Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, the method and the device for measuring and calculating the base station position based on multi-center fusion in the present application can be used in the mobile communication field under the condition of measuring and calculating the base station position, and can also be used in any field except the mobile communication field under the condition of measuring and calculating the base station position.
It should be noted that, the collected information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for presentation, analyzed data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the related data are collected, stored, used, processed, transmitted, provided, disclosed, applied, etc. and processed, all comply with related laws and regulations and standards, necessary security measures are adopted, no prejudice to the public welfare is provided, and corresponding operation entrance is provided for the user to select authorization or rejection. If an interface is arranged between the system and the related user or institution, a corresponding operation inlet is provided for the user, so that the user can choose to agree or reject the automatic decision result; if the user selects refusal, the expert decision flow is entered.
The following embodiments of the present invention are applicable to various base station location measurement systems/applications/devices. The method and the device define global centroid, regional centroid and regional gravity center based on the geographic data of the user equipment, signal receiving intensity and other data, and calculate the geographic position of the target base station through multi-center fusion, so that the method and the device can better cope with complex situations of signal propagation in different scenes compared with a single factor positioning algorithm, and can greatly improve calculation accuracy based on a multi-center fusion calculation mode.
According to the position measuring and calculating method, geographic rasterization division is carried out on the sampled data so as to improve the data processing precision, and the raster precision can be set according to different scenes, so that the position measuring and calculating method can adapt to diversified geographic environments, and the precision of measuring and calculating results is greatly improved.
The present invention will be described in detail with reference to the following examples.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a base station location measurement method based on multi-center fusion, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
The method according to the first embodiment of the present application may be implemented in a mobile terminal, a computer terminal or a similar computing device. Fig. 1 shows a block diagram of a hardware architecture of a computer terminal (or mobile device) for implementing a multi-center converged base station location calculation method. As shown in fig. 1, the computer terminal 10 (or mobile device) may include one or more (shown in the figures as 102a,102 b.) processors 102 (the processors 102 may include, but are not limited to, a microprocessor MCU or a programmable logic device FPGA or the like processing means), a memory 104 for storing data, and a transmission means 106 for communication functions. Among other things, a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS BUS), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors 102 and/or other data processing circuits described above may be referred to generally herein as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computer terminal 10 (or mobile device). As referred to in embodiments of the application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination connected to the interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the method for measuring and calculating the position of the base station in the embodiment of the present application, and the processor 102 executes the software programs and modules stored in the memory 104, thereby executing various functional applications and data processing, that is, implementing the method for measuring and calculating the position of the base station. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 106 is arranged to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).
In the above operating environment, the present application provides a method for measuring and calculating the base station position as shown in fig. 2, and the implementation subject of the method is a base station position measuring and calculating system based on multi-center fusion.
Fig. 2 is a flowchart of an alternative base station location measurement method based on multi-center fusion according to an embodiment of the present invention, as shown in fig. 2, the method includes the steps of:
Step S201, obtaining device data reported by user equipment in a plurality of coverage areas corresponding to a target base station, and preprocessing the device data to obtain a sampling data set.
With the development of mobile network technologies, particularly the wide application of Minimized DRIVE TEST (MDT, minimization of drive tests) data, the base station positioning technology based on network data is receiving more and more attention. MDT data is automatically reported by User Equipment (UE) in daily use, and the MDT data comprises parameters such as received signal strength, signal receiving strength, signal to interference plus noise ratio and the like, and the data provides precious real-time information for wireless network optimization.
In the embodiment of the invention, the position information and the signal receiving intensity of the user equipment are taken as consideration factors, the regional centroid, the regional gravity center, the regional centroid and the global centroid in each coverage area of the target base station are defined, the rays from the global centroid to the regional centroid are selected as reference lines, and the weights related to the regional gravity center and the regional centroid are calculated based on the rays. The weight of the region centroid is determined by considering the length of the region centroid and global centroid line, and the angle between the line and the reference line. This process simulates the propagation path and direction of the signal in a real environment. In practice, the signal propagation direction will tend to be in a user-dense region, and the center of gravity of the region and the manner in which the region is strongly calculated are related to the user distribution and signal strength. Therefore, the factors considered in the positioning process are tightly combined with the actual propagation direction of the signal, so that the method and the device more accord with the actual propagation direction, and the positioning accuracy of the base station can be improved.
In the above step S101, the target base station to be measured may be determined based on a base station location measurement request, which may originate from a network optimization team of the network operator, a base station maintenance department, or a third party network analysis service provider, etc. When the network has the conditions of coverage problem, low signal-to-noise ratio or poor user experience, the problem can be positioned through base station position measurement, so that corresponding optimization measures are adopted. The location measurement request typically contains several key information including target base station identification, request source information, attribute parameters, performance index requirements, where the attribute parameters may include service provider, communication technology (i.e., network type), signal transmission range (i.e., frequency band), coverage parameters (e.g., city and rural), and usage sites Jing Canshu (e.g., macro and pico stations).
When a base station position measuring and calculating request is received, the system firstly analyzes various parameters in the request, and determines a target base station to be measured and calculated based on the parameters. And obtain MDT data (i.e., device data) in all coverage areas of the target base station, where "coverage area" refers to a geographical range that can be reached by the target base station signal, and is generally determined by the transmitting Power, antenna configuration and surrounding environment of the base station, for example, one base station can cover multiple cells, each cell can be used as a coverage area, and MDT data of all user devices in the cell can be obtained, where the MDT data is reported by the user devices, and the MDT data includes latitude and longitude of the user devices and reference signal receiving strength (REFERENCE SIGNAL RECEIVED Power, abbreviated as RSRP). The user equipment exchanges data with network infrastructure such as a base station through wireless signals, and various functions such as voice communication, data transmission, multimedia service and the like are realized. User devices encompass various types of mobile and stationary devices including, but not limited to, smart phones, tablet computers, notebook computers, internet of things devices, vehicle communication systems, wearable devices. The reference signal Receiving Strength (RSRP) is the strength of a target base station signal received by the user equipment and reflects the distance of signal transmission and the environmental influence.
The preprocessing stage aims at cleaning and arranging original equipment data to remove abnormal values, fill missing data, standardize signal strength and the like, meanwhile remove discrete points in the original data to obtain sampling points and sampling data of all the sampling points, and finally obtain a sampling data set.
An optional embodiment of the method comprises the steps of performing data cleaning on the device data to obtain cleaned device data, wherein the data cleaning comprises missing value processing and abnormal value processing, setting a data effective interval, and screening the cleaned device data based on the data effective interval to obtain screened device data, wherein the screened device data are all in the data effective interval.
