Traffic distribution determination method, device, equipment, storage medium and program product

Abstract

The embodiment of the present application provides a method, device, equipment, storage medium and program product for determining traffic distribution, the method comprising: obtaining multiple route data, multiple site data and multiple gateway data of multiple mobile objects in a target area; performing feature extraction processing on the multiple route data to obtain service feature data of multiple mobile objects; determining the initial trajectory data of multiple mobile objects based on the multiple route data and the multiple site data; determining the wave positions and gateway data corresponding to the multiple mobile objects based on the initial trajectory data of the multiple mobile objects and the multiple gateway data; determining the traffic distribution information of each wave position and each gateway in the target area based on the service feature data of the multiple mobile objects and the wave positions and gateway data corresponding to the multiple mobile objects. The method can improve the accuracy of determining the traffic distribution information of each wave position and each gateway in the target area within the target time period.

Description

Technical Field

[0001] The embodiments of the present application relate to technical fields such as wireless communications, and in particular, to a method, apparatus, device, storage medium, and program product for determining traffic distribution. Background Art

[0002] In the satellite network scenario, the satellite network can provide communication services for multiple mobile objects based on beam positions or gateways. Satellite network companies rationally allocate the network resources of satellite network beams or gateways so that the satellite network can better provide communication services for each mobile object. For example, the satellite network can be a low-orbit constellation Internet, and the mobile objects can be airplanes, ships, or drones.

[0003] In related technologies, satellite network companies can construct a traffic matrix model, and use the traffic matrix model to simulate the traffic distribution information of each wave position of the satellite network, and reasonably allocate network resources of the satellite network to each wave position based on the traffic distribution information.

[0004] However, the traffic matrix model has low accuracy in simulating the traffic distribution information of each wave position of the satellite network. Summary of the invention

[0005] The embodiments of the present application provide a method, apparatus, device, storage medium and program product for determining traffic distribution, so as to solve the technical problem in the related art that the traffic matrix model has low accuracy when simulating the traffic distribution information of each wave position of the satellite network.

[0006] In a first aspect, an embodiment of the present application provides a method for determining traffic distribution, which is applied to a first device, and the method includes:

[0007] Acquire multiple route data of multiple mobile objects in the target area, data of multiple sites, and data of multiple gateways;

[0008] Performing feature extraction processing on the plurality of route data to obtain service feature data of the plurality of mobile objects;

[0009] Determining initial trajectory data of the plurality of mobile objects according to the plurality of route data and the data of the plurality of sites;

[0010] Determine the wave positions and the data of the gateways corresponding to the multiple mobile objects according to the initial trajectory data of the multiple mobile objects and the data of the multiple gateways;

[0011] The flow distribution information of each wave position and each gateway station in the target area is determined according to the service characteristic data of the multiple mobile objects and the data of the wave positions and gateway stations corresponding to the multiple mobile objects.

[0012] In a possible implementation, determining the initial trajectory data of the plurality of mobile objects according to the plurality of route data and the data of the plurality of sites includes:

[0013] Performing matching processing on the data of the plurality of sites and the data of the plurality of routes to obtain a plurality of groups of matching data corresponding to the plurality of mobile objects;

[0014] Determining the flight duration of the plurality of mobile objects according to the plurality of route data;

[0015] Determine, according to the plurality of route data, a plurality of navigation positions corresponding to each of the plurality of mobile objects at a plurality of sampling moments, wherein the plurality of sampling moments are determined by a sampling period and a navigation duration of each mobile object;

[0016] Initial trajectory data of the plurality of mobile objects are generated according to the plurality of route data, the plurality of sets of matching data, and the plurality of navigation positions corresponding to the plurality of mobile objects at a plurality of sampling moments.

[0017] In a possible implementation, for any mobile object, determining the flight time of the mobile object according to the plurality of route data includes:

[0018] Determining a departure position and an arrival position of the mobile object according to the route data of the mobile object in the plurality of route data;

[0019] Determining the navigation distance of the mobile object according to the departure position and the arrival position;

[0020] determining a navigation speed of the mobile object;

[0021] The sailing duration is determined according to the sailing speed and the sailing distance.

[0022] In a possible implementation, determining a plurality of navigation positions corresponding to the mobile object at a plurality of sampling moments includes:

[0023] determining an azimuth between the departure location and the arrival location;

[0024] The multiple navigation positions of the mobile object are determined according to the azimuth and the navigation distance.

[0025] In a possible implementation, matching the data of the plurality of sites with the plurality of route data to obtain a plurality of groups of matching data corresponding to the plurality of mobile objects includes:

[0026] Determining locations of the plurality of sites based on the data of the plurality of sites;

[0027] Determining departure locations and arrival locations of the plurality of mobile objects based on the plurality of route data;

[0028] Determining matching relationships between the multiple mobile objects and the multiple sites according to the locations of the multiple sites, the departure locations and the arrival locations of the multiple mobile objects;

[0029] The multiple groups of matching data are determined according to the matching relationship, the data of the multiple sites and the multiple route data.

[0030] In a possible implementation, for any mobile object, determining the wave position and the data of the gateway corresponding to the mobile object according to the initial trajectory data of the mobile object and the data of the plurality of gateways includes:

[0031] Determining target trajectory data of the moving object within a target time period according to the initial trajectory data of the moving object;

[0032] Determining, based on the target trajectory data of the mobile object, a plurality of predicted positions of the mobile object corresponding to a plurality of predicted moments within a target time period;

[0033] Determining data of wave positions corresponding to the mobile object according to the multiple predicted positions;

[0034] The data of the gateway corresponding to the mobile object is determined according to the multiple predicted positions and the data of the multiple gateways.

[0035] In a possible implementation manner, determining data of the wave position corresponding to the mobile object according to the multiple predicted positions includes:

[0036] Determine the wave position identifier corresponding to each predicted position, and obtain multiple wave position identifiers;

[0037] According to the multiple wave position identifiers, the multiple predicted positions are divided into multiple first position sets, and the wave position identifiers of the predicted positions in the first position sets are the same;

[0038] A first prediction time period corresponding to the plurality of first position sets is determined within the target time period, and data of the wave position is determined according to the first prediction time period corresponding to the plurality of first position sets and the wave position identifier.

[0039] In a possible implementation manner, determining the data of the gateway corresponding to the mobile object according to the multiple predicted positions and the data of the multiple gateways includes:

[0040] Determining locations of the plurality of gateways based on the data of the plurality of gateways;

[0041] Determining, according to the multiple predicted positions and the positions of the multiple gateways, candidate gateways corresponding to the multiple predicted positions from among the multiple gateways;

[0042] According to the candidate gateways corresponding to the multiple predicted positions, the multiple predicted positions are divided into at least one second position set, wherein the candidate gateways corresponding to the predicted positions in the second position set are the same;

[0043] A second predicted time period corresponding to the at least one second location set is determined within the target time period, and data of the gateway is determined according to the second predicted time period corresponding to the at least one second location set and the to-be-selected gateway.

[0044] In a possible implementation, for any predicted location, determining a candidate gateway corresponding to the predicted location from among the multiple gateways according to the predicted location and the locations of the multiple gateways includes:

[0045] Determine, according to the predicted position and the positions of the plurality of gateways, a distance between the predicted position and each gateway to obtain a plurality of distances;

[0046] determining a shortest distance among the plurality of distances;

[0047] The gateway corresponding to the shortest distance is determined as the candidate gateway corresponding to the predicted position.

[0048] In a possible implementation, determining target trajectory data of the moving object within a target period of time according to the initial trajectory data of the moving object includes:

[0049] Determining the flight duration of the mobile object;

[0050] The initial trajectory data of the mobile object is preprocessed according to the navigation duration and the duration of the target time period to obtain the target trajectory data of the mobile object.

[0051] In a possible implementation, determining the flow distribution information of each wave position and each gateway in the target area according to the service characteristic data of the multiple mobile objects and the wave positions and gateway data corresponding to the multiple mobile objects includes:

[0052] Determine, according to the data of the wave positions corresponding to the multiple mobile objects, a first mobile object corresponding to each wave position among the multiple mobile objects, and determine, according to the service characteristic data of the first mobile object and the data of the wave position corresponding to the first mobile object, traffic distribution information of each wave position;

[0053] According to the data of the gateways corresponding to the multiple mobile objects, a second mobile object corresponding to each gateway is determined among the multiple mobile objects, and according to the service characteristic data of the second mobile object and the data of the gateway corresponding to the second mobile object, traffic distribution information of each gateway is determined.

[0054] In a possible implementation, the method further includes:

[0055] According to the flow distribution information of each wave position and each gateway station, network optimization processing is performed on the satellite network in the target area.

[0056] In a possible implementation, the method further includes:

[0057] Sending the traffic distribution information of each wavelength and each gateway to the second device;

[0058] The flow distribution information of each wave position and each gateway station is used by the second device to perform network optimization processing on the satellite network in the target area.

[0059] In a possible implementation manner, performing network optimization processing on the satellite network in the target area includes:

[0060] performing resource optimization processing on the network resources of the satellite network according to the flow distribution information of each wave position and each gateway station; and / or,

[0061] According to the flow distribution information of each wave position and each gateway station, routing optimization processing is performed on the routing information of the satellite network;

[0062] The network optimization process includes the resource optimization process and the route optimization process.

[0063] In a second aspect, an embodiment of the present application provides a method for determining traffic distribution, which is applied to a second device, and the method includes:

[0064] receiving the flow distribution information of each wavelength and each gateway station in the target area sent by the first device;

[0065] The flow distribution information of each wave position and each gateway station is used to perform network optimization processing on the satellite network in the target area.

[0066] In a possible implementation manner, performing network optimization processing on the satellite network in the target area includes:

[0067] performing resource optimization processing on the network resources of the satellite network according to the flow distribution information of each wave position and each gateway station; and / or,

[0068] According to the flow distribution information of each wave position and each gateway station, routing optimization processing is performed on the routing information of the satellite network;

[0069] The network optimization process includes the resource optimization process and the route optimization process.

[0070] In a third aspect, an embodiment of the present application provides a flow distribution determination device, applied to a first device, including:

[0071] A transceiver module, used to obtain multiple route data of multiple mobile objects in a target area, data of multiple sites and data of multiple gateways;

[0072] A processing module, used for performing feature extraction processing on the plurality of route data to obtain service feature data of the plurality of mobile objects;

[0073] The processing module is further used to determine the initial trajectory data of the plurality of mobile objects based on the plurality of route data and the data of the plurality of sites;

[0074] The processing module is further used to determine the wave positions and gateway data corresponding to the multiple mobile objects according to the initial trajectory data of the multiple mobile objects and the data of the multiple gateways;

[0075] The processing module is further used to determine the flow distribution information of each wave position and each gateway station in the target area according to the service characteristic data of the multiple mobile objects and the data of the wave positions and gateway stations corresponding to the multiple mobile objects.

[0076] In a possible implementation manner, the processing module is specifically configured to:

[0077] Performing matching processing on the data of the plurality of sites and the data of the plurality of routes to obtain a plurality of groups of matching data corresponding to the plurality of mobile objects;

[0078] Determining the flight duration of the plurality of mobile objects according to the plurality of route data;

[0079] Determine, according to the plurality of route data, a plurality of navigation positions corresponding to each of the plurality of mobile objects at a plurality of sampling moments, wherein the plurality of sampling moments are determined by a sampling period and a navigation duration of each mobile object;

[0080] Initial trajectory data of the plurality of mobile objects are generated according to the plurality of route data, the plurality of sets of matching data, and the plurality of navigation positions corresponding to the plurality of mobile objects at a plurality of sampling moments.

