CN112929887B - Method and device for setting room substation based on flow prediction and electronic equipment - Google Patents
Method and device for setting room substation based on flow prediction and electronic equipment Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The embodiment of the invention provides a method, a device and electronic equipment for setting a room substation based on flow prediction, which are used for predicting future network data of each cell according to historical network data of each cell in a preset area in which the room substation needs to be set. And determining a high-load cell by the predicted network data, further determining high-load buildings in the high-load cell, and further determining a site area in which the indoor substation needs to be arranged according to the position information of the high-load buildings. According to the network utilization data predicted by the change rule of the historical network utilization data, multiple factors influencing the network utilization data can be synthesized to determine the network utilization data which is matched with the actual network utilization data, so that the determined high-load cell is matched with the actual network utilization data, and further reasonable setting of the site area of the indoor substation is facilitated. Meanwhile, the process of determining the indoor substation does not need to be manually participated, so that the planning efficiency of the indoor substation is improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, and an electronic device for setting a substation based on flow prediction.
Background
There are some cells, because of the excessive traffic, the load of the base station is increased, which affects the normal communication process, and the load of the base station is commonly shared by adding the indoor substation, so as to ensure the normal communication process. For example, by adding a room subsystem, telephone traffic sinking is carried out on the base station, and normal operation of user service is ensured. The existing high-load telephone traffic subsidence mainly collects KPI (Key Performance Indicators, key performance index), network complaint information and the like through OMC (Operation and Maintenance Center, operation maintenance center), manually analyzes and identifies a high-load cell, and carries out service subsidence adjustment on the high-load cell. And business diversion is carried out by a newly added room subsystem and the like so as to reduce the load pressure of the macro station which grows too fast.
However, the existing method for realizing telephone traffic sinking mainly comprises the steps of manually collecting a large amount of data for analysis, and making planning decisions and formulation, such as a frequency superposition scheme, a room division splitting/new construction scheme and the like. The manual optimization method can consider the dimensions of various problem points (such as test feedback, user complaints, market feedback and the like), but requires a large amount of human resource investment, and is long in time consumption and complex in process. Therefore, the manual optimization method is only applicable to optimization in a small range, but not applicable to optimization in a large range and whole network. In addition, the existing method does not always consider the coverage range of a base station and the influence of the existing indoor substation in the process of planning the indoor substation, and resource waste is easy to cause.
In summary, the existing method for determining the indoor substation is realized manually, whether the cell is a high-load cell is simply evaluated through experience, and the evaluation result cannot reflect the actual situation, so that the planning of the indoor substation is unreasonable.
Disclosure of Invention
The embodiment of the invention provides a method, a device and electronic equipment for setting a room substation based on flow prediction, which are used for solving the problems that in the prior art, the existing method for determining the room substation is realized manually, whether a cell is a high-load cell is simply evaluated through experience, and the evaluation result cannot reflect the actual situation, so that the planning of the room substation is unreasonable.
In view of the above technical problems, in a first aspect, an embodiment of the present invention provides a method for setting a chamber substation based on flow prediction, including:
according to historical network data of each cell in a preset area, determining predicted network data of each cell;
determining a high-load cell from each cell according to the predicted network data, and determining a high-load building from a building in the high-load cell according to the distribution information of the terminals in each high-load cell;
and determining a site area in which the indoor substation is required to be arranged in the preset area according to the position information of each high-load building.
In a second aspect, an embodiment of the present invention provides an apparatus for setting a chamber substation based on flow prediction, including:
the prediction module is used for determining network utilization data predicted for each cell according to the historical network utilization data of each cell in the preset area;
the first determining module is used for determining a high-load cell from the cells according to the predicted network data and determining a high-load building from the buildings in the high-load cell according to the distribution information of the terminals in each high-load cell;
and the second determining module is used for determining a site area in which the indoor substation is required to be arranged in the preset area according to the position information of each high-load building.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for setting up a chamber substation based on flow prediction described above when the program is executed.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of setting up a cell substation based on flow prediction.
According to the method, the device and the electronic equipment for setting the indoor substation based on the flow prediction, which are provided by the embodiment of the invention, the preset area where the indoor substation needs to be set is predicted according to the historical network data of each cell in the preset area, and the future network data of each cell is predicted according to the historical network data of each cell in the preset area. And determining a high-load cell by the predicted network data, further determining high-load buildings in the high-load cell, and further determining a site area in which the indoor substation needs to be arranged according to the position information of the high-load buildings. According to the network utilization data predicted by the change rule of the historical network utilization data, multiple factors influencing the network utilization data can be synthesized to determine the network utilization data which is matched with the actual network utilization data, so that the determined high-load cell is matched with the actual network utilization data, and further reasonable setting of the site area of the indoor substation is facilitated. Meanwhile, the process of determining the indoor substation does not need to be manually participated, so that the planning efficiency of the indoor substation is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a setup room substation provided in an embodiment of the present invention;
FIG. 2 is a flow chart of a method for setting up a chamber substation based on flow prediction according to another embodiment of the present invention;
FIG. 3 is a flow chart of network data prediction according to another embodiment of the present invention;
fig. 4 is a schematic flow chart of determining a high load cell according to another embodiment of the present invention;
FIG. 5 is a schematic diagram of a high load building determination process provided by another embodiment of the present invention;
fig. 6 is a schematic diagram of a terminal number generation according to a high load cell connected in each building in a cell according to another embodiment of the present invention;
FIG. 7 is a schematic diagram of a process for determining a compartmental site provided by another embodiment of the present invention;
FIG. 8 is a block diagram of an apparatus for setting up a chamber substation based on flow prediction according to another embodiment of the present invention;
fig. 9 is a schematic physical structure of an electronic device according to another embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to resolve the load of the high load cell base station, it is generally necessary to provide a room substation in a region where high load buildings are concentrated in the cell. Fig. 1 is a schematic flow chart of setting a chamber substation according to the present embodiment, referring to fig. 1, a process for setting a chamber substation generally includes: firstly, high-load area analysis is carried out according to a high-load cell and traffic conditions, wherein the traffic conditions need to consider traffic growth caused by three conditions, namely natural growth, traffic excitation and marketing. And then carrying out a planning scheme decision-making and making process according to the analysis result of the high-load area, wherein the process can be realized in the following ways: frequency superposition scheme, room splitting/new scheme, and small micro-station new scheme. Finally, regional scheme evaluation is carried out, wherein the regional capacity bearing coincidence evaluation and the high-load cell evaluation are used for evaluating the capacity of the planned indoor substation for decomposing the load of the high-load cell base station.
