Shared site detection method and device, equipment, medium and program product

Abstract

The invention provides a shared site detection method and device, equipment, a medium and a program product. The method comprises the following steps: identifying a newly added PCI according to MDT data in a preset area in a preset historical time period; the number of the newly added PCIs is at least one; mapping the user position data of the newly added PCI to a rectangular plane coordinate system; based on the user position data of the newly added PCI, fitting in the rectangular plane coordinate system to obtain a minimum circumscribed triangle; determining target base station data based on the minimum external triangle through a spatial optimization algorithm; and determining a suspected shared site based on the target base station data. By adopting the method provided by the embodiment of the invention, the operation cost can be reduced, and the accuracy and timeliness of shared site identification can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a shared site detection method, device, equipment, medium and program product. Background Art

[0002] In the related technologies, with the continuous development of Internet communication technology, communication equipment is constantly changing, and the number of shared sites is also increasing. As a rigid expenditure of the operator's network line, the cost of shared sites (such as towers) is usually shared by all parties who set up equipment at shared sites. However, due to the large scale and frequency of equipment changes, it is difficult to accurately locate the equipment changes of operators, which will lead to poor timeliness and low accuracy of equipment updates, which in turn will affect the poor timeliness and accuracy of shared site updates. Summary of the invention

[0003] The present disclosure provides a shared site detection method, device, equipment, medium and program product. The technical solution of the present disclosure is as follows:

[0004] In a first aspect, the present disclosure provides a shared site detection method, comprising:

[0005] Identify a newly added PCI according to MDT data in a preset area