Method and apparatus for evaluating base station cell coverage

Abstract

The application provides a method and device for evaluating base station cell coverage effect, relates to the field of wireless communication, and can improve the accuracy and rationality of base station resource co-construction and sharing. The method comprises the following steps: acquiring service amount data of N first base station cells; inputting the service amount data into a time series prediction model to output prediction results of future service amounts of the N first base station cells; determining a target base station cell from the N first base station cells based on the prediction results; combining the target base station cell with M second base station cells to obtain X base station cells, and predicting coverage ranges and user perception data of the X base station cells; evaluating the coverage effects of the X base station cells based on historical user perception data, predicted user perception data and the coverage ranges of the X base station cells; the operators to which the first base station cells and the second base station cells belong are different; N, M and X are all integers greater than 1. The application is used in the process of evaluating base station cell coverage effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless communication, in particular to a method and device for evaluating cell coverage effect of a base station. BACKGROUND

[0002] In the prior art, when two operators share network resources, on the one hand, the coverage area needs to be increased, and on the other hand, the influence of the increased resource load on user perception needs to be considered. Therefore, in the initial stage of base station co-construction and sharing, for the county and below areas with low resource utilization, the two operators can perform regional sharing in patches.

[0003] However, as the scale of sharing continues to expand and deepen, the patch sharing mode cannot meet the requirement of targeted and rapid positioning of shared base station resources due to the limitation of resource load. The demand for precise matching of resources puts forward higher requirements for the precision of co-construction and sharing.

[0004] Therefore, how to effectively improve the accuracy and rationality of base station resource co-construction and sharing is a problem to be solved at present. SUMMARY

[0005] The present application provides a method and device for evaluating cell coverage effect of a base station, which can effectively improve the accuracy and rationality of base station resource co-construction and sharing.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for evaluating cell coverage effect of a base station, the method comprising: obtaining traffic data of N first base station cells; inputting the traffic data into a time series prediction model to output a prediction result of future traffic of the N first base station cells; determining a target base station cell from the N first base station cells based on the prediction result; combining the target base station cell with M second base station cells to obtain X base station cells, and predicting coverage range and user perception data of the X base station cells; evaluating the coverage effect of the X base station cells based on historical user perception data, predicted user perception data and the coverage range of the X base station cells; wherein the first base station cells and the second base station cells belong to different operators; N, M and X are integers greater than 1.

[0008] Based on the technical solution, the method for evaluating the coverage effect of the base station cell provided by the embodiment of the application evaluates the coverage effect of the combined base station cell to obtain the evaluation result of the coverage effect of the combined base station cell, so that in the case that the evaluation result is not ideal, a new base station cell is replaced until the evaluation result is ideal, and then the original base station cell and the base station cell resource with the ideal evaluation result are combined to obtain a new base station cell resource, thereby effectively improving the accuracy and rationality of the base station co-construction and sharing.

[0009] In a first possible implementation manner of the first aspect, a target base station cell is determined from the N first base station cells according to a preset condition; the preset condition includes at least one of the following: a physical resource block (PRB) utilization rate of the target base station cell is less than a first preset value; and a downlink traffic of the target base station cell is less than a second preset value.

[0010] In a second possible implementation manner of the first aspect, the time series prediction model includes a first target function and a second target function; the first target function is used for performing average value resampling on the traffic data; the second target function is used for marking a specific date; and based on the marked specific date and the resampled traffic data, future traffic of the N first base station cells is predicted to output a prediction result of the future traffic of the N first base station cells.

[0011] In a third possible implementation manner of the first aspect, the coverage range of the X base station cells is divided into a plurality of regions by using a Thiessen polygon algorithm, and the plurality of regions are converted into geographic information; based on the geographic information, user perception data in the coverage range of the X base station cells is predicted; based on the predicted user perception data, a weak coverage proportion of the user perception data in the X base station cells is calculated; and based on the weak coverage proportion and the coverage range, the coverage effect of the X base station cells is evaluated.

[0012] In a second aspect, the present application provides a device for evaluating the coverage effect of a base station cell, comprising: an acquisition unit, a processing unit and an evaluation unit; the acquisition unit is configured to acquire traffic data of N first base station cells; the processing unit is configured to input the traffic data into a time series prediction model to output a prediction result of future traffic of the N first base station cells; the processing unit is further configured to determine a target base station cell from the N first base station cells based on the prediction result; the processing unit is further configured to combine the target base station cell with M second base station cells to obtain X base station cells, and predict the coverage range and user perception data of the X base station cells; the evaluation unit is configured to evaluate the coverage effect of the X base station cells based on historical user perception data, predicted user perception data and the coverage range of the X base station cells; wherein the first base station cell and the second base station cell belong to different operators; N, M and X are all integers greater than 1.