In order to enable the set indoor substation to effectively share the load of the base station and ensure the stability of the whole area communication, fig. 2 is a schematic flow chart of a method for setting the indoor substation based on the flow prediction provided in this embodiment, where the method is performed by an apparatus for planning the area of the station of the set indoor substation, and the apparatus is a server, a computer or an apparatus dedicated to planning the indoor substation. Referring to fig. 2, the method includes the steps of:
Step 201: and determining the predicted network data of each cell according to the historical network data of each cell in the preset area.
The step aims at predicting the load of each cell base station in a future period through historical network data capable of reflecting the load of each cell base station, and the predicted network data is generated according to the change rule of the historical network data, so that the predicted network data is more matched with the actual situation, the station area of the indoor substation is determined based on the predicted network data, the load of the base station can be better shared, and the aim of better improving the network service quality is fulfilled.
Further, according to the historical network data, determining the network data predicted for each cell by a model pair trained in advance through machine learning; the "model trained by machine learning" is obtained by training network data that varies with time and over a historical period of time, and specifically, the "model trained by machine learning" is a time-series model, or an existing model that can predict according to a data variation rule, and the network data predicted for each cell according to the historical network data, for example, the model may be a propset model.
Step 202: and determining a high-load cell from the cells according to the predicted network data, and determining a high-load building from the buildings in the high-load cell according to the distribution information of the terminals in each high-load cell.
Because the predicted network data can better reflect the actual network data of the cell in the future, the high-load cell in the preset area can be screened out according to the predicted network data. Further, based on the determined high-load cell, since the terminals are not uniformly distributed in the cell, the embodiment determines a high-load building in which the terminals are concentrated according to the terminal distribution information. These high load buildings will be considered areas where the setting of the substation is to be performed.
Step 203: and determining a site area in which the indoor substation is required to be arranged in the preset area according to the position information of each high-load building.
The high-load building is usually a region with more concentrated terminal distribution, and the indoor substation is arranged in the region, so that the load of the base station of the high-load cell can be effectively shared, and the network service quality of the load building is greatly improved.
According to the method for setting the indoor substation based on the flow prediction, for a preset area where the indoor substation needs to be set, the network data of each cell in the future is predicted according to the historical network data of each cell in the preset area. And determining a high-load cell by the predicted network data, further determining high-load buildings in the high-load cell, and further determining a site area in which the indoor substation needs to be arranged according to the position information of the high-load buildings. According to the network utilization data predicted by the change rule of the historical network utilization data, multiple factors influencing the network utilization data can be synthesized to determine the network utilization data which is matched with the actual network utilization data, so that the determined high-load cell is matched with the actual network utilization data, and further reasonable setting of the site area of the indoor substation is facilitated. Meanwhile, the process of determining the indoor substation does not need to be manually participated, so that the planning efficiency of the indoor substation is improved.
Further, on the basis of the above embodiment, the step 201 includes:
for any first cell in a preset area, acquiring historical network data corresponding to each set time interval of the first cell in a first historical time period, inputting the acquired historical network data into a prediction model, and outputting the network data predicted for the first cell by the prediction model;
the prediction model predicts according to the trend of the input historical network data along with the time change; the historical network data includes at least one of the following data: the method comprises the steps of accessing an effective RRC (Radio Resource Control, radio resource control layer) user quantity of the first cell, an uplink Physical Resource Block (PRB) (Physical Resource Block ) utilization rate of the first cell, an uplink flow of the first cell, a downlink PRB utilization rate of the first cell and a downlink flow of the first cell.
Before inputting the obtained historical network data into the prediction model, the method further comprises the following steps: and eliminating abnormal points in the acquired historical network data, wherein the abnormal points are curves where the historical network data deviate, and the distance between the abnormal points and the closest points on the curves is larger than a set distance threshold.
Specifically, before the obtained historical network data is input into the prediction model, the S-H-ESD (Seasonal Hybrid ESD Test (Extreme Studentized Deviate test, extreme deviation test) and time series anomaly detection algorithm) are adopted to detect anomaly points, and the anomaly points detected in the past are removed.
