Method, device and system for determining site location

Abstract

The present application discloses a method, device, and system for determining site locations, which pertain to the field of communications. The method comprises: obtaining a weak coverage area in a communication system, wherein the weak coverage area is determined based on communication indicator data collected by multiple information collection points in the communication system; obtaining at least one weak coverage cluster determined based on the weak coverage area; and for each of the weak coverage clusters, using a first constant clustering algorithm to determine the site location of a site to be deployed in the weak coverage cluster. The present application improves the efficiency of determining site locations and the reliability of the ultimately obtained site locations. The present application is used for determining site locations.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a method, device, and system for determining a site location. Background Art

[0002] The communication system includes a network management device and multiple stations. The network management device is used to manage the multiple stations. The stations are used to provide communication services to terminal devices. The location of the stations determines the quality of service provided to the terminal devices.

[0003] Currently, when a new site needs to be opened within a planned communications system, personnel must determine an initial site location based on parameters such as the terrain type, building height, and logical topology of the area covered by the communications system. They then use simulation software to simulate the communication performance of the site at this initial location. Based on the simulation results, personnel continuously optimize the site location until the final site location is determined. This algorithm for determining site locations is called an artificial white-box algorithm.

[0004] However, the manual white-box algorithm requires staff to participate in the initial determination of the site location and the continuous optimization of the site location. The efficiency of site location determination is low, and the reliability of the final site location is low. Summary of the Invention

[0005] The present invention provides a method, device, and system for determining a site location. This method addresses the current issues of low site location determination efficiency and low reliability. The technical solution is as follows:

[0006] In a first aspect, a method for determining a site location is provided. The method may be performed by an analysis device, and the method includes:

[0007] A weak coverage area in a communication system is obtained, where the weak coverage area is an area determined based on communication indicator data collected by multiple information collection points in the communication system; at least one weak coverage cluster determined based on the weak coverage area is obtained; and for each of the weak coverage clusters, a first constant clustering algorithm is used to determine the site location of a site to be deployed in the weak coverage cluster.

[0008] The method for determining the site location provided in the embodiment of the present application is that the analysis device automatically uses the first constant clustering algorithm to determine the number of sites to be deployed in each weak coverage cluster, without the need to manually set the number of sites to be deployed, which can improve the efficiency of determining the site location and improve the accuracy and reliability of the final site location.

[0009] Optionally, the analysis device may determine the at least one weak coverage cluster using a non-deterministic clustering algorithm based on the location information of the information collection point in the weak coverage area. The analysis device automatically divides the weak coverage area into at least one weak coverage cluster, eliminating the need for manual division of the weak coverage area. This can improve the efficiency of determining the site location and improve the reliability of the ultimately obtained site location.

[0010] The process of determining the at least one weak coverage cluster by using a non-indeterminate clustering algorithm based on the location information of the information collection point in the weak coverage area may include:

[0011] Based on the location information of the information collection point in the weak coverage area, the indefinite number clustering algorithm is used to divide the weak coverage area into multiple candidate weak coverage clusters; the candidate weak coverage cluster with the number of information collection points greater than the specified collection point number threshold in the multiple candidate weak coverage clusters is determined as the at least one weak coverage cluster. By eliminating weak coverage clusters that do not meet the conditions, the computational complexity can be reduced and the efficiency of determining the site location can be improved.

[0012] Optionally, the process of determining the site location of the site to be deployed in the weak coverage cluster by using the first constant clustering algorithm includes:

[0013] Determine the number of sites to be deployed in the weak coverage cluster; and determine the site locations of the sites to be deployed in the weak coverage cluster using the first constant number clustering algorithm based on the number of sites to be deployed in the weak coverage cluster and the location information of the information collection point in the weak coverage cluster.

[0014] The use of the aforementioned first constant clustering algorithm to determine site locations improves the efficiency of the site determination process while satisfying the correlation between sites and weak coverage areas, and also makes the process more adaptable.

[0015] Optionally, the process of determining the number of sites to be deployed in the weak coverage cluster includes: obtaining the maximum distance between every two information collection points in the weak coverage cluster; obtaining the ratio of the maximum distance to the specified station spacing; and determining the number of sites to be deployed based on the obtained ratio.

[0016] Among them, when the ratio is an integer, the ratio can be directly determined as the number of sites to be deployed; when the ratio is not an integer, the rounded-down value of the ratio (in some actual scenarios, it can also be a rounded-up value or a rounded value) can be determined as the number of sites to be deployed. The designated inter-site distance is the distance between the designated sites to be deployed, which can be the average inter-site distance (Average Inter-Site Distance) of the sites to be deployed, which is related to the application environment of the communication system to be deployed at the sites to be deployed. For example, for different urban environments and different frequency bands, the designated inter-site distance is different. By considering the distance between each two information collection points in the weak coverage cluster and the designated inter-site distance, the number of sites to be deployed that is finally determined can ensure that the service coverage of the sites to be deployed covers most or even all of the information collection points in the weak coverage cluster as much as possible, so that the sites to be deployed provide effective communication services to the terminal devices at the location of each information collection point after deployment, thereby improving the accuracy of subsequent determination of the base station location.

[0017] Optionally, the process of determining the site locations of the sites to be deployed in the weak coverage cluster using the first constant number clustering algorithm based on the number of sites to be deployed in the weak coverage cluster and the location information of the information collection point in the weak coverage cluster includes:

[0018] Based on the number of sites to be deployed and the weak coverage cluster, the first constant number clustering algorithm is used to determine the same number of sub-areas as the number of sites to be deployed in the weak coverage cluster; for each of the sub-areas, based on the location information of the information collection point in the sub-area, a site location of the site to be deployed is determined in the sub-area.

[0019] Because the divided sub-areas correspond one-to-one with the sites to be deployed, the center point of each sub-area can be determined as the location of the site to be deployed in that sub-area. This ensures that the service coverage of the site deployed at that location fully or substantially covers the corresponding sub-area, providing comprehensive and effective communication services to the terminal devices in the corresponding sub-area.

[0020] Optionally, the process of obtaining the number of target cells corresponding to the site to be deployed in the sub-area includes:

[0021] Based on the site location in the sub-area and the location information of the information collection point in the sub-area, the angle of the information collection point in the sub-area relative to the site to be deployed is determined. Based on the second constant clustering algorithm and the angle of the information collection point in the sub-area relative to the site to be deployed, m candidate cell azimuths are obtained. The m candidate cell azimuths correspond one-to-one to the m candidate cell numbers. The m candidate cell numbers are respectively a value between 1 and n, where n is a specified cell number threshold. Based on the m candidate cell azimuths, the target cell number is determined from the m candidate cell numbers.

[0022] Correspondingly, the process of obtaining the cell azimuth angle corresponding to the site to be deployed determined by the second constant clustering algorithm based on the target cell number includes: determining the cell azimuth angle corresponding to the target cell number as the cell azimuth angle corresponding to the site to be deployed.

[0023] In the above steps, after obtaining the angle of the information collection point in the sub-area relative to the site to be deployed, the analysis device sets some optional numbers of candidate cells and then obtains the corresponding azimuth angles of the candidate cells, thereby screening the m number of candidate cells based on the determined azimuth angles of the candidate cells to obtain a reasonable number of target cells.

[0024] In an optional implementation, the process of obtaining m candidate cell azimuth angles based on the second constant clustering algorithm and the angle of the information collection point in the sub-area relative to the site to be deployed includes: a process of obtaining the m candidate cell azimuth angles. The process of obtaining the azimuth angle of each candidate cell includes:

[0025] Based on the angle of the information collection point in the sub-area relative to the site to be deployed, and the first number of candidate cells, the second constant clustering algorithm is used to determine at least one subcluster in the sub-area, where the first number of candidate cells is any one of the m number of candidate cells; the angle of the center point of each subcluster relative to the site to be deployed is determined; based on the angle of the center point of each subcluster relative to the site to be deployed, the antenna main lobe direction of each cell in the sub-area is determined; based on the antenna main lobe direction of each cell and the specified main lobe angle range, the cell azimuth of each cell in the sub-area is determined.

[0026] It should be noted that in the aforementioned steps, how to select the number of m candidate cells from 1 to n determines the efficiency of determining the number of target cells. Accordingly, for different selection methods, the analysis device determines the number of target cells in the number of m candidate cells in different ways. This application example uses the following two optional methods as examples to illustrate the process of the analysis device determining the number of target cells in the number of m candidate cells:

[0027] In a first optional method, before the analysis device performs the analysis based on the m candidate cell numbers, the second constant clustering algorithm, and the angle of the information collection point in the sub-area relative to the site to be deployed, the analysis device obtains the set m candidate cell numbers, that is, the m candidate cell numbers are known before the process of obtaining the candidate cell azimuth angle is executed. For example, the m candidate cell numbers are m values ​​from 1 to n that are continuous, partially continuous, or intermittent; or, m=n, that is, 1 to n are respectively used as the m candidate cell numbers. The process of the analysis device determining the target cell number is as follows: based on the m candidate cell numbers and the cell azimuth angles of the corresponding sub-areas, m partitioning methods corresponding to the m candidate cell numbers are determined, and among the m partitioning methods, the candidate partitioning method with a cell overlap range less than a specified overlap range threshold (wherein, the non-overlapping case can be regarded as a cell overlap range of 0) is selected, and the largest candidate cell number among the candidate cell numbers corresponding to the alternative partitioning method is determined as the target cell number.

