Base station position prediction method, device, equipment and storage medium

Abstract

The present application relates to the technical field of wireless communication, and discloses a base station position prediction method, device and equipment and storage medium, the method comprises the following steps: determining the target re-encoding identifier corresponding to each rival base station based on the original MR data; obtaining a preset number of target measurement points connected with the rival base station according to the target re-encoding identifier; and predicting the base station position corresponding to the rival base station based on each target measurement point and a preset reverse triangular positioning algorithm. Compared with the prior art in which the center point of the measurement point connected with the base station is taken as the base station position, the obtained base station position is not accurate. Since the target measurement points connected with the rival base station are obtained according to the target re-encoding identifier corresponding to each rival base station in the present application, and the position of each rival base station is predicted based on the target measurement points and the preset reverse triangular positioning algorithm, the technical problem that the real position of the rival base station cannot be predicted in the prior art, thereby failing to provide support for subsequent network optimization, is solved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method, apparatus, device, and storage medium for predicting base station location. Background Technology

[0002] Currently, 5G networks are in a phase of large-scale construction and deployment, and the planning and design of base stations is a crucial component of network planning and design. Base station locations are core resource data for mobile networks, and operators can use the locations of their own base stations and those of competitors, as well as the coverage of both, to support subsequent network optimization and planning.

[0003] Currently, operators typically record and store the location information of their own base stations. However, for competing base stations, since the location of the measurement point connecting the base station is some distance away from the base station location, the existing method of using the center point of the measurement point connecting the base station as the base station location is not accurate. At the same time, since competing base stations are all neighboring cells without unique identifiers, it is impossible to predict the true location of competing base stations, thus failing to provide a reference for subsequent network optimization. Summary of the Invention

[0004] The main objective of this invention is to provide a base station location prediction method, apparatus, device, and storage medium, aiming to solve the technical problem in the prior art that the actual location of competitor base stations cannot be predicted, thus failing to provide support for subsequent network optimization.

[0005] To achieve the above objectives, the present invention provides a base station location prediction method, the base station location prediction method comprising:

[0006] The target recoding identifier corresponding to each competing base station is determined based on the original MR data;

[0007] A preset number of target measurement points connected to the competing base station are obtained based on the target recoding identifier;

[0008] The location of the base station corresponding to the competitor's base station is predicted based on each target measurement point and a preset reverse triangulation algorithm.

[0009] Optionally, the step of determining the target recoding identifier corresponding to each competing base station based on the original MR data includes:

[0010] The original MR data is converted to a new format to obtain the initial base station data corresponding to each competing base station.

[0011] The initial base station data is sorted to obtain a base station data sequence;

[0012] The data of all adjacent base stations in the base station data sequence are compared, and the target recoding identifier corresponding to each competing base station is determined based on the comparison result.

[0013] Optionally, the step of obtaining a preset number of target measurement points connected to the competing base station based on the target recoding identifier includes:

[0014] All measurement points associated with the competing base station are obtained based on the target recoding identifier;

[0015] Based on a preset number of signal power values, extract the merged measurement points corresponding to the signal power values ​​from all the measurement points;

[0016] Obtain the centroid of the measurement point corresponding to the merged measurement point, and determine the centroid of the measurement point as a preset number of target measurement points connected to the competing base station.

[0017] Optionally, the step of predicting the location of the competing base station based on each of the target measurement points and a preset reverse triangulation algorithm includes:

[0018] The target distance between each target measurement point and the corresponding competitor base station is obtained by averaging the signal power linearly.

[0019] The preset number of circles are obtained based on each target measurement point and the target distance, and the position prediction strategy is determined according to the number of intersections between the circles.

[0020] Based on the location prediction strategy and the preset reverse triangulation algorithm, the location of the base station corresponding to the competitor's base station is predicted.

[0021] Optionally, after the step of predicting the location of the competing base station based on each of the target measurement points and a preset reverse triangulation algorithm, the method further includes:

[0022] Obtain the main cell coverage type associated with the competing base station, and determine the median value of all distances corresponding to the main cell coverage type based on the main cell coverage type and a preset dictionary table;

[0023] The base station merging distance threshold is determined based on the median value of all distances.

[0024] The base station locations are merged according to the distance threshold level corresponding to the base station merging distance threshold;

[0025] The target base station location corresponding to the competing base station is determined based on the merged base station location.

