CN115426030A - Satellite energy-saving method and device based on big data - Google Patents
Satellite energy-saving method and device based on big data Download PDFInfo
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- CN115426030A CN115426030A CN202211083945.5A CN202211083945A CN115426030A CN 115426030 A CN115426030 A CN 115426030A CN 202211083945 A CN202211083945 A CN 202211083945A CN 115426030 A CN115426030 A CN 115426030A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/1851—Systems using a satellite or space-based relay
- H04B7/18519—Operations control, administration or maintenance
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/1853—Satellite systems for providing telephony service to a mobile station, i.e. mobile satellite service
- H04B7/18539—Arrangements for managing radio, resources, i.e. for establishing or releasing a connection
- H04B7/18543—Arrangements for managing radio, resources, i.e. for establishing or releasing a connection for adaptation of transmission parameters, e.g. power control
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application provides a satellite energy-saving method and a satellite energy-saving device based on big data, wherein the satellite energy-saving method comprises the following steps: acquiring current satellite position information of a satellite group according to the running orbit of the satellite; determining a target area corresponding to the current position of the satellite group based on the satellite position information, and acquiring big data information corresponding to the target area in a historical time period; analyzing the big data information, and determining a network use peak time period corresponding to the target area in a plurality of target time periods; acquiring current time, and determining a satellite communication demand vector according to the current time and the network use peak time period; and determining the number of satellites needing to be activated at present according to the satellite communication demand vector and a preset load threshold, and activating the satellites corresponding to the number of the satellites in the satellite group. According to the satellite energy-saving method and the satellite energy-saving device, the effect of satellite energy saving is achieved.
Description
Technical Field
The application relates to the technical field of satellite communication, in particular to a satellite energy-saving method and a satellite energy-saving device based on big data.
Background
With the development of network communication technology and the introduction of the 6G (6 th generation mobile communication technology) technology, a satellite can cover the land and the sea, and the space barrier of communication is broken. Even if the satellite can cover a large range, if the satellite covers a large range in some areas with rare people, such as deserts, remote sea areas, original forests and other areas with rare people, the precious satellite energy is wasted, and when the satellite is emergently moved in case of emergencies or natural disasters, a large amount of satellite energy is consumed. However, at present, the only means by which a satellite can obtain energy is to obtain solar energy through irradiation of sunlight, but the satellite also has a backlight time period, so that energy conservation of the satellite is a very challenging problem.
Disclosure of Invention
In view of this, an object of the present application is to provide a satellite energy saving method and a satellite energy saving device based on big data, which combine with analysis and application of the big data to assist in adjusting and controlling activation or dormancy of a satellite, so as to avoid invalid coverage of the satellite when a satellite group passes through an unmanned area or an area with little human smoke, thereby achieving an effect of satellite energy saving.
In a first aspect, an embodiment of the present application provides a satellite energy saving method based on big data, where the satellite energy saving method includes:
acquiring current satellite position information of a satellite group according to the running orbit of the satellite;
determining a target area corresponding to the current position of the satellite group based on the satellite position information, and acquiring big data information corresponding to the target area in a historical time period; the historical time period comprises a plurality of target time periods, and each target time period corresponds to different big data information;
analyzing the big data information, and determining a network use peak time period corresponding to the target area in a plurality of target time periods;
acquiring current time, and determining a satellite communication demand vector according to the current time and the network use peak time period;
and determining the number of satellites needing to be activated at present according to the satellite communication demand vector and a preset load threshold, and activating the satellites corresponding to the number of the satellites in the satellite group.
Further, the big data information comprises the number of network registered users in the target region; the analyzing the big data information and determining a network usage peak time period corresponding to the target area in a plurality of target time periods includes:
and determining a target time period with the highest number of network registered users in a plurality of target time periods according to the number of the network registered users corresponding to each target time period, and determining the target time period with the highest number of the network registered users as the network peak time period.
Further, the determining a satellite communication demand vector according to the current time and the network usage peak time period includes:
judging whether the current time is in the network use peak time period or not;
if so, determining a first preset load rate as the satellite communication demand vector;
if not, determining a second preset load rate as the satellite communication demand vector; and the first preset load rate is greater than the second preset load rate.
