtained by initializing the chromosome vectors. Then, based on a genetic algorithm and a fitness function related to movement distance and supply-demand ratio, the chromosome population is iteratively optimized to determine the chromosome vector with the minimum fitness.

[0068] This allows for the determination of the coverage area scanned by each second beam within each beam-hopping cycle, enabling dynamic allocation of the second beams used to assist the first beam in scanning the coverage area. Furthermore, it solves the technical problem in existing technologies where relying solely on conventional scanning beams not only makes dynamic satellite resource scheduling difficult but also fails to meet the communication needs of the coverage area, thus affecting the quality of satellite communication services.

[0069] Optionally, the operation of constructing the fitness function includes: pre-determining a first weight corresponding to the moving distance and a second weight corresponding to the supply-demand ratio; and constructing the fitness function based on the moving distance, the first weight, the supply-demand ratio, and the second weight.

[0070] Specifically, firstly, the distance to be moved is predetermined. The corresponding first weight and the supply-demand ratio The corresponding second weight .

[0071] Then, set Indicates the first A second beam In the One hop beam cycle The scanned coverage area Indicates the first A second beam In the One hop beam cycle The scanned coverage area Indicates the second beam In adjacent hop beam cycles and hopping beam period The distance moved between. Among them, and From genes The decision has been made. And, , .

[0072] Calculate the distance traveled The formula is as follows:

[0073] .

[0074] in, Indicates the second beam In each hop beam cycle The average distance traveled between them. Indicates the hopping beam period The quantity. Indicates the second beam The quantity. The sequence number indicates the hopping beam period. This indicates the sequence number of the second beam. That is, , .

[0075] Furthermore, the hopping beam period This is considered as a complete scan cycle. It determines the coverage areas within the previous complete scan cycle. Downlink data volume As the various coverage areas within the current overall scanning cycle The demand.

[0076] Calculate the hopping beam period of each beam in the current overall scan period. 10 pairs of internal satellites cover the area communication capabilities The formula is as follows:

[0077] .

[0078] in, This represents the period of each hop beam in the current overall scanning period. 10 pairs of internal satellites cover the area communication capabilities. Indicates the period of beam skipping 10 pairs of internal satellites cover the area communication capabilities. Indicates the hopping beam period The quantity. The sequence number indicates the hopping beam period. This indicates the sequence number of the coverage area. That is, , .

[0079] Using Shannon's formula, calculate the beam skipping period 10 pairs of internal satellites cover the area communication capabilities The formula is as follows:

[0080] .

[0081] in, Represents a hop beam period Duration. Indicates the first beam The bandwidth. Indicates the second beam The bandwidth. This indicates the signal-to-noise ratio of satellite 10. This indicates the signal power of satellite 10. This indicates the noise power of satellite 10. Indicates the first One hop beam cycle Inner A second beam Scan the first Coverage area , The value can be either 0 or 1. That is, when At that time, the first One hop beam cycle Inner A second beam No scan Coverage area ;when At that time, the first One hop beam cycle Inner A second beam Scan the first Coverage area .

[0082] Then, calculate the coverage area of ​​satellite 10. The supply-demand ratio between The formula is as follows:

[0083] .

[0084] in, Indicates the period of each hop beam. 10 pairs of internal satellites cover the area Communication capabilities and coverage area The relationship between needs. Indicates the coverage area The demand. Indicates the period of each hop beam. 10 pairs of internal satellites cover the area communication capabilities.

[0085] Furthermore, according to the first weight Distance of movement Second weight and variance Construct the fitness function as follows:

[0086] .

[0087] in, This represents the fitness function. This indicates the first weight. Indicates the distance traveled. This indicates the second weight. Indicates the supply-demand ratio The variance.

[0088] Optionally, according to the genetic algorithm, the operation of iteratively optimizing the chromosome population through the fitness function to determine the chromosome vector with the minimum fitness includes: calculating the first variance of the supply-demand ratio of chromosome vectors in the chromosome population; calculating the fitness of the chromosome population based on the movement distance, the first weight, the first variance, and the second weight; selecting chromosome vectors in the chromosome population with fitness less than a preset threshold; and performing crossover and mutation operations on the chromosome vectors with fitness less than the preset threshold, and iteratively optimizing to determine the chromosome vector with the minimum fitness.