[0026] Optionally, before the step of obtaining the primary cell coverage type associated with the competing base station and determining all median distance values ​​corresponding to the primary cell coverage type based on the primary cell coverage type and a preset dictionary table, the method further includes:

[0027] Determine the minimum base station distance between base stations in this network based on the base station operating parameter table;

[0028] The median value of the base station distance corresponding to each base station coverage type is determined based on each base station coverage type and the minimum base station distance;

[0029] A preset dictionary table is generated based on the coverage type of each base station and the median distance value of the base station.

[0030] Optionally, the step of merging the base station locations according to the distance threshold level corresponding to the base station merging distance threshold includes:

[0031] The base station merging distance threshold is classified into levels according to a preset level classification standard;

[0032] Based on the classification results, the distance threshold level corresponding to the base station merging distance threshold is determined, and the base station locations are merged based on the distance threshold level.

[0033] Furthermore, to achieve the above objectives, the present invention also proposes a base station location prediction device, the device comprising:

[0034] The identifier determination module is used to determine the target recoding identifier corresponding to each competing base station based on the original MR data;

[0035] The measurement point acquisition module is used to acquire a preset number of target measurement points connected to the competing base station based on the target recoding identifier;

[0036] The location prediction module is used to predict the location of the base station corresponding to the competitor's base station based on each of the target measurement points and a preset reverse triangulation algorithm.

[0037] Furthermore, to achieve the above objectives, the present invention also proposes a base station location prediction device, the device comprising: a memory, a processor, and a base station location prediction program stored in the memory and executable on the processor, the base station location prediction program being configured to implement the steps of the base station location prediction method as described above.

[0038] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a base station location prediction program, which, when executed by a processor, implements the steps of the base station location prediction method as described above.

[0039] This invention discloses a method for determining the target recoding identifier corresponding to each competing base station based on the original MR data; obtaining a preset number of target measurement points connected to the competing base stations based on the target recoding identifier; and predicting the base station location corresponding to the competing base station based on each target measurement point and a preset reverse triangulation algorithm. Compared to the prior art, which uses the center point of the measurement points connected to the base station as the base station location, resulting in inaccurate base station locations, this invention solves the technical problem in the prior art that it is impossible to predict the true location of competing base stations, thus failing to provide support for subsequent network optimization. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the base station location prediction device in the hardware operating environment involved in the embodiments of the present invention;

[0041] Figure 2 This is a flowchart illustrating the first embodiment of the base station location prediction method of the present invention;

[0042] Figure 3 This is a flowchart illustrating the second embodiment of the base station location prediction method of the present invention;

[0043] Figure 4 This is a schematic diagram illustrating the ideal distance relationship between competing base stations and measurement points in the second embodiment of the base station location prediction method of the present invention;

[0044] Figure 5 This is a schematic diagram of the first distance relationship between competing base stations and measurement points in the second embodiment of the base station location prediction method of the present invention;

[0045] Figure 6 This is a schematic diagram of the second distance relationship between competing base stations and measurement points in the second embodiment of the base station location prediction method of the present invention;

[0046] Figure 7 This is a schematic diagram of the third distance relationship between competing base stations and measurement points in the second embodiment of the base station location prediction method of the present invention;

[0047] Figure 8 This is a flowchart illustrating the third embodiment of the base station location prediction method of the present invention;

[0048] Figure 9 This is a schematic diagram of the process for merging the locations of competing base stations in the third embodiment of the base station location prediction method of the present invention;

[0049] Figure 10 This is a structural block diagram of the first embodiment of the base station location prediction device of the present invention.

[0050] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0052] Reference Figure 1 , Figure 1 This is a schematic diagram of the base station location prediction device structure in the hardware operating environment involved in the embodiments of the present invention.

[0053] like Figure 1 As shown, the base station location prediction device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0054] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the base station location prediction device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0055] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a base station location prediction program.

[0056] exist Figure 1In the base station location prediction device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the base station location prediction device of the present invention can be set in the base station location prediction device. The base station location prediction device calls the base station location prediction program stored in the memory 1005 through the processor 1001 and executes the base station location prediction method provided in the embodiment of the present invention.

[0057] This invention provides a base station location prediction method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the base station location prediction method of the present invention.

[0058] In this embodiment, the base station location prediction method includes the following steps:

[0059] Step S10: Determine the target recoding identifier corresponding to each competing base station based on the original MR data.

[0060] It should be noted that the execution subject of the method in this embodiment can be a base station location prediction device that predicts the location of 5G competing base stations, or other base station location prediction systems that can achieve the same or similar functions and include such a base station location prediction device. Here, the base station location prediction method provided in this embodiment and the following embodiments will be specifically described using a base station location prediction system (hereinafter referred to as the system).