[0013] In a first possible implementation manner of the second aspect, the processing unit is specifically configured to determine the target base station cell from the N first base station cells according to a preset condition; the preset condition comprises at least one of the following: the PRB utilization rate of the target base station cell is less than a first preset value; the downlink traffic of the target base station cell is less than a second preset value.

[0014] In a second possible implementation manner of the second aspect, the processing unit is specifically configured to:

[0015] The traffic data is average value resampled through a first target function in the time series prediction model; a specific date is marked through a second target function in the time series prediction model; the future traffic of the N first base station cells is predicted based on the marked specific date and the resampled traffic data, to output the prediction result of the future traffic of the N first base station cells.

[0016] In a third possible implementation manner of the second aspect, the evaluation unit is specifically configured to:

[0017] The coverage range of the X base station cells is divided into a plurality of regions through a Thiessen polygon algorithm, and the plurality of regions are converted into geographic information; the user perception data in the coverage range of the X base station cells is predicted based on the geographic information; the weak coverage proportion of the user perception data in the X base station cells is calculated based on the predicted user perception data; the coverage effect of the X base station cells is evaluated based on the weak coverage proportion and the coverage range.

[0018] In a third aspect, the present application provides a device for evaluating cell coverage effect of a base station, the device comprising: a processor and a communication interface; the communication interface and the processor are coupled; the processor is configured to run computer programs or instructions to implement the method for evaluating cell coverage effect of a base station as described in the first aspect and any possible implementation manner of the first aspect.

[0019] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium stores instructions, when the instructions are run on a terminal, the terminal executes the method for evaluating cell coverage effect of a base station as described in the first aspect and any possible implementation manner of the first aspect.

[0020] In a fifth aspect, the present application provides a computer program product comprising instructions, when the computer program product is run on a device for evaluating cell coverage effect of a base station, the device for evaluating cell coverage effect of a base station executes the method for evaluating cell coverage effect of a base station as described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a sixth aspect, the present application provides a chip, the chip comprising a processor and a communication interface, the communication interface and the processor are coupled; the processor is configured to run computer programs or instructions to implement the method for evaluating cell coverage effect of a base station as described in the first aspect and any possible implementation manner of the first aspect.

[0022] Specifically, the chip provided in the embodiments of the present application further comprises a memory for storing computer programs or instructions. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 One of the flowcharts of the method for evaluating cell coverage effect of a base station provided in the embodiments of the present application;

[0024] Figure 2 An example diagram of the coverage range in another method for evaluating cell coverage effect of a base station provided in the embodiments of the present application;

[0025] Figure 3 The second flowchart of another method for evaluating cell coverage effect of a base station provided in the embodiments of the present application;

[0026] Figure 4 One of the structural schematic diagrams of the device for evaluating cell coverage effect of a base station provided in the embodiments of the present application;

[0027] Figure 5 The second structural schematic diagram of the device for evaluating cell coverage effect of a base station provided in the embodiments of the present application;

[0028] Figure 6Figure 3 is a structural schematic diagram of a device for evaluating cell coverage effect of a base station according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The method and device for evaluating cell coverage effect of a base station according to the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0030] The term "and / or" used herein is merely used to describe an associated relationship between associated objects, and can represent three relationships, for example, A and / or B can represent three cases of A alone, A and B together, and B alone.

[0031] The terms "first" and "second" and the like in the specification of the present application and the accompanying drawings are used to distinguish different objects, or to distinguish different treatments of the same object, and are not used to describe a specific order of the objects.

[0032] In addition, the terms "include" and "have" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0033] It should be noted that in the embodiments of the present application, the words "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Rather, the use of "exemplary" or "for example" is intended to present concepts in a concrete manner.

[0034] In the description of the present application, "a plurality of" means two or more, unless otherwise specified.

[0035] In the following, the terms related to the embodiments of the present application are explained to facilitate the understanding of the reader.

[0036] The Voronoi diagram, also known as the Voronoi diagram, is named after Georgy Voronoi, and is composed of a series of continuous polygons composed of vertical bisectors connecting two adjacent points. The Voronoi diagram divides the area covered by the base station into Voronoi regions or adjacent regions. Each Voronoi polygon contains only one base station, and the base station in the Voronoi polygon is closest to the corresponding base station.

