Beam hopping scheduling method, device and storage medium based on genetic algorithm

Abstract

The present application discloses a beam hopping scheduling method, device and storage medium based on a genetic algorithm, including: determining a cluster corresponding to a target beam, and determining each wave position within the cluster; determining the demand ratio information of each wave position, and determining the time slot number ratio corresponding to each wave position according to the demand ratio information; randomly generating a plurality of corresponding first beam hopping patterns when the time slot number ratio corresponding to each wave position is determined; generating a corresponding path optimization function based on each first beam hopping pattern, and determining the optimal solution corresponding to the target path optimization function among the plurality of path optimization functions based on a genetic algorithm; determining the corresponding target beam hopping pattern when the optimal solution corresponding to the target path optimization function is determined; and scheduling the target beam according to the target beam hopping pattern. Thus, the technical effect of improving communication efficiency can be achieved.

Description

Technical Field

[0001] The present application relates to the field of satellite communication technology, and in particular to a beam hopping scheduling method, device and storage medium based on a genetic algorithm. Background Art

[0002] With the continuous development of satellite technology, and due to the different locations and service requests of the terminal devices corresponding to the users, each beam transmitted by the satellite usually has different business requirements and channel conditions, so the spatial variability and temporal variability of business requirements are very obvious. Therefore, the traditional multi-beam satellite system can no longer meet the unbalanced characteristics of satellite business requirements.

[0003] In response to the above problems, researchers proposed a beam-hopping technology solution, which uses time slicing technology to effectively allocate the working range of the beam, thereby improving the efficiency of satellite resource utilization and meeting the dynamic business needs of users. Due to the above performance advantages, beam-hopping technology has attracted widespread attention, and in order to better apply beam-hopping technology to actual satellite communication systems, researchers have made many useful studies from different angles. However, whether it is a beam-hopping algorithm based on deep learning, a beam-hopping algorithm based on multi-objective optimization, a beam-hopping algorithm based on dynamic clustering, or a beam-hopping algorithm based on linear or nonlinearity, none of them considers the path size that the beam-hopping needs to go through to traverse each wave position in the cluster.

[0004] Furthermore, since the existing beam hopping algorithm does not consider the path size that the beam hopping needs to go through to traverse each beam position in the cluster, when the beam hopping actually traverses each beam position in the cluster, the traversal time may be long due to the long path, resulting in low communication efficiency.

[0005] The publication number is CN117750510A, and the name is a real-time multi-satellite collaborative service planning method and device for a giant constellation communication system. The method includes: step 1, determining the satellite-to-ground service time; step 2, satellite service demand expansion processing; step 3, ground service area satellite service pre-planning: based on the expanded satellite service demand and the ground virtual service time slot division, combined with the previous satellite resource scheduling cycle planning result, according to the ground service area virtual service time slot cycle and the minimum interval between service satellites as constraints, according to the satellite-to-ground link parameter change law, each ground service area virtual service time slot set is initialized, and a satellite beam hopping plan is generated; step 4: satellite service time planning adaptation: according to the system setting rules, the satellite service time is globally optimized, and each ground service area virtual service time slot set and each satellite beam hopping time slot planning set are generated to realize satellite collaborative service time planning.

[0006] The publication number is CN117639884A, and the name is a method and system for automatically calculating a dynamic connection scheme for a high-throughput satellite ring backup. The method includes: disassembling the target backup ring to obtain objects in the target backup ring and the connection relationships between objects; generating an initial backup matrix based on the objects and the connection relationships between objects using the shortest path search algorithm; optimizing the initial backup matrix by solving the minimum spanning tree to obtain a backup relationship matrix with a multi-level backup scheme; and using the backup relationship matrix to find a direct switching scheme and / or an iterative evacuation scheme.

[0007] With regard to the technical problem that the above-mentioned prior art does not take into account the size of the path that the beam hopping needs to go through to traverse each wave position in the cluster, when the beam hopping actually traverses each wave position in the cluster, a long traversal time may occur due to the long path, resulting in low communication efficiency, and no effective solution has been proposed so far. Summary of the invention

[0008] The embodiments of the present disclosure provide a beam hopping scheduling method, device and storage medium based on a genetic algorithm, so as to at least solve the technical problem in the prior art that, because the size of the path that the beam hopping needs to traverse each wave position in the cluster is not taken into consideration, when the beam hopping actually traverses each wave position in the cluster, a long traversal time may occur due to the long path, thereby reducing the communication efficiency.

[0009] According to one aspect of an embodiment of the present disclosure, a beam hopping scheduling method based on a genetic algorithm is provided, including: determining a cluster corresponding to a target beam, and determining each wave position within the cluster; determining demand ratio information of each wave position, and determining the ratio of the number of time slots corresponding to each wave position according to the demand ratio information; when the ratio of the number of time slots corresponding to each wave position is determined, randomly generating a corresponding plurality of first beam hopping patterns, wherein the sum of the lengths of the time slots corresponding to each wave position in each first beam hopping pattern is the same, and the order of traversing each wave position corresponding to each first beam hopping pattern is different; based on each first beam hopping pattern, generating a corresponding path optimization function, and determining an optimal solution corresponding to a target path optimization function among the plurality of path optimization functions based on a genetic algorithm, wherein the path optimization function is used to indicate the sum of the paths of the target beam traversing each wave position within the corresponding cluster according to the corresponding first beam hopping pattern, and the target path optimization function is used to indicate the minimum sum of the paths in the path optimization function; when the optimal solution corresponding to the target path optimization function is determined, determining a corresponding target beam hopping pattern; and scheduling the target beam according to the target beam hopping pattern.

[0010] According to another aspect of an embodiment of the present disclosure, a storage medium is further provided, the storage medium including a stored program, wherein when the program is running, a processor executes any one of the methods described above.

