Functional characteristic resource scheduling method and device

Abstract

The present application provides a method, device, electronic device and computer program product for scheduling functional characteristics resources, and relates to the field of communication technology. The method comprises: determining the resource demand site of the current scheduling period based on the first historical traffic data of several sites in the target area in the previous scheduling period; after the resource demand site loads the available resources of the target functional characteristics and within the current scheduling period, based on the second historical traffic data of the resource demand site, the target cell is screened out from the cells under the resource demand site with a preset operation and maintenance period, so that the target functional characteristics switch of the target cell is set to the on state within the operation and maintenance period. The present application realizes the allocation of functional characteristics resources between sites and the automatic operation and maintenance of functional characteristics within a site according to historical traffic data, thereby being able to simply and effectively improve the evaluation efficiency and application benefits of network functional characteristics.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device, electronic device and computer program product for scheduling functional characteristic resources. Background Art

[0002] 4G networks (4th generation mobile networks) are the long-term evolution of technical standards developed by the 3GPP (The 3rd Generation Partnership Project). 5G networks (5th generation mobile networks) are the fifth generation mobile communication technology networks developed by the 3GPP. The ITU IMT-2020 specification calls for speeds of up to 20Gbit / s. The application of LTE and 5G NR features can effectively improve network perception.

[0003] Reasonable application of functional features in 4 / 5G networks can improve network quality and user perception. Functional feature resource scheduling should be carried out in a timely manner according to changes in network users and services. Currently, functional feature scheduling is mainly based on user and service fluctuations, and is regularly sorted out and scheduled based on manually formulated principles.

[0004] Since the existing 4 / 5G functional features are mainly sorted and applied based on manually formulated principles, and are limited by the need to load licenses at the site level, the existing license scheduling and functional feature application have relatively long scheduling and application cycles. In addition, the overall work implementation requires a large amount of manpower and cost resources. As a result, the overall functional feature operation and maintenance efficiency is low, which may affect user perception in some scenarios. Summary of the Invention

[0005] The embodiments of the present application provide a method, device, electronic device, and computer program product for scheduling functional feature resources to solve the technical problem of low efficiency in functional feature operation and maintenance in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for scheduling functional characteristic resources, including:

[0007] Determine the resource demand site in the current scheduling period based on the first historical traffic data of several sites in the target area in the previous scheduling period;

[0008] After the resource demand site loads available resources of the target functional characteristics and within the current scheduling period, based on the second historical traffic data of the resource demand site, a target cell is selected from cells under the resource demand site in a preset operation and maintenance period, so that the target functional characteristic switch of the target cell is set to an on state within the operation and maintenance period;

[0009] Among them, the scheduling period is a positive integer multiple of the operation and maintenance period; the number of resource demand sites and the number of available resources of the target functional characteristics have a first corresponding relationship; the number of target cells in each operation and maintenance period and the number of available resources of the target functional characteristics have a second corresponding relationship.

[0010] In one embodiment, determining the resource demand site in the current scheduling period based on the first historical traffic data of the plurality of sites in the target area in the previous scheduling period includes:

[0011] According to the traffic statistics of all cells under the plurality of sites in the target area obtained by counting the first historical traffic data, cells whose historical traffic meets a preset condition are screened out and marked as demand cells;

[0012] The resource demand sites of the current scheduling period are determined according to the number of cells marked as demand cells under each site; wherein the number of resource demand sites is determined according to the number of available resources of the target functional characteristics and the first corresponding relationship.

[0013] In one embodiment, determining the resource demand site in the current scheduling period based on the first historical traffic data of the plurality of sites in the target area in the previous scheduling period further includes:

[0014] Constructing a traffic information matrix with m rows and n columns based on the first historical traffic data; wherein m represents the number of all cells under the plurality of sites in the target area, and n represents the number of unit times divided into each scheduling period;

[0015] Based on the available resource quantity of the target functional characteristic and the first corresponding relationship, performing sparse processing on the traffic information matrix to obtain a first sparse matrix;

[0016] The first sparse matrix is ​​summed and reorganized row by row to obtain a demand information matrix, and then the resource demand site of the current scheduling period is determined according to the demand information matrix.

[0017] In one embodiment, the target cell screening method includes:

[0018] In each operation and maintenance cycle, based on the traffic statistics of the cells under the resource demand site counted by the second historical traffic data, the cells whose historical traffic meets the preset conditions are screened out as the target cells; wherein, the second historical traffic data includes the traffic statistics information of each cell in each operation and maintenance cycle in the past several scheduling cycles; the number of the target cells is determined based on the number of available resources of the target functional characteristics and the second corresponding relationship.

[0019] In one embodiment, the target cell screening method further includes:

[0020] In each operation and maintenance cycle, a traffic data matrix with i rows and j columns is constructed based on the second historical traffic data; wherein i represents the number of cells under the resource demand site, and j represents the number of historical scheduling cycles in the second historical traffic data;

[0021] Based on the available resource quantity of the target functional characteristic and the second corresponding relationship, performing sparse processing on the traffic data matrix to obtain a second sparse matrix;

[0022] The second sparse matrix is ​​summed up row by row and reorganized to obtain a screening information matrix, and then the target cell of the current operation and maintenance cycle is screened out according to the screening information matrix.