[0028] In a second optional manner, before the analysis device performs the analysis based on the m candidate cell numbers, the second constant clustering algorithm, and the angles of the information collection points in the sub-area relative to the site to be deployed, the m candidate cell numbers are unset, that is, the m candidate cell numbers are m values ​​between 1 and n, but the specific values ​​are unknown. Therefore, when executing the aforementioned process of acquiring the m candidate cell azimuth angles, the aforementioned process of acquiring the candidate cell azimuth angles can be executed in an increasing (e.g., increasing from 1 or 2) or decreasing (e.g., decreasing from n) order according to the corresponding candidate cell numbers until the cell overlap range in the partitioning scheme determined based on the current candidate cell number and the cell azimuth angles of the corresponding sub-area is less than a specified overlap range threshold, thereby meeting a cutoff condition, i.e., the aforementioned process of acquiring the candidate cell azimuth angles can be terminated. The analysis device then determines the target cell number by determining the current candidate cell number corresponding to the partitioning scheme with an overlap range less than the specified overlap range threshold (i.e., the currently determined partitioning scheme, i.e., the partitioning scheme when the cutoff condition is met) as the target cell number.

[0029] In this second optional method, the alternative cell azimuths corresponding to different numbers of alternative cells are tried one by one in an increasing or decreasing manner. After trying the appropriate alternative cell azimuth, the corresponding number of alternative cells is determined as the target cell number. Compared with the aforementioned first optional method, the number of times the alternative cell azimuth acquisition process is executed can be reduced, thereby reducing the computational cost and improving the efficiency of determining the cell azimuth corresponding to the site to be deployed.

[0030] Corresponding to the first and second optional modes, the process of determining the cell azimuth angles corresponding to the sites to be deployed using the second constant clustering algorithm based on the number of target cells can be implemented by the following two schematic implementable methods:

[0031] In the first possible implementation, the analysis device performs a cell azimuth acquisition process based on the target cell number. This cell azimuth acquisition process is identical to the candidate cell azimuth acquisition process, except that the first candidate cell number is updated to the target cell number, and the candidate cell azimuth determined based on the target cell number is used as the cell azimuth corresponding to the site to be deployed. This means that the aforementioned candidate cell azimuth acquisition process is repeated to obtain the corresponding cell azimuth.

[0032] In the second possible implementation method, since the number of target cells is selected from m candidate cell numbers, and the alternative cell azimuths corresponding to the m candidate cell numbers have been determined through the process of obtaining the alternative cell azimuths, the alternative cell azimuths corresponding to the target cell number can be directly obtained from the alternative cell azimuths corresponding to the m candidate cell numbers as the cell azimuth corresponding to the site to be deployed. The cell azimuth corresponding to the site to be deployed is the cell azimuth determined by the second constant clustering algorithm.

[0033] Optionally, after determining a site location of the site to be deployed in the sub-area, the analysis device may further obtain a cell azimuth angle corresponding to the site to be deployed.

[0034] In one optional approach, the analysis device can present a sub-area distribution map showing the distribution of each sub-area and the location of the site. Based on this map, personnel can empirically determine the number of target cells and their azimuth angles. Accordingly, the analysis device can directly receive input from personnel regarding the number of target cells and their azimuth angles. This allows for rapid acquisition of these numbers.

[0035] In another optional approach, the analysis device can automatically determine the target cell number and cell azimuth angle corresponding to the site to be deployed. For example, for each sub-area, the target cell number corresponding to the site to be deployed in that sub-area is obtained; based on this target cell number, a second constant clustering algorithm is used to determine the cell azimuth angle corresponding to the site to be deployed. Automatic determination of the target cell number and cell azimuth angle corresponding to the site to be deployed by the analysis device ensures the rationality of the set target cell number, achieves adaptability to data from different situations, improves the efficiency of determining the cell azimuth angle, and enhances the reliability of the resulting cell azimuth angle.

[0036] Optionally, the location information of the information collection point can be the grid information of the grid points converted from the longitude and latitude information of the information collection point. Since the number of grid points obtained after rasterization processing is less than or equal to the number of information collection points, and the conversion from the spherical coordinate system to the plane coordinate system is realized, the information collection point is converted into a grid point to determine the aforementioned weak coverage area, which is equivalent to reducing the number of objects to be calculated and reducing the complexity of the calculation. It can realize quantitative analysis of the data, thereby reducing the computational cost and improving the efficiency of determining the weak coverage area.

[0037] Optionally, the location information of the information collection point may also be the latitude and longitude information of the information collection point.

[0038] In a second aspect, a device for determining a site location is provided, comprising: multiple functional modules, wherein the multiple functional modules interact with each other to implement the method of the first aspect and its respective embodiments. The multiple functional modules can be implemented using software, hardware, or a combination of software and hardware, and the multiple functional modules can be arbitrarily combined or divided based on the specific implementation.

[0039] In a third aspect, a device for determining a site location is provided, comprising: a processor and a memory;

[0040] The memory is used to store a computer program, wherein the computer program includes program instructions;

[0041] The processor is used to call the computer program to implement the method for determining the site location as described in any one of the first aspects.

[0042] In a fourth aspect, a computer storage medium is provided, on which instructions are stored. When the instructions are executed by a processor, the method for determining the site location as described in any one of the first aspects is implemented.

[0043] In a fifth aspect, a communication system is provided, comprising: a plurality of information collection points, a plurality of sites, a management device and an analysis device,

[0044] The management device is used to manage the multiple sites;

[0045] The site is used to transmit data between the management device and the information collection point;

[0046] The analysis device includes the site location determination device described in any one of the second aspect or the third aspect.

[0047] In a sixth aspect, a chip is provided, which includes a programmable logic circuit and / or program instructions. When the chip is running, it implements the method for determining the site location as described in any one of the first aspects.

[0048] In a seventh aspect, a computer program product is provided, wherein instructions are stored in the computer program product. When the instructions are executed on a computer, the computer is caused to execute the method for determining the site location as described in any one of the first aspects.

[0049] The beneficial effects of the technical solutions provided in the embodiments of the present application include at least:

[0050] To sum up, the method for determining the site location provided in the embodiment of the present application is that the analysis device automatically adopts the first constant clustering algorithm to determine the number of sites to be deployed in each weak coverage cluster, without the need to manually set the number of sites to be deployed, which can improve the efficiency of determining the site location and improve the accuracy and reliability of the final site location.

[0051] Furthermore, compared with the manual white-box algorithm, the present application adopts a non-indeterminate clustering algorithm to determine the at least one weak coverage cluster, which reduces the complexity of the station deployment task and improves the automation capability of the station deployment work, so that the station deployment work in the communication system can cope with larger data volumes and more complex data scenarios.

[0052] Furthermore, using the aforementioned first constant clustering algorithm to determine site locations improves the efficiency of the site determination process while ensuring the correlation between sites and weak coverage areas. This also makes the process more adaptable. Using the second constant clustering algorithm to determine cell azimuth angles improves the efficiency of determining cell azimuth angles and increases the reliability of the resulting cell azimuth angles.

[0053] When the analysis device obtains at least one weak coverage cluster, it automatically filters out abnormal or isolated data (i.e., data that does not meet the conditions), saving the manual operation of screening abnormal data points (such as abnormal information collection points or abnormal grid points), effectively improving computing efficiency, and achieving a higher level of automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of an application scenario involved in a method for determining a site location provided in an embodiment of the present application;

[0055] Figure 2 This is a schematic diagram of another application scenario involved in a method for determining a site location provided in an embodiment of the present application;

[0056] Figure 3 This is a flow chart of a method for determining a site location provided in an embodiment of the present application;

[0057] Figure 4 A schematic diagram of a coordinate system conversion process provided in an embodiment of the present application;

[0058] Figure 5A schematic grid diagram provided for an embodiment of the present application;

[0059] Figure 6 It will Figure 5 A schematic diagram of an area where grid points are located after filtering out grid points whose second communication index is not greater than the second communication index threshold;

[0060] Figure 7 Another schematic grid diagram provided for an embodiment of the present application;

[0061] Figure 8 Another schematic grid diagram provided in an embodiment of the present application;

[0062] Figure 9 This is another schematic grid diagram provided in an embodiment of the present application;

[0063] Figure 10 A schematic antenna pattern provided in an embodiment of the present application;

[0064] Figure 11 yes Figure 9 A schematic diagram of the cell azimuth angles corresponding to the sites to be deployed in the sub-area shown;

[0065] Figure 12 is a block diagram of a device for determining a site location provided in an exemplary embodiment of the present application;

[0066] Figure 13 is a block diagram of a first determination module provided in an exemplary embodiment of the present application;

[0067] Figure 14 is a block diagram of another device for determining a site location provided by an illustrative embodiment of the present application;

[0068] Figure 15 This is a block diagram of another device for determining a site location provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0070] To facilitate readers' understanding, the embodiment of the present application briefly introduces the clustering algorithm involved in the method for determining the site location provided.

[0071] In the field of machine learning, clustering algorithms are a type of unsupervised learning algorithm. They are used to classify a collection of data into subsets (also known as classes, clusters, or regions) by identifying relationships between the data. Clustering algorithms can be categorized as either fixed-number or non-fixed-number clustering algorithms, depending on whether the algorithm requires input for the number of subsets.

[0072] Among them, the fixed number clustering algorithm means that before clustering, the number of subsets obtained by the final classification needs to be defined and input as a parameter into the clustering process. The algorithm will divide a set of multiple data into a predetermined number of subsets, that is, the number of subsets input above. In the embodiment of the present application, the fixed number clustering algorithm can be a fixed number clustering algorithm such as the k-means clustering algorithm (k-means) or the clustering algorithm based on random selection (CLARANS).

[0073] A non-definite number clustering algorithm refers to one that does not require the input of the number of subsets before clustering. The algorithm will infer the corresponding number of subsets based on its own principles and the data situation. In the embodiment of the present application, the definite number clustering algorithm can be a non-definite number clustering algorithm such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH).

[0074] Please refer to Figure 1 , Figure 1 This is a schematic diagram of an application scenario involved in the method for determining the site location provided in the embodiment of this application. Figure 1 As shown, the application scenario includes an analysis device 101 , a management device 102 , a site 103 and an information collection point 104 . Figure 1 The number of analysis devices, management devices, sites, and information collection points is for illustration only and does not limit the application scenarios involved in the method for determining site locations provided in the embodiments of the present application. The network involved in the application scenario may be a second-generation (2G) communication system, a third-generation (3G) communication system, a long-term evolution (LTE) communication system, or a fifth-generation (5G) communication system.

