CN116321243B - Mobility management method of base station - Google Patents
Mobility management method of base station Download PDFInfo
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- CN116321243B CN116321243B CN202310011240.0A CN202310011240A CN116321243B CN 116321243 B CN116321243 B CN 116321243B CN 202310011240 A CN202310011240 A CN 202310011240A CN 116321243 B CN116321243 B CN 116321243B
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- 238000007726 management method Methods 0.000 title claims abstract description 23
- 238000012423 maintenance Methods 0.000 claims abstract description 25
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 20
- 230000017525 heat dissipation Effects 0.000 claims abstract description 10
- 238000012544 monitoring process Methods 0.000 claims abstract description 6
- 230000001174 ascending effect Effects 0.000 claims abstract description 4
- 238000010801 machine learning Methods 0.000 claims abstract description 4
- 238000012937 correction Methods 0.000 claims description 11
- 230000007613 environmental effect Effects 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 2
- 238000004891 communication Methods 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 5
- 238000000034 method Methods 0.000 description 5
- 241000282461 Canis lupus Species 0.000 description 4
- 238000005265 energy consumption Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 2
- 230000000630 rising effect Effects 0.000 description 2
- 239000000725 suspension Substances 0.000 description 2
- 241000282421 Canidae Species 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000007791 dehumidification Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/30—Automatic controllers with an auxiliary heating device affecting the sensing element, e.g. for anticipating change of temperature
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q1/00—Details of selecting apparatus or arrangements
- H04Q1/02—Constructional details
- H04Q1/035—Cooling of active equipments, e.g. air ducts
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/06—Testing, supervising or monitoring using simulated traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
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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 mobility management method of a base station, which belongs to the technical field of communication base stations and specifically comprises the following steps: determining an importance value of the base station based on the daily average flow of the base station in the last month, determining expected operation time of the base station based on standby time of the base station and maintenance time of the base station when the importance value of the base station is larger than a first threshold value, further determining required power consumption of the base station, and starting a heat dissipation device of the base station when the operation temperature of the base station is larger than the required power consumption, further improving the reliability of the operation of the base station by adopting a prediction model of a machine learning algorithm based on the temperature of an operation environment, the predicted flow of the base station in the expected operation time and the current operation temperature of the base station, obtaining the highest operation temperature of the base station in the expected operation time, and monitoring the operation temperature of the base station in real time when the highest operation temperature is larger than the first threshold value, and starting the heat dissipation device of the base station when the operation temperature of the base station is in an ascending state.
Description
Technical Field
The application belongs to the technical field of communication base stations, and particularly relates to a mobility management method of a base station.
Background
In order to realize management of a communication base station, in the method, the device, the electronic equipment and the storage medium for managing a backup battery of the base station of the patent grant bulletin No. CN113113677B of the grant patent, the maintenance time of the base station is calculated according to the geographic position information of the base station; adjusting the electric quantity distribution mode of the backup battery according to the geographic position information of the base station and the maintenance time of the base station; further comprises: correcting an electrical threshold parameter in a current power distribution mode of the backup battery according to the geographic position information of the base station, wherein the electrical threshold parameter comprises at least one of a maximum output current threshold value, a maximum output power threshold value and a maximum output voltage threshold value of the backup battery, but has the following technical problems:
1. the standby power supply time is not considered based on the importance degree of the base station, and for a more important base station, if a maintenance time delay or other unexpected situations occur, the operation stability of the base station with higher importance degree may be obviously affected.
2. The power supply adjustment of the heat sink of the base station is not considered based on the temperature of the operating environment, the predicted flow rate, and the operating temperature of the base station, and for the base station with less predicted flow rate and less operating temperature of the base station, the power saving control of the heat sink is performed at the first time, but if the above factors are not combined, the power consumption is greatly increased, and damage to the base station equipment or unexpected suspension may be caused due to higher temperature.
The application provides a mobility management method of a base station aiming at the technical problems.
Disclosure of Invention
In order to achieve the purpose of the application, the application adopts the following technical scheme:
according to an aspect of the present application, there is provided a mobility management method of a base station.