[0089] Specifically, first, the processor calculates the chromosome vector of the chromosome population based on the initialized chromosome population. supply and demand ratio First variance .

[0090] Then, the processor determines the weight based on the first weight. Distance of movement Second weight and first variance Calculate the fitness of the chromosome population. The formula is as follows:

[0091] .

[0092] in, This indicates the fitness of the corresponding chromosome population. This indicates the first weight. Indicates the distance traveled. This indicates the second weight. Chromosome vectors representing a population of chromosomes supply and demand ratio The first variance.

[0093] The processor bases its data on the vectors of each chromosome in the chromosome population. Corresponding fitness And a preset fitness threshold, and select fitness. Chromosome vectors less than a preset threshold .

[0094] Then, the processor, based on a pre-set crossover rate, adjusts the selected fitness... Chromosome vectors less than a preset threshold Perform a crossover operation. Then, based on a pre-set mutation rate, adjust the resulting chromosome vectors. Perform mutation operations.

[0095] Thus, the selection, crossover, and mutation operations described above complete the first round of iterative updates in the genetic algorithm. Multiple iterative calculations are performed following these steps until a predetermined number of iterations is reached, at which point the chromosome vector with the lowest fitness can be determined. .

[0096] Optionally, the operation of determining the chromosome vector with the minimum fitness includes: calculating the second variance of the supply-demand ratio of the chromosome vectors of the iteratively optimized chromosome population based on the iteratively optimized chromosome population; calculating the fitness of the chromosome vectors of the iteratively optimized chromosome population based on the movement distance, the first weight, the second variance, and the second weight; and determining the chromosome vector corresponding to the minimum movement distance and the minimum second variance.

[0097] Specifically, first, the processor calculates the chromosome vector of the iteratively optimized chromosome population. supply and demand ratio Second variance .

[0098] Then, the processor determines the weight based on the first weight. Distance of movement Second weight and the second variance Calculate the chromosome vector of the chromosome population after iterative optimization. Corresponding fitness The formula is as follows:

[0099] .

[0100] in, Chromosome vectors representing the iteratively optimized chromosome population The corresponding fitness level. This indicates the first weight. Indicates the distance traveled. This indicates the second weight. Chromosome vectors representing the iteratively optimized chromosome population supply and demand ratio The second variance.

[0101] Finally, based on the chromosome vectors of the iteratively optimized chromosome population... Corresponding fitness Determine fitness Minimum chromosome vector That is, determining the distance to move. Minimum and second variance Minimum chromosome vector Among them, the distance traveled Minimum indicates the second beam In each hop beam cycle The average distance moved between them is the smallest, and the second variance is the smallest. Minimum representation of satellite 10 and coverage area The supply-demand ratio between Most balanced.

[0102] Thus, according to the first aspect of this embodiment, via the first beam of satellite 10 Corresponding coverage area Used to assist the first beam The second beam and with the second beam Corresponding beam skipping period It can realize chromosome vector The definition of chromosome vector. Then, based on the chromosome vector... and distance traveled and supply-demand ratio Related fitness function By using a genetic algorithm to iteratively optimize the chromosome population, the chromosome vector with the minimum fitness can be determined. Furthermore, it can determine the chromosome vector in each hopping beam cycle. Inside, each second beam The scanned coverage area Furthermore, it is possible to dynamically allocate the second beam. Assisting the first beam Scan coverage area To ensure the quality of satellite communication services.

[0103] In addition, refer to Figure 1 As shown, according to a second aspect of this embodiment, a storage medium is provided. The storage medium includes a stored program, wherein, when the program is executed, a processor performs any of the methods described above.

[0104] Therefore, according to this embodiment, a chromosome vector can be defined by the coverage area corresponding to the first beam of the satellite, the second beam used to assist the first beam, and the hopping beam period corresponding to the second beam. Then, based on the chromosome vector and a fitness function related to the travel distance and the supply-demand ratio, a genetic algorithm is used to iteratively optimize the chromosome population, thereby determining the chromosome vector with the minimum fitness. Furthermore, the coverage area scanned by each second beam within each hopping beam period can be determined. Consequently, the second beam can be dynamically allocated to assist the first beam in scanning the coverage area, ensuring the quality of satellite communication services.

