CN109996246B - Energy-saving method, device, equipment and medium for base station cell - Google Patents
Energy-saving method, device, equipment and medium for base station cell Download PDFInfo
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- CN109996246B CN109996246B CN201711491977.8A CN201711491977A CN109996246B CN 109996246 B CN109996246 B CN 109996246B CN 201711491977 A CN201711491977 A CN 201711491977A CN 109996246 B CN109996246 B CN 109996246B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
- H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
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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 invention discloses an energy-saving method, an energy-saving device, energy-saving equipment and an energy-saving medium for a base station cell, wherein the method comprises the following steps: acquiring service data of a cell in real time; calculating the service data of the cell at the next moment according to the acquired service data of the cell at the current moment through a pre-established service data model; classifying the cells according to a preset algorithm according to the service data of the cell at the next moment obtained by calculation, and determining the energy-saving class of the cell at the next moment; and controlling the energy-saving time period of the cell according to the energy-saving category of the cell.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to an energy saving method, apparatus, device, and medium for a base station cell.
Background
The 4G is formed by a plurality of frequency bands to form hot spot coverage, wherein low-frequency band sites provide continuous coverage, and high-frequency band sites provide discontinuous coverage. Therefore, the operation of blocking part of cells when the service of the overlapping coverage area site is idle can make the base station in an energy-saving state, correspondingly, the operation of activating all the cells of the base station can make the base station in a non-energy-saving state, and the technical scheme provided by the current base station equipment manufacturer is as follows:
firstly, energy conservation is controlled based on a strategy of a time period, and energy conservation is started in the time period by setting the starting and ending time of energy conservation. For the energy saving time period of each day, three types are distinguished again: special dates such as weekdays, weekends, holidays, and the like.
Controlling energy conservation based on system load, and starting the system to save energy after entering an energy-saving time period; however, because the time period is set by the user, and sudden traffic may occur in the network, the time period may not be set reasonably; therefore, for different energy saving scenarios, a strategy for determining the current load condition of the system needs to be further set.
Based on the energy-saving cell awakening condition, in order to achieve the purposes of reducing energy consumption and not influencing services at the same time, after the energy-saving cell enters an energy-saving state, the energy-saving cell needs to be awakened at a proper time;
the energy conservation of the base station can be realized through the setting of the scheme strategy.
The existing base station energy-saving technology has the following defects:
firstly, aiming at different network scenes, network data needs to be collected manually, the state and service characteristics of cells in each base station are analyzed, and multiple settings of power-saving strategies are carried out, so that the labor cost is increased.
Secondly, due to the fluctuation of the service, a certain error probability always exists through manual experience analysis, and the energy-saving strategy of the base station is difficult to reasonably and accurately configure, so that the energy-saving effect of the base station is reduced.
Thirdly, the original technical scheme cannot predict the change rule of the cell service according to the real-time service or the timed awakening condition, so that the frequent starting of the power-saving operation is caused, and in addition, a certain time delay exists when the awakening is executed, so that the user perception is easily deteriorated.
Disclosure of Invention
Embodiments of the present invention provide an energy saving method, apparatus, device, and medium for a base station cell, so as to solve at least one technical problem described above.
In a first aspect, an embodiment of the present invention provides a method for saving energy in a base station cell, where the method includes:
acquiring service data of a cell in real time;
calculating the service data of the cell at the next moment according to the acquired service data of the cell at the current moment through a pre-established service data model;
classifying the cells according to a preset algorithm and the calculated service data of the cells at the next moment, and determining the energy-saving type of the cells at the next moment;
and controlling the energy-saving time period of the cell according to the energy-saving category of the cell.
In a second aspect, an embodiment of the present invention provides a base station cell apparatus, where the apparatus includes:
the acquisition module is used for acquiring the service data of the cell in real time;
the pre-estimation module is used for calculating the service data of the cell at the next moment according to the acquired service data of the cell at the current moment through a pre-established service data model;
the classification module is used for classifying the cells according to a preset algorithm and the service data of the cells at the next moment calculated by the estimation module, and determining the energy-saving class of the cells at the next moment;
and the energy-saving control module is used for controlling the energy-saving time interval of the cell according to the energy-saving category of the cell.
