5G base station energy consumption detection and analysis method and system

Abstract

The present invention provides a 5G base station energy consumption detection and analysis system and method, which belongs to the technical field of communication base stations, and specifically includes: determining the deviation between the energy consumption monitoring data of a reliable monitoring base station at different times and a preset energy consumption amount, and determining the energy consumption abnormality coefficient and the energy consumption abnormality date of different dates based on the deviation, obtaining the distribution data of the energy consumption abnormality date of a specified area, and determining the 5G base station in the specified area that needs to be optimized in combination with the energy consumption abnormality coefficient of different energy consumption abnormal dates, obtaining the communication flow of the specified area in different time periods, and determining the layout density optimization strategy of the 5G base station in the specified area in combination with the energy consumption monitoring data of the reliable monitoring base station in the specified area under different communication flows, thereby ensuring the operational reliability of the 5G base station.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication base stations, and in particular, relates to a 5G base station energy consumption detection and analysis system and method. Background Art

[0002] In order to realize the collection and analysis of energy consumption data of 5G base stations, in the invention patent application CN202311416849.2 "A communication base station energy consumption analysis system and method based on 5G technology", the network traffic and ring deviation coefficient of the communication base station in each reference period are counted, so that the communication base station can match the corresponding reference range for energy consumption monitoring under different network traffic and external environment. However, it is not difficult to find the following technical problems through analysis:

[0003] The existing technical solutions have neglected to optimize the layout of 5G base stations according to the analysis results of the energy consumption of 5G base stations. In specific existing technical solutions, the layout of base stations is often optimized according to the communication traffic of 5G base stations, and the distribution density of 5G base stations is targeted at areas with large communication traffic. However, in some areas, due to the influence of external environment and weather factors, the energy consumption is greater under the same communication traffic, and excessive energy consumption may cause the power supply module of the 5G base station to be in an overloaded state, which will inevitably lead to the poor operation reliability of the 5G base station. Therefore, if the layout of 5G base stations cannot be optimized in combination with the analysis results of the energy consumption of 5G base stations, the security and reliability of the communication system cannot be guaranteed.

[0004] In response to the above technical problems, the present invention provides a 5G base station energy consumption detection and analysis method and system. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a 5G base station energy consumption detection and analysis method is provided.

[0007] A 5G base station energy consumption detection and analysis method, specifically comprising:

[0008] S1 divides the historical energy consumption monitoring period of the 5G base station into a plurality of similar monitoring period groups based on weather data and communication traffic, and determines a reliable monitoring base station among the 5G base stations by using similar situations of energy consumption monitoring data of different similar monitoring period groups;

[0009] S2 divides the 5G base station into multiple areas using the service objects of the 5G base station, and determines the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times and the preset energy consumption based on the energy consumption monitoring data of the reliable monitoring base station in the specified area on different dates, and determines the energy consumption abnormality coefficient and energy consumption abnormality date on different dates based on the deviation;

[0010] S3 obtains the distribution data of the energy consumption abnormality date of the specified area, and when it is determined that the 5G base station in the specified area needs to be optimized by combining the energy consumption abnormality coefficients of different energy consumption abnormality dates, proceeds to the next step;

[0011] S4 obtains the communication traffic of the designated area in different time periods, and determines the layout density optimization strategy of the 5G base stations in the designated area in combination with the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication traffic.

[0012] A further technical solution is that the weather data includes humidity, temperature and light intensity.

[0013] A further technical solution is that the communication traffic is determined based on historical monitoring data of the communication equipment of the 5G base station.

[0014] A further technical solution is to divide the historical energy consumption monitoring period of the 5G base station into multiple similar monitoring period groups, including:

[0015] Determine the communication flow interval in which the historical energy consumption monitoring period is located by using the communication flow of different historical energy consumption monitoring periods, and divide the historical energy consumption monitoring periods in the same communication flow interval into the same candidate group;

[0016] Determine the deviation of weather data in different dimensions between historical energy consumption monitoring periods according to weather data of historical energy consumption monitoring periods in different candidate groups, and determine the weather similarity coefficient between historical energy consumption monitoring periods using the average value of the deviation of weather data in different dimensions;

[0017] The historical energy consumption monitoring periods in the same candidate group whose weather similarity coefficient is greater than the preset similarity coefficient are divided into the same similar monitoring period group.