[0011] According to another aspect of the embodiments of the present disclosure, a beam hopping scheduling device based on a genetic algorithm is also provided, including: a beam position determination module, used to determine a cluster corresponding to a target beam, and determine each beam position within the cluster; a time slot ratio determination module, used to determine demand ratio information of each beam position, and determine the ratio of the number of time slots corresponding to each beam position based on the demand ratio information; a random generation module, used to randomly generate a corresponding plurality of first beam hopping patterns when the ratio of the number of time slots corresponding to each beam position is determined, wherein the sum of the time slot lengths corresponding to each beam position in each first beam hopping pattern is the same, and the order of traversing each beam position corresponding to each first beam hopping pattern is different The same; a target path optimization function determination module is used to generate a corresponding path optimization function based on each first hopping beam pattern, and determine the optimal solution corresponding to the target path optimization function among multiple path optimization functions based on a genetic algorithm, wherein the path optimization function is used to indicate the sum of the paths of each wave position in the corresponding cluster traversed by the target beam according to the corresponding first hopping beam pattern, and the target path optimization function is used to indicate the sum of the minimum paths in the path optimization function; a target hopping beam pattern determination module is used to determine the corresponding target hopping beam pattern when determining the optimal solution corresponding to the target path optimization function; and a scheduling module is used to schedule the target beam according to the target hopping beam pattern.

[0012] According to another aspect of the embodiment of the present disclosure, a beam hopping scheduling device based on a genetic algorithm is also provided, including: a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: determining a cluster corresponding to a target beam, and determining each wave position within the cluster; determining demand ratio information of each wave position, and determining the ratio of the number of time slots corresponding to each wave position based on the demand ratio information; when determining the ratio of the number of time slots corresponding to each wave position, randomly generating a corresponding plurality of first beam hopping patterns, wherein the sum of the lengths of the time slots corresponding to each wave position in each first beam hopping pattern is the same, and The order of traversing each wave position corresponding to each first hopping beam pattern is different; based on each first hopping beam pattern, a corresponding path optimization function is generated, and based on a genetic algorithm, an optimal solution corresponding to a target path optimization function in multiple path optimization functions is determined, wherein the path optimization function is used to indicate the sum of paths of each wave position in the corresponding cluster traversed by the target beam according to the corresponding first hopping beam pattern, and the target path optimization function is used to indicate the sum of the minimum paths in the path optimization function; when the optimal solution corresponding to the target path optimization function is determined, the corresponding target hopping beam pattern is determined; and the target beam is scheduled according to the target hopping beam pattern.

[0013] The present application provides a beam hopping scheduling method based on a genetic algorithm. First, the processor determines the cluster corresponding to the target beam and determines each wave position within the cluster. Then, the processor determines the demand ratio information of each wave position, and determines the ratio of the number of time slots corresponding to each wave position based on the demand ratio information. Further, when the ratio of the number of time slots corresponding to each wave position is determined, a corresponding plurality of first beam hopping patterns are randomly generated. Afterwards, the processor generates a corresponding path optimization function based on each first beam hopping pattern, and determines the optimal solution corresponding to the target path optimization function among the plurality of path optimization functions based on a genetic algorithm. And when the optimal solution corresponding to the target path optimization function is determined, the corresponding target beam hopping pattern is determined. Finally, the processor schedules the target beam according to the target beam hopping pattern.

[0014] With reference to the above-mentioned contents, it can be known that, before scheduling the target beam, the present application predetermines the target beam hopping pattern corresponding to the target path optimization function, so that the target beam can be scheduled based on the target beam hopping pattern. It is worth noting that since the optimal solution corresponding to the target path optimization function is used to indicate the sum of the minimum paths of the target beam traversing each wave position in the corresponding cluster according to the corresponding first beam hopping pattern, when determining the target beam hopping pattern corresponding to the optimal solution of the target path optimization function, the sum of the paths of the target beam traversing all wave positions in a complete beam hopping cycle can be minimized. Thereby, when the target beam actually traverses each wave position in the cluster, the technical effect of improving communication efficiency can be achieved.

[0015] This solves the technical problem in the prior art that, because the size of the path that the beam hopping needs to traverse to each wave position in the cluster is not taken into consideration, when the beam hopping actually traverses each wave position in the cluster, a long traversal time may occur due to the long path, resulting in low communication efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0017] Figure 1 A schematic diagram of the communication coverage of the satellite according to Embodiment 1 of the present application;

[0018] Figure 2 is a schematic diagram of the hardware architecture of the satellite according to Embodiment 1 of the present application;

[0019] Figure 3 is a modular schematic diagram of a satellite communication system according to Embodiment 1 of the present application;

[0020] Figure 4 is a flow chart of the method for beam hopping scheduling based on genetic algorithm according to Embodiment 1 of the present application;

[0021] Figure 5 is a schematic diagram of a beam hopping pattern corresponding to beam Q1 according to Embodiment 1 of the present application;

[0022] Figure 6 is a schematic diagram of multiple first beam hopping patterns according to Embodiment 1 of the present application;

[0023] Figure 7 is a schematic diagram of a device for beam hopping scheduling based on a genetic algorithm according to Embodiment 2 of the present application;

[0024] Figure 8 It is a schematic diagram of a device for beam hopping scheduling based on genetic algorithm as described in Example 3 of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0027] Example 1

[0028] According to this embodiment, a method embodiment of beam hopping scheduling based on a genetic algorithm is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 1 Schematic diagram of the communication coverage of the satellite according to the embodiment of the present application. Figure 1 As shown, the satellite 110 interacts with the terminal device within the first communication coverage area 120 through the satellite communication system. It is worth noting that the satellite 110 generates multiple beams Q1~Q t , and each beam has its own second communication coverage range 131~13r. For example, beam Q1 corresponds to the second communication coverage range 131, and the satellite communication system of satellite 110 can interact with the terminal device in the second communication coverage range 131 through beam Q1. Thus, multiple beams Q1~Q t , and each beam Q1~Q t When the communication coverage of Figure 1 A first communication coverage area 120 is shown.

[0030] Furthermore, each beam Q1~Q t The second communication coverage areas 131~13r have corresponding clusters Z1~Z t , and each cluster Z1~Z t There are multiple beam positions. t Communication can be performed with the terminal devices in each beam position in sequence according to the beam hopping pattern and the time slot size corresponding to each beam position.

[0031] For example, the wave position covered by beam Q1 The corresponding time slot size is , the number of time slots is ; and the wave position covered by beam Q1 The corresponding time slot size is , the number of time slots is ; And so on; and the wave position covered by beam Q1 The corresponding time slot size is , the number of time slots is .

[0032] For example, the wave position covered by beam Q2 The corresponding time slot size is , the number of time slots is ; and the wave position covered by beam Q2 The corresponding time slot size is , the number of time slots is ; And so on; and the wave position covered by beam Q2 The corresponding time slot size is , the number of time slots is .

