A system for providing smart energy efficiency by means of distributed artificial intelligence and data analysis

Abstract

The present invention relates to a system (1) for providing smart energy efficiency for mobile networks, that aim to reduce the sustainability and environmental impacts of emission points serving a mobile network, by means of distributed artificial intelligence and data analysis.

Description

[0001] A SYSTEM FOR PROVIDING SMART ENERGY EFFICIENCY BY MEANS OF DISTRIBUTED ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS

[0002] Technical Field

[0003] The present invention relates to a system for providing smart energy efficiency for mobile networks, that aim to reduce the sustainability and environmental impacts of emission points serving a mobile network, by means of distributed artificial intelligence and data analysis.

[0004] Background of the Invention

[0005] Today, sustainability and environmental impacts of emission points serving a mobile network are among the important issues in terms of companies. Energy efficiency in mobile networks is closely related to the efficiency of direct voltage power supply units (PSUs) used in base stations and data centers and it should be dynamically adjusted depending on the network density.

[0006] Therefore, it is understood that there is need for a system for providing smart energy efficiency for mobile networks, that aim to reduce the sustainability and environmental impacts of emission points serving a mobile network, by means of distributed artificial intelligence and data analysis.

[0007] The Chinese patent document no. CN116094701, an application included in the state of the art, discloses an energy data speed limiting method based on quantum network distributed processing, storage device and mobile terminal. The invention which is subject to the said Chinese patent document discloses a distributed energy data processing speed limiting method based on a quantum network, a storage device and a mobile terminal; the method comprises a quantum communication network and a plurality of QNCPs; each QNCP comprises a network communication module and a key pool module. A source node QNCP determines a sending path and sends message information according to routing table information, and a next- hop QNCP node receives the message information and sends the message information to the quantum communication network. The messages are stored in a to-be-processed message queue in sequence, and response message information is sent to the sending end QNCP; the sending end QNCP selects an encryption mode according to the security level of message information needing to be sent, carries out encryption processing and sends the message information to a next-hop QNCP node of a sending path; and after receiving the response message, the next-hop QNCP node analyzes the response message, and if the message is a response completion message, sending is completed.

[0008] Summary of the Invention

[0009] An objective of the present invention is to realize a system which is developed for providing smart energy efficiency for mobile networks, that aim to reduce the sustainability and environmental impacts of emission points serving a mobile network, by means of distributed artificial intelligence and data analysis.

[0010] Another objective of the present invention is to realize a system which is developed for supporting mobile network operators to reduce high energy costs and carbon emissions, by reducing energy consumption upon minimizing the energy losses of the network. Another objective of the present invention is to realize a system which is developed for offering a structure suggestion that will obtain maximum benefit from renewable solutions to support the green transformation policies of companies.

[0011] Detailed Description of the Invention

[0012] “A System for Providing Smart Energy Efficiency by means of Distributed Artificial Intelligence and Data Analysis” realized to fulfil the objective of the present invention is shown in the figure attached, in which:

[0013] Figure 1 is a schematic view of the inventive system.

[0014] The components illustrated in the figure are individually numbered, where the numbers refer to the following:

[0015] 1. System

[0016] 2. Database

[0017] 3. Server

[0018] The inventive system (1) for providing smart energy efficiency for mobile networks, that aim to reduce the sustainability and environmental impacts of emission points serving a mobile network, by means of distributed artificial intelligence and data analysis; comprises at least one database (2) which is configured to keep record of the data of energy consumed by stations of emission points and the data of network density; and at least one server (3) which is configured to realize smart energy management for mobile networks; to analyze energy consumption and to estimate future energy needs; to manage and analyze power supply units; to check the number and status of PSUs recommended to operate according to the current status of the network, in accordance with the limits given according to the equipment in the network and to check whether there are limits that can be applied to the network; to provide smart energy efficiency in mobile networks and to monitor the overall performance of the system; to report the energy efficiency results in detail; to evaluate the decisions received from the distributed artificial intelligence model and to implement the necessary optimizations for energy management.

[0019] The database (2) included in the inventive system (1) is configured to establish communication with the server (3) by using any communication protocol.

[0020] The server (3) included in the inventive system (1) is configured to establish communication with the database (2) by using any communication protocol. The server (3) is configured to increase the energy efficiency of the radio network and data centers. The server (3) is configured to dynamically adjust energy efficiency according to the network density and to enable a future action to be taken by means of predictive artificial intelligence models. The server (3) is configured to realize smart energy management for mobile networks. The server (3) is configured to collect mobile network data from the fields by means of request method and to save it in the database

[0021] (2), to process and analyze this data, to take energy management decisions locally or at the center, to predict future consumption trends by means of forecasting models, and to train distributed artificial intelligence models in the light of these data by means of a universal artificial intelligence algorithm. The server (3) is configured to collect data received from sensors, energy consumption devices and other data sources. The server

