CN111132179B - Cell scheduling method, system, device and storage medium - Google Patents
Cell scheduling method, system, device and storage medium Download PDFInfo
- Publication number
- CN111132179B CN111132179B CN201911367120.4A CN201911367120A CN111132179B CN 111132179 B CN111132179 B CN 111132179B CN 201911367120 A CN201911367120 A CN 201911367120A CN 111132179 B CN111132179 B CN 111132179B
- Authority
- CN
- China
- Prior art keywords
- cell
- cells
- scheduling
- matching
- load
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a cell scheduling method, a system, a device and a storage medium, wherein the method comprises the steps of acquiring resource historical data of each cell, predicting future operation indexes of each cell by using a prophet algorithm, judging the load condition of each cell in a future time period, calculating the matching degree of each cell, respectively determining the best matching cell for each cell, performing complementary scheduling between the cell and the best matching cell, and the like. The prophet algorithm is used for predicting the future operation index, so that an accurate prediction result can be provided; and determining the best matching cell of each cell according to the prediction result, wherein more time exists between each cell and the best matching cell of each cell, and the best matching cells of each cell and each cell are in a complementary state, so that complementary scheduling is performed between each cell and each best matching cell, a higher success rate can be achieved, and the effect of balancing resources and loads can be better realized. The invention is widely applied to the technical field of wireless communication.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a cell scheduling method, system, apparatus, and storage medium.
Background
In the field of wireless communication, the load of a cellular network cell has an obvious tidal phenomenon, and the load conditions of different cells at the same time may be different, which causes the problem of scheduling resources of the cell, and mainly causes the cell with lower load to share part of the load for the cell with higher load by allocating carrier resources and other modes.
In the prior art, a patent document with publication number "CN105530650A" discloses a method and an apparatus for network resource planning, where the method includes: the method comprises the steps of periodically detecting carrier resource configuration data of each cell in a network to be planned, wherein the network to be planned is a network capable of being processed in a centralized mode and comprises a baseband resource pool, and the baseband resource pool comprises a plurality of preset carrier resources; acquiring the carrier resource utilization rate of each cell according to the carrier resource configuration data; judging whether the carrier resource utilization rate exceeds a preset range or not; if so, judging that resource adjustment needs to be carried out on the cell corresponding to the carrier resource utilization rate, so that the adjusted carrier resource utilization rate is in a preset range, and acquiring adjustment data; and replanning the carrier configuration condition of each cell in the network to be planned according to the adjustment data. The scheme solves the tidal effect, avoids carrier redundancy after capacity expansion, but treats each cell in isolation, neglects the cooperative relationship among different cells, and therefore, the planning effect on each cell cannot fully meet the practical requirement.
A patent document with publication number "CN107509202A" discloses a super-dense network resource allocation method based on variation prediction of the number of access users, and the method provides a super-dense network resource allocation method based on variation prediction of the number of access users, and firstly, a typical "tide" mode is identified through semi-supervised classification; then predicting the change of the number of base station access users, sequencing and screening the input features by taking the out-of-bag errors of random forest features as an index for measuring the importance of the features, and carrying out support vector machine regression prediction on the obtained features; and finally, dynamically adjusting the resources of the small stations in proportion to realize resource migration between the small stations. The method provides historical rule information of people number change for the prediction of Support Vector machine Regression (SVR) based on semi-supervised classification, benefits from the feature selection of random forests, and reduces the calculation complexity of SVR prediction without reducing the accuracy. According to the scheme, the base station resources are dynamically adjusted according to the predicted user number, the user fairness and the average throughput are improved, but the network resource allocation is carried out by depending on a single working parameter of a cell, and if the deviation of the prediction result of the user number is large, the network resource allocation effect is poor.
Disclosure of Invention
In view of at least one of the above technical problems, an object of the present invention is to provide a cell scheduling method, system, device and storage medium.
