CN113055904A - Base station data analysis method, base station data analysis device and computer readable storage medium - Google Patents
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Abstract
The disclosure provides a base station data analysis method and device and a computer readable storage medium, and relates to the technical field of big data. The disclosed base station data analysis method comprises: acquiring base station load data according to a target tidal cycle; acquiring a list of clusters taking each data point in base station load data as a core based on an improved DBSCAN algorithm, wherein the improved DBSCAN algorithm is that only forward and backward scanning is carried out in sequence on the basis of the DBSCAN algorithm, and the difference value between a newly added point in a cluster and a core point is in a preset range; determining an energy saving period under the target tidal cycle based on the base station load data for each cluster in the list of clusters, and a predetermined cluster load threshold, a predetermined tidal coefficient threshold, and a predetermined minimum continuous period threshold. By the method, the accuracy of determining the tidal load of the base station can be improved, and the differentiation and the fine management of the energy conservation of the base station can be optimized.
Description
Technical Field
The present disclosure relates to the field of big data technologies, and in particular, to a method and an apparatus for analyzing base station data, and a computer-readable storage medium.
Background
Due to the new characteristics of high frequency, large bandwidth, large-scale antenna and the like, the energy consumption of the 5G base station is at least 3-4 times that of the 4G base station, and the requirements of the 5G base station on energy conservation and consumption reduction are very urgent. Actually, the 4G base station generally has the problems of over coverage and low load, and has urgent need for energy saving of the base station, and currently, energy saving measures for the 4G base station are also being implemented.
The existing energy-saving scheme of the base station mainly utilizes the tidal effect of the base station load to implement the energy-saving strategy at the tidal time, but currently, no more complete definition is provided for the tide, and the energy-saving time is still under the extensive management stage, for example, the energy-saving strategy is implemented at 1-6 points in the morning for all the base stations.
Disclosure of Invention
It is an object of the present disclosure to improve the accuracy of base station tidal load determination and to improve energy savings.
According to an aspect of some embodiments of the present disclosure, a base station data analysis method is provided, including: acquiring base station load data according to a target tidal cycle; acquiring a list of clusters taking each data point in base station load data as a core Based on an improved DBSCAN (Density-Based noisy Application space Clustering) algorithm, wherein the improved DBSCAN algorithm is that only forward and backward scanning is carried out in sequence on the basis of the DBSCAN algorithm, and the difference value between a newly added point in a cluster and the core point is in a preset range; determining an energy saving period under the target tidal cycle based on the base station load data for each cluster in the list of clusters, and a predetermined cluster load threshold, a predetermined tidal coefficient threshold, and a predetermined minimum continuous period threshold.
In some embodiments, acquiring base station load data from the target tidal cycle comprises: determining an acquisition period according to the target tidal period; and determining base station load data corresponding to the target tidal cycle through a preset strategy according to the load data in the acquisition cycle, wherein each acquisition cycle corresponds to one data point.
In some embodiments, determining the energy saving period at the target tidal cycle based on the base station load data for each cluster in the list of clusters and the predetermined cluster load threshold, the predetermined tidal coefficient threshold, and the predetermined minimum continuous period threshold comprises: determining the cluster with the largest number of data points in the cluster list as a candidate target cluster; determining cluster load and tide coefficient of the candidate target cluster; removing data points contained in the candidate target cluster from the cluster list, and continuing to execute the operation of determining the candidate target cluster until the number of the data points in each cluster in the cluster list is less than the number of the data points corresponding to the preset minimum continuous time period threshold; and determining the time period corresponding to the cluster with the cluster load less than or equal to a preset cluster load threshold value and the tide coefficient less than or equal to a preset tide coefficient threshold value in the candidate target cluster, wherein the time period is the energy-saving time period in the target tide cycle.
In some embodiments, obtaining the list of clusters centered on each data point in the base station load data based on the modified DBSCAN algorithm comprises, for each data point as a starting point and a core point, sequentially performing: judging whether the difference value of the current data point and the value of the forward or backward adjacent data point is within a preset difference value range; under the condition that the difference value between the value of the current data point and the value of the adjacent data point is determined to be in a preset difference value range, judging whether the difference value between the adjacent data point and the core point is in the preset difference value range or not; under the condition that the difference value between the adjacent data point and the core point is in a preset range, bringing the adjacent data point into a cluster taking the current core point as the core point, taking the adjacent data point as the current data point, and continuously executing the operation of judging whether the difference value between the values of the current data point and the adjacent data point is in the preset difference value range; otherwise, the adjacent data points do not belong to the cluster taking the current core point as the core point, and the cluster determination taking the current core point as the core point is completed.