Specifically, when preprocessing the device data, the device data is first subjected to data cleaning, so as to remove or adjust the missing value and the abnormal value in the device data. For missing values, interpolation, statistical-based prediction methods, or filling by referring to the average of neighboring device data, etc., may be employed to ensure the integrity of the data set and the feasibility of subsequent analysis. Outliers are points in the data set that deviate significantly from normal values, possibly due to equipment failure, measurement errors, or extreme environmental conditions. These outliers are identified and rejected by statistical analysis methods, such as calculating the mean and standard deviation of the dataset. Further, after the cleaned device data is obtained, a data effective interval is set according to the value of the reference signal receiving intensity, the device data in the interval is reserved through the data effective interval, and the data outside the interval is removed, so that inaccurate sampling points are removed, for example, the effective interval of RSRP is set to be [ -120,160], and the sampling points in the interval are screened out. The screened data set not only removes abnormal and unreasonable data, but also ensures the integrity of the data by filling the missing value, thereby improving the accuracy and reliability of the position measurement of the base station.
An optional embodiment of the method for preprocessing the device data further comprises the steps of sorting all the device data according to the reference signal receiving intensity to obtain a sorted list, splitting the sorted list according to coverage areas of the device data corresponding to the sampling points in the sorted list to obtain sorted sub-lists corresponding to the coverage areas, and intercepting the device data in the sorted sub-lists corresponding to the coverage areas according to a preset intercepting proportion to obtain intercepted device data.
Specifically, for the filtered device data, the preprocessing operation further includes sorting and partition filtering all devices according to the reference signal receiving intensity, firstly, for the device data which is filtered and confirmed to be in the effective interval, the system sorts all the device data according to the reference signal receiving intensity (RSRP), and creates a sort list. The purpose of this step is to highlight the sampling point where the signal quality is optimal, since in wireless communication a higher RSRP value generally means that the communication quality between the user equipment and the base station is better, the signal path is more direct and less affected by external interference and multipath effects. Therefore, based on RSRP sequencing, the data points with high signal quality and more reliable position information can be prioritized, and the accuracy of base station position measurement is improved. And then, splitting the ordered list into a plurality of sub-lists corresponding to the coverage areas, namely the ordered sub-list, according to the coverage areas of the sampling points corresponding to the device data in the ordered list.
Finally, according to a preset intercepting proportion (for example, the front 80 percent can be set), intercepting the equipment data in the sequencing sub-list corresponding to each coverage area to obtain intercepted equipment data. The choice of the cut-out ratio should be based on comprehensive consideration of signal quality and position measurement requirements, aiming at retaining the data point with highest signal strength and least influence by environmental factors, reducing the calculation amount and improving the processing efficiency. The intercepted equipment data set is more refined, focuses on the sampling point with the highest signal quality, and provides high-quality input data for a subsequent positioning algorithm.
The device data is preprocessed to obtain a sampling data set, wherein the sampling data set comprises sampling points and sampling data of the sampling points, one user device corresponds to one sampling point, and the sampling data at least comprises position coordinates of the sampling points and reference signal receiving intensity.
Step S202, determining a global centroid and a regional centroid of each coverage region based on the position coordinates of the sampling points.
In the above step S202, the latitude and longitude coordinates of the sampling points are extracted from the sampling data set, and the global centroid is calculated based on the latitude and longitude coordinates of all the sampling points. The global centroid represents the geometric center position of all sampling points in the coverage area of the whole target base station, and can reflect the center trend of the whole signal coverage area. The centroid of the region is then calculated based on the latitude and longitude coordinates of the sampling points within each coverage region, which more accurately represents the centers of signal propagation and user distribution within each coverage region, enabling finer spatial distribution characteristics to be captured.
Further, the step of determining the global centroid and the regional centroid of each coverage area based on the position coordinates of the sampling points comprises the steps of calculating global average longitude and latitude values based on the position coordinates of all the sampling points in all the coverage areas, obtaining the global centroid based on the global average longitude and latitude values, calculating regional average longitude and latitude values based on the position coordinates of all the sampling points in each coverage area, and obtaining the regional centroid of each coverage area based on the regional average longitude and latitude values.
Specifically, the global centroid serves as the center of the overall signal footprint and can serve as a key reference point in subsequent calculations. In calculating the influence of the center of gravity and the centroid on the centroid of the cell, a directional reference is provided for the analysis of the signal propagation path based on the direction of rays from the global centroid to the regional centroid. The global centroid is determined based on the average longitude and latitude of all sampling points, and longitude and latitude coordinate information of all sampling points is summarized. These latitude and longitude values are summed, i.e., the sum of all longitude values and the sum of all latitude values. These sums are then divided by the total number of sampling points, respectively, to obtain an average of the longitude and latitude, to obtain the position coordinates of the global centroid. The global average longitude and latitude value represents the central position of all signal receiving points in the coverage area, and provides a macroscopic position reference point for subsequent positioning analysis.
The regional centroid within each coverage region accurately reflects the central location of signal activity within the region, providing finer positional information than the global centroid. Through the regional centroid, the signal radiation centers of the base station for different cells can be estimated more accurately, and the positioning accuracy is improved. And aiming at each coverage area of the target base station, calculating longitude and latitude average values of all sampling points in the coverage area, and thus obtaining the area centroid in the coverage area.
In step S203, each sampling point and sampling data in the sampling data set are mapped into the geographic grids, and the center of gravity of N areas of each coverage area and the center of gravity of M areas of each coverage area are determined based on the sampling points and sampling data in each geographic grid.
In the step S203, each sampling point in the sampling data set is mapped to a geographic grid system, specifically, a coverage area is divided into finer geographic grids, and the precision of the geographic grids can be set according to the rough geographic environment where the target base station is located, so that the continuous geographic space is divided into manageable small units through geographic grid division, which is beneficial to statistics and comparison of the data points.
Secondly, according to statistics of the number of sampling points in the geographic grids and calculation of signal receiving intensity, a plurality of centers corresponding to each area range, namely N area centers of gravity and M area centers of gravity, can be determined, the area centers of gravity are determined based on the number of the sampling points in each geographic grid, the most dense geographic positions of the user equipment in the coverage area are reflected, and the positions are often areas with the best signal propagation effect, so that possible positions of the base station can be effectively indicated. Regional emphasis is determined based on the reference signal received strengths of the sampling points within each geographic grid. The area emphasis emphasizes the peak areas of signal strength, typically near the base station, thus helping to pinpoint the signal source.