[0105] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0106] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0107] Example 2

[0108] Figure 6 A beam scanning allocation apparatus according to a first aspect of this embodiment is shown, which corresponds to the method described according to a first aspect of Embodiment 1. Reference Figure 6As shown, the device includes: a beam determination module 610, used to determine the coverage areas corresponding to multiple first beams of a satellite, and multiple second beams for assisting each first beam in scanning the coverage area; a hopping beam period determination module 620, used to determine the hopping beam period corresponding to the second beam, and within the hopping beam period, the second beam is used to switch and scan the coverage area; an initialization module 630, used to define a chromosome vector and initialize the chromosome vector to obtain a chromosome population, wherein the chromosome vector represents the coverage area scanned by each second beam within each hopping beam period; and a fitness function construction module. 640 is used to construct a fitness function, which is related to the movement distance and the supply-demand ratio. The movement distance represents the distance the second beam switches to scan the coverage area during each hop beam cycle, and the supply-demand ratio represents the relationship between the satellite's communication capability and the demand for coverage area. 650 is used to iteratively optimize the chromosome population using the fitness function based on the genetic algorithm to determine the chromosome vector with the minimum fitness. 660 is used to determine the coverage area scanned by each second beam during each hop beam cycle based on the chromosome vector with the minimum fitness.

[0109] Optionally, the fitness function construction module 640 includes: a weight determination submodule, used to predetermine a first weight corresponding to the moving distance and a second weight corresponding to the supply-demand ratio; and a construction submodule, used to construct a fitness function based on the moving distance, the first weight, the supply-demand ratio, and the second weight.

[0110] Optionally, the genetic algorithm module 650 includes: a first variance calculation submodule, used to calculate the first variance of the supply-demand ratio of chromosome vectors in the chromosome population based on the chromosome population; a first fitness calculation submodule, used to calculate the fitness of the chromosome population based on the movement distance, the first weight, the first variance, and the second weight; a selection submodule, used to select chromosome vectors in the chromosome population whose fitness is less than a preset threshold; and an iterative optimization submodule, used to perform crossover and mutation operations on chromosome vectors whose fitness is less than the preset threshold, and perform iterative optimization to determine the chromosome vector with the minimum fitness.

[0111] Optionally, the genetic algorithm module 650 further includes: a second variance submodule, used to calculate the second variance of the supply-demand ratio of chromosome vectors of the iteratively optimized chromosome population based on the iteratively optimized chromosome population; a second fitness calculation submodule, used to calculate the fitness of chromosome vectors of the iteratively optimized chromosome population based on the movement distance, the first weight, the second variance, and the second weight; and a chromosome vector determination submodule, used to determine the chromosome vector corresponding to the minimum movement distance and the minimum second variance.

[0112] Therefore, according to this embodiment, a chromosome vector can be defined by the coverage area corresponding to the first beam of the satellite, the second beam used to assist the first beam, and the hopping beam period corresponding to the second beam. Then, based on the chromosome vector and a fitness function related to the travel distance and the supply-demand ratio, a genetic algorithm is used to iteratively optimize the chromosome population, thereby determining the chromosome vector with the minimum fitness. Furthermore, the coverage area scanned by each second beam within each hopping beam period can be determined. Consequently, the second beam can be dynamically allocated to assist the first beam in scanning the coverage area, ensuring the quality of satellite communication services.

[0113] Example 3

[0114] Figure 7 A beam scanning allocation apparatus according to a first aspect of this embodiment is shown, which corresponds to the method described according to a first aspect of Embodiment 1. Reference Figure 7 As shown, the device includes: a processor 710; and a memory 720 connected to the processor 710, for providing the processor 710 with instructions to process the following steps: determining coverage areas corresponding to multiple first beams of a satellite, and multiple second beams for assisting each first beam in scanning the coverage area; determining a hopping beam period corresponding to a second beam, wherein the second beam is used to switch and scan the coverage area within the hopping beam period; defining a chromosome vector and initializing the chromosome vector to obtain a chromosome population, wherein the chromosome vector represents the coverage area scanned by each second beam within each hopping beam period; constructing a fitness function, wherein the fitness function is related to the movement distance and the supply-demand ratio, wherein the movement distance represents the distance moved by the second beam during switching the coverage area within each hopping beam period, and the supply-demand ratio represents the relationship between the communication capability of the satellite and the demand for the coverage area; iteratively optimizing the chromosome population through the fitness function using a genetic algorithm to determine the chromosome vector with the minimum fitness; and determining the coverage area scanned by each second beam within each hopping beam period based on the chromosome vector with the minimum fitness.