[0018] A further technical solution is that the energy consumption anomaly coefficient of the specific date has a value range between 0 and 1, wherein when the energy consumption anomaly coefficient of the specific date is greater than a preset anomaly coefficient, the specific date is determined to be an energy consumption anomaly date.

[0019] A further technical solution is that the method for determining the energy consumption abnormality coefficient is:

[0020] Based on the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times on a specific date and the preset energy consumption, determine the duration of time that the energy consumption monitoring data of different reliable monitoring base stations is greater than the preset energy consumption;

[0021] Determine the base station energy consumption anomaly coefficients of different reliable monitoring base stations according to the proportion of time periods when the energy consumption monitoring data of different reliable monitoring base stations is greater than the preset energy consumption amount;

[0022] The energy consumption abnormality coefficient of the specific date is determined by summing the base station energy consumption abnormality coefficients of different reliable monitoring base stations.

[0023] A further technical solution is that the method for determining the layout density optimization strategy of the 5G base stations in the designated area is:

[0024] Based on the communication traffic of the designated area in different time periods, determining the proportion of the number of time periods in different communication traffic intervals, and determining the frequency of different communication traffic intervals according to the proportion of the number of time periods;

[0025] Determine the average energy consumption of the reliable monitoring base stations in different communication flow intervals according to the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication flow rates in different communication flow intervals, and determine the energy consumption optimization interval in the communication flow interval by using the average energy consumption;

[0026] The energy consumption optimization demand coefficient is determined based on the frequency of different energy consumption optimization intervals, and the layout density optimization strategy of 5G base stations in the specified area is determined using the energy consumption optimization demand coefficient.

[0027] A further technical solution is to use the energy consumption optimization requirement coefficient to determine the layout density optimization strategy of the 5G base stations in the specified area, specifically including:

[0028] The energy consumption optimization requirement coefficient is used to determine the corresponding preset layout density of the specified area, and the layout density optimization strategy of the 5G base stations in the specified area is determined based on the preset layout density and the current layout density of the 5G base stations in the specified area.

[0029] On the other hand, the present application provides a 5G base station energy consumption detection and analysis system, which adopts the above-mentioned 5G base station energy consumption detection and analysis method, specifically including:

[0030] Monitoring base station classification module, abnormal date screening module, optimization processing judgment module, layout optimization module;

[0031] The monitoring base station classification module is responsible for dividing the historical energy consumption monitoring period of the 5G base station into a plurality of similar monitoring period groups based on weather data and communication traffic, and determining the reliable monitoring base station among the 5G base stations by using the similarity of energy consumption monitoring data of different similar monitoring period groups;

[0032] The abnormal date screening module is responsible for dividing the 5G base station into multiple areas by using the service objects of the 5G base station, and determining the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times and the preset energy consumption based on the energy consumption monitoring data of the reliable monitoring base station in the specified area on different dates, and determining the energy consumption abnormality coefficient and energy consumption abnormality date on different dates based on the deviation;

[0033] The optimization processing judgment module is responsible for obtaining the distribution data of the energy consumption abnormality date of the specified area, and determining whether the 5G base station in the specified area needs to be optimized based on the energy consumption abnormality coefficients of different energy consumption abnormality dates;

[0034] The layout optimization module is responsible for obtaining the communication traffic in the designated area at different time periods, and determining the layout density optimization strategy of the 5G base stations in the designated area in combination with the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication traffic.

[0035] The beneficial effects of the present invention are:

[0036] 1. The similarity of energy consumption monitoring data in different similar monitoring time groups is used to determine reliable monitoring base stations among 5G base stations, thereby realizing the screening of base stations with more reliable energy consumption monitoring from the perspective of similar situations in energy consumption monitoring areas under the same conditions, avoiding the inability to accurately determine the actual energy consumption of 5G base stations in a specified area due to unreliable energy consumption monitoring data, and laying the foundation for further improving the operational reliability of 5G base stations.