[0037] In the prior art, when the two operators share network resources, on the one hand, the coverage area needs to be considered, and on the other hand, the influence of the increased resource load after sharing on user perception needs to be considered. Therefore, in the initial stage of base station co-construction and sharing, for the county and below areas with low resource utilization, the two operators can perform regional sharing in patches. However, as the scale of sharing is continuously expanded and deepened, the patch sharing area mode cannot meet the requirement of targeted and rapid positioning of shared base station resources due to the limitation of resource load. The demand for precise matching of resources puts forward higher requirements for co-construction and sharing precision.

[0038] Currently, the method for co-construction and sharing of base station resources includes the following:

[0039] 1. Selecting base stations with resource utilization less than 10% from all unshared base stations of the two parties, and determining that these base stations can improve the coverage of the local area according to the experience of network optimization personnel, and selecting the base station resources for co-construction and sharing that meet the above two conditions;

[0040] 2. Calculating the distance of the nearest to-be-shared sites of the two parties, combining the base station coverage radius, and the angle between the site and the nearest same-operator and different-operator site distance and vector, to determine whether the co-construction and sharing condition is met.

[0041] 3. Based on the Thiessen polyhedron cutting method, the to-be-constructed base station coverage simulation area is divided in three-dimensional space to determine the coverage area of the proposed base station.

[0042] 4. The to-be-planned area for co-construction and sharing is divided into multiple grids by grid division, and the average income per user, age, flagship terminal type, fourth-generation communication technology user quantity, network age, traffic data, busy hour utilization rate, and service demand in the grid are filled to determine whether the two parties have a demand for co-construction and sharing in the planning area.

[0043] Therefore, the current method for co-construction and sharing of base station resources can cause the following problems:

[0044] 1. The selection of co-construction and sharing base station resources is limited to the judgment of whether it is a low-traffic cell based on the current resource utilization, and the range of resource sharing is determined based on this, and the target of resource utilization for co-construction and sharing in this range needs to rely on the experience of network optimization personnel. However, with the change of business, resource utilization is a dynamic process, and the current index cannot reflect the influence of the trend on resource utilization.

[0045] 2, From the distance between the base stations, the angle of the vector to determine the rationality of co-construction and sharing, it is difficult to consider the relationship between all related base station resources around the co-construction and sharing base station. After adding new co-construction and sharing resources in the region, the coverage area of the surrounding adjacent cells will change in area. If these changes are not analyzed and judged, it will cause local over-dense or over-dense coverage, and cannot achieve balanced use of resources.

[0046] 3, Using the Voronoi polygon to plan the base station coverage area can simulate the coverage range and mutual relationship of the base station, but the result does not realize the evaluation of the influence on the existing base station resource coverage, and cannot quickly and accurately judge the following problems: (1) Whether the use of shared resources is superimposed in the original strong coverage area to cause interference and resource repeated coverage, (2) Whether the use of shared resources can effectively improve the weak coverage area.

[0047] 4, Although the commercial value of users in the region can be judged through the consumption attributes, traffic attributes and other data of users in the grid, we need to accurately analyze and judge the influence of the new base station resource space distribution on the existing resource coverage network coverage after sharing from the perspective of the rationality of the base station resource coverage after sharing.

[0048] Therefore, how to effectively improve the accuracy and rationality of base station resource co-construction and sharing is a problem to be solved at present.

[0049] In order to solve the problem of how to effectively improve the accuracy and rationality of base station resource co-construction and sharing in the prior art, the present application provides a method for evaluating base station cell coverage effect, obtaining service data of N first base station cells; inputting the service data into a time series prediction model to output a prediction result of future service of the N first base station cells; determining a target base station cell from the N first base station cells based on the prediction result; combining the target base station cell with M second base station cells to obtain X base station cells, and predicting coverage range and user perception data of the X base station cells; based on historical user perception data, predicted user perception data and coverage range of the X base station cells, evaluating coverage effect of the X base station cells; wherein the first base station cell and the second base station cell belong to different operators; N, M and X are integers greater than 1. In this way, by evaluating the coverage effect of the combined base station cells, the evaluation result of the coverage effect of the combined base station cells is obtained, so that in the case that the evaluation result is not ideal, the new base station cell is replaced, until the evaluation result is ideal, the original base station cell and the base station cell resource with ideal evaluation result are combined to obtain new base station cell resource, and then the accuracy and rationality of base station co-construction and sharing can be effectively improved.

[0050] For example Figure 1As shown, a flowchart of a method for evaluating cell coverage effect of a base station provided in an embodiment of the present application is provided, and the method comprises the following steps S101-S105:

[0051] S101, obtaining service data of N first base station cells.