In some embodiments, the base station data analysis method further comprises: and sequentially appointing the target tidal cycle so as to determine corresponding base station load data according to the target tidal cycle and determine the energy-saving time period corresponding to the target tidal cycle.
In some embodiments, the target tidal cycle comprises one or more of an annual cycle, a quarterly cycle, a monthly cycle, or a daily cycle.
In some embodiments, the base station data analysis method further comprises: the base station is configured to be in an energy saving supply mode during an energy saving period.
By the method, the base station load period classification can be realized based on the improved DBSCAN, tides are analyzed in various periodic period classification scenes, corresponding energy-saving periods are output, the accuracy of determining the tide load of the base station is improved, and the differentiation and fine management of base station energy saving are optimized.
According to an aspect of some embodiments of the present disclosure, there is provided a base station data analysis apparatus including: a data acquisition unit configured to acquire base station load data according to a target tidal cycle; the cluster unit is configured to acquire a list of clusters taking each data point in the base station load data as a core based on an improved DBSCAN algorithm, wherein the improved DBSCAN algorithm is that only forward and backward scanning is performed in sequence on the basis of the DBSCAN algorithm, and the difference value between a newly added point in a cluster and the core point is in a preset range; an energy-saving period determination unit configured to determine an energy-saving period in a target tidal cycle based on the base station load data for each cluster in the list of clusters, and a predetermined cluster load threshold, a predetermined tidal coefficient threshold, and a predetermined minimum continuous period threshold.
In some embodiments, the base station data analysis apparatus further comprises: a target modification unit configured to sequentially designate the target tidal cycles so that the energy saving period determination unit determines the energy saving periods corresponding to the target tidal cycles.
According to an aspect of some embodiments of the present disclosure, there is provided a base station data analysis apparatus including: a memory; and a processor coupled to the memory, the processor configured to perform any of the above base station data analysis methods based on instructions stored in the memory.
The base station data analysis device can realize base station load period classification based on the improved DBSCAN, analyzes tides under various periodic period classification scenes, outputs corresponding energy-saving periods, improves the accuracy of base station tide load determination, and optimizes differentiation and fine management of base station energy saving.
According to an aspect of some embodiments of the present disclosure, a computer-readable storage medium is proposed, on which computer program instructions are stored, which instructions, when executed by a processor, implement the steps of any of the above base station data analysis methods.
By executing the instructions on the computer-readable storage medium, base station load period classification can be realized based on the improved DBSCAN, tides are analyzed under various periodic period classification scenes, corresponding energy-saving periods are output, the accuracy of base station tidal load determination is improved, and differentiation and fine management of base station energy saving are optimized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a flow chart of some embodiments of a base station data analysis method of the present disclosure.
Fig. 2 is a flowchart of some embodiments of determining an energy saving period in the base station data analysis method of the present disclosure.
Fig. 3 is a flow chart of some embodiments of determining a list of clusters in a base station data analysis method of the present disclosure.
Fig. 4 is a schematic diagram of some embodiments of a base station data analysis method of the present disclosure.
Fig. 5 is a schematic diagram of another embodiment of a base station data analysis method according to the present disclosure.
Fig. 6 is a schematic diagram of some embodiments of a base station data analysis apparatus of the present disclosure.
Fig. 7 is a schematic diagram of another embodiment of a base station data analysis apparatus according to the present disclosure.
Fig. 8 is a schematic diagram of a base station data analysis apparatus according to still other embodiments of the disclosure.
Detailed Description
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
A flow chart of some embodiments of a base station data analysis method of the present disclosure is shown in fig. 1.