The mobility management method of the base station is characterized by comprising the following steps:
s11, determining an importance degree value of a base station based on the daily average flow of the base station in the month of the last month, judging whether the importance degree value of the base station is larger than a first threshold value, if so, determining the standby time of the base station based on the importance degree value of the base station, and if not, determining the standby time of the base station to be 0;
s12, determining expected operation time of the base station based on standby time of the base station and maintenance time of the base station, determining required power consumption of the base station based on the expected operation time of the base station, judging whether the standby battery capacity of the base station is larger than the required power consumption, if so, not performing power consumption control, and if not, entering step S13;
s13, based on the temperature of the operating environment, the predicted flow of the base station in the expected operating time and the current operating temperature of the base station, a prediction model of a machine learning algorithm is adopted to obtain the highest operating temperature of the base station in the expected operating time, whether the highest operating temperature is greater than a first threshold value or not is judged, if yes, the step S14 is entered, and if not, a heat dissipating device of the base station is closed;
s14, monitoring the operation temperature of the base station in real time, and starting a heat dissipation device of the base station when the operation temperature of the base station is greater than a second threshold value and the operation temperature of the base station is in an ascending state.
The standby time of the base station is determined according to the importance value of the base station, so that the base station with higher importance can have longer expected operation time, the operation stability and reliability of the base station with higher importance are further ensured, and the problems of sudden disconnection and the like caused by the unexpected maintenance time due to the delay of maintenance time or other reasons are prevented.
The base station meeting the power supply requirement does not need to control the power consumption by judging the capacity of the backup battery, so that the overall processing efficiency is further ensured on the basis of ensuring the running stability, and the number of the needed judgment is reduced.
By constructing the prediction model, the judgment of the operating temperature of the base station is not only carried out on the current operating temperature, but also carried out from the predicted value of the future operating temperature, so that the reliability of the overall judgment is further improved, and meanwhile, the base station with lower operating temperature can control the temperature of the heat radiating device at the beginning, and unnecessary electric energy consumption is reduced.
And through the judgment of the second threshold value and the rising state, the starting and the running temperature of the heat radiating device are connected together, the electric energy consumption is further reduced, and the running stability and reliability of the base station are improved.
In another aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a program stored in the memory and capable of running on the processor, where the processor implements the mobility management method of a base station when executing the program.
In another aspect, the present application provides a computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a mobility management method of a base station as described above.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 is a flowchart of a mobility management method of a base station according to embodiment 1;
FIG. 2 is a flowchart of specific steps for determining the maximum operating temperature of a base station at a desired run time according to embodiment 1;
fig. 3 is a frame diagram of a computer storage medium in embodiment 3.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
Technical problem thinking: in the present stage, in the maintenance process of the base station for serving mobile communication, power is often required to be supplied to the base station by a backup battery, for the base station, a plurality of modules are often provided, a core module is a communication module, and other modules such as a ventilation and heat dissipation module, a dehumidification module and the like serve the base station, but the backup power supply time is not considered based on the importance degree of the base station, and for a more important base station, if the maintenance time delay or other unexpected situations occur, the operation stability of the base station with higher importance degree is possibly affected obviously; the power supply adjustment of the heat sink of the base station is not considered based on the temperature of the operating environment, the predicted flow rate, and the operating temperature of the base station, and for the base station with less predicted flow rate and less operating temperature of the base station, the power saving control of the heat sink is performed at the first time, but if the above factors are not combined, the power consumption is greatly increased, and damage to the base station equipment or unexpected suspension may be caused due to higher temperature.
Example 1
To solve the above-described problems, according to one aspect of the present application, as shown in fig. 1,
the mobility management method of the base station is characterized by comprising the following steps:
s11, determining an importance degree value of a base station based on the daily average flow of the base station in the month of the last month, judging whether the importance degree value of the base station is larger than a first threshold value, if so, determining the standby time of the base station based on the importance degree value of the base station, and if not, determining the standby time of the base station to be 0;
specifically, the first threshold is determined according to the number of base stations and population density of the city in which the base stations are located, and generally the value range is between 0.5 and 0.7.
Specifically, the importance value of the base station is determined in a mapping manner, and the value range is between 0 and 1, wherein the greater the daily average flow of the base station in the month of the last month is, the greater the importance value of the base station is.