Further, the determining, according to the satellite communication demand vector and a preset load threshold, the number of satellites that need to be activated at present, and activating the satellites corresponding to the number of satellites in the satellite group includes:
judging whether the satellite communication demand vector is greater than or equal to the load threshold value;
if so, determining a first preset quantity as the satellite quantity;
if not, determining a second preset quantity as the quantity of the satellites; wherein the first preset number is greater than the second preset number;
determining satellites to be activated corresponding to the number of the satellites in the satellite group, and determining the satellites in the satellite group except the satellites to be activated as dormant satellites;
sending an activation instruction to the satellite to be activated, and controlling the satellite to be activated to enter an activation state;
and sending a sleep instruction to the sleep satellite to control the sleep satellite to enter a sleep state.
In a second aspect, an embodiment of the present application further provides a satellite energy saving device based on big data, where the satellite energy saving device includes:
the position information acquisition module is used for acquiring the current satellite position information of the satellite group according to the running orbit of the satellite;
the big data information acquisition module is used for determining a target area corresponding to the current position of the satellite group based on the satellite position information and acquiring big data information corresponding to the target area in a historical time period; the historical time period comprises a plurality of target time periods, and each target time period corresponds to different big data information;
the peak time period determining module is used for analyzing the big data information and determining a network use peak time period corresponding to the target area in a plurality of target time periods;
the demand vector determining module is used for acquiring the current time and determining a satellite communication demand vector according to the current time and the network utilization peak time period;
and the satellite activation module is used for determining the number of satellites needing to be activated at present according to the satellite communication demand vector and a preset load threshold value, and activating the satellites corresponding to the number of the satellites in the satellite group.
Further, the big data information comprises the number of network registered users in the target region; when the peak time period determining module is configured to analyze the big data information and determine that a network corresponding to the target area uses a peak time period in a plurality of target time periods, the peak time period determining module is further configured to:
and determining a target time period with the highest number of network registered users in a plurality of target time periods according to the number of the network registered users corresponding to each target time period, and determining the target time period with the highest number of the network registered users as the network peak time period.
Further, when the demand vector determination module is configured to determine a satellite communication demand vector according to the current time and the peak time period used by the network, the demand vector determination module is further configured to:
judging whether the current time is in the network use peak time period or not;
if so, determining a first preset load rate as the satellite communication demand vector;
if not, determining a second preset load rate as the satellite communication demand vector; wherein the first preset load rate is greater than the second preset load rate.
Further, when the satellite activation module is configured to determine the number of satellites that need to be activated currently according to the satellite communication demand vector and a preset load threshold, and activate the satellites corresponding to the number of satellites in the satellite group, the satellite activation module is further configured to:
judging whether the satellite communication demand vector is greater than or equal to the load threshold value;
if so, determining a first preset quantity as the quantity of the satellites;
if not, determining a second preset quantity as the quantity of the satellites; wherein the first preset number is greater than the second preset number;
determining satellites to be activated corresponding to the number of the satellites in the satellite group, and determining the satellites in the satellite group except the satellites to be activated as dormant satellites;
sending an activation instruction to the satellite to be activated, and controlling the satellite to be activated to enter an activation state;
and sending a sleep instruction to the sleep satellite to control the sleep satellite to enter a sleep state.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is operating, the machine readable instructions when executed by the processor performing the steps of the big data based satellite energy saving method as described above.
In a fourth aspect, the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps of the big-data based satellite energy saving method as described above.
According to the satellite energy-saving method based on the big data, firstly, the current satellite position information of a satellite group is obtained according to the running orbit of a satellite; then, determining a target area corresponding to the current position of the satellite group based on the satellite position information, and acquiring big data information corresponding to the target area in a historical time period; analyzing the big data information, and determining a network use peak time period corresponding to the target area in a plurality of target time periods; acquiring current time, and determining a satellite communication demand vector according to the current time and the network use peak time period; and finally, determining the number of satellites needing to be activated at present according to the satellite communication demand vector and a preset load threshold, and activating the satellites corresponding to the number of the satellites in the satellite group.