[0075] The analysis device 101 and management device 102 can be deployed on the same device or on different devices. For example, when the analysis device 101 and management device 102 are deployed on different devices, the analysis device 101 can be a computer, a server, a server cluster consisting of several servers, or a cloud computing service center. The analysis device 101 is deployed with intelligent site placement software or plug-ins for determining site locations. The management device 102 can be a computer, a server, a server cluster consisting of several servers, or a cloud computing service center. The management device 102 can be an operations support system (OSS) or other device connected to the analysis device. The management device 102 is deployed with a software management system. The analysis device 101 and management device 102 are connected via a wired or wireless network. Site 103 can be a device that provides network information to information collection points, such as a router, switch, or base station. The base station can be a traditional base station (Node B), an evolved base station (eVolution Node B, eNB), or a 5G base station. The information collection point 104 can be a device with a network connection function (such as the function of accessing a wireless network), such as a smart phone, Internet of Things (IoT), desktop computer, laptop computer, tablet computer, multimedia player, e-reader or wearable device, etc.

[0076] The information collection point 104 is used to upload the collected communication indicator data to the management device 102 through the site 103. The analysis device 101 is used to obtain and analyze the communication indicator data from the management device 102 to determine the site location. This embodiment of the application is only a schematic illustration of the source of the communication indicator data, but is not limited thereto.

[0077] Optionally, in Figure 1 Building on the illustrated scenario, this application scenario may also include a storage device 105 for storing data provided by information collection point 104. This storage device 105 may be a distributed storage device, and analysis device 101 may read and write data stored in this storage device 105. In this way, when information collection point 104 has a large amount of data, data storage in storage device 105 can reduce the load on analysis device 101 and improve its data analysis efficiency. It should be noted that when the amount of data provided by information collection point 104 is small, storage device 105 may not be required.

[0078] The present application embodiment provides a method for determining a site location, which can be performed by the aforementioned Figure 1 or Figure 2The analysis equipment in the implementation, such as Figure 3 As shown, the method includes:

[0079] Step 301: An analysis device obtains communication indicator data collected by multiple information collection points in a communication system.

[0080] Communication indicator data is data used to measure communication quality, and the communication quality of the location where the information collection point that collects the data is located can be determined based on the communication indicator data. The communication indicator data includes one or more service data in the communication system. For example, user downlink throughput, transmission rate, Reference Signal Receiving Power (RSRP) and / or Signal to Interference plus Noise Ratio (SINR). When the terminal equipment in the communication system has higher requirements for communication quality and needs to add new sites, the analysis device can obtain the communication indicator data collected by multiple information collection points in the communication system, thereby determining the location of the site to be deployed (this location is also called the site addition location) based on the obtained communication indicator data.

[0081] The analysis device can obtain communication indicator data in a variety of ways. In one optional way, the analysis device can directly receive the communication indicator data input by the staff, and the communication indicator data is collected by multiple information collection points and output to the staff through a designated device (such as the aforementioned management device); in another optional way, such as Figure 1 or Figure 2 As shown, the analysis device can obtain the communication indicator data collected by multiple information collection points from the management device. For example, after receiving the trigger instruction input by the staff, the management device can issue a data collection command, or periodically issue a data collection command, which is forwarded by the site. After each information collection point receives it, it will report the business indicator data to the management device through the site, so that the analysis device can extract the business indicator data, or the management device can send the business indicator data to the analysis device. It is worth noting that the analysis device can also obtain the communication indicator data in other ways. For example, after the information collection point collects the communication indicator data, the communication indicator data is directly sent to the analysis device. The embodiment of the present application does not limit the way in which the analysis device obtains the communication indicator data.

[0082] Step 302: The analysis device determines a weak coverage area in the communication system based on the communication indicator data collected by multiple information collection points.

[0083] A weak coverage area refers to an area with weak communication service coverage. In this area, the communication service provided by the site is poor, resulting in poor communication quality of the terminal device. There are many ways to obtain a weak coverage area. The following examples are used as examples to illustrate:

[0084] In the first optional manner, for each information collection point, the analysis device can determine a first communication index value based on the communication index data collected by the information collection point, and compare the first communication index value with the first communication index threshold. When the first communication index value is less than the first communication index threshold, it indicates that the communication quality of the corresponding information collection point is poor, and the analysis device determines that the information collection point is located in a weak coverage area; when the first communication index value is not less than the first communication index threshold, it indicates that the communication quality of the corresponding information collection point is good, and the analysis device determines that the information collection point is not located in a weak coverage area. The weak coverage area finally obtained includes one or more information collection points whose first communication index values ​​are less than the first communication index threshold, that is, the weak coverage area is a set of information collection points. In this manner, each information collection point can be represented by the location information of the information collection point, and the location information of the information collection point can be the latitude and longitude information (i.e., longitude and latitude) of the information collection point. The latitude and longitude information of each information collection point can correspond one-to-one to each communication index data collected by the information collection point. For example, the corresponding longitude and latitude information and communication indicator data can be reported to the management device via the site through the same message by the information collection point and recorded by the management device. Alternatively, each communication indicator data item can carry the corresponding longitude and latitude information, which acts as a tag for the corresponding communication indicator data. After obtaining multiple communication indicator data items, the analysis device calibrates the corresponding information collection point according to the location information corresponding to the communication indicator data item.

[0085] Optionally, the first communication indicator value may be the value of a service data item in the communication indicator data, such as downlink throughput, transmission rate, RSRP, or SINR. Alternatively, the first communication indicator value may be a value determined based on the values ​​of multiple service data items in the communication indicator data, such as weighted values ​​of the multiple service data items, such as weighted values ​​of RSRP and SINR. In this way, the analysis device can support determining communication indicator values ​​based on different service data items, thereby achieving greater algorithm applicability during site deployment.

[0086] In the second optional manner, the analysis device can obtain at least one grid point and determine the weak coverage area in the communication system based on the communication indicator data corresponding to the at least one grid point. The at least one grid point corresponds to multiple information collection points in the communication system. In this manner, the information collection point is converted into a grid point, and each information collection point can be represented by the location information of the information collection point. The location information of the information collection point can be the grid information of the grid point converted from the longitude and latitude information of the information collection point (also called grid horizontal and vertical coordinate information, including grid horizontal coordinate and grid vertical coordinate). The method for obtaining the longitude and latitude information of the information collection point can refer to the first optional manner mentioned above, and this application will not elaborate on this.

[0087] There are many ways for the analysis device to obtain at least one grid point. In an optional example, the analysis device can perform grid processing on the multiple information collection points to obtain grid points corresponding to the multiple information collection points.

[0088] Among them, the process of rasterization is essentially the process of coordinate system conversion, that is, converting the geographic coordinate system (Geographic Coordinate System) where the information collection point is located into a grid coordinate system (Grid Coordinate System). The geographic coordinate system is a spherical coordinate system, which uses a three-dimensional sphere to define the position on the earth; the grid coordinate system is usually a plane coordinate system, which is used to define the position of the grid on the earth's spherical surface. The conversion process of the aforementioned coordinate system is essentially the process of converting the longitude and latitude information on the earth's surface to the longitude and latitude information on the plane. For example, for a certain data collection point, the data collection point in the geographic coordinate system and the corresponding grid point in the grid coordinate system satisfy the following grid conversion formula:

[0089]

[0090]

[0091] Where Lo represents the longitude of a data collection point in the geographic coordinate system, gx represents the grid longitude corresponding to the longitude Lo of the data collection point in the grid coordinate system, La represents the latitude of a data collection point in the geographic coordinate system, gy represents the grid latitude corresponding to the latitude La of the data collection point in the grid coordinate system, and gr represents the grid resolution, which is a specified value. The grid resolution represents the number of angles corresponding to a grid. The greater the grid resolution, the larger the area corresponding to the converted grid in the geographic coordinate system. Floor indicates rounding down. s is a specified value, s≥0, which is used to retain the number of decimal places after the longitude Lo and latitude La during the grid conversion process. The larger s is, the more digits are retained. For example, s=5 means that when using the grid conversion formula to calculate the abscissa and ordinate of the grid point, 5 decimal places after the longitude Lo and latitude La are retained.

[0092] It is worth noting that the above-mentioned grid conversion formula is only a schematic formula of the embodiment of the present application. The analysis device can also use other grid conversion formulas to determine the horizontal and vertical coordinates of the grid points. The embodiment of the present application does not limit this.

[0093] like Figure 4 As shown, Figure 4 A schematic diagram of a coordinate system conversion process provided in an embodiment of the present application is provided. Figure 4 In the figure, the left side is a schematic geographic coordinate system, and the right side is a schematic grid coordinate system. Each grid point obtained by the coordinate system conversion process corresponds to one or more data collection points (it can also be considered that one or more data collection points can be represented by one grid point). Figure 4 In this example, an area W (the shaded area in the figure) in a geographic coordinate system includes two data collection points (represented by dots in the geographic coordinate system). After coordinate system transformation, this area W is converted into a grid point w (represented by a dot in the grid coordinate system). This grid point w then corresponds to the two data collection points. Accordingly, the communication indicator data corresponding to this grid point can be determined based on the communication indicator data of the data collection point corresponding to this grid point. For example, the communication indicator data corresponding to this grid point can be the average, maximum, minimum, or weighted average of the communication indicator data of the data collection point corresponding to this grid point.

[0094] For example, assuming that the communication indicator data is RSRP, the RSRP corresponding to the grid point may be the average value of the RSRPs of the data collection points corresponding to the grid point. Then, the RSRP corresponding to any grid point satisfies the following RSRP calculation formula:

[0095]

[0096] Wherein, grid_RSRP represents the RSRP corresponding to any grid point, gN represents the number of RSRPs collected by the data collection points corresponding to any grid point, Represents the sum of RSRPs collected by the data collection points corresponding to any grid point.