In urban environments, buildings are dense and users are often concentrated within the buildings. The construction may have an influence on signal propagation such as shielding and reflection, and thus signal propagation becomes complicated. The embodiment of the invention fully considers the actual situation, and in the calculation process, discrete points are removed by carrying out statistical analysis on the data in the grid so as to reduce the interference of abnormal data on positioning. When the gravity center and the heart are determined, the gravity center and the heart of the region are defined according to the number of sampling points in the grid and the average RSRP, and a distance threshold value and an included angle threshold value are set to screen the appropriate longitude and latitude of the center of the grid. This approach can highlight the role of user-dense areas and areas of higher signal strength, giving more weight to radiation that propagates more "clean" (i.e., less reflected, closer to line-of-sight conditions) and more users. That is, when the base station is located, the location result can more accurately reflect the position of the base station in the actual complex environment by considering the effect of user aggregation and the effect of signal receiving intensity in the building, thereby improving the location precision.
Further, the step of mapping each sample point and sample data in the sample data set into a geogrid comprises establishing K geogrids for each coverage area, wherein K is a positive integer, and mapping the sample points and sample data into the geogrids based on the position coordinates of the sample points.
Specifically, the rasterizing of the sampled data is to divide each sampled point into two-dimensional geographic grids according to the longitude and latitude of the device reported by the user equipment, where the geographic grids divide the geographic space into a series of uniform and non-overlapping grid units, and each unit (grid) represents a specific geographic area. For example, a square grid of 50 meters×50 meters may be used, and longitude and latitude coordinates of the device data may be associated with these grids. The purpose of doing so is for space aggregation, merge the sampling point that geographic position is close to in same grid, be convenient for follow-up according to geographical region carries out data statistics analysis, reduce data volume, improve processing efficiency, simultaneously also can catch the signal strength change characteristic of local region, when establishing geographical grid, can also synchronous construction plane rectangular coordinate system, the longitude and latitude coordinate of each sampling point is converted into plane rectangular coordinate, the unification of parameter in the calculation process of being convenient for.
In an alternative embodiment, after rasterizing the sampled data, for each geographic grid, counting the number of sampling points in the geographic grid, treating the geographic grid with the number of sampling points smaller than a preset number range as a grid containing scattered discrete points, and deleting the identified scattered discrete points from the sampled data set.
The method comprises the steps of determining the center of gravity of N areas of each coverage area based on sampling points and sampling data in each geographic grid, counting the number of the sampling points in each geographic grid, sorting the geographic grids in each coverage area based on the number of the sampling points to obtain a first sorting list, screening the first sorting list based on a center of gravity distance threshold and a center of gravity included angle threshold to obtain a screened first sorting list, wherein the center of gravity distance threshold is the maximum value of the distance between the center of gravity of the area and the centroid of the area in the coverage area, the center of gravity included angle threshold is the maximum value of the included angle between the center of gravity of the area in the coverage area and the centroid of the area, selecting N geographic grids with the number of the sampling points larger than the threshold from the screened first sorting list, and taking the center point of each geographic grid as the center of gravity of the area to obtain N area centers.
Specifically, the center of gravity of the area is calculated based on the number of sampling points in the geographic grids, when the center of gravity of the area is selected, firstly, the number of sampling points in each geographic grid is counted, the area with dense signal reception of user equipment in the coverage area is identified, and the geographic grids in each coverage area are arranged in descending order according to the counted number of sampling points in each geographic grid to form a first ordering list. The descending order ensures that the grid with the largest number of sampling points is prioritized, and provides basis for identifying the center of gravity of the area. Based on the first ordered list, two preset thresholds, namely a gravity center distance threshold and a gravity center included angle threshold, are applied to further screening. The center of gravity distance threshold limits the maximum allowable distance between the center of gravity of the region and the centroid of the region, ensuring that the selected center of gravity of the region is within the effective range of signal coverage. The center of gravity included angle threshold value limits the maximum included angle between the connecting line of the center of gravity of the region and the centroid of the region and the preset reference line, namely the rays of the connecting line of the global centroid and the centroid of the region, and ensures that the connecting line direction is consistent with the main direction of signal propagation, thereby improving the positioning accuracy. N geographic grids with larger sampling points are selected from the screened first sequencing sub-list, and the center points of the geographic grids are used as regional centers, so that N regional centers of gravity are obtained.
The method comprises the steps of determining M regional centers of each coverage area based on sampling points and sampling data in each geographic grid, wherein the step of determining M regional centers of each coverage area based on the sampling points and the sampling data comprises the steps of calculating reference signal receiving intensity average values of all sampling points in each geographic grid based on reference signal receiving intensity in the sampling data to obtain reference signal receiving intensity average values corresponding to each geographic grid, the step of sorting the geographic grids in each coverage area based on the reference signal receiving intensity average values corresponding to each geographic grid to obtain a second sorting list, the step of screening the second sorting list based on a center-of-gravity distance threshold and a center-of-gravity included angle threshold to obtain a screened second sorting list, the center-of-gravity distance threshold is the maximum value of the distance between the regional centers of gravity and the regional centers of gravity in the coverage area, the center-of-gravity included angle threshold is the maximum value of the included angle between the regional centers of gravity and the regional centers in the coverage area, and the step of selecting M geographic grids with reference signal receiving intensity average values larger than the preset reference signal receiving intensity threshold from the second sorting list, and taking the center points of each geographic grid as regional centers of gravity.
Specifically, the regional centroid is determined according to the average signal receiving intensity of the geographic grids, and when determining the regional centroid of each regional range, the RSRP average value of all sampling points in each geographic grid is collected and calculated firstly. This process aims to quantify the average intensity of the signal in different regions, providing a basis for subsequent determination of grid ordering and region centering. And performing descending order arrangement according to the RSRP average value corresponding to each geographic grid, and generating a second ordered list. The geographical grid with the strongest signal, namely the focal area of signal propagation, is identified, and a foundation is laid for the selection of area intense. And screening the second ordered list by using a preset heart-strengthening distance threshold and a heart-strengthening included angle threshold. The centroid distance threshold defines a maximum allowable distance between the region centroid and the region centroid, ensuring that the selected centroid is within an effective range of signal coverage. The threshold value of the heart-strengthening included angle controls the maximum allowed included angle between the connecting line of the heart and the area centroid and the main direction of signal propagation, and ensures that the selection of the heart strengthening is matched with the actual mode of signal propagation. And selecting the first M geographical grids with larger RSRP average values from the screened second ordered list, and defining the central points of the grids as regional hearts in the regional range to obtain M regional hearts.
The center of gravity distance threshold, the center of gravity included angle threshold, the center of gravity distance threshold and the center of gravity included angle threshold are set, the influence of unreasonable center of gravity and center of gravity on the coordinate of the measuring and calculating base station is further avoided, and meanwhile, weights are distributed to the centroids of each region based on the screened region center of gravity and the region center of gravity, so that more clean (namely fewer reflections and closer to the sight distance condition) propagation paths on the region reference line and more regional centroids of users are given more weights.
Step S204, combining the center of gravity and the regional center of gravity of each coverage area to obtain a plurality of regional center combinations, configuring centroid weights for regional centroids of each coverage area based on the global centroid and each regional center combination, and calculating candidate position coordinates of a target base station under the regional center combination based on the centroid weights and the position coordinates of the regional centroids to obtain a candidate position coordinate set.