[0052] In an embodiment of the present application, the service data can be a physical resource unit (Physical Resource Block, PRB) utilization rate, or can be downlink traffic.

[0053] In an embodiment of the present application, N is an integer greater than 1.

[0054] S102, inputting the service data into a time series prediction model to output a prediction result of future service of the N first base station cells.

[0055] In an embodiment of the present application, the time series prediction model is fitted by using the SARIMAX() function of the statsmodels module of python.

[0056] In an embodiment of the present application, the prediction result is used to represent the service data of the first base station cell in the future period of time.

[0057] S103, determining a target base station cell from the N first base station cells based on the prediction result.

[0058] In an embodiment of the present application, the base station cell is determined from the N first base station cells according to a preset condition.

[0059] In an embodiment of the present application, the target base station cell is one or more of the N first base station cells.

[0060] Optionally, in an embodiment of the present application, the preset condition comprises at least one of the following: the PRB utilization rate of the target base station cell is less than a first preset value; the downlink traffic of the target base station cell is less than a second preset value.

[0061] Exemplarily, the first preset value and the second preset value can be user-defined settings, or can be set by a terminal device, which is not limited in the present application.

[0062] For example, when the first preset value is 10%, the base station cell with a PRB utilization rate less than 10% in the first base station cell can be the target base station cell.

[0063] In this way, the conversion of demand and shared resources can be facilitated. The original co-construction and sharing full-time staff can focus more on user demand and business demand without needing to master professional network optimization technology, and can place more experience on resource utilization and cost control. Through the establishment of a data platform and scientific and accurate calculation, the pressure of data collection, data screening, calculation and analysis of control personnel is reduced; the original multi-specialty work is programmed to manage, shorten the control target generation and response cycle.

[0064] S104, combining the target base station cell with the M second base station cells to obtain X base station cells, and predicting coverage ranges and user perception data of the X base station cells.

[0065] In the embodiments of the present application, M and X are integers greater than 1.

[0066] In the embodiments of the present application, the X base station cells include the target base station cell and the M base station cells.

[0067] In the embodiments of the present application, the coverage range is a range covered by signals of the X base station cells.

[0068] In the embodiments of the present application, the user perception data is used to represent the quality of signals or user experience in the area covered by the base station cell.

[0069] In the embodiments of the present application, base station parameters of the target base station cell and the X base station cells are combined.

[0070] In the embodiments of the present application, the coverage ranges covered by the X base station cells are predicted by a Thiessen polygon algorithm.

[0071] In this way, by constructing a Thiessen polygon centered on the base station, the geographical location relationship between the base station and the surrounding adjacent base stations can be scientifically associated, so that the coverage range of the base station can be more accurately simulated.

[0072] S105, evaluating the coverage effect of the X base station cells based on historical user perception data, predicted user perception data and coverage ranges of the X base station cells.

[0073] In the embodiments of the present application, the historical user perception data includes user perception data of the N first base station cells and the M second base station cells.

[0074] In the embodiments of the present application, the evaluation result can be reasonable or unreasonable.

[0075] In an example, when the evaluation result is reasonable, the target base station cell is combined with the second base station cell to obtain a combined base station cell. That is, when the evaluation result is reasonable, it indicates that if the target base station cell is combined with the second base station cell, the coverage area of the base station cell can be effectively improved, the weak coverage ratio is reduced, and the user experience is improved.

[0076] In an example, when the evaluation result is unreasonable, the next target base station cell that meets the preset condition is selected. That is, when the evaluation result is unreasonable, it indicates that if the target base station cell is combined with the second base station cell, the coverage area of the base station cell cannot be effectively improved, the weak coverage ratio is reduced, and the user experience is improved.

[0077] It should be noted that the first base station cell and the second base station cell belong to different operators. In addition, the base station cell includes resources and working parameters used by the cell where the base station is located.

[0078] The method for evaluating the coverage effect of the base station cell provided by the embodiments of the present application combines the base station cell resources of different operators, predicts the coverage range of the combined base station cell resources, and then evaluates the coverage effect of the combined base station cell through historical user perception data, predicted user perception data, and the coverage range of the combined base station cell. Thus, in the case of an undesirable evaluation result, a new base station cell is replaced until the evaluation result is desirable, the original base station cell is combined with the base station cell resources with a desirable evaluation result to obtain new base station cell resources, and thus the accuracy and rationality of base station co-construction and sharing can be effectively improved.