[0097] In another optional example, the analysis device may directly receive grid information (i.e., horizontal and vertical coordinate information of the grid point) of at least one grid point input by a staff member or a designated device. For example, the designated device is the aforementioned management device having the aforementioned coordinate system conversion function.

[0098] For example, the analysis device can use the following process to determine the weak coverage area in the communication system based on the communication index data corresponding to at least one grid point obtained: for each grid point, the analysis device can determine the second communication index value based on the communication index data corresponding to the grid point, and compare the second communication index value with the second communication index threshold. When the second communication index value is less than the second communication index threshold, it means that the communication quality of the information collection point corresponding to the grid point is poor, and the analysis device determines that the grid point is located in the weak coverage area; when the second communication index value is not less than the second communication index threshold, it means that the communication quality of the information collection point corresponding to the grid point is good, and the analysis device determines that the grid point is not located in the weak coverage area. The weak coverage area finally obtained includes one or more grid points whose second communication index values ​​are less than the second communication index threshold, that is, the weak coverage area is a collection of grid points. Among them, the method for determining the second communication index value can refer to the method for determining the first communication index value mentioned above, and the first communication index threshold can be the same as or different from the second communication index threshold. The embodiment of the present application does not limit this.

[0099] For example, assuming that the second communication indicator value is grid_RSRP and the second communication indicator threshold is RSRP_Threhold, the analysis device can traverse all grid points and determine the area where the grid points with grid_RSRP less than RSRP_Threhold belong as a weak coverage area. The weak coverage area satisfies the following formula:

[0100] {grid optimize} = {grid|grid_RSRP <RSRP_Threhold}

[0101] Among them, {grid optimize} represents a weak coverage area, which is a set of grid points (grid) where grid_RSRP is less than RSRP_Threhold.

[0102] Figure 5A schematic grid diagram is provided in an embodiment of the present application, which describes the distribution of grid points obtained by converting the aforementioned multiple data collection points in a grid coordinate system. Figure 5 The horizontal axis represents the horizontal axis of the grid coordinate system, and the vertical axis represents the vertical axis of the grid coordinate system. Each dot represents a grid point. Different depths of the dots represent different values ​​of the second communication index. Figure 5 Assuming that the darker the color of the dot, the smaller the second communication index value, then Figure 5 In the case of the grid point distribution of the grid diagram shown in FIG, the weak coverage area determined by the aforementioned second communication index threshold is as follows Figure 6 As shown, that is Figure 6 It will Figure 5 A schematic diagram of an area where grid points are located after filtering out grid points whose second communication index is not greater than the second communication index threshold.

[0103] Since the number of grid points obtained after rasterization processing is less than or equal to the number of information collection points, and the conversion from spherical coordinate system to plane coordinate system is realized, therefore, by converting the information collection points into grid points to determine the aforementioned weak coverage area, it is equivalent to reducing the number of objects to be calculated and reducing the complexity of the calculation. It can realize quantitative analysis of the data, thereby reducing the computational cost and improving the efficiency of determining the weak coverage area.

[0104] It is worth noting that the aforementioned steps 301 and 302 are merely an example of obtaining a weak coverage area in a communication system. The analysis device may also obtain the weak coverage area in the communication system through other means. For example, the analysis device may receive information about the weak coverage area in the communication system input by a staff member or transmitted by other devices. The weak coverage area is determined based on communication indicator data collected by multiple information collection points in the communication system.

[0105] Step 303: The analysis device obtains at least one weak coverage cluster determined based on the weak coverage area.

[0106] Optionally, the analysis device may use a non-definite number clustering algorithm to determine at least one weak coverage cluster based on the location information of the information collection points in the weak coverage area. As mentioned above, the non-definite number clustering algorithm may be a DBSCAN algorithm or a BIRCH algorithm. The analysis device automatically divides the weak coverage area into at least one weak coverage cluster, and there is no need to manually divide the weak coverage area, which can improve the efficiency of determining the site location and improve the reliability of the site location finally obtained. Moreover, since the analysis device automatically uses a non-definite number clustering algorithm to determine at least one weak coverage cluster, it can be applicable to data sources of different sizes (i.e., location information of different numbers of information collection points), which expands the scale of the station layout problem that can be solved and improves the flexibility and solution efficiency of station layout planning.

[0107] Optionally, the process of determining the at least one weak coverage cluster includes:

[0108] Step A1: The analysis device divides the weak coverage area into multiple candidate weak coverage clusters using a non-constant number clustering algorithm based on the location information of the information collection points in the weak coverage area.

[0109] For example, after obtaining the weak coverage area, based on the location information of all information collection points, the non-definite number clustering algorithm is called to cluster the weak coverage area to obtain multiple candidate weak coverage clusters. The multiple candidate weak coverage clusters are equivalent to the subsets obtained by the aforementioned non-definite number clustering algorithm. Since the non-definite number clustering algorithm does not need to specify the number of subsets (i.e., the number of clusters in step A1), when the non-definite number clustering algorithm is mobilized, the location information of all information collection points in the weak coverage area is input into the model of the non-definite number clustering algorithm, and the clustering result can be output by the model of the non-definite number clustering algorithm.

[0110] As previously mentioned, the location information of the information collection point can be the latitude and longitude information of the information collection point; alternatively, the location information of the information collection point can be the grid information of the grid points converted from the latitude and longitude information of the information collection point. Through rasterization processing, one grid point can correspond to multiple information collection points, which can reduce computational complexity and improve the efficiency of determining the site location.

[0111] Step A2: The analyzing device determines, among the multiple candidate weak coverage clusters, a candidate weak coverage cluster whose number of information collection points is greater than a specified collection point number threshold as at least one weak coverage cluster.

[0112] Since there may be some discrete points among the information collection points, the number of information collection points included in the candidate weak coverage cluster formed with other information collection points is too small. If sites are subsequently deployed for the candidate weak coverage cluster containing fewer information collection points, it is easy to increase the network layout cost and also affect the site layout efficiency of the entire communication system. Therefore, the candidate weak coverage cluster containing fewer information collection points can be eliminated (also called filtering). Therefore, the analysis device can determine the candidate weak coverage cluster whose number of information collection points in multiple candidate weak coverage clusters is greater than the specified collection point number threshold as at least one weak coverage cluster. Similarly, if the information collection points are converted into grid points, the candidate weak coverage cluster whose number of grid points is greater than the specified collection point number threshold can be eliminated. Then the aforementioned determination of the candidate weak coverage cluster whose number of information collection points in multiple candidate weak coverage clusters is greater than the specified collection point number threshold as at least one weak coverage cluster is equivalent to determining the candidate weak coverage cluster whose number of grid points in multiple candidate weak coverage clusters is greater than the specified grid point number threshold as at least one weak coverage cluster. By eliminating candidate weak coverage clusters that do not meet the conditions (i.e., the number of information collection points is not greater than the specified collection point number threshold or the number of grid points is not greater than the specified grid point number threshold), the computational complexity can be reduced and the efficiency of determining site locations can be improved.

[0113] It is worth noting that the specific implementation of the aforementioned step A1 and step A2 may vary based on the different models of different non-deterministic clustering algorithms.

[0114] In an optional manner, the non-determined clustering algorithm does not support the elimination of discrete points. Then, in step A1, the analysis device may input the location information of the information collection points in the weak coverage area (such as the latitude and longitude information of the information collection points, or the grid information of the grid points) into the model of the non-determined clustering algorithm, and determine multiple candidate weak coverage clusters based on the classification results output by the model of the non-determined clustering algorithm; in step A2, the analysis device eliminates the candidate weak coverage clusters whose number of information collection points is not greater than a specified collection point number threshold (or the number of grid points is not greater than a specified grid point number threshold) among the multiple candidate weak coverage clusters, thereby obtaining at least one weak coverage cluster.

[0115] Assuming that the information collection points are converted into grid points to determine the weak coverage clusters, then Figure 7 and Figure 8 As shown, the alternative weak coverage clusters determined by step A1 are weak coverage clusters 1 to 5, and the weak coverage clusters obtained by eliminating the alternative weak coverage clusters whose number of grid points is not greater than the specified grid point number threshold are weak coverage cluster 1 and weak coverage cluster 5. Then, at least one weak coverage cluster finally obtained by clustering is weak coverage cluster 1 and weak coverage cluster 5.

[0116] In step A1, taking the non-definite number clustering algorithm as the DBSCAN algorithm and the location information of the information collection point as the grid information of the grid point as an example, the model of the DBSCAN algorithm can be expressed by the following first clustering formula:

[0117] cluster grid =DBSCAN(X grid ,Y grid );

[0118] Among them, X grid Indicates the horizontal coordinate of a grid point input, Y grid Indicates the grid ordinate of a certain grid point input, cluster grid Indicates the cluster number of the output grid point. The DBSCAN algorithm model can assign a cluster number to each grid point of the input location information. Grid points with the same cluster number belong to the same candidate weak coverage cluster.

[0119] In another optional manner, the non-definite number clustering algorithm supports the elimination of discrete points. Then, in step A1, the analysis device can input the location information of the information collection points in the weak coverage area (such as the latitude and longitude information of the information collection points, or the grid information of the grid points) into the model of the non-definite number clustering algorithm, and the model of the non-definite number clustering algorithm determines multiple candidate weak coverage clusters; in step A2, the model of the non-definite number clustering algorithm eliminates the candidate weak coverage clusters whose number of information collection points is not greater than the specified collection point number threshold (or the number of grid points is not greater than the specified grid point number threshold) in the multiple candidate weak coverage clusters, and outputs the classification result. The analysis device determines at least one weak coverage cluster based on the classification result output by the model of the non-definite number clustering algorithm.