[0023] In one embodiment, the scheduling cycle is one day long, and the operation and maintenance cycle is one hour long.

[0024] In one embodiment, the target functional characteristics are high-order modulation functional characteristics or carrier aggregation functional characteristics.

[0025] In a second aspect, an embodiment of the present application provides a functional characteristic resource scheduling device, including:

[0026] A demand allocation module is used to determine the resource demand site of the current scheduling period based on the first historical flow data of several sites in the target area in the previous scheduling period;

[0027] a resource operation and maintenance module, configured to, after the resource demand site loads available resources of the target functional characteristics and within the current scheduling period, screen out a target cell from cells under the resource demand site based on the second historical traffic data of the resource demand site within a preset operation and maintenance period, so that a target functional characteristic switch of the target cell is set to an on state within the operation and maintenance period;

[0028] Among them, the scheduling period is a positive integer multiple of the operation and maintenance period; the number of resource demand sites and the number of available resources of the target functional characteristics have a first corresponding relationship; the number of target cells in each operation and maintenance period and the number of available resources of the target functional characteristics have a second corresponding relationship.

[0029] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory storing a computer program, wherein when the processor executes the program, the steps of the functional characteristic resource scheduling method described in the first aspect are implemented.

[0030] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the functional characteristic resource scheduling method described in the first aspect.

[0031] The functional characteristic resource scheduling method, device, electronic device and computer program product provided in the embodiments of the present application realize functional characteristic resource allocation by calculating resource requirements based on historical traffic data of different cells at the site level, and after loading the resources, realize automatic operation and maintenance of functional characteristics according to the historical traffic data at the cell level within the site, thereby simply and effectively improving the evaluation efficiency and application benefits of network functional characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 This is one of the flow diagrams of the function characteristic resource scheduling method provided in the embodiment of the present application;

[0034] Figure 2 This is the second flow chart of the function characteristic resource scheduling method provided in the embodiment of the present application;

[0035] Figure 3 This is one of the structural diagrams of the functional characteristic resource scheduling device provided in the embodiment of the present application;

[0036] Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0038] Figure 1 This is a resource scheduling method for functional features. Figure 1 , an embodiment of the present application provides a method for scheduling functional characteristic resources, which may include:

[0039] S1. Determine the resource demand site in the current scheduling period based on the first historical traffic data of several sites in the target area in the previous scheduling period;

[0040] S2. After the resource demand site loads the available resources of the target functional characteristics and within the current scheduling period, based on the second historical traffic data of the resource demand site, the target cell is screened out from the cells under the resource demand site according to a preset operation and maintenance period, so that the target functional characteristic switch of the target cell is set to the on state within the operation and maintenance period; in an embodiment of the present application, the target functional characteristic is a high-order modulation functional characteristic or a carrier aggregation functional characteristic.

[0041] The scheduling period is a positive integer multiple of the operation and maintenance period; the number of resource-demanding sites has a first correspondence with the number of available resources for the target functional characteristics; and the number of target cells within each operation and maintenance period has a second correspondence with the number of available resources for the target functional characteristics. In this embodiment of the present application, the scheduling period is one day, and the operation and maintenance period is one hour.

[0042] In the embodiment of the present application, the historical traffic data of each site in the target area is first obtained. For example, one day can be used as a scheduling cycle, and the resource demand for the next scheduling cycle can be predicted based on the historical traffic data of the previous scheduling cycle, so as to perform resource allocation. The historical traffic data includes the historical traffic information of all cells under each site, and can include the specific historical traffic information of these cells in each subdivided unit time (for example, if a scheduling cycle is divided into 24 hours, one hour is used as a subdivided unit time). It should be noted that the number of resource demand sites selected has a first corresponding relationship with the number of available resources of the target functional characteristics. For example, if the number of available resources of the target functional characteristics is 2 (the specific value can be set according to actual needs and is not specifically limited in the embodiment of the present application), the number of resource demand sites selected can be 1 or 2, that is, it can be set to allocate all 2 available resources to one resource demand site selected, or it can be set to allocate 2 available resources to two resource demand sites selected.

[0043] After the resource-demanding site is determined, the available resources for the functional characteristic are loaded into the resource-demanding site. After the available resources for the target functional characteristic are loaded, automatic operation and maintenance of the target functional characteristic is performed on each cell under the resource-demanding site. Specifically, each scheduling cycle is divided into several operation and maintenance cycles (a scheduling cycle is a positive integer multiple of the operation and maintenance cycle, for example, a scheduling cycle of one day and an operation and maintenance cycle of one hour). Based on the acquired second historical traffic data, the traffic statistics of each cell in the same time period over the past several scheduling cycles are determined. For example, if resource-demanding site A includes three cells: A1, A2, and A3, based on the traffic statistics of A1, A2, and A3 in the same time period (for example, from 0:00 to 1:00) over the past seven days (seven scheduling cycles), it can be determined which cell (for example, A1) has the greater traffic demand in that time period. In this case, the switch of the target functional characteristic of cell A1 is set to on in the future time period (from 0:00 to 1:00). It can be understood that, during this time period, except for the target cell whose target functional characteristic switch is set to the on state, the target functional characteristic switches of the remaining cells are all set to the off state.