Compared with the satellite energy-saving method in the prior art, the method has the advantages that the big data information of the target area is utilized to carry out data analysis, the network use peak time period of the target area is analyzed, the satellite communication demand vector is determined according to the network use peak time period and the current time, and the satellite quantity of the satellite which needs to be activated at present is determined according to the satellite communication demand vector. Therefore, the method and the device accurately analyze the real demand of the target area on the network by utilizing the characteristics of large data volume, high speed, high truth and the like of big data analysis, and efficiently and accurately assist in adjusting the satellite state. And then the number of the satellites needing to be activated is adjusted according to the real demand, so that unnecessary data transmission of the satellites can be reduced when the demand of the target area for the network is small, the energy conservation of the satellites can be realized in an unmanned area or an area with less people smoke, the invalid coverage of the satellites in the unmanned area or the area with less people smoke is avoided, and the energy conservation effect of the satellites is achieved. When the demand of the target area for the network is large, a large number of satellites are activated, and therefore the network in a densely-populated city can be guaranteed to be smooth. The application helps to regulate and control the activation or dormancy of the satellite by combining the analysis and application of big data, and avoids the invalid coverage of the satellite when a satellite group passes through an unmanned area or an area with rare people, thereby achieving the effect of energy conservation of the satellite.
In order to make the aforementioned objects, features and advantages of the present application comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of a big data based satellite energy saving method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for activating a satellite according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a big data based satellite energy saving device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that one skilled in the art can obtain without inventive effort based on the embodiments of the present application falls within the scope of protection of the present application.
First, an application scenario to which the present application is applicable will be described. The method and the device can be applied to the technical field of satellite communication.
With the development of network communication technology and the introduction of the 6G (6 th generation mobile communication technology) technology, a satellite can cover the land and the sea, and the space barrier of communication is broken. Even if the satellite can cover a large area, if the satellite also needs to cover a large area in areas with rare people, such as desert, remote sea area, primitive forest and other areas with wide people, the precious satellite energy is not easily wasted, and the satellite needs to be moved in an emergency when an emergency or natural disaster happens, so that a large amount of satellite energy is consumed. However, currently, the only means for a satellite to obtain energy is to obtain solar energy through the irradiation of sunlight, but the satellite also has a backlight time period, so that the energy saving of the satellite is a very challenging problem.
Based on the above, the embodiment of the application provides a satellite energy saving method based on big data, which combines analysis and application of the big data to assist in adjusting and controlling activation or dormancy of a satellite, so that invalid coverage of the satellite when a satellite group passes through an unmanned area or an area with rare people is avoided, and an effect of satellite energy saving is achieved.
Referring to fig. 1, fig. 1 is a flowchart illustrating a satellite energy saving method based on big data according to an embodiment of the present disclosure. As shown in fig. 1, a method for saving energy of a satellite based on big data according to an embodiment of the present application includes:
s101, acquiring current satellite position information of the satellite group according to the running orbit of the satellite.
The satellite position information refers to position information corresponding to a position where the satellite group is currently located. Here, the satellite position information may be longitude and latitude information corresponding to a position where the satellite group is currently located.
In step S101, in a specific implementation, the satellite position information of the satellite group at present is obtained according to the actual orbit of the satellite.
S102, determining a target area corresponding to the current position of the satellite group based on the satellite position information, and collecting big data information corresponding to the target area in a historical time period.