[0033] And so on.

[0034] For example, with beam Q t Wavelengths covered The corresponding time slot size is , the number of time slots is ; and beam Q t Wavelengths covered The corresponding time slot size is , the number of time slots is ; and so on; with beam Q t Wavelengths covered The corresponding time slot size is , the number of time slots is .

[0035] It is worth noting that in the embodiment of the present application, since the time slot length of each wave position corresponding to the target beam has been initialized before the time slot length of each wave position corresponding to the target beam is determined, the number of time slots corresponding to each wave position of the target beam corresponds to the time slot size, which will not be repeated here.

[0036] Figure 2 It further shows Figure 1 Schematic diagram of the hardware architecture of satellite 110. Figure 2 As shown, satellite 110 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, so that the processor can access the memory, read the 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 is also connected to a bus such as a CAN bus. Therefore, the processor can communicate with the satellite-borne peripherals connected to the bus through the bus managed by the bus management module. In addition, the processor is also connected to communication devices such as cameras, star sensors, measurement and control transponders, and data transmission equipment via the communication interface. It can be understood by those skilled in the art that Figure 2 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2 More or fewer components as shown, or with Figure 2 Different configurations are shown.

[0037] It should be noted that Figure 2The one or more processors and / or other data processing circuits shown in the figure may generally be referred to as "data processing circuitry" herein. The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other elements in the computing device. As involved in the embodiments of the present disclosure, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0038] Figure 2 The memory shown in the figure can be used to store software programs and modules of application software, such as the program instruction / data storage device corresponding to the beam hopping scheduling method based on genetic algorithm in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the beam hopping scheduling method based on genetic algorithm of the above-mentioned application program is realized. The memory may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0039] It should be noted that, in some optional embodiments, the above Figure 2 The devices shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. Figure 2 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the above described apparatus.

[0040] Figure 3 is a modular schematic diagram of a satellite communication system according to an embodiment of the present application. Figure 3 As shown, the satellite communication system includes: a data acquisition module, an optimization module, a scheduling module and a beam transmission module. The data acquisition module is used to collect the demand information of each beam position in the cluster corresponding to the target beam. The optimization module is used to determine the target path optimization function among multiple path optimization functions according to the genetic algorithm. The scheduling module is used to schedule the target beam according to the target beam hopping pattern corresponding to the target path optimization function. The beam transmission module is used to transmit multiple beams.

[0041] In the above operating environment, according to the first aspect of this embodiment, a beam hopping scheduling method based on a genetic algorithm is provided. The method is composed of Figure 3 The satellite communication system shown in is implemented. Figure 4 A schematic diagram showing the process of the method is shown in FIG. Figure 4 As shown, the method includes:

[0042] S402: Determine a cluster corresponding to the target beam, and determine each beam position within the cluster;

[0043] S404: Determine demand ratio information of each wave position, and determine the time slot ratio corresponding to each wave position according to the demand ratio information;

[0044] S406: when the time slot ratios corresponding to the respective beam positions are determined, a plurality of corresponding first beam hopping patterns are randomly generated, wherein the sum of the time slot lengths corresponding to the respective beam positions in each first beam hopping pattern is the same, and the order of traversing the respective beam positions corresponding to the respective first beam hopping patterns is different;

[0045] S408: Generate a corresponding path optimization function based on each first hop beam pattern, and determine a target path optimization function among the multiple path optimization functions, wherein the path optimization function is used to indicate the sum of paths of each beam position in the corresponding cluster traversed by the target beam according to the corresponding first hop beam pattern, and the target path optimization function is used to indicate the minimum sum of paths in the path optimization function;

[0046] S410: When the target path optimization function is determined, determine a corresponding target beam hopping pattern; and

[0047] S412: Schedule the target beam according to the target beam hopping pattern.

[0048] Specifically, the beam transmitting module in the satellite communication system corresponding to the satellite 110 can transmit different beams Q1~Q t , and each beam Q1~Q t Can cover multiple wave positions, multiple wave positions are composed of each beam Q1~Q t The corresponding clusters Z1~Z t Therefore, the satellite communication system can use each beam Q1~Q t Communicate with the communication device in the corresponding beam. For example, beam Q1 corresponds to cluster Z1, and cluster Z1 includes multiple beams. , so that the satellite communication system can communicate with multiple beam positions through beam Q1 The communication devices in the cluster communicate with each other; beam Q2 corresponds to cluster Z2, and cluster Z2 includes multiple beam positions , so that the satellite communication system can communicate with multiple beam positions through beam Q2 Communication equipment within the communication; ....; beam Q t Corresponding to cluster Z t , and cluster Z t Includes multiple wave positions , so that the satellite communication system can use beam Q t With multiple wave positions Communicate with the communication equipment within.

[0049] Thus, first, the satellite communication system determines a cluster corresponding to the target beam, and further determines each beam position within the cluster (S402). For example, the satellite communication system determines a plurality of beams Q1 to Q t The beam Q in x is the target beam. x Corresponding to cluster Z x , and cluster Z x Includes multiple wave positions , so the target beam Q x By traversing the cluster Z x Each wave position within , and wave position The communication equipment in the communication The communication devices within may include, for example, mobile terminals, terminal devices, base stations or drones.

[0050] Afterwards, the data acquisition module in the satellite communication system determines the demand ratio information of each beam position, and determines the time slot ratio corresponding to each beam position according to the demand ratio information (S404). Referring to the above content, it can be known that each beam position includes multiple communication devices, and the satellite communication system communicates with the communication devices in the corresponding beam position through the beam. x In this case, the data acquisition module can determine the target beam Q x The corresponding wave positions The data acquisition module can then collect the information related to each wave position based on the collected information. Corresponding demand information, determine the corresponding wave position The corresponding demand ratio information. The demand information is used to indicate the target beam Q x The number of communication devices in communication, in this embodiment, the number of communication devices is used to indicate the number of terminal devices. It should be noted by those skilled in the art that the above only defines the number of communication devices as the number of terminal devices, and the actual situation is not limited to this.