[0081] In a possible implementation, for any moving object, the processing module is further configured to:

[0082] Determining a departure position and an arrival position of the mobile object according to the route data of the mobile object in the plurality of route data;

[0083] Determining the navigation distance of the mobile object according to the departure position and the arrival position;

[0084] determining a navigation speed of the mobile object;

[0085] The sailing duration is determined according to the sailing speed and the sailing distance.

[0086] In a possible implementation manner, the processing module is further configured to:

[0087] determining an azimuth between the departure location and the arrival location;

[0088] The multiple navigation positions of the mobile object are determined according to the azimuth and the navigation distance.

[0089] In a possible implementation manner, the processing module is further configured to:

[0090] Determining locations of the plurality of sites based on the data of the plurality of sites;

[0091] Determining departure locations and arrival locations of the plurality of mobile objects based on the plurality of route data;

[0092] Determining matching relationships between the multiple mobile objects and the multiple sites according to the locations of the multiple sites, the departure locations and the arrival locations of the multiple mobile objects;

[0093] The multiple groups of matching data are determined according to the matching relationship, the data of the multiple sites and the multiple route data.

[0094] In a possible implementation, for any moving object, the processing module is further configured to:

[0095] Determining target trajectory data of the moving object within a target time period according to the initial trajectory data of the moving object;

[0096] Determining, based on the target trajectory data of the mobile object, a plurality of predicted positions of the mobile object corresponding to a plurality of predicted moments within a target time period;

[0097] Determining data of wave positions corresponding to the mobile object according to the multiple predicted positions;

[0098] The data of the gateway corresponding to the mobile object is determined according to the multiple predicted positions and the data of the multiple gateways.

[0099] In a possible implementation manner, the processing module is further configured to:

[0100] Determine the wave position identifier corresponding to each predicted position, and obtain multiple wave position identifiers;

[0101] According to the multiple wave position identifiers, the multiple predicted positions are divided into multiple first position sets, and the wave position identifiers of the predicted positions in the first position sets are the same;

[0102] A first prediction time period corresponding to the plurality of first position sets is determined within the target time period, and data of the wave position is determined according to the first prediction time period corresponding to the plurality of first position sets and the wave position identifier.

[0103] In a possible implementation manner, the processing module is further configured to:

[0104] Determining locations of the plurality of gateways based on the data of the plurality of gateways;

[0105] Determining, according to the multiple predicted positions and the positions of the multiple gateways, candidate gateways corresponding to the multiple predicted positions from among the multiple gateways;

[0106] According to the candidate gateways corresponding to the multiple predicted positions, the multiple predicted positions are divided into at least one second position set, wherein the candidate gateways corresponding to the predicted positions in the second position set are the same;

[0107] A second predicted time period corresponding to the at least one second location set is determined within the target time period, and data of the gateway is determined according to the second predicted time period corresponding to the at least one second location set and the to-be-selected gateway.

[0108] In a possible implementation, for any predicted position, the processing module is further configured to:

[0109] Determine, according to the predicted position and the positions of the plurality of gateways, a distance between the predicted position and each gateway to obtain a plurality of distances;

[0110] determining a shortest distance among the plurality of distances;

[0111] The gateway corresponding to the shortest distance is determined as the candidate gateway corresponding to the predicted position.

[0112] In a possible implementation manner, the processing module is further configured to:

[0113] Determining the flight duration of the mobile object;

[0114] The initial trajectory data of the mobile object is preprocessed according to the navigation duration and the duration of the target time period to obtain the target trajectory data of the mobile object.

[0115] In a possible implementation manner, the processing module is further configured to:

[0116] Determine, according to the data of the wave positions corresponding to the multiple mobile objects, a first mobile object corresponding to each wave position among the multiple mobile objects, and determine, according to the service characteristic data of the first mobile object and the data of the wave position corresponding to the first mobile object, traffic distribution information of each wave position;

[0117] According to the data of the gateways corresponding to the multiple mobile objects, a second mobile object corresponding to each gateway is determined among the multiple mobile objects, and according to the service characteristic data of the second mobile object and the data of the gateway corresponding to the second mobile object, traffic distribution information of each gateway is determined.

[0118] In a possible implementation manner, the processing module is further configured to:

[0119] According to the flow distribution information of each wave position and each gateway station, network optimization processing is performed on the satellite network in the target area.

[0120] In a possible implementation, the transceiver module is further configured to:

[0121] Sending the traffic distribution information of each wavelength and each gateway to the second device;

[0122] The flow distribution information of each wave position and each gateway station is used by the second device to perform network optimization processing on the satellite network in the target area.

[0123] In a possible implementation manner, performing network optimization processing on the satellite network in the target area includes:

[0124] performing resource optimization processing on the network resources of the satellite network according to the flow distribution information of each wave position and each gateway station; and / or,

[0125] According to the flow distribution information of each wave position and each gateway station, routing optimization processing is performed on the routing information of the satellite network;

[0126] The network optimization process includes the resource optimization process and the route optimization process.

[0127] In a fourth aspect, an embodiment of the present application provides a flow distribution determination device, which is applied to a second device, including:

[0128] A transceiver module, used for receiving the flow distribution information of each wavelength and each gateway in the target area sent by the first device;

[0129] The flow distribution information of each wave position and each gateway station is used to perform network optimization processing on the satellite network in the target area.

[0130] In a possible implementation manner, performing network optimization processing on the satellite network in the target area includes:

[0131] performing resource optimization processing on the network resources of the satellite network according to the flow distribution information of each wave position and each gateway station; and / or,

[0132] According to the flow distribution information of each wave position and each gateway station, routing optimization processing is performed on the routing information of the satellite network;

[0133] The network optimization process includes the resource optimization process and the route optimization process.

[0134] In a fifth aspect, an embodiment of the present application provides a flow distribution determination device, including:

[0135] at least one processor; and

[0136] a memory communicatively connected to the at least one processor; wherein,

[0137] The memory stores instructions to be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the methods described in the first aspect.

[0138] In a sixth aspect, an embodiment of the present application provides a flow distribution determination device, including:

[0139] at least one processor; and

[0140] a memory communicatively connected to the at least one processor; wherein,

[0141] The memory stores instructions to be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of the second aspects.

[0142] In a seventh aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in any one of the first aspects.

[0143] In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in any one of the second aspects.

[0144] In a ninth aspect, an embodiment of the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements any one of the methods described in the first aspect.

[0145] In a tenth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any method described in the second aspect.

[0146] The traffic distribution determination method, apparatus, device, storage medium and program product provided in the embodiments of the present application, on the basis of not relying on historical traffic distribution information, the method considers from two perspectives of the access network side and the feeding side of the satellite network, obtains multiple route data of multiple mobile objects in the target area, data of multiple sites and data of multiple gateways; based on the spatiotemporal distribution characteristics of the multiple route data of multiple mobile objects in the target area, the data of multiple sites and the data of multiple gateways, conveniently and quickly determines the wave positions and gateway data corresponding to the multiple mobile objects, and combines the service feature data of the multiple mobile objects and the wave positions and gateway data corresponding to the multiple mobile objects to accurately determine the traffic distribution information of each wave position and each gateway in the target area during the target period, so that the satellite network enterprise can optimize the satellite network in the target area based on the traffic distribution information of each wave position and each gateway, so that the satellite network enterprise can better provide communication services for each mobile object through each wave position and each gateway. BRIEF DESCRIPTION OF THE DRAWINGS

[0147] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0148] Figure 1A A schematic diagram of a satellite network communication system provided in an embodiment of the present application;

[0149] Figure 1B A schematic diagram of the prediction process of a traffic prediction model provided in an embodiment of the present application;

[0150] Figure 2 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0151] Figure 3 One of the flow charts of the method for determining flow distribution provided in the embodiment of the present application;

[0152] Figure 4 A schematic diagram of a mapping distribution diagram of wave positions in a target area provided in an embodiment of the present application;

[0153] Figure 5 A schematic diagram of a mapping distribution diagram of gateway stations in a target area provided in an embodiment of the present application;

[0154] Figure 6 A second flow chart of a method for determining flow distribution provided in an embodiment of the present application;

[0155] Figure 7 A schematic diagram of an initial trajectory of a moving object provided in an embodiment of the present application;

[0156] Figure 8 The third flowchart of the method for determining flow distribution provided in the embodiment of the present application;

[0157] Fig. 9 A fourth flow chart of a method for determining flow distribution provided in an embodiment of the present application;

[0158] Fig.10 A schematic diagram of target trajectory data of a mobile object provided in an embodiment of the present application;

[0159] Fig.11 A schematic diagram of geographical area division provided for an embodiment of the present application;

[0160] Fig.12 A schematic diagram of the wave position coverage area provided in an embodiment of the present application;

[0161] Fig.13 A schematic diagram of the distribution of moving objects in a target area in different time periods provided in an embodiment of the present application;

[0162] Fig.14 A fifth flow chart of a method for determining flow distribution provided in an embodiment of the present application;

[0163] Fig.15 A schematic diagram of a switching process of a gateway provided in an embodiment of the present application;

[0164] Fig.16 Flow chart of the flow distribution determination method provided in the embodiment of the present application is as follows:

[0165] Fig.17 One of the structural schematic diagrams of the flow distribution determination device provided in an embodiment of the present application;

[0166] Fig.18 A second structural diagram of a flow distribution determination device provided in an embodiment of the present application;

[0167] Fig.19A schematic diagram of the structure of a flow distribution determination device provided in an embodiment of the present application.

[0168] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0169] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0170] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0171] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

[0172] It should be noted that the traffic distribution determination method, device, equipment, storage medium and program product provided in the embodiments of the present application can be used in the field of wireless communications, and can also be used in any field other than the field of wireless communications, for example, the field of Internet technology. The embodiments of the present application do not limit the application fields of the traffic distribution determination method, device, equipment, storage medium and program product.

[0173] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0174] To facilitate understanding, the communication system involved in the embodiments of the present application is introduced.

[0175] Figure 1A A schematic diagram of a satellite network communication system provided in an embodiment of the present application. Figure 1A The communication system includes a satellite network, a ground gateway and a mobile object. The satellite network may include multiple satellites; the mobile object may carry multiple satellite communication terminals. For example, the mobile object may be an aircraft, and the satellite communication terminal may be a satellite communication terminal in the aircraft, etc.

[0176] It should be noted that the embodiment of the present application is illustrated by taking the number of mobile objects accessing the satellite network as 1 and the number of ground gateway stations as 1. In some embodiments, the number of mobile objects and ground gateway stations accessing the satellite network can be any number, and the present application does not limit the number of mobile objects and ground gateway stations accessing the satellite network.

[0177] At present, the traffic prediction method of ground cellular network can be used to predict the traffic of satellite network. The traffic prediction of ground cellular network can be divided into two categories: time prediction and space-time prediction. Among them:

[0178] Time prediction is generally for a single base station. The traffic data of user terminals or other communication devices connected to the single base station in the future period can be predicted based on the historical traffic data of the user terminals or other communication devices connected to the single base station and their time dependency.

[0179] Spatiotemporal prediction is generally aimed at multiple base stations or multiple regions. Based on the historical traffic data of user terminals (or other communication devices) in these multiple base stations or multiple regions and their spatiotemporal dependency, the traffic data of user terminals (or other communication devices) connected to these multiple base stations or multiple regions in future time periods can be predicted.

[0180] Technicians can perform traffic prediction for terrestrial cellular networks based on a traffic prediction model that can generate various input and output pairs by using a sliding window.