The target area refers to an area on the earth through which the satellite group is passing. Here, the target area may be a country or a city, and the present application is not limited thereto. The historical time period refers to a past time period, and as an alternative embodiment, the historical time period may be a past day, a week previous to the current time, or a month previous to the current time. For example, if the current time is 2022, 9, 1, and fourteen, the historical period may be 2022, 8, 31, 00 to 2022, 8, 31, 23, or the historical period may be 2022, 8, 22, monday to 2022, 8, 28, sunday, or the historical period may be 2022, 8, 1, to 2022, 8, 31, days. Big data information refers to data information generated by the target region during a historical period of time. Here, the big data information may be a total population number in the target area or a number of network-registered users in the target area, or the like. Here, the history period includes a plurality of target periods each corresponding to different large data information. Specifically, the target time period refers to a plurality of sub-time points within the history time period. For example, when the historical period is 2022 year 8 month 31 day 00 to 2022 year 8 month 31 day 23, the target period may be each hour in the historical period; the target time period may be each day of the historical time period when the historical time period is from 22 days a day at 8 months in 2022 to 28 days a day at 8 months in 2022; when the historical period is from 1/8/2022 to 31/8/2022, the target period may be each day of the historical period.
Here, it should be noted that the above examples of the history time period and the target time period are merely examples, and actually, the history time period and the target time period are not limited to the above examples.
In the specific implementation of step S102, the satellite group is located based on the satellite position information of the current position of the satellite group, and the target area corresponding to the current position of the satellite group is determined. Specifically, the latitude and longitude ranges corresponding to all regions on the earth are traversed, a target latitude and longitude range containing satellite position information of the satellite group is determined, and then the corresponding target region is determined according to the target latitude and longitude range. After the target area is determined, big data information generated in the target area in a historical time period is collected.
S103, analyzing the big data information, and determining a network use peak time period corresponding to the target area in a plurality of target time periods.
It should be noted that the peak time period of network usage refers to a time period in which the network demand is the largest in the target area. Here, as an example, continuing the example in step S102 above, when the historical time period is the past day and the target time period is each hour in the historical time period, the determined network usage peak time period should also be one hour; when the historical time period is the last week of the current time and the target time period is each day in the historical time period, determining that the network use peak time period should be one day; when the historical time period is the last month of the current time and the target time period is each day in the historical time period, the determined network use peak time period should also be one day.
In step S103, in a specific implementation, after the big data information generated in the target area in the historical time period is collected, the big data information is analyzed to obtain a network usage peak time period corresponding to the target area.
According to the embodiment provided by the application, when big data analysis is performed, big data information of a target area can be collected through a big data cloud, and then the collected big data information is uploaded to a network element of a ground 5G core network NWDAF (network data analysis function) for analysis. The NWDAF is a data analysis network element based on network data automatic perception and analysis network, participates in the whole life cycle of network planning, construction, operation and maintenance, optimization and operation, enables the network to be easy to maintain and control, improves the utilization rate of network resources, and improves the user experience. The NWDAF can solve the problems of data security and signal overhead caused by reporting of a large amount of measurement data, and can also solve the problem of low delay, thereby improving the rate of analyzing big data.
As an optional implementation, the big data information includes the number of network registered users in the target region.
The number of network-registered users refers to the number of users who perform network communication using the network electronic device in the target area.
Specifically, the number of network-registered users in the target area may be determined by tracking the network when the user uses the network electronic device.
For step S103, the analyzing the big data information to determine the network usage peak time period corresponding to the target area includes:
and determining a target time period with the highest number of network registered users in a plurality of target time periods according to the number of the network registered users corresponding to each target time period, and determining the target time period with the highest number of the network registered users as the network peak time period.
For the above steps, in a specific implementation, after the number of network registered users corresponding to each target time period is obtained in step S102, the number of network registered users corresponding to each target time period is compared, a target time period with the highest number of network registered users is determined in a plurality of target time periods, and the target time period with the highest number of network registered users is determined as the network peak time period.
And S104, acquiring the current time, and determining a satellite communication demand vector according to the current time and the network use peak time period.
It should be noted that the satellite communication demand vector refers to a load rate of a target area for a satellite demand under a specified satellite quantity, and the quantity of the satellites to be started can be determined according to the satellite communication demand vector within a preset load threshold of the satellite group. Here, the satellite communication demand vector has a dynamic value, for example, the value of the satellite communication demand vector may float upward before the network utilization peak time period; the value of the satellite communications demand vector may float downward, varying from time to time, after peak periods of network usage.
For the above step S104, in a specific implementation, after determining a network peak time period corresponding to a target area, a current time is obtained, and a satellite communication demand vector is determined according to the current time and the network peak time period.