In a third aspect, an embodiment of the present invention provides an energy saving device for a base station cell, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of the first aspect of the embodiments described above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, implement the method of the first aspect in the foregoing embodiments.
According to the energy-saving method, the energy-saving device, the energy-saving equipment and the energy-saving medium for the base station cell, the service data of the next moment can be deduced through the service data acquired in real time according to the service data model, and the energy-saving category of the cell can be estimated according to the service data, so that the control on the energy-saving time period of the cell is controlled, and the energy-saving time period of the cell is more reasonable.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for saving power of a base station cell according to an embodiment of the present invention;
FIG. 2 is a flow chart of business data modeling in another embodiment of the present invention;
fig. 3 is a schematic diagram of an energy saving apparatus of a base station cell according to another embodiment of the present invention;
fig. 4 is a schematic diagram of an energy saving apparatus of a base station cell according to another embodiment of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, an embodiment of the present invention provides an energy saving method for a base station cell, where the method includes the following steps:
s01, acquiring the service data of the cell in real time;
s02, calculating the service data of the cell at the next moment according to the acquired service data of the cell at the current moment through a pre-established service data model;
s03, classifying the cells according to the calculated service data of the cells at the next moment and a preset algorithm, and determining the energy-saving type of the cells at the next moment;
and S04, controlling the energy-saving time interval of the cell according to the energy-saving type of the cell.
In the energy saving method for the base station cell provided by the embodiment of the invention, the service data of the next moment can be deduced according to the service data model and the service data obtained in real time, and the energy saving category of the cell can be estimated according to the service data, so that the control on the energy saving time period of the cell is controlled, and the energy saving time period of the cell is more reasonable.
The invention provides an energy-saving method of a base station cell, which applies a service data model to predict service data at the next moment according to the service data of the cell at the current moment acquired in real time; the method also applies a preset algorithm to judge the energy-saving category of the cell to which the estimated service data belongs so as to control the energy-saving time period of the cell.
The method for saving energy in the base station cell in the embodiment specifically comprises the following steps:
and S11, acquiring the service data of the cell in real time. The service data includes the number of users, the utilization rate of radio resources, and the total throughput.
S12, calculating the service data of the cell at the next moment according to the acquired service data of the cell at the current moment through a pre-established service data model; the service data model is the corresponding relation between service data of the K time period and the service data of the K-1 time period.
The correspondence between the service data of which the service data model is the K time period and the service data of which the service data model is the K-1 time period is derived according to a system state equation, so that the background of the correspondence between the service data of which the service data model is the K time period and the service data of which the service data model is the K-1 time period is explained first.
Firstly, a system state equation is introduced, and a specific formula is as follows:
X(k)＝AX(k-1)+BU(k)+W(k)… … formula (1);
if the system measurements are added:
Z(k)＝HX(k)+V(k)… … formula (2);
in the two formulas, X(k)Indicating the state of the system at time k, Z(k)Representing the measured value at time k, U(k)Is the amount of control over the system at time k. A and B are system parameters, H is a parameter of the measurement system, W(k)And V(k)Representing process and measured noise, respectively, the variance is combined below to estimate the optimized output of the system.
The system can then be predicted for the next state using the process model of the system. If the present system state is k, the present state is predicted according to equation (1):
X(k|k-1)＝AX(k-1|k-1)+BU(k)… … formula (3);
X(k|k-1)is to use the result of the last state prediction, and X(k-1|k-1)Is the result of the last state being optimal, U(k)Then the control quantity of the current state is, if the prediction system has no control quantity, we can set it to 0.
To this end, the system results have been updated, but correspond to X(k|k-1)The covariance of (a) has not been updated. The covariance is denoted by P, introducing the formula:
P(k|k-1)＝AP(k-1|k-1)a' + Q … … formula (4);
in the formula (4), P(k|k-1)Is exactly X(k|k-1)Corresponding covariance, P(k-1|k-1)Is X(k-1|k-1)The corresponding covariance, A' is the transpose of A, and Q is the covariance of the system process.