In the step S204, the centroid weights are configured for the region centroids according to each combination by combining the region centroids and the region centroids of the coverage regions, the candidate position coordinates of the target base station are calculated according to the centroid weights, and the target base station is positioned based on the plurality of candidate position coordinates, so as to improve the position measurement accuracy of the target base station. And combining the gravity center of each area screened in each area range with the gravity center of each area, and constructing a plurality of possible interpretations of the base station position in the coverage area by combining the highest signal coverage density point (the gravity center of the area) with the peak signal strength point (the gravity center of the area). For each region center combination, the distance and angle from the global centroid to the region centroid of the combination and the distance and angle from the region centroid to the region center of gravity and the region centroid are calculated. Based on these distances and angles, centroid weights are assigned to the region centroids. Thereby giving more weight to the centroid of the region where the propagation path on the reference line is cleaner (i.e., less reflected, closer to line of sight conditions) and more to the user within the coverage area. And calculating candidate position coordinates of the target base station based on the centroid weight and the position coordinates of the regional centroids, and repeatedly executing the steps until all regional center combinations are selected, so that a candidate position coordinate set is generated.
The method comprises the steps of obtaining a reference line of each coverage area based on rays of a connecting line between the global centroid and the regional centroids in each coverage area, calculating gravity center related weights according to the reference line and the connecting line between the regional gravity centers and the regional centroids in the regional center combination in the coverage area, calculating strong heart related weights according to the reference line and the connecting line between the regional strong centers and the regional centroids in the regional center combination in the coverage area, and obtaining the centroid weights of the regional centroids of each coverage area under the regional center combination based on the gravity center related weights and the strong heart related weights corresponding to the regional center combination.
Specifically, the weight data configured for the global centroid comprises a gravity center related weight and a gravity center related weight, wherein the gravity center related weight comprises a gravity center distance weight and a gravity center included angle weight, and the gravity center related weight comprises a gravity center distance weight and a gravity center included angle weight. Rays from the global centroid to the line between the regional centroids within each coverage area are defined as the reference lines of each coverage area, and the propagation path and direction of the analog signal in the actual environment. The direction of signal propagation may tend to be in areas of dense user and areas of higher signal strength, while the center of gravity and the manner in which the emphasis is calculated is related to the user distribution and signal strength. The relevant weights are configured for the regional centroid through the regional gravity center and the regional centroid, so that factors considered in the process of positioning the target base station are tightly combined with the actual propagation direction of the signal, the actual conditions of the propagation path and the propagation direction are more met, and the positioning accuracy of the base station can be further improved.
Further, the gravity center related weights comprise gravity center distance weights and gravity center included angle weights, and the step of calculating the gravity center related weights according to the reference line and the connecting line between the regional gravity center and the regional centroid in the regional center combination in the coverage region comprises the steps of calculating the gravity center distance weights according to the distance value of the connecting line between the regional gravity center and the regional centroid in the regional center combination in the coverage region and calculating the gravity center included angle weights according to the included angle between the connecting line between the regional gravity center and the regional centroid in the regional center combination in the coverage region and the reference line.
Specifically, the configuration of the gravity center related weight considers the effect of user gathering and use, calculates the gravity center distance weight according to the distance value of the connecting line between the gravity center of the region and the centroid of the region, and the calculation formula of the gravity center distance weight is expressed as follows: , wherein,The weight of the distance between the centers of gravity,For a reference line within the coverage areaIs a length of (c). Secondly, configuring a gravity center included angle weight according to an included angle between a connecting line of the gravity center of the region and the centroid of the region and a reference line in the coverage area, wherein a calculation formula of the gravity center included angle weight is as follows: , wherein,Is the weight of the included angle of the gravity center,Is the included angle between the center of gravity of the region and the connecting line of the centroid of the region and the reference line,Is the connection line between the center of gravity of the region and the centroid of the region the threshold value of the included angle between the reference line and the reference line, i.e. the center of gravity angle threshold.
Further, the heart-strengthening correlation weights comprise heart-strengthening distance weights and heart-strengthening included angle weights, and the step of calculating the heart-strengthening correlation weights according to the reference line and the connecting line of the regional heart and the regional centroid in the regional center combination in the coverage region comprises calculating the heart-strengthening distance weights according to the distance value of the connecting line between the regional heart and the regional centroid in the regional center combination in the coverage region, and calculating the heart-strengthening included angle weights according to the included angle between the connecting line between the regional heart and the regional centroid in the regional center combination in the coverage region and the reference line.
Specifically, regarding that the signal influence of less reflection defines a heart-strengthening related weight, a heart-strengthening distance threshold value is calculated according to a distance value of a connecting line between the area heart and the area centroid, and a calculation formula of the heart-strengthening distance threshold value can be expressed as follows: , wherein,As the threshold value of the distance between the hearts,Is the distance value of the connecting line between the region centroid and the region centroid,Is the path propagation loss distance. Further, the calculation formula of the heart-strengthening included angle weight according to the included angle between the connecting line between the area heart-strengthening and the area centroid and the reference line in the coverage area can be expressed as: , wherein,For the weight of the heart-strengthening included angle,For the angle between the line between the region centroid and the reference line within the coverage area,The threshold value of the included angle between the connecting line between the region centroid and the reference line in the coverage area is the threshold value of the included angle of the centroid.
Further, according to a set of region center combinations, a weight value may be configured for the region centroid, where the weight value of the region centroid may be expressed as: wherein, theFor distinguishing between the different physical properties,For refining parameter properties: Time of dayCharacterizing center-of-gravity related weights whenWhen it characterizes a cardiac related weight; Corresponding to the parameter related to the included angle,Corresponding to a distance-related parameter.
Based on the weight values of the regional centroids and the position coordinates of the regional centroids under the combination of a plurality of regional centers in the regional scope, the candidate position coordinates of the target base station can be calculated, so that the potential position of the target base station can be determined, and specifically, the candidate position coordinates of the target base station can be expressed as:
wherein, theIs the abscissa of the centroid of the region,And respectively calculating the abscissa and the ordinate of the candidate position for the ordinate of the region centroid to obtain the candidate position coordinate.
Step S205, calculating expected values of all candidate position coordinates in the candidate position coordinate set to obtain target position coordinates of the target base station, and determining the target position of the target base station based on the target position coordinates.
In the step S205, the potential position of the target base station is obtained according to different combinations of the center of gravity of the area and the area centroid. And obtaining expectations of all potential positions to obtain a final measuring position of the target base station, and finally converting the plane coordinates into longitude and latitude coordinates to serve as a final target position of the base station. Therefore, base station position measurement and calculation based on multi-center fusion is realized, and positioning accuracy and stability are remarkably improved.