For step S104, the determining a satellite communication demand vector according to the current time and the network peak time period includes:
step 1041, determining whether the current time is in the network peak time period.
Step 1042, if yes, determining a first preset load rate as the satellite communication demand vector.
And step 1043, if not, determining a second preset load rate as the satellite communication demand vector.
The preset load rate refers to a load rate of a preset target area for a specified number of satellites. Here, the first preset load rate is greater than the second preset load rate. For example, the first preset load rate may be set to 80%, and the second preset load rate may be set to 40%, which is not particularly limited in this application.
For the above steps 1041 to 1043, in specific implementation, it is first determined whether the current time is in the peak time period of network usage. If the current time is within the network usage peak time period, and the current satellite communication demand vector of the target area is considered to be higher, the step 1042 is executed to determine the first preset load rate as the satellite communication demand vector. If the current time is not located in the peak time period of network usage, and the current satellite communication demand vector of the target area is considered to be low, the above step 1043 is executed to determine the second preset load rate as the satellite communication demand vector.
Taking as an example, continuing the embodiment in step S102, the current time is 9/1/10 in 2022, the historical time period is 8/31/0 in 2022 to 31/23 in 8/31/59 in 2022, the target time period is each hour in the historical time period, and the determined network peak usage time period is 10 in one day: 00, which may determine that the current time is within the network usage peak time period, the above step 1042 is executed to determine the first preset load rate as the satellite communication demand vector. Second, when the embodiment in step S102 is continued, the current time is 2022, 9, month, 1, fourteen, the historical time period is 2022, 8, month, 22, monday, to 2022, 8, month, 28, monday, the target time period is each day in the historical time period, the determined network peak time period is wednesday in a week, which is that it may be determined that the current time is not located in the network peak time period, step 1043 is executed to determine the second preset load rate as the satellite communication demand vector. Third, if the current time is 9/1/2022, the historical time period is 8/1/2022 to 8/31/2022, the target time period is each day in the historical time period, and the determined network peak use time period is the first day in one month, which may be determined that the current time is within the network peak use time period, step 1042 described above is executed to determine the first preset load factor as the satellite communication demand vector.
And S105, determining the number of satellites needing to be activated at present according to the satellite communication demand vector and a preset load threshold, and activating the satellites corresponding to the number of satellites in the satellite group.
It should be noted that the load threshold refers to a load rate that is preset and used for determining whether the target area has a large network demand in the current time. For example, the load threshold may be set to 60% in advance, and the present application is not particularly limited thereto. The number of satellites refers to the number of satellites that need to be currently activated.
In step S105, in a specific implementation, the number of satellites that need to be activated at present is determined according to the satellite communication demand vector determined in step S104 and a preset load threshold, and the satellites corresponding to the number of satellites in the satellite group are activated.
Referring to fig. 2, fig. 2 is a flowchart illustrating a satellite activation method according to an embodiment of the present disclosure. As shown in fig. 2, for the step S105, determining the number of satellites that need to be activated currently according to the satellite communication demand vector and a preset load threshold, and activating the satellites corresponding to the number of satellites in the satellite group includes:
s201, judging whether the satellite communication demand vector is larger than or equal to the load threshold value;
and S202, if so, determining a first preset quantity as the quantity of the satellites.
S203, if not, determining a second preset quantity as the quantity of the satellites.
It should be noted that the preset number refers to a preset number of satellites that need to be activated in the satellite group. Here, the first preset number is greater than the second preset number. And the first preset quantity and the second preset quantity are both smaller than the quantity of the satellites in the satellite group. For example, the first predetermined number may be set to 80, and the second predetermined number may be set to 30, which is not particularly limited in this application.