[0115] Optionally, the operation of constructing the fitness function includes: pre-determining a first weight corresponding to the moving distance and a second weight corresponding to the supply-demand ratio; and constructing the fitness function based on the moving distance, the first weight, the supply-demand ratio, and the second weight.

[0116] Optionally, according to the genetic algorithm, the operation of iteratively optimizing the chromosome population through the fitness function to determine the chromosome vector with the minimum fitness includes: calculating the first variance of the supply-demand ratio of chromosome vectors in the chromosome population; calculating the fitness of the chromosome population based on the movement distance, the first weight, the first variance, and the second weight; selecting chromosome vectors in the chromosome population with fitness less than a preset threshold; and performing crossover and mutation operations on the chromosome vectors with fitness less than the preset threshold, and iteratively optimizing to determine the chromosome vector with the minimum fitness.

[0117] Optionally, the operation of determining the chromosome vector with the minimum fitness includes: calculating the second variance of the supply-demand ratio of the chromosome vectors of the iteratively optimized chromosome population based on the iteratively optimized chromosome population; calculating the fitness of the chromosome vectors of the iteratively optimized chromosome population based on the movement distance, the first weight, the second variance, and the second weight; and the chromosome vector corresponding to the minimum movement distance and the minimum second variance.

[0118] Therefore, according to this embodiment, a chromosome vector can be defined by the coverage area corresponding to the first beam of the satellite, the second beam used to assist the first beam, and the hopping beam period corresponding to the second beam. Then, based on the chromosome vector and a fitness function related to the travel distance and the supply-demand ratio, a genetic algorithm is used to iteratively optimize the chromosome population, thereby determining the chromosome vector with the minimum fitness. Furthermore, the coverage area scanned by each second beam within each hopping beam period can be determined. Consequently, the second beam can be dynamically allocated to assist the first beam in scanning the coverage area, ensuring the quality of satellite communication services.

[0119] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0120] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0125] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A beam scanning allocation method, characterized in that, include: Determine the coverage areas corresponding to multiple first beams of the satellite, and multiple second beams for assisting each first beam in scanning the coverage areas; A hopping beam period corresponding to the second beam is determined, and within the hopping beam period, the second beam is used to switch and scan the coverage area; Define a chromosome vector and initialize the chromosome vector to obtain a chromosome population, wherein the chromosome vector represents the coverage area scanned by each second beam in each hopping beam period; A fitness function is constructed, wherein the fitness function is related to the moving distance and the supply-demand ratio, wherein the moving distance represents the distance the coverage area scanned by the second beam switching moves within each hopping beam cycle, and the supply-demand ratio represents the relationship between the communication capability of the satellite and the demand of the coverage area; According to the genetic algorithm, the chromosome population is iteratively optimized using the fitness function to determine the chromosome vector with the minimum fitness. as well as Based on the chromosome vector with the lowest fitness, the coverage area scanned by each second beam is determined within each of the hopping beam periods.

2. The method according to claim 1, characterized in that, The operations for constructing the fitness function include: A first weight corresponding to the travel distance and a second weight corresponding to the supply-demand ratio are predetermined; and The fitness function is constructed based on the movement distance, the first weight, the supply-demand ratio, and the second weight.

3. The method according to claim 2, characterized in that, According to the genetic algorithm, the operation of iteratively optimizing the chromosome population using the fitness function to determine the chromosome vector with the minimum fitness includes: Based on the chromosome population, calculate the first variance of the supply-demand ratio of the chromosome vectors of the chromosome population; The fitness of the chromosome population is calculated based on the movement distance, the first weight, the first variance, and the second weight. Select chromosome vectors from the chromosome population whose fitness is less than a preset threshold; and The chromosome vectors with fitness less than a preset threshold are subjected to crossover and mutation operations, and iterative optimization is performed to determine the chromosome vector with the minimum fitness.