In one aspect, an embodiment of the present invention includes a cell scheduling method, including:
. Acquiring resource historical data of each cell;
predicting future operation indexes of the cells according to the historical resource data by using a prophet algorithm;
judging the load condition of each cell in the future time period according to the future operation index of each cell;
calculating the matching degree between the cells according to the load conditions;
respectively determining the best matching cells for each cell according to each matching degree;
complementary scheduling between the cell and its best matching cell
Further, the resource history data includes uplink traffic, downlink traffic, uplink utilization rate, downlink utilization rate, effective RRC number, week attribute information, and holiday information.
Further, the step of obtaining resource history data of each cell specifically includes:
carrying out grading assignment and summation processing on the uplink flow, the downlink flow, the uplink utilization rate, the downlink utilization rate and the effective RRC number corresponding to the same cell;
and converting each week attribute information and each holiday information into a dummy variable respectively.
Further, the step of determining the load condition of each cell in a future time period according to the future operation index of each cell specifically includes:
comparing the future operation index with a preset high-load standard value and a preset low-load standard value;
under the condition that the corresponding future operation index is higher than the high-load standard value, judging the cell as a high-load cell in a future time period;
and under the condition that the corresponding future operation index is lower than the low-load standard value, judging the cell as a low-load cell in a future time interval.
Further, the step of calculating the matching degree between the cells according to each of the load conditions specifically includes:
for any two of the cells, detecting the duration that they are in a complementary state in a future time period; the complementary state means that the load conditions of the two cells are different in the same future time period;
and calculating the proportion of the duration of the complementary state to the sum of the durations of the future periods as the matching degree between the two cells.
Further, the step of calculating the matching degree between the cells according to each of the load conditions specifically further includes:
for any two of the cells, detecting the service scenarios they serve;
setting a matching weight between the two cells according to the corresponding relation between the service scenes;
and adjusting the matching degree between the two cells according to the matching weight.
Further, the step of determining the best matching cell for each cell according to each matching degree specifically includes:
selecting a target cell from each cell, and setting continuous complementary duration corresponding to the target cell;
respectively detecting the effective days between the target cell and the rest of the cells; in each day forming the valid days, the time length of the target cell and the corresponding cell in the complementary state is not less than the continuous complementary time length;
respectively calculating scheduling parameters between the target cell and the rest of the cells; the scheduling parameter is a product of a matching degree between the two cells, the valid days and the continuous complementary duration;
and selecting the cell with the maximum scheduling parameter from the rest of the cells as the best matching cell of the target cell.
In another aspect, an embodiment of the present invention further includes a cell scheduling system, including:
a first module, configured to obtain resource history data of each cell;
a second module, configured to predict future operation indicators of each of the cells according to the resource history data using a prophet algorithm;
a third module, configured to determine, according to a future operation index of each cell, a load condition of each cell in a future time period;
a fourth module, configured to calculate a matching degree between the cells according to each of the load conditions;
a fifth module, configured to determine, according to each matching degree, an optimal matching cell for each cell;
a sixth module for performing complementary scheduling between the cell and its best matching cell.
On the other hand, the embodiment of the present invention further includes a cell scheduling apparatus, including a memory and a processor, where the memory is used to store at least one program, and the processor is used to load the at least one program to execute the cell scheduling method.
In another aspect, embodiments of the present invention further include a storage medium having stored therein processor-executable instructions, which when executed by a processor, are configured to perform the cell scheduling method according to the embodiments.
The invention has the beneficial effects that: by using a prophet algorithm, a future operation index is predicted according to resource historical data such as uplink flow, downlink flow, uplink utilization rate, downlink utilization rate, effective RRC number, week attribute information, holiday information and the like, so that factors such as trend, periodic change, accidental change, errors and the like of the resource historical data can be fully considered, and an accurate prediction result is provided; the best matching cell of each cell is determined according to the prediction result of the prophet algorithm, more time exists between each cell and the best matching cell of each cell, the cells are in a complementary state, and complementary scheduling is performed between each cell and the respective best matching cell of each cell, so that a higher success rate can be achieved, and the effect of balancing resources and loads can be better realized.
Drawings
Fig. 1 is a flowchart of the cell scheduling method in the embodiment;
FIG. 2 is a diagram illustrating the results of analyzing the load status of a plurality of cells in the embodiment;
fig. 3 is a diagram illustrating the result of analyzing the matching degree between each two of the cells in the embodiment.