[0044] The functional characteristic resource scheduling method provided in the embodiment of the present application realizes the functional characteristic resource allocation between sites and the automatic operation and maintenance of functional characteristics within a site based on historical traffic data, thereby simply and effectively improving the evaluation efficiency and application benefits of network functional characteristics.

[0045] In one embodiment, step S1 may include:

[0046] According to the traffic statistics of all cells under the plurality of sites in the target area obtained by counting the first historical traffic data, cells whose historical traffic meets a preset condition are screened out and marked as demand cells;

[0047] The resource demand sites of the current scheduling period are determined according to the number of cells marked as demand cells under each site; wherein the number of resource demand sites is determined according to the number of available resources of the target functional characteristics and the first corresponding relationship.

[0048] It should be noted that the selection of the resource demand site can be determined according to the above two steps. Specifically, first, based on the traffic statistics of all cells under several sites in the target area of ​​the first historical traffic data statistics, the cells whose historical traffic meets the preset conditions are screened out and marked as demand cells. The following is an example of screening out a demand cell. Example 1: Divide a scheduling cycle (1 day) into 24 hours. In each hour, mark the cell corresponding to the data with the largest historical traffic. After completing all 24 markings, determine which cell has been marked the most times, and then determine this cell as the demand cell (if there are multiple cells with the same number of markings, a random algorithm is used to select a cell); Example 2: Within a scheduling cycle (1 day), determine which cell has the largest total historical traffic within the cycle, and then determine this cell as the demand cell (if there are multiple cells with the same total historical traffic, a random algorithm is used to select a demand cell).

[0049] Then, the resource demand cells are determined based on the number of cells marked as demand cells corresponding to each site. For example, if the number of resource demand sites is determined to be 1 based on the number of available resources of the target functional characteristics and the first corresponding relationship, it means that only one site will be selected as the resource demand site, and the number of demand cells mentioned above is also determined to be marked as 1. If it is determined that two resource demand sites can be selected, then two demand cells can also be marked accordingly. At this time, it is possible that the two demand cells belong to the same site, then the site is directly determined as the resource demand site, and both available resources can be allocated to the resource demand site, or only one available resource can be allocated to the resource demand site (the subsequent automatic operation and maintenance of the functional characteristics switch only turns on one switch).

[0050] The functional characteristic resource scheduling method provided in the embodiment of the present application, by refining the basis for judging the resource demand site to the cell level of each site, can more accurately analyze the timing fluctuation characteristics of cell traffic between sites, thereby more effectively improving the accuracy and effectiveness of functional characteristic resource scheduling between sites, and further improving the efficiency of functional characteristic utilization.

[0051] In one embodiment, determining the resource demand site in the current scheduling period based on the first historical traffic data of the plurality of sites in the target area in the previous scheduling period may further include:

[0052] Constructing a traffic information matrix with m rows and n columns based on the first historical traffic data; wherein m represents the number of all cells under the plurality of sites in the target area, and n represents the number of unit times divided into each scheduling period;

[0053] Based on the available resource quantity of the target functional characteristic and the first corresponding relationship, performing sparse processing on the traffic information matrix to obtain a first sparse matrix;

[0054] The first sparse matrix is ​​summed and reorganized row by row to obtain a demand information matrix, and then the resource demand site of the current scheduling period is determined according to the demand information matrix.

[0055] It should be noted that the selection of resource-demand sites can be further determined using matrix representation and operations. Specifically, the first historical traffic data is first organized using a matrix to construct an m-row, n-column traffic information matrix. For example, if several sites in the target area have a total of 6 cells under their jurisdiction, and each scheduling cycle is divided into 24 hours of data, a 6*24 traffic information matrix is ​​constructed, where each row represents the historical traffic of the mth cell in the previous scheduling cycle for 24 hours, and each column represents the historical traffic of the six cells in the nth hour of the previous scheduling cycle.

[0056] The traffic information matrix is ​​then sparsified. Assuming the number of available resources is 1, only the maximum value in a single column is assigned a value of 1, and the remaining elements are assigned a value of 0, resulting in the first sparse matrix. This matrix is ​​then summed and reorganized along its row dimensions to obtain a 1-row, 6-column demand information matrix. The values ​​in this matrix are then used to determine the demand cell. The site to which this demand cell belongs is the resource demand site.

[0057] The functional characteristic resource scheduling method provided in the embodiment of the present application converts the process of determining the resource demand site into matrix representation and calculation, thereby being able to more accurately analyze the timing fluctuation characteristics of cell traffic between sites, and then more effectively realize the scheduling of functional characteristic resources between sites based on these timing fluctuation characteristics, thereby further improving the efficiency of functional characteristic utilization.