[0051] In the data acquisition module, determine the In the case of corresponding demand ratio information, the The corresponding demand ratio information is determined as For example, the data acquisition module determines the ratio of the number of time slots corresponding to the wave position. The corresponding demand ratio information is , thus with the wave position The corresponding ratio of time slots is ; Data acquisition module determines the wave position The corresponding demand ratio information is , thus with the wave position The corresponding ratio of time slots is ;......;Data acquisition module determines the wave position The corresponding demand ratio information is , thus with the wave position The corresponding ratio of time slots is .

[0052] When the data acquisition module determines the ratio of the number of time slots corresponding to each wave position, a plurality of corresponding first beam hopping patterns are randomly generated (S406), wherein the sum of the lengths of the time slots corresponding to each wave position in each first beam hopping pattern is the same, and the order of traversing each wave position corresponding to each first beam hopping pattern is different.

[0053] Those skilled in the art should note that the beam hopping pattern includes the order in which the beam traverses each beam position in the cluster and the time slot size. Figure 5 is a schematic diagram of a beam hopping pattern corresponding to beam Q1 according to an embodiment of the present application. Figure 5 The beam hopping pattern shown shows that beam Q1 traverses the wave position The order is wave position , wave position , wave position , wave position 、......、wave position , and wave position The corresponding unit time slot length is , and wave position The corresponding unit time slot length is , and wave position The corresponding unit time slot length is , and wave position The corresponding unit time slot length is ,......, and wave position The corresponding unit time slot length is . And with the unit time slot length The corresponding time slot ID is , and unit time slot length The corresponding time slot ID is , and unit time slot length The corresponding time slot ID is , ..., and the unit time slot length The corresponding time slot is ID .

[0054] It is also worth noting that in this embodiment, the unit time slot length corresponding to each wave position is initialized. , unit time slot length , unit time slot length And the unit time slot length And when the unit time slot length corresponding to each wave position is the same, the target beam Q x Each wave position The corresponding number of time slots corresponds to the time slot size, thus ensuring that each wave position The corresponding total time slot length corresponds to. Referring to the above example, it can be seen that due to the wave position The corresponding total time slot length is larger, so when the unit time slot length is the same, it is the same as the wave position. The corresponding number of time slots is large.

[0055] Therefore, the total time slot length in a beam hopping cycle is constant and consistent with the target beam Q x Each wave position When the corresponding unit time slot lengths are the same, the optimization module can be based on the The corresponding time slot ratio , randomly generate multiple first hop beam patterns. Figure 6 is a schematic diagram of multiple first beam hopping patterns according to an embodiment of the present application. Figure 6 As shown, the first hop beam pattern corresponding to the target beam may be, for example, the target beam Q x According to wave position , wave position , wave position , wave position 、...、wave position The target beam Q x According to wave position Wave position Wave position , wave position 、...、wave position The order of traversing each wave position in the cluster; ...; target beam Q x According to wave position Wave position , wave position , wave position 、...、wave position In addition, it is worth noting that the multiple first hop beam patterns above are related to the beam positions The corresponding number of time slots More.

[0056] Furthermore, it is worth noting that although the optimization module can be based on the The corresponding time slot ratio , randomly generate corresponding multiple first hop beam patterns. However, in this embodiment, each first hop beam pattern generated by the optimization module corresponds to traversing each beam position For example, refer to Figure 6 As shown, the order of the first hop beam pattern cannot be: , wave position , wave position , wave position 、...、wave position The above contents will be described in detail later, so they will not be elaborated here.

[0057] After the optimization module randomly generates a plurality of corresponding first hop beam patterns, a corresponding path optimization function is further generated based on each first hop beam pattern, and an optimal solution corresponding to the target path optimization function among the plurality of path optimization functions is determined based on a genetic algorithm (S408). The path optimization function is used to indicate the target beam Q x Traverse each beam position in the corresponding cluster according to the corresponding first hop beam pattern For example, in the case of the target beam Q x The corresponding cluster includes multiple wave positions , and the optimization module can determine the The corresponding center point Therefore, when the optimization module determines multiple first beam hopping patterns, the sum of the paths corresponding to the respective first beam hopping patterns may be determined.

[0058] refer to Figure 6 As shown, for example, in the optimization module, the The corresponding center point In the case of the first hop beam pattern (the order of traversing the wave positions is the wave position , wave position , wave position , wave position 、...、wave position ) The corresponding path is .

[0059] The first hop beam pattern (the order of traversing the wave positions is the wave position , wave position , wave position , wave position 、...、wave position ) The corresponding path is .

[0060] And so on.

[0061] The first hop beam pattern (the order of traversing the wave positions is the wave position , wave position , wave position , wave position 、...、wave position ) The corresponding path is .

[0062] And according to the above content, it can be known that each time slot in the first hop beam pattern corresponds to a unique time slot ID. That is to say, when the optimization module determines the center point corresponding to each first hop beam pattern, the path that the target beam needs to go through all the wave positions can be further determined according to the distance of the center point of each wave position. And the path that the target beam needs to go through all the wave positions can be expressed by the following formula (i.e., path optimization function):

[0063]

[0064] in, represents the path optimization function corresponding to the j-th first hop beam pattern, and j =1~ J . represents the first beam hopping pattern corresponding to each first beam hopping pattern. i - 1 time slot ID, represents the first beam hopping pattern corresponding to each first beam hopping pattern. i timeslot ID, Indicates i The time slot corresponding to the time slot ID is i -The distance between time slots corresponding to 1 time slot ID.

[0065] Thus, with the first hop beam pattern The corresponding path optimization function can be expressed as: . With the second second hop beam pattern The corresponding path optimization function can be expressed as: And so on. With the Jth second hop beam pattern The corresponding path optimization function can be expressed as: .

[0066] The optimization module then determines multiple path optimization functions based on genetic algorithms. Optimization function for target path The corresponding optimal solution. Among them, the target path optimization function The corresponding optimal solution is used to indicate the sum of the minimum paths for the target beam to traverse each beam position in the corresponding cluster according to the corresponding first hop beam pattern.

[0067] Referring to the above content, it can be seen that the target beam has different path optimization functions corresponding to the sum of the paths traversed by the corresponding first hop beam pattern. , so that in the above multiple path optimization functions There must be a target path optimization function with the smallest sum of paths That is, the sum of the paths of the target beam traversing each beam position in the corresponding cluster according to the first hop beam pattern corresponding to the target path optimization function is the smallest. The sum of the corresponding paths is the smallest, then the path optimization function is the target path optimization function, and the optimal solution corresponding to the target path optimization function is the first first-hop beam pattern. Thus, the target beam is transmitted according to the first first-hop beam pattern (that is, the order of the beam positions is the beam position , wave position , wave position , wave position 、...、wave position ) The sum of the paths required to traverse each wave position is minimized.