In the specific implementation of steps S201 to S203, after determining the satellite communication demand vector, it is determined whether the satellite communication demand vector is greater than or equal to the load threshold. If the satellite communication demand vector is greater than or equal to the load threshold, it is determined that the current demand of the target area for the network is large, for example, when the satellite coverage area is a place such as a city where population gathers, the demand of the user terminal for the network is large, and a large number of satellites need to be activated, the above step S202 is executed, and a large first preset number is determined as the number of satellites. If the satellite communication demand vector is smaller than the load threshold, it is determined that the current demand of the target area for the network is smaller, for example, when the satellite action area is a sparsely populated area, such as a desert, a remote sea area, an original forest, and other sparsely populated areas, and at this time, the demand of the user terminal for the network is smaller, it is not necessary to activate a large number of satellites, and the energy saving mode can be started, and then the above step S203 is executed to determine the smaller second preset number as the number of satellites.
S204, determining the satellites to be activated corresponding to the number of the satellites in the satellite group, and determining the satellites in the satellite group except the satellites to be activated as dormant satellites.
S205, sending an activation instruction to the satellite to be activated, and controlling the satellite to be activated to enter an activation state.
S206, sending a sleep instruction to the sleep satellite, and controlling the sleep satellite to enter a sleep state.
It should be noted that the satellite to be activated refers to a satellite that needs to be activated for data transmission. A dormant satellite refers to a satellite that may enter a dormant state. The activation instruction refers to an instruction to wake up a satellite to be activated. The sleep command refers to a command for causing the satellite to enter a sleep state.
In specific implementation, after the number of satellites is determined, the number of satellites to be activated corresponding to the number of satellites is determined in the satellite group, and the satellites in the satellite group except for the number of the satellites to be activated are determined as the dormant satellites. And sending an activation instruction to the satellite to be activated, and controlling the satellite to be activated to enter an activation state. And sending a sleep instruction to the sleep satellite to control the sleep satellite to enter a sleep state. Therefore, the dormant satellite is awakened or put into a dormant state by sending the activating instruction and the dormancy instruction, so that unnecessary data transmission of the satellite can be reduced when the demand of a target area on a network is small, the energy conservation of the satellite can be realized in an unmanned area or an area with less people, the invalid coverage of the satellite in the unmanned area or the area with less people is avoided, and the energy-saving effect of the satellite is achieved. When the demand of the target area for the network is large, a large number of satellites are activated, and therefore the network in a densely-populated city can be guaranteed to be smooth.
According to the satellite energy-saving method based on the big data, firstly, the current satellite position information of a satellite group is obtained according to the running orbit of a satellite; then, determining a target area corresponding to the current position of the satellite group based on the satellite position information, and acquiring big data information corresponding to the target area in a historical time period; analyzing the big data information, and determining a network use peak time period corresponding to the target area in a plurality of target time periods; acquiring current time, and determining a satellite communication demand vector according to the current time and the network use peak time period; and finally, determining the number of satellites needing to be activated at present according to the satellite communication demand vector and a preset load threshold, and activating the satellites corresponding to the number of satellites in the satellite group.
Compared with the satellite energy-saving method in the prior art, the method has the advantages that the big data information of the target area is utilized to carry out data analysis, the network use peak time period of the target area is analyzed, the satellite communication demand vector is determined according to the network use peak time period and the current time, and the satellite quantity of the satellite which needs to be activated at present is determined according to the satellite communication demand vector. Therefore, the method and the device accurately analyze the real demand of the target area on the network by utilizing the characteristics of large data volume, high speed, high truth and the like of big data analysis, and efficiently and accurately assist in adjusting the satellite state. And then the number of the satellites needing to be activated is adjusted according to the real demand, so that unnecessary data transmission of the satellites can be reduced when the demand of the target area for the network is small, the energy conservation of the satellites can be realized in an unmanned area or an area with less people, the invalid coverage of the satellites in the unmanned area or the area with less people is avoided, and the energy-saving effect of the satellites is achieved. When the target area has a large demand for the network, a large number of satellites are activated, which in turn can ensure that the network is clear in a densely populated city. The application helps to regulate and control the activation or dormancy of the satellite by combining the analysis and application of big data, and avoids the invalid coverage of the satellite when a satellite group passes through an unmanned area or an area with rare people, thereby achieving the effect of energy conservation of the satellite.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a satellite energy saving device based on big data according to an embodiment of the present disclosure. As shown in fig. 3, the satellite power saving device 300 includes:
a position information obtaining module 301, configured to obtain current satellite position information of a satellite group according to a running orbit of a satellite;
a big data information obtaining module 302, configured to determine, based on the satellite position information, a target area corresponding to a current position of the satellite group, and collect big data information corresponding to the target area within a historical time period; the historical time period comprises a plurality of target time periods, and each target time period corresponds to different big data information;
a peak time period determining module 303, configured to analyze the big data information, and determine a network usage peak time period corresponding to the target area in multiple target time periods;
a demand vector determination module 304, configured to obtain current time, and determine a satellite communication demand vector according to the current time and the network usage peak time period;
a satellite activation module 305, configured to determine the number of satellites that need to be activated currently according to the satellite communication demand vector and a preset load threshold, and activate the satellites corresponding to the number of satellites in the satellite group.