The principle of S-H-ESD is introduced: the algorithm replaces trend components in STL (sequential-Trend decomposition procedure based on Loess) decomposition with the median of the original data, then removes periodic components and trend components from the original data to obtain residual components, and finally carries out G-ESD (Standard Practice for Application of Generalized-ESD, generalized extreme deviation test) test on the residual components to identify abnormal points. Wherein the algorithm mistakes the holiday effect data, considers outliers, and should remove the holiday period data at the time of detection.
The first historical time period is a time period before the current time point, and the time length of the first historical time period is greater than or equal to 6 months. For example, the first historical period of time is 6 months or 1 year before the current point in time.
The set time interval is an interval for dividing the first history period, and is, for example, 1 hour, n hours (n is a natural number), days, weeks, or months. The shorter the set time interval is, the higher the accuracy of the predicted network data is, and the predicted network data is more consistent with the actual situation.
Wherein the predictive model is a time series model.
Wherein the prediction model is a prophet model.
The propset model is described here: the propset model structure is y (t) =g (t) +s (t) +h (t) +e (t). Wherein g (t) represents a trend function for analyzing non-periodic variations in the time series; s (t) represents a periodic variation such as a periodicity of week, day, year; h (t) represents an influence caused by accidental one or several days such as holidays; epsilon represents the error term: the model accounts for the effects of errors. The propset model has no complex characteristic engineering, and can predict the model only by inputting time sequence data meeting the requirements, is insensitive to the missing value and is sensitive to the abnormal value, so that the original data is firstly subjected to abnormal value processing.
In general, the type of the history network data input into the prediction model is the same as the type of the predicted network data output from the prediction model, and for example, if the input history network data is the number of valid RRC users, the output predicted network data is also the number of valid RRC users.
Note that, the effective RRC user number in this embodiment refers to the number of terminals having data transmission among terminals accessing a cell. For example, if the terminal accessing the cell is inactive, it should not be counted in the number of valid RRC users. The data of the number of the effective RRC users accessing the first cell, the uplink utilization rate of the first cell, the uplink flow of the first cell, the downlink utilization rate of the first cell and the downlink flow of the first cell can be obtained through OMC.
For example, assuming that "the number of effective RRC users, the uplink PRB utilization, and the uplink traffic" are related to the preset conditions of the high-load cell, the historical network data input into the prediction model includes: and in the first historical time period, the effective RRC user quantity of the first cell, the uplink PRB utilization rate of the first cell and the uplink flow of the first cell corresponding to each set time interval.
Similarly, assuming that the "effective RRC user number, downlink PRB utilization, and downlink traffic" are related to the preset conditions of the high load cell, the historical network data input into the prediction model includes: and in the first historical time period, the effective RRC user quantity of the first cell, the downlink PRB utilization rate of the first cell and the downlink flow of the first cell corresponding to each set time interval.
Fig. 3 is a schematic flow chart of network data prediction provided in this embodiment, referring to fig. 3, taking prediction of the number of valid RRC users accessing the first cell as an example, the number of valid RRC users accessing the first cell six months before the current time point is collected from the OMC, and the set time interval is set to 1 hour. And detecting abnormal points, removing the abnormal points, and then predicting the effective RRC users to the prediction model according to the average value of the effective RRC users in each hour of each day in the six months, so that the prediction model predicts the effective RRC users according to the change of the effective RRC users in each hour of each day in the six months. For example, if the number of valid RRC users in a week after the current time is to be predicted, the prediction model outputs the number of valid RRC users that change over time and time each day in a week after the current time.
In addition, since the network data has obvious rules of holiday effect, annual periodicity and the like, the selected historical network data is preferably in the last two years, and the data in the last three months can be predicted, but the effect is slightly worse due to insufficient data quantity.
According to the embodiment, the prediction of the network data of the first cell is realized through the prediction model, the predicted data is generated according to the change rule of the historical network data, and the real change condition of the network data is reflected.
Further, based on the above embodiment, the step 202 includes:
judging whether the first cell meets a preset condition or not according to network data predicted for the first cell for any first cell in a preset area, and if so, judging that the first cell is a high-load cell;
acquiring MR (Measurement Report ) reported by a terminal in a second historical time period, judging whether the first building is in a high-load cell or not according to the acquired MR, wherein the accessed cell is the first terminal number of the high-load cell, and if the ratio of the accessed cell to the second terminal number is larger than a preset ratio, the first building is the high-load building, and if the ratio of the accessed cell to the second terminal number is the number of all terminals in the first building;
Wherein the preset conditions include at least one of the following conditions:
first condition: in the predicted time period, the average value of the number of effective RRC users accessing the first cell is greater than or equal to a first threshold value, the average value of the utilization rate of the uplink PRB of the first cell is greater than or equal to a second threshold value, and the average value of the utilization rate of the uplink PRB of the first cell is greater than or equal to a second threshold value
The average value of the uplink flow of the first cell is larger than or equal to a third threshold value;
second condition: and in the predicted time period, the average value of the number of the effective RRC users accessing the first cell is larger than or equal to a fourth threshold value, the average value of the utilization rate of the downlink PRB of the first cell is larger than or equal to a fifth threshold value, and the downlink flow of the first cell is larger than or equal to a sixth threshold value.