Through the steps, equipment data reported by user equipment in a plurality of coverage areas corresponding to a target base station are obtained, the equipment data are preprocessed to obtain a sampling data set, wherein the sampling data set comprises sampling points and sampling data of the sampling points, the sampling data at least comprise position coordinates of the sampling points and reference signal receiving intensity, a global centroid and region centroids of the coverage areas are determined based on the position coordinates of the sampling points, then the sampling points and the sampling data in the sampling data set are mapped into a geographic grid, N region barycenters of the coverage areas and M region barycenters of the coverage areas are determined based on the sampling points and the sampling data in the geographic grid, wherein N and M are positive integers, the region barycenters are determined based on the number of the sampling points in the geographic grid, the region barycenters are determined based on the reference signal receiving intensity of the sampling points in the geographic grid, a plurality of region center combinations are obtained, the global centroid and the region centroid combinations are obtained based on the position coordinates of the global centroid and the region centroid combinations of the coverage areas, the region centroid weights of the coverage areas are calculated based on the global centroid and the region centroid combinations, the candidate coordinate values of the target base station position of the target base station are obtained, and the target position of the target position is calculated based on the candidate coordinate set of the target position of the target base station.
In this embodiment, the area division is performed by using the geographic gridding multiple sampling points, so that the sampling points can be analyzed more finely to obtain the distribution characteristics, and meanwhile, the global centroid, the area center of gravity and the area center of gravity are defined by combining the data such as the position information and the signal receiving intensity data, so as to realize the multi-center fusion measurement of the base station position, comprehensively consider the factors such as the mean value of the base station signal coverage, the aggregation degree of the user equipment and the signal intensity, and more scientifically and reasonably measure the base station position, and obtain the technical effect of improving the accuracy of the base station position measurement. Further, the technical problem of lower accuracy of measuring results when the base station position is automatically measured in the related art is solved.
The following detailed description is directed to alternative embodiments.
In the embodiment of the invention, the whole process involves a plurality of links such as data preprocessing, clear screening, geographic rasterization, statistical analysis, centroid, gravity center, centroid calculation, weight calculation and the like through a multi-center fusion sector fitting positioning algorithm. And (3) performing pre-classification and geographic rasterization processing on the data by collecting and cleaning MDT data reported by the user equipment, including longitude and latitude and RSRP. And then, carrying out statistical analysis on the data in the grid, removing discrete points, and calculating the global centroid of all data sampling points, the centroid, the gravity center and the centroid of each cell. And then, respectively screening out 3 centers of gravity and a strong center according to a threshold value, and calculating the centroid weight of the cell by using the subsequent combination. Then, constructing a multi-centroid connecting line, selecting a combination of gravity centers and strong centers each time, and obtaining a centroid-centroid cell reference line l_i, a gravity center-centroid line segment l_z and a strong center-centroid line segment l_q by connecting a cell centroid with a clustered centroid, a gravity center with a global centroid, and a strong center with a global centroid. Further, the cell centroid weights are determined by the effects of center of gravity and centroid, and the potential locations of the target sites under the corresponding center of gravity-centroid combination are determined later by weighted averaging of all cells. Finally, the final estimated coordinates are determined by the expectations of all potential locations.
The multi-center fusion sector fitting positioning algorithm disclosed by the invention covers rich information such as longitude and latitude, RSRP and the like by collecting a large amount of user MDT data. In the data processing process, the data are subjected to geographic rasterization, and proper raster precision is selected according to different regional scenes. The method can adapt to various geographic environments, and can effectively process and analyze data in areas where urban high buildings stand, mountainous areas with complex terrains or open plain areas. In addition, in the calculation process, a plurality of factors such as centroid, gravity center and heart strengthening are comprehensively considered, and compared with a single-factor positioning algorithm, the method can better cope with complex signal propagation conditions in different scenes, greatly improves positioning accuracy and applicability in various application scenes, and enhances universality of the application scenes.
In wireless communications, signal propagation is not an ideal straight line propagation and may be affected by a variety of factors, such as building shielding, topography, etc. When the position of a target base station is determined, rays from a global centroid to a cell centroid are selected as reference lines, and the gravity center and the weight of the heart are calculated based on the reference lines. The cell centroid weight is determined by considering the length of the centroid and global centroid line, and the angle of the line and reference line. This process simulates the propagation path and direction of the signal in a real environment. In practice, the direction of signal propagation may be prone to user-dense areas, while the center of gravity and the manner in which the emphasis is calculated is related to the user distribution and signal strength. Therefore, factors considered in the positioning process of the algorithm are tightly combined with the actual propagation direction of the signal, so that the algorithm is more in line with the actual propagation direction, and the positioning accuracy of the base station can be improved.
In urban environments, buildings are dense and users are often concentrated within the buildings. The construction may have an influence on signal propagation such as shielding and reflection, and thus signal propagation becomes complicated. The invention fully considers the actual situation, and in the calculation process, discrete points are removed by carrying out statistical analysis on the data in the grid so as to reduce the interference of abnormal data on positioning. When the center of gravity and the center of gravity are determined, the center of gravity of the cell and the center of gravity of the cell are respectively defined according to the number of sampling points in the grid and the average RSRP, and a distance threshold value and an included angle threshold value are set to screen the longitude and latitude of the center of the grid. This approach can highlight the role of user-dense areas and areas of higher signal strength, giving more weight to radiation that propagates more "clean" (i.e., less reflected, closer to line-of-sight conditions) and more users. That is, when positioning the base station, the positioning result reflects the position of the base station in the actual complex environment more accurately by considering the effect of user aggregation in the building, so that the positioning accuracy can be improved.
Fig. 3 is a schematic diagram of an optional multi-center-based base station location fitting measurement procedure according to an embodiment of the present invention, and as shown in fig. 3, the multi-center fusion-based location measurement procedure specifically includes:
Step one, collecting MDT data of all cells (in the embodiment of the invention, the coverage area is specifically one cell, and the coverage area can be represented by other types of geographic features), wherein the MDT data is reported by user equipment and comprises longitude and latitude and RSRP, and the RSRP represents the receiving intensity of a reference signal.
And step two, cleaning the data, and screening strong data to clean RSRP sampling points.
The RSRP sampled data is cleaned to remove inaccurate sampling points. Setting the effective interval of RSRP as [ -120,160], reserving the RSRP sampling points in the interval, sorting the screened RSRP sampling points according to the signal intensity from strong to weak, and then intercepting the first 80% of strong RSRP sampling points.
And thirdly, rasterizing the geographic plane.
The processed device data is mapped into a two-dimensional geographic grid system, and grid precision can be selected according to regional scenes.
And step four, deleting the discrete points.