[0079] Optionally, in the step S102 of inputting the traffic data into the time series prediction model to output the prediction result of the future traffic of the N first base station cells in the embodiments of the present application, the following steps S102a to S102c are included:

[0080] S102a, the traffic data is average value resampled through a first target function in the time series prediction model.

[0081] Exemplarily, the first target function can be a resample() function in a pandas library of python.

[0082] Exemplarily, the average value resampling can be sampling the traffic data of the first base station cell in units of weeks.

[0083] S102b, a specific date is marked through a second target function in the time series prediction model.

[0084] Exemplarily, the second target function can be a function of a chinese_calendar library of python.

[0085] Exemplarily, the specific date can be a holiday or a weekday.

[0086] Exemplarily, the specific date is marked to distinguish holidays from weekdays.

[0087] S102c, based on the marked specific date and the sampled traffic data, predicting future traffic of the N first base station cells to output a prediction result of the future traffic of the N first base station cells.

[0088] Exemplarily, the marked specific date and the sampled traffic data of the first base station cell are input into a time series analysis model SARIMAX, and then an order parameter (p, d, q) and a seasonal component (P, D, Q, S) in the time series analysis model are trained by using a grid search and a hyperparameter optimization method, and appropriate model parameters are selected by a score of an information criterion (Akaike, AIC) value.

[0089] Further, a new time series analysis model is fitted using the model parameters to predict future traffic of the first base station cell.

[0090] Therefore, due to the change of the traffic load of the base station cell, in addition to the number of users and the usage amount of services, the change of the surrounding wireless resource environment, faults, emergencies and the like also have a certain relationship, and these changes are reflected in the historical data of a certain time sequence. If only the data of a certain time period is extracted to measure the traffic load of the cell, the prediction result can be affected by uncertain interference terms. However, the time series model and the traffic collection method provided in the embodiments of the present application can effectively reduce the interference terms and improve the prediction accuracy.

[0091] Optionally, in the embodiment of the present application, in the process of the above step S105 "evaluating the coverage effect of the X base station cells based on the historical user perception data, the predicted user perception data and the coverage range of the X base station cells", the following steps S105a to S105d are included.

[0092] S105a, dividing the coverage range of the X base station cells into a plurality of regions by a Thiessen polygon algorithm, and converting the plurality of regions into geographic information.

[0093] Exemplarily, the above-mentioned Thiessen polygon algorithm is the Voronoi function introduced in the scipy.spatial library in python. By performing the Voronoi function operation on the longitude and latitude information parameters in the base station parameters of the X base station cells, and extracting the geographic information in the result (vor) to generate the Thiessen polygon to construct the edge frame, that is, the range covered by the above-mentioned X base station cells is divided into multiple regions.

[0094] Specifically, the algorithm for generating the Thiessen polygon to construct the edge frame is as follows:

[0095]

[0096] For example, as shown in Figure 2 , the coverage range of the X base station cells is re-predicted by the Thiessen polygon algorithm. Among them, Figure 2 (a) in (a) represents the edge frame (i.e. the above-mentioned region) constructed based on the Thiessen polygon algorithm for the coverage range of the M second base station cells before introducing the low traffic resource (i.e. the above-mentioned target base station cell), Figure 2 (b) in (b) represents the edge frame constructed based on the Thiessen polygon algorithm for the coverage range of the X base station cells after introducing the low traffic resource.

[0097] Exemplarily, the multiple regions divided by the X base station cells are converted into geographic information by the geopandas library of python. That is, the geographic information of the Thiessen polygon to construct the edge frame is generated by the geopandas library of python.

[0098] Specifically, the algorithm for generating geographic information is as follows:

[0099] areaplace = pd.DataFrame(areaplace, columns=['areaband'])

[0100] areageo = gpd.GeoDataFrame(areaplace, geometry='areaband', crs=4326)

[0101] S105b, based on the geographic information, predict the user perception data in the coverage range of the X base station cells.

[0102] Exemplarily, the above-mentioned predicted user perception data can be obtained by using the following algorithm. Specifically, selectpoint = townstopgeo[townstopgeo['pointband'].within(b['areaband'])]

[0103] S105c, based on the predicted user perception data, calculate the weak coverage proportion of the user perception data in the X base station cells.

[0104] Illustratively, the predicted user perception data is the user perception data of the X base station cells.

[0105] S105d, based on the weak coverage proportion and the coverage range, evaluate the coverage effect of the X base station cells.

[0106] Illustratively, in the case that the weak coverage proportion of the X base station cells is smaller than the weak coverage proportion of the M second base station cells, and / or the coverage range of the X base station cells is larger than the coverage range of the M second base station cells, the evaluation result is reasonable, and the target base station cell is the optimal base station cell.