[0061] It should be understood that the aforementioned raw MR data can be the raw network data measured by the user terminal. The MR (Measurement Report) carries relevant information about the uplink and downlink wireless links. In practical applications, in-depth analysis based on MR is one of the effective means of network performance evaluation and optimization, such as network problem localization, network coverage analysis, and neighbor cell optimization.

[0062] It is understood that the aforementioned competing base stations can be base stations from other operators' networks, and this embodiment does not impose any restrictions on this.

[0063] It should be noted that the aforementioned target recoding identifier can be the identifier obtained by recoding the identifiers of each competing base station. In practical applications, since the identifier of a competing base station is "frequency point + PCI (Physical Cell Identifier)," which is a non-unique identifier and may be duplicated, when predicting the location of each competing base station, the identifier of the competing base station must first be recoded to obtain a unique identifier corresponding to the competing base station for base station location prediction.

[0064] Furthermore, in order to obtain the unique identifier of each competing base station to predict the location of each competing base station, step S10 may specifically include: converting the format of the original MR data to obtain the initial base station data corresponding to each competing base station; sorting the initial base station data to obtain a base station data sequence; comparing all adjacent base station data in the base station data sequence, and determining the target recoding identifier corresponding to each competing base station based on the comparison result.

[0065] It should be understood that the aforementioned initial base station data can be data in a preset data format corresponding to each competing base station obtained after converting the original MR data. In practical applications, this embodiment can convert the original MR data to obtain initial base station data. The format of the initial base station data can be {“neighboring competing base station frequency + PCI”, “associated primary cell location”}. After obtaining the initial base station locations corresponding to each competing base station, data deduplication can be performed, thereby reducing the amount of data processing and improving data processing efficiency. The parameters in the initial base station data can be obtained based on 5G MR data and the 5G local network base station operating parameter table.

[0066] It is understood that in this embodiment, the base station data sequence can be obtained by sequentially sorting the data in the order of {"neighboring cell competing base station frequency point + PCI", "associated primary cell location"}.

[0067] It should be noted that the aforementioned adjacent base station data can refer to the data of two adjacent base stations in the base station data sequence. In practical applications, all adjacent base station data in the base station data sequence can be compared to determine whether the "neighboring competing base station frequency point + PCI" in the data of two adjacent base stations is the same. Based on the determination result, the competing base station identifiers corresponding to each competing base station are recoded to obtain the target recoding identifiers corresponding to each competing base station.

[0068] In practical implementation, since competitor base station identifiers are "frequency point + PCI," which are not unique identifiers, base stations with the same "frequency point + PCI" may not be the same base station. Therefore, before predicting the location of each competitor base station, it is necessary to recode the competitor base station identifiers to obtain a unique target recoded identifier corresponding to each competitor base station. First, the original MR data can be converted to obtain initial base station data, which has the format {"neighboring cell competitor base station frequency point + PCI", "associated main cell location"}. The data is then deduplicated and sorted sequentially according to the order {"neighboring cell competitor base station frequency point + PCI", "associated main cell location"} to obtain the base station data sequence. Finally, each data in the base station data sequence is traversed to compare all adjacent base station data in the sequence. If the "neighboring cell competing base station frequency + PCI" in the next data is different from the previous data, the competing base station identifier is recoded. If the "neighboring cell competing base station frequency + PCI" in the next data is the same as the previous data, but the distance between the primary cell locations corresponding to the two data is greater than a given threshold, the competing base station identifier also needs to be recoded. Otherwise, the base station identifier remains consistent with the previous one.

[0069] Step S20: Obtain a preset number of target measurement points connected to the competing base station based on the target recoding identifier.

[0070] It should be noted that existing solutions can use triangulation to accurately locate measurement points in 5G MR data, while this embodiment can infer the base station location based on the measurement point location. Traditional triangulation calculates the distance from the measurement point to the three base stations connected to it using RSSI (Received Signal Strength Indication). Using this distance as the radius and the base station as the center, three circles are drawn. All three circles should intersect with the measurement point; the common intersection point of the three circles is the precise location of the measurement point. The reverse triangulation algorithm, on the other hand, is based on three measurement points connected to competing base stations, each with different locations and the strongest signal strength. It calculates the distance between these three measurement points and the base station using RSSI, and draws three circles with these three measurement points as centers and the distances as radii. All three circles should intersect with the base station location; the common intersection point of the three circles is the location of the competing base station. Therefore, this embodiment can obtain a preset number of target measurement points connected to competing base stations to predict the location of competing base stations. The preset number can be three, and the target measurement points can be three measurement points connected to each competing base station, each with different locations and the strongest signal strength.