In step 101, base station load data is acquired from a target tidal cycle. In some embodiments, the target tidal cycle may include a year cycle, a quarter cycle, a month cycle, and a day cycle, with the data acquisition period corresponding to each data point in the base station load data being different for each target tidal cycle. For example, for the case that the target tidal cycle is a daily cycle, the sampled data within one hour or half hour may be calculated based on a predetermined strategy, such as taking an average, median, maximum, etc. of the downlink PRBs to obtain the base station load data.
In step 102, a list of clusters centered around each data point in the base station load data is obtained based on the modified DBSCAN algorithm. The DBSCAN is a clustering algorithm based on density, and is characterized in that a scanning radius (eps) and a minimum contained point number (minPts) are set, neighborhood scanning is carried out to generate core points to form clusters, and the clusters are expanded through continuous recursive processing.
The improved DBSCAN algorithm is based on the DBSCAN algorithm, only forward and backward scanning is carried out in sequence, and the difference value between the newly added point and the core point in the cluster is in a preset range. For example, the day tide, arranges data in sequence from 0 to 23 points, firstly presets 0 point as the core point, forwards 23 points, or backwards 1 point to check whether the data is within the scanning radius, if the data is included in the cluster and continues to scan forwards 22 points, or backwards 2 points, if the load difference with 0 point is also within the scanning radius, the inclusion of the cluster continues to circulate until no point can be included in the cluster, and then continues to perform cluster expansion by using 1 point as the core point until the cluster expansion is completed at 24 time points.
In step 103, an energy saving period for the target tidal cycle is determined based on the base station load data for each cluster in the list of clusters, as well as a predetermined cluster load threshold, a predetermined tidal coefficient threshold, and a predetermined minimum continuous period threshold. In some embodiments, the base station is configured to be in an energy saving supply mode during an energy saving period, thereby reducing energy consumption.
By the method, the base station load period classification can be realized based on the improved DBSCAN, the load tide is analyzed under various periodic period classification scenes, the corresponding energy-saving period is output, the accuracy of determining the base station tide load is improved, and the differentiation and fine management of base station energy saving are optimized.
In some embodiments, as shown in FIG. 1, step 104 may also be included.
In step 104, in the event that an energy saving period for the full target tidal cycle is not available, the target tidal cycle is modified and step 101 is performed. In some embodiments, the historical data is classified in time periods sequentially from a large period to a small period, and for base station data with large-period tide, tide analysis of small period is respectively performed for tide and tide of large period until day tide is analyzed, and energy-saving time periods under different classifications can be output by combining cluster load and tide coefficient.
By such a method, a smaller level of tides can be further acquired on the basis of acquiring large periodic time period classification.
In some embodiments, the base station data analysis method can perform data analysis by taking a single base station or a base station within a certain range as a unit, and further perform zone division and base station division energy-saving period determination and mode adjustment, so that the problem that the energy-saving period of the base station is set by adopting a one-time mode in the traditional energy-saving strategy without considering the difference between the base station and the base station (such as a market/office building and a dormitory/teaching building) and the problem that the difference between the tide and the tide of the same base station in different periods (such as school vacation and factory vacation) are not considered is solved, differential management is realized, and the fineness of base station energy-saving management is improved.
A flow chart of some embodiments of determining the energy saving period in the base station data analysis method of the present disclosure is shown in fig. 2.
In step 201, the cluster with the largest number of data points in the cluster list is determined as the candidate target cluster.
In step 202, cluster loads and tidal coefficients for the candidate target clusters are determined. In some embodiments, the cluster load may be an average, median, maximum, etc. of each load within the cluster; the tidal coefficient can be calculated using the following formula:
the tidal coefficient is the cluster load-the minimum load of the period/the load of the period is extremely poor.
In step 203, data points included in the candidate target cluster are culled from the list of clusters.
In step 204, it is determined whether there are clusters remaining in the cluster list, the number of data points of which is less than the number of data points corresponding to the predetermined minimum continuous period threshold, for example, all of the remaining clusters are single-point clusters.
If the number of the remaining clusters of data points is less than the number of data points corresponding to the predetermined minimum continuous time period threshold, go to step 205; otherwise, it is determined that the candidate target cluster has not been screened, step 201 is executed to determine a next candidate target cluster.