Detailed Description
Example 1
In this embodiment, the cell scheduling method is composed of a plurality of scheduling periods, and each scheduling period executes steps S1 to S6 shown in fig. 1 at least once. The set of cells to be scheduled may not be exactly the same in each scheduling period, but the principle of operation to be performed in each scheduling period is the same, and therefore the description may be made with respect to only the steps performed in one of the scheduling periods.
The cell scheduling method may be performed by a server provided in a computer room. The server is connected with the base station so as to acquire necessary data and control the base station to complete the scheduling of the cell.
The server firstly detects all base stations, and the detected cells which can be scheduled are defined to the execution range of the following steps S1-S6, and the cells which can not be scheduled due to the fact that hardware does not support capacity expansion and reduction, full load and capacity expansion cannot be carried out are removed.
In each scheduling period, the server executes the following steps:
s1, acquiring resource historical data of each cell.
The resource historical data refers to the operation parameters of each cell in a past period of time, and includes data such as uplink flow, downlink flow, uplink utilization rate, downlink utilization rate and effective RRC number with the granularity of hours, and week attribute information and holiday information with the granularity of days.
The resource history data is used for processing by a prophet algorithm, and in order to make the resource history data conform to a format required by the prophet algorithm, the following steps S101-S102 are executed to standardize the resource history data:
s101, performing grading assignment and summation processing on the uplink flow, the downlink flow, the uplink utilization rate, the downlink utilization rate and the effective RRC number corresponding to the same cell; specifically, different levels are defined for uplink traffic, downlink traffic, uplink utilization, downlink utilization, and the effective RRC number, each level corresponds to a different assignment, and the assignments may be positive integers such as 0, 1, and 2; then, the uplink flow, the downlink flow, the uplink utilization rate, the downlink utilization rate and the effective RRC number of each cell are respectively in which level, so that the corresponding assignment is converted; adding assignments corresponding to uplink flow, downlink flow, uplink utilization rate, downlink utilization rate and effective RRC number of each cell together, wherein the obtained result is a standardized processing result of data such as the uplink flow, the downlink flow, the uplink utilization rate, the downlink utilization rate and the effective RRC number; through grading assignment, parameters such as uplink flow, downlink flow, uplink utilization rate, downlink utilization rate, effective RRC number and the like of different units and orders can be converted into assignments of the same unit and order, and then the influence of the parameters is comprehensively considered through a summing method;
s102, converting each week attribute information and each holiday information into a dummy variable respectively; by converting the qualitatively described week attribute information and the holiday information into the quantitatively described dummy variables, the prophet algorithm can perform corresponding processing.
After step S1 is executed, the following step S2 is executed.
And S2, predicting future operation indexes of the cells according to the resource historical data by using a prophet algorithm.
Prophet is a FaceBook open source timing framework, and the principle of Prophet is to analyze various time sequence characteristics: periodicity, trending, holiday effects, and partial outliers. In the aspect of trend, the method supports the addition of change points and realizes piecewise linear fitting. In terms of period, it uses Fourier series to build a period model (sin + cos), and in terms of holidays and emergencies, users can specify holidays and N days before and after the holidays in a table manner. Prophet can be viewed as an integrated solution for timing.
The prophet algorithm is constructed as follows:
y(t)＝g(t)+s(t)+h(t)+∈；
wherein g (t) is a trend (trend) function for analyzing aperiodic changes in the time series. s (t) represents a periodic variation, such as a one week or one year periodicity. h (t) represents the effect of occasional one or more days such as holidays. E is an error term representing the effect of the error that the model does not take into account.
The prediction duration of the prophet algorithm is adjustable. The longer the set prediction duration is, the longer the prophet algorithm can output future operation indexes, and the more beneficial the working personnel to carry out cell scheduling in advance, but the prediction accuracy is reduced. Therefore, when the prophet algorithm is applied, an optimal prediction duration should be comprehensively considered and set, which may be to test future operation indexes output by different prediction durations through the prophet algorithm before step S2 is executed, then actually measure actual operation indexes of the cell, analyze a difference between the predicted future operation indexes and the actually measured values, and consider using the optimal prediction duration, that is, the prediction duration corresponding to the minimum difference.