And counting the number of sampling points in each grid in the rasterized data. The number of sampling points is smaller thanIs considered to contain sporadic discrete points, and the identified sporadic discrete points are deleted from the dataset.
And fifthly, calculating a global centroid and a cell centroid.
Obtaining the average longitude and latitude of all sampling points as the global centroid,) Acquiring a cellIs the area centroid [ ],). Rays emanating from the target base station that "clean" through the user-dense area are sought. Meanwhile, taking the accumulation effect of users in the building into consideration, selecting rays from the global centroid to the district centroid as the districtReference line of (2)。
Step six, the center of gravity of the cell and the cell is strong.
And calculating the center of gravity of the cell according to the number of sampling points in the grid. Ordering the grids according to the sequence of the number of sampling points in the grids from more to less, and according to the distance threshold value of the center of gravity and the centroid of the cellSum of angle thresholdScreening the first N grids, wherein the central point of the screened grids is the center of gravity of the cell, defining the cell center of gravity according to the average RSRP in the grids, sequencing the grids from high to low, and determining the threshold value according to the distance between the center of gravity and the centroid of the cellSum of angle thresholdScreening the first M grids, wherein the central point of the screened grids is the cell center. By setting the above-mentioned threshold values, the influence of the unreasonable center of gravity and the centroid on the estimated base station coordinates is further avoided, and at the same time, the method is used for distributing weights to each cell centroid, so that more 'clean' (i.e. less reflection and closer to the line-of-sight condition) propagation paths on the cell reference lines and more cell centroids of users are given more weights.
And seventhly, combining the center of gravity of the cell and the center of gravity of the cell, and calculating the suspected position.
And calculating the gravity center related weight. Taking into account the effects of user aggregate usage, based on center of gravity-centroid line segmentsLength of (2)A kind of electronic deviceAnd cell reference lineIncluded angle of (2)The calculation formula for calculating the gravity center correlation weight of the centroid of the cell is as follows:
,,
wherein, theRepresents the weight of the included angle of the gravity center,For a strong distance-related weight of the heart,For cell reference linesLength.
And calculating the heart-strengthening correlation weight. Taking into account the influence of less reflected signals, according to a centroid-centroid line segmentLength of (2)A kind of electronic deviceAnd cell reference lineIncluded angle of (2)The calculation formula for calculating the centroid related weight of the centroid of the cell is as follows:
,,
wherein, theFor the weight of the heart-strengthening included angle,For the weight of the distance between the hearts,Is the path propagation loss distance.
And calculating the centroid weight of the cell. Thereby obtaining a pair of cell centers of gravity and a cell under the combination of cell center of gravityThe centroid weights are:
wherein, theFor distinguishing between the different physical properties,For refining parameter properties: Time of dayCharacterizing center-of-gravity related weights whenWhen it characterizes a cardiac related weight; Corresponding to the parameter related to the included angle,Corresponding to a distance-related parameter.
And calculating the potential position of the target base station. The estimated potential target base station coordinates are calculated as follows:
Step eight, judging whether all combinations are taken, if yes, executing step nine, and if not, repeatedly executing step seven to step eight;
And step nine, determining the final position of the target base station based on the suspected position. And obtaining the potential position of the target base station according to different cell barycenters and cell centroid combinations. And obtaining the expected positions of all the potential positions to obtain the position coordinates of the target base station, and converting the plane coordinates into longitude and latitude coordinates serving as final target coordinates of the target base station.
And step ten, ending.
The following describes in detail another embodiment.
Example two
The base station position measuring and calculating device based on multi-center fusion provided in this embodiment includes a plurality of implementation units, each implementation unit corresponds to each implementation step in the first embodiment, and specific implementation and beneficial effects of each implementation unit may refer to the foregoing method embodiment and will not be described herein.
Fig. 4 is a schematic diagram of an alternative base station position measurement device based on multi-center fusion according to an embodiment of the present invention, and as shown in fig. 4, the base station position measurement device based on multi-center fusion may include an acquisition unit 41, a determination unit 42, a mapping unit 43, a configuration unit 44, and a calculation unit 45, where,
An obtaining unit 41, configured to obtain device data reported by user equipment in multiple coverage areas corresponding to a target base station, and pre-process the device data to obtain a sample data set, where the sample data set includes sample points and sample data of each sample point, and the sample data at least includes position coordinates of the sample points and reference signal receiving intensity;
A determining unit 42 for determining a global centroid and a region centroid of each coverage region based on the position coordinates of the sampling points;
A mapping unit 43, configured to map each sampling point and sampling data in the sampling data set into a geographic grid, determine N regional centroids of each coverage area and M regional centroids of each coverage area based on the sampling points and sampling data in each geographic grid, where N and M are both positive integers, the regional centroids are determined based on the number of sampling points in each geographic grid, and the regional centroids are determined based on the reference signal reception intensities of the sampling points in each geographic grid;
A configuration unit 44, configured to combine the center of gravity and the region centroid of each coverage area to obtain a plurality of region center combinations, configure centroid weights for the region centroids of each coverage area based on the global centroid and each region center combination, and calculate candidate position coordinates of the target base station under the region center combination based on the centroid weights and the position coordinates of the region centroids to obtain a candidate position coordinate set;
And a calculating unit 45, configured to calculate expected values of all candidate position coordinates in the candidate position coordinate set, obtain a target position coordinate of the target base station, and determine a target position of the target base station based on the target position coordinate.
The base station position measuring and calculating device obtains device data reported by user equipment in a plurality of coverage areas corresponding to a target base station through an obtaining unit 41, and preprocesses the device data to obtain a sampling data set, wherein the sampling data set comprises sampling points and sampling data of the sampling points, the sampling data at least comprises position coordinates of the sampling points and reference signal receiving intensity, a determining unit 42 determines global centroid and regional centroids of the coverage areas based on the position coordinates of the sampling points, a mapping unit 43 maps the sampling points and the sampling data in the sampling data set into geographic grids, N regional centroids of the coverage areas and M regional centroids of the coverage areas are determined based on the sampling points and the sampling data in the geographic grids, N and M are positive integers, the regional centroids are determined based on the number of the sampling points in the geographic grids, the regional centroids are determined based on the reference signal receiving intensity of the sampling points in the geographic grids, a configuration unit 44 is used for combining the regional centroids and the regional centroids of the coverage areas to obtain a plurality of regional centroids, the global centroid and the regional centroids of the coverage areas are combined based on the position coordinates of the target base station, the position of the target base station is calculated based on the coordinate set of the target base station, and the position of the target base station is calculated based on the coordinate set of the target position of the target base station is calculated.