Specifically, the present embodiment provides two conditions defining "how to determine whether or not to be a high load cell", which are the first conditions, respectively: [ "the number of effective RRC users reaches the threshold" and "the utilization of uplink PRB reaches the threshold" and "the uplink traffic reaches the threshold" ] and a second condition: [ "effective RRC user number reaches threshold" and "downlink PRB utilization reaches threshold" and "downlink traffic reaches threshold" ]. Whether a cell is a high load cell can be determined by either of these two conditions. Fig. 4 is a schematic flow chart of determining a high-load cell according to the present embodiment, referring to fig. 4, a prediction model predicts application network data corresponding to a future week according to each input historical application network data, and directly outputs a list of the high-load cell after determining according to the first condition or the second condition.
Wherein the second historical time period is any time period prior to the current time, for example, one week, one month, or one year prior to the current time point. Because the distribution of the terminals in the cells has a certain regularity, the embodiment takes a second historical time period, and determines the high-load building from the buildings of the high-load cells through the distribution regularity of the users in the second historical time period.
Specifically, the position of the terminal and the accessed cell are determined according to the MR data reported by the terminal, the terminals with high-load cells accessed in the terminals in the first building in the second time period are counted, the ratio of the terminals to the total number of the terminals in the first building is calculated, and if the ratio is larger than the preset ratio, the building is the high-load building.
Fig. 5 is a schematic diagram of a high-load building determining process provided in this embodiment, referring to fig. 5, after determining a high-load cell, performing sample (one terminal is one sample) proportion analysis on terminals in each building (i.e. calculating the ratio of the number of the first terminals to the number of the second terminals), and determining the high-load building according to the result of the sample proportion analysis. Fig. 6 is a schematic diagram generated according to the number of terminals connected to the high-load cells in each building in the cells, the high-load building squares determined in the above manner are marked, and building points outside the high-load building are filled, so that the distribution situation of the high-load cells can be clearly shown through fig. 6, and the distribution area of the high-load building can be rapidly determined. Namely, the area with concentrated distribution of the high-load buildings can be presented, and the distribution condition and the relative proportion of the high-load buildings in the cells can be visually represented.
The method provided by the embodiment realizes the judging process of the high-load building, accurately judges the high-load building, and is beneficial to the reasonable determination of the follow-up indoor substation.
Further, on the basis of the above embodiments, the step 203 includes:
acquiring a first set and a second set, wherein the first set comprises high-load buildings which are all or partially positioned in a high-load cell signal coverage overlapping area, and the second set comprises high-load buildings which do not belong to the first set;
after the site area of the building needing to be set is determined according to the position information of the high-load building in the first set and the existing indoor substation, the site area of the building needing to be set is determined according to the position information of the high-load building in the second set, the existing indoor substation and the determined site area.
The signal coverage overlapping areas of any two second cells and the third cell are as follows: and taking the position of the base station of the second cell as the center of a circle, taking the position of the base station of the preset radius as the center of a circle, taking the position of the base station of the third cell as the center of a circle, taking the position of the base station of the preset radius as the center of a circle, and taking the angle of the lobe of the preset radius as the 120-degree, wherein the intersection area of the lobe of the second cell and the lobe of the third cell is the signal coverage overlapping area of the second cell and the third cell.
Wherein the preset radius is 280 meters.
When the site area for setting the indoor substation is determined, the high-load building is divided into a first set and a second set, the site area for setting the indoor substation is determined for the first set, and then the site area for setting the indoor substation is determined for the second set. The high-load buildings in the first set are positioned in the signal coverage overlapping areas of two or more high-load cells, and the indoor substation arranged in the signal coverage overlapping areas can share the loads of the high-load cells at the same time, so that the high-load building has obvious telephone traffic sinking effect. Therefore, by planning the indoor substation preferentially for the signal coverage area overlapping area, the base station load of the high-load cell can be reduced to the maximum extent, and the method has the effects of reducing the base station load and improving the network quality.
Further, on the basis of the foregoing embodiments, after determining the site area where the indoor substation needs to be set according to the location information of the high-load building in the first set and the existing indoor substation, determining the site area where the indoor substation needs to be set according to the location information of the high-load building in the second set, the existing indoor substation, and the determined site area includes:
If a room substation exists in an area with a distance from any second building in the first set being smaller than or equal to a first preset distance, marking the second building to obtain a first subset consisting of high-load buildings which are not marked in the first set;
performing clustering operation according to the position information of the high-load buildings in the first subset, judging whether the first type of clusters meet station setting conditions or not for each determined first type of clusters, and if so, determining that the area where the first type of clusters are located is a station area where a chamber substation needs to be set;
if a room substation exists in an area with a distance from the third building being smaller than or equal to a second preset distance or a cluster center point of a first cluster determined as a site area exists in any third building in the second set, marking the third building, and obtaining a second subset consisting of high-load buildings which are not marked in the second set;
performing clustering operation according to the position information of the high-load buildings in the second subset, judging whether the second class cluster meets the station setting conditions or not for each determined second class cluster, and if so, determining the area where the second class cluster is located as a station area where a room substation needs to be set;
Wherein the station setting condition includes at least one of the following conditions: the minimum value of the distance between the cluster center point of the cluster and the base station of each high-load cell is smaller than a fourth preset distance, the total number of terminals accessing the area where the cluster is located according to MR statistics is larger than a preset number in a third historical time period, high-load buildings with the height larger than the preset height exist in the cluster, and the occupied area of the high-load buildings with the area larger than or equal to the preset area exist in the cluster.