[0058] In one embodiment, the target cell screening method may include:

[0059] In each operation and maintenance cycle, based on the traffic statistics of the cells under the resource demand site counted by the second historical traffic data, the cells whose historical traffic meets the preset conditions are screened out as the target cells; wherein, the second historical traffic data includes the traffic statistics information of each cell in each operation and maintenance cycle in the past several scheduling cycles; the number of the target cells is determined based on the number of available resources of the target functional characteristics and the second corresponding relationship.

[0060] It should be noted that the method for screening the target cell can be determined according to the above steps. Specifically, in each operation and maintenance cycle, based on the traffic statistics of the cells under the resource demand site, for example: there are three cells under the resource demand site, the traffic statistics of each cell include the historical traffic of the cell in the same period (if the operation and maintenance cycle is one hour, the same time starting and ending points of each day are used as the same period, such as 0-1 o'clock every day) in the past n scheduling cycles (for example, one scheduling cycle is one day, here we take the data of the past 7 days). Based on these historical traffic data, calculate which cell has the largest total historical traffic in the same period of the past 7 days, and use this cell as the target cell.

[0061] The functional characteristic resource scheduling method provided in the embodiment of the present application realizes the prediction of the time series characteristics of the cell traffic within the site by using the traffic statistical information of each operation and maintenance period of the cells under the resource demand site in the past several scheduling periods, thereby being able to more accurately analyze the time series fluctuation characteristics of the cell traffic within the site, and further more effectively improve the accuracy and effectiveness of the automatic operation and maintenance of the functional characteristic resources of each cell in the site, thereby further improving the efficiency of functional characteristic utilization.

[0062] In one embodiment, the target cell screening method further includes:

[0063] In each operation and maintenance cycle, a traffic data matrix with i rows and j columns is constructed based on the second historical traffic data; wherein i represents the number of cells under the resource demand site, and j represents the number of historical scheduling cycles in the second historical traffic data;

[0064] Based on the available resource quantity of the target functional characteristic and the second corresponding relationship, performing sparse processing on the traffic data matrix to obtain a second sparse matrix;

[0065] The second sparse matrix is ​​summed up row by row and reorganized to obtain a screening information matrix, and then the target cell of the current operation and maintenance cycle is screened out according to the screening information matrix.

[0066] It should be noted that the target screening can be further determined in the form of matrix representation and operation. Specifically, the second historical traffic data is first listed using a matrix to construct a traffic data matrix with i rows and j columns. For example, if the resource demand site has 3 cells and the number of historical scheduling cycles in the second historical traffic data is 7, then a 3*7 traffic data matrix is ​​constructed, where each row represents the historical traffic of the i-th cell in the same operation and maintenance cycle (such as 0-1) within the 7 historical scheduling cycles, and each column represents the historical traffic of the three cells in the same operation and maintenance cycle within the j-th historical scheduling cycle.

[0067] The traffic information matrix is ​​then sparsified. Assuming the number of available resources is 1, only the maximum value in a single column is assigned a value of 1, and the remaining elements are assigned a value of 0, resulting in a second sparse matrix. This matrix is ​​then summed and reorganized along the row dimension to obtain a 1-row, 3-column screening information matrix. The target cell for the operation and maintenance cycle (0-1 hours) is then determined based on the values ​​in this matrix.

[0068] The functional characteristic resource scheduling method provided in the embodiment of the present application converts the screening process of the target cells in the station into matrix representation and calculation, so as to more accurately analyze the timing fluctuation characteristics of the cell traffic in the station, thereby more effectively improving the accuracy and effectiveness of the automatic operation and maintenance of the functional characteristic resources of each cell in the station, and further improving the utilization efficiency of the functional characteristics.

[0069] See Figure 2 Based on the above solution, in order to facilitate a better understanding of the functional characteristic resource scheduling method provided by the present invention, the following is a detailed description:

[0070] In response to the technical problems raised by the above background technology, the embodiments of the present application provide a functional feature resource scheduling method. Based on the fluctuations in user traffic between cells at the same site and the time series changes in user traffic within the cell, it realizes automatic real-time scheduling of functional feature license resources of the entire network cell on demand, creates an autonomous driving network, and achieves the goals of automatic operation and maintenance and cost reduction and efficiency improvement.

[0071] It should be noted that the embodiments of this application predict and evaluate the demand for cell-specific software license resources based on inter-cell user traffic fluctuations and intra-cell user traffic time series changes, enabling on-demand automatic real-time scheduling of features. This is primarily based on the current high-order modulation and carrier aggregation features available in 4 / 5G, and based on cell-level traffic time series characteristics, enabling on-demand automatic scheduling of features, thereby effectively improving feature utilization efficiency and reducing feature license resource investment costs.

[0072] The embodiment of the present application is mainly implemented in three steps: step 1 realizes the calculation of inter-station functional feature license resource requirements, step 2 realizes functional feature license loading, and step 3 realizes intra-station cell-level time series data processing, sorting and automatic operation and maintenance.