After performing step S2 using the optimal predicted duration, a future operation index for each cell is obtained. The future operation index is actually a general term for a series of operation parameters distributed over a future time period and is used to describe the operation status of the corresponding cell at each time point or time segment over the future time period.
After step S2 is performed, the following step S3 is performed.
And S3, judging the load condition of each cell in the future time period according to the future operation index of each cell.
In this embodiment, the step S3 refers to a combination of the following steps S301 to S303:
s301, comparing the future operation index with a preset high-load standard value and a preset low-load standard value;
s302, under the condition that the corresponding future operation index is higher than the high-load standard value, judging the cell as a high-load cell in a future time period;
and S303, under the condition that the corresponding future operation index is lower than the low-load standard value, judging the cell as a low-load cell in a future time period.
Fig. 2 is a result of performing the above steps S1-S2 and S301-S303 for the a cell, the B cell, and the C cell, where (a) part corresponds to the a cell, (B) part corresponds to the B cell, and (C) part corresponds to the C cell. Referring to fig. 2, in different future time periods, the future operation index of each cell may be higher than the high load criterion value or lower than the low load criterion value, and accordingly determined to be a high load cell or a low load cell, respectively.
The load situation of each cell at different future time periods is marked with a numerical value, resulting in the curve shown in fig. 2. Specifically, when a cell belongs to a high load cell, its value is marked as 1, and when a cell belongs to a low load cell, its value is marked as 0, i.e.
Where i is the cell index.
On the basis of the determination result of the load situation of each cell shown in fig. 2, step S4 may be executed to calculate the matching degree between any two cells.
And S4, calculating the matching degree between the cells according to the load conditions. In this embodiment, the step S4 refers to a combination of the following steps S401 to S402:
s401, for any two cells, detecting the duration of the cells in a complementary state in a future time period; the complementary state means that the load conditions of the two cells are different in the same future time period;
s402, calculating the proportion of the duration of the complementary state to the sum of the durations of the future time periods as the matching degree between the two cells.
Referring to part (a) and part (B) in fig. 2, in any same future time period, the a cell and the B cell have different load conditions, that is, in any future time period, one of the a cell and the B cell belongs to a high load cell, and the other belongs to a low load cell, so that the a cell and the B cell are in a complementary state in any future time period, and the matching degree between the a cell and the B cell is 100%; referring to part (a) and part (C) of fig. 2, in some future time periods, the a cell and the C cell have different load conditions, and in other future time periods, the a cell and the C cell have the same load condition, so that the a cell and the C cell are in a complementary state in some future time periods, and the obtained result is the matching degree between the a cell and the C cell by obtaining the time length of the a cell and the C cell in the complementary state and dividing the time length by the total time length of all future time periods considered; referring to parts (B) and (C) in fig. 2, in some future periods, the B cell and the C cell have different load conditions, and in other future periods, the B cell and the C cell have the same load condition, so that the B cell and the C cell are in a complementary state in some future periods, and the obtained result is the matching degree between the a cell and the B cell by obtaining the time length of the a cell and the B cell in the complementary state and dividing the time length by the total time length of all future periods under consideration.
In order to calculate and display the matching degree between the cells more intuitively, the curves shown in fig. 2 are calculated to obtain the curve shown in fig. 3, and the formula used for calculation is F (i,j) ＝f i (x)-f j (x) Or F (i,j) ＝f i (x)⊕f j (x) In which F is (i,j) Indicates the degree of matching between the cell numbered i and the cell numbered j, and ≧ indicates the exclusive or operation. Obtaining a part (a) curve in fig. 3 by performing curve operation on a part (a) and a part (B) in fig. 2, wherein when the curve value is 1, the cell a and the cell B are in a complementary state; performing operation on the curves of the part (a) and the part (C) in fig. 2 to obtain the curve of the part (b) in fig. 3, wherein when the value of the curve is 1, the curve indicates that the cell a and the cell C are in a complementary state; the curve of part (B) and part (C) in fig. 2 are calculated to obtain the curve of part (C) in fig. 3, and when the curve takes the value of 1, it indicates that the cell B and the cell C are in mutual relationshipAnd (5) complementing the state.