[0071] Furthermore, in order to achieve accurate prediction of the location of the competing base station, step S20 may specifically include: obtaining all measurement points associated with the competing base station according to the target recoding identifier; extracting the merged measurement points corresponding to the signal power values ​​from all the measurement points according to a preset number of signal power values; obtaining the centroid of the measurement point corresponding to the merged measurement point, and determining the centroid of the measurement point as a preset number of target measurement points connected to the competing base station.

[0072] It should be noted that due to the inherent errors in data acquisition, abnormal data can be deleted before predicting the location of competing base stations to prevent them from affecting the prediction. In practical applications, groups can be formed based on the target recoding identifiers after recoding of each competing base station. The DBSCAN algorithm can then be used to cluster all measurement points under each competing base station, and outliers identified by the model can be removed.

[0073] It should be understood that the above signal power values ​​can be the cell common reference signal (CRS) power values ​​received by the terminal, such as RSRP (Reference Signal Receiving Power). The values ​​are linear averages of the power of a single RE within the measurement bandwidth, reflecting the strength of the useful signal in the cell.

[0074] It is understandable that the aforementioned merged measurement points can be measurement points with the same signal strength among all measurement points associated with each competing base station. Correspondingly, the centroids of the aforementioned measurement points can be the centroids obtained after clustering the merged measurement points.

[0075] In practical implementation, since there may be situations where the signal strength RSRP values ​​of measurement points connected to the same base station are the same but their locations are different, this embodiment can extract all measurement points corresponding to the three highest signal strength RSRP values ​​from all measurement points associated with each competing base station to obtain three merged measurement points. The centroids of the measurement points of each merged measurement point are then grouped and statistically analyzed according to the RSRP values ​​to obtain three centroids. These three centroids are then determined as the three target measurement points of the competing base station, which serve as the measurement point locations required for the next step of reverse triangulation.

[0076] Step S30: Predict the location of the base station corresponding to the competitor's base station based on each of the target measurement points and the preset reverse triangulation algorithm.

[0077] It should be noted that the above-mentioned preset reverse triangulation positioning algorithm can be an improved reverse triangulation positioning algorithm.

[0078] In the specific implementation, after obtaining the three target measurement points connected to each competitor's base station, the distance between the three target measurement points and the corresponding competitor's base station can be calculated by RSSI. Then, three circles are drawn with the three measurement points as the center and the distance as the radius. All three circles should intersect with the base station location. That is, the common intersection point of the three circles is the location of the competitor's base station.

[0079] This embodiment discloses a method for determining the target recoding identifier corresponding to each competing base station based on the original MR data; obtaining a preset number of target measurement points connected to the competing base stations based on the target recoding identifier; and predicting the base station location corresponding to the competing base station based on each target measurement point and a preset reverse triangulation algorithm. Compared with the prior art, which uses the center point of the measurement point connected to the base station as the base station location, resulting in inaccurate base station locations, this embodiment solves the technical problem in the prior art that it is impossible to predict the true location of competing base stations, thus failing to provide support for subsequent network optimization.

[0080] refer to Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the base station location prediction method of the present invention.

[0081] Based on the first embodiment described above, in this embodiment, step S30 includes:

[0082] Step S301: Obtain the target distance between each target measurement point and the corresponding competitor base station by averaging the signal power linearly.

[0083] It should be noted that the above linear average signal power can be the linear average of the power of all signals received by the terminal (including useful and interfering signals on the same frequency, adjacent channel interference, thermal noise, etc.), such as RSSI. RSSI reflects the load intensity on the resource.

[0084] It should be understood that the aforementioned target distance can be the distance between the three target measurement points and their corresponding competing base stations.

[0085] Step S302: Obtain the preset number of circles based on each target measurement point and the target distance, and determine the position prediction strategy according to the number of intersections between the circles.

[0086] It should be noted that in this embodiment, a circle can be drawn with each target measurement point as the center and the distance between each target measurement point and the corresponding competitor base station as the radius, and the corresponding location prediction strategy can be determined based on the number of intersections between the circles.

[0087] It should be understood that the above location prediction strategy can be a strategy for predicting the location of competitor base stations.

[0088] Step S303: Predict the location of the base station corresponding to the competitor's base station based on the location prediction strategy and the preset reverse triangulation algorithm.