[0037] 2. Based on the communication traffic in the designated area at different time periods and the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication traffic, the layout density optimization strategy of the 5G base stations in the designated area is implemented, thereby realizing the screening of the communication traffic intervals that need energy consumption optimization based on the differences in energy consumption monitoring data under different communication traffic. By optimizing the layout density, it is ensured that different 5G base stations can work reliably in different communication traffic intervals, thereby further improving the operational reliability and stability of the communication system.

[0038] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings;

[0041] Figure 1 It is a flow chart of a 5G base station energy consumption detection and analysis method;

[0042] Figure 2 is a flow chart of a method for determining a reliable monitoring base station in a 5G base station;

[0043] Figure 3 is a flow chart of a method for determining an abnormal coefficient of energy consumption;

[0044] Figure 4 is a flow chart of a method for determining a layout density optimization strategy for 5G base stations in a specified area;

[0045] Figure 5 It is a framework diagram of a 5G base station energy consumption detection and analysis system. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0047] In some areas, due to the influence of external environment and weather factors, the energy consumption of 5G base stations is greater under the same communication traffic. Excessive energy consumption may cause the power supply module of the 5G base station to be in an overloaded state, which will inevitably lead to the operation reliability of the 5G base station to deteriorate. Therefore, if the layout of the 5G base station cannot be optimized in combination with the analysis results of the energy consumption of the 5G base station, the security and reliability of the communication system cannot be guaranteed.

[0048] Reliable monitoring base station among 5G base stations: a 5G base station whose energy consumption monitoring data deviation in different similar monitoring time groups is within 3%.

[0049] Energy consumption abnormality coefficient: It is determined based on the deviation between the energy consumption monitoring data and the preset energy consumption and the ratio of the preset energy consumption. When the energy consumption abnormality coefficient is greater than 0.1, the date is determined as the energy consumption abnormality date.

[0050] Determine whether the 5G base stations in the specified area need to be optimized: This is to avoid the 5G base stations in the specified area from running in an overloaded state, so that the operation reliability of the 5G base stations in the specified area cannot meet the requirements. It is specifically determined based on the product of the proportion of the number of energy consumption abnormality dates in the specified area and the average value of the energy consumption abnormality coefficient on different energy consumption abnormality dates. When the proportion is 0.2 and the average value of the energy consumption abnormality coefficient is 0.2, the comprehensive abnormality coefficient is 0.04. When the comprehensive abnormality coefficient is greater than 0.01, it is determined that the 5G base stations in the specified area need to be optimized.

[0051] Determination of the layout density optimization strategy of 5G base stations in the specified area: determine the energy consumption optimization interval based on the average energy consumption in the reliable monitoring base station in different communication traffic intervals, determine the energy consumption optimization demand coefficient based on the sum of the operating time proportions of different energy consumption optimization intervals, and use the energy consumption optimization demand coefficient to determine the layout density optimization strategy of 5G base stations in the specified area. Example

[0052] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a 5G base station energy consumption detection and analysis method is provided, which specifically includes:

[0053] S1 divides the historical energy consumption monitoring period of the 5G base station into a plurality of similar monitoring period groups based on weather data and communication traffic, and determines a reliable monitoring base station among the 5G base stations by using similar situations of energy consumption monitoring data of different similar monitoring period groups;

[0054] Furthermore, the weather data includes humidity, temperature and sunlight.

[0055] It can be understood that the communication traffic is determined based on the historical monitoring data of the communication equipment of the 5G base station.

[0056] It should be noted that the historical energy consumption monitoring period of 5G base stations is divided into multiple similar monitoring period groups, including:

[0057] Determine the communication flow interval in which the historical energy consumption monitoring period is located by using the communication flow of different historical energy consumption monitoring periods, and divide the historical energy consumption monitoring periods in the same communication flow interval into the same candidate group;

[0058] Determine the deviation of weather data in different dimensions between historical energy consumption monitoring periods according to weather data of historical energy consumption monitoring periods in different candidate groups, and determine the weather similarity coefficient between historical energy consumption monitoring periods using the average value of the deviation of weather data in different dimensions;

[0059] The historical energy consumption monitoring periods in the same candidate group whose weather similarity coefficient is greater than the preset similarity coefficient are divided into the same similar monitoring period group.