In step 205, the screening of the candidate target cluster is completed, the cluster load of the candidate target cluster is further compared with a predetermined cluster load threshold, the tide coefficient of the candidate target cluster is compared with a predetermined tide coefficient threshold, and a time interval corresponding to the candidate target cluster, in which the cluster load is less than or equal to the predetermined cluster load threshold and the tide coefficient is less than or equal to the predetermined tide coefficient threshold, is determined as an energy-saving time interval in the target tide cycle.
By the method, the time interval meeting the requirement of the energy-saving time interval can be obtained by circular screening on the basis of the cluster list, the obtained time intervals are not overlapped, the time interval with the longest time and stable load can be obtained first, and therefore the accuracy of obtaining the energy-saving time interval is improved.
A flow chart of some embodiments of determining a list of clusters in a base station data analysis method of the present disclosure is shown in fig. 3. Firstly, a core point is determined, and with the core point as an initial current data point, the following steps are performed:
in step 301, it is determined whether the difference between the values of the current data point and the adjacent data points (data points corresponding to forward or backward time points) is within a predetermined difference range. If the difference is within the predetermined range, go to step 302; if not, go to step 303. In some embodiments, a neighboring data point refers to a data point that is located on a side of the current data point that is further from the core point.
In step 302, it is determined whether the difference between the neighboring data points and the core point is within a predetermined difference range. If the difference is within the predetermined range, go to step 304; if not, go to step 303.
In step 303, it is determined that the neighboring data point does not belong to the cluster using the current core point as the core point, and the cluster determination using the current core point as the core point is completed.
In step 304, neighboring data points are included in the cluster having the current core point as the core point.
In step 305, the adjacent data point is taken as the current data point, and the process continues to step 301.
By the method, improvement can be performed based on a DBSCAN algorithm, the neighborhood scanning is limited to be carried out only the first time or the next time, and the application of the DBSCAN algorithm in the time series data processing process is realized; in addition, the load difference between the newly added point and the core point is required to be within a parameter limit range, so that the continuity of the time period and the stability of the load in the cluster are ensured.
In some embodiments, cluster expansion is then performed by arranging the data chronologically and presetting the time points as core points, as shown in fig. 4. Calculating cluster load and tide coefficient for an output cluster list which takes each time point as a core point, preferably selecting the cluster with the maximum number of points as a candidate target cluster, then eliminating the points in the candidate target cluster, checking whether the remaining clusters have only one point, if not, continuing the selection process until the remaining clusters only contain one point per se, and obtaining the following information of each candidate target cluster:
TABLE 1 calculation of various types of information for clusters in case of tidal calculation
As shown in fig. 4, the cluster with 0 point as the core point advances to 22 points, and 22 points and 21 points are closely connected according to the DBSCAN algorithm in the photo-correlation technique, and can be incorporated into the cluster, but in the present disclosure, load difference comparison with 0 point of the core is required, and then data corresponding to 21 points is removed outside the cluster. In this example, the cluster containing the longest period, the tidal coefficient 0.004, and the cluster load 0.038. The energy saving period can be output by combining the cluster load, the tidal coefficient and the minimum continuous time period number. For example, setting the cluster load to 0.1 as the energy saving threshold, and the minimum continuous period to 3, the two clusters of 22-7 points and 20-21 points in the example satisfy the threshold of the cluster load, and the two clusters are continuous, and the total time length is greater than 3, so that the whole period of 21-7 is the energy saving period. And the 13 o' clock period, although the cluster load is sufficiently low, cannot be outputted as the energy saving period because its continuous period is only 1.
In some implementation processes, time interval classification is carried out on historical data from a large period to a small period in sequence, and for base station data with large-period tide, tide analysis is respectively carried out on tide of the large period and tide of the small period until day tide is analyzed, and energy-saving time intervals under different classifications can be output by combining cluster load and tide coefficient. As shown in fig. 5. The two lines are the daily average load conditions of the two base stations counted daily, wherein the one framed by the box has obvious annual tidal effect, and the annual tide, tidal tide and tidal are identified to perform the tidal analysis respectively to obtain the corresponding energy-saving time period, and the obtained input can be shown in the following table.
TABLE 2 hierarchical tidal analysis schematic table
A schematic diagram of some embodiments of a base station data analysis apparatus of the present disclosure is shown in fig. 6.