Since there may be existing indoor substations in the preset area, in order to avoid repeated establishment of the indoor substations, the influence of the existing indoor substations is eliminated before the clustering operation is performed on the high-load buildings in the first set. Accordingly, prior to clustering the high-load buildings in the second set, the effects of existing outstations and outstations already located in the overlapping area of signal coverage need to be eliminated.
Wherein the first preset distance, the second preset distance and the third preset distance may all be set to 80 meters.
Wherein the fourth preset distance may be set to 300 meters.
The preset number is determined according to the duration of the third historical time period, and the longer the duration of the third historical time period is, the larger the preset number is. For example, if the duration of the third history period is 1 hour, the preset number is 5000.
Wherein the preset height may be set to 30 meters.
Wherein the preset area may be set to 1000 square meters.
The cluster center point of the cluster is determined according to the position information of each high-load building in the cluster. For example, if the position information is a position coordinate, the position coordinate of the cluster center point of the cluster is an average value of the position coordinates of each high-load building in the cluster.
Further, all the determined site areas are output.
Fig. 7 is a schematic diagram of a process of determining a substation in the present embodiment, referring to fig. 7, in the process, a substation is planned in a sector intersection area first, and in the process of planning, a high-load building covered by an existing substation is removed first. Then, the room substation is planned in the non-sector intersection area, and in the process, the high-load building covered by the existing room substation and the planned room substation needs to be removed first. And finally, outputting the planned result.
Specifically, the station setting conditions provided in this embodiment include that a room substation can be set up by satisfying any one of them:
first station setting condition: the minimum value of the distance between the cluster center point of the cluster and the base station of each high-load cell is smaller than a fourth preset distance, the total number of terminals accessed to the area where the cluster is located according to MR statistics is larger than a preset number in a third historical time period, and high-load buildings with the height larger than the preset height exist in the cluster. Specifically, the distance between the center point of the cluster in the cluster and the base station of the high-load cell is not more than 300 meters, the number of sampling points in the cluster is not less than 5000, no room coverage exists, and the building height in the cluster is not less than 30 meters.
Second station setting condition: the minimum value of the distance between the cluster center point of the cluster and the base station of each high-load cell is smaller than a fourth preset distance, the total number of terminals accessing the area where the cluster is located according to MR statistics is larger than the preset number in a third historical time period, and the high-load buildings with occupied area larger than or equal to the preset area exist in the cluster. Specifically, the distance between the cluster center point in the cluster and the high-load cell base station is less than or equal to 300 meters, the sampling points are more than or equal to 5000, no room coverage exists, the building height is 18 meters < = 30 meters, and the building area is more than or equal to 1000 square meters.
The embodiment combines the information of building and macro station distance, building height, building area and the like to carry out high-load indoor station planning, and the planning process considers the influence of planned indoor stations and existing indoor stations, so that repeated station establishment of the indoor stations is avoided, and resources are saved. The method makes up for the low efficiency of the existing manual analysis and optimization, and has long time consumption and complex process.
The clustering operation is described as follows:
wherein performing a clustering operation (which may employ an algorithm of K-Means) from the subset (first subset or second subset) includes:
the method comprises the steps of circularly executing class cluster number determining operation until the determined class cluster number is greater than or equal to a preset number, or determining a maximum value in profile coefficients corresponding to the number of various classes of clusters under the condition that the determined class cluster number, and taking the determined maximum class cluster number or the class cluster number corresponding to the maximum value as the class cluster number for carrying out clustering operation on each high-load building of a subset;
The cluster number determining operation comprises the following steps: the method comprises the steps of obtaining the number of current class clusters, circularly executing class cluster center determining operation until the number of times of executing the class cluster center determining operation is larger than the preset number of times, or increasing the number of the current class clusters by 1 after the theoretical class cluster center of executing the class cluster center determining operation is the same as the actual class cluster center, and taking the class clusters which are determined by the last time of executing the class cluster center determining operation and belong to each high-load building as the class clusters which are determined by clustering operation under the current class cluster number; the actual cluster center is calculated according to the position coordinates of each high-load building after determining the cluster to which each high-load building belongs by using the theoretical cluster center;
the cluster center determining operation comprises the following steps:
when the method is executed for the first time, high-load buildings with the same number as the current number of the categories are randomly selected to be used as theoretical category cluster centers which are determined for the first time, the category clusters which each high-load building belongs to are divided according to the distance between each high-load building and each theoretical category cluster center, and the actual category cluster centers of each category cluster are calculated according to the position coordinates of each high-load building;
when the method is not executed for the first time, the actual cluster center determined by the previous execution cluster center determining operation is used as the theoretical cluster center of the current execution cluster center determining operation, the clusters of each high-load building are divided according to the distance between each high-load building and each theoretical cluster center, and the actual cluster centers of each cluster are calculated according to the position coordinates of each high-load building;
According to the position coordinates of each high-load building, calculating the actual cluster center of each cluster, including: and calculating the average value of the position coordinates of all high-load buildings contained in a certain cluster to obtain the position coordinates of the center of the actual cluster.
When the number of the class clusters is K, the corresponding contour coefficient SCK is calculated by the following formula:
SCK＝s(1)+s(2)+…+s(i)+…+s(n)
where n represents the total number of high-load buildings subjected to clustering operation, and s (i) represents the cluster-like coefficient of the ith high-load building.
Cluster-like coefficient s (i) is calculated by the following formula:
wherein b (i) represents the dissimilarity between classes corresponding to the ith high-load building, and a (i) represents the dissimilarity between classes corresponding to the ith high-load building.