Specifically, the calculation formula of the standby time of the base station is as follows:
wherein G is 1 The importance of the base station is represented by T1, T1 is the importance value of the base station, T1 is the basic standby time, the value is 1 hour, min () is the minimum function, and T is the standby time of the base station in hours.
S12, determining expected operation time of the base station based on standby time of the base station and maintenance time of the base station, determining required power consumption of the base station based on the expected operation time of the base station, judging whether the standby battery capacity of the base station is larger than the required power consumption, if so, not performing power consumption control, and if not, entering step S13;
specifically, the expected operating time of a base station is equal to the sum of the standby time of the base station and the maintenance time of the base station.
Specifically, the required power consumption of the base station is determined according to the product of the expected running time of the base station and the time-averaged power consumption of the base station.
Specifically, the time-average power consumption of the base station is determined according to the time-average power consumption of the base station in the near week, or according to the time-average power consumption of the same date type in the near week, wherein the date type comprises a working day, a weekend and a holiday.
For example, if the expected operation time of the base station is 8 hours and the required power amount of the base station is 0.5KWh per hour, the required power consumption of the base station is 4KWh, and when the back battery capacity is 5KWh, the power consumption control is not required.
S13, based on the temperature of the operating environment, the predicted flow of the base station in the expected operating time and the current operating temperature of the base station, a prediction model of a machine learning algorithm is adopted to obtain the highest operating temperature of the base station in the expected operating time, whether the highest operating temperature is greater than a first threshold value or not is judged, if yes, the step S14 is entered, and if not, a heat dissipating device of the base station is closed;
as a specific example, as shown in fig. 2, the specific steps for determining the highest operating temperature of the base station at the expected operating time are:
s21, obtaining a maintenance operation period of the base station based on the current time and the expected operation time, and taking the maximum value of the environmental temperature of the base station in the maintenance operation period as the temperature of the operation environment;
for example, when the current time is 10 am and the expected operation time is 8 hours, the maintenance operation period of the base station is 10 am to 6 pm, and if the maximum value of the environmental temperature between 10 am and 6 pm of the base station is 36 degrees, the temperature of the operation environment is 36 degrees.
Specifically, the average value of the environmental temperature of the base station in the maintenance operation period can also be used as the temperature of the operation environment.
S22, based on the current date type, obtaining the predicted flow of the base station at the expected running time through the average flow of the base station at the same maintenance running time period under the same date type of the last week;
for example, when the current date type is workday and the average traffic of the base station from 10 a.m. to 6 p.m. on the workday of the last week is 6Gbs, the predicted traffic of the base station at the expected operation time is 6Gbs.
Specifically, the predicted flow of the base station in the expected running time can be obtained according to the average flow of the same maintenance running time period on the same date in the last month.
For example, if the current date is wednesday, the average flow from 10 a.m. to 6 a.m. on wednesday in the next month needs to be selected as the predicted flow of the base station at the expected operating time.
Specifically, the average flow is the average of all flows from 10 a.m. to 6 a.m. between different days.
S23, obtaining the highest predicted working temperature of the base station in the expected operation time by adopting a predicted model of BP algorithm optimized based on GWO algorithm based on the temperature of the operation environment, the predicted flow of the base station in the expected operation time and the current operation temperature of the base station;
specifically, the specific steps of the GWO algorithm are as follows:
step1: initializing the population number of the wolves and the positions of the individuals, and determining the maximum iteration times;
step2: calculating the fitness of individuals in the wolf group, and arranging the positions of the wolf individuals according to the fitness value to determine the initial position and the current optimal fitness;
step3: updating parameters and updating individual positions of the wolf clusters;
step4: calculating individual fitness, and judging whether to update the position of the wolf group and the value of the current optimal fitness;
step5: judging whether the iteration termination condition is met, if not, returning to the Step3; if the condition is satisfied, outputting an optimal result, and ending the algorithm.
And S24, correcting the highest predicted working temperature based on the average value of the environmental temperature of the base station in the maintenance operation period to obtain the highest working temperature of the base station in the expected operation time.
Specifically, the calculation formula of the attenuation factor of the GWO algorithm is as follows:
wherein a is max A is the maximum value of attenuation factor min For the minimum value of the attenuation factor, t is the current iteration number, t max For the maximum iteration number, K1 is a constant, and the value is between 0 and 1, and the value of the application is 0.7.