[0089] Understandably, referring to Figure 4 , Figure 4 This is a schematic diagram illustrating the ideal distance relationship between competing base stations and measurement points in the second embodiment of the base station location prediction method of the present invention. Figure 4 As shown, Figure 4 The diagram illustrates the distance relationship between competitor base station locations and measurement points under ideal conditions. Here, u1, u2, and u3 represent the locations of the three measurement points, and R... u1 R u2 R u3 The distances between the three measurement points and the base station are represented by c1 to c6, which are the intersections of three circles. c4, c5, and c6 are the common intersections, representing the locations of competing base stations. However, in reality, due to noise and obstacles, the signal strength received by the terminal may be lower than expected, resulting in a calculated distance that is larger than anticipated. This can lead to discrepancies such as... Figure 5 , Figure 6 and Figure 7 Of the three scenarios shown, Figure 5 This is a schematic diagram of the first distance relationship between competing base stations and measurement points in the second embodiment of the base station location prediction method of the present invention; Figure 6 This is a schematic diagram of the second distance relationship between competing base stations and measurement points in the second embodiment of the base station location prediction method of the present invention; Figure 7 This is a schematic diagram illustrating the third distance relationship between competing base stations and measurement points in the second embodiment of the base station location prediction method of the present invention. Figure 5 , Figure 6 and Figure 7 The meaning of the parameters in Figure 4 The parameters in the above-mentioned embodiment have the same meaning. In this case, the location of the competing base station in each case can be predicted by the above-mentioned preset reverse triangulation positioning algorithm.

[0090] It should be noted that, as Figure 5 As shown, Figure 5 The three circles have six intersection points c1 to c6 at different positions, and the corresponding position prediction strategy can be the first position prediction strategy. In practical applications, this embodiment can calculate the distance between the six intersection points separately, and use the weighted centroid of the three closest intersection points (i.e., Figure 5 Point B in the diagram is used as the location of the competing base station. The signal strength values ​​of the three measurement points are used as weights, with stronger signal strength having higher weights.

[0091] It should be understood that, such as Figure 6As shown, Figure 6 The three circles have four intersection points c1 to c4 at different positions, and the corresponding position prediction strategy can be the second position prediction strategy. In practical applications, this embodiment can calculate the distance between the four intersection points separately, and use the weighted centroid of the two closest intersection points (i.e., Figure 6 Point B in the diagram is used as the location of the competitor's base station.

[0092] It is understandable that, such as Figure 7 As shown, Figure 7 The three circles have two intersection points, c1 and c2, in different positions. The corresponding position prediction strategy can be a third position prediction strategy. In practical applications, this embodiment can calculate the distances between the two intersection points and the center point of another circle that does not intersect with the two circles, denoted as D(u2,c1) and D(u2,c2), respectively. If D(u2,c1)>D(u2,c2), then the position of intersection point c1 is taken as the position of the competing base station; if D(u2,c2)>D(u2,c1), then the position of intersection point c2 is taken as the position of the competing base station; if D(u2,c2)=D(u2,c1), then the distances between the centroids of all measurement points and the two intersection points are compared. If the centroid is closer to c1, then the position of c1 is the position of the competing base station, otherwise the opposite is true.

[0093] In the specific implementation, the distance between three target measurement points and their connected competitor base stations can be calculated using RSSI. Then, three circles are drawn with each of the three target measurement points as the center and the distance between each target measurement point and its connected competitor base station as the radius. The corresponding position prediction strategy is determined based on the number of intersections between the three circles. Based on the corresponding position prediction strategy, the position of the competitor base station is predicted using the principle of the preset reverse triangulation algorithm.

[0094] This embodiment obtains the target distance between each target measurement point and the corresponding competitor base station by averaging the signal power linearly. Based on each target measurement point and the target distance, a preset number of circles are obtained. The location prediction strategy is determined according to the number of intersections between the circles. Finally, the location of the competitor base station is predicted based on the location prediction strategy and the preset reverse triangulation algorithm, thereby achieving accurate prediction of the location of the competitor base station.

[0095] refer to Figure 8 , Figure 8 This is a flowchart illustrating the third embodiment of the base station location prediction method of the present invention.

[0096] Based on the above embodiments, in order to improve the accuracy of competitor base station location prediction, in this embodiment, after step S30, the method further includes:

[0097] Step S40: Obtain the main cell coverage type associated with the competing base station, and determine all median distance values ​​corresponding to the main cell coverage type based on the main cell coverage type and a preset dictionary table.

[0098] It should be noted that, considering the standard distance for base station construction intervals, in order to improve the accuracy and rationality of predicting the location of competitor base stations, the predicted location of competitor base stations in step S30 can be further processed to correct the location of competitor base stations and obtain a more reasonable and accurate location of competitor base stations.

[0099] It should be understood that the above-mentioned main cell coverage type is the coverage type of the main cell associated with the competing base station.