Wherein, in the formula for calculating the cluster coefficient s (i), the calculation of the dissimilarity b (i) between classes corresponding to the ith high-load building includes:
and calculating the average distance bij of all the high-load buildings in any other cluster (any cluster where the non-ith high-load building is) Cj of the ith high-load building as the dissimilarity between the ith high-load building and the cluster Cj. And taking the minimum value in each calculated average distance bij as the dissimilarity between classes of the ith high-load building. That is, the inter-class dissimilarity b (i) can be calculated by the following formula: b (i) =min { bi1, bi2, …, bij, …, bi (m-1) }
Wherein m is the number of clusters for clustering, bij is the dissimilarity between the ith high-load building and the jth cluster.
In the above formula for calculating the cluster coefficient s (i), the intra-class dissimilarity a (i) corresponding to the ith high-load building is equal to the average distance from the ith high-load building to the other high-load buildings in the same cluster (i.e. the cluster in which the ith high-load building is located).
Fig. 8 is a block diagram of the apparatus for setting up a station based on flow prediction provided in the present embodiment, referring to fig. 8, which includes a prediction module 801, a first determination module 802 and a second determination module 803, wherein,
a prediction module 801, configured to determine network data predicted for each cell according to historical network data for each cell in a preset area;
a first determining module 802, configured to determine a high-load cell from the cells according to the predicted network data, and determine a high-load building from a building in the high-load cell according to distribution information of terminals in each high-load cell;
and the second determining module 803 is configured to determine a site area in which the substation is to be set in the preset area according to the location information of each high-load building.
The device for setting the chamber substation based on the flow prediction provided in this embodiment is applicable to the method for setting the chamber substation based on the flow prediction provided in the above embodiment, and will not be described herein.
Further, on the basis of the above embodiment, the prediction module is further configured to:
for any first cell in a preset area, acquiring historical network data corresponding to each set time interval of the first cell in a first historical time period, inputting the acquired historical network data into a prediction model, and outputting the network data predicted for the first cell by the prediction model;
the prediction model predicts according to the trend of the input historical network data along with the time change; the historical network data includes at least one of the following data: the method comprises the steps of accessing the effective RRC user quantity of a first cell, the utilization rate of an uplink physical resource block PRB of the first cell, the uplink flow of the first cell, the utilization rate of a downlink PRB of the first cell and the downlink flow of the first cell.
Further, on the basis of the above embodiments, the first determining module is further configured to:
judging whether the first cell meets a preset condition or not according to network data predicted for the first cell for any first cell in a preset area, and if so, judging that the first cell is a high-load cell;
acquiring a measurement report MR reported by a terminal in a second historical time period, judging whether the first building is located in a first building according to the acquired MR, wherein the accessed cell is a first terminal number of the high-load cell, and if the ratio of the first terminal number to the second terminal number is larger than a preset ratio, the first building is the high-load building, and if the ratio is larger than the preset ratio, the second terminal number is the number of all terminals located in the first building;
Wherein the preset conditions include at least one of the following conditions:
first condition: in the predicted time period, the average value of the number of effective RRC users accessing the first cell is greater than or equal to a first threshold value, the average value of the utilization rate of the uplink PRB of the first cell is greater than or equal to a second threshold value, and the average value of the utilization rate of the uplink PRB of the first cell is greater than or equal to a second threshold value
The average value of the uplink flow of the first cell is larger than or equal to a third threshold value;
second condition: and in the predicted time period, the average value of the number of the effective RRC users accessing the first cell is larger than or equal to a fourth threshold value, the average value of the utilization rate of the downlink PRB of the first cell is larger than or equal to a fifth threshold value, and the downlink flow of the first cell is larger than or equal to a sixth threshold value.
Further, on the basis of the above embodiments, the second determining module is further configured to:
acquiring a first set and a second set, wherein the first set comprises high-load buildings which are all or partially positioned in a high-load cell signal coverage overlapping area, and the second set comprises high-load buildings which do not belong to the first set;
after the site area of the building needing to be set is determined according to the position information of the high-load building in the first set and the existing indoor substation, the site area of the building needing to be set is determined according to the position information of the high-load building in the second set, the existing indoor substation and the determined site area.
Further, on the basis of the above embodiments, the second determining module is further configured to:
if a room substation exists in an area with a distance from any second building in the first set being smaller than or equal to a first preset distance, marking the second building to obtain a first subset consisting of high-load buildings which are not marked in the first set;
performing clustering operation according to the position information of the high-load buildings in the first subset, judging whether the first type of clusters meet station setting conditions or not for each determined first type of clusters, and if so, determining that the area where the first type of clusters are located is a station area where a chamber substation needs to be set;
if a room substation exists in an area with a distance from the third building being smaller than or equal to a second preset distance or a cluster center point of a first cluster determined as a site area exists in any third building in the second set, marking the third building, and obtaining a second subset consisting of high-load buildings which are not marked in the second set;
performing clustering operation according to the position information of the high-load buildings in the second subset, judging whether the second class cluster meets the station setting conditions or not for each determined second class cluster, and if so, determining the area where the second class cluster is located as a station area where a room substation needs to be set;
Wherein the station setting condition includes at least one of the following conditions: the minimum value of the distance between the cluster center point of the cluster and the base station of each high-load cell is smaller than a fourth preset distance, the total number of terminals accessing the area where the cluster is located according to MR statistics is larger than a preset number in a third historical time period, high-load buildings with the height larger than the preset height exist in the cluster, and the occupied area of the high-load buildings with the area larger than or equal to the preset area exist in the cluster.