[0181] Figure 1B A schematic diagram of the prediction process of a traffic prediction model provided in an embodiment of the present application. Figure 1BThe traffic prediction model can predict the traffic data (output data) in the future time step based on the historical traffic data (input data) in the past time step. By continuously sliding the window from left to right, a pair of data is generated: input data and output data.

[0182] At present, technicians usually build the traffic prediction model based on statistical models or deep learning models. For example, the statistical model can be an autoregressive integrated moving average model (ARIMA), and the deep learning model can be a long short-term memory network (LSTM). Specifically, the statistical model or deep learning model can be trained through supervised learning based on the existing historical traffic data of the ground cellular network to obtain the traffic prediction model.

[0183] However, in some business scenarios, the satellite network enterprise in the business scenario did not provide the service object with satellite network access capabilities during the historical period, making it impossible for the technicians to obtain the historical traffic data of the service object, and thus unable to train the traffic prediction model suitable for this part of the business scenario based on the historical traffic data, making it impossible for the technicians to perform satellite network traffic prediction in this part of the business scenario. For example, the business scenario can be an aviation business scenario, a navigation business scenario, or an aerospace business scenario, and the service object can be a satellite communication terminal on an aircraft or a ship, and the service object can also be a satellite communication terminal carried by a drone.

[0184] In related technologies, technicians can also collect information about the top 100 cities with the largest populations in the world to establish a traffic matrix model, and use the traffic matrix model to simulate the traffic distribution information of each wave position of the satellite network, and reasonably allocate network resources of the satellite network to each wave position based on the traffic distribution information. For example, the information of the top 100 cities can be shown in Table 1.

[0185] Table 1

[0186]

[0187] The traffic matrix model can use the following two modes to predict the traffic distribution information of each service object:

[0188] General mode: In this mode, 100 cities are randomly paired, forming a total of 50 node pairs and 50 business flows.

[0189] Dedicated mode: In this mode, you need to first select a specified node pair, for example, the specified node pair is (A, B), and calculate the constellation network path of (A, B), for example, the constellation network path can be expressed as (A, R1, R2, ..., Rn, B); then randomly pair the remaining 98 nodes in pairs to obtain 49 node pairs, and calculate the constellation network path of each node pair. According to the constellation network paths of these 49 node pairs, determine the node pairs with the same source or target satellite, delete these node pairs with the same source or target satellite, and finally use the screened node pairs as "background traffic".

[0190] However, although the traffic matrix model can predict the traffic distribution of the satellite network at each wave position to a certain extent, the traffic matrix model is relatively simple, and the business scenarios it predicts are quite different from the actual business scenarios at each wave position, resulting in the low accuracy of the traffic matrix model in predicting the traffic distribution information at each wave position. In addition, the network service types provided by satellite network companies to each service object (for example, aircraft) may include Internet access types. Under the Internet access type, the service object needs to be connected to the Internet through a ground gateway station. In this case, the satellite network company also needs to pay attention to the traffic distribution information of each gateway station in the target area.

[0191] It should be noted that the terms "service object" and "mobile object" in this specification can be used interchangeably.

[0192] It is understandable that when the satellite network provides communication services for various mobile objects (for example, airplanes or ships), the routes of these mobile objects moving in a certain area have significant spatiotemporal distribution characteristics, so that the wave positions and gateways corresponding to these mobile objects have significant spatiotemporal distribution characteristics. In view of this, an embodiment of the present application provides a method for determining traffic distribution, which includes: obtaining multiple route data, multiple site data, and multiple gateway data of multiple mobile objects in a target area; performing feature extraction processing on the multiple route data to obtain service feature data of the multiple mobile objects; determining the initial trajectory data of the multiple mobile objects based on the multiple route data and the multiple site data; determining the wave positions and gateway data corresponding to the multiple mobile objects based on the initial trajectory data of the multiple mobile objects and the multiple gateway data; determining the traffic distribution information of each wave position and each gateway in the target area based on the service feature data of the multiple mobile objects and the wave positions and gateway data corresponding to the multiple mobile objects. The method is based on the consideration of the access network side and the feeding side of the satellite network from the perspective of no reliance on historical traffic distribution information. Based on the spatiotemporal distribution characteristics of multiple route data of multiple mobile objects, data of multiple sites and data of multiple gateways in the target area, the method conveniently and quickly determines the wave position and gateway data corresponding to each mobile object. In addition, the method combines the service feature data of multiple mobile objects and the wave position and gateway data corresponding to multiple mobile objects to accurately determine the traffic distribution information of each wave position and each gateway in the target area during the target period, so that the satellite network enterprise can optimize the satellite network based on the traffic distribution information, so that the satellite network enterprise can better provide communication services for each mobile object through each wave position and each gateway.

[0193] Figure 2 This is a schematic diagram of an application scenario provided by the embodiment of this application. Figure 2 The traffic distribution determination device can obtain multiple route data, multiple site data, and multiple gateway data of multiple mobile objects in the target area. For example, if the mobile object is an airplane, the route is the route of the airplane, and the site can be the airport where the airplane stops; if the mobile object is a ship, the route is the route of the ship, and the site can be the port where the ship stops.

[0194] The traffic distribution determination device can determine the traffic distribution information of each wave position in the target area and the traffic distribution information of each gateway station based on the multiple route data, the multiple site data and the multiple gateway station data.

[0195] In some embodiments, the traffic distribution determination device or other device can determine the allocation strategy of the satellite network's access network resources and gateway station feeding resources, and optimize the routing protocol algorithm of the bearer network based on the traffic distribution information of each wave position in the target area and the traffic distribution information of each gateway station, so that the satellite network can provide better communication services to each satellite communication terminal in the target area.

[0196] The method shown in the present application is described below through specific embodiments. It should be noted that the following embodiments can exist independently or in combination with each other, and the same or similar contents will not be described repeatedly in different embodiments.

[0197] Figure 3 This is one of the flow charts of the method for determining flow distribution provided in the embodiment of the present application. Figure 2 The flow distribution determination device in the embodiment is executed, or it can also be executed by the flow distribution determination device in the flow distribution determination device, and the flow distribution determination device can be implemented by software or by a combination of software and hardware. Figure 3 The flow distribution determination method may specifically include the following steps:

[0198] S301, obtaining multiple route data of multiple mobile objects in a target area, data of multiple sites, and data of multiple gateways.

[0199] The mobile object may be aviation equipment, navigation equipment or aerospace equipment. For example, the aviation equipment may be an airplane or a drone, the navigation equipment may be a ship, and the aerospace equipment may be a spacecraft.

[0200] In some embodiments, a database may be pre-established, and the route data of multiple mobile objects and the data of multiple sites in each area may be stored in the database. The traffic distribution determination device may send an acquisition request to the database to obtain the route data of multiple mobile objects and the data of multiple sites in the target area.

[0201] In the embodiment of the present application, various regions can be divided based on boundary conditions such as continental boundaries, national boundaries, provincial boundaries, and city boundaries. For example, the target region can be a region of a certain province.

[0202] The route may include a flight route of aviation equipment or aerospace equipment, and the route may also include a water route of marine equipment. The site may be a place where aviation equipment stops, such as an airport; the site may also be a place where marine equipment stops, such as a port. The gateway station may be a gateway station preset by the satellite network for ground access to the Internet.

[0203] The route data may include the mobile object's identification, departure location, arrival location, equipment type, specifications, and service type.

[0204] For example, equipment types may include: aviation equipment, aerospace equipment or navigation equipment, etc. Furthermore, equipment types may be classified more finely, such as aviation equipment-aircraft or aviation equipment-drone, etc. Specification types may include: extra-large, large, medium, small and medium, small, etc. Service types may include: passenger transport, freight transport and detection, etc.

[0205] The identifier of the mobile object may be a name or a code (eg, an index in a database) of the mobile object, so that other data of the mobile object may be quickly retrieved from the database based on the identifier of the mobile object.

[0206] Optionally, the coordinates of the departure position can be expressed as {departure latitude (DepLat), departure longitude (DepLon)}, or the coordinates of the departure position can also be expressed as {departure latitude (DepLat), departure longitude (DepLon), departure altitude}. The coordinates of the arrival position can be expressed as {arrival latitude (ArrLat), arrival longitude (ArrLon)}, or the coordinates of the arrival position can also be expressed as {arrival latitude (ArrLat), arrival longitude (ArrLon), arrival altitude}.

[0207] The route data may be stored in a database in a table format. For example, the route data may be as shown in Table 2.

[0208] Table 2

[0209]

[0210] For example, in Table 2, the departure position of the mobile object L0001 is {latitude D1, longitude D1}, the arrival position is {latitude A1, longitude A1}, the mobile object L0001 is aviation equipment - aircraft, the specification is large, and the service type is passenger transport.

[0211] The data of a site may include the type, name, location and area identifier of the site. For example, the location of the site may be represented as {latitude (Lat), longitude (Lon), altitude (Elevation, El)}.

[0212] Exemplarily, the types of the site may include: extra large, large, medium, small and small, the name of the site may include the Chinese name and English name of the site, and the area identifier may be the name or index code of the area where the site is located. For example, the area identifier may be a code defined by the International Organization for Standardization (ISO).

[0213] In some examples, the site data may also include a Global Positioning System (GPS) identifier.

[0214] The data of each site may be stored in a database in a table format. For example, the data of each site may be as shown in Table 3.

[0215] Table 3

[0216]

[0217] For example, in Table 3, Airport 1 is a small airport, the location of Airport 1 is {latitude 1, longitude 1, altitude 1}, and Airport 1 is located in the A area.

[0218] S302: Perform feature extraction processing on multiple route data to obtain business feature data of multiple mobile objects.

[0219] In some embodiments, for any mobile object, the first characteristic data of the mobile object is extracted from the route data of the mobile object based on a preset field; based on the first characteristic data and a preset correspondence, the second characteristic data of the mobile object is determined; wherein the business characteristic data of the mobile object may include the first characteristic data and the second characteristic data.

[0220] The first characteristic data may include the identification and type of the mobile object, and the number of passengers or equipment carried by the mobile object.

[0221] Exemplarily, the preset fields may include: type, number of passengers, and number of devices.

[0222] The preset correspondence may include multiple types of mobile objects and second characteristic data corresponding to each type. It can be understood that technicians can collect network requirements of various types of mobile objects and, based on the network requirements of various types of mobile objects, pre-configure appropriate second characteristic data for each type of mobile objects to obtain the preset correspondence.

[0223] The second characteristic data may include network service type, network protocol type, single-machine bandwidth, round-trip time (RTT) threshold and packet loss rate, etc., wherein the single-machine bandwidth may include uplink bandwidth and downlink bandwidth, and the network service type may include the following: Internet service type, communication service type, monitoring service type and emergency service type.

[0224] Exemplarily, the network protocol type may be Transmission Control Protocol (TCP).

[0225] In some examples, the Internet service type refers to the satellite network providing Internet services to passengers or equipment carried on the mobile object; the communication service type refers to the satellite network being used to implement communication services between the mobile object and the site; the surveillance service type refers to the satellite network providing network communication services for some surveillance services to the mobile object, for example, the network communication service can be a remote sensing data backhaul service; the emergency service type refers to the satellite network providing network communication services for some emergency services to the mobile object.

[0226] In some embodiments, the preset correspondence relationship may be stored in advance in the traffic distribution determination device; or, the preset correspondence relationship may be stored in a database, and the traffic distribution determination device may request the database to obtain the preset correspondence relationship. The preset correspondence relationship may be stored in the form of a table, for example, the preset correspondence relationship may be as shown in Table 4.