It can be clearly seen from fig. 3 which future time periods between the a cell, the B cell and the C cell are in a complementary state, and according to fig. 3, the matching degree between any two cells can be calculated in an automatic or manual mode.
On the basis of performing steps S401-S402, the following steps S403-S405 may also be performed:
s403, for any two cells, detecting service scenes served by the two cells; the service scene mainly refers to the range covered by a cell;
s404, setting matching weight between the two cells according to the corresponding relation between the service scenes;
s405, adjusting the matching degree between the two cells according to the matching weight.
In this embodiment, the coverage area of the cell a is a residential area, and the service clients are residents in the residential area; the coverage area of the cell B is an office building, and the service customers are employees working in the office building; the area covered by the C cell is a shopping mall, and the service clients are staff and customers of the shopping mall. For example, staff in an office building mainly come from residents in a residential area, and customers in a shopping mall come from more than one place except the residential area, so that the load condition of the cell a and the load condition of the cell B are more likely to be in different states at the same time, and the load condition of the cell B and the load condition of the cell C are more likely to be in the same state, it is necessary to perform step S404 empirically, and set the matching weight between the two cells in consideration of the corresponding relationship between the service scenes of the different cells.
After the matching weight between every two cells is set, the curve shown in fig. 3 is adjusted by using the matching weight, specifically, the value of each curve is multiplied by the corresponding matching weight, and the obtained value is used as the adjusted result. Through the adjustment of the curve, the influence of the actual application scene on the load condition can be fully considered.
On the basis of fig. 3 and the matching degree between any two cells obtained from fig. 3, the best matching cell may be determined for each cell by considering only the matching degree, specifically: firstly, sequencing all cells according to a certain sequencing rule to obtain a list; selecting a target cell from the first position of the list, screening out a cell with the highest matching degree as a corresponding best matching cell, and then deleting the target cell and the corresponding best matching cell from the list; the above process is repeated again in the list to complete the matching for all cells. If the total number of cells is odd, one cell will eventually fail to match, and this cell may serve as a backup cell for the other cells.
In addition to fig. 3 and the matching degree between any two cells obtained from fig. 3, more factors may be considered to determine the best matching cell for each cell, so step S5 is a combination of the following steps S501-S504:
s501, selecting a target cell from each cell; setting the continuous complementary duration of the target cell pair to S hours; taking hours as granularity, and investigating the complementary condition of the target cell and other cells in a future time period;
s502, if a cell and a target cell have a future time interval which reaches or exceeds a continuous complementary time length by S hours in a future day, and the cell and the target cell are in a complementary state, defining the day as 'effective'; counting the number of days between the cell and the target cell, which are valid, within the range which can be predicted by the prophet algorithm, so as to obtain the number of valid days between the cell and the target cell;
s503, respectively calculating scheduling parameters between the target cell and the rest of the cells; the scheduling parameter is a product of a matching degree between the two cells, the effective number of days calculated according to the step S502, and the continuous complementary duration calculated according to the step S501;
s504, comparing the scheduling parameters between the target cell and the rest of the cells, screening out the largest scheduling parameter, and finding out the corresponding cell, wherein the found cell is the best matching cell of the target cell.
The target cell is reselected and steps S501-S504 are repeatedly performed to select a corresponding best matching cell for the newly selected target cell.
In performing steps S501-S504 to determine the best matching cell for the target cell, in addition to the degree of matching between the target cell and the remaining cells, the number of active days between the target cell and the remaining cells and the length of hours in the complementary state for each "active" day are taken into account. Since the larger the number of valid days and the longer the duration in the complementary state in each day, the more the buffer time left for scheduling between cells, the better the scheduling effect can be obtained, the best matching cell is selected by executing steps S501 to S504, and the influence of various factors in actual scheduling can be considered more comprehensively than merely considering the matching degree between cells, and the selected best matching cell is a cell more suitable for scheduling.