In this embodiment, the area division is performed by using the geographic gridding multiple sampling points, so that the sampling points can be analyzed more finely to obtain the distribution characteristics, and meanwhile, the global centroid, the area center of gravity and the area center of gravity are defined by combining the data such as the position information and the signal receiving intensity data, so as to realize the multi-center fusion measurement of the base station position, comprehensively consider the factors such as the mean value of the base station signal coverage, the aggregation degree of the user equipment and the signal intensity, and more scientifically and reasonably measure the base station position, and obtain the technical effect of improving the accuracy of the base station position measurement. Further, the technical problem of lower accuracy of measuring results when the base station position is automatically measured in the related art is solved.
Further, the determining unit 42 includes a first calculating module configured to calculate global average longitude and latitude values based on position coordinates of all sampling points in all coverage areas, and obtain global centroids based on the global average longitude and latitude values, and a second calculating module configured to calculate regional average longitude and latitude values based on position coordinates of all sampling points in each coverage area, and obtain regional centroids of each coverage area based on the regional average longitude and latitude values.
Further, the mapping unit 43 comprises a first establishing module for establishing K geographical grids for each coverage area, wherein K is a positive integer, and a first mapping module for mapping the sampling points and the sampling data to the geographical grids based on the position coordinates of the sampling points.
Further, the mapping unit 43 includes a first statistics module configured to count the number of sampling points in each geographic grid, order the geographic grids in each coverage area based on the number of sampling points to obtain a first ordered list, a first screening module configured to screen the first ordered list based on a gravity center distance threshold and a gravity center included angle threshold, to obtain a screened first ordered list, where the gravity center distance threshold is a maximum value of a distance between a gravity center of an area in a coverage area and a centroid of the area, the gravity center included angle threshold is a maximum value of an included angle between the gravity center of the area in the coverage area and the centroid, and a first selection module configured to select N geographic grids with the number of sampling points greater than the threshold from the screened first ordered list, and take a center point of each geographic grid as the gravity center of the area to obtain N area centers.
Further, the mapping unit 43 includes a third calculation module configured to calculate a reference signal reception intensity average value of all sampling points in each geographic grid based on the reference signal reception intensity in the sampling data to obtain a reference signal reception intensity average value corresponding to each geographic grid, a first sorting module configured to sort the geographic grids in each coverage area based on the reference signal reception intensity average value corresponding to each geographic grid to obtain a second sorting list, and a second screening module configured to screen the second sorting list based on a centroid distance threshold and a centroid angle threshold, to obtain a screened second sorting list, where the centroid distance threshold is a maximum value of a distance between a region centroid and a region centroid in a preset coverage area, and the centroid angle threshold is a maximum value of an included angle between a region centroid and a region centroid in the preset coverage area, and a second selection module configured to select M geographic grids whose reference signal reception intensity average values are greater than the preset reference signal reception intensity threshold from the second sorting list, and take a center point of each geographic grid as a region centroid, to obtain M region centroids.
Further, the configuration unit 44 includes a first obtaining module configured to obtain a reference line of each coverage area based on a ray of a line between a global centroid and an area centroid in each coverage area, a fourth calculating module configured to calculate a centroid related weight according to the reference line and a line between an area centroid and an area centroid in an area center combination of the coverage areas, a fifth calculating module configured to calculate a centroid related weight according to the reference line and a line between an area centroid and an area centroid in an area center combination of the coverage areas, and a second obtaining module configured to obtain a centroid weight of an area centroid of each coverage area in the area center combination based on the centroid related weight and the centroid related weight corresponding to the area center combination.
The fourth calculation module comprises a first calculation sub-module and a second calculation sub-module, wherein the first calculation sub-module is used for calculating the gravity center distance weight according to the distance value of the connecting line between the regional gravity center and the regional centroid in the regional center combination in the coverage region, and the second calculation sub-module is used for calculating the gravity center included angle weight according to the included angle between the connecting line between the regional gravity center and the regional centroid in the regional center combination in the coverage region and the reference line.
Further, the center-of-gravity related weights comprise center-of-gravity distance weights and center-of-gravity included angle weights, and the fifth calculation module comprises a third calculation sub-module used for calculating the center-of-gravity distance weights according to the distance value of the connecting line between the center of gravity and the centroid of the region in the region center combination in the coverage region, and a fourth calculation sub-module used for calculating the center-of-gravity included angle weights according to the included angle between the connecting line between the center of gravity and the centroid of the region in the region center combination in the coverage region and the reference line.
Here, the acquiring unit 41, the determining unit 42, the mapping unit 43, the configuring unit 44, and the calculating unit 45 correspond to steps S201 to S205 in the first embodiment, and the above units are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the first embodiment. It should be noted that the above-described modules or units may be hardware components or software components stored in a memory (e.g., the memory 104) and processed by one or more processors (e.g., the processors 102a,102b, 102 n), or may be executed as part of an apparatus in the computer terminal 10 provided in the first embodiment.
The invention is described below in connection with alternative embodiments.
Example III
An embodiment of the present invention may also provide an electronic device, where fig. 5 is a block diagram of a hardware structure of an electronic device (or a mobile device) that performs an optional method for measuring a base station location according to an embodiment of the present invention, and as shown in fig. 5, the electronic device may include one or more (only one is shown in fig. 5) processors 502, a memory 504, a memory controller, and a peripheral interface, where the peripheral interface is connected to a radio frequency module, an audio module, and a display.
The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the methods and apparatuses in the embodiments of the present application, and the processor executes the software programs and modules stored in the memory, thereby performing various functional applications and data processing, that is, implementing the methods described above. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located with respect to the processor, the remote memory being connectable to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor can call information and application programs stored in a memory through a transmission device to execute the following steps of acquiring equipment data reported by user equipment in a plurality of coverage areas corresponding to a target base station, preprocessing the equipment data to obtain a sampling data set, wherein the sampling data set comprises sampling points and sampling data of the sampling points, the sampling data at least comprises position coordinates of the sampling points and reference signal receiving intensity, determining global centroid and regional centroids of the coverage areas based on the position coordinates of the sampling points, mapping the sampling points and the sampling data in the sampling data set into geographic grids, determining N regional centroids of the coverage areas and M regional centroids of the coverage areas based on the sampling points and the sampling data in the geographic grids, wherein N and M are positive integers, the regional centroids are determined based on the number of the sampling points in the geographic grids, the regional centroids are determined based on the reference signal receiving intensity of the sampling points in the geographic grids, combining the regional centroids and the regional centroids of the coverage areas to obtain a plurality of regional centroids, configuring the global centroid and the regional centroids of the coverage areas as regional centroids based on the position coordinates of the global centroid and the regional centroids, calculating the candidate base station coordinate position of the target base station, and calculating the candidate base station coordinate position of the target base station based on the candidate coordinate set.
