CN116669054B - 5G base station optimization planning method and storage medium - Google Patents
5G base station optimization planning method and storage medium Download PDFInfo
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- CN116669054B CN116669054B CN202310943761.XA CN202310943761A CN116669054B CN 116669054 B CN116669054 B CN 116669054B CN 202310943761 A CN202310943761 A CN 202310943761A CN 116669054 B CN116669054 B CN 116669054B
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- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000013439 planning Methods 0.000 title claims description 16
- 238000005457 optimization Methods 0.000 title claims description 8
- 238000011156 evaluation Methods 0.000 claims abstract description 25
- 238000010606 normalization Methods 0.000 claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 238000012544 monitoring process Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims description 20
- 230000005484 gravity Effects 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 10
- 238000013528 artificial neural network Methods 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 9
- 230000005855 radiation Effects 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000005611 electricity Effects 0.000 abstract description 8
- 238000007726 management method Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000010923 batch production Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/24—Cell structures
- H04W16/26—Cell enhancers or enhancement, e.g. for tunnels, building shadow
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Abstract
According to the method, the attribute information database of the power equipment in the specific geographic area is pre-established, the attribute information comprises the number, longitude and latitude information, environment information, capacity information, occupied area and load information of the power equipment, so that the data set is richer and more accurate, the attribute information is processed by using a normalization algorithm, a power equipment state monitoring model is established, the optimal position for deploying the 5G base station is obtained by combining the evaluation result of the power equipment and the 5G base station characteristics to be deployed, and the 5G base station can be rapidly and accurately deployed on the basis of the existing power equipment, so that the balance between signal coverage and electricity consumption cost is realized.
Description
Technical Field
The application relates to the technical field of base station deployment planning, in particular to a 5G base station optimization planning method and a storage medium.
Background
In the prior art, an attempt to deploy 5G equipment in a transformer substation exists, on one hand, the transformer substation can directly provide reliable uninterrupted power supply guarantee and professional maintenance service for the 5G base station, and the service quality of the 5G network is effectively improved. On the other hand, compared with 5G equipment built on a commercial building equal-rotation power supply main body building, the electric power infrastructure such as a sharing transformer substation can enjoy the direct power supply electricity price on the electricity price, and the electric power cost of the 5G base station is reduced. Therefore, how to flexibly and accurately deploy the 5G base station and how to reduce the electricity cost of the 5G base station under the condition of ensuring signal coverage become the problems to be solved.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a 5G base station optimization planning method and a storage medium, which are used for satisfying the balance between signal coverage and electricity consumption cost and realizing the purpose of rapidly, accurately and effectively providing a proper position for base station site selection.
To achieve the above object, the present application provides:
A5G base station optimization planning method comprises the following steps:
s1, pre-establishing an attribute information database of power equipment in a specific geographic area, wherein the attribute information comprises the number, longitude and latitude information, environment information, capacity information, occupied area and load information of the power equipment;
s2, carrying out normalization processing on the attribute information, and establishing a power equipment state monitoring model based on the normalized data;
s3, establishing an evaluation system of the running state of the power equipment, and evaluating the state of the power equipment based on the evaluation system;
s4, establishing 5G base station characteristic databases of different manufacturers, wherein the base station characteristic databases comprise power consumption, volume and radiation size of the base station;
and S5, screening out the preferred address of the 5G base station based on the evaluation result and the base station characteristic database.
Preferably, before the normalization processing is performed on the attribute information, the method further includes: the attribute information is preprocessed, wherein the preprocessing comprises the processing of missing values, abnormal values and blank values.
Preferably, the processing of the missing value, the outlier and the blank value specifically includes: and eliminating the missing value, the abnormal value and the blank value, and requiring the power equipment to upload the related attribute information again, and if the information is still abnormal information after being uploaded again, processing according to the default value.
Preferably, the environmental information includes an ambient temperature and an atmospheric humidity; the capacity information includes rated current, rated voltage, rated load.
Preferably, the normalization process is a deep neural network based normalization process, the input to the deep neural network is a batch process input x = { x1, … …, xm }, where β is a learning parameter and m is the number of training samples.