[0100] It is understandable that the aforementioned preset dictionary table can be a pre-established table that stores the correspondence between base station coverage types and median distance values ​​of base stations.

[0101] Furthermore, to improve data processing efficiency, before step S40, the method further includes: determining the minimum base station distance between base stations in the network based on the network base station operating parameter table; determining the median base station distance corresponding to each base station coverage type based on the coverage type of each base station and the minimum base station distance; and generating a preset dictionary table based on the coverage type of each base station and the median base station distance.

[0102] It should be noted that the minimum base station distance mentioned above can be the minimum distance between base stations within the same network. In practical applications, the distance between each base station and other base stations can be calculated based on the network's base station operating parameter table to obtain the minimum base station distance. The network's base station operating parameter table can be a table of operating parameters for the base stations within the network, and the minimum base station distance can be any value other than 0m.

[0103] It should be understood that this embodiment can statistically summarize the base station distances according to the base station coverage type and calculate the median value of the base station distance under each coverage type.

[0104] It is understandable that after obtaining the median distance value of base stations under each base station coverage type, a preset dictionary table in the form of {"base station coverage type": median distance value of base stations} can be generated.

[0105] Step S50: Determine the base station merging distance threshold based on the median value of all distances.

[0106] It should be noted that the aforementioned base station merging distance threshold can be the maximum distance value for merging the locations of competing base stations. In practical applications, after querying the preset dictionary table for all median distance values ​​corresponding to the coverage type of the primary cell associated with the competing base station, the minimum value of the associated median distance value can be extracted as the base station merging distance threshold for that competing base station.

[0107] Step S60: Merge the base station locations according to the distance threshold level corresponding to the base station merging distance threshold.

[0108] Furthermore, in order to make the distance threshold layering more reasonable, step S60 may include: classifying the base station merging distance threshold according to a preset level classification standard; determining the distance threshold level corresponding to the base station merging distance threshold according to the level classification result, and merging the base station locations based on the distance threshold level.

[0109] It should be understood that the aforementioned preset level classification standard can be a standard for classifying distance thresholds according to a 100m standard, and this embodiment does not limit this. In practical applications, if this embodiment classifies all distance thresholds according to a 100m standard, that is, if the distance threshold = 178m, then its corresponding distance threshold level is 200m; if the distance threshold = 249m, then its corresponding distance threshold level is 300m. After determining the distance threshold level corresponding to the base station merging distance threshold, the locations of competing base stations initially predicted in step S30 can be merged based on different distance threshold levels. That is, if the distance between base stations under this distance threshold level is within the distance threshold range, then the competing base stations that meet the requirements will be merged.

[0110] Step S70: Determine the target base station location corresponding to the competing base station based on the merged base station location.

[0111] It is understandable that the target base station location can be a corrected version of the initially predicted locations of competing base stations. In practical applications, after merging the locations of competing base stations, their centroids can be taken as the target base station location.

[0112] In the specific implementation, refer to Figure 9 , Figure 9 This is a schematic diagram illustrating the process of merging competitor base station locations in the third embodiment of the base station location prediction method of the present invention. Figure 9As shown, firstly, the minimum distance between base stations in the network (excluding 0m) can be calculated based on the network's base station operating parameters table. Then, the median distance value is calculated and categorized according to base station coverage type. Next, a preset dictionary table is generated based on the base station coverage type and the median distance value. When merging the locations of competing base stations, the coverage type of the primary cell associated with the competing base station can be viewed. Based on the primary cell coverage type, the median distance value corresponding to the competing base station is found in the preset dictionary table, and the minimum value of the associated median distance value is extracted to generate the merging distance threshold for competing base stations. Simultaneously, to make the distance threshold hierarchy more reasonable, all distance thresholds can be classified into levels according to a 100m standard. The distance threshold level can then be determined based on the level classification results. Finally, the initially predicted locations of competing base stations are merged based on different distance threshold levels, and their centroids are taken as the final target base station locations.

[0113] This embodiment determines the median distance corresponding to the main cell coverage type based on the coverage type associated with competing base stations and a preset dictionary table. It then determines a base station merging distance threshold based on the median distance, and merges base station locations according to the distance threshold level corresponding to the merging distance threshold to determine the target base station location of competing base stations. This allows for correction of base station locations, making the corrected target base station location more accurate. Simultaneously, it determines the median base station distance corresponding to each base station coverage type based on the minimum base station distance between each base station coverage type and the local network base stations, and generates a preset dictionary table based on each base station coverage type and the median base station distance. This reduces data processing volume, improves data processing efficiency, and enhances the efficiency of competing base station location prediction.