[0120] In step A1, taking the non-definite number clustering algorithm as the DBSCAN algorithm and the location information of the information collection point as the grid information of the grid point as an example, the model of the DBSCAN algorithm can be expressed by the following second clustering formula:

[0121] cluster grid =DBSCAN(X grid ,Y grid ), N_cluster grid ≥N;

[0122] Among them, X grid Indicates the horizontal coordinate of a grid point input, Y grid Indicates the grid ordinate of a certain grid point input, cluster grid Indicates the cluster number of the output grid point, N_cluster grid Indicates the number of grid points included in the cluster indicated by the cluster number of a grid point, where N is the specified grid point number threshold. After receiving the grid information of the input grid points, the model of the DBSCAN algorithm clusters the grid points and assigns cluster numbers to clusters whose number of grid points exceeds the specified grid point number threshold. Accordingly, the corresponding cluster number is output for the grid points in the assigned clusters. Clusters whose number of grid points does not exceed the specified grid point number threshold are not assigned cluster numbers. Accordingly, the grid points in the clusters not assigned cluster numbers do not output cluster numbers, or output information indicating that they cannot be clustered. Ultimately, grid points with the same cluster number belong to the same weak coverage cluster, and grid points without cluster numbers are filtered out.

[0123] In the above two optional methods, when the location information of the information collection point is the latitude and longitude information of the information collection point, the non-deterministic clustering algorithm can also adopt the above-mentioned DBSCAN algorithm, and the weak coverage cluster determination process can also refer to the weak coverage cluster determination process when the location information of the above-mentioned information collection point is the grid information of the grid point. The embodiment of this application will not go into details about this.

[0124] Step 304: For each weak coverage cluster, the analysis device uses a first constant number clustering algorithm to determine the site locations of the sites to be deployed in the weak coverage cluster.

[0125] The first non-definite number clustering algorithm may be a k-means algorithm or a CLARANS algorithm. The process of using the first definite number clustering algorithm to determine the site locations of the sites to be deployed in the weak coverage cluster may include:

[0126] Step B1: The analysis device determines the number of sites to be deployed in the weak coverage cluster.

[0127] In an optional implementation, the analysis device may present a distribution diagram of the weak coverage clusters, where the distribution diagram shows the distribution of each weak coverage cluster, which may be as follows: Figure 8 The grid coordinate system shown in the figure allows staff to empirically determine the number of sites to be deployed based on this distribution map. Accordingly, the analysis device directly receives the number of sites to be deployed input by the staff, i.e., this number is a pre-set value. In another optional implementation, the analysis device can determine the number of sites to be deployed in weak coverage areas. Automatically determining the number of sites to be deployed in each weak coverage area by the analysis device can improve the efficiency of site location determination and enhance the reliability of the resulting site locations.

[0128] For example, the process of determining the number of sites to be deployed in a weak coverage cluster by an analysis device includes: obtaining the maximum distance between each two information collection points in the weak coverage cluster by the analysis device; obtaining a first ratio of the maximum distance between each two information collection points to the specified inter-site distance; and determining the number of sites to be deployed based on the obtained first ratio. When the first ratio is an integer, the first ratio can be directly determined as the number of sites to be deployed; when the first ratio is not an integer, the rounded-down value of the first ratio (in some actual scenarios, it can also be a rounded-up value or a rounded-up value) can be determined as the number of sites to be deployed. The specified inter-site distance is the distance between the specified sites to be deployed, which can be the average inter-site distance (Average Inter-Site Distance) of the sites to be deployed, which is related to the application environment of the communication system to be deployed at the sites to be deployed. For example, for different urban environments and different frequency bands, the specified inter-site distance is different. By considering the distance between each two information collection points in the weak coverage cluster and the designated station spacing, the number of sites to be deployed can be ultimately determined to ensure that the service coverage of the sites to be deployed covers as many or even all of the information collection points in the weak coverage cluster as possible, so that after deployment, the sites to be deployed can provide effective communication services to the terminal devices at the location of each information collection point, thereby improving the accuracy of subsequent determination of the base station location.

[0129] The analysis device may first determine the distance between each two information collection points in the weak coverage cluster and obtain the maximum distance among the determined distances. Then, a first ratio calculation formula is used to determine the ratio of the maximum distance to the designated station spacing. The first ratio determination formula is as follows:

[0130]

[0131] Wherein, B1 represents the first ratio, P represents the information collection point, Indicates that x1 and y1 belong to the information collection point, distance(x1, y1) indicates the distance between the information collection points x1 and y1, and Av1 indicates the distance between the specified stations.

[0132] Similarly, if the information collection points are converted into grid points, the aforementioned analysis device obtains the maximum distance in the distance between every two information collection points in the weak coverage cluster, which can be replaced by: the analysis device obtains the maximum distance in the distance between every two grid points in the weak coverage cluster; the aforementioned ratio of the maximum distance in the distance between every two information collection points to the specified station spacing can be replaced by: obtaining the second ratio of the maximum distance in the distance between every two grid points to the target station spacing, and the target station spacing is determined based on the aforementioned specified station spacing, for example, the target station spacing is the specified station spacing after rasterization processing.

[0133] The analysis device can first determine the distance between each two grid points in the weak coverage cluster and obtain the maximum distance among the determined distances. Then, a second ratio calculation formula is used to determine the ratio of the maximum distance to the designated station spacing. The second ratio determination formula is as follows:

[0134]

[0135] Among them, B2 represents the second ratio, grid represents the grid point, Indicates that x2 and y2 belong to the grid points, distance(x2, y2) means obtaining the distance between the grid points x2 and y2, and Av2 represents the target station distance.

[0136] Among them, the first ratio and the second ratio calculated using the aforementioned first ratio determination formula and the second ratio determination formula are the same or similar, and the number of sites to be deployed finally determined is usually the same, or the difference is within an acceptable range.

[0137] Step B2: The analysis device uses a first constant number clustering algorithm to determine the site locations of the sites to be deployed in the weak coverage cluster based on the number of sites to be deployed in the weak coverage cluster and the location information of the information collection points in the weak coverage cluster.

[0138] Optionally, the process of using the first constant clustering algorithm to determine the site location of the site to be deployed in the weak coverage cluster includes:

[0139] Step B21: The analysis device uses a first constant clustering algorithm based on the number of sites to be deployed and the weak coverage clusters to determine the same number of sub-areas as the number of sites to be deployed in the weak coverage clusters. That is, the number of sub-areas in each weak coverage cluster is equal to the number of sites to be deployed in the weak coverage cluster.

[0140] For example, after obtaining the weak coverage cluster, the analysis device calls the first constant number clustering algorithm based on the location information of all information collection points, and the weak coverage cluster can be partitioned (that is, clustered or classified) to obtain multiple sub-areas. The multiple sub-areas are equivalent to the subsets obtained by the aforementioned constant number clustering algorithm. Since the constant number clustering algorithm needs to specify the number of subsets, that is, the number of sites to be deployed in step B21 (that is, the number of partitions when partitioning), when mobilizing the first constant number clustering algorithm, the location information of all information collection points in the weak coverage cluster and the number of sites to be deployed are input into the model of the first constant number clustering algorithm, and the partitioning result can be output by the model of the first constant number clustering algorithm.

[0141] In step B21, taking the first constant clustering algorithm as the k-means algorithm and the location information of the information collection point as the grid information of the grid point as an example, the model of the k-means algorithm can be expressed by the following partition formula:

[0142] cluster grid =Kmeans(k=Num site ,X grid , Y grid );

[0143] Among them, X grid Indicates the horizontal coordinate of a grid point input, Y grid Indicates the grid ordinate of a certain grid point input, cluster grid Indicates the output area code (also called cluster number) of the grid point. That is, the k-means algorithm model can assign an area code to each grid point of the input location information, and grid points with the same area code belong to the same sub-area.

[0144] like Figure 8 and Figure 9 As shown, in step B21, the weak coverage cluster 1 is partitioned into sub-areas 11 and 12, and the weak coverage cluster 5 is partitioned into sub-areas 51 and 52. Thus, the original two weak coverage clusters are further divided into four sub-areas.

[0145] Step B22: For each sub-area, the analysis device determines a site location of a site to be deployed in the sub-area based on the location information of the information collection points in the sub-area.

[0146] As can be seen from step B1 above, the number of sites to be deployed in the weak coverage cluster determined by the analysis device is related to the designated inter-station spacing. If the number of sites to be deployed is determined using the first or second ratio determination formulas above, each sub-area is divided based on the service coverage of a site to be deployed. One site to be deployed can be deployed in each of these sub-areas. In other words, there is a one-to-one correspondence between the sub-areas and the sites to be deployed.

[0147] Because the divided sub-areas correspond one-to-one with the sites to be deployed, the center point of each sub-area can be determined as the location of the site to be deployed in that sub-area. This ensures that the service coverage of the site deployed at that location fully or substantially covers the corresponding sub-area, providing comprehensive and effective communication services to the terminal devices in the corresponding sub-area.

[0148] In an optional manner, when the location information of the information collection point is the latitude and longitude information of the information collection point, the center point of each sub-area can be determined by the following first center point determination formula:

[0149]

[0150] Among them, lo represents longitude, la represents latitude, represents the sum of the longitude and latitude of all information collection points in any sub-region, P_num represents the total number of information collection points in any sub-region, and center(lo, la) represents the center point of any sub-region.

[0151] In another optional manner, when the position information of the information collection point is grid information of grid points, for any sub-area, the center point of the sub-area can be determined by the following second center point determination formula:

[0152]

[0153] Among them, X grid Indicates the horizontal coordinate of the grid, Y grid Represents the grid vertical coordinate, represents the sum of the horizontal and vertical coordinates of all grid points in any sub-region, grid_num represents the total number of grid points in any sub-region, center(X grid , Y grid ) represents the center point of any sub-region.