The processor may also invoke the information stored in the memory and the application program via the transmission device to perform the steps of determining a global centroid based on the position coordinates of the sampling points and a regional centroid of each coverage area, including calculating global average longitude and latitude values based on the position coordinates of all the sampling points within all the coverage areas, obtaining a global centroid based on the global average longitude and latitude values, calculating regional average longitude and latitude values based on the position coordinates of all the sampling points within each coverage area, and obtaining a regional centroid of each coverage area based on the regional average longitude and latitude values.
The processor may also invoke the information stored by the memory and the application program via the transmission device to perform the steps of mapping each sample point and sample data in the sample data set into a geographic grid including establishing K geographic grids for each coverage area, where K is a positive integer, and mapping the sample points and sample data into the geographic grids based on the position coordinates of the sample points.
The processor can also call information and application programs stored in the memory through the transmission device to execute the following steps of determining N regional barycenters of each coverage area based on sampling points and sampling data in each geographic grating, counting the number of the sampling points in each geographic grating, sorting the geographic gratings in each coverage area based on the number of the sampling points to obtain a first sorting list, screening the first sorting list based on a barycenter distance threshold and a barycenter included angle threshold to obtain a screened first sorting list, wherein the barycenter distance threshold is the maximum value of the distance between the regional barycenter and the regional centroid of the coverage area, the barycenter included angle threshold is the maximum value of the included angle between the regional barycenter and the regional centroid of the coverage area, selecting N geographic gratings with the number of the sampling points larger than the threshold from the screened first sorting list, and taking the central point of each geographic grating as the regional barycenter to obtain N regional barycenters.
The processor can also call information and application programs stored in the memory through the transmission device to execute the following steps of determining M regional centers of each coverage area based on sampling points and sampling data in each geographic grid, wherein the step of determining M regional centers of each coverage area based on the sampling points and the sampling data comprises the steps of calculating reference signal receiving intensity average values of all sampling points in each geographic grid based on reference signal receiving intensity in the sampling data to obtain reference signal receiving intensity average values corresponding to each geographic grid, sorting geographic grids in each coverage area based on the reference signal receiving intensity average values corresponding to each geographic grid to obtain a second sorting list, screening the second sorting list based on a center distance threshold and a center included angle threshold to obtain a screened second sorting list, the center distance threshold is the maximum value of the distance between the regional centers of each regional center and the regional centers of each regional center, the center included angle is the maximum value of the regional center of each regional center and the regional center of each geographic center is selected from the second sorting list to be the M geographic grids, and the center point of each geographic center point is selected as the regional center of the regional center.
The processor can also call information and application programs stored in the memory through the transmission device to execute the following steps of configuring centroid weights for the regional centroids of all the coverage areas based on the global centroid and each regional center combination, obtaining reference lines of all the coverage areas based on rays of connecting lines between the global centroid and regional centroids in all the coverage areas, calculating gravity center related weights according to the reference lines and connecting lines between regional centroids and regional centroids in the regional center combination of the coverage areas, calculating centroid related weights according to the reference lines and connecting lines between regional centroids and regional centroids in the regional center combination of the coverage areas, and obtaining the centroid weights of regional centroids of all the coverage areas under the regional center combination based on the gravity center related weights and the centroid related weights corresponding to the regional center combination of the coverage areas.
The processor can also call information and application programs stored in the memory through the transmission device to execute the following steps that the gravity center related weights comprise gravity center distance weights and gravity center included angle weights, and the gravity center related weights are calculated according to the reference line and the connecting line between the gravity center of the region and the centroid of the region in the regional center combination in the coverage region.
The processor can also call information and application programs stored in the memory through the transmission device to execute the following steps of the heart-strengthening related weights including heart-strengthening distance weights and heart-strengthening included angle weights, and the step of calculating the heart-strengthening related weights according to the reference line and the connecting line of the regional heart and the regional centroid in the regional center combination in the coverage area includes calculating the heart-strengthening distance weights according to the distance value of the connecting line between the regional heart and the regional centroid in the regional center combination in the coverage area, and calculating the heart-strengthening included angle weights according to the included angle between the connecting line between the regional heart and the regional centroid in the regional center combination in the coverage area and the reference line.
The embodiment of the invention provides a base station position measuring and calculating scheme based on multi-center fusion. The geographical gridding multi-sampling points are used for area division, so that the sampling points can be analyzed more finely to obtain distribution characteristics, meanwhile, the global centroid, the area center of gravity and the area center of gravity are defined by combining data such as position information, signal receiving intensity data and the like, the multi-center fusion measurement of the base station position is realized, factors such as the mean value of base station signal coverage, the aggregation degree of user equipment and signal intensity are comprehensively considered, the base station position can be measured more scientifically and reasonably, and the technical effect of improving the measurement accuracy of the base station position is achieved. Further, the technical problem of lower accuracy of measuring results when the base station position is automatically measured in the related art is solved.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is only illustrative, and the electronic device may be a terminal device such as a smart phone, a tablet computer, a palm computer, a Mobile internet device (Mobile INTERNET DEVICES, MID), a PAD, and the like. Fig. 5 is not limited to the structure of the electronic device. For example, the electronic device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 5, or have a different configuration than shown in FIG. 5.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device related hardware, and the program may be stored in a computer readable storage medium, where the storage medium may include a flash disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, etc.
The invention is described below in connection with alternative embodiments.
Example IV
The embodiment of the invention also provides a computer readable storage medium. Alternatively, in an embodiment of the present invention, the computer readable storage medium may be used to store program codes executed by the method for measuring and calculating a position of a base station according to the first embodiment.
Alternatively, in the embodiment of the present invention, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
The embodiment of the invention also provides a computer program product, which is suitable for executing the program of the step of the measuring and calculating method of the base station position when being executed on data processing equipment, wherein equipment data reported by user equipment in a plurality of coverage areas corresponding to a target base station are acquired and preprocessed to obtain a sampling data set, the sampling data set comprises sampling points and sampling data of the sampling points, the sampling data at least comprises position coordinates of the sampling points and reference signal receiving intensity, global centroid and regional centroid of the coverage areas are determined based on the position coordinates of the sampling points, each sampling point and the sampling data in the sampling data set are mapped into a geographic grid, N regional barycenters of the coverage areas and M regional barycenters of the coverage areas are determined based on the sampling points and the sampling data in the geographic grid, the regional barycenters are all positive integers, the regional barycenters are determined based on the number of the sampling points in the geographic grid, the regional barycenters are determined based on the reference signal receiving intensity of the sampling points in the geographic grid, the regional barycenters are combined to obtain a plurality of regional center combinations, the global centroid combinations are determined based on the position coordinates of the global centroid and the target centroid, the position of the target centroid is calculated based on the position of the target centroid, the candidate base station is calculated, and the position of the candidate base station is calculated based on the position of the candidate coordinate set of the target centroid.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. The storage medium includes a U disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, etc. which can store the program code.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.