[0068] The content of the above-mentioned optimization module determining the optimal solution corresponding to the target path optimization function among multiple path optimization functions will be described in detail later, so it will not be repeated here.

[0069] When the optimization module determines the optimal solution corresponding to the target path optimization function, the corresponding target beam hopping pattern is further determined (S410). Referring to the above content, it can be known that each first beam hopping pattern corresponds to a unique path optimization function. For example, the first first beam hopping pattern corresponds to the path optimization function , the second second hop beam pattern corresponds to the path optimization function , ..., the Jth first hop beam pattern corresponds to the path optimization function . Thus, the target path optimization function is determined in the optimization module After that, we can further determine the target path optimization function The corresponding beam hopping pattern. For example, the optimization module determines the path optimization function Optimize function for target path , so with the path optimization function The corresponding first first hopping beam pattern is the target hopping beam pattern, that is, the sum of the paths that the target beam needs to go through when traversing each beam position according to the first first hopping beam pattern is the smallest.

[0070] Finally, the scheduling module schedules the target beam according to the target beam hopping pattern (S412). After the optimization module determines the target beam hopping pattern, the transmission module transmits the target beam, and the scheduling module schedules the target beam according to the target beam hopping pattern, so that the target beam traverses each beam position in the order indicated by the target beam hopping pattern.

[0071] As described in the background technology, whether it is a beam hopping algorithm based on deep learning, a beam hopping algorithm based on multi-objective optimization, a beam hopping algorithm based on dynamic clustering, or a beam hopping algorithm based on linear or nonlinear, none of them considers the size of the path that the beam hopping needs to go through to traverse each wave position in the cluster. Furthermore, since the existing beam hopping algorithm does not consider the size of the path that the beam hopping needs to go through to traverse each wave position in the cluster, when the beam hopping actually traverses each wave position in the cluster, there may be a problem of long traversal time due to the long path, thereby causing low communication efficiency.

[0072] In view of this, the present application provides a beam hopping scheduling method based on a genetic algorithm. And before scheduling the target beam, the present application predetermines the target beam hopping pattern corresponding to the target path optimization function, so that the target beam can be scheduled based on the target beam hopping pattern. It is worth noting that since the optimal solution corresponding to the target path optimization function is used to indicate the sum of the minimum paths of the target beam traversing each wave position in the corresponding cluster according to the corresponding first beam hopping pattern, when the target beam hopping pattern corresponding to the optimal solution of the target path optimization function is determined, the sum of the paths of the target beam traversing all wave positions in a complete beam hopping cycle can be minimized. Thereby, when the target beam actually traverses each wave position in the cluster, the technical effect of improving communication efficiency can be achieved.

[0073] This solves the technical problem in the prior art that, because the size of the path that the beam hopping needs to traverse to each wave position in the cluster is not taken into consideration, when the beam hopping actually traverses each wave position in the cluster, a long traversal time may occur due to the long path, resulting in low communication efficiency.

[0074] Optionally, the operation of determining demand ratio information for each wave position, and determining the ratio of the number of time slots corresponding to each wave position based on the demand ratio information, includes: collecting demand information corresponding to each wave position, and determining the demand ratio information for each wave position based on the demand information corresponding to each wave position, wherein the demand information is used to indicate the number of communication devices communicating through the target beam; and determining the ratio of the number of time slots corresponding to each wave position based on the demand ratio information.

[0075] Specifically, the data acquisition module can, for example, xThe historical communication data determines the target beam Q x The corresponding wave positions The data acquisition module can then collect the information related to each wave position based on the collected information. Corresponding demand information, determine the corresponding wave position The corresponding demand ratio information. The demand information is used to instruct the satellite communication system to pass the target beam Q x The number of communication devices in communication, the demand ratio information is used to indicate the The ratio of the corresponding demand information to the total demand information corresponding to all wave positions. In this embodiment, the number of communication devices is used to indicate the number of terminal devices. It should be noted by those skilled in the art that the above only determines the number of communication devices as the number of terminal devices, and the actual situation is not limited to this.

[0076] In the data acquisition module, determine the In the case of corresponding demand ratio information, the The corresponding demand ratio information is determined as That is, in relation to each wave position The larger the corresponding demand ratio information is, the more time slots are required for the wave position to meet the communication needs of all terminal devices in the wave position.

[0077] For example, the data acquisition module determines the wave position The corresponding demand ratio information is , thus with the wave position The corresponding ratio of time slots is ; Data acquisition module determines the wave position The corresponding demand ratio information is , thus with the wave position The corresponding ratio of time slots is ;......;Data acquisition module determines the wave position The corresponding demand ratio information is , thus with the wave position The corresponding ratio of time slots is .

[0078] Therefore, by collecting the demand information corresponding to each wave position and determining the ratio of the number of time slots corresponding to each wave position, the technical effect of being able to meet the communication needs of the communication equipment in each wave position and providing the necessary basis for the subsequent determination of the target beam hopping pattern is achieved.

[0079] Optionally, it also includes: for multiple randomly generated first hopping beam patterns, screening out multiple second hopping beam patterns according to predetermined constraints, wherein the constraints are used to indicate that two wave positions in adjacent time slots in each first hopping beam pattern are different; and generating a corresponding path optimization function based on each second hopping beam pattern.

[0080] Specifically, since the first hopping beam pattern is randomly generated by the optimization module, there must be a situation where two adjacent time slots have the same beam position in multiple first hopping beam patterns. For example, any first hopping beam pattern in multiple first hopping beam patterns can be: , wave position , wave position , wave position 、...、wave position . Thus, the target beam is based on the time slot ID , Time Slot ID , Time Slot ID , ..., time slot ID The target beam traverses each beam position in the cluster in the order of the first hop beam pattern. Therefore, it can be seen from the above that the target beam needs to traverse the beam positions twice in adjacent time slots. .

[0081] If the target beam traverses the wave position twice in adjacent time slots , the communication of communication devices in other wave positions may be affected. Therefore, in this embodiment, it is necessary to avoid the situation where adjacent time slots traverse the same wave position.