[0022] (3) is configured to clean data received from sensors, energy consumption devices and other data sources and to convert it into a standard form and to transmit it to the database (2). The server (3) is configured to analyze energy consumption and to predict future energy needs by using the collected data. The server (3) is configured to manage energy resources, optimize the use of green energy and to determine the number of PSUs (Power Supply Units) to be actively used based on the results of energy consumption analysis. The server (3) is configured to use the obtained data in order to increase the performance of distributed artificial intelligence modules and accordingly to update local models. The server (3) is configured to transfer energy management decisions to related devices. The server (3) is configured to manage and analyze power supply units. The server (3) is configured to update the number of PSUs that will operate according to network density as a result of analysis and to increase the efficiency of power supplies. The server (3) is configured to check the number and status of PSUs recommended to operate according to the current status of the network, in accordance with the limits given according to the equipment in the network and to check whether there are limits that can be applied to the network. The server (3) is configured to carry out transaction of edge computing by updating the status of PSUs as a result of analysis. The server (3) is configured to dynamically check the related points by directly integrating the obtained outputs into the radio units at the end. The server (3) is configured to ensure effective management of energy resources. The server (3) is configured to support the overall energy management of the system by transmitting energy data. The server (3) is configured to provide smart energy efficiency in mobile networks and to monitor the overall performance of the system (1). The server (3) is configured to monitor the entire system (1), to report energy efficiency results and to perform performance analysis. The server (3) is configured to monitor the data received and to report the energy efficiency results in detail. The server (3) is configured to analyze its overall performance and to make adjustments if necessary. The server (3) is configured to evaluate decisions received from the distributed artificial intelligence model and to apply the necessary optimizations for energy management Industrial Applicability of the Invention

[0023] With the inventive system (1), it is possible to provide smart energy efficiency for mobile networks, that aim to reduce the sustainability and environmental impacts of emission points serving a mobile network, by means of distributed artificial intelligence and data analysis.

[0024] Within these basic concepts; it is possible to develop various embodiments of the inventive “System (1) for Providing Smart Energy Efficiency by means of Distributed Artificial Intelligence and Data Analysis”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS1. A system (1) for providing smart energy efficiency for mobile networks, that aim to reduce the sustainability and environmental impacts of emission points serving a mobile network, by means of distributed artificial intelligence and data analysis; comprising at least one database (2) which is configured to keep record of the data of energy consumed by stations of emission points and the data of network density; and characterized by at least one server (3) which is configured to realize smart energy management for mobile networks; to analyze energy consumption and to estimate future energy needs; to manage and analyze power supply units; to check the number and status of PSUs recommended to operate according to the current status of the network, in accordance with the limits given according to the equipment in the network and to check whether there are limits that can be applied to the network; to provide smart energy efficiency in mobile networks and to monitor the overall performance of the system; to report the energy efficiency results in detail; to evaluate the decisions received from the distributed artificial intelligence model and to implement the necessary optimizations for energy management.

2. A system (1) according to Claim 1; characterized by the database (2) which (2) is configured to establish communication with the server (3) by using any communication protocol.

3. A system (1) according to Claim 1 or 2; characterized by the server (3) which is configured to establish communication with the database (2) by using any communication protocol.

4. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to increase the energy efficiency of the radio network and data centers.

5. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to dynamically adjust energy efficiency according to the network density and to enable a future action to be taken by means of predictive artificial intelligence models.

6. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to realize smart energy management for mobile networks.

7. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to collect mobile network data from the fields by means of request method and to save it in the database (2), to process and analyze this data, to take energy management decisions locally or at the center, to predict future consumption trends by means of forecasting models, and to train distributed artificial intelligence models in the light of these data by means of a universal artificial intelligence algorithm.

8. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to collect data received from sensors, energy consumption devices and other data sources.

9. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to clean data received from sensors, energy consumption devices and other data sources and to convert it into a standard form and to transmit it to the database (2).

10. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to analyze energy consumption and to predict future energy needs by using the collected data.

11. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to manage energy resources, optimize the use of green energy and to determine the number of PSUs (Power Supply Units) to be actively used based on the results of energy consumption analysis.

12. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to use the obtained data in order to increase the performance of distributed artificial intelligence modules and accordingly to update local models.

13. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to transfer energy management decisions to related devices.

14. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to manage and analyze power supply units.

15. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to update the number of PSUs that will operate according to network density as a result of analysis and to increase the efficiency of power supplies.

16. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to check the number and status of PSUs recommended to operate according to the current status of the network, in accordance with the limits given according to the equipment in the network and to check whether there are limits that can be applied to the network.

17. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to carry out transaction of edge computing by updating the status of PSUs as a result of analysis.

18. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to dynamically check the related points by directly integrating the obtained outputs into the radio units at the end.

19. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to ensure effective management of energy resources.

20. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to support the overall energy management of the system by transmitting energy data.

21. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to provide smart energy efficiency in mobile networks and to monitor the overall performance of the system (1).

22. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to monitor the entire system (1), to report energy efficiency results and to perform performance analysis.

23. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to monitor the data received and to report the energy efficiency results in detail.

24. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to analyze its overall performance and to make adjustments if necessary.

25. A system (1) according to any of the preceding claims; characterized by the server (3) which is configured to evaluate decisions received from the distributed artificial intelligence model and to apply the necessary optimizations for energy management

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