Preferably, the calculation process of the deep neural network is as follows: (1) calculating the average value of the output data of the last layer:the method comprises the steps of carrying out a first treatment on the surface of the (2) calculating standard deviation of the output data of the previous layer: />The method comprises the steps of carrying out a first treatment on the surface of the (3) Normalizing to obtain->The method comprises the steps of carrying out a first treatment on the surface of the (4) Reconstructing, namely reconstructing the data obtained by the normalization processing to obtain +.>Wherein->Is a minimum value infinitely close to 0, and γ is a learning parameter.
Preferably, the evaluation system specifically comprises: and setting corresponding specific gravity values for different power equipment attribute information, multiplying the attribute information by the corresponding specific gravity values, and summing to obtain an evaluation result of the power equipment.
Preferably, the power equipment has the highest specific gravity of load information and the lowest specific gravity of longitude and latitude information.
Preferably, the screening the preferred address of the 5G base station based on the evaluation result and the base station database specifically includes: and traversing all the power equipment by adopting a hierarchical traversal algorithm, and screening out the preferred address of the 5G base station based on the evaluation result and the base station characteristics of the power equipment.
The application also provides a computer readable storage medium storing a computer program which when executed by a processor implements a 5G base station optimization planning method as described above.
Compared with the prior art, the application has the advantages that:
according to the method, the attribute information database of the power equipment in the specific geographic area is pre-established, the attribute information comprises the number, longitude and latitude information, environment information, capacity information, occupied area and load information of the power equipment, so that the data set is richer and more accurate, the attribute information is processed by using a normalization algorithm, a power equipment state monitoring model is established, the optimal position for deploying the 5G base station is obtained by combining the evaluation result of the power equipment and the 5G base station characteristics to be deployed, and the 5G base station can be rapidly and accurately deployed on the basis of the existing power equipment, so that the balance between signal coverage and electricity consumption cost is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method according to an embodiment of the application.
Fig. 2 is a schematic structural diagram of a storage medium according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
Referring to fig. 1, in an embodiment of the present application, the method includes the following steps:
s1, pre-establishing an attribute information database of power equipment in a specific geographic area, wherein the attribute information comprises the number, longitude and latitude information, environment information, capacity information, occupied area and load information of the power equipment;
it should be noted that, the above-mentioned power equipment can be transformer substation, block terminal, generator, transformer etc. the geographical position that these equipment set up is generally comparatively fixed, and the natural environment that is located is comparatively stable yet, therefore can set up 5G basic station near above-mentioned power equipment to be convenient for realize changeing the power supply, reduce the electricity cost of 5G basic station.
The serial number, capacity, longitude and latitude information and occupied area of the power equipment belong to fixed attributes of the power equipment, and the attributes are not changed after the power equipment is erected, so that the power equipment can be uploaded to a management platform after the power equipment is erected.
The environmental information includes, but is not limited to, temperature, humidity, etc., where the temperature and humidity belong to environmental attributes of the power device and are key factors affecting the operation state of the power device, and temperature and humidity sensors may be disposed on the power device to monitor the two state information and upload the two state information to the management platform according to a certain period of time.
S2, carrying out normalization processing on the attribute information, and establishing a power equipment state monitoring model based on the normalized data;
in a specific embodiment, the fixed attribute data and the environmental attribute data may be integrated before the normalization process to form an attribute information database of the power device, and the attribute information database may be updated during the operation of the power device.
Preferably, before the normalization processing is performed on the attribute information, the method further includes: the attribute information is preprocessed, wherein the preprocessing comprises the processing of missing values, abnormal values and blank values.
Preferably, the processing of the missing value, the outlier and the blank value specifically includes: and eliminating the missing value, the abnormal value and the blank value, and requiring the power equipment to upload the related attribute information again, and if the information is still abnormal information after being uploaded again, processing according to the default value.
In the actual operation process, the situation that the data uploaded by the power equipment is wrong, such as null value, abnormal value, partial value loss and the like, may be caused by various reasons, at this time, the management platform can actively request the power equipment to upload the relevant attribute information again for correction, and if the uploaded data is still wrong, the data is processed according to the default value.
Normalization is a means for effectively reducing the data range and simplifying the calculation process, and because the attribute information of the power equipment is not in a conventional format, the processing efficiency of the data can be improved through normalization processing.