Fig. 9 is a schematic diagram showing the physical structure of the electronic device provided in the present embodiment.
Referring to fig. 9, the electronic apparatus includes: processor 901, communication interface (Communications Interface) 902, memory 903 and communication bus 904, wherein processor 901, communication interface 902 and memory 903 communicate with each other via communication bus 904. The processor 901 may call logic instructions in the memory 903 to perform the following method: according to historical network data of each cell in a preset area, determining predicted network data of each cell; determining a high-load cell from each cell according to the predicted network data, and determining a high-load building from a building in the high-load cell according to the distribution information of the terminals in each high-load cell; and determining a site area in which the indoor substation is required to be arranged in the preset area according to the position information of each high-load building.
Further, the logic instructions in the memory 903 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the above-described method embodiments, for example, comprising: according to historical network data of each cell in a preset area, determining predicted network data of each cell; determining a high-load cell from each cell according to the predicted network data, and determining a high-load building from a building in the high-load cell according to the distribution information of the terminals in each high-load cell; and determining a site area in which the indoor substation is required to be arranged in the preset area according to the position information of each high-load building.
The present embodiment provides a non-transitory computer readable storage medium having stored thereon a computer program that is executed by a processor to perform the method of: according to historical network data of each cell in a preset area, determining predicted network data of each cell; determining a high-load cell from each cell according to the predicted network data, and determining a high-load building from a building in the high-load cell according to the distribution information of the terminals in each high-load cell; and determining a site area in which the indoor substation is required to be arranged in the preset area according to the position information of each high-load building.
The above-described embodiments of electronic devices and the like are merely illustrative, wherein the elements described as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the embodiments of the present invention, and are not limited thereto; although embodiments of the present invention have been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of setting up a cell site based on flow prediction, comprising:
according to historical network data of each cell in a preset area, determining predicted network data of each cell;
determining a high-load cell from each cell according to the predicted network data, and determining a high-load building from a building in the high-load cell according to the distribution information of the terminals in each high-load cell;
determining a site area in which a room substation is required to be arranged in the preset area according to the position information of each high-load building;
the determining the site area in which the indoor substation needs to be set in the preset area according to the position information of each high-load building comprises the following steps:
acquiring a first set and a second set, wherein the first set comprises high-load buildings which are all or partially positioned in a high-load cell signal coverage overlapping area, and the second set comprises high-load buildings which do not belong to the first set;
after the site area of the building needing to be set is determined according to the position information of the high-load building in the first set and the existing indoor substation, the site area of the building needing to be set is determined according to the position information of the high-load building in the second set, the existing indoor substation and the determined site area.
2. The method for setting up a station according to claim 1, wherein the determining the predicted network data for each cell based on the historical network data for each cell in the preset area comprises:
for any first cell in a preset area, acquiring historical network data corresponding to each set time interval of the first cell in a first historical time period, inputting the acquired historical network data into a prediction model, and outputting the network data predicted for the first cell by the prediction model;
the prediction model predicts according to the trend of the input historical network data along with the time change; the historical network data includes at least one of the following data: the method comprises the steps of accessing the effective RRC user quantity of the first cell, the uplink utilization rate of the first cell, the uplink flow of the first cell, the downlink utilization rate of the first cell and the downlink flow of the first cell.
3. The method for setting up a substation according to claim 1, wherein the determining a high load cell from each cell according to the predicted network data, determining a high load building from a building in each high load cell according to the distribution information of the terminals in each high load cell, comprises:
Judging whether the first cell meets a preset condition or not according to network data predicted for the first cell for any first cell in a preset area, and if so, judging that the first cell is a high-load cell;
acquiring a measurement report MR reported by a terminal in a second historical time period, judging whether the first building is located in a first building according to the acquired MR, wherein the accessed cell is a first terminal number of the high-load cell, and if the ratio of the first terminal number to the second terminal number is larger than a preset ratio, the first building is the high-load building, and if the ratio is larger than the preset ratio, the second terminal number is the number of all terminals located in the first building;
wherein the preset conditions include at least one of the following conditions:
first condition: in the predicted time period, the average value of the number of effective RRC users accessing the first cell is larger than or equal to a first threshold value, the average value of the utilization rate of the uplink material resource blocks PRB of the first cell is larger than or equal to a second threshold value, and the average value of the uplink flow of the first cell is larger than or equal to a third threshold value;
second condition: and in the predicted time period, the average value of the number of the effective RRC users accessing the first cell is larger than or equal to a fourth threshold value, the average value of the utilization rate of the downlink PRB of the first cell is larger than or equal to a fifth threshold value, and the downlink flow of the first cell is larger than or equal to a sixth threshold value.