The data acquisition unit 601 can acquire base station load data according to a target tidal cycle. In some embodiments, the target tidal cycle may include a year cycle, a quarter cycle, a month cycle, and a day cycle, with the data acquisition period corresponding to each data point in the base station load data being different for each target tidal cycle. For example, for the case that the target tidal cycle is a daily cycle, the sampled data within one hour or half hour may be calculated based on a predetermined strategy, such as taking an average, median, maximum, etc. of the downlink PRBs to obtain the base station load data.
The clustering unit 602 can obtain a list of clusters with each data point in the base station load data as a core based on an improved DBSCAN algorithm, where the improved DBSCAN algorithm is based on the DBSCAN algorithm, and only scans forwards and backwards in sequence, and a difference value between a new point in a cluster and a core point is within a predetermined range. For example, the day tide, arranges data in sequence from 0 to 23 points, firstly presets 0 point as the core point, forwards 23 points, or backwards 1 point to check whether the data is within the scanning radius, if the data is included in the cluster and continues to scan forwards 22 points, or backwards 2 points, if the load difference with 0 point is also within the scanning radius, the inclusion of the cluster continues to circulate until no point can be included in the cluster, and then continues to perform cluster expansion by using 1 point as the core point until the cluster expansion is completed at 24 time points.
The energy-saving period determination unit 603 can determine an energy-saving period in a target tidal cycle based on the base station load data of each cluster in the list of clusters, and a predetermined cluster load threshold, a predetermined tidal coefficient threshold, and a predetermined minimum continuous period threshold. In some embodiments, the base station is configured to be in an energy saving supply mode during an energy saving period, thereby reducing energy consumption.
The device can realize base station load period classification based on the improved DBSCAN, analyzes tides under various periodic period classification scenes, outputs corresponding energy-saving periods, improves the accuracy of base station tide load determination, and optimizes differentiation and fine management of base station energy saving.
In some embodiments, as shown in fig. 6, the base station data analysis apparatus may further include a target modification unit 604 capable of modifying the target tidal cycle in case that the energy saving period determination unit 603 fails to obtain the energy saving periods of all the target tidal cycles, and further activating the data acquisition unit 601 to acquire the energy saving period in the next cycle. In some embodiments, the historical data is classified in time periods sequentially from a large period to a small period, and for base station data with large-period tide, tide analysis of small period is respectively performed for tide and tide of large period until day tide is analyzed, and energy-saving time periods under different classifications can be output by combining cluster load and tide coefficient.
The device can further acquire smaller-level tides on the basis of acquiring large-cycle time interval classification, and is convenient for further fine management.
Fig. 7 is a schematic structural diagram of an embodiment of a base station data analysis apparatus according to the present disclosure. The base station data analysis apparatus includes a memory 701 and a processor 702. Wherein: the memory 701 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory is for storing instructions in the corresponding embodiments of the base station data analysis method above. Processor 702 is coupled to memory 701 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 702 is configured to execute instructions stored in the memory, and is capable of improving the accuracy of the tidal load determination of the base station, and optimizing differentiation and fine management of energy conservation of the base station.
In one embodiment, as also shown in fig. 8, the base station data analysis apparatus 800 includes a memory 801 and a processor 802. The processor 802 is coupled to the memory 801 by a BUS 803. The base station data analysis apparatus 800 may be further connected to an external storage device 805 through a storage interface 804 to call external data, and may be further connected to a network or another computer system (not shown) through a network interface 806. And will not be described in detail herein.
In the embodiment, the data instructions are stored in the memory and processed by the processor, so that the accuracy of determining the tidal load of the base station can be improved, and the differentiation and fine management of energy conservation of the base station can be optimized.
In another embodiment, a computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the method in the corresponding embodiment of the base station data analysis method. As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Thus far, the present disclosure has been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
Finally, it should be noted that: the above examples are intended only to illustrate the technical solutions of the present disclosure and not to limit them; although the present disclosure has been described in detail with reference to preferred embodiments, those of ordinary skill in the art will understand that: modifications to the specific embodiments of the disclosure or equivalent substitutions for parts of the technical features may still be made; all such modifications are intended to be included within the scope of the claims of this disclosure without departing from the spirit thereof.