[0154] like Figure 9As shown, through step B22, the site location P1 to be deployed can be determined in sub-area 11, the site location P2 to be deployed can be determined in sub-area 12, the site location P3 to be deployed can be determined in sub-area 51, and the site location P4 to be deployed can be determined in sub-area 52.

[0155] It is worth noting that, in actual implementation, other points in the sub-area can also be determined as the location of the site to be deployed in the sub-area based on the specific shape of the sub-area. The embodiment of the present application does not limit the location of the site to be deployed in the sub-area. In addition, the aforementioned embodiment is only described by taking the one-to-one correspondence between the divided sub-areas and the sites to be deployed as an example. In actual implementation, each sub-area and the site to be deployed can also have other corresponding relationships, such as a one-to-many or many-to-one correspondence relationship, which is not limited in the embodiment of the present application.

[0156] This application introduces a first constant clustering algorithm when determining the site location. The site location determination process has higher adaptability and can be applied to different data distributions and different topological structures of information collection points in the communication system.

[0157] Step 305: The analysis device obtains the cell azimuth angle corresponding to the site to be deployed.

[0158] In a communication system, the service area of ​​a site can be divided into multiple cells. A cell is an area corresponding to all or part of a site (such as a base station). The cell can be an area covered by an omnidirectional antenna or a sector antenna. In an embodiment of the present application, the cells and antennas of the site correspond one to one, that is, the area covered by each antenna is a cell. The antenna has a main lobe, which is the maximum radiation beam located on the antenna pattern and is the main coverage area of ​​the antenna energy. The antenna pattern refers to a graph in which the relative field strength of the radiation field changes with direction at a certain distance from the antenna, and is usually represented by two mutually perpendicular plane patterns in the direction of maximum radiation of the antenna. When deploying the antennas of the site, it is necessary to determine the number of target cells and the cell azimuth. The target cell number refers to the number of cells that need to be deployed at each site, which is equal to the number of antennas that need to be deployed. The cell azimuth corresponds to the antenna azimuth. The antenna azimuth refers to the angle experienced by rotating clockwise from the plane of the reference direction (such as the north direction) to coincide with the plane where the antenna is located. For example, if a site corresponds to three cells, the antenna azimuth angles are usually 0 degrees, 120 degrees, and 240 degrees. In the embodiment of the present application, the antenna azimuth angle corresponding to each cell is the angle between a ray and a reference direction, where the ray is the center line of the cell azimuth angle, and the angle of the cell azimuth angle is equal to the main lobe angle range of the antenna corresponding to the cell. The main lobe angle range refers to the opening angle of the main lobe coverage range on the antenna pattern (also called the horizontal azimuth opening angle).

[0159] like Figure 10 As shown, Figure 10 An exemplary antenna pattern is provided in an embodiment of the present application, which describes the relationship between the antenna azimuth angle and the cell azimuth angle. Figure 10 Assuming that the north direction is the reference direction, the antenna azimuth angle corresponding to the main lobe R of the antenna is 0°, and the coverage range of the main lobe is 60°, then the cell azimuth angle α is 60°, and the center line of the cell azimuth angle is the 0° line, that is, the direction of the cell azimuth angle is due north, that is, the 0° direction.

[0160] There are many ways for the analysis device to obtain the cell azimuth angle corresponding to the site to be deployed. In one optional way, the analysis device can present a distribution map of the sub-areas, which shows the distribution of each sub-area and the site location, which can be as follows: Figure 9 The grid coordinate system shown in the figure allows personnel to determine the target cell number and cell azimuth based on experience based on this distribution map. Accordingly, the analysis device can directly receive the target cell number and cell azimuth input from the personnel. This allows for rapid acquisition of the target cell number and cell azimuth.

[0161] In another optional manner, the analysis device can automatically determine the target cell number and cell azimuth corresponding to the site to be deployed. For example, for each sub-area, the analysis device obtains the target cell number corresponding to the site to be deployed in the sub-area, and based on the target cell number, uses a second constant clustering algorithm to determine the cell azimuth corresponding to the site to be deployed. The second constant clustering algorithm can be a k-means or CLARANS algorithm. Automatically determining the target cell number and cell azimuth corresponding to the site to be deployed by the analysis device can ensure the rationality of the set target cell number, achieve adaptability to data in different situations, improve the efficiency of determining the cell azimuth, and improve the reliability of the cell azimuth obtained in the end.

[0162] The analysis device may obtain the number of target cells corresponding to the site to be deployed in the sub-area by the following process:

[0163] Step C1: The analysis device determines the angle of the information collection point in the sub-area relative to the site to be deployed based on the site location in the sub-area and the location information of the information collection point in the sub-area.

[0164] The process of the analysis device obtaining the angle in the sub-area may include: first converting the coordinate system of the sub-area into a polar coordinate system, where the pole of the polar coordinate system is the site to be deployed, wherein when the sub-area is determined by the latitude and longitude information of the aforementioned information collection point, the coordinate system in which it is located is a geographic coordinate system, and when the sub-area is determined by the grid information of the aforementioned grid point, the coordinate system in which it is located is a grid coordinate system; then, based on the site location in the sub-area and the location information of the information collection point in the sub-area, the analysis device can determine the angle of the information collection point (also called the polar angle of the information collection point) in the polar coordinate system in which the sub-area is located. For example, a ray Ox is drawn from the site to be deployed O, called the polar axis. For any information collection point P, based on the site to be deployed O and the information collection point P, a line segment OP is determined, and the angle θ from Ox to OP is determined as the angle of the information collection point P.

[0165] In the embodiment of the present application, the sub-region is determined by the grid information of the aforementioned grid points as an example. The angle of the information collection point P is represented by the angle GridAngle of the grid point corresponding to the information collection point P. The angle GridAngle of the grid point satisfies:

[0166] GridAngle=TranAngle(Xgrid, Ygrid);

[0167] Among them, Xgrid represents the grid horizontal coordinate of the grid point, Ygrid represents the grid vertical coordinate of the grid point, and TranAngle represents the acquisition angle.

[0168] Step C2: The analysis device obtains m candidate cell azimuths based on the number of m candidate cells, the second constant clustering algorithm, and the angle of the information collection point in the sub-area relative to the site to be deployed. The m candidate cell azimuths correspond one-to-one to the number of m candidate cells.

[0169] Step C3: The analysis device determines the number of target cells from the m candidate cells based on the azimuth angles of the m candidate cells.

[0170] In the aforementioned steps C2 to C3, after obtaining the angle of the information collection point in the sub-area relative to the site to be deployed, the analysis device sets a number of optional candidate cells through step C2, and then obtains the corresponding candidate cell azimuth angles, thereby screening the m candidate cells based on the determined candidate cell azimuth angles to obtain a reasonable number of target cells. In step C2, the analysis device performs the process of obtaining the candidate cell azimuth angles m times, thereby obtaining m candidate cells. Assuming that the first candidate cell number is any one of the m candidate cells, the embodiment of the present application takes the first candidate cell number as an example to illustrate the process of obtaining the candidate cell azimuth angle, which includes:

[0171] Step C21: The analysis device uses a second constant clustering algorithm to determine at least one subcluster in the subregion based on the angle of the information collection point in the subregion relative to the site to be deployed and the first number of candidate cells.

[0172] For example, the analysis device calls a second constant number clustering algorithm based on the angle of the information collection point in the sub-region relative to the site to be deployed, and the sub-region can be clustered (i.e., partitioned or classified) to obtain at least one sub-cluster. The at least one sub-cluster is equivalent to the subset obtained by the aforementioned constant number clustering algorithm. Since the constant number clustering algorithm requires the specification of the number of subsets, i.e., the first candidate cell number in step C21 (i.e., the number of clusters during clustering), when mobilizing the second constant number clustering algorithm, the angle information converted from the position information of all information collection points in the sub-region and the first candidate cell number need to be input into the model of the second constant number clustering algorithm, and the partitioning result can be output by the model of the second constant number clustering algorithm. Among them, the position information of the information collection point can be the longitude and latitude information of the information collection point, or the grid information of the grid point. Accordingly, the angle information of the information collection point can be the angle of the information collection point (i.e., the angle converted from the longitude and latitude information of the information collection point), or the angle of the grid point (i.e., the angle converted from the grid information of the grid point).

[0173] Taking the second constant clustering algorithm as the k-means algorithm and the angle information of the information collection point as the angle of the grid point as an example, the model of the k-means algorithm can be expressed by the following clustering formula:

[0174] Cluster angle =Kmeans(k,GridAngle);

[0175] Among them, k is equal to the number of first candidate cells, GridAngle represents the angle of a certain information collection point input relative to the site to be deployed, and Cluster angle This represents the cluster number of the angle of the output information collection point relative to the site to be deployed. In other words, the k-means algorithm model can assign a cluster number to each information collection point (or grid point) in the input angle information. Information collection points with the same area number belong to the same sub-cluster.

[0176] Step C22: The analysis device determines the angle of the center point of each sub-cluster relative to the site to be deployed.

[0177] For each sub-cluster, the angle of the center point of the sub-cluster relative to the site to be deployed refers to the center angle of the sub-cluster, that is, the average angle of the information collection points in the sub-cluster relative to the site to be deployed.

[0178] In an optional manner, when the angle information of the information collection point is the angle of the information collection point, the center angle of each sub-cluster may be determined by the following first center angle determination formula:

[0179]

[0180] Among them, centerangle represents the angle of the center point of the sub-cluster relative to the site to be deployed. It represents the sum of the angles of all information collection points in the sub-cluster, and P1_num represents the total number of information collection points in the sub-cluster.

[0181] In another optional manner, when the angle information of the information collection point is the angle of the grid point, the center angle of each sub-cluster can be determined by the following second center angle determination formula:

[0182]

[0183] Among them, centerangle represents the angle of the center point of the sub-cluster relative to the site to be deployed. It represents the sum of the angles of all grid points in the sub-cluster, and grid1_num represents the total number of grid points in the sub-cluster.