By combining the predicted values and the measured values, an optimal estimated value X of the current state (k) can be obtained(k|k)：
X(k|k)＝X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1)) … … formula (5);
middle KgIs the kalman gain:
Kg(k)＝P(k|k-1)H′/(HP(k|k-1)h' + Q) … … formula (6);
according to the above formulas (5) and (6), we obtain the optimal estimated value X(k|k). However, in order to make the algorithm run continuously, X in the k state needs to be updated(k|k)Variance of (a):
P(k|k)＝(I-Kg(k)H)P(k|k-1)… … formula (7);
where I is a matrix, and in the single measurement model I equals l. When the system enters the k + l state, P(k|k)Becomes P (k-1| k-1) of the formula (4). Thus, the algorithm can be operated on automatically and recursively. The service data model in this embodiment is obtained by following the above-mentioned formula (3) -formula (7).
Specifically, as shown in fig. 2, the service data model in this embodiment may be obtained by the following steps:
s1, initializing a system parameter matrix according to the service data, wherein the system parameter matrix comprises A(X(0))、B、H(0)；
S2, according to A(X(0))、B、H(0)And an initial covariance matrix according to formula X(k|k-1)＝AX(K-1|K-1)+BU(k)Calculating to obtain an initial estimated value matrix, and according to a formula Kg(k)＝P(k|k-1)H′/(HP(k|k-1)H' + Q), calculating to obtain an initial gain matrix;
s3, according to the service data of the service data system k-1 time, according to the formula X(k|k-1)＝AX(k-1|k-1)+BU(k)And calculating to obtain the predicted state A of the service data system at the moment k(x(k))；
S4, according to formula P(k|k-1)＝AP(k-1|k-1)A′+Q、A(x(k))And P(k-1|k-2)Computing a covariance matrix P(k|k-1)Wherein P is(k-1|k-2)According to formula P(k|k)＝(I-Kg(k)H)P(k|k-1)Obtaining; a' is a transposed matrix of A; p is(k|k-1)Is X(k|k-1)A corresponding covariance; p(k-1|k-1)Is X(k-1|k-1)A corresponding covariance;Kg(k)gain matrix Kg of traffic data system at time k(k)；
S5, according to the formula Kg(k)＝P(k|k-1)H′/(HP(k|k-1)H' + R), calculating gain matrix Kg of the service data system at the time k(k)；
S6, according to the gain matrix Kg(k)And formula X(k|k)＝X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1)) To obtain the optimal prediction estimation value matrix
S7, judging whether the service data at all moments are estimated to be finished or not, if so, ending; if not, repeating S3-S6.
According to the steps, A in the business data model(x(k))The method is determined according to historical service data before the k +1 moment, so that a service data model is a time-varying system, and the variance of system noise and observation noise is updated in real time, so that the model can adapt to the change of the service data in the first N time periods. In addition, H in the observation equation(k)And is also established by adopting a statistical method according to historical data.
Wherein, the optimal prediction estimation value matrix of the computing service
Mainly receives a gain K(k)Sum covariance P(k+1/k)The influence of (2) can be started from two aspects in order to enable the optimal predicted estimated traffic value to be closer to the real traffic, namely firstly, the estimated predicted value is corrected in a mode of maximizing the gain, and secondly, the predicted value is directly corrected. The traffic prediction has a certain hysteresis, and particularly for a time period with large traffic variation, the prediction accuracy is low. For the situation, the optimal prediction estimation value of the service can be directly estimated
And (6) correcting. At this moment IAdding a service correction coefficient TRA _ Rate to obtain a formula:
TRA in the above formula(k+1/k)Namely the prediction correction value of the service data, and the main key point of the improvement of the algorithm is TRA _ Rate(k+1/k)In the acquisition, it is assumed that enough service data exist and the service volume has a certain periodic characteristic, and the service correction coefficient can be acquired by using the service data in the historical time period.
And S13, classifying the cells according to the calculated service data of the cell at the next moment and a preset algorithm, and determining the energy saving type of the cell at the next moment.
Specifically, the calculated service data of the cell includes the number of users, the utilization rate of the radio resources, and the total throughput of the cell, and different weighting factors can be given to the service data of the cell in combination with the number of users, the utilization rate of the radio resources, and the total throughput, so as to establish a preliminary model.