[0060] It is understandable that if Figure 2 As shown, the method for determining the reliable monitoring base station in the 5G base station is:

[0061] The historical energy consumption monitoring period in the same similar monitoring period group is used as the energy consumption comparison period, and the basic energy consumption monitoring amount of different similar monitoring period groups is determined by the average value of the energy consumption monitoring amount of different energy consumption comparison periods;

[0062] Determine the energy consumption deviation period in different similar monitoring period groups according to the deviation between the energy consumption monitoring amount and the basic energy consumption monitoring amount, and determine the energy consumption monitoring deviation coefficient of different similar monitoring period groups according to the number ratio of the energy consumption deviation period;

[0063] The weight coefficients of different similar monitoring period groups are determined by the number of historical energy consumption monitoring periods in different similar monitoring period groups, the energy consumption deviation coefficient of the 5G base station is determined based on the product of the weight coefficients of different similar monitoring period groups and the energy consumption monitoring deviation coefficient, and the reliable monitoring base station among the 5G base stations is determined based on the energy consumption deviation coefficient.

[0064] Furthermore, the energy consumption deviation period is a historical energy consumption monitoring period in which the deviation between the energy consumption monitoring amount and the basic energy consumption monitoring amount is greater than a preset deviation.

[0065] It should also be noted that when the energy consumption deviation coefficient of the 5G base station is not within the preset deviation coefficient range, the 5G base station is determined to be a reliable monitoring base station.

[0066] In another embodiment, the method for determining the reliable monitoring base station in the 5G base station is:

[0067] The historical energy consumption monitoring period in the same similar monitoring period group is used as the energy consumption comparison period, and the basic energy consumption monitoring amount of different similar monitoring period groups is determined by the average value of the energy consumption monitoring amount of different energy consumption comparison periods;

[0068] Determine the energy consumption deviation period in different similar monitoring period groups according to the deviation between the energy consumption monitoring amount and the basic energy consumption monitoring amount;

[0069] The monitoring deviation factor of the energy consumption deviation period is determined by the deviation between the energy consumption monitoring amount and the basic energy consumption monitoring amount during the energy consumption deviation period, the energy consumption deviation coefficient of the 5G base station is determined according to the sum of the monitoring deviation factors of different energy consumption deviation periods, and the reliable monitoring base station among the 5G base stations is determined according to the energy consumption deviation coefficient.

[0070] In another embodiment, the method for determining the reliable monitoring base station in the 5G base station is:

[0071] S11 uses the historical energy consumption monitoring period in the same similar monitoring period group as the energy consumption comparison period, determines the basic energy consumption monitoring amount of different similar monitoring period groups by the average value of the energy consumption monitoring amount of different energy consumption comparison periods, and determines whether there is an energy consumption deviation period according to the deviation between the energy consumption monitoring amount and the basic energy consumption monitoring amount. If yes, proceed to the next step; if not, determine that the 5G base station is a reliable monitoring base station;

[0072] S12 determines the energy consumption deviation periods in different similar monitoring period groups, and determines whether the number of the energy consumption deviation periods is greater than the number of preset periods. If so, proceed to the next step; if not, proceed to step S15;

[0073] S13 determines whether the number of similar monitoring period groups whose number of energy consumption deviation periods is greater than the number of preset deviation periods meets the requirement. If not, it is determined that the 5G base station is not a reliable monitoring base station. If so, proceed to the next step.