[0082] Therefore, the optimization module preliminarily removes the first hopping beam patterns with the same two wave positions in adjacent time slots from the multiple first hopping beam patterns, and only retains the first hopping beam patterns with different two wave positions in adjacent time slots, so that the generation optimization module can screen out multiple second hopping beam patterns.

[0083] Finally, the optimization module generates a corresponding path optimization function based on each second beam hopping pattern. The operation of the optimization module generating a corresponding path optimization function based on each second beam hopping pattern is the same as the operation of the optimization module generating a corresponding path optimization function based on each first beam hopping pattern, so it will not be repeated here.

[0084] Therefore, the optimization module eliminates the first hopping beam patterns with the same two wave positions in adjacent time slots from multiple first hopping beam patterns based on predetermined constraints, and only retains the operation of multiple second hopping beam patterns, thereby achieving the technical effect of ensuring that the communication needs of communication equipment in each wave position are met.

[0085] Optionally, the operation of determining the optimal solution corresponding to the target path optimization function among multiple path optimization functions based on the genetic algorithm includes: determining each path optimization function as a fitness function corresponding to each first hopping beam pattern; determining multiple third hopping beam patterns based on the multiple first hopping beam patterns, wherein the multiple first hopping beam patterns are initialized hopping beam patterns, and the multiple third hopping beam patterns are sub-generation hopping beam patterns of the multiple first hopping beam patterns; cross-copying the wave positions in each third hopping beam pattern, and generating multiple fourth hopping beam patterns; mutating each fourth hopping beam pattern, and generating multiple fifth hopping beam patterns; and repeating the iteration until a termination condition is met, and outputting the optimal solution corresponding to the target path optimization function, wherein the termination condition is used to indicate a preset number of iterations.

[0086] Specifically, for example, referring to the above-mentioned contents, the optimization module randomly generates a corresponding plurality of first beam hopping patterns (i.e., initializes the population) when determining the time slot ratio corresponding to each beam position. The plurality of first beam hopping patterns are "chromosomes", and each time slot in each first beam hopping pattern is a "gene".

[0087] When the optimization module randomly generates a plurality of corresponding first hopping beam patterns and determines a path optimization function corresponding to each first hopping beam pattern, the path optimization function corresponding to each first hopping beam pattern is further determined as a fitness function.

[0088] Then, the optimization module determines multiple third hopping beam patterns based on the multiple first hopping beam patterns. The multiple first hopping beam patterns are initialized hopping beam patterns, and the multiple third hopping beam patterns are sub-generation hopping beam patterns of the multiple first hopping beam patterns. For example, when the fitness function corresponding to each first hopping beam pattern is determined, the optimization module can select some first hopping beam patterns as the first generation according to the fitness corresponding to each first hopping beam pattern, and use them to generate the sub-generation (that is, multiple third hopping beam patterns). And it is worth noting that in this embodiment, the roulette wheel selection method is selected for operation collection. That is, the higher the fitness of the first hopping beam pattern, the higher the probability of being selected to generate the sub-generation.

[0089] Further, the optimization module performs a cross-copy operation on the beam positions in each third hop beam pattern and generates multiple fourth hop beam patterns. The cross-copy operation on each third hop beam pattern by the optimization module helps to explore different areas of the solution space.

[0090] Then, the optimization module performs a mutation operation on each fourth hop beam pattern and generates a corresponding fifth hop beam pattern. The optimization module performs a mutation operation on each fourth hop beam pattern to help increase diversity and prevent the algorithm from converging to a local optimal solution too early.

[0091] Therefore, after the optimization module cross-copies and mutates the beam positions in each third hopping beam pattern, multiple fifth hopping beam patterns (i.e., a new generation population) can be generated. And because the multiple third hopping beam patterns (i.e., a sub-generation population) are generated based on the multiple first hopping beam patterns with higher fitness, the fitness of the multiple fifth hopping beam patterns finally generated is higher.

[0092] Finally, the above steps are repeated until the termination condition is met. The termination condition may be, for example, reaching a preset number of iterations or the average fitness no longer significantly improving. When the termination condition is met, the optimization module outputs the optimal solution corresponding to the target path optimization function. The optimal solution corresponding to the target path optimization function is used to indicate that the path that the target beam needs to go through to traverse each wave position in the cluster is the smallest.

[0093] And in the case of determining the optimal solution corresponding to the target path optimization function, the target beam hopping pattern corresponding to the target optimization function can be determined.

[0094] Therefore, by utilizing a genetic algorithm to determine the operation of the target path optimization function, the technical effect of being able to determine the minimum sum of paths that the target beam needs to traverse the wave positions is achieved, thereby being able to improve communication efficiency.

[0095] Therefore, according to the first aspect of this embodiment, when the target beam actually traverses each beam position in the cluster, the technical effect of improving communication efficiency can be achieved.

[0096] In addition, reference Figure 1 As shown, according to the second aspect of this embodiment, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0097] Therefore, according to this embodiment, when the target beam actually traverses each beam position in the cluster, the technical effect of improving communication efficiency can be achieved.

[0098] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0099] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0100] Example 2

[0101] Figure 7 FIG. 7 shows a beam hopping scheduling device 700 based on a genetic algorithm according to this embodiment, and the device 700 corresponds to the method according to embodiment 1. Figure 7 As shown, the device 700 includes: a wave position determination module 710, which is used to determine the cluster corresponding to the target beam and determine each wave position in the cluster; a time slot ratio determination module 720, which is used to determine the demand ratio information of each wave position, and determine the ratio of the number of time slots corresponding to each wave position according to the demand ratio information; a random generation module 730, which is used to randomly generate a corresponding plurality of first hopping beam patterns when the ratio of the number of time slots corresponding to each wave position is determined, wherein the sum of the time slot lengths corresponding to each wave position in each first hopping beam pattern is the same, and the order of traversing each wave position corresponding to each first hopping beam pattern is different; a target path optimization function determination module 740, used to generate a corresponding path optimization function based on each first hopping beam pattern, and determine the optimal solution corresponding to the target path optimization function among multiple path optimization functions based on a genetic algorithm, wherein the path optimization function is used to indicate the sum of the paths of each wave position in the corresponding cluster traversed by the target beam according to the corresponding first hopping beam pattern, and the target path optimization function is used to indicate the sum of the minimum paths in the path optimization function; a target hopping beam pattern determination module 750, used to determine the corresponding target hopping beam pattern when determining the optimal solution corresponding to the target path optimization function; and a scheduling module 760, used to schedule the target beam according to the target hopping beam pattern.