4. The method for setting up a station according to claim 1, wherein after determining a station area where a station is to be set up according to the location information of a high-load building in the first set and an existing station, determining a station area where a station is to be set up according to the location information of a high-load building in the second set, an existing station, and the determined station area includes:
if a room substation exists in an area with a distance from any second building in the first set being smaller than or equal to a first preset distance, marking the second building to obtain a first subset consisting of high-load buildings which are not marked in the first set;
performing clustering operation according to the position information of the high-load buildings in the first subset, judging whether the first type of clusters meet station setting conditions or not for each determined first type of clusters, and if so, determining that the area where the first type of clusters are located is a station area where a chamber substation needs to be set;
if a room substation exists in an area with a distance from the third building being smaller than or equal to a second preset distance or a cluster center point of a first cluster determined as a site area exists in any third building in the second set, marking the third building, and obtaining a second subset consisting of high-load buildings which are not marked in the second set;
Performing clustering operation according to the position information of the high-load buildings in the second subset, judging whether the second class cluster meets the station setting conditions or not for each determined second class cluster, and if so, determining the area where the second class cluster is located as a station area where a room substation needs to be set;
wherein the station setting condition includes at least one of the following conditions: the minimum value of the distance between the cluster center point of the cluster and the base station of each high-load cell is smaller than a fourth preset distance, the total number of terminals accessing the area where the cluster is located according to MR statistics is larger than a preset number in a third historical time period, high-load buildings with the height larger than the preset height exist in the cluster, and the occupied area of the high-load buildings with the area larger than or equal to the preset area exist in the cluster.
5. An apparatus for setting up a cell site based on flow prediction, comprising:
the prediction module is used for determining network utilization data predicted for each cell according to the historical network utilization data of each cell in the preset area;
the first determining module is used for determining a high-load cell from the cells according to the predicted network data and determining a high-load building from the buildings in the high-load cell according to the distribution information of the terminals in each high-load cell;
The second determining module is used for determining a site area in which a room substation is required to be arranged in the preset area according to the position information of each high-load building;
the determining the site area in which the indoor substation needs to be set in the preset area according to the position information of each high-load building comprises the following steps:
acquiring a first set and a second set, wherein the first set comprises high-load buildings which are all or partially positioned in a high-load cell signal coverage overlapping area, and the second set comprises high-load buildings which do not belong to the first set;
after the site area of the building needing to be set is determined according to the position information of the high-load building in the first set and the existing indoor substation, the site area of the building needing to be set is determined according to the position information of the high-load building in the second set, the existing indoor substation and the determined site area.
6. The flow prediction based set room substation device according to claim 5, wherein the prediction module is further configured to:
for any first cell in a preset area, acquiring historical network data corresponding to each set time interval of the first cell in a first historical time period, inputting the acquired historical network data into a prediction model, and outputting the network data predicted for the first cell by the prediction model;
The prediction model predicts according to the trend of the input historical network data along with the time change; the historical network data includes at least one of the following data: the method comprises the steps of accessing the effective RRC user quantity of the first cell, the uplink utilization rate of the first cell, the uplink flow of the first cell, the downlink utilization rate of the first cell and the downlink flow of the first cell.
7. The flow prediction based setup room substation device of claim 5, wherein the first determination module is further configured to:
judging whether the first cell meets a preset condition or not according to network data predicted for the first cell for any first cell in a preset area, and if so, judging that the first cell is a high-load cell;
acquiring a measurement report MR reported by a terminal in a second historical time period, judging whether the first building is located in a first building according to the acquired MR, wherein the accessed cell is a first terminal number of the high-load cell, and if the ratio of the first terminal number to the second terminal number is larger than a preset ratio, the first building is the high-load building, and if the ratio is larger than the preset ratio, the second terminal number is the number of all terminals located in the first building;
Wherein the preset conditions include at least one of the following conditions:
first condition: in the predicted time period, the average value of the number of the effective RRC users accessing the first cell is larger than or equal to a first threshold value, the average value of the uplink utilization rate of the first cell is larger than or equal to a second threshold value, and the average value of the uplink flow of the first cell is larger than or equal to a third threshold value;
second condition: and in the predicted time period, the average value of the number of the effective RRC users accessing the first cell is larger than or equal to a fourth threshold value, the average value of the downlink utilization rate of the first cell is larger than or equal to a fifth threshold value, and the downlink flow of the first cell is larger than or equal to a sixth threshold value.
8. The flow prediction based set room substation device according to claim 5, wherein the second determination module is further configured to:
if a room substation exists in an area with a distance from any second building in the first set being smaller than or equal to a first preset distance, marking the second building to obtain a first subset consisting of high-load buildings which are not marked in the first set;
Performing clustering operation according to the position information of the high-load buildings in the first subset, judging whether the first type of clusters meet station setting conditions or not for each determined first type of clusters, and if so, determining that the area where the first type of clusters are located is a station area where a chamber substation needs to be set;
if a room substation exists in an area with a distance from the third building being smaller than or equal to a second preset distance or a cluster center point of a first cluster determined as a site area exists in any third building in the second set, marking the third building, and obtaining a second subset consisting of high-load buildings which are not marked in the second set;
performing clustering operation according to the position information of the high-load buildings in the second subset, judging whether the second class cluster meets the station setting conditions or not for each determined second class cluster, and if so, determining the area where the second class cluster is located as a station area where a room substation needs to be set;
wherein the station setting condition includes at least one of the following conditions: the minimum value of the distance between the cluster center point of the cluster and the base station of each high-load cell is smaller than a fourth preset distance, the total number of terminals accessing the area where the cluster is located according to MR statistics is larger than a preset number in a third historical time period, high-load buildings with the height larger than the preset height exist in the cluster, and the occupied area of the high-load buildings with the area larger than or equal to the preset area exist in the cluster.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method of setting up a cell substation based on flow prediction as claimed in any one of claims 1 to 4 when the program is executed.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the method of setting up a cell substation based on flow prediction as claimed in any one of claims 1 to 4.
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