[0073] Step 1: Calculate license resource requirements for inter-site functional features.

[0074] The effectiveness of major terminal features currently depends on the configuration of feature licenses and the activation of corresponding parameter settings. These features take effect only after the feature license resources are configured and the parameter settings are enabled.

[0075] This step uses an algorithm to calculate the license resource requirements for site-level 4 / 5G features. The specific implementation process of step one is as follows:

[0076] 1.1 First, cell-level traffic statistics data (equivalent to the historical traffic data in the above embodiment) are extracted.

[0077] This small step completes the extraction of cell-level data traffic, and the extraction cycle can be 24 hours.

[0078] 1.2 The second step is to build the cell-level traffic data matrix.

[0079] The total number of cells in the statistical area is taken as rows, and the 1-24 hours in a day are taken as columns, and the traffic statistics corresponding to each cell in each hour are filled in. The matrix R is obtained. m*n , where the element is N m*n ,Right now:

[0080] The matrix R m*n It is an m*n matrix, where m is the number of cells in the statistical area and n is the 24-hour cell-level traffic.

[0081] 1.3 Then comes the data sparsification of the cell-level traffic data matrix.

[0082] According to the matrix data obtained by the above process, the matrix R is adjusted according to the available license scale of the actual functional characteristics. m*n Perform sparseness and generate a new matrix T m*n The specific sparsification rules are as follows:

[0083] 1) Sort each column of traffic data in ascending order;

[0084] 2) When the traffic data size ranking corresponding to the column element is greater than the available license size, the element is filled with 1; otherwise, the element is filled with 0;

[0085] 1.4 Finally, the functional feature license resource allocation calculation is implemented based on the matrix data reordering.

[0086] The matrix T m*n Sum according to the row dimension to get the matrix T' m . For the matrix T' m Sort in ascending order and determine the investment in license resources for functional features at a single site based on the available license size for the functional features.

[0087] Step 2: Load the feature license.

[0088] This step loads the feature license based on the license resource requirement data obtained in step 1.

[0089] Step 3: Implement cell-level time series data processing, sorting, and automatic operation and maintenance within the station.

[0090] This step is to calculate and implement the cell-level traffic sorting under the site based on the cell-level time series characteristics.

[0091] The specific implementation process is as follows:

[0092] 3.1 First, extract the historical traffic time series data at the community level

[0093] The historical traffic data of all cells under the site are extracted, and the historical traffic data of all cells under the site in the same period within 7 days are formed into a matrix U i*j , where i represents the number of cells under the site, j represents the historical traffic data of the same period for 7 days, and the matrix U i*j as follows:

[0094] The matrix row i equals the number of cells under the site, and the matrix column j equals the historical traffic volume for the same period over the past seven days. This step is used to fill the matrix with raw traffic data, facilitating subsequent data fitting based on the algorithm.

[0095] 3.2 Next is the fitting of cell-level traffic time series data

[0096] Based on the matrix U i*j The original traffic data of the cell is aggregated and the cell-level traffic time series data is fitted through the following steps.

[0097] 1) Obtain the number of licenses for loading features at the site according to step 2.

[0098] 2) Upgrade and sort the cell traffic under the site in each column using the column as the dimension.

[0099] 3) If the element ranking value is higher than the site configuration license size, the element value is filled with 1; otherwise, the element value is filled with 0, and the sparse matrix V is obtained. i*j , to achieve matrix array sparseness.

[0100] 3.3 Next is the cell-level traffic sorting within the site

[0101] For the sparse matrix V i*j Sum up the rows to get the new matrix V' i , the one-dimensional matrix V i The data in the is sorted in descending order. If the element ranking value is higher than the site configuration license size, the corresponding cell has the feature switch enabled; otherwise, the corresponding cell has the feature switch disabled.

[0102] 3.4 Finally, realize the automatic operation and maintenance of functional features.

[0103] Based on the site-level feature license resource scale allocated in step 2, cell-level traffic sorting within the site is re-performed every hour, enabling automatic operation and maintenance of cell-level feature features on an hourly basis.

[0104] In order to more clearly illustrate the present application, specific embodiments are listed below for illustration:

[0105] Step 1: First, statistical calculations are performed using the LTE network's downlink 256QAM high-order modulation feature as an example. The target area includes Site A and Site B. First, the 24-hour traffic volume for cells A1-A3 at Site A and cells B1-B3 at Site B is extracted.

[0106] Secondly, based on the data extracted in step 1, construct the matrix R m*n , where m is the size of the six cells under sites A and B, and n is the number of hours in the past 24 days. Fill the matrix with the acquired 24-hour traffic volume (historical traffic data) for each cell.

[0107]

[0108] R m*n It is a 6*24 matrix.

[0109] Then, the matrix is ​​sparsely populated based on the license size. Assuming there is only one available 256QAM feature license, in this step, only the maximum value in a single column is assigned a value of 1, and the remaining elements are assigned a value of 0. The sparse matrix T is obtained. m*n :

[0110]

[0111] T m*n It is a 6*24 sparse matrix.