[0227] Table 4

[0228]

[0229] In an example, the service characteristic data of the mobile object may be as shown in Table 5.

[0230] Table 5

[0231]

[0232] S303: Determine initial trajectory data of multiple mobile objects according to the multiple route data and the multiple station data.

[0233] The initial trajectory data may include the identification of the mobile object, the navigation duration and navigation distance of the mobile object, the identification of the departure site, the identification of the arrival site, the area identification of the departure location, the area identification of the arrival location, the coordinates of the departure location, the coordinates of the arrival location, and the coordinates of multiple sampling locations between the departure location and the arrival location.

[0234] The initial trajectory data may be stored in a table format. For example, the initial trajectory data may be as shown in Table 6.

[0235] Table 6

[0236]

[0237] It should be noted that the detailed process of determining the initial trajectory data will be described in Figure 6 The details are described in the embodiments and will not be described again here.

[0238] S304: Determine the wave positions corresponding to the multiple mobile objects and the data of the multiple gateways according to the initial trajectory data of the multiple mobile objects and the data of the multiple gateways.

[0239] It can be understood that the wave positions corresponding to the mobile object may include all the wave positions involved in the navigation process of the mobile object, and the signal gateway stations corresponding to the mobile object may include all the signal gateway stations involved in the navigation process of the mobile object.

[0240] In some embodiments, the wave position data may include an identifier of the moving object, a plurality of first prediction time periods, and a wave position identifier corresponding to the moving object in each first prediction time period.

[0241] The data of the wave position may be stored in a table form. For example, the data of the wave position may be as shown in Table 7.

[0242] Table 7

[0243]

[0244] In Table 7, T 出发 and T 到达 They represent the departure time and arrival time of L0001, T sample2 , T sample3 , T sample4 , T sample50 They respectively represent the 2nd, 3rd, 4th and 50th sampling moments of L0001 during its movement.

[0245] In some embodiments, the data of the gateway may include an identifier of the mobile object, at least one second prediction period, and an identifier of the gateway corresponding to the mobile object in each second prediction period.

[0246] The data of the gateway station may be stored in the form of a table. For example, the data of the gateway station may be as shown in Table 8.

[0247] Table 8

[0248]

[0249] In Table 8, T 出发 and T 到达 They represent the departure time and arrival time of L0001, T sample15 , Tsample16 , T sample35 , T sample36 They respectively represent the 15th, 16th, 35th and 36th sampling moments of L0001 during the movement process.

[0250] It should be noted that the specific implementation process of determining the wave position and the data of the signal gateway corresponding to each mobile object will be described in Fig. 9 The details are described in the examples.

[0251] S305: Determine the flow distribution information of each wave position and each gateway station in the target area according to the service characteristic data of the multiple mobile objects and the data of the wave positions and gateway stations corresponding to the multiple mobile objects.

[0252] In some embodiments, the traffic distribution information of each wave position can be determined in the following manner: based on the data of the wave positions corresponding to multiple mobile objects, the first mobile object corresponding to each wave position is determined among the multiple mobile objects, and the traffic distribution information of each wave position is determined based on the service characteristic data of the first mobile object and the data of the wave position corresponding to the first mobile object.

[0253] Optionally, the wave position identifier corresponding to each mobile object can be determined based on the data of the wave position corresponding to each mobile object; for any wave position, matching processing can be performed based on the wave position identifier of the wave position and the wave position identifiers corresponding to the multiple mobile objects, and the mobile object that successfully matches the wave position identifier of the wave position is determined as the first mobile object.

[0254] The traffic distribution information of the wave position may include: the wave position identifier, the identifier of the mobile object served by the wave position, and the service characteristic data. The traffic distribution information of the wave position may also include the service period during which the wave position provides services to the mobile object within the target period. The service period may include the service start time and the service end time. For example, in Table 7, L0001 needs the communication service provided by wave position 1, and the service period provided by wave position 1 is T 出发 ~T sample2 .

[0255] The flow distribution information of the wave position can be stored in a table form. For example, the flow distribution information of the wave position can be stored as a data table shown in Table 9.

[0256] Table 9

[0257]

[0258] Please refer to Table 5. The service characteristic data of the mobile object may include one or more of the mobile object's identification, type, number of passengers or devices carried by the mobile object, network service type (type), network protocol type (protocol), uplink bandwidth (bwFwd), downlink bandwidth (bwRev), RTT threshold or packet loss rate.

[0259] Optionally, a flow distribution diagram of the wave positions in the target area may be generated according to the flow distribution information of each wave position in the target area.

[0260] In some embodiments, a mapping distribution diagram of the number of wave positions and mobile objects in the target area may also be generated according to the first mobile object served by each wave position. Figure 4 This is a schematic diagram of a mapping distribution diagram of wave positions in a target area provided in an embodiment of the present application. Figure 4 The horizontal axis of the mapping distribution diagram of the wave position represents time, and the vertical axis represents the number of mobile objects served by the satellite network. Different line types represent different wave positions. For example, the solid line represents wave position 1 and the dotted line represents wave position 2. The number of mobile objects served by wave position 1 and wave position 2 at each time can be clearly seen in the mapping distribution diagram.

[0261] It should be noted that Figure 4 The mapping distribution diagram shown is illustrated by taking two wave positions in the target area as an example. In other embodiments, the number of wave positions in the target area can be any number, and the embodiment of the present application does not limit the number of wave positions in the target area. The wave positions in the target area can be a wave position set consisting of all wave positions covering the area, or a subset of the wave position set.

[0262] In some embodiments, a second mobile object corresponding to each gateway can be determined among the multiple mobile objects based on data of gateways corresponding to the multiple mobile objects, and traffic distribution information of each gateway can be determined based on service feature data of the second mobile object and data of the gateway corresponding to the second mobile object.

[0263] Optionally, the gateway identifier corresponding to each mobile object can be determined based on the data of the gateway corresponding to each mobile object; for any gateway, matching processing can be performed based on the gateway identifier of the gateway and the gateway identifiers corresponding to the multiple mobile objects, and the mobile object that successfully matches the gateway identifier of the gateway is determined as the second mobile object.

[0264] The traffic distribution information of the gateway station may include: the gateway station identifier, the identifier and service characteristic data of the mobile object served by the gateway station, and the service period during which the gateway station provides services for the mobile object within the target period. For example, in Table 8, L0001 needs gateway station 1 to provide communication services, and the service period during which gateway station 1 provides services is T出发 ~T sample15 .

[0265] The traffic distribution information of the gateway station may be stored in a table form. For example, the traffic distribution information of the gateway station may be stored as a data table shown in Table 10:

[0266] Table 10

[0267]

[0268] Optionally, a traffic distribution map of the gateways in the target area may be generated according to the traffic distribution information of each gateway in the target area.

[0269] In some embodiments, a mapping distribution diagram of the number of each gateway and the mobile objects in the target area may also be generated according to the second mobile objects accessed by each gateway. Figure 5 This is a schematic diagram of a mapping distribution diagram of gateway stations in a target area provided in an embodiment of the present application. Figure 5 The horizontal axis of the mapping distribution diagram of the gateway station represents time, and the vertical axis represents the number of mobile objects served by the satellite network. Different line types represent different gateways. For example, the solid line represents gateway 1, and the dotted line represents gateway 2. The number of mobile objects served by gateway 1 and gateway 2 at each time can be clearly seen in the mapping distribution diagram of the gateway station.

[0270] It should be noted that Figure 5 The mapping distribution diagram shown is illustrated by taking two gateways in the target area as an example. In other embodiments, the number of gateways in the target area can be any number, and the embodiment of the present application does not limit the number of gateways in the target area.

[0271] The traffic distribution determination method provided in the embodiment of the present application can conveniently and quickly determine the wave positions and gateway data corresponding to multiple mobile objects based on the spatiotemporal distribution characteristics of multiple route data, multiple site data, and multiple gateway data of multiple mobile objects in the target area, and accurately determine the traffic distribution information of each wave position and each gateway in the target area during the target time period in combination with the service feature data of the multiple mobile objects and the wave positions and gateway data corresponding to the multiple mobile objects, so that the satellite network enterprise can perform network optimization processing on the satellite network in the target area based on the traffic distribution information of each wave position and each gateway, so that the satellite network enterprise can better provide communication services for each mobile object through each wave position and each gateway.

[0272] The flow distribution determination method provided in the embodiment of the present application involves a process of determining initial trajectory data of multiple mobile objects based on multiple route data and multiple station data. Figure 6, which explains the process in detail.

[0273] Figure 6 The second flow chart of the method for determining the flow distribution provided in the embodiment of the present application is as follows. Figure 2 The flow distribution determination device in the embodiment is executed, or it can also be executed by the flow distribution determination means in the flow distribution determination device, and the flow distribution determination means can be implemented by software or by a combination of software and hardware.

[0274] See also Figure 6 , the method specifically comprises the following steps:

[0275] S601, matching processing is performed on data of multiple sites and data of multiple routes to obtain multiple groups of matching data corresponding to multiple mobile objects.

[0276] Each set of matching data may be used to indicate the departure point and arrival point of the corresponding route of each mobile object.

[0277] In some embodiments, S601 may specifically include the following steps (1) to (4):

[0278] (1) Determine the locations of multiple sites based on data from multiple sites.

[0279] The data of each station may include the location of each station, and the location of each station may be extracted from the data of each station, and the location of each station may be expressed as {latitude, longitude}. For example, from the data table shown in Table 3, it can be queried that the location of Airport 1 is {latitude 1, longitude 1}.

[0280] (2) Determine the departure and arrival locations of multiple moving objects based on multiple route data.

[0281] It can be understood that, for any one of the multiple objects, the route data of the mobile object may include the departure position and arrival position of the mobile object, and the departure position and arrival position of the mobile object can be extracted from the route data of the mobile object. The departure position of the mobile object can be expressed as {latitude Di, longitude Di}, and the arrival position can be expressed as {latitude Ai, longitude Ai}. For example, from the data table shown in Table 2, it can be queried that the departure position of L0001 is {latitude D1, longitude D1}, and the arrival position is {latitude A1, longitude A1}.

[0282] (3) According to the positions of the multiple sites, the departure positions and the arrival positions of the multiple mobile objects, the matching relationships between the multiple mobile objects and the multiple sites are determined.

[0283] In some embodiments, for any mobile object, a first deviation value between the position of each station and the starting position of the mobile object can be determined, as well as a second deviation value between the position of each station and the arrival position of the mobile object can be determined; the station with the smallest first deviation value is determined as the departure station of the mobile object, and the station with the smallest second deviation value is determined as the arrival station of the mobile object.

[0284] Optionally, when determining the deviation value between the location of each site and the departure location or arrival location of the mobile object, the longitude and latitude of the location of each site and the longitude and latitude of the departure location and arrival location of each mobile object can also be preprocessed to ensure fast and accurate matching of each site and each mobile object.

[0285] In one example, the preprocessing method may be percentile truncation processing. The matching relationship between each mobile object and the departure station or the arrival station may satisfy the following expression:

[0286]

[0287]

[0288] in, , Can be used to represent moving objects Latitude ( ) and longitude ( ) is used for percentile truncation. For example, if the moving object is an airplane, the percentile truncation expressions for the latitude and longitude of the airplane can be expressed as: ; , Respectively represent the site Latitude ( ) and longitude ( ) is used for percentile truncation. For example, if the station is an airport, the percentile truncation expressions for the latitude and longitude of the airport can be expressed as .