[0107] In this way, the two parties complement each other through low-traffic-area coverage, effectively expand the coverage area, greatly increase the single-cell traffic volume, greatly improve the network operation efficiency, and protect the user perception of both parties.

[0108] The method for evaluating the coverage effect of the base station cell provided in the present application will be exemplarily described below, as shown in Figure 3 .

[0109] Step A1, collect the traffic volume (such as 4G cell downlink peak flow) of the unshared base station cells (i.e. the above-mentioned first base station cells) of China Telecom and China Unicom.

[0110] Step A2, use the time series prediction method to evaluate the business trend of the collected data, and judge whether the cell (i.e. the above-mentioned first base station cell) load can reach the low-load cell sharing threshold requirement (such as busy time PRB utilization <10%) according to the predicted trend result, if the cell meets the requirement, proceed to step A3.

[0111] Step A3, combine the existing cell parameters (i.e. the above-mentioned second base station cell) with the low-load cell (i.e. the above-mentioned target base station cell) that can be shared by the other party.

[0112] Step A4, predict the coverage range of the cell under the new resource distribution condition with the combined parameters.

[0113] Step A5, judge the rationality of sharing the other party's low-traffic resources in space, and evaluate the effect of network improvement after sharing by using the historical user perception data collected before the combination, from the dual perspectives of user perception and geographical distribution, to predict the influence of network sharing coverage resources on coverage.

[0114] Thus, by screening low-service base station shared resources based on time series, the influence of accidental factors in the screening process is avoided. Furthermore, the screening results are intelligently predicted for coverage. By constructing a geographic data structure for the coverage area, and based on the characteristics of user-perceived distribution patterns and signal coverage strength, mathematical tools such as geographic inclusion relationships and area calculations are used to identify and predict the coverage impact after sharing. Compared with existing base station resource sharing methods, this achieves the goals of process automation and accurate prediction, better realizing the objectives of resource conservation, improved coverage after sharing, and enhanced user experience. It also has stronger generalization capabilities and is easier to deploy.

[0115] This application embodiment can divide the device for evaluating the coverage effect of a base station cell into functional modules or functional units according to the above method example. For example, each function can be divided into a separate functional module or functional unit, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or in software functional modules or functional units. The module or unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0116] like Figure 4 The diagram shown is a structural schematic of a device 700 for evaluating the coverage effect of a base station cell provided in an embodiment of this application. The device includes: an acquisition unit 701, a processing unit 702, and an evaluation unit 703.

[0117] The system includes an acquisition unit 701 for acquiring traffic data from N first base station cells; a processing unit 702 for inputting the traffic data into a time series prediction model to output a prediction result of the future traffic volume of the N first base station cells; the processing unit 702 is also used to determine a target base station cell from the N first base station cells based on the prediction result; the processing unit 702 is also used to combine the target base station cell with M second base station cells to obtain X base station cells, and predict the coverage area and user perception data of the X base station cells; and an evaluation unit 703 is used to evaluate the coverage effect of the X base station cells based on historical user perception data, predicted user perception data, and the coverage area of ​​the X base station cells; wherein the first base station cells and the second base station cells belong to different operators; and N, M, and X are all integers greater than 1.

[0118] Optionally, in this embodiment of the application, the processing unit 702 is specifically used to determine a target base station cell from N first base station cells according to preset conditions; the preset conditions include at least one of the following: the PRB utilization rate of the target base station cell is less than a first preset value; the downlink traffic of the target base station cell is less than a second preset value.

[0119] Optionally, in the embodiment of the present application, the processing unit 702 is specifically used for:

[0120] averaging value resampling of the traffic data through a first objective function in the time series prediction model; marking a specific date through a second objective function in the time series prediction model; and predicting future traffic of the N first base station cells based on the marked specific date and the resampled traffic data, to output a prediction result of the future traffic of the N first base station cells.

[0121] Optionally, in the embodiment of the present application, the evaluation unit 703 is specifically used for:

[0122] dividing the coverage of the X base station cells into a plurality of areas through a Thiessen polygon algorithm, and converting the plurality of areas into geographic information; predicting user perception data in the coverage of the X base station cells based on the geographic information; calculating a weak coverage proportion of the user perception data in the X base station cells based on the predicted user perception data; and evaluating the coverage effect of the X base station cells based on the weak coverage proportion and the coverage.