Wherein, the number of users [ N, N + 1): the user number interval is divided into 720 user number intervals from 0 to 720. Radio resource utilization weighting factor: k1 ═ 0.3; user number weighting factor: k2 ═ 0.4; total throughput weighting factor: k3 is 0.3. The weighting factors can be customized according to the network. Comprehensive coefficient: Σ (x, y, z) ═ k1 ×% + k2 × (1-y%) + k3 × z%.
The classification is carried out according to the real-time service comprehensive coefficient calculation value of the cell, the classification threshold can be self-defined and adjusted according to the practical application condition, and the classification result is shown in the following table:
| cell classification | Super-idle cell | Idle cell | General idle cell | Common cell | High load region |
| Number of users | (0,2) | [2,5) | [5,8) | [8,200) | [200,+∞) |
| Radio resource utilization | (0％,2％) | [2％,5％) | [5％,8％) | [8％,80％) | [80％,+∞) |
| Total throughput | (0,20) | [20,50) | [50,80) | [80,5000) | [5000,+∞) |
Setting a target coefficient by combining the actual network situation and the index requirements of each region, comparing the comprehensive coefficient with the target coefficient, further defining corresponding conditions and thresholds according to the influence degree of the energy conservation of the cell on the network for clustering according to different clustering conditions, dividing the cells meeting the conditions into a first energy-saving cell, a second energy-saving cell and a third energy-saving cell, wherein the energy-saving cells and the corresponding compensation cells simultaneously meet the corresponding conditions, and the clustering conditions are as shown in the following table:
wherein, the Top compensation cell: the adjacent areas of the cell are sorted from large to small according to the switching number, for example, TOP5 compensates the cell, namely, the cell with the largest adjacent area switching number is 5 cells. The electricity-saving compensation cell: the real-time service conditions of the adjacent cells of the cell belong to three categories, namely a super idle cell, an idle cell and a general idle cell, and the cells can be used as power-saving compensation cells. The adjacent area relationship is overlapped: two different cells have the same neighbor cell.
And S14, controlling the energy-saving time interval of the cell according to the energy-saving type of the cell.
Through the calculation of the power saving model algorithm based on the business rules, as long as the time intervals of any energy saving cell in the first, second and third energy saving cells are defined as the 'energy saving' state of the cell, and the rest time intervals which do not meet the energy saving condition are defined as the 'normal' state of the cell, the result example of establishing the power saving model based on the business rules is shown in the following table:
| time period | [0,1] | [1,2] | [2,3] | [3,4] | …… | [Tn-1,Tn] |
| Cell A | Can save energy | Can save energy | Can save energy | Can save energy | Can save energy | Can save energy |
| Cell B | Is normal and normal | Is normal and normal | Is normal | Is normal | Is normal | Is normal |
| Cell C | Can save energy | Can save energy | Is normal | Can save energy | Can save energy | Is normal |
| Cell D | Is normal | Is normal | Is normal | Can save energy | Can save energy | Is normal |
Specifically, the control of the energy saving time of the energy saving cell: 1) energy-saving period strategy: considering the difference of the requirements of different time periods on the network quality of the user, for example, the energy-saving implementation is only set for a class of energy-saving cells which have a light influence on the network due to energy saving in early busy hours (generally 9:00-12:00) and late busy hours (generally 21:00-23:00) of the whole network service, and the class of energy-saving cells, i.e. one, two and three classes of energy-saving cells, are allowed to participate in power saving at 2-5 points of the whole network service, and different time period strategies can be defined for various energy-saving cells, as shown in the following table:
| power saving classification | Power saving time interval strategy |
| Electricity-saving cell | 0:00 to 23:00 |
| Class II power-saving cell | 0:00 to 9:00, 12:00 to 21:00 |
| Three-class power-saving cell | 2:00-5:00 |
2) Implementation of the energy-saving cell: and when the classified energy-saving cells accord with the time interval strategy and meet the condition that the next N time intervals in the cell energy-saving model are all in an energy-saving state (N can be defined), executing cell blocking operation to enable the cells to be in the energy-saving state.