[0074] S14 determines the energy consumption monitoring deviation coefficients of different similar monitoring period groups by using the number of energy consumption deviation periods and the proportion of the number, and determines whether there is a similar monitoring period group whose energy consumption monitoring deviation coefficient does not meet the requirements. If so, it is determined that the 5G base station does not belong to a reliable monitoring base station. If not, proceed to the next step;

[0075] S15 determines the weight coefficients of different similar monitoring period groups according to the number of historical energy consumption monitoring periods of different similar monitoring period groups, determines the energy consumption deviation coefficient of the 5G base station based on the product of the weight coefficients of different similar monitoring period groups and the energy consumption monitoring deviation coefficient, and determines the reliable monitoring base station among the 5G base stations according to the energy consumption deviation coefficient.

[0076] S2 divides the 5G base station into multiple areas using the service objects of the 5G base station, and determines the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times and the preset energy consumption based on the energy consumption monitoring data of the reliable monitoring base station in the specified area on different dates, and determines the energy consumption abnormality coefficient and energy consumption abnormality date on different dates based on the deviation;

[0077] Furthermore, the 5G base station is divided into multiple areas, specifically including:

[0078] The setting location of the 5G base station is used to determine the service object of the 5G base station, and 5G base stations with the same service objects are divided into the same area.

[0079] Specifically, the preset energy consumption is determined according to the rated power of the power supply device of the 5G base station.

[0080] Specifically, Figure 3 As shown, the method for determining the energy consumption abnormality coefficient is:

[0081] Based on the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times on a specific date and the preset energy consumption, the time when the base station has abnormal energy consumption is determined, and it is used as the abnormal energy consumption time. The basic energy consumption abnormality coefficient of the specific date is determined by the proportion of the number of abnormal energy consumption times;

[0082] Determine the number of energy consumption abnormal base stations at different energy consumption abnormal moments, and determine energy consumption abnormal factors at different energy consumption abnormal moments in combination with the deviation between energy consumption monitoring data of different energy consumption abnormal base stations and preset energy consumption amounts;

[0083] The energy consumption abnormality coefficient on a specific date is determined by multiplying the average value of the energy consumption abnormality factors at different energy consumption abnormality moments by the basic energy consumption abnormality coefficient.

[0084] It should be noted that, when the energy consumption monitoring data of the reliable monitoring base station is greater than the preset energy consumption, the reliable monitoring base station is determined to be an energy consumption abnormal base station.

[0085] Furthermore, the energy consumption anomaly coefficient of the specific date has a value range between 0 and 1, wherein when the energy consumption anomaly coefficient of the specific date is greater than a preset anomaly coefficient, the specific date is determined to be an energy consumption anomaly date.

[0086] It should also be noted that the method for determining the energy consumption abnormality coefficient is:

[0087] Based on the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times on a specific date and the preset energy consumption, determine the duration of time that the energy consumption monitoring data of different reliable monitoring base stations is greater than the preset energy consumption;

[0088] Determine the base station energy consumption anomaly coefficients of different reliable monitoring base stations according to the proportion of time periods when the energy consumption monitoring data of different reliable monitoring base stations is greater than the preset energy consumption amount;

[0089] The energy consumption abnormality coefficient of the specific date is determined by summing the base station energy consumption abnormality coefficients of different reliable monitoring base stations.

[0090] It should also be noted that the method for determining the energy consumption abnormality coefficient is:

[0091] S21 determines whether there is an abnormal energy consumption base station on the specific date based on the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times on the specific date and the preset energy consumption amount. If so, proceed to the next step. If not, determine that the specific date is not an abnormal energy consumption date.

[0092] S22 determines the time when there is an abnormal energy consumption base station, and takes it as the abnormal energy consumption time, and determines whether the number of the abnormal energy consumption time is less than the preset time number, if yes, proceeds to the next step, if no, proceeds to step S25;

[0093] S23 determines the number of energy consumption abnormal base stations at different energy consumption abnormal moments, and determines whether the number of energy consumption abnormal moments when the number of energy consumption abnormal base stations is greater than the preset number of base stations meets the requirement, if so, proceeds to the next step, if not, proceeds to step S25;

[0094] S24 determines whether there is an abnormal energy consumption base station whose deviation does not meet the requirement based on the deviation of the energy consumption monitoring data of different abnormal energy consumption base stations and the preset energy consumption amount. If so, proceed to the next step. If not, determine that the specific date does not belong to the abnormal energy consumption date.