In a specific embodiment, the power equipment state monitoring model may be a visual monitoring management platform based on a BIM technology, and the management platform displays the power equipment based on a map and longitude and latitude information of the power equipment, and displays normalized data in real time.
S3, establishing an evaluation system of the running state of the power equipment, and evaluating the state of the power equipment based on the evaluation system;
in the embodiment of the application, the evaluation system scores the running state of the power equipment based on the attribute information of the power equipment, and evaluates the load state and the environment state of the power equipment based on the result of the scoring.
S4, establishing 5G base station characteristic databases of different manufacturers, wherein the base station characteristic databases comprise power consumption, volume, radiation size and the like of the base station;
the 5G base stations are provided by different communication equipment providers, and the power consumption, the volume and the radiation of the 5G base stations of different manufacturers are different, so that the data of different 5G base stations can be input into a management platform in advance so as to select sites according to the actual conditions of the 5G base stations, wherein the radiation is a very special parameter, and because a plurality of electric equipment are arranged in a resident living area, the resident is sensitive to the radiation of the base stations at present, and therefore, the radiation is a factor to be considered when the electric equipment is in the resident living area and the base stations are selected.
And S5, screening out the preferred address of the 5G base station based on the evaluation result and the base station characteristic database.
Preferably, the environmental information includes an ambient temperature and an atmospheric humidity; the capacity information includes rated current, rated voltage, rated load.
Preferably, the normalization process is a deep neural network based normalization process, the input to the deep neural network is a batch process input x = { x1, … …, xm }, where β is a learning parameter and m is the number of training samples.
Preferably, the calculation process of the deep neural network is as follows: (1) calculating the average value of the output data of the last layer:the method comprises the steps of carrying out a first treatment on the surface of the (2) calculating standard deviation of the output data of the previous layer: />The method comprises the steps of carrying out a first treatment on the surface of the (3) Normalizing to obtain->The method comprises the steps of carrying out a first treatment on the surface of the (4) Reconstructing, namely reconstructing the data obtained by the normalization processing to obtain +.>Wherein->Is a minimum value infinitely close to 0, and γ is a learning parameter.
Preferably, the evaluation system specifically comprises: and setting corresponding specific gravity values for different power equipment attribute information, multiplying the attribute information by the corresponding specific gravity values, and summing to obtain an evaluation result of the power equipment.
Preferably, the power equipment has the highest specific gravity of load information and the lowest specific gravity of longitude and latitude information.
The load is a factor considered first when the 5G base station is erected, so that overload of the power equipment caused by the erection of the 5G base station is avoided; the environment information is inferior, because the 5G base station has certain requirements on temperature and humidity; while the volume of the 5G base station is also considered in connection with the footprint of the power equipment.
Preferably, the screening the preferred address of the 5G base station based on the evaluation result and the base station database specifically includes: and traversing all the power equipment by adopting a hierarchical traversal algorithm, and screening out the preferred address of the 5G base station based on the evaluation result and the base station characteristics of the power equipment.
In a specific embodiment of the present application, the hierarchical traversal algorithm may use a binary tree traversal algorithm, which is a common algorithm in the art, and will not be described herein.
Through pre-establishing an attribute information database of the power equipment in a specific geographic area, wherein the attribute information comprises the serial number, longitude and latitude information, environment information, capacity information, occupied area and load information of the power equipment, so that the data set is richer and more accurate, the attribute information is processed by using a normalization algorithm, a power equipment state monitoring model is established, and the optimal position for deploying the 5G base station is obtained by combining an evaluation result of the power equipment and 5G base station characteristics to be deployed, so that the 5G base station can be rapidly and accurately deployed on the basis of the existing power equipment, and the balance between signal coverage and electricity consumption cost is realized.
As shown in fig. 2, the embodiment of the present application further provides a storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for optimizing and planning a 5G base station is implemented. The storage medium includes various media capable of storing program codes, such as a usb disk, a removable hard disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk, or an optical disk.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer-readable storage media, which may include computer-readable storage media (or non-transitory media) and communication media (or transitory media).
The term computer-readable storage medium includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
By way of example, the computer readable storage medium may be an internal storage unit of the electronic device of the foregoing embodiments, such as a hard disk or a memory of the electronic device. The computer readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device.