[0114] Furthermore, this embodiment of the invention also proposes a storage medium storing a base station location prediction program, which, when executed by a processor, implements the steps of the base station location prediction method described above.

[0115] Reference Figure 10 , Figure 10 This is a structural block diagram of the first embodiment of the base station location prediction device of the present invention.

[0116] like Figure 10 As shown, the base station location prediction device proposed in this embodiment of the invention includes:

[0117] The identifier determination module 501 is used to determine the target recoding identifier corresponding to each competing base station based on the original MR data;

[0118] The measurement point acquisition module 502 is used to acquire a preset number of target measurement points connected to the competing base station according to the target recoding identifier;

[0119] The location prediction module 503 is used to predict the location of the base station corresponding to the competitor's base station based on each of the target measurement points and a preset reverse triangulation algorithm.

[0120] The identifier determination module 501 is further configured to convert the format of the original MR data to obtain the initial base station data corresponding to each competing base station; sort the initial base station data to obtain a base station data sequence; compare all adjacent base station data in the base station data sequence, and determine the target recoding identifier corresponding to each competing base station based on the comparison result.

[0121] The measurement point acquisition module 502 is further configured to acquire all measurement points associated with the competing base station according to the target recoding identifier; extract the merged measurement points corresponding to the signal power values ​​from all the measurement points according to a preset number of signal power values; acquire the centroid of the measurement point corresponding to the merged measurement point; and determine the centroid of the measurement point as a preset number of target measurement points connected to the competing base station.

[0122] This embodiment of the base station location prediction device discloses determining the target recoding identifier corresponding to each competing base station based on the original MR data; obtaining a preset number of target measurement points connected to the competing base stations based on the target recoding identifier; and predicting the base station location corresponding to the competing base station based on each target measurement point and a preset reverse triangulation algorithm. Compared with the prior art, which uses the center point of the measurement point connected to the base station as the base station location, resulting in inaccurate base station locations, this embodiment solves the technical problem in the prior art that it is impossible to predict the true location of competing base stations, thus failing to provide support for subsequent network optimization.

[0123] Based on the first embodiment of the base station location prediction device of the present invention, a second embodiment of the base station location prediction device of the present invention is proposed.

[0124] In this embodiment, the location prediction module 503 is further configured to obtain the target distance between each target measurement point and the corresponding competitor base station by means of the linear average value of the signal power; obtain the preset number of circles based on each target measurement point and the target distance, and determine the location prediction strategy according to the number of intersections between the circles; and predict the base station location corresponding to the competitor base station based on the location prediction strategy and the preset reverse triangulation algorithm.

[0125] This embodiment obtains the target distance between each target measurement point and the corresponding competitor base station by averaging the signal power linearly. Based on each target measurement point and the target distance, a preset number of circles are obtained. The location prediction strategy is determined according to the number of intersections between the circles. Finally, the location of the competitor base station is predicted based on the location prediction strategy and the preset reverse triangulation algorithm, thereby achieving accurate prediction of the location of the competitor base station.

[0126] Based on the above-described embodiments, a third embodiment of the base station location prediction device of the present invention is proposed.

[0127] In this embodiment, the location prediction module 503 is further configured to obtain the main cell coverage type associated with the competing base station, and determine all median distance values ​​corresponding to the main cell coverage type based on the main cell coverage type and a preset dictionary table; determine a base station merging distance threshold based on all median distance values; merge the base station locations according to the distance threshold level corresponding to the base station merging distance threshold; and determine the target base station location corresponding to the competing base station based on the merged base station location.

[0128] The location prediction module 503 is further configured to determine the minimum base station distance between base stations in the network based on the network base station operating parameter table; determine the median value of the base station distance corresponding to each base station coverage type based on each base station coverage type and the minimum base station distance; and generate a preset dictionary table based on each base station coverage type and the median value of the base station distance.

[0129] The location prediction module 503 is further configured to classify the base station merging distance threshold according to a preset classification standard; determine the distance threshold level corresponding to the base station merging distance threshold based on the classification result; and merge the base station locations based on the distance threshold level.

[0130] This embodiment determines the median distance corresponding to the main cell coverage type based on the coverage type associated with competing base stations and a preset dictionary table. It then determines a base station merging distance threshold based on the median distance, and merges base station locations according to the distance threshold level corresponding to the merging distance threshold to determine the target base station location of competing base stations. This allows for correction of base station locations, making the corrected target base station location more accurate. Simultaneously, it determines the median base station distance corresponding to each base station coverage type based on the minimum base station distance between each base station coverage type and the local network base stations, and generates a preset dictionary table based on each base station coverage type and the median base station distance. This reduces data processing volume, improves data processing efficiency, and enhances the efficiency of competing base station location prediction.