The strategy ensures global estimation capability of the algorithm in the early search stage through nonlinear adjustment of the self value, and provides a unusual convergence rate when the algorithm performs optimizing search; the local estimation capability of the algorithm in the later search stage can be enhanced by a smaller self-value, so that the solution precision is improved.
Specifically, the GWO algorithm is adopted to optimize the learning rate of the BP algorithm.
S14, monitoring the operation temperature of the base station in real time, and starting a heat dissipation device of the base station when the operation temperature of the base station is greater than a second threshold value and the operation temperature of the base station is in an ascending state.
Specific examples of the specific steps for determining the opening of the heat dissipation device are:
s31, monitoring the operation temperature of the base station in real time, and when the operation temperature of the base station is greater than a second threshold value, the second threshold value is smaller than a first threshold value, and the step S32 is carried out;
in a specific example, the operating temperature is 40 degrees, the second threshold is 39 degrees, and the first threshold is 50 degrees.
Specifically, it is also necessary to determine according to the temperature change condition within the previous first time threshold, if the temperature in the previous 30 minutes is gradually increased from 35 degrees to 40 degrees, step S32 is required, and if the temperature is gradually decreased from 45 degrees to 40 degrees, step S32 is not required.
S32, based on the running temperature of the base station, the current environment temperature of the base station, the total flow data of the base station in a first time threshold in the future adopt a temperature prediction model based on an ELM algorithm to obtain the running temperature of the base station after the first time threshold in the future, wherein the total flow data of the base station in the first time threshold in the future is determined according to the total flow data of the base station in the first time threshold in the past.
Specifically, the total flow data of the base station in the previous first time threshold is 0.3GB, and the total flow data in the future first time threshold is 0.3GB.
S33, judging whether the running temperature of the base station after the first time threshold is greater than a first threshold, if so, starting the heat dissipation device, if not, correcting the second threshold based on the importance value of the base station and the generation times of the running temperature of the base station after the first time threshold, obtaining a second correction threshold, taking the second correction threshold as the second threshold, and returning to the step S31.
Specifically, the second threshold is 39 degrees celsius, the importance value of the base station is 0.8, the number of times of generating the running temperature of the base station after the first time threshold in the future is 3, and then the second correction threshold is 39+0.8+3/10=40.1 degrees celsius.
For example, the second correction threshold is greater than the second threshold, the second correction threshold is smaller than the first threshold, and the greater the importance value of the base station, the greater the number of times of generating the operating temperature of the base station after the first time threshold in the future, the greater the second correction threshold.
The standby time of the base station is determined according to the importance value of the base station, so that the base station with higher importance can have longer expected operation time, the operation stability and reliability of the base station with higher importance are further ensured, and the problems of sudden disconnection and the like caused by the unexpected maintenance time due to the delay of maintenance time or other reasons are prevented.
The base station meeting the power supply requirement does not need to control the power consumption by judging the capacity of the backup battery, so that the overall processing efficiency is further ensured on the basis of ensuring the running stability, and the number of the needed judgment is reduced.
By constructing the prediction model, the judgment of the operating temperature of the base station is not only carried out on the current operating temperature, but also carried out from the predicted value of the future operating temperature, so that the reliability of the overall judgment is further improved, and meanwhile, the base station with lower operating temperature can control the temperature of the heat radiating device at the beginning, and unnecessary electric energy consumption is reduced.
And through the judgment of the second threshold value and the rising state, the starting and the running temperature of the heat radiating device are connected together, the electric energy consumption is further reduced, and the running stability and reliability of the base station are improved.
Example 2
The embodiment of the application provides an electronic device, which comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the processor realizes the mobility management method of the base station when executing the program.
Example 3
As shown in fig. 3, in another aspect, the present application provides a computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a mobility management method of a base station as described above.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other manners as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present application as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present application. The technical scope of the present application is not limited to the description, but must be determined according to the scope of claims.