[0102] Optionally, the time slot ratio determination module 720 includes: a demand ratio information determination module, which is used to collect demand information corresponding to each wave position, and determine the demand ratio information of each wave position based on the demand information corresponding to each wave position, wherein the demand information is used to indicate the number of communication devices communicating through the target beam; and a time slot ratio determination sub-module, which is used to determine the ratio of the number of time slots corresponding to each wave position based on the demand ratio information.

[0103] Optionally, the device 700 also includes: a screening module, used to screen out multiple second hopping beam patterns according to predetermined constraints for the randomly generated multiple first hopping beam patterns, wherein the constraints are used to indicate that two wave positions in adjacent time slots in each first hopping beam pattern are different; and a path optimization function generation module, used to generate a corresponding path optimization function based on each second hopping beam pattern.

[0104] Optionally, the target path optimization function determination module 740 includes: a fitness determination module, which is used to determine each path optimization function as a fitness function corresponding to each first hopping beam pattern; a third hopping beam pattern determination module, which is used to determine multiple third hopping beam patterns based on multiple first hopping beam patterns, wherein the multiple first hopping beam patterns are initialized hopping beam patterns, and the multiple third hopping beam patterns are sub-generation hopping beam patterns of the multiple first hopping beam patterns; a fourth hopping beam pattern generation module, which is used to cross-copy the wave positions in each third hopping beam pattern and generate multiple fourth hopping beam patterns; a fifth hopping beam pattern generation module, which is used to mutate each fourth hopping beam pattern and generate multiple fifth hopping beam patterns; and a target path optimization function determination submodule, which is used to repeat the iteration until the termination condition is met, and output the optimal solution corresponding to the target path optimization function, wherein the termination condition is used to indicate a preset number of iterations.

[0105] Therefore, according to this embodiment, when the target beam actually traverses each beam position in the cluster, the technical effect of improving communication efficiency can be achieved.

[0106] Example 3

[0107] Figure 8 FIG. 8 shows a beam hopping scheduling device 800 based on a genetic algorithm according to this embodiment, and the device 800 corresponds to the method according to embodiment 1. Figure 8As shown, the device 800 includes: a processor 810; and a memory 820, connected to the processor 810, and used to provide the processor 810 with instructions for processing the following processing steps: determining a cluster corresponding to a target beam, and determining each wave position in the cluster; determining demand ratio information of each wave position, and determining the ratio of the number of time slots corresponding to each wave position according to the demand ratio information; when determining the ratio of the number of time slots corresponding to each wave position, randomly generating a corresponding plurality of first hopping beam patterns, wherein the sum of the lengths of the time slots corresponding to each wave position in each first hopping beam pattern is the same, and ... The order of traversing each wave position corresponding to the scheme is different; based on each first hopping beam pattern, a corresponding path optimization function is generated, and based on a genetic algorithm, an optimal solution corresponding to a target path optimization function in multiple path optimization functions is determined, wherein the path optimization function is used to indicate the sum of paths of each wave position in the corresponding cluster traversed by the target beam according to the corresponding first hopping beam pattern, and the target path optimization function is used to indicate the sum of the minimum paths in the path optimization function; when the optimal solution corresponding to the target path optimization function is determined, the corresponding target hopping beam pattern is determined; and the target beam is scheduled according to the target hopping beam pattern.

[0108] Optionally, the operation of determining demand ratio information for each wave position, and determining the ratio of the number of time slots corresponding to each wave position based on the demand ratio information, includes: collecting demand information corresponding to each wave position, and determining the demand ratio information for each wave position based on the demand information corresponding to each wave position, wherein the demand information is used to indicate the number of communication devices communicating through the target beam; and determining the ratio of the number of time slots corresponding to each wave position based on the demand ratio information.

[0109] Optionally, the device 800 also includes: for multiple randomly generated first hopping beam patterns, screening out multiple second hopping beam patterns according to predetermined constraints, wherein the constraints are used to indicate that two wave positions in adjacent time slots in each first hopping beam pattern are different; and generating a corresponding path optimization function based on each second hopping beam pattern.

[0110] Optionally, the operation of determining the optimal solution corresponding to the target path optimization function among multiple path optimization functions based on the genetic algorithm includes: determining each path optimization function as a fitness function corresponding to each first hopping beam pattern; determining multiple third hopping beam patterns based on the multiple first hopping beam patterns, wherein the multiple first hopping beam patterns are initialized hopping beam patterns, and the multiple third hopping beam patterns are sub-generation hopping beam patterns of the multiple first hopping beam patterns; cross-copying the wave positions in each third hopping beam pattern, and generating multiple fourth hopping beam patterns; mutating each fourth hopping beam pattern, and generating multiple fifth hopping beam patterns; and repeating the iteration until a termination condition is met, and outputting the optimal solution corresponding to the target path optimization function, wherein the termination condition is used to indicate a preset number of iterations.

[0111] Therefore, according to this embodiment, when the target beam actually traverses each beam position in the cluster, the technical effect of improving communication efficiency can be achieved.

[0112] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0113] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] If the integrated unit is implemented in the form of 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. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0118] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A beam hopping scheduling method based on genetic algorithm, characterized in that: include: Determining a cluster corresponding to a target beam, and determining each beam position within the cluster; Determine demand ratio information of each of the wave positions, and determine the ratio of the number of time slots corresponding to each of the wave positions according to the demand ratio information; In the case of determining the ratio of the number of time slots corresponding to the respective beam positions, randomly generating a plurality of first beam hopping patterns corresponding to the cluster, wherein the sum of the lengths of the time slots corresponding to the respective beam positions in each first beam hopping pattern is the same, and the order of traversing the respective beam positions corresponding to the respective first beam hopping patterns is different; Generate a corresponding path optimization function based on each of the first hopping beam patterns, and determine an optimal solution corresponding to a target path optimization function among multiple path optimization functions based on a genetic algorithm, wherein the path optimization function is used to indicate the sum of paths of each beam position in the corresponding cluster traversed by the target beam according to the corresponding first hopping beam pattern, and the target path optimization function is used to indicate the minimum sum of paths in the path optimization function; Upon determining an optimal solution corresponding to the target path optimization function, determining a corresponding target beam hopping pattern; as well as The target beam is scheduled according to the target beam hopping pattern.