Claims (11)
1. A base station data analysis method comprises the following steps:
acquiring base station load data according to a target tidal cycle;
acquiring a list of clusters taking each data point in the base station load data as a core based on an improved density-based application space clustering (DBSCAN) algorithm with noise, wherein the improved DBSCAN algorithm is that only forward and backward scanning is performed in sequence on the basis of the DBSCAN algorithm, and the difference value between a newly added point in a cluster and the core point is in a preset range;
determining an energy saving period under a target tidal cycle according to the base station load data of each cluster in the list of clusters, and a predetermined cluster load threshold, a predetermined tidal coefficient threshold and a predetermined minimum continuous period threshold.
2. The method of claim 1, wherein the acquiring base station load data from a target tidal cycle comprises:
determining an acquisition period from the target tidal period;
and determining base station load data corresponding to the target tidal cycle through a preset strategy according to the load data in the acquisition cycles, wherein each acquisition cycle corresponds to one data point.
3. The method of claim 1, wherein said determining an energy saving period at a target tidal cycle from the base station load data for each cluster in the list of clusters and a predetermined cluster load threshold, a predetermined tidal coefficient threshold, and a predetermined minimum continuous period threshold comprises:
determining the cluster with the largest number of data points in the cluster list as a candidate target cluster;
determining cluster loads and tidal coefficients for the candidate target clusters;
removing the data points contained in the candidate target cluster from the cluster list, and continuing to execute the operation of determining the candidate target cluster until the number of the data points in each cluster in the cluster list is less than the number of the data points corresponding to the preset minimum continuous time period threshold;
and determining a time period corresponding to the cluster with the cluster load less than or equal to a preset cluster load threshold value and the tide coefficient less than or equal to the preset tide coefficient threshold value in the candidate target cluster, wherein the time period is an energy-saving time period in the target tide cycle.
4. The method of claim 1, wherein,
the obtaining of the list of clusters with each data point in the base station load data as a core based on the improved DBSCAN algorithm includes sequentially performing, for each data point as a starting point and a core point:
judging whether the difference value of the current data point and the value of the forward or backward adjacent data point is within a preset difference value range;
under the condition that the difference value between the values of the current data point and the adjacent data point is determined to be within a preset difference value range, judging whether the difference value between the adjacent data point and the core point is within the preset difference value range or not;
when the difference value between the adjacent data point and the core point is within a predetermined range, bringing the adjacent data point into a cluster using the current core point as the core point, using the adjacent data point as the current data point, and continuing to perform an operation of determining whether the difference value between the values of the current data point and the adjacent data point is within the predetermined difference value range;
otherwise, the adjacent data points do not belong to the cluster taking the current core point as the core point, and the cluster determination taking the current core point as the core point is completed.
5. The method of claim 1, further comprising:
and sequentially appointing the target tidal cycle so as to determine corresponding base station load data according to the target tidal cycle and determine the energy-saving time period corresponding to the target tidal cycle.
6. The method of claim 1, 2 or 5, wherein the target tidal cycle comprises one or more of a yearly cycle, a quarterly cycle, a monthly cycle or a daily cycle.
7. The method of claim 1, further comprising:
configuring the base station to be in an energy-saving supply mode during the energy-saving period.
8. A base station data analysis apparatus comprising:
a data acquisition unit configured to acquire base station load data according to a target tidal cycle;
a clustering unit configured to obtain a list of clusters using each data point in the base station load data as a core based on an improved density-based application space clustering (DBSCAN) algorithm with noise, wherein the improved DBSCAN algorithm is based on the DBSCAN algorithm, only forward and backward scanning is performed in sequence, and the difference value between a new point in a cluster and the core point is within a predetermined range;
an energy saving period determination unit configured to determine an energy saving period in a target tidal cycle based on the base station load data for each cluster in the list of clusters, and a predetermined cluster load threshold, a predetermined tidal coefficient threshold, and a predetermined minimum continuous period threshold.
9. The apparatus of claim 8, further comprising:
a target modification unit configured to sequentially designate the target tidal cycle so that the energy saving period determination unit determines an energy saving period corresponding to the target tidal cycle.
10. A base station data analysis apparatus comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of any of claims 1-7 based on instructions stored in the memory.
11. A computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 7.
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