After determining the best matching cells for the cells to be scheduled, if scheduling is required, step S6 is performed to perform complementary scheduling between the cell and its best matching cell. Specifically, performing complementary scheduling refers to avoiding service quality degradation and resource waste caused by one cell being too busy and the other cell being too idle by allocating resources and loads between the cell and its best matching cell so that their loads are in a balanced state when the cell is in a high load state and its best matching cell is in a low load state or when the cell is in a low load state and its best matching cell is in a high load state.
In the present embodiment, the principle of executing steps S1 to S6 is as follows: by using a prophet algorithm, a future operation index is predicted according to resource historical data such as uplink flow, downlink flow, uplink utilization rate, downlink utilization rate, effective RRC number, week attribute information, holiday information and the like, so that factors such as trend, periodic change, accidental change, errors and the like of the resource historical data can be fully considered, and an accurate prediction result is provided; the best matching cell of each cell is determined according to the prediction result of the prophet algorithm, more time exists between each cell and the best matching cell of each cell, the cells are in a complementary state, and complementary scheduling is performed between each cell and the respective best matching cell of each cell, so that a higher success rate can be achieved, and the effect of balancing resources and loads can be better realized.
Example 2
In this embodiment, the cell scheduling method in embodiment 1 is programmed into a computer program, and the computer program is written into computer hardware, so that a cell scheduling system, a cell scheduling apparatus, and a storage medium can be obtained.
The cell scheduling system comprises:
a first module, configured to obtain resource history data of each cell;
a second module, configured to predict future operation indicators of each cell according to the resource history data by using a prophet algorithm;
a third module, configured to determine, according to a future operation index of each cell, a load condition of each cell in a future time period;
a fourth module, configured to calculate a matching degree between the cells according to each of the load conditions;
a fifth module, configured to determine, according to each matching degree, an optimal matching cell for each cell;
a sixth module for performing complementary scheduling between the cell and its best matching cell.
The first module, the second module, the third module, the fourth module, the fifth module and the sixth module may be hardware modules, software modules or a combination of hardware modules and software modules having corresponding functions on a computer device such as a server.
The cell scheduling apparatus includes a memory for storing at least one program and a processor for loading the at least one program to perform the cell scheduling method.
The steps S1 to S6, S101 to S102, S301 to S303, S401 to S405, and S501 to S504 are written as a driver, and written into a storage medium of the existing display device, and when a computer program in the storage medium is read out and executed, the control method can be executed, so that the existing display device can be the display device in embodiment 1, thereby achieving the beneficial effects described in embodiment 1.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described by the present embodiments (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated onto a computing platform, such as a hard disk, optically read and/or write storage media, RAM, ROM, etc., so that it is readable by a programmable computer, which when read by the computer can be used to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (8)
1. A cell scheduling method, characterized in that the following steps are performed in each scheduling period:
acquiring resource historical data of each cell;
predicting the future operation index of each cell according to the resource historical data by using a prophet algorithm;
judging the load condition of each cell in the future time period according to the future operation index of each cell;
calculating the matching degree between the cells according to the load conditions;
respectively determining the best matching cells for each cell according to each matching degree;
performing complementary scheduling between the cell and its best matching cell;
the step of calculating the matching degree between the cells according to the load conditions specifically includes:
for any two of the cells, detecting a duration that they are in a complementary state in a future time period; the complementary state refers to that the load conditions of the two cells are different in the same future time period;
calculating the proportion of the duration of the complementary state to the sum of the durations of the future time periods as the matching degree between the two cells;
for any two of the cells, detecting the service scenes served by the cells;
setting a matching weight between the two cells according to the corresponding relation between the service scenes;
and adjusting the matching degree between the two cells according to the matching weight.
2. The method of claim 1, wherein the resource history data comprises uplink traffic, downlink traffic, uplink utilization, downlink utilization, number of active RRC's, week attribute information, and holiday information.