[0112] Finally, the sparse matrix T m*n Sum the row dimensions to get the matrix T' m , T' m is [0 22

[0113] 2…], where the element value corresponding to cell A2 is 22. Therefore, given that there is only one 256QAM feature license, this feature license resource is allocated to site A, where cell A2 is located.

[0114] Step 2: Load the 256QAM feature license to site A.

[0115] Since a 256QAM feature license has been loaded at site A, step three will perform automatic operation and maintenance of the 256QAM feature based on the timing characteristics of site A.

[0116] First, extract the 7*24 hour traffic data of cells A1-A3 under site A for the past week and form the matrix U i*j Taking Monday 00:00 as an example, output U i*j matrix.

[0117]

[0118] The rows represent the A1-A3 cells, and the columns represent the traffic data corresponding to 0:00 for the past seven days in the past week.

[0119] Secondly, cell-level traffic fitting is achieved. Since only one 256QAM high-order modulation license resource is configured under site A, the maximum value in the column dimension is marked as 1 and the other elements are marked as 0, and the sparse V i*j matrix

[0120]

[0121] Then according to the V obtained after sparse i*j The matrix V is obtained by summing the rows i , V i =[1 6 0]. Since the value corresponding to the element of cell A2 is the largest in this period, the 256QAM feature switch is turned on for cell A2 at time 0, and the 256QAM feature switch is turned off for cells A1 and A3.

[0122] Then, at 1, recalculate according to the third step to get the new V iAssuming Vi = [4 3 0] at 1:00 AM on Monday, the 256QAM feature switch is enabled for cell A1 at 1:00 AM, and disabled for cells A2 and A3. This process continues in this manner, achieving automated O&M of these features until midnight, when the next inter-site resource scheduling cycle begins.

[0123] It should be noted that the embodiments of this application mainly include the following key points:

[0124] 1. Compared with the traditional method of implementing functional characteristics application based on sites, this embodiment implements functional characteristics application based on time series prediction by analyzing the time series fluctuation characteristics of traffic in different cells at the site level.

[0125] 2. This embodiment implements a method for site license resource configuration and intra-site cell traffic time series prediction based on data matrix sparsification.

[0126] 3. This embodiment implements a method for scheduling intra-station cell-level functional characteristics through system automatic operation and maintenance.

[0127] It should be noted that the current strategy for applying functional features in existing networks is based on site-level deployment, resulting in underutilization of functional feature resources. This embodiment of the application uses a time series-based prediction method to implement site-level cell-level functional feature scheduling, significantly improving resource utilization efficiency in a single-site, multi-cell environment.

[0128] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0129] (1) The embodiments of the present application innovatively implement automatic operation and maintenance of cell functional characteristics at the site level. By combining the time series fluctuation characteristics of traffic in different cells at the site level, the functional characteristics are scheduled at the cell level on an hourly basis, optimizing the original method of enabling functional characteristics at the site level. The efficiency of functional characteristics utilization in multi-cell scenarios within the site is significantly improved.

[0130] (2) Under the premise that site-level licenses are limited by vendor equipment, the embodiments of the present application use algorithms to achieve reasonable allocation of site-level license resources and maximize the benefits of license resource deployment.

[0131] (3) The embodiment of the present application realizes site-level license resource allocation statistics and estimation of cell time series data under the site through data sparsification, guiding the automatic operation and maintenance of functional characteristics at hourly granularity, and the algorithm is simple and efficient.

[0132] (4) The embodiments of the present application realize the automation of functional characteristics operation and maintenance, reduce human resource investment, do not need to change the network structure, have no network risks, and are simple to operate.

[0133] (5) After the implementation of the embodiment of the present application, the efficiency of functional feature scheduling is significantly improved, and the operation is simple and efficient.

[0134] In summary, the embodiments of the present application can simply and effectively improve the evaluation efficiency and application benefits of 4 / 5G functional characteristics.

[0135] The following describes a functional characteristic resource scheduling device provided in an embodiment of the present application. The functional characteristic resource scheduling device described below and the functional characteristic resource scheduling method described above can refer to each other.

[0136] See Figure 3 , an embodiment of the present application provides a functional characteristic resource scheduling device, comprising:

[0137] Demand allocation module 1, for determining resource demand sites in the current scheduling period based on first historical traffic data of several sites in the target area in the previous scheduling period;

[0138] The resource operation and maintenance module 2 is configured to, after the resource demand site loads the available resources of the target functional characteristics and within the current scheduling period, screen out a target cell from the cells under the resource demand site based on the second historical traffic data of the resource demand site in a preset operation and maintenance period, so that the target functional characteristic switch of the target cell is set to an on state within the operation and maintenance period;

[0139] Among them, the scheduling period is a positive integer multiple of the operation and maintenance period; the number of resource demand sites and the number of available resources of the target functional characteristics have a first corresponding relationship; the number of target cells in each operation and maintenance period and the number of available resources of the target functional characteristics have a second corresponding relationship.