The foregoing is merely exemplary of embodiments of the present application to enable those skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. The 5G base station optimization planning method is characterized by comprising the following steps of:
s1, pre-establishing an attribute information database of power equipment in a specific geographic area, wherein the attribute information comprises the number, longitude and latitude information, environment information, capacity information, occupied area and load information of the power equipment;
s2, carrying out normalization processing on the attribute information, and establishing a power equipment state monitoring model based on the normalized data;
s3, establishing an evaluation system of the running state of the power equipment, and evaluating the state of the power equipment based on the evaluation system;
s4, establishing 5G base station characteristic databases of different manufacturers, wherein the base station characteristic databases comprise power consumption, volume and radiation size of the base station;
s5, screening out the preferred address of the 5G base station based on the evaluation result and the base station characteristic database;
the evaluation system specifically includes: and setting corresponding specific gravity values for different power equipment attribute information, multiplying the attribute information by the corresponding specific gravity values, and summing to obtain an evaluation result of the power equipment.
2. The planning method according to claim 1, characterized by further comprising, before normalizing the attribute information: the attribute information is preprocessed, wherein the preprocessing comprises the processing of missing values, abnormal values and blank values.
3. The method according to claim 2, wherein the processing of the missing values, the outliers and the blank values specifically includes: and eliminating the missing value, the abnormal value and the blank value, and requiring the power equipment to upload the related attribute information again, and if the information is still abnormal information after being uploaded again, processing according to the default value.
4. The planning method of claim 1, wherein the environmental information includes an ambient temperature and an atmospheric humidity; the capacity information includes rated current, rated voltage, rated load.
5. The planning method of claim 1, wherein the normalization process is a deep neural network based normalization process, and the input to the deep neural network is a batch input x: β= { x 1 .,……,x m Where β is the learning parameter and m is the number of training samples.
6. The planning method of claim 5, wherein the calculation process of the deep neural network is: (1) calculating the average value of the output data of the last layer:the method comprises the steps of carrying out a first treatment on the surface of the (2) calculating standard deviation of the output data of the previous layer:the method comprises the steps of carrying out a first treatment on the surface of the (3) Normalizing to obtain->The method comprises the steps of carrying out a first treatment on the surface of the (4) Reconstructing, namely reconstructing the data obtained by the normalization processing to obtain +.>Wherein->Is a minimum value infinitely close to 0, and γ is a learning parameter.
7. The planning method according to claim 1, wherein the power equipment has a highest specific gravity of load information and a lowest specific gravity of latitude and longitude information.
8. The planning method according to claim 1, wherein the screening out the preferred address of the 5G base station based on the evaluation result and the base station database specifically includes: and traversing all the power equipment by adopting a hierarchical traversal algorithm, and screening out the preferred address of the 5G base station based on the evaluation result and the base station characteristics of the power equipment.
9. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements a 5G base station optimization planning method according to any of claims 1 to 8.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004312273A (en) * | 2003-04-04 | 2004-11-04 | Nec Corp | Air link load generating apparatus, air link evaluation system, and air link evaluation method |
| EP1860900A1 (en) * | 2006-05-23 | 2007-11-28 | Nederlandse Organisatie voor Toegepast-Natuuurwetenschappelijk Onderzoek TNO | System and method for radio network planning with HSDPA analysis |
| JP2017163393A (en) * | 2016-03-10 | 2017-09-14 | 株式会社日立製作所 | Radio communication system, radio equipment, radio communication method, lift control system and transformer station control system |