3) Awakening the energy-saving cell: and if the random compensation cell comprises a normal state in the next N time intervals (N can be defined) in the power saving model or the real-time service of the random compensation cell meets the classification condition of the high-load cell, activating all energy-saving cells corresponding to the compensation cell to enable the cell to be in the normal state.
In the energy saving method for the base station cell provided by the embodiment of the invention, the service data of the next moment can be deduced according to the service data model and the service data obtained in real time, and the energy saving category of the cell can be estimated according to the service data, so that the control on the energy saving time period of the cell is controlled, and the energy saving time period of the cell is more reasonable.
Referring to fig. 3, another embodiment of the present invention provides an energy saving apparatus for a base station, including: an acquisition module 301, an estimation module 302, a classification module 303 and an energy-saving control module 304; the apparatus in this embodiment can be applied to the method of embodiment 2. The acquiring module 301 is configured to acquire service data of a cell in real time; the pre-estimation module 302 is configured to calculate, according to the acquired service data of the current time of the cell, service data of the next time of the cell through a pre-established service data model; the classification module 303 is configured to classify the cell according to a preset algorithm according to the service data of the cell at the next time calculated by the estimation module, and determine an energy saving class of the cell at the next time; the energy saving control module 304 is configured to control the energy saving time period of the cell according to the energy saving category to which the cell belongs.
In the energy-saving device for the base station cell provided by the embodiment of the invention, the service data of the next moment can be deduced according to the service data model and the service data obtained in real time, and the energy-saving category of the cell can be estimated according to the service data, so that the control on the energy-saving time period of the cell is controlled, and the energy-saving time period of the cell is more reasonable.
Another embodiment of the present invention provides an energy saving device for a base station cell, where the energy saving method for a base station cell according to the above embodiments of the present invention may be implemented by an energy saving device for a base station cell. Fig. 4 is a schematic diagram illustrating a hardware structure of an energy saving device for a base station cell according to an embodiment of the present invention.
The energy saving device of the base station cell may comprise a processor 401 and a memory 402 storing computer program instructions.
Specifically, the processor 401 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement the energy saving method of any one of the base station cells in the above embodiments.
In one example, the power saving device of the base station cell may also include a communication interface 403 and a bus 410. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 to complete communication therebetween.
The communication interface 403 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present invention.
Embodiments of the present invention may be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement the method of energy saving for a base station cell of any of the above embodiments.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (8)
1. A method for saving energy in a base station cell, the method comprising:
acquiring service data of a cell in real time;
calculating the service data of the cell at the next moment according to the acquired service data of the cell at the current moment through a pre-established service data model;
classifying the cells according to the calculated service data of the cells at the next moment and a preset algorithm, and determining the energy-saving categories of the cells at the next moment, wherein the energy-saving categories comprise a first-class energy-saving cell, a second-class energy-saving cell and a third-class energy-saving cell;
controlling energy-saving time intervals of the cell according to the energy-saving category of the cell, wherein the energy-saving time intervals comprise a first time interval, a second time interval and a third time interval;
the controlling the energy-saving time interval of the cell according to the energy-saving category to which the cell belongs comprises:
determining the energy-saving time interval of the cell as a first time interval under the condition that the cell is a type of energy-saving cell;
determining the energy-saving time interval of the cell as a second time interval under the condition that the cell is a type II energy-saving cell;
determining the energy-saving time interval of the cell as a third time interval under the condition that the cell is a three-type energy-saving cell;
the network influence degree of the energy-saving cell of the first type is lighter than that of the energy-saving cell of the second type, the network influence degree of the energy-saving cell of the second type is lighter than that of the energy-saving cell of the third type, the requirement of the user on the network quality in the first time interval is higher than that of the user on the network quality in the second time interval, and the requirement of the user on the network quality in the second time interval is higher than that of the user on the network quality in the third time interval.