4. The method according to claim 3, characterized in that, The operation of determining the chromosome vector with the minimum fitness includes: Based on the iteratively optimized chromosome population, calculate the second variance of the supply-demand ratio of chromosome vectors in the iteratively optimized chromosome population. Based on the movement distance, the first weight, the second variance, and the second weight, calculate the fitness of the chromosome vector of the iteratively optimized chromosome population; and Determine the chromosome vector corresponding to the minimum movement distance and the minimum second variance.

5. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, the method described in any one of claims 1 to 4 is performed by a processor.

6. A beam scanning and distribution device, characterized in that, include: A beam determination module is used to determine the coverage area corresponding to a plurality of first beams of a satellite, and a plurality of second beams to assist each first beam in scanning the coverage area; A beam-hopping period determination module is used to determine the beam-hopping period corresponding to the second beam, and within the beam-hopping period, the second beam is used to switch and scan the coverage area; An initialization module is used to define a chromosome vector and initialize the chromosome vector to obtain a chromosome population, wherein the chromosome vector represents the coverage area scanned by each second beam in each hopping beam period; A fitness function construction module is used to construct a fitness function, wherein the fitness function is related to the moving distance and the supply-demand ratio, wherein the moving distance represents the distance the coverage area scanned by the second beam switching moves within each hop beam cycle, and the supply-demand ratio represents the relationship between the communication capability of the satellite and the demand of the coverage area; The genetic algorithm module is used to iteratively optimize the chromosome population using the fitness function according to the genetic algorithm, and determine the chromosome vector with the minimum fitness. as well as The coverage area determination module is used to determine the coverage area scanned by each second beam within each hopping beam period based on the chromosome vector with the lowest fitness.

7. The apparatus according to claim 6, characterized in that, The fitness function construction module includes: The weight determination submodule is used to pre-determine a first weight corresponding to the moving distance and a second weight corresponding to the supply-demand ratio; and A submodule is constructed to build the fitness function based on the movement distance, the first weight, the supply-demand ratio, and the second weight.

8. The apparatus according to claim 7, characterized in that, The genetic algorithm module includes: The first variance calculation submodule is used to calculate the first variance of the supply-demand ratio of the chromosome vectors of the chromosome population based on the chromosome population. The first fitness calculation submodule is used to calculate the fitness of the chromosome population based on the movement distance, the first weight, the first variance, and the second weight. The selection submodule is used to select chromosome vectors in the chromosome population whose fitness is less than a preset threshold; and The iterative optimization submodule is used to perform crossover and mutation operations on chromosome vectors with fitness less than a preset threshold, and to perform iterative optimization to determine the chromosome vector with the minimum fitness.

9. The apparatus according to claim 8, characterized in that, The genetic algorithm module further includes: The second variance submodule is used to calculate the second variance of the supply-demand ratio of chromosome vectors in the iteratively optimized chromosome population based on the iteratively optimized chromosome population. The second fitness calculation submodule is used to calculate the fitness of the chromosome vector of the iteratively optimized chromosome population based on the movement distance, the first weight, the second variance, and the second weight; and The chromosome vector determination submodule is used to determine the chromosome vector corresponding to the minimum movement distance and the minimum second variance.

10. A beam scanning and distribution device, characterized in that, include: processor; as well as A memory, connected to the processor, for providing the processor with instructions to perform the following processing steps: Determine the coverage areas corresponding to multiple first beams of the satellite, and multiple second beams for assisting each first beam in scanning the coverage areas; A hopping beam period corresponding to the second beam is determined, and within the hopping beam period, the second beam is used to switch and scan the coverage area; Define a chromosome vector and initialize the chromosome vector to obtain a chromosome population, wherein the chromosome vector represents the coverage area scanned by each second beam in each hopping beam period; A fitness function is constructed, wherein the fitness function is related to the moving distance and the supply-demand ratio, wherein the moving distance represents the distance the coverage area scanned by the second beam switching moves within each hopping beam cycle, and the supply-demand ratio represents the relationship between the communication capability of the satellite and the demand of the coverage area; According to the genetic algorithm, the chromosome population is iteratively optimized using the fitness function to determine the chromosome vector with the minimum fitness. as well as Based on the chromosome vector with the lowest fitness, the coverage area scanned by each second beam is determined within each of the hopping beam periods.

Images