| CN107708126A (en) * | 2017-07-19 | 2018-02-16 | 国网浙江省电力公司嘉兴供电公司 | A kind of network stations optimization method based on TD LTE230 networks |
| CN108280872A (en) * | 2017-12-11 | 2018-07-13 | 云南电网有限责任公司红河供电局 | UWB locating base stations deployment scheme and evaluation method based on substation's threedimensional model |
| CN112512000A (en) * | 2020-11-25 | 2021-03-16 | 云南电网有限责任公司电力科学研究院 | Power distribution Internet of things node deployment optimization method |
| CN113011681A (en) * | 2021-04-23 | 2021-06-22 | 广东电网有限责任公司江门供电局 | Optimization method and system for deploying 5G base station based on transformer substation resources |
| WO2021247448A1 (en) * | 2020-06-01 | 2021-12-09 | Intel Corporation | Federated learning optimizations |
| CN114004417A (en) * | 2021-11-09 | 2022-02-01 | 广东电网有限责任公司江门供电局 | Method and device for building power facility shared base station |
| CN114298433A (en) * | 2021-12-31 | 2022-04-08 | 中国电力科学研究院有限公司 | Site selection method, device, equipment and medium for 5G base station in transformer substation |
| CN114741872A (en) * | 2022-04-08 | 2022-07-12 | 国家电网有限公司大数据中心 | Base station address data processing method and device, electronic equipment and storage medium |
| CN115391962A (en) * | 2022-08-15 | 2022-11-25 | 华北电力大学 | Communication base station and power distribution network collaborative planning method, device, equipment and medium |
| CN115456357A (en) * | 2022-08-19 | 2022-12-09 | 三峡大学 | Decision-making method for 5G base station antenna deployment of transformer substation |
| CN116245260A (en) * | 2023-05-12 | 2023-06-09 | 国网吉林省电力有限公司信息通信公司 | Optimization method for deploying 5G base station based on substation resources |
| WO2023103598A1 (en) * | 2021-12-06 | 2023-06-15 | 中兴通讯股份有限公司 | Base station activation method and apparatus, electronic device, and computer-readable storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7986953B2 (en) * | 2009-06-01 | 2011-07-26 | Microsoft Corporation | Location determination with geographic and bias tuning |
-
2023
- 2023-07-31 CN CN202310943761.XA patent/CN116669054B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004312273A (en) * | 2003-04-04 | 2004-11-04 | Nec Corp | Air link load generating apparatus, air link evaluation system, and air link evaluation method |
| EP1860900A1 (en) * | 2006-05-23 | 2007-11-28 | Nederlandse Organisatie voor Toegepast-Natuuurwetenschappelijk Onderzoek TNO | System and method for radio network planning with HSDPA analysis |
| JP2017163393A (en) * | 2016-03-10 | 2017-09-14 | 株式会社日立製作所 | Radio communication system, radio equipment, radio communication method, lift control system and transformer station control system |
| CN107708126A (en) * | 2017-07-19 | 2018-02-16 | 国网浙江省电力公司嘉兴供电公司 | A kind of network stations optimization method based on TD LTE230 networks |
| CN108280872A (en) * | 2017-12-11 | 2018-07-13 | 云南电网有限责任公司红河供电局 | UWB locating base stations deployment scheme and evaluation method based on substation's threedimensional model |
| WO2021247448A1 (en) * | 2020-06-01 | 2021-12-09 | Intel Corporation | Federated learning optimizations |
| CN112512000A (en) * | 2020-11-25 | 2021-03-16 | 云南电网有限责任公司电力科学研究院 | Power distribution Internet of things node deployment optimization method |
| CN113011681A (en) * | 2021-04-23 | 2021-06-22 | 广东电网有限责任公司江门供电局 | Optimization method and system for deploying 5G base station based on transformer substation resources |
| CN114004417A (en) * | 2021-11-09 | 2022-02-01 | 广东电网有限责任公司江门供电局 | Method and device for building power facility shared base station |
| WO2023103598A1 (en) * | 2021-12-06 | 2023-06-15 | 中兴通讯股份有限公司 | Base station activation method and apparatus, electronic device, and computer-readable storage medium |
| CN114298433A (en) * | 2021-12-31 | 2022-04-08 | 中国电力科学研究院有限公司 | Site selection method, device, equipment and medium for 5G base station in transformer substation |
| CN114741872A (en) * | 2022-04-08 | 2022-07-12 | 国家电网有限公司大数据中心 | Base station address data processing method and device, electronic equipment and storage medium |
| CN115391962A (en) * | 2022-08-15 | 2022-11-25 | 华北电力大学 | Communication base station and power distribution network collaborative planning method, device, equipment and medium |
| CN115456357A (en) * | 2022-08-19 | 2022-12-09 | 三峡大学 | Decision-making method for 5G base station antenna deployment of transformer substation |
| CN116245260A (en) * | 2023-05-12 | 2023-06-09 | 国网吉林省电力有限公司信息通信公司 | Optimization method for deploying 5G base station based on substation resources |
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| CN116669054A (en) | 2023-08-29 |
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