[0095] S25 determines the basic energy consumption abnormality coefficient of a specific date by the proportion of the number of energy consumption abnormal moments, determines the number of energy consumption abnormal base stations at different energy consumption abnormal moments, and determines the energy consumption abnormality factors at different energy consumption abnormal moments based on the deviation between the energy consumption monitoring data of different energy consumption abnormal base stations and the preset energy consumption. The energy consumption abnormality coefficient of a specific date is determined by multiplying the average value of the energy consumption abnormality factors at different energy consumption abnormal moments and the basic energy consumption abnormality coefficient.

[0096] S3 obtains the distribution data of the energy consumption abnormality date of the specified area, and when it is determined that the 5G base station in the specified area needs to be optimized by combining the energy consumption abnormality coefficients of different energy consumption abnormality dates, proceeds to the next step;

[0097] Further, it is determined that the 5G base stations in the designated area need to be optimized, specifically including:

[0098] Dividing the energy consumption abnormality dates into different date intervals according to the distribution data of the energy consumption abnormality dates in the designated area;

[0099] Determine the interval abnormality coefficients of different date intervals by multiplying the proportion of the number of energy consumption abnormality dates in different date intervals by the average value of the energy consumption abnormality coefficients of different energy consumption abnormality dates;

[0100] Determine the basic weight values ​​for different date intervals, and determine the base station energy consumption anomaly values ​​of the specified area in combination with the interval anomaly coefficients of the different date intervals, and use the base station energy consumption anomaly values ​​to determine whether the 5G base stations in the specified area need to be optimized.

[0101] It should be noted that when the abnormal energy consumption value of the base station in the specified area is greater than the preset abnormal threshold, it is determined that the 5G base station in the specified area needs to be optimized.

[0102] Specifically, determining that the 5G base stations in the designated area need to be optimized includes:

[0103] S31 obtains the number of energy consumption abnormal dates in the specified area, and determines whether the number of energy consumption abnormal dates in the specified area is greater than the preset number of dates. If so, it is determined that the 5G base station in the specified area needs to be optimized. If not, proceed to the next step.

[0104] S32 divides the energy consumption abnormality dates into different date intervals according to the distribution data of the energy consumption abnormality dates in the specified area, and determines whether there is a date interval in which the number of energy consumption abnormality dates accounts for a greater proportion than the preset date proportion. If yes, proceed to the next step; if not, proceed to step S36;

[0105] S33 determines whether the number of date intervals in which the proportion of abnormal energy consumption dates is greater than the preset date proportion meets the requirement, if so, proceeds to the next step, if not, determines that the 5G base stations in the specified area need to be optimized;

[0106] S34 determines the interval abnormality coefficients of different date intervals by multiplying the proportion of the number of energy consumption abnormality dates in different date intervals by the average value of the energy consumption abnormality coefficients of different energy consumption abnormality dates, and judges whether the nipple abnormality coefficient of the preset date interval meets the requirements. If so, proceed to the next step; if not, determine that the 5G base station in the specified area needs to be optimized;

[0107] S35 determines whether the number of date intervals whose interval anomaly coefficients do not meet the requirements meets the requirements, if so, proceeds to the next step, if not, determines that the 5G base stations in the specified area need to be optimized;

[0108] S36 determines the basic weight values ​​of different date intervals, and determines the base station energy consumption abnormality values ​​of the specified area in combination with the interval abnormality coefficients of different date intervals, and uses the base station energy consumption abnormality values ​​to determine whether the 5G base stations in the specified area need to be optimized.

[0109] S4 obtains the communication traffic of the designated area in different time periods, and determines the layout density optimization strategy of the 5G base stations in the designated area in combination with the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication traffic.