2. The method according to claim 1, characterized in that The operation of determining demand ratio information of each of the wave positions, and determining the ratio of the number of time slots corresponding to each of the wave positions according to the demand ratio information, includes: Collecting demand information corresponding to each of the beam positions, and determining demand ratio information of each of the beam positions based on the demand information corresponding to each of the beam positions, wherein the demand information is used to indicate the number of communication devices communicating through the target beam; and Based on the demand ratio information, the ratio of the number of time slots corresponding to the respective wave positions is determined.

3. The method according to claim 1, characterized in that Also includes: For the randomly generated multiple first hopping beam patterns, screening out multiple second hopping beam patterns according to a predetermined constraint condition, wherein the constraint condition is used to indicate that two beam positions of adjacent time slots in each of the first hopping beam patterns are different; as well as Based on each second hop beam pattern, a corresponding path optimization function is generated.

4. The method according to claim 1, characterized in that: The operation of determining the optimal solution corresponding to the target path optimization function among multiple path optimization functions based on the genetic algorithm includes: Determining each of the path optimization functions as a fitness function corresponding to each of the first beam hopping patterns; Determining a plurality of third beam hopping patterns based on the plurality of first beam hopping patterns, wherein the plurality of first beam hopping patterns are initialized beam hopping patterns, and the plurality of third beam hopping patterns are sub-generation beam hopping patterns of the plurality of first beam hopping patterns; Cross-copying the beam positions in each third hopping beam pattern and generating a plurality of fourth hopping beam patterns; performing a mutation operation on each fourth hopping beam pattern to generate a plurality of fifth hopping beam patterns; and The iteration is repeated until a termination condition is met, and an optimal solution corresponding to the target path optimization function is output, wherein the termination condition is used to indicate a preset number of iterations.

5. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 4.

6. A beam hopping scheduling device based on genetic algorithm, characterized in that: include: A beam position determination module, used to determine a cluster corresponding to a target beam and to determine each beam position within the cluster; A time slot ratio determination module, used to determine the demand ratio information of each wave position, and determine the ratio of the number of time slots corresponding to each wave position according to the demand ratio information; A random generation module, configured to randomly generate a plurality of first beam hopping patterns corresponding to the cluster when determining a ratio of the number of time slots corresponding to the respective beam positions, wherein the sum of the lengths of the time slots corresponding to the respective beam positions in each first beam hopping pattern is the same, and the order of traversing the respective beam positions corresponding to the respective first beam hopping patterns is different; a target path optimization function determination module, configured to generate a corresponding path optimization function based on each of the first beam hopping patterns, and determine an optimal solution corresponding to the target path optimization function among multiple path optimization functions based on a genetic algorithm, wherein the path optimization function is configured to indicate the sum of paths of each beam position in the corresponding cluster traversed by the target beam according to the corresponding first beam hopping pattern, and the target path optimization function is configured to indicate the sum of the minimum paths in the path optimization function; a target beam hopping pattern determination module, configured to determine a corresponding target beam hopping pattern when an optimal solution corresponding to the target path optimization function is determined; as well as A scheduling module is used to schedule the target beam according to the target beam hopping pattern.

7. The device according to claim 6, characterized in that The time slot ratio determination module includes: a demand ratio information determination module, configured to collect demand information corresponding to each of the beam positions, and determine demand ratio information of each of the beam positions based on the demand information corresponding to each of the beam positions, wherein the demand information is used to indicate the number of communication devices communicating through the target beam; and The time slot ratio determination submodule is used to determine the ratio of the number of time slots corresponding to each of the wave positions based on the demand ratio information.

8. The device according to claim 7, characterized in that The device also includes: a screening module, configured to screen out a plurality of second beam hopping patterns according to a predetermined constraint condition for the randomly generated plurality of first beam hopping patterns, wherein the constraint condition is used to indicate that two beam positions of adjacent time slots in each of the first beam hopping patterns are different; and The path optimization function generating module is used to generate a corresponding path optimization function based on each second hop beam pattern.

9. The device according to claim 8, characterized in that The target path optimization function determination module includes: A fitness determination module, configured to determine each of the path optimization functions as a fitness function corresponding to each of the first hopping beam patterns; a third hopping beam pattern determining module, configured to determine a plurality of third hopping beam patterns based on the plurality of first hopping beam patterns, wherein the plurality of first hopping beam patterns are initialized beam hopping patterns, and the plurality of third hopping beam patterns are sub-generation beam hopping patterns of the plurality of first hopping beam patterns; A fourth hopping beam pattern generating module, used for cross-copying the beam positions in each third hopping beam pattern and generating a plurality of fourth hopping beam patterns; a fifth hopping beam pattern generating module, configured to perform a mutation operation on each fourth hopping beam pattern and generate a plurality of fifth hopping beam patterns; and The target path optimization function determination submodule is used to repeat iterations until a termination condition is met and output an optimal solution corresponding to the target path optimization function, wherein the termination condition is used to indicate a preset number of iterations.

10. A beam hopping scheduling device based on genetic algorithm, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Determining a cluster corresponding to a target beam, and determining each beam position within the cluster; Determine demand ratio information of each of the wave positions, and determine the ratio of the number of time slots corresponding to each of the wave positions according to the demand ratio information; In the case of determining the ratio of the number of time slots corresponding to the respective beam positions, randomly generating a plurality of first beam hopping patterns corresponding to the cluster, wherein the sum of the lengths of the time slots corresponding to the respective beam positions in each first beam hopping pattern is the same, and the order of traversing the respective beam positions corresponding to the respective first beam hopping patterns is different; Generate a corresponding path optimization function based on each of the first hopping beam patterns, and determine an optimal solution corresponding to a target path optimization function among multiple path optimization functions based on a genetic algorithm, wherein the path optimization function is used to indicate the sum of paths of each beam position in the corresponding cluster traversed by the target beam according to the corresponding first hopping beam pattern, and the target path optimization function is used to indicate the minimum sum of paths in the path optimization function; Upon determining an optimal solution corresponding to the target path optimization function, determining a corresponding target beam hopping pattern; as well as The target beam is scheduled according to the target beam hopping pattern.

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