[0289] (4) Determine multiple sets of matching data based on the matching relationship, the data of multiple sites, and the data of multiple routes.

[0290] Each set of matching data may include an identifier of the mobile object, an identifier of a departure station and an identifier of an arrival station of the mobile object, a departure position, an arrival position, an area identifier of the departure position, and an area identifier of the arrival position.

[0291] Each set of matching data may be stored in the flow distribution determination device in the form of a data table. For example, a set of matching data may be stored with reference to the data table shown in Table 11.

[0292] Table 11

[0293]

[0294] S602: Determine the flight duration of multiple mobile objects according to multiple route data.

[0295] S603. Determine, based on the plurality of route data, a plurality of navigation positions corresponding to each of the plurality of mobile objects at a plurality of sampling moments, wherein the plurality of sampling moments are determined by a sampling period and the navigation duration of each mobile object.

[0296] In some embodiments, for any mobile object, the route data of the mobile object may include the navigation duration of the mobile object and the navigation position of the mobile object at each moment during the navigation process. The navigation duration can be extracted from the route data, and multiple navigation positions corresponding to multiple sampling moments can be extracted from the route data based on multiple sampling moments.

[0297] It should be noted that the sampling period needs to ensure that the navigation position of the mobile object within the coverage range of each corresponding wave position can be collected to determine the relationship between the mobile object and the corresponding wave positions. The navigation speed of the mobile object is v, and the shortest distance of the coverage range of each wave position involved in the route of the mobile object is s, then t=s / v, t is downsampled and rounded to get t1, then the value of the sampling period can be any time length less than t1. For example, the navigation speed is 700km / h, the wave position diameter (the shortest distance covered by the wave position) is 12km, and t is calculated to be 61.7 seconds. T is downsampled and rounded to get t1 as 61 seconds, and the sampling period can be 60 seconds.

[0298] The number of the plurality of sampling moments may be determined by rounding down. For example, if the flight duration is 6334 seconds and the sampling period is 60 seconds, the number of the plurality of sampling moments may be 105.

[0299] It should be noted that, in the case where the route data does not include the flight duration of the mobile object and the multiple navigation positions of the mobile object at multiple times, the flight duration of the mobile object and the multiple navigation positions of the mobile object will be determined in Figure 8 The details are described in the examples.

[0300] S604: Generate initial trajectory data of multiple mobile objects according to multiple route data, multiple sets of matching data, and multiple navigation positions corresponding to multiple mobile objects at multiple sampling times.

[0301] The initial trajectory data of each mobile object can be shown in Table 6. For any mobile object, the initial trajectory data of the mobile object can be generated in the following manner: determine the route data of the mobile object from a plurality of route data, and obtain the mobile object's identification, navigation time, navigation distance, departure position, arrival position, area identification of the departure position, and area identification of the arrival position according to the route data of the mobile object; determine the matching data of the mobile object from a plurality of sets of matching data, and determine the identification of the departure site and the identification of the arrival site corresponding to the mobile object in the matching data of the mobile object; generate the initial trajectory data of the mobile object according to the mobile object's identification, navigation time, navigation distance, area identification of the departure position, area identification of the arrival position, identification of the departure site, identification of the arrival site, departure position, arrival position, and a plurality of navigation positions corresponding to the mobile object at a plurality of sampling times.

[0302] The traffic distribution determination method provided in the embodiment of the present application can determine the initial trajectory data of multiple mobile objects based on multiple route data and data of multiple stations, so as to quickly determine the wave positions and signal gateway data corresponding to the multiple mobile objects based on the initial trajectory data of the multiple mobile objects.

[0303] In some embodiments, an initial trajectory diagram of the moving object may also be generated based on the initial trajectory data of the moving object. Figure 7 A schematic diagram of the initial trajectory of a moving object provided in an embodiment of the present application. Figure 7 The initial trajectory diagram can show the user the identification of the moving object, the identification of the departure site, the identification of the arrival site, the identification of multiple wave positions passed by the navigation trajectory, the departure position, the arrival position, and multiple navigation positions between the departure position and the arrival position.

[0304] The flow distribution determination method provided in the embodiment of the present application also involves a process of determining the navigation duration of a mobile object and multiple navigation positions of the mobile object. Figure 8 , taking any moving object as an example, the detailed process of determining the navigation duration of the moving object and multiple navigation positions of the moving object is described.

[0305] Figure 8 The third flow chart of the method for determining the flow distribution provided in the embodiment of the present application. The method can be Figure 2 The flow distribution determination device in the embodiment is executed, or it can also be executed by the flow distribution determination means in the flow distribution determination device, and the flow distribution determination means can be implemented by software or by a combination of software and hardware.

[0306] See also Figure 8 , the method specifically comprises the following steps:

[0307] S801. Determine a departure position and an arrival position of a mobile object according to the route data of the mobile object in a plurality of route data.

[0308] It should be noted that the specific execution process of S801 can refer to the specific execution process of step (2) of step S601, which will not be described in detail here.

[0309] S802: Determine the navigation distance of the mobile object according to the departure position and the arrival position.

[0310] In some embodiments, the navigation distance can be represented by a geodesic line, and the calculation process of the geodesic line satisfies the following expression:

[0311] ;

[0312] .

[0313] in, represents the calculated geodesic result, represents the calculation of geodesic lines, represents the starting position of the moving object, represents the arrival position of the moving object, Indicates the geodesic distance (unit: km).

[0314] S803: Determine the navigation speed of the moving object.

[0315] In some embodiments, the route data of the mobile object may include the navigation speed of the mobile object. For example, if the mobile object is an airplane, the navigation speed of the airplane may be 700 km / h or 0.2 km / s.

[0316] In some embodiments, the navigation speed may also be a preset navigation speed input by a tester into the flow distribution determination device, so as to simulate the initial trajectory data of the moving object under different navigation scenarios.

[0317] S804: Determine the sailing time according to the sailing speed and the sailing distance.

[0318] In some embodiments, the navigation duration may be calculated based on the geodesic distance and navigation speed obtained in step S803, and the calculation process may satisfy the following expression:

[0319]

[0320] in, Indicates the duration of the flight (in seconds). Indicates the navigation speed of the moving object (unit: km / s).

[0321] S805. Determine multiple sampling times according to the navigation duration and the sampling period.

[0322] In some embodiments, the number of the plurality of sampling moments may satisfy the following expression:

[0323]

[0324] in, It can represent the number of multiple sampling moments, which can be obtained by calculating the flight time. The ratio of the sampling period is rounded down to the nearest integer.

[0325] S806: Determine the azimuth between the departure position and the arrival position.

[0326] In some embodiments, the calculation of the azimuth angle may satisfy the following expression:

[0327]

[0328] in, It can represent the azimuth of a moving object from its starting position to its arrival position. It can be used to calculate the azimuth of the departure position and the arrival position.

[0329] S807: Determine multiple navigation positions of the mobile object according to the azimuth and the navigation distance.

[0330] In some embodiments, the multiple navigation positions of the mobile object may satisfy the following expression:

[0331]

[0332] in, Indicates that the moving object is on the geodesic Up along the azimuth Direction, collection The set of navigation positions at sampling moments; Indicates sampling processing.

[0333] The traffic distribution determination method provided in the embodiment of the present application can determine the departure position and arrival position of the mobile object based on the route data of the mobile object when there is no navigation distance and navigation position at each time in the route data of the mobile object, and further determine the navigation distance and azimuth of the mobile object based on the departure position and arrival position, so as to simulate the navigation position of the mobile object at different sampling times through the azimuth and navigation distance, and then obtain the initial trajectory data of the mobile object, so as to quickly determine the wave position and gateway data corresponding to the mobile object based on the initial trajectory data of the mobile object.

[0334] The above method embodiment also involves the following process: for any mobile object, the wave position and the data of the signal gateway corresponding to the mobile object can be determined according to the initial trajectory data of the mobile object. Fig. 9 , which explains the process in detail.

[0335] Fig. 9 The fourth flow chart of the method for determining the flow distribution provided in the embodiment of the present application. The method can be Figure 2 The flow distribution determination device in the embodiment is executed, or it can also be executed by the flow distribution determination means in the flow distribution determination device, and the flow distribution determination means can be implemented by software or by a combination of software and hardware.

[0336] See also Fig. 9 , the method specifically comprises the following steps:

[0337] S901. Determine target trajectory data of the moving object within a target period of time according to initial trajectory data of the moving object.

[0338] In some embodiments, the target trajectory data of the mobile object can be determined in the following manner: determine the navigation duration of the mobile object; pre-process the initial trajectory data of the mobile object according to the navigation duration and the duration of the target time period to obtain the target trajectory data of the mobile object.

[0339] It should be noted that the specific execution process of "determining the flight time of the mobile object" in this step can refer to the specific process of "determining the flight time of the mobile object" in the above embodiment, and will not be repeated here.

[0340] In some embodiments, multiple flight periods of multiple mobile objects in the target area may be obtained, and the target period may be determined based on the multiple flight periods. For example, the target period may be an average value, a median value, or a maximum value of the multiple periods.

[0341] The target period can be a period set by the user. For example, if the user needs to predict the traffic distribution information of each wave position and each gateway station from 9:00 to 12:00 every day, the target period can be set to 9:00~12:00.

[0342] In some embodiments, if the flight time of the mobile object is less than the duration of the target time period, the initial trajectory data of the mobile object can be padded; if the flight time of the mobile object is greater than the duration of the target time period, the initial trajectory data of the mobile object can be truncated.

[0343] Fig.10 This is a schematic diagram of the target trajectory data of a moving object provided in an embodiment of the present application. Fig.10, the flight duration of mobile object 1, mobile object 2 and mobile object 4 is less than the duration of the target period, and the initial trajectory data of these three mobile objects need to be padded to obtain the target trajectory data of these three mobile objects. The target trajectory data of these three mobile objects include the initial trajectory data and the padded trajectory data. The flight duration of mobile object 3 may be greater than the duration of the target period. Based on the duration of the target period, the initial trajectory data within the flight duration of mobile object 3 can be randomly truncated to obtain the target trajectory data of mobile object 3.

[0344] In some embodiments, in order to simulate the randomness of the distribution of moving objects, when the initial trajectory data of each moving object is padded, random interpolation padding can be performed before and after the flight time of each moving object, and the inserted value can be a preset blank value.

[0345] See also Fig.10 , the number distribution of mobile objects in the target area during the target period has the characteristics of temporal and spatial distribution. In the T1 and T7 periods, mobile object 3 is navigating in the target area; in the T2 period, mobile object 3 and mobile object 2 are navigating in the target area; in the T3 period, mobile object 2~mobile object 4 are navigating in the target area; in the T4 period, mobile object 1~mobile object 4 are navigating in the target area; in the T5 period, mobile object 2~mobile object 4 are navigating in the target area; in the T6 period, mobile object 3 and mobile object 4 are navigating in the target area.

[0346] S902: Determine multiple predicted positions of the mobile object corresponding to multiple predicted moments within a target time period based on the target trajectory data of the mobile object.

[0347] Optionally, a plurality of prediction moments may be determined based on information input by the user, and a plurality of prediction positions corresponding to the plurality of prediction moments may be determined according to target trajectory data of the mobile object.

[0348] S903: Determine data of the wave position corresponding to the moving object according to the multiple predicted positions.

[0349] In some embodiments, the step may further include the following steps S9031-S9034:

[0350] S9031. Determine the wave position identifier corresponding to each predicted position, and obtain multiple wave position identifiers.