[0110] Specifically, Figure 4 As shown, the method for determining the layout density optimization strategy of the 5G base stations in the specified area is:

[0111] Based on the communication traffic of the designated area in different time periods, determining the proportion of the number of time periods in different communication traffic intervals, and determining the frequency of different communication traffic intervals according to the proportion of the number of time periods;

[0112] Determine the average energy consumption of the reliable monitoring base stations in different communication flow intervals according to the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication flow rates in different communication flow intervals, and determine the energy consumption optimization interval in the communication flow interval by using the average energy consumption;

[0113] The energy consumption optimization demand coefficient is determined based on the frequency of different energy consumption optimization intervals, and the layout density optimization strategy of 5G base stations in the specified area is determined using the energy consumption optimization demand coefficient.

[0114] Furthermore, the energy consumption optimization requirement coefficient is used to determine the layout density optimization strategy of the 5G base stations in the specified area, specifically including:

[0115] The energy consumption optimization requirement coefficient is used to determine the corresponding preset layout density of the specified area, and the layout density optimization strategy of the 5G base stations in the specified area is determined based on the preset layout density and the current layout density of the 5G base stations in the specified area.

[0116] Real-time Example 2

[0117] On the other hand, Figure 5 As shown, the present application provides a 5G base station energy consumption detection and analysis system, which adopts the above-mentioned 5G base station energy consumption detection and analysis method, specifically including:

[0118] Monitoring base station classification module, abnormal date screening module, optimization processing judgment module, layout optimization module;

[0119] The monitoring base station classification module is responsible for dividing the historical energy consumption monitoring period of the 5G base station into a plurality of similar monitoring period groups based on weather data and communication traffic, and determining the reliable monitoring base station among the 5G base stations by using the similarity of energy consumption monitoring data of different similar monitoring period groups;

[0120] The abnormal date screening module is responsible for dividing the 5G base station into multiple areas by using the service objects of the 5G base station, and determining the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times and the preset energy consumption based on the energy consumption monitoring data of the reliable monitoring base station in the specified area on different dates, and determining the energy consumption abnormality coefficient and energy consumption abnormality date on different dates based on the deviation;

[0121] The optimization processing judgment module is responsible for obtaining the distribution data of the energy consumption abnormality date of the specified area, and determining whether the 5G base station in the specified area needs to be optimized based on the energy consumption abnormality coefficients of different energy consumption abnormality dates;

[0122] The layout optimization module is responsible for obtaining the communication traffic in the designated area at different time periods, and determining the layout density optimization strategy of the 5G base stations in the designated area in combination with the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication traffic.

[0123] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0124] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A 5G base station energy consumption detection and analysis method, characterized in that: Specifically include: Based on weather data and communication traffic, the historical energy consumption monitoring period of the 5G base station is divided into a plurality of similar monitoring period groups, and the similarity of energy consumption monitoring data of different similar monitoring period groups is used to determine the reliable monitoring base station among the 5G base stations; The 5G base station is divided into multiple areas by using the service objects of the 5G base station, and the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times and the preset energy consumption is determined based on the energy consumption monitoring data of the reliable monitoring base station in the specified area on different dates, and the energy consumption abnormality coefficient and energy consumption abnormality date on different dates are determined based on the deviation; Obtain the distribution data of the energy consumption abnormality date of the specified area, and when it is determined that the 5G base station in the specified area needs to be optimized by combining the energy consumption abnormality coefficients of different energy consumption abnormality dates, proceed to the next step; Obtain the communication traffic of the designated area at different time periods, and determine the layout density optimization strategy of the 5G base stations in the designated area in combination with the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication traffic; The method for determining a reliable monitoring base station in the 5G base station is: The historical energy consumption monitoring period in the same similar monitoring period group is used as the energy consumption comparison period, and the basic energy consumption monitoring amount of different similar monitoring period groups is determined by the average value of the energy consumption monitoring amount of different energy consumption comparison periods; Determine the energy consumption deviation period in different similar monitoring period groups according to the deviation between the energy consumption monitoring amount and the basic energy consumption monitoring amount, and determine the energy consumption monitoring deviation coefficient of different similar monitoring period groups according to the number ratio of the energy consumption deviation period; Determine the weight coefficients of different similar monitoring period groups according to the number of historical energy consumption monitoring periods of different similar monitoring period groups, determine the energy consumption deviation coefficient of the 5G base station based on the product of the weight coefficients of different similar monitoring period groups and the energy consumption monitoring deviation coefficient, and determine the reliable monitoring base station among the 5G base stations according to the energy consumption deviation coefficient; The energy consumption deviation period is a historical energy consumption monitoring period in which the deviation between the energy consumption monitoring amount and the basic energy consumption monitoring amount is greater than a preset deviation amount; The historical energy consumption monitoring period of the 5G base station is divided into multiple similar monitoring period groups, including: Determine the communication flow interval in which the historical energy consumption monitoring period is located by using the communication flow of different historical energy consumption monitoring periods, and divide the historical energy consumption monitoring periods in the same communication flow interval into the same candidate group; Determine the deviation of weather data in different dimensions between historical energy consumption monitoring periods according to weather data of historical energy consumption monitoring periods in different candidate groups, and determine the weather similarity coefficient between historical energy consumption monitoring periods using the average value of the deviation of weather data in different dimensions; The historical energy consumption monitoring periods in the same candidate group whose weather similarity coefficient is greater than the preset similarity coefficient are divided into the same similar monitoring period group.