Claims (9)
1. The mobility management method of the base station is characterized by comprising the following steps:
s11, determining an importance degree value of a base station based on the average daily flow of the base station in the last month, judging whether the importance degree value of the base station is larger than a first threshold value, if so, determining the standby time of the base station based on the importance degree value of the base station, and if not, determining the standby time of the base station to be 0;
s12, determining expected operation time of the base station based on standby time of the base station and maintenance time of the base station, determining required power consumption of the base station based on the expected operation time of the base station, judging whether the standby battery capacity of the base station is larger than the required power consumption, if so, not performing power consumption control, and if not, entering step S13;
the calculation formula of the standby time of the base station is as follows:wherein G is 1 For the importance of the base station, T 1 Taking the basic standby time as 1 hour, taking min () as a minimum function, and taking T as the standby time of the base station, wherein the unit is hours;
s13, based on the temperature of an operating environment, the predicted flow of the base station in expected operating time and the current operating temperature of the base station, obtaining the highest operating temperature of the base station in the expected operating time by adopting a prediction model of a machine learning algorithm, judging whether the highest operating temperature is greater than a first threshold, if so, entering a step S14, and if not, closing a heat dissipation device of the base station;
s14, monitoring the operation temperature of the base station in real time, and starting a heat dissipation device of the base station when the operation temperature of the base station is greater than a second threshold value and the operation temperature of the base station is in an ascending state.
2. The mobility management method of claim 1 wherein the importance level value of the base station is determined by mapping, and the range of the importance level value is between 0 and 1, wherein the greater the average daily traffic of the base station in the last month, the greater the importance level value of the base station.
3. The mobility management method of claim 1 wherein the required power consumption of the base station is determined from a product of a desired run time of the base station and a time-averaged power consumption of the base station.
4. The mobility management method of claim 1, wherein the specific step of determining the highest operating temperature of the base station at the desired operating time is:
s21, obtaining a maintenance operation period of the base station based on the current time and the expected operation time, and taking the maximum value of the environmental temperature of the base station in the maintenance operation period as the temperature of the operation environment;
s22, based on the current date type, obtaining the predicted flow of the base station at the expected running time through the average flow of the base station at the same maintenance running time period under the same date type of the last week;
s23, obtaining the highest predicted working temperature of the base station in the expected operation time by adopting a predicted model of BP algorithm optimized based on GWO algorithm based on the temperature of the operation environment, the predicted flow of the base station in the expected operation time and the current operation temperature of the base station;
and S24, correcting the highest predicted working temperature based on the average value of the environmental temperature of the base station in the maintenance operation period to obtain the highest working temperature of the base station in the expected operation time.
5. The mobility management method of claim 4 wherein said GWO algorithm is employed to optimize a learning rate of said BP algorithm.
6. The mobility management method of claim 1, wherein the specific step of determining that the heat sink is turned on is:
s31, monitoring the operation temperature of the base station in real time, and when the operation temperature of the base station is greater than a second threshold value, the second threshold value is smaller than a first threshold value, and the step S32 is carried out;
s32, based on the running temperature of the base station, the current environment temperature of the base station, the total flow data of the base station in a first time threshold in the future, and a temperature prediction model based on an ELM algorithm, the running temperature of the base station after the first time threshold in the future is obtained, wherein the total flow data of the base station in the first time threshold in the future is determined according to the total flow data of the base station in the first time threshold in the past;
s33, judging whether the running temperature of the base station after the first time threshold is greater than a first threshold, if so, starting the heat dissipation device, if not, correcting the second threshold based on the importance value of the base station and the generation times of the running temperature of the base station after the first time threshold, obtaining a second correction threshold, taking the second correction threshold as the second threshold, and returning to the step S31.
7. The mobility management method of claim 6 wherein the second correction threshold is greater than a second threshold, the second correction threshold is less than a first threshold, and the greater the importance level value of the base station, the greater the number of times the operating temperature of the base station is generated after a first time threshold in the future, the greater the second correction threshold.
8. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing a mobility management method of a base station according to any one of claims 1-7 when the program is executed by the processor.
9. A computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a mobility management method of a base station according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310011240.0A CN116321243B (en) | 2023-01-05 | 2023-01-05 | Mobility management method of base station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310011240.0A CN116321243B (en) | 2023-01-05 | 2023-01-05 | Mobility management method of base station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116321243A CN116321243A (en) | 2023-06-23 |
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