[0123] In the device for evaluating the coverage effect of the base station cell provided in the embodiment of the present application, the device acquires traffic data of N first base station cells; inputs the traffic data into a time series prediction model, to output a prediction result of future traffic of the N first base station cells; determines a target base station cell from the N first base station cells based on the prediction result; combines the target base station cell with M second base station cells to obtain X base station cells, and predicts the coverage and user perception data of the X base station cells; evaluates the coverage effect of the X base station cells based on historical user perception data, predicted user perception data and the coverage of the X base station cells; wherein the first base station cells and the second base station cells belong to different operators; N, M and X are all integers greater than 1. In this way, the coverage effect of the combined base station cells is evaluated to obtain an evaluation result of the coverage effect of the combined base station cells, so that in the case that the evaluation result is not ideal, a new base station cell is replaced until the evaluation result is ideal, the original base station cell and the base station cell resource with the ideal evaluation result are combined to obtain a new base station cell resource, and thus the accuracy and rationality of the base station co-construction and sharing can be effectively improved.

[0124] When implemented by hardware, the communication unit 703 in the embodiment of the present application can be integrated on a communication interface, and the processing unit 702 can be integrated on a processor. The specific implementation manner is as shown in Figure 5 .

[0125] Figure 5Another possible structural diagram of the apparatus for evaluating base station cell coverage effect involved in the above embodiments is shown. The apparatus for evaluating base station cell coverage effect includes a processor 302 and a communication interface 303. The processor 302 is configured to control and manage actions of the apparatus for evaluating base station cell coverage effect, for example, perform steps performed by the processing unit 702 described above, and / or perform other processes of the techniques described herein. The communication interface 303 is configured to support communication of the apparatus for evaluating base station cell coverage effect with other network entities, for example, perform steps performed by the communication unit 703 described above. The apparatus for evaluating base station cell coverage effect can further include a memory 301 and a bus 304, where the memory 301 is configured to store program codes and data of the apparatus for evaluating base station cell coverage effect.

[0126] The memory 301 can be a memory in the apparatus for evaluating base station cell coverage effect, and can include a volatile memory such as a random access memory, and can also include a non-volatile memory such as a read-only memory, a flash memory, a hard disk or a solid state disk, and can also include a combination of the above-mentioned memories.

[0127] The processor 302 described above can be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array, or other programmable logic device, transistor logic device, hardware component, or any combination thereof, which can implement or execute the various exemplary logical blocks, modules and circuits described in connection with the disclosure. The processor can implement or execute the various exemplary logical blocks, modules and circuits described in connection with the disclosure. The processor can also be a combination of computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0128] The bus 304 can be an extended industry standard architecture (EISA) bus or the like. The bus 304 can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 5 Only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0129] Figure 6 is a structural diagram of a chip 170 provided by the embodiments of the present application. The chip 170 includes one or more (including two) processors 1710 and a communication interface 1730.

[0130] Optionally, the chip 170 further includes a memory 1740, which can include read-only memory and random access memory, and provides the processor 1710 with operation instructions and data. A part of the memory 1740 can further include non-volatile random access memory (NVRAM).

[0131] In some embodiments, the memory 1740 stores the following elements, execution modules or data structures, or a subset thereof, or an extended set thereof.

[0132] In the embodiments of the present application, corresponding operations are performed by calling operation instructions stored in the memory 1740 (the operation instructions can be stored in an operating system).

[0133] The processor 1710 can implement or execute the various exemplary logical blocks, units and circuits described in connection with the disclosure of the present application. The processor can be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, transistor logic device, hardware component or any combination thereof. It can implement or execute the various exemplary logical blocks, units and circuits described in connection with the disclosure of the present application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0134] The memory 1740 can include volatile memory, such as random access memory; the memory can also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid state disk; the memory can also include a combination of the above types of memory.

[0135] The bus 1720 can be an Extended Industry Standard Architecture (EISA) bus or the like. The bus 1720 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one line is used to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0136] Those skilled in the art can clearly understand the above-mentioned method embodiments through the description of the above-mentioned implementation manners. For the convenience and brevity of description, only the above-mentioned division of functional modules is exemplified. In actual application, the above-mentioned functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0137] The embodiment of the present application provides a computer program product containing instructions, which, when executed on a computer, cause the computer to perform the method of evaluating the cell coverage effect of the base station in the foregoing method embodiments.

[0138] The embodiment of the present application also provides a computer readable storage medium, which stores instructions, and when the instructions are executed on a computer, the computer performs the method of evaluating the cell coverage effect of the base station in the method flow shown in the foregoing method embodiments.

[0139] The computer readable storage medium may, for example, be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor, so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). In the embodiment of the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus.