[0351] In some embodiments, when performing geospatial data analysis, a multi-level geospatial discrete grid index system can be used to divide the geographic space into multiple appropriate regions, and a satellite (e.g., a low-orbit satellite) can determine each region as the coverage area of ​​a beam (Spot Beam) of the satellite, which can be a phased array beam.

[0352] For example, the geospatial discrete grid indexing system can be Uber's open source H3, which can divide the global geographical areas into 0-15 levels, and the granularity of the regions (cells) divided at each level is different. At the 4th level, H3 divides the world into 288,122 regions, and each region at the 4th level can be determined as the coverage area of ​​a satellite beam.

[0353] Fig.11 A schematic diagram of geographical area division provided for the embodiment of this application. Fig.11 , the global geographical area can be divided into multiple small areas (each hexagonal area in the figure), each area can be regarded as a wave position for identification, and the mapping relationship between the location information of each area and the wave position identification can be stored in the database. In actual use, the wave position identification corresponding to the area can be quickly determined based on the mapping relationship and the location information of each area.

[0354] Fig.12 For a schematic diagram of the wave position coverage area provided in the embodiment of this application, please refer to Fig.12 , the satellite can transmit multiple beams, and the projection area of ​​each beam on the earth (or called "coverage area") is a wave position, which can be Fig.12 A hexagonal area shown can determine the mapping relationship between the wave position and the location information of the area, and store the mapping relationship in a database.

[0355] The mapping relationship may include location information of multiple areas and a wave position identifier corresponding to each location information.

[0356] For any predicted position, matching processing can be performed based on the predicted position and the position information of multiple areas in the mapping relationship, and the wave position identifier corresponding to the position information of the successfully matched area is determined as the wave position identifier corresponding to the predicted position.

[0357] Fig.13 This is a schematic diagram of the distribution of moving objects in the target area at different times provided by the embodiment of the present application. Fig.13 ,From time 0 to time 5, each mobile object at different airports navigates along a preset route, and its navigation trajectory may involve multiple wave positions during the navigation process.

[0358] S9032. Divide the multiple predicted positions into multiple first position sets according to the multiple wave position identifiers.

[0359] The wave position identifiers of the predicted positions in the first position set are the same.

[0360] For example, the wave position identifiers corresponding to multiple predicted positions may be as shown in Table 12:

[0361] Table 12

[0362]

[0363] Please refer to Table 12. The wave position identifiers corresponding to the prediction position 1 and the prediction position 2 are the same. The prediction position 1 and the prediction position 2 can be divided into the first position set 1; similarly, the prediction position 3 and the prediction position 4 can also be divided into the first position set 2.

[0364] S9033. Determine first prediction time periods corresponding to multiple first position sets within the target time period, and determine wave position data according to the first prediction time periods corresponding to the multiple first position sets and wave position identifiers.

[0365] For any first position set, the minimum and maximum values ​​of the prediction time can be determined in the first position set, and the first prediction period can be determined based on the maximum and minimum values. For example, referring to Table 12, the first prediction period corresponding to the first position set 1 is t1~t2.

[0366] The data of the wave position may include the identifier of the mobile object, the first prediction time period corresponding to each first position set, and the wave position identifier. For example, the data of the wave position may be as shown in Table 7.

[0367] The traffic distribution determination method provided in the embodiment of the present application can determine the wave position identifier corresponding to each mobile object when it moves in the target area based on the mapping relationship between the coverage area of ​​each wave position of the satellite and the geographical areas divided on the earth, and then determine the mapping relationship between each mobile object and each wave position of the satellite during the movement, and determine the traffic distribution information corresponding to each wave position based on the mapping relationship, so as to facilitate the subsequent reasonable allocation of network resources for each wave position.

[0368] S904: Determine the data of the gateway corresponding to the mobile object according to the multiple predicted positions and the data of the multiple gateways.

[0369] In some embodiments, data of gateways in each region may be collected in advance and stored in a database in a classified manner. In actual use, the traffic distribution determination device may send a query request to the database to query the database for data of multiple gateways in the target region.

[0370] The data of the gateway station may include the identification, location, number of antenna units (Num Antennas), minimum elevation angle (Min Elevation) of the gateway station, and the regional identification of the area where the gateway station is located. The identification of the gateway station may include the index and name of the gateway station.

[0371] For example, data of multiple gateways may be stored in a database in the form of a data table. For example, the data table may be as shown in Table 13.

[0372] Table 13

[0373]

[0374] It should be noted that the specific process of determining the target gateway data corresponding to the mobile object will be described in Fig.14 The details are described in the examples.

[0375] The traffic distribution determination method provided in the embodiment of the present application considers the access network side and the feeding side of the satellite network, and based on the spatiotemporal distribution characteristics of multiple route data of multiple mobile objects, data of multiple sites, and data of multiple gateways in the target area, conveniently and quickly determines the wave positions and gateway data corresponding to the multiple mobile objects, and combines the service feature data of the multiple mobile objects and the wave positions and gateway data corresponding to the multiple mobile objects to accurately determine the traffic distribution information of each wave position and each gateway in the target area within the target time period, so that the satellite network enterprise can optimize the satellite network in the target area based on the traffic distribution information of each wave position and each gateway, so that each wave position and each gateway can better provide communication services for each mobile object.

[0376] Fig.14 Flow chart 5 of the method for determining flow distribution provided in the embodiment of the present application. The method can be Figure 2 The flow distribution determination device in the embodiment is executed, or it can also be executed by the flow distribution determination device in the flow distribution determination device, and the flow distribution determination device can be implemented by software or by a combination of software and hardware. Fig.14 , the method may specifically include the following steps:

[0377] S1401. Determine locations of multiple gateways based on data of multiple gateways.

[0378] The data of the gateway station may include the location of the gateway station. The location of the gateway station may be extracted from the data of the gateway station. The location of the gateway station may be expressed as {latitude, longitude}.

[0379] S1402: Determine, according to the multiple predicted positions and the positions of the multiple gateways, candidate gateways corresponding to the multiple predicted positions from the multiple gateways.

[0380] In some embodiments, for any predicted location, the candidate gateway station corresponding to the predicted location can be determined in the following manner: according to the predicted location and the locations of multiple gateway stations, the distance between the predicted location and each gateway station is determined to obtain multiple distances; the shortest distance is determined among the multiple distances; and the gateway station corresponding to the shortest distance is determined as the candidate gateway station corresponding to the predicted location.

[0381] In some examples, the distance between the predicted location and each gateway may be a geodesic distance between the predicted location and each gateway.

[0382] The following expression can be used to determine the candidate signal gateway corresponding to each predicted position:

[0383]

[0384]

[0385] For each predicted location, perform the following steps:

[0386]

[0387]

[0388] in, It can represent the target trajectory data of the selected moving object i ; Can represent predicted position; It can indicate the preset duration, the default value is 60 seconds; It can be expressed as a preset duration The target trajectory data of the mobile object i is sampled multiple times to obtain multiple predicted positions; A gateway list consisting of data from multiple gateways can be represented; It can indicate the ID of the selected gateway. Can represent geodesic distance; Can represent any predicted position; It may mean that among the multiple gateways in the gateway list, a gateway having the shortest geodesic distance to the predicted position is selected as a candidate gateway corresponding to the predicted position; Indicates the matching process between the mobile object i and the gateway; Indicates the prediction time.

[0389] In some embodiments, data of the corresponding gateway of each mobile object within the target time period can be stored. The data of the gateway can include the predicted time, predicted position, the identifier of the mobile object, the identifier of the gateway to be selected, and the distance between the predicted position of the mobile object at the current predicted time and the gateway to be selected.

[0390] The data of the gateway may be stored in the form of a data table. For example, the data table may be as shown in Table 14.

[0391] Table 14

[0392]

[0393] S1403: Divide the multiple predicted positions into at least one second position set according to the candidate gateways corresponding to the multiple predicted positions.

[0394] The candidate gateways corresponding to the predicted positions in the second position set are the same. For example, in Table 14, the candidate gateways corresponding to the predicted positions 1 and 2 are both gateway 1, so the predicted positions 1 and 2 can be divided into a second position set.

[0395] It should be noted that at least one means one or more, and a plurality includes 2 or more.

[0396] It is understandable that, within the target time period, a certain mobile object may be matched to multiple different gateways, which may involve a gateway switching process.

[0397] Fig.15 A schematic diagram of a switching process of a gateway provided in an embodiment of the present application. Fig.15 There are 5 gateways in the target area, namely, gateway 1~gateway 5. During the process of the mobile object navigating from the departure position to the arrival position, during the period T1~T5, the candidate gateways corresponding to the mobile object are gateway 1, gateway 2, gateway 3, gateway 4 and gateway 5 respectively. The mobile object experiences 5 gateway switching during the navigation process. The mobile object is connected to gateway 1, gateway 2, gateway 3, gateway 4 and gateway 5 in sequence.

[0398] In some embodiments, the method may also split the traffic of each mobile object at the gateway station in the target time period into multiple sub-traffic according to the number of switched gateway stations. Fig.15 The traffic of the mobile object at the gateway in the target time period can be split into sub-traffic corresponding to five gateways.

[0399] In some embodiments, the splitting process of the traffic of the gateway station can be implemented according to the following expression:

[0400]

[0401]

[0402]

[0403]

[0404]

[0405] in, Represented as mobile objects and gateways a list of at least one second position set determined by a mapping relationship between; for The first, second, and nth second position sets in the For each second position set in the , is determined as the time when the mobile object starts to access the gateway corresponding to the second location set; the end time of the second prediction period corresponding to the second location set can be , determined as the time when the mobile object is disconnected from the gateway corresponding to the second position set; The traffic distribution information of the gateway of the mobile object may include the identifier of the mobile object, the identifier of the gateway, and the time when the mobile object starts to access the gateway. The time when the mobile object disconnects from the gateway .

[0406] S1404: Determine a second predicted time period corresponding to at least one second location set within the target time period, and determine data of a gateway according to the second predicted time period corresponding to the at least one second location set and the gateway to be selected.

[0407] For any second position set, the minimum and maximum values ​​of the prediction time can be determined in the second position set, and the second prediction period can be determined based on the maximum and minimum values. For example, referring to Table 14, the second prediction period corresponding to the second position set is t1~t2.

[0408] The data of the gateway station may include an identifier of the mobile object, at least one second prediction period, and an identifier of the gateway station corresponding to the mobile object in each second prediction period. For example, the data of the gateway station may be as shown in Table 8.

[0409] The traffic distribution determination method provided in the embodiment of the present application can determine the traffic distribution information of each gateway based on the data of the gateways of multiple mobile objects.

[0410] In some embodiments, the calculation process of the traffic distribution information of each gateway station may satisfy the following expression:

[0411]

[0412]

[0413]

[0414]

[0415]

[0416]

[0417] in, Indicates the gateway Matching with moving objects The obtained gateway station list; It can be expressed as The first, second, and nth gateways in ; Can be represented as a time list The first, second, and nth moments in ; is the number of mobile objects accessing each gateway after traversing all the time periods of the gateway; Indicates that at the target time The number distribution information of mobile objects accessing the gateway; Indicates that at the target time Traffic distribution information of mobile objects accessing the gateway.

[0418] In some embodiments, the traffic of the access mobile objects can be calculated by multiplying the number of the access mobile objects by the second characteristic data (eg, bandwidth) of each mobile object.

[0419] The traffic distribution determination method provided in the embodiment of the present application can be considered from the perspective of the satellite network feeding side. By determining the mapping relationship between multiple mobile objects and the gateway station in the target area, the traffic distribution information of each gateway station in the target area during the target time period can be accurately determined, so that the satellite network enterprise can reasonably allocate the network resources of the satellite network to each gateway station based on the traffic distribution information.