2. The 5G base station energy consumption detection and analysis method according to claim 1, characterized in that: The weather data includes humidity, temperature and sunlight.

3. The 5G base station energy consumption detection and analysis method according to claim 1, characterized in that: The communication traffic is determined based on historical monitoring data of the communication equipment of the 5G base station.

4. The 5G base station energy consumption detection and analysis method according to claim 1, characterized in that: The 5G base stations are divided into multiple areas, including: The setting location of the 5G base station is used to determine the service object of the 5G base station, and 5G base stations with the same service objects are divided into the same area.

5. The 5G base station energy consumption detection and analysis method according to claim 1, characterized in that: The preset energy consumption is determined based on the rated power of the power supply device of the 5G base station.

6. The 5G base station energy consumption detection and analysis method according to claim 1, characterized in that: The method for determining the layout density optimization strategy of the 5G base stations in the specified area is: Based on the communication traffic of the designated area in different time periods, determining the proportion of the number of time periods in different communication traffic intervals, and determining the frequency of different communication traffic intervals according to the proportion of the number of time periods; Determine the average energy consumption of the reliable monitoring base stations in different communication flow intervals according to the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication flow rates in different communication flow intervals, and determine the energy consumption optimization interval in the communication flow interval by using the average energy consumption; The energy consumption optimization demand coefficient is determined based on the frequency of different energy consumption optimization intervals, and the layout density optimization strategy of 5G base stations in the specified area is determined using the energy consumption optimization demand coefficient.

7. A 5G base station energy consumption detection and analysis system, using a 5G base station energy consumption detection and analysis method according to any one of claims 1 to 6, characterized in that: Specifically include: Monitoring base station classification module, abnormal date screening module, optimization processing judgment module, layout optimization module; The monitoring base station classification module is responsible for dividing the historical energy consumption monitoring period of the 5G base station into a plurality of similar monitoring period groups based on weather data and communication traffic, and determining the reliable monitoring base station among the 5G base stations by using the similarity of energy consumption monitoring data of different similar monitoring period groups; The abnormal date screening module is responsible for dividing the 5G base station into multiple areas by using the service objects of the 5G base station, and determining the deviation between the energy consumption monitoring data of the reliable monitoring base station at different times and the preset energy consumption based on the energy consumption monitoring data of the reliable monitoring base station in the specified area on different dates, and determining the energy consumption abnormality coefficient and energy consumption abnormality date on different dates based on the deviation; The optimization processing judgment module is responsible for obtaining the distribution data of the energy consumption abnormality date of the specified area, and determining whether the 5G base station in the specified area needs to be optimized based on the energy consumption abnormality coefficients of different energy consumption abnormality dates; The layout optimization module is responsible for obtaining the communication traffic in the designated area at different time periods, and determining the layout density optimization strategy of the 5G base stations in the designated area in combination with the energy consumption monitoring data of the reliable monitoring base stations in the designated area under different communication traffic.

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