[0140] Furthermore, the demand allocation module 1 is specifically configured to:

[0141] According to the traffic statistics of all cells under the plurality of sites in the target area obtained by counting the first historical traffic data, cells whose historical traffic meets a preset condition are screened out and marked as demand cells;

[0142] The resource demand sites of the current scheduling period are determined according to the number of cells marked as demand cells under each site; wherein the number of resource demand sites is determined according to the number of available resources of the target functional characteristics and the first corresponding relationship.

[0143] Furthermore, the demand allocation module 1 is further configured to:

[0144] Constructing a traffic information matrix with m rows and n columns based on the first historical traffic data; wherein m represents the number of all cells under the plurality of sites in the target area, and n represents the number of unit times divided into each scheduling period;

[0145] Based on the available resource quantity of the target functional characteristic and the first corresponding relationship, performing sparse processing on the traffic information matrix to obtain a first sparse matrix;

[0146] The first sparse matrix is ​​summed and reorganized row by row to obtain a demand information matrix, and then the resource demand site of the current scheduling period is determined according to the demand information matrix.

[0147] Furthermore, the target cell screening method includes:

[0148] In each operation and maintenance cycle, based on the traffic statistics of the cells under the resource demand site counted by the second historical traffic data, the cells whose historical traffic meets the preset conditions are screened out as the target cells; wherein, the second historical traffic data includes the traffic statistics information of each cell in each operation and maintenance cycle in the past several scheduling cycles; the number of the target cells is determined based on the number of available resources of the target functional characteristics and the second corresponding relationship.

[0149] Furthermore, the target cell screening method further includes:

[0150] In each operation and maintenance cycle, a traffic data matrix with i rows and j columns is constructed based on the second historical traffic data; wherein i represents the number of cells under the resource demand site, and j represents the number of historical scheduling cycles in the second historical traffic data;

[0151] Based on the available resource quantity of the target functional characteristic and the second corresponding relationship, performing sparse processing on the traffic data matrix to obtain a second sparse matrix;

[0152] The second sparse matrix is ​​summed up row by row and reorganized to obtain a screening information matrix, and then the target cell of the current operation and maintenance cycle is screened out according to the screening information matrix.

[0153] Furthermore, the duration of the scheduling cycle is one day, and the duration of the operation and maintenance cycle is one hour.

[0154] Furthermore, the target functional characteristics are high-order modulation functional characteristics or carrier aggregation functional characteristics.

[0155] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention. The functional characteristic resource scheduling device provided by the embodiment of this application can implement the functional characteristic resource scheduling method provided by any method embodiment of the present invention.

[0156] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call a computer program in the memory 430 to execute the steps of the function characteristic resource scheduling method, for example, including:

[0157] Determine the resource demand site in the current scheduling period based on the first historical traffic data of several sites in the target area in the previous scheduling period;

[0158] After the resource demand site loads available resources of the target functional characteristics and within the current scheduling period, based on the second historical traffic data of the resource demand site, a target cell is selected from cells under the resource demand site in a preset operation and maintenance period, so that the target functional characteristic switch of the target cell is set to an on state within the operation and maintenance period;

[0159] Among them, the scheduling period is a positive integer multiple of the operation and maintenance period; the number of resource demand sites and the number of available resources of the target functional characteristics have a first corresponding relationship; the number of target cells in each operation and maintenance period and the number of available resources of the target functional characteristics have a second corresponding relationship.

[0160] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0161] On the other hand, embodiments of the present application further provide a computer program product, comprising a computer program. The computer program may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the method for scheduling functional feature resources provided in the above embodiments, for example, including:

[0162] Determine the resource demand site in the current scheduling period based on the first historical traffic data of several sites in the target area in the previous scheduling period;

[0163] After the resource demand site loads available resources of the target functional characteristics and within the current scheduling period, based on the second historical traffic data of the resource demand site, a target cell is selected from cells under the resource demand site in a preset operation and maintenance period, so that the target functional characteristic switch of the target cell is set to an on state within the operation and maintenance period;

[0164] Among them, the scheduling period is a positive integer multiple of the operation and maintenance period; the number of resource demand sites and the number of available resources of the target functional characteristics have a first corresponding relationship; the number of target cells in each operation and maintenance period and the number of available resources of the target functional characteristics have a second corresponding relationship.

[0165] On the other hand, an embodiment of the present application further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, wherein the computer program is configured to cause a processor to execute the steps of the methods provided in the above embodiments, for example, including:

[0166] Determine the resource demand site in the current scheduling period based on the first historical traffic data of several sites in the target area in the previous scheduling period;

[0167] After the resource demand site loads available resources of the target functional characteristics and within the current scheduling period, based on the second historical traffic data of the resource demand site, a target cell is selected from cells under the resource demand site in a preset operation and maintenance period, so that the target functional characteristic switch of the target cell is set to an on state within the operation and maintenance period;

[0168] Among them, the scheduling period is a positive integer multiple of the operation and maintenance period; the number of resource demand sites and the number of available resources of the target functional characteristics have a first corresponding relationship; the number of target cells in each operation and maintenance period and the number of available resources of the target functional characteristics have a second corresponding relationship.