Further, the big data information comprises the number of network registered users in the target region; when the peak time period determining module 303 is configured to analyze the big data information and determine that a network corresponding to the target area uses a peak time period in a plurality of target time periods, the peak time period determining module 303 is further configured to:
and determining a target time period with the highest number of network registered users in a plurality of target time periods according to the number of the network registered users corresponding to each target time period, and determining the target time period with the highest number of the network registered users as the network peak time period.
Further, when the demand vector determination module 304 is configured to determine a satellite communication demand vector according to the current time and the network usage peak time period, the demand vector determination module 304 is further configured to:
judging whether the current time is in the network use peak time period or not;
if so, determining a first preset load rate as the satellite communication demand vector;
if not, determining a second preset load rate as the satellite communication demand vector; wherein the first preset load rate is greater than the second preset load rate.
Further, when the satellite activation module 305 is configured to determine, according to the satellite communication demand vector and a preset load threshold, the number of satellites that need to be activated currently, and activate the satellites corresponding to the number of satellites in the satellite group, the satellite activation module 305 is further configured to:
determining whether the satellite communication demand vector is greater than or equal to the load threshold;
if so, determining a first preset quantity as the quantity of the satellites;
if not, determining a second preset quantity as the quantity of the satellites; wherein the first preset number is greater than the second preset number;
determining satellites to be activated corresponding to the number of the satellites in the satellite group, and determining the satellites in the satellite group except the satellites to be activated as dormant satellites;
sending an activation instruction to the satellite to be activated, and controlling the satellite to be activated to enter an activation state;
and sending a sleep instruction to the sleep satellite to control the sleep satellite to enter a sleep state.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 4, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.
The memory 420 stores machine-readable instructions executable by the processor 410, when the electronic device 400 runs, the processor 410 communicates with the memory 420 through the bus 430, and when the machine-readable instructions are executed by the processor 410, the steps of the satellite energy saving method based on big data in the method embodiments shown in fig. 1 and fig. 2 may be performed.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the satellite energy saving method based on big data in the method embodiments shown in fig. 1 and fig. 2 may be executed.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit the technical solutions, and the scope of the present application is not limited to the above-mentioned embodiments, although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A satellite energy saving method based on big data is characterized by comprising the following steps:
acquiring current satellite position information of a satellite group according to the running orbit of the satellite;
determining a target area corresponding to the current position of the satellite group based on the satellite position information, and acquiring big data information corresponding to the target area in a historical time period; the historical time periods comprise a plurality of target time periods, and each target time period corresponds to different big data information;
analyzing the big data information, and determining a network use peak time period corresponding to the target area in a plurality of target time periods;
acquiring current time, and determining a satellite communication demand vector according to the current time and the network use peak time period;
and determining the number of satellites needing to be activated at present according to the satellite communication demand vector and a preset load threshold, and activating the satellites corresponding to the number of the satellites in the satellite group.
2. The satellite power saving method of claim 1, wherein the big data information comprises a number of network registered users in the target region; the analyzing the big data information and determining the network use peak time period corresponding to the target area in a plurality of target time periods comprises the following steps:
and determining a target time period with the highest number of network registered users in a plurality of target time periods according to the number of the network registered users corresponding to each target time period, and determining the target time period with the highest number of the network registered users as the network peak time period.