3. The method according to claim 2, wherein the step of obtaining the resource history data of each cell specifically comprises:
carrying out grading assignment and summation processing on the uplink flow, the downlink flow, the uplink utilization rate, the downlink utilization rate and the effective RRC number corresponding to the same cell;
and converting each week attribute information and each holiday information into a dummy variable respectively.
4. The method according to claim 1, wherein the step of determining the load condition of each cell in a future time period according to the future operation index of each cell specifically comprises:
comparing the future operation index with a preset high-load standard value and a preset low-load standard value;
under the condition that the corresponding future operation index is higher than the high-load standard value, judging the cell as a high-load cell in a future time period;
and under the condition that the corresponding future operation index is lower than the low-load standard value, judging the cell as a low-load cell in a future time period.
5. The method according to claim 1, wherein the step of determining the best matching cell for each cell according to each matching degree comprises:
selecting a target cell from each cell, and setting continuous complementary duration corresponding to the target cell;
respectively detecting the effective days between the target cell and the rest of the cells; in each day forming the valid days, the time length of the target cell and the corresponding cell in the complementary state is not less than the continuous complementary time length;
respectively calculating scheduling parameters between the target cell and the rest of the cells; the scheduling parameter is the product of the matching degree between the two cells, the valid days and the continuous complementary duration;
and selecting the cell with the maximum scheduling parameter from the rest of the cells as the best matching cell of the target cell.
6. A cell scheduling system, comprising:
a first module, configured to obtain resource history data of each cell;
a second module, configured to predict future operation indicators of each cell according to the resource history data by using a prophet algorithm;
a third module, configured to determine, according to a future operation index of each cell, a load condition of each cell in a future time period;
a fourth module, configured to calculate a matching degree between the cells according to each of the load conditions;
a fifth module, configured to determine a best matching cell for each cell according to each matching degree;
a sixth module for performing complementary scheduling between the cell and its best matching cell;
the step of calculating the matching degree between the cells according to each of the load conditions specifically includes:
for any two of the cells, detecting a duration that they are in a complementary state in a future time period; the complementary state means that the load conditions of the two cells are different in the same future time period;
calculating the proportion of the duration of the complementary state to the sum of the durations of the future time periods as the matching degree between the two cells;
for any two of the cells, detecting the service scenes served by the cells;
setting a matching weight between the two cells according to the corresponding relation between the service scenes;
and adjusting the matching degree between the two cells according to the matching weight.
7. A cell scheduling apparatus comprising a memory for storing at least one program and a processor for loading the at least one program to perform the method of any of claims 1-5.
8. A storage medium having stored therein processor-executable instructions, which when executed by a processor, are configured to perform the method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911367120.4A CN111132179B (en) | 2019-12-26 | 2019-12-26 | Cell scheduling method, system, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911367120.4A CN111132179B (en) | 2019-12-26 | 2019-12-26 | Cell scheduling method, system, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111132179A CN111132179A (en) | 2020-05-08 |
| CN111132179B true CN111132179B (en) | 2023-03-10 |
Family
ID=70503176
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911367120.4A Active CN111132179B (en) | 2019-12-26 | 2019-12-26 | Cell scheduling method, system, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111132179B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113923096B (en) * | 2020-06-22 | 2023-05-30 | 中国联合网络通信集团有限公司 | Network element fault early warning method and device, electronic equipment and storage medium |
| CN112351440B (en) * | 2020-09-28 | 2024-03-12 | 北京拓明科技有限公司 | Method and system for predicting pooling capacity expansion of 5G/4G high-load cell |