[0184] Step C23: The analysis device determines the cell azimuth angle of each cell in the sub-area based on the angle of the center point of each sub-cluster relative to the site to be deployed and the specified main lobe angle range.

[0185] The angle of the center point of each subcluster in the subregion relative to the site to be deployed can reflect the direction of the antenna main lobe of each cell. Therefore, based on the obtained angle, the analysis device can directly determine the direction of the cell's antenna main lobe, that is, the antenna azimuth. Different antennas have different capabilities for transmitting and receiving signals, and therefore have different main lobe angle ranges. The analysis device can receive the set main lobe angle ranges for each cell as the specified main lobe angle range, and then determine the cell azimuth of each cell in the subregion based on the angle of the center point of each subcluster relative to the site to be deployed and the specified main lobe angle range. For example, the specified main lobe angle range is a value between 60° and 120°.

[0186] Still Figure 10 For example, assuming that the polar axis Ox of the site to be deployed is due north and the main lobe angle range is specified to be 60°, then the cell azimuth is 60° towards due north, that is, Figure 10 The angle α in .

[0187] It should be noted that in the aforementioned steps C2 and C3, the number of m candidate cells is a value of n integers from 1 to n. Among them, n is the threshold value of the number of designated cells, for example, n = 3. How to select the number of m candidate cells from 1 to n determines the efficiency of determining the number of target cells. Accordingly, for different selection methods, the method of determining the number of target cells from the number of m candidate cells in the aforementioned step C3 is also different. This application example uses the following two optional methods as examples to illustrate step C3:

[0188] In the first optional method, before step C2, the set number of m candidate cells is obtained, that is, the number of m candidate cells is known before the process of obtaining the azimuth angle of the candidate cells is performed. For example, the number of m candidate cells is m values ​​that are continuous, partially continuous, or intermittent from 1 to n; or, m=n, that is, 1 to n are respectively used as the number of m candidate cells. Then the process of determining the target number of cells in step C3 is: based on the number of m candidate cells and the cell azimuth angles of the corresponding sub-areas, m types of partitioning methods corresponding to the m number of candidate cells are determined, and the candidate partitioning method with a cell overlap range less than a specified overlap range threshold (wherein the non-overlapping case can be regarded as a cell overlap range of 0) is selected from the m types of partitioning methods, and the largest number of candidate cells corresponding to the candidate partitioning method is determined as the target number of cells. In an example, the aforementioned cell overlap range can be an overlap range between two cells, and accordingly, the specified overlap range threshold is a specified overlap range threshold between two cells. For example, the specified overlap range threshold is 5°. Assuming that in the first partitioning method, the number of target cells is 3 and the overlap range between each two cells is 2°, then 2°<5°, and the first partitioning method can be determined as an alternative partitioning method. In another example, the aforementioned cell overlap range can be the sum of the overlap ranges between each two cells. Accordingly, the specified overlap range threshold is the overall specified overlap range threshold. For example, the specified overlap range threshold is 5°. Assuming that in the first partitioning method, the number of target cells is 3 and the overlap range between each two cells is 2°, then 2°×3>5°, and the first partitioning method does not belong to the alternative partitioning method.

[0189] In a second optional manner, before step C2, the number of m candidate cells is not set, that is, the number of m candidate cells is m values ​​between 1 and n, but the specific values ​​are unknown. Then, when executing the aforementioned step C2, the aforementioned process of acquiring the candidate cell azimuth angles can be performed in the order of increasing (e.g., increasing from 1 or 2) or decreasing (e.g., decreasing from n) the corresponding candidate cell numbers until the cell overlap range in the partitioning method determined based on the current number of candidate cells and the cell azimuth angles of the corresponding sub-area is less than a specified overlap range threshold, then the cutoff condition is met, and the aforementioned process of acquiring the candidate cell azimuth angles can be stopped; then, the process of determining the target cell number in step C3 is: the current number of candidate cells corresponding to the partitioning method with an overlap range less than the specified overlap range threshold (i.e., the partitioning method currently determined, i.e., the partitioning method when the cutoff condition is met) is determined as the target cell number. In this second optional method, the alternative cell azimuths corresponding to different numbers of alternative cells are tried one by one in an increasing or decreasing manner. After trying the appropriate alternative cell azimuth, the corresponding number of alternative cells is determined as the target cell number. Compared with the aforementioned first optional method, the number of times the alternative cell azimuth acquisition process is executed can be reduced, thereby reducing the computational cost and improving the efficiency of determining the cell azimuth corresponding to the site to be deployed.

[0190] Corresponding to the first and second optional modes, the process of determining the cell azimuth angles corresponding to the sites to be deployed using the second constant clustering algorithm based on the number of target cells can be implemented by the following two schematic implementable methods:

[0191] In the first possible implementation, the analysis device performs a cell azimuth acquisition process based on the target cell number. This cell azimuth acquisition process is identical to the candidate cell azimuth acquisition process, except that the first candidate cell number is updated to the target cell number, and the candidate cell azimuth determined based on the target cell number is used as the cell azimuth corresponding to the site to be deployed. This means that the aforementioned candidate cell azimuth acquisition process is repeated to obtain the corresponding cell azimuth.

[0192] In the second possible implementation method, since the number of target cells is selected from m candidate cell numbers, and the alternative cell azimuths corresponding to the m candidate cell numbers have been determined through the process of obtaining the alternative cell azimuths, the alternative cell azimuths corresponding to the target cell number can be directly obtained from the alternative cell azimuths corresponding to the m candidate cell numbers as the cell azimuth corresponding to the site to be deployed. The cell azimuth corresponding to the site to be deployed is the cell azimuth determined by the second constant clustering algorithm.

[0193] For ease of understanding, the embodiment of the present application assumes that the analysis device performs the aforementioned process of acquiring the candidate cell azimuth angles in descending order of the corresponding candidate cell number starting from n, and the aforementioned steps C2 and C3 may include the following steps:

[0194] Step D1: Set the number of candidate cells to n.

[0195] Step D2: Use the process of acquiring the candidate cell azimuth angles of the aforementioned step C2 to obtain n candidate cell azimuth angles corresponding to n candidate cells; and execute step D3.

[0196] Step D3: Detect whether the cell overlap range is less than the specified overlap range threshold in the partitioning method based on the number of alternative cells being n and the azimuth angles of n alternative cells. If the cell overlap range is less than the specified overlap range threshold, execute step D4; if the cell overlap range is not less than the specified overlap range threshold, execute step D5.

[0197] Step D4: Determine the number of candidate cells corresponding to the current partitioning method as the number of target cells.

[0198] Step D5: Update the number of candidate cells to n-1. Execute step D2 again.

[0199] like Figure 11 As shown, Figure 11 yes Figure 9 The diagram shows the cell azimuth angles corresponding to the sites to be deployed in sub-area 11. Using the second constant clustering algorithm, it is determined that sub-area 11 includes three subclusters. The cell azimuth angles corresponding to the three subclusters are angles α, β, and θ, respectively. All three cell azimuth angles are 60°.

[0200] After determining the site location and cell azimuth of each site to be deployed, the analysis equipment can output the site location and cell azimuth of each site to be deployed. Staff can then deploy sites based on the output results, effectively improving site deployment efficiency and enhancing accuracy and reliability.

[0201] It should be noted that in the aforementioned embodiments, the location information of the information collection point is only described as the grid information of the grid point or the latitude and longitude information of the information collection point. In the embodiments of the present application, the location information of the information collection point can also be represented by other types of information, as long as it can effectively identify the location of the information collection point. The embodiments of the present application do not limit this.

[0202] The order of the steps of the site location determination method provided in the embodiment of the present application can be appropriately adjusted, and the steps can be increased or decreased accordingly according to the situation. Any technician familiar with this technical field can easily think of different methods within the technical scope disclosed in this application, and they should all be covered within the scope of protection of this application, so they will not be repeated here.

[0203] To sum up, the method for determining the site location provided in the embodiment of the present application is that the analysis device automatically adopts the first constant clustering algorithm to determine the number of sites to be deployed in each weak coverage cluster, without the need to manually set the number of sites to be deployed, which can improve the efficiency of determining the site location and improve the accuracy and reliability of the final site location.

[0204] Furthermore, compared with the manual white-box algorithm, the present application adopts a non-indeterminate clustering algorithm to determine the at least one weak coverage cluster, which reduces the complexity of the station deployment task and improves the automation capability of the station deployment work, so that the station deployment work in the communication system can cope with larger data volumes and more complex data scenarios.

[0205] Furthermore, using the aforementioned first constant clustering algorithm to determine site locations improves the efficiency of the site determination process while ensuring the correlation between sites and weak coverage areas. This also makes the process more adaptable. Using the second constant clustering algorithm to determine cell azimuth angles improves the efficiency of determining cell azimuth angles and increases the reliability of the resulting cell azimuth angles.

[0206] In the aforementioned step A2, when obtaining at least one weak coverage cluster, the analysis device automatically filters out abnormal or isolated data (i.e., data that does not meet the conditions), saving the manual operation of screening abnormal data points (such as abnormal information collection points or abnormal grid points), effectively improving computing efficiency, and achieving a higher level of automation.

[0207] Figure 12 6 is a block diagram of a device 60 for determining a site location provided in an embodiment of the present application. The device includes:

[0208] A first acquisition module 601 is configured to acquire a weak coverage area in a communication system, where the weak coverage area is an area determined based on communication indicator data collected by multiple information collection points in the communication system;

[0209] A second acquisition module 602 is configured to acquire at least one weak coverage cluster determined based on the weak coverage area;

[0210] The first determining module 603 is configured to determine, for each weak coverage cluster, the site locations of the sites to be deployed in the weak coverage cluster by using a first constant number clustering algorithm.