[0420] Fig.16 This is a flow chart of the flow distribution determination method provided in the embodiment of the present application. Fig.16 , the method specifically comprises the following steps:

[0421] S1601. The first device obtains multiple route data of multiple mobile objects, data of multiple sites, and data of multiple gateways in a target area.

[0422] S1602: The first device performs feature extraction processing on a plurality of route data to obtain service feature data of a plurality of mobile objects.

[0423] S1603: The first device determines initial trajectory data of multiple mobile objects based on multiple route data and multiple site data.

[0424] S1604: The first device determines the wave positions corresponding to the multiple mobile objects and the data of the multiple gateways according to the initial trajectory data of the multiple mobile objects and the data of the multiple gateways.

[0425] S1605: The first device determines flow distribution information of each wavelength and each gateway in the target area according to the service characteristic data of the multiple mobile objects and the wavelengths and gateway data corresponding to the multiple mobile objects.

[0426] It should be noted that the specific execution process of steps S1601 to S1605 can refer to the specific execution process of steps S301 to S305, which will not be repeated here.

[0427] In some embodiments, the first device may also perform network optimization processing on the satellite network in the target area according to the traffic distribution information of each wave position and each gateway station.

[0428] In some embodiments, the first device performs network optimization processing on the satellite network in the target area, which may include: performing resource optimization processing on the network resources of the satellite network according to the traffic distribution information of each wave position and each gateway station; and / or performing route optimization processing on the routing information of the satellite network according to the traffic distribution information of each wave position and each gateway station; the network optimization processing includes resource optimization processing and route optimization processing.

[0429] In some embodiments, the first device may determine the access network resource allocation strategy of the satellite network based on the traffic distribution information of each wave position, and optimize the access network resources of the satellite network based on the access network resource allocation strategy; may determine the gateway station feeding resource allocation strategy of the satellite network based on the traffic distribution information of each gateway station, and optimize the gateway station feeding resources of the satellite network based on the gateway station feeding resource allocation strategy.

[0430] The routing information may include a routing protocol and / or algorithm. In some embodiments, the first device may determine a routing protocol and / or algorithm on the bearer network side of the satellite network based on the traffic distribution information of each wave position and each gateway station, so as to perform routing optimization processing on the bearer network through the routing protocol and / or algorithm.

[0431] S1606: The first device sends traffic distribution information of each wavelength and each gateway in the target area to the second device.

[0432] The flow distribution information of each wave position and each gateway station can be used by the second device to perform network optimization processing on the satellite network in the target area.

[0433] In some embodiments, the first device and the second device may be used to perform network optimization processing on a satellite network. The first device and / or the second device may perform satellite communication with a satellite communication terminal, and the first device and / or the second device may be located in space or on the ground. The first device and / or the second device may be referred to as a space base station, a satellite-borne base station, a satellite, a satellite communication node, a satellite communication module, or a base station, etc., and the first device and / or the second device may also be referred to as a management device.

[0434] S1607: The second device performs network optimization processing on the satellite network in the target area according to the flow distribution information of each wave position and each gateway station.

[0435] It should be noted that the second device may perform network optimization processing on the satellite network in the target area with reference to the specific execution process of the first device performing network optimization processing on the satellite network in the target area in step S1605, and perform network optimization processing on the satellite network in the target area.

[0436] The traffic distribution determination method provided in the embodiment of the present application can communicate and interact with the second device through the first device, so that the second device can obtain the traffic distribution information of each wave position and each gateway station in the target area through the first device, and based on the traffic distribution information of each wave position and each gateway station, perform network optimization processing on the satellite network in the target area to optimize the network resources and routing information of each wave position and each gateway station in the target area, so as to provide better communication services for each satellite communication terminal served by the satellite network in the target area.

[0437] Fig.17 This is one of the structural schematic diagrams of the flow distribution determination device provided in the embodiment of the present application. The flow distribution determination device can be applied to the first device in the above embodiment, see Fig.17 , the flow distribution determining device 1700 may include:

[0438] The transceiver module 1701 is used to obtain multiple route data of multiple mobile objects, data of multiple sites and data of multiple gateways in the target area;

[0439] The processing module 1702 is used to perform feature extraction processing on the plurality of route data to obtain service feature data of the plurality of mobile objects;

[0440] The processing module 1702 is further configured to determine initial trajectory data of the plurality of mobile objects based on the plurality of route data and the plurality of station data;

[0441] The processing module 1702 is further configured to determine the wave positions and gateway data corresponding to the multiple mobile objects according to the initial trajectory data of the multiple mobile objects and the data of the multiple gateways;

[0442] The processing module 1702 is further configured to determine the flow distribution information of each wave position and each gateway station in the target area according to the service characteristic data of the multiple mobile objects and the wave positions and gateway station data corresponding to the multiple mobile objects.

[0443] In a possible implementation, the processing module 1702 is specifically configured to:

[0444] Performing matching processing on the data of the plurality of sites and the data of the plurality of routes to obtain a plurality of groups of matching data corresponding to the plurality of mobile objects;

[0445] Determining the flight duration of the plurality of mobile objects according to the plurality of route data;

[0446] Determine, according to the plurality of route data, a plurality of navigation positions corresponding to each of the plurality of mobile objects at a plurality of sampling moments, wherein the plurality of sampling moments are determined by a sampling period and a navigation duration of each mobile object;

[0447] Initial trajectory data of the plurality of mobile objects are generated according to the plurality of route data, the plurality of sets of matching data, and the plurality of navigation positions corresponding to the plurality of mobile objects at a plurality of sampling moments.

[0448] In a possible implementation, for any moving object, the processing module 1702 is further configured to:

[0449] Determining a departure position and an arrival position of the mobile object according to the route data of the mobile object in the plurality of route data;

[0450] Determining the navigation distance of the mobile object according to the departure position and the arrival position;

[0451] determining a navigation speed of the mobile object;

[0452] The sailing duration is determined according to the sailing speed and the sailing distance.

[0453] In a possible implementation, the processing module 1702 is further configured to:

[0454] determining an azimuth between the departure location and the arrival location;

[0455] The multiple navigation positions of the mobile object are determined according to the azimuth and the navigation distance.

[0456] In a possible implementation, the processing module 1702 is further configured to:

[0457] Determining locations of the plurality of sites based on the data of the plurality of sites;

[0458] Determining departure locations and arrival locations of the plurality of mobile objects based on the plurality of route data;

[0459] Determining matching relationships between the multiple mobile objects and the multiple sites according to the locations of the multiple sites, the departure locations and the arrival locations of the multiple mobile objects;

[0460] The multiple groups of matching data are determined according to the matching relationship, the data of the multiple sites and the multiple route data.

[0461] In a possible implementation, for any moving object, the processing module 1702 is further configured to:

[0462] Determining target trajectory data of the moving object within a target time period according to the initial trajectory data of the moving object;

[0463] Determining, based on the target trajectory data of the mobile object, a plurality of predicted positions of the mobile object corresponding to a plurality of predicted moments within a target time period;

[0464] Determining data of wave positions corresponding to the mobile object according to the multiple predicted positions;

[0465] The data of the gateway corresponding to the mobile object is determined according to the multiple predicted positions and the data of the multiple gateways.

[0466] In a possible implementation, the processing module 1702 is further configured to:

[0467] Determine the wave position identifier corresponding to each predicted position, and obtain multiple wave position identifiers;

[0468] According to the multiple wave position identifiers, the multiple predicted positions are divided into multiple first position sets, and the wave position identifiers of the predicted positions in the first position sets are the same;

[0469] A first prediction time period corresponding to the plurality of first position sets is determined within the target time period, and data of the wave position is determined according to the first prediction time period corresponding to the plurality of first position sets and the wave position identifier.

[0470] In a possible implementation, the processing module 1702 is further configured to:

[0471] Determining locations of the plurality of gateways based on the data of the plurality of gateways;

[0472] Determining, according to the multiple predicted positions and the positions of the multiple gateways, candidate gateways corresponding to the multiple predicted positions from among the multiple gateways;

[0473] According to the candidate gateways corresponding to the multiple predicted positions, the multiple predicted positions are divided into at least one second position set, wherein the candidate gateways corresponding to the predicted positions in the second position set are the same;

[0474] A second predicted time period corresponding to the at least one second location set is determined within the target time period, and data of the gateway is determined according to the second predicted time period corresponding to the at least one second location set and the to-be-selected gateway.

[0475] In a possible implementation, for any predicted position, the processing module 1702 is further configured to:

[0476] Determine, according to the predicted position and the positions of the plurality of gateways, a distance between the predicted position and each gateway to obtain a plurality of distances;

[0477] determining a shortest distance among the plurality of distances;

[0478] The gateway corresponding to the shortest distance is determined as the candidate gateway corresponding to the predicted position.

[0479] In a possible implementation, the processing module 1702 is further configured to:

[0480] Determining the flight duration of the mobile object;

[0481] The initial trajectory data of the mobile object is preprocessed according to the navigation duration and the duration of the target time period to obtain the target trajectory data of the mobile object.

[0482] In a possible implementation, the processing module 1702 is further configured to:

[0483] Determine, according to the data of the wave positions corresponding to the multiple mobile objects, a first mobile object corresponding to each wave position among the multiple mobile objects, and determine, according to the service characteristic data of the first mobile object and the data of the wave position corresponding to the first mobile object, traffic distribution information of each wave position;

[0484] According to the data of the gateways corresponding to the multiple mobile objects, a second mobile object corresponding to each gateway is determined among the multiple mobile objects, and according to the service characteristic data of the second mobile object and the data of the gateway corresponding to the second mobile object, traffic distribution information of each gateway is determined.

[0485] In a possible implementation, the processing module 1702 is further configured to:

[0486] According to the flow distribution information of each wave position and each gateway station, network optimization processing is performed on the satellite network in the target area.

[0487] In a possible implementation, the transceiver module 1701 is further configured to:

[0488] Sending the traffic distribution information of each wavelength and each gateway to the second device;

[0489] The flow distribution information of each wave position and each gateway station is used by the second device to perform network optimization processing on the satellite network in the target area.

[0490] In a possible implementation manner, performing network optimization processing on the satellite network in the target area includes:

[0491] performing resource optimization processing on the network resources of the satellite network according to the flow distribution information of each wave position and each gateway station; and / or,

[0492] According to the flow distribution information of each wave position and each gateway station, routing optimization processing is performed on the routing information of the satellite network;

[0493] The network optimization process includes the resource optimization process and the route optimization process.

[0494] The flow distribution determination device 1700 provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0495] Fig.18 This is a second structural diagram of the flow distribution determination device provided in the embodiment of the present application. The flow distribution determination device can be applied to the second device in the above embodiment, see Fig.18 , the flow distribution determining device 1800 may include:

[0496] The transceiver module 1801 is used to receive the traffic distribution information of each wavelength and each gateway in the target area sent by the first device;

[0497] The flow distribution information of each wave position and each gateway station is used to perform network optimization processing on the satellite network in the target area.

[0498] In a possible implementation manner, performing network optimization processing on the satellite network in the target area includes:

[0499] performing resource optimization processing on the network resources of the satellite network according to the flow distribution information of each wave position and each gateway station; and / or,

[0500] According to the flow distribution information of each wave position and each gateway station, routing optimization processing is performed on the routing information of the satellite network;

[0501] The network optimization process includes the resource optimization process and the route optimization process.