| CN112492637B (en) * | 2020-12-28 | 2023-06-27 | 中国联合网络通信集团有限公司 | Method and device for predicting cell traffic |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104144431A (en) * | 2013-05-10 | 2014-11-12 | 中国电信股份有限公司 | Method and device for mobile network status prediction and mobile network |
| CN105792376A (en) * | 2014-12-23 | 2016-07-20 | 中国移动通信集团上海有限公司 | Method and device for scheduling of carrier resources |
| CN109451012A (en) * | 2018-11-01 | 2019-03-08 | 宜通世纪物联网研究院(广州)有限公司 | A kind of end cloud collaboration load equilibration scheduling method, system and storage medium |
| CN110061882A (en) * | 2019-04-26 | 2019-07-26 | 南京华苏科技有限公司 | The method of License dynamic adjustment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015114739A1 (en) * | 2014-01-28 | 2015-08-06 | 富士通株式会社 | Wireless terminal and information processing apparatus |
-
2019
- 2019-12-26 CN CN201911367120.4A patent/CN111132179B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104144431A (en) * | 2013-05-10 | 2014-11-12 | 中国电信股份有限公司 | Method and device for mobile network status prediction and mobile network |
| CN105792376A (en) * | 2014-12-23 | 2016-07-20 | 中国移动通信集团上海有限公司 | Method and device for scheduling of carrier resources |
| CN109451012A (en) * | 2018-11-01 | 2019-03-08 | 宜通世纪物联网研究院(广州)有限公司 | A kind of end cloud collaboration load equilibration scheduling method, system and storage medium |
| CN110061882A (en) * | 2019-04-26 | 2019-07-26 | 南京华苏科技有限公司 | The method of License dynamic adjustment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111132179A (en) | 2020-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111132179B (en) | Cell scheduling method, system, device and storage medium | |
| US10474504B2 (en) | Distributed node intra-group task scheduling method and system | |
| WO2016033973A1 (en) | Method and system for predicting resource occupancy | |
| Hendriks et al. | Strategic allocation of cyclically calling vessels for multi-terminal container operators | |
| CN110996377B (en) | Base station energy saving method, system, device and storage medium | |
| EP3309724A1 (en) | Methods and systems related to allocating field engineering resources for power plant maintenance | |
| US20190334785A1 (en) | Forecasting underutilization of a computing resource | |
| CN110662245A (en) | Base station load early warning method and device based on deep learning | |
| CN111198808A (en) | Method, device, storage medium and electronic equipment for predicting performance index | |
| CN113434253A (en) | Cluster resource scheduling method, device, equipment and storage medium | |
| CN103297623A (en) | Telephone traffic predicting method and device | |
| US11310125B2 (en) | AI-enabled adaptive TCA thresholding for SLA assurance | |
| CN103699443A (en) | Task distributing method and scanner | |
| CN111741427A (en) | Service complaint processing method, device and equipment | |
| US20220180291A1 (en) | Method and system for workflow assignment | |
| CN113240226A (en) | Resource distribution method and device between satellite ground stations | |
| JP6635895B2 (en) | Power demand control system, power demand control method, aggregator system, customer power management system, and program | |
| CN114786190A (en) | Flow prediction method and device and storage medium | |
| CN111800807A (en) | Method and device for alarming number of base station users | |
| CN114595970A (en) | Resource scheduling intelligent decision method and device, electronic equipment and storage medium | |
| EP2681967A1 (en) | Resource managing method, resource management device and apparatus for supporting operation of a radio communication network | |
| CN116737804B (en) | Gas data hierarchical processing method and system based on intelligent gas Internet of things | |
| CN111105050B (en) | Fan maintenance plan generation method, device, equipment and storage medium | |
| CN103826228A (en) | Method and device for planning and allocating resources | |
| US20210337430A1 (en) | Forecasting Traffic Demand Experienced by Wireless Communication System with Application to Channel Allocation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Lu Yufang Inventor after: Ding Xiaoli Inventor after: Peng Siyu Inventor after: Huang Dan Inventor after: Zheng Tao Inventor before: Lu Yufang Inventor before: Ding Xiaoli Inventor before: Peng Siyu Inventor before: Huang Dan Inventor before: Zheng Tao |
|
| CB03 | Change of inventor or designer information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 510630 room 1101, building 1, No.16 Keyun Road, Tianhe District, Guangzhou City, Guangdong Province (office use only) Patentee after: Guangdong Yitong Lianyun Intelligent Information Co.,Ltd. Address before: 510630 building 1101, No.16 Keyun Road, Tianhe District, Guangzhou City, Guangdong Province Patentee before: YITONG CENTURY INTERNET OF THINGS RESEARCH INSTITUTE (GUANGZHOU) Co.,Ltd. |
|
| CP03 | Change of name, title or address |