3. The satellite energy saving method of claim 1, wherein the determining a satellite communication demand vector based on the current time and the network usage peak time period comprises:
judging whether the current time is in the network use peak time period or not;
if so, determining a first preset load rate as the satellite communication demand vector;
if not, determining a second preset load rate as the satellite communication demand vector; wherein the first preset load rate is greater than the second preset load rate.
4. The satellite energy saving method according to claim 1, wherein the determining, according to the satellite communication demand vector and a preset load threshold, the number of satellites that need to be currently activated, and activating the satellites corresponding to the number of satellites in the satellite group includes:
determining whether the satellite communication demand vector is greater than or equal to the load threshold;
if so, determining a first preset quantity as the satellite quantity;
if not, determining a second preset quantity as the quantity of the satellites; wherein the first preset number is greater than the second preset number;
determining satellites to be activated corresponding to the number of the satellites in the satellite group, and determining the satellites in the satellite group except the satellites to be activated as dormant satellites;
sending an activation instruction to the satellite to be activated, and controlling the satellite to be activated to enter an activation state;
and sending a sleep instruction to the sleep satellite to control the sleep satellite to enter a sleep state.
5. A big data based satellite energy saving device, the satellite energy saving device comprising:
the position information acquisition module is used for acquiring the current satellite position information of the satellite group according to the running orbit of the satellite;
the big data information acquisition module is used for determining a target area corresponding to the current position of the satellite group based on the satellite position information and acquiring big data information corresponding to the target area in a historical time period; the historical time period comprises a plurality of target time periods, and each target time period corresponds to different big data information;
the peak time period determining module is used for analyzing the big data information and determining a network use peak time period corresponding to the target area in a plurality of target time periods;
the demand vector determining module is used for acquiring the current time and determining a satellite communication demand vector according to the current time and the network utilization peak time period;
and the satellite activation module is used for determining the number of satellites needing to be activated at present according to the satellite communication demand vector and a preset load threshold value, and activating the satellites corresponding to the number of the satellites in the satellite group.
6. The satellite power saving device of claim 5, wherein the big data information comprises a number of network registered users in the target region; the peak time period determining module is configured to, when analyzing the big data information and determining that a network corresponding to the target area uses a peak time period in a plurality of target time periods, further:
and determining a target time period with the highest number of network registered users in a plurality of target time periods according to the number of the network registered users corresponding to each target time period, and determining the target time period with the highest number of the network registered users as the network peak time period.
7. The satellite energy saving device of claim 5, wherein when the demand vector determination module is configured to determine a satellite communication demand vector based on the current time and the network usage peak time period, the demand vector determination module is further configured to:
judging whether the current time is in the network use peak time period or not;
if so, determining a first preset load rate as the satellite communication demand vector;
if not, determining a second preset load rate as the satellite communication demand vector; wherein the first preset load rate is greater than the second preset load rate.
8. The satellite energy saving device according to claim 5, wherein the satellite activation module, when configured to determine a number of satellites that need to be activated currently according to the satellite communication demand vector and a preset load threshold, and activate the satellites corresponding to the number of satellites in the satellite group, is further configured to:
judging whether the satellite communication demand vector is greater than or equal to the load threshold value;
if so, determining a first preset quantity as the quantity of the satellites;
if not, determining a second preset quantity as the quantity of the satellites; wherein the first preset number is greater than the second preset number;
determining satellites to be activated corresponding to the number of the satellites in the satellite group, and determining the satellites in the satellite group except the satellites to be activated as dormant satellites;
sending an activation instruction to the satellite to be activated, and controlling the satellite to be activated to enter an activation state;
and sending a sleep instruction to the sleep satellite to control the sleep satellite to enter a sleep state.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions being executable by the processor to perform the steps of the big-data based satellite power saving method according to any one of claims 1 to 4.
10. A computer-readable storage medium, having a computer program stored thereon, which, when being executed by a processor, performs the steps of the big data based satellite power saving method according to any one of claims 1 to 4.
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