2. The method of claim 1, wherein the traffic data model is a correspondence between traffic data of a K time period and a K-1 time period; the correspondence between the k time period and the service data of the k-1 time period is obtained by adopting the following steps:
s1, initializing a system parameter matrix according to the service data, wherein the system parameter matrix comprises A(X(0))、B、H(0)；
S2, according to A(X(0))、B、H(0)And an initial covariance matrix according to formula X(k|k-1) ＝ AX(K-1|K-1)+BU(k)Calculating to obtain an initial estimated value matrix and Kg according to the formula(k)＝P(k|k-1)H′/(HP(k|k-1)H' + Q), calculating to obtain an initial gain matrix; wherein, X(k|k-1)Predicting k time for the service data system according to the service data state at the k-1 momentThe state of the carved service data; x(k-1|k-1)The service data system predicts the optimal service data state at the k-1 moment; u shape(k)Is the control quantity of the k time to the service data system; a and B are system parameter matrices; h is a parameter matrix of the measurement system; h' is a transposed matrix of H; q is the covariance of the business data system process;
s3, according to the service data of the service data system k-1 time, according to the formula X(k|k-1)＝AX(k-1|k-1)+BU(k)And calculating to obtain the predicted state A of the service data system at the moment k(x(k))；
S4, according to formula P(k|k-1)＝AP(k-1|k-1)A′+Q、A(x(k))And P(k-1|k-2)Computing a covariance matrix P(k|k-1)Wherein P is(k-1|k-2)According to formula P(k|k)＝(I-Kg(k)H)P(k|k-1)Obtaining; a' is a transposed matrix of A; p(k|k-1)Is X(k|k-1)A corresponding covariance; p(k-1|k-1)Is X(k-1|k-1)A corresponding covariance; kg(k)Gain matrix Kg of traffic data system at time k(k)；
S5, according to the formula Kg(k)＝P(k|k-1)H′/(HP(k|k-1)H' + R), calculating gain matrix Kg of the service data system at the time k(k)；
S6, according to the gain matrix Kg(k)And formula X(k|k)＝X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1)) To obtain the optimal prediction estimation value matrix
S7, judging whether the service data at all moments are estimated to be finished or not, if so, ending; if not, repeating S3-S6.
3. The method of claim 1, wherein the traffic data comprises a number of users, radio resource utilization, and total throughput.
4. The method according to claim 3, wherein the step of classifying the cell according to the obtained service data of the cell and a preset algorithm to determine the energy saving class of the cell specifically comprises:
giving different weighting factors to the number of users, the utilization rate of wireless resources and the total throughput in the service data of the cell, and establishing a preliminary model;
calculating the comprehensive coefficient of the real-time service data of the cell through the preliminary model;
classifying the cells according to the comprehensive coefficients;
and obtaining the energy-saving category of the classified cell according to a preset clustering condition.
5. The method of claim 4, wherein the clustering condition comprises: and the classified cells correspond to energy-saving compensation cell conditions and/or network state conditions.
6. An energy saving apparatus of a base station, the apparatus comprising:
the acquisition module is used for acquiring the service data of the cell in real time;
the pre-estimation module is used for calculating the service data of the cell at the next moment according to the acquired service data of the cell at the current moment through a pre-established service data model;
the classification module is used for classifying the cells according to the service data of the cell at the next moment calculated by the estimation module and a preset algorithm, and determining the energy-saving classes of the cell at the next moment, wherein the energy-saving classes comprise a class-I energy-saving cell, a class-II energy-saving cell and a class-III energy-saving cell;
the energy-saving control module is used for controlling the energy-saving time interval of the cell according to the energy-saving category of the cell, wherein the energy-saving time interval comprises a first time interval, a second time interval and a third time interval;
the energy-saving control module is specifically configured to determine that an energy-saving time period of the cell is a first time period when the cell is a type of energy-saving cell;
determining the energy-saving time interval of the cell as a second time interval under the condition that the cell is a type II energy-saving cell;
determining the energy-saving time interval of the cell as a third time interval under the condition that the cell is a three-type energy-saving cell;
the network influence degree of the energy-saving cells of the first type is lower than that of the energy-saving cells of the second type, the network influence degree of the energy-saving cells of the second type is lower than that of the energy-saving cells of the third type, the requirement of the user on the network quality in the first time interval is higher than that in the second time interval, and the requirement of the user on the network quality in the second time interval is higher than that in the third time interval.
7. An energy saving apparatus of a base station, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any of claims 1-5.
8. A computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any one of claims 1-5.
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