[0502] The flow distribution determination device 1800 provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0503] Fig.19 This is a schematic diagram of the structure of the flow distribution determination device provided in the embodiment of the present application. Fig.19 The traffic distribution determination device 1900 is, for example, the first device or the second device in the above embodiment, and the traffic distribution determination device 1900 may include: a memory 1901 and a processor 1902. Exemplarily, the memory 1901 and the processor 1902 are interconnected via a bus 1903.

[0504] The memory 1901 is used to store program instructions;

[0505] The processor 1902 is used to execute the program instructions stored in the memory, so as to enable the traffic distribution determination device 1900 to perform the method shown in the above method embodiment.

[0506] It is understandable that the flow distribution determination device 1900 may include at least one processor and a memory connected to the at least one processor in communication. The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method shown in the above embodiment.

[0507] The flow distribution determination device provided in the embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0508] An embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the traffic distribution determination method implemented by the first device in the above embodiment.

[0509] An embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the traffic distribution determination method implemented by the second device in the above embodiment.

[0510] An embodiment of the present application may also provide a computer program product, including a computer program, which, when executed by a processor, can implement the traffic distribution determination method implemented by the first device in the above embodiment.

[0511] An embodiment of the present application may also provide a computer program product, including a computer program, which, when executed by a processor, can implement the traffic distribution determination method implemented by the second device in the above embodiment.

[0512] All or part of the steps of the above-mentioned method embodiments can be completed by hardware related to program instructions. The above-mentioned program can be stored in a readable memory. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the above-mentioned memory (storage medium) includes: read-only memory (ROM), random access memory (RAM), flash memory, hard disk, solid state drive, magnetic tape, floppy disk, optical disc and any combination thereof.

[0513] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processing unit of the computer or other programmable data processing device generate a device for implementing the functions specified in one process or multiple processes in the flowchart and / or one box or multiple boxes in the block diagram.

[0514] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0515] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0516] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0517] In the present application, the term "include" and its variations may refer to non-restrictive inclusion; the term "or" and its variations may refer to "and / or". The terms "first", "second", etc. in the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. In the present application, "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previously associated objects are in an "or" relationship.

Claims

1. A method for determining flow distribution, characterized in that: Applied to a first device, the method includes: Acquire multiple route data of multiple mobile objects in the target area, data of multiple sites, and data of multiple gateways; Performing feature extraction processing on the plurality of route data to obtain service feature data of the plurality of mobile objects; Determining initial trajectory data of the plurality of mobile objects according to the plurality of route data and the data of the plurality of sites; Determine the wave positions and the data of the gateways corresponding to the multiple mobile objects according to the initial trajectory data of the multiple mobile objects and the data of the multiple gateways; The flow distribution information of each wave position and each gateway station in the target area is determined according to the service characteristic data of the multiple mobile objects and the data of the wave positions and gateway stations corresponding to the multiple mobile objects.

2. The method according to claim 1, characterized in that Determining initial trajectory data of the plurality of mobile objects according to the plurality of route data and the plurality of station data includes: Performing matching processing on the data of the plurality of sites and the data of the plurality of routes to obtain a plurality of groups of matching data corresponding to the plurality of mobile objects; Determining the flight duration of the plurality of mobile objects according to the plurality of route data; Determine, according to the plurality of route data, a plurality of navigation positions corresponding to each of the plurality of mobile objects at a plurality of sampling moments, wherein the plurality of sampling moments are determined by a sampling period and a navigation duration of each mobile object; Initial trajectory data of the plurality of mobile objects are generated according to the plurality of route data, the plurality of sets of matching data, and the plurality of navigation positions corresponding to the plurality of mobile objects at a plurality of sampling moments.

3. The method according to claim 2, characterized in that For any mobile object, determining the flight time of the mobile object according to the plurality of route data includes: Determining a departure position and an arrival position of the mobile object according to the route data of the mobile object in the plurality of route data; Determining the navigation distance of the mobile object according to the departure position and the arrival position; determining a navigation speed of the mobile object; The sailing duration is determined according to the sailing speed and the sailing distance.

4. The method according to claim 3, characterized in that Determining a plurality of navigation positions corresponding to the mobile object at a plurality of sampling moments comprises: determining an azimuth between the departure location and the arrival location; The multiple navigation positions of the mobile object are determined according to the azimuth and the navigation distance.

5. The method according to any one of claims 2 to 4, characterized in that: Matching the data of the plurality of sites with the plurality of route data to obtain a plurality of groups of matching data corresponding to the plurality of mobile objects includes: Determining locations of the plurality of sites based on the data of the plurality of sites; Determining departure locations and arrival locations of the plurality of mobile objects based on the plurality of route data; Determining matching relationships between the multiple mobile objects and the multiple sites according to the locations of the multiple sites, the departure locations and the arrival locations of the multiple mobile objects; The multiple groups of matching data are determined according to the matching relationship, the data of the multiple sites and the multiple route data.

6. The method according to any one of claims 1 to 4, characterized in that: For any mobile object, determining the wave position and the data of the gateway corresponding to the mobile object according to the initial trajectory data of the mobile object and the data of the plurality of gateways includes: Determining target trajectory data of the moving object within a target time period according to the initial trajectory data of the moving object; Determining, based on the target trajectory data of the mobile object, a plurality of predicted positions of the mobile object corresponding to a plurality of predicted moments within a target time period; Determining data of wave positions corresponding to the mobile object according to the multiple predicted positions; The data of the gateway corresponding to the mobile object is determined according to the multiple predicted positions and the data of the multiple gateways.

7. The method according to claim 6, characterized in that Determining data of wave positions corresponding to the mobile object according to the multiple predicted positions includes: Determine the wave position identifier corresponding to each predicted position, and obtain multiple wave position identifiers; According to the multiple wave position identifiers, the multiple predicted positions are divided into multiple first position sets, and the wave position identifiers of the predicted positions in the first position sets are the same; A first prediction time period corresponding to the plurality of first position sets is determined within the target time period, and data of the wave position is determined according to the first prediction time period corresponding to the plurality of first position sets and the wave position identifier.

8. The method according to claim 6, characterized in that Determining the data of the gateway corresponding to the mobile object according to the multiple predicted positions and the data of the multiple gateways includes: Determining locations of the plurality of gateways based on the data of the plurality of gateways; Determining, according to the multiple predicted positions and the positions of the multiple gateways, candidate gateways corresponding to the multiple predicted positions from among the multiple gateways; According to the candidate gateways corresponding to the multiple predicted positions, the multiple predicted positions are divided into at least one second position set, wherein the candidate gateways corresponding to the predicted positions in the second position set are the same; A second predicted time period corresponding to the at least one second location set is determined within the target time period, and data of the gateway is determined according to the second predicted time period corresponding to the at least one second location set and the to-be-selected gateway.

9. The method according to claim 8, characterized in that For any predicted location; according to the predicted location and the locations of the plurality of gateways, determining a candidate gateway corresponding to the predicted location from the plurality of gateways, comprising: Determine, according to the predicted position and the positions of the plurality of gateways, a distance between the predicted position and each gateway to obtain a plurality of distances; determining a shortest distance among the plurality of distances; The gateway corresponding to the shortest distance is determined as the candidate gateway corresponding to the predicted position.

10. The method according to claim 6, characterized in that Determining target trajectory data of the moving object within a target time period according to the initial trajectory data of the moving object includes: Determining the flight duration of the mobile object; The initial trajectory data of the mobile object is preprocessed according to the navigation duration and the duration of the target time period to obtain the target trajectory data of the mobile object.

11. The method according to any one of claims 1 to 4, characterized in that: Determining flow distribution information of each wave position and each gateway station in the target area according to the service characteristic data of the multiple mobile objects and the data of the wave positions and gateway stations corresponding to the multiple mobile objects, including: Determine, according to the data of the wave positions corresponding to the multiple mobile objects, a first mobile object corresponding to each wave position among the multiple mobile objects, and determine, according to the service characteristic data of the first mobile object and the data of the wave position corresponding to the first mobile object, traffic distribution information of each wave position; According to the data of the gateways corresponding to the multiple mobile objects, a second mobile object corresponding to each gateway is determined among the multiple mobile objects, and according to the service characteristic data of the second mobile object and the data of the gateway corresponding to the second mobile object, traffic distribution information of each gateway is determined.

12. The method according to claim 1, characterized in that The method further comprises: According to the flow distribution information of each wave position and each gateway station, network optimization processing is performed on the satellite network in the target area.

13. The method according to claim 1, characterized in that The method further comprises: Sending the traffic distribution information of each wavelength and each gateway to the second device; The flow distribution information of each wave position and each gateway station is used by the second device to perform network optimization processing on the satellite network in the target area.

14. The method according to claim 12 or 13, characterized in that Performing network optimization processing on the satellite network in the target area, including: performing resource optimization processing on the network resources of the satellite network according to the flow distribution information of each wave position and each gateway station; and / or, According to the flow distribution information of each wave position and each gateway station, routing optimization processing is performed on the routing information of the satellite network; The network optimization process includes the resource optimization process and the route optimization process.

15. A method for determining flow distribution, characterized in that: Applied to the second device, the method includes: receiving traffic distribution information of each wavelength and each gateway in a target area sent by a first device; the traffic distribution information of each wavelength and each gateway is determined by the first device according to service feature data of a plurality of mobile objects in the target area and data of wavelengths and gateways corresponding to the plurality of mobile objects; According to the flow distribution information of each wave position and each gateway station, network optimization processing is performed on the satellite network in the target area.

16. The method according to claim 15, characterized in that According to the flow distribution information of each wave position and each gateway station, the satellite network in the target area is optimized, including: performing resource optimization processing on the network resources of the satellite network according to the flow distribution information of each wave position and each gateway station; and / or, According to the flow distribution information of each wave position and each gateway station, routing optimization processing is performed on the routing information of the satellite network; The network optimization process includes the resource optimization process and the route optimization process.

17. A flow distribution determination device, characterized in that: Applied to a first device, comprising: A transceiver module, used to obtain multiple route data of multiple mobile objects in a target area, data of multiple sites and data of multiple gateways; A processing module, used for performing feature extraction processing on the plurality of route data to obtain service feature data of the plurality of mobile objects; The processing module is further used to determine the initial trajectory data of the plurality of mobile objects based on the plurality of route data and the data of the plurality of sites; The processing module is further used to determine the wave positions and gateway data corresponding to the multiple mobile objects according to the initial trajectory data of the multiple mobile objects and the data of the multiple gateways; The processing module is further used to determine the flow distribution information of each wave position and each gateway station in the target area according to the service characteristic data of the multiple mobile objects and the data of the wave positions and gateway stations corresponding to the multiple mobile objects.

18. A flow distribution determination device, characterized in that: Applied to the second device, comprising: a transceiver module, configured to receive traffic distribution information of each wavelength and each gateway in a target area sent by a first device; the traffic distribution information of each wavelength and each gateway is determined by the first device according to service feature data of a plurality of mobile objects in the target area and data of wavelengths and gateways corresponding to the plurality of mobile objects; According to the flow distribution information of each wave position and each gateway station, network optimization processing is performed on the satellite network in the target area.

19. A flow distribution determination device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions to be executed by the at least one processor, where the instructions are executed by the at least one processor so as to enable the at least one processor to perform the method according to any one of claims 1 to 14.

20. A flow distribution determination device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions to be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of claim 15 or 16.

21. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 14 when the computer-executable instructions are executed by a processor.

22. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method of claim 15 or 16 when the computer-executable instructions are executed by a processor.

23. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 14 when being executed by a processor.

24. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to claim 15 or 16.

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