[0169] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSDs)), etc.

[0170] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0171] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for scheduling functional resource, characterized in that: include: Determine the resource demand site in the current scheduling period based on the first historical traffic data of several sites in the target area in the previous scheduling period; After the resource demand site loads available resources of the target functional characteristics and within the current scheduling period, based on the second historical traffic data of the resource demand site, a target cell is selected from cells under the resource demand site in a preset operation and maintenance period, so that the target functional characteristic switch of the target cell is set to an on state within the operation and maintenance period; Among them, the scheduling cycle is a positive integer multiple of the operation and maintenance cycle; the number of resource-demand sites and the number of available resources of the target functional characteristics have a first corresponding relationship; the number of target cells in each operation and maintenance cycle and the number of available resources of the target functional characteristics have a second corresponding relationship; the first historical traffic data includes the traffic statistics of all cells under several sites in the target area; the second historical traffic data includes the traffic statistics information of each cell in each operation and maintenance cycle in the past several scheduling cycles.

2. The method for scheduling functional characteristic resources according to claim 1, wherein: The determining of the resource demand site in the current scheduling period according to the first historical traffic data of the plurality of sites in the target area in the previous scheduling period includes: According to the traffic statistics of all cells under the plurality of sites in the target area obtained by counting the first historical traffic data, cells whose historical traffic meets a preset condition are screened out and marked as demand cells; The resource demand sites of the current scheduling period are determined according to the number of cells marked as demand cells under each site; wherein the number of resource demand sites is determined according to the number of available resources of the target functional characteristics and the first corresponding relationship.

3. The method for scheduling functional characteristic resources according to claim 1, wherein: The method of determining the resource demand site in the current scheduling period based on the first historical traffic data of the plurality of sites in the target area in the previous scheduling period further includes: Constructing a traffic information matrix with m rows and n columns based on the first historical traffic data; wherein m represents the number of all cells under the plurality of sites in the target area, and n represents the number of unit times divided into each scheduling period; Based on the available resource quantity of the target functional characteristic and the first corresponding relationship, performing sparse processing on the traffic information matrix to obtain a first sparse matrix; The first sparse matrix is ​​summed and reorganized row by row to obtain a demand information matrix, and then the resource demand site of the current scheduling period is determined according to the demand information matrix.

4. The method for scheduling functional characteristic resources according to claim 1, wherein: The target cell screening method includes: During each operation and maintenance cycle, based on the traffic statistics of the cells under the resource demand site according to the second historical traffic data statistics, the cells whose historical traffic meets the preset conditions are screened out as the target cells; the number of the target cells is determined based on the number of available resources of the target functional characteristics and the second corresponding relationship.

5. The method for scheduling functional characteristic resources according to claim 1, wherein: The target cell screening method further includes: In each operation and maintenance cycle, a traffic data matrix with i rows and j columns is constructed based on the second historical traffic data; wherein i represents the number of cells under the resource demand site, and j represents the number of historical scheduling cycles in the second historical traffic data; Based on the available resource quantity of the target functional characteristic and the second corresponding relationship, performing sparse processing on the traffic data matrix to obtain a second sparse matrix; The second sparse matrix is ​​summed up row by row and reorganized to obtain a screening information matrix, and then the target cell of the current operation and maintenance cycle is screened out according to the screening information matrix.

6. The method for scheduling functional characteristic resources according to claim 1, wherein: The duration of the scheduling cycle is one day, and the duration of the operation and maintenance cycle is one hour.

7. The method for scheduling functional characteristic resources according to claim 1, wherein: The target functional characteristics are high-order modulation functional characteristics or carrier aggregation functional characteristics.

8. A functional characteristic resource scheduling device, characterized in that: include: A demand allocation module is used to determine the resource demand site of the current scheduling period based on the first historical flow data of several sites in the target area in the previous scheduling period; a resource operation and maintenance module, configured to, after the resource demand site loads available resources of the target functional characteristics and within the current scheduling period, screen out a target cell from cells under the resource demand site based on the second historical traffic data of the resource demand site within a preset operation and maintenance period, so that a target functional characteristic switch of the target cell is set to an on state within the operation and maintenance period; Among them, the scheduling cycle is a positive integer multiple of the operation and maintenance cycle; the number of resource-demand sites and the number of available resources of the target functional characteristics have a first corresponding relationship; the number of target cells in each operation and maintenance cycle and the number of available resources of the target functional characteristics have a second corresponding relationship; the first historical traffic data includes the traffic statistics of all cells under several sites in the target area; the second historical traffic data includes the traffic statistics information of each cell in each operation and maintenance cycle in the past several scheduling cycles.

9. An electronic device comprising a processor and a memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of the function characteristic resource scheduling method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the function characteristic resource scheduling method according to any one of claims 1 to 7 are implemented.

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