[0140] Embodiments of the present application provide a computer program product comprising instructions which, when executed on a computer, cause the computer to carry out the method of assessing base station cell coverage effect as described in Figures 1 to 3

[0141] Since the device for assessing base station cell coverage effect, the computer readable storage medium and the computer program product in the embodiments of the present application can be applied to the above-mentioned method, the technical effects they can obtain can also be referred to the above-mentioned method embodiments, and the embodiments of the present application will not be repeated here.

[0142] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented by other manners. For example, the above-mentioned device embodiments are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0143] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the present embodiment.

[0144] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0145] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.​

Claims

1. A method of evaluating the effect of a base station cell coverage, characterized by, The method comprises: obtaining traffic data of N first base station cells; inputting the traffic data into a time series prediction model to output a prediction result of future traffic of the N first base station cells; determining a target base station cell from the N first base station cells based on the prediction result; combining the target base station cell with M second base station cells to obtain X base station cells, and predicting coverage ranges and user perception data of the X base station cells; dividing the coverage ranges of the X base station cells into multiple regions by a Thiessen polygon algorithm, and converting the multiple regions into geographic information; predicting user perception data in the coverage ranges of the X base station cells based on the geographic information; calculating a weak coverage proportion of user perception data in the X base station cells based on the predicted user perception data; evaluating coverage effects of the X base station cells based on the weak coverage proportion and the coverage ranges. The first base station cells and the second base station cells belong to different operators; N, M and X are all integers greater than 1.

2. The method of claim 1, wherein, The determining of the target base station cell from the N first base station cells based on the prediction result comprises: determining the target base station cell from the N first base station cells according to a preset condition. The preset condition comprises at least one of the following: a physical resource block (PRB) utilization rate of the target base station cell is less than a first preset value; and / or a downlink traffic of the target base station cell is less than a second preset value.

3. The method of claim 1, wherein, The inputting of the traffic data into the time series prediction model to output the prediction result of future traffic of the N first base station cells comprises: averaging value resampling of the traffic data by a first objective function in the time series prediction model; labeling a specific date by a second objective function in the time series prediction model; predicting future traffic of the N first base station cells based on the labeled specific date and the resampled traffic data to output the prediction result of future traffic of the N first base station cells.

4. An apparatus for evaluating a base station cell coverage effect, the apparatus comprising: The device comprises an acquisition unit, a processing unit and an evaluation unit. The acquisition unit is configured to acquire traffic data of N first base station cells. The processing unit is configured to input the traffic data acquired by the acquisition unit into a time series prediction model to output a prediction result of future traffic of the N first base station cells. The processing unit is further configured to determine a target base station cell from the N first base station cells based on the prediction result. The processing unit is further configured to combine the target base station cell with M second base station cells to obtain X base station cells, and predict coverage ranges and user perception data of the X base station cells. The evaluation unit is specifically configured to: divide the coverage ranges of the X base station cells into multiple regions by a Thiessen polygon algorithm, and convert the multiple regions into geographic information; predict user perception data in the coverage ranges of the X base station cells based on the geographic information; and calculate a weak coverage proportion of user perception data in the X base station cells based on the predicted user perception data. Based on the predicted user perception data, a weak coverage proportion of user perception data in the X base station cells is calculated; Based on the weak coverage proportion and the coverage range, the coverage effect of the X base station cells is evaluated; Wherein, the first base station cell and the second base station cell belong to different operators; N, M and X are all integers greater than 1.

5. The apparatus of claim 4, wherein, Comprise: The processing unit is specifically configured to determine a target base station cell from the N first base station cells according to a preset condition; The preset condition comprises at least one of the following: The physical resource unit (PRB) utilization rate of the target base station cell is less than a first preset value; The downlink traffic of the target base station cell is less than a second preset value.

6. The apparatus of claim 4, wherein, Comprise: The processing unit is specifically configured to: Through a first target function in the time series prediction model, the traffic data is average value resampled; Through a second target function in the time series prediction model, a specific date is marked; Based on the marked specific date and the sampled traffic data, the future traffic of the N first base station cells is predicted to output the prediction result of the future traffic of the N first base station cells.

7. An apparatus for evaluating the coverage effect of a base station cell, characterized in that, Comprise: A processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run computer programs or instructions to realize the method for evaluating the coverage effect of the base station cell as claimed in any one of claims 1 to 3.

8. A computer-readable storage medium having stored therein instructions, the computer-readable storage medium comprising: When the computer executes the instructions, the computer executes the method for evaluating the coverage effect of the base station cell as claimed in any one of claims 1 to 3.

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