[0131] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0132] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0133] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0134] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A base station location prediction method, characterized in that, The base station location prediction method includes: The target recoding identifier corresponding to each competing base station is determined based on the original MR data; A preset number of target measurement points connected to the competing base station are obtained based on the target recoding identifier; Based on the target measurement points and the preset reverse triangulation algorithm, predict the location of the base station corresponding to the competitor's base station; The step of predicting the location of the competing base station based on each of the target measurement points and a preset reverse triangulation algorithm includes: The target distance between each target measurement point and the corresponding competitor base station is obtained by averaging the signal power linearly. A preset number of circles are drawn with each target measurement point as the center and the target distance as the radius, and the position prediction strategy is determined based on the number of intersections between the circles. Based on the location prediction strategy and the preset reverse triangulation algorithm, the location of the base station corresponding to the competitor's base station is predicted.

2. The base station location prediction method as described in claim 1, characterized in that, The step of determining the target recoding identifier corresponding to each competing base station based on the original MR data includes: The original MR data is converted to a new format to obtain the initial base station data corresponding to each competing base station. The initial base station data is sorted to obtain a base station data sequence; The data of all adjacent base stations in the base station data sequence are compared, and the target recoding identifier corresponding to each competing base station is determined based on the comparison result.

3. The base station location prediction method as described in claim 1, characterized in that, The step of obtaining a preset number of target measurement points connected to the competing base station based on the target recoding identifier includes: All measurement points associated with the competing base station are obtained based on the target recoding identifier; Based on a preset number of signal power values, extract the merged measurement points corresponding to the signal power values ​​from all the measurement points; Obtain the centroid of the measurement point corresponding to the merged measurement point, and determine the centroid of the measurement point as a preset number of target measurement points connected to the competing base station.

4. The base station location prediction method as described in claim 1, characterized in that, After the step of predicting the location of the competing base station based on each of the target measurement points and a preset reverse triangulation algorithm, the method further includes: Obtain the main cell coverage type associated with the competing base station, and determine the median value of all distances corresponding to the main cell coverage type based on the main cell coverage type and a preset dictionary table; The base station merging distance threshold is determined based on the median value of all distances. The base station locations are merged according to the distance threshold level corresponding to the base station merging distance threshold; The target base station location corresponding to the competing base station is determined based on the merged base station location.

5. The base station location prediction method as described in claim 4, characterized in that, Before the step of obtaining the primary cell coverage type associated with the competing base station and determining all median distance values ​​corresponding to the primary cell coverage type based on the primary cell coverage type and a preset dictionary table, the method further includes: Determine the minimum base station distance between base stations in this network based on the network base station operating parameter table; The median value of the base station distance corresponding to each base station coverage type is determined based on each base station coverage type and the minimum base station distance; A preset dictionary table is generated based on the coverage type of each base station and the median distance value of the base station.

6. The base station location prediction method as described in claim 4, characterized in that, The step of merging the base station locations according to the distance threshold level corresponding to the base station merging distance threshold includes: The base station merging distance threshold is classified into levels according to a preset level classification standard; Based on the classification results, the distance threshold level corresponding to the base station merging distance threshold is determined, and the base station locations are merged based on the distance threshold level.

7. A base station location prediction device, characterized in that, The device includes: The identifier determination module is used to determine the target recoding identifier corresponding to each competing base station based on the original MR data; The measurement point acquisition module is used to acquire a preset number of target measurement points connected to the competing base station based on the target recoding identifier; The location prediction module is used to predict the location of the base station corresponding to the competitor's base station based on each of the target measurement points and a preset reverse triangulation algorithm; The location prediction module is further configured to obtain the target distance between each target measurement point and the corresponding competitor base station by means of the linear average value of the signal power; draw the preset number of circles with each target measurement point as the center and the target distance as the radius, and determine the location prediction strategy according to the number of intersections between the circles; and predict the base station location corresponding to the competitor base station based on the location prediction strategy and the preset reverse triangulation algorithm.

8. A base station location prediction device, characterized in that, The device includes: a memory, a processor, and a base station location prediction program stored in the memory and executable on the processor, the base station location prediction program being configured to implement the steps of the base station location prediction method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a base station location prediction program, which, when executed by a processor, implements the steps of the base station location prediction method as described in any one of claims 1 to 6.

Images