[0211] To sum up, the device for determining the site location provided in the embodiment of the present application automatically uses the first constant clustering algorithm by the first determination module to determine the number of sites to be deployed in each weak coverage cluster. There is no need to manually set the number of sites to be deployed, which can improve the efficiency of determining the site location and improve the accuracy and reliability of the final site location.

[0212] Figure 136 is a block diagram of a first determination module 603 provided in an embodiment of the present application. The first determination module 603 includes:

[0213] A first determining submodule 6031 is configured to determine the number of sites to be deployed in a weak coverage cluster;

[0214] The second determining submodule 6032 is configured to determine the site locations of the sites to be deployed in the weak coverage cluster using a first constant number clustering algorithm based on the number of sites to be deployed in the weak coverage cluster and the location information of the information collection points in the weak coverage cluster.

[0215] Optionally, the first determination submodule 6031 is configured to: obtain the maximum distance between every two information collection points in the weak coverage cluster; obtain the ratio of the maximum distance to the designated station spacing; and determine the number of sites to be deployed based on the obtained ratio.

[0216] Optionally, the second determination submodule 6032 is used to: based on the number of sites to be deployed and the weak coverage clusters, use the first constant clustering algorithm to determine the same number of sub-areas as the number of sites to be deployed in the weak coverage clusters; for each sub-area, based on the location information of the information collection point in the sub-area, determine the site location of a site to be deployed in the sub-area.

[0217] Figure 14 is a block diagram of another device 60 for determining a site location provided in an embodiment of the present application. The device 60 further includes:

[0218] The third acquisition module 604 is used to determine the site location of a site to be deployed in the sub-area, and then, for each sub-area, obtain the number of target cells corresponding to the site to be deployed in the sub-area; the second determination module 605 is used to determine the cell azimuth corresponding to the site to be deployed based on the target cell number using a second constant clustering algorithm.

[0219] Optionally, the first acquisition module 601 is configured to:

[0220] Based on the position information of the information collection points in the weak coverage area, a non-deterministic clustering algorithm is used to determine at least one weak coverage cluster.

[0221] Optionally, the first acquisition module is used to: divide the weak coverage area into multiple candidate weak coverage clusters based on the location information of the information collection points in the weak coverage area by using a non-definite number clustering algorithm; and determine the candidate weak coverage cluster in which the number of information collection points in the multiple candidate weak coverage clusters is greater than a specified collection point number threshold as at least one weak coverage cluster.

[0222] Optionally, the location information of the information collection point is grid information of the grid point converted from the latitude and longitude information of the information collection point; or, the location information of the information collection point is the latitude and longitude information of the information collection point.

[0223] To sum up, the device for determining the site location provided in the embodiment of the present application automatically uses the first constant clustering algorithm by the first determination module to determine the number of sites to be deployed in each weak coverage cluster. There is no need to manually set the number of sites to be deployed, which can improve the efficiency of determining the site location and improve the accuracy and reliability of the final site location.

[0224] Figure 15 1 is a block diagram of a device for determining a site location provided in an embodiment of the present application. The device for determining a site location may be an analysis device. Figure 15 As shown, the analysis device 150 includes a processor 1501 and a memory 1502 .

[0225] Memory 1501, used to store computer programs, where the computer programs include program instructions;

[0226] Processor 1502 is used to call a computer program to implement the method for determining the site location provided in the embodiment of the present application.

[0227] Optionally, the information collection point 150 further includes a communication bus 1503 and a communication interface 1504 .

[0228] The processor 1501 includes one or more processing cores, and the processor 1501 executes various functional applications and data processing by running computer programs.

[0229] The memory 1502 can be used to store computer programs. Optionally, the memory can store an operating system and at least one application unit required for a function. The operating system can be a real-time operating system (RTX), Linux, UNIX, Windows, or OSX.

[0230] There may be multiple communication interfaces 1504, which are used to communicate with other storage devices or information collection points. For example, in the embodiment of the present application, the communication interface 1504 may be used to receive sample data sent by an information collection point in the communication system.

[0231] The memory 1502 and the communication interface 1504 are connected to the processor 1501 via the communication bus 1503 respectively.

[0232] An embodiment of the present application provides a computer storage medium having instructions stored thereon. When the instructions are executed by a processor, the method for determining the site location provided in the embodiment of the present application is implemented.

[0233] An embodiment of the present application provides a communication system, comprising: multiple information collection points, multiple sites, a management device, and an analysis device, wherein the management device is used to manage the multiple sites; the sites are used to transmit data between the management device and the information collection points; and the analysis device includes a site location determination device as described in any of the aforementioned embodiments.

[0234] For example, the architecture of the communication system can refer to the aforementioned Figure 1 or Figure 2 The system architecture in the application environment shown is not described in detail in the embodiments of the present application.

[0235] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0236] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0237] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product, which includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium, or a semiconductor medium (e.g., a solid-state hard disk).

[0238] In this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance. The term "plurality" refers to two or more, unless expressly limited otherwise. A referring to B means that A is the same as B or that A is a simple variant of B.

[0239] The above description is merely an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for determining a site location, characterized in that: include: Acquire a weak coverage area in a communication system, where the weak coverage area is determined based on communication indicator data collected by multiple information collection points in the communication system; Determining at least one weak coverage cluster using a non-deterministic clustering algorithm based on the position information of the information collection point in the weak coverage area; For each of the weak coverage clusters, determine the number of sites to be deployed in the weak coverage cluster, and based on the number of sites to be deployed and the weak coverage cluster, use a first constant number clustering algorithm to determine, in the weak coverage cluster, a number of sub-areas that is equal to the number of sites to be deployed; For each of the sub-areas, based on the location information of the information collection points in the sub-area, a site location of a site to be deployed is determined in the sub-area.

2. The method according to claim 1, characterized in that The determining the number of sites to be deployed in the weak coverage cluster includes: Obtaining the maximum distance among the distances between every two information collection points in the weak coverage cluster; Obtaining a ratio of the maximum distance to the designated station spacing; Based on the obtained ratio, the number of sites to be deployed is determined.

3. The method according to claim 1 or 2, characterized in that After determining a site location of a site to be deployed in the sub-area, the method further includes: For each of the sub-areas, obtaining the number of target cells corresponding to the to-be-deployed sites in the sub-area; Based on the target number of cells, a second constant clustering algorithm is used to determine the cell azimuth angle corresponding to the to-be-deployed site.

4. The method according to claim 1 or 2, characterized in that The determining the at least one weak coverage cluster by using a non-definite number clustering algorithm based on the position information of the information collection point in the weak coverage area includes: Based on the position information of the information collection points in the weak coverage area, the weak coverage area is divided into a plurality of candidate weak coverage clusters using the indefinite number clustering algorithm; A candidate weak coverage cluster having a number of information collection points greater than a specified collection point number threshold among the multiple candidate weak coverage clusters is determined as the at least one weak coverage cluster.

5. The method according to claim 1 or 2, characterized in that The location information of the information collection point is the grid information of the grid point converted from the latitude and longitude information of the information collection point; Alternatively, the location information of the information collection point is the latitude and longitude information of the information collection point.

6. A device for determining a site location, characterized in that: include: A first acquisition module is configured to acquire a weak coverage area in the communication system, where the weak coverage area is an area determined based on communication indicator data collected by multiple information collection points in the communication system; A second acquisition module is configured to determine at least one weak coverage cluster using a non-definite number clustering algorithm based on the position information of the information collection point in the weak coverage area; A first determining submodule is configured to determine, for each of the weak coverage clusters, the number of sites to be deployed in the weak coverage cluster; The second determination submodule is used to determine, for each of the weak coverage clusters, based on the number of the sites to be deployed and the weak coverage cluster, a number of sub-areas in the weak coverage cluster that is the same as the number of the sites to be deployed, using a first constant clustering algorithm; for each of the sub-areas, based on the location information of the information collection point in the sub-area, determine the site location of one of the sites to be deployed in the sub-area.

7. The device according to claim 6, characterized in that The first determining submodule is configured to: Obtaining the maximum distance among the distances between every two information collection points in the weak coverage cluster; Obtaining a ratio of the maximum distance to the designated station spacing; Based on the obtained ratio, the number of sites to be deployed is determined.

8. The device according to claim 6 or 7, characterized in that The device further comprises: A third acquisition module is configured to, after determining a site location of a site to be deployed in the sub-area, acquire, for each sub-area, the number of target cells corresponding to the site to be deployed in the sub-area; The second determination module is configured to determine the cell azimuth angle corresponding to the to-be-deployed site by adopting a second constant clustering algorithm based on the target cell number.

9. The device according to claim 6 or 7, characterized in that The first acquisition module is configured to: Based on the position information of the information collection points in the weak coverage area, the weak coverage area is divided into a plurality of candidate weak coverage clusters using the indefinite number clustering algorithm; A candidate weak coverage cluster having a number of information collection points greater than a specified collection point number threshold among the multiple candidate weak coverage clusters is determined as the at least one weak coverage cluster.

10. The device according to claim 6 or 7, characterized in that The location information of the information collection point is the grid information of the grid point converted from the latitude and longitude information of the information collection point; Alternatively, the location information of the information collection point is the latitude and longitude information of the information collection point.

11. A device for determining a site location, characterized in that: include: processor and memory; The memory is used to store a computer program, wherein the computer program includes program instructions; The processor is configured to call the computer program to implement the method for determining a site location according to any one of claims 1 to 5.

12. A computer storage medium, characterized in that The computer storage medium stores instructions, and when the instructions are executed by the processor, the method for determining the site location according to any one of claims 1 to 5 is implemented.

13. A communication system, characterized in that: include: Multiple information collection points, multiple sites, management equipment and analysis equipment, The management device is used to manage the multiple sites; The site is used to transmit data between the management device and the information collection point; The analysis device comprises the station location determination device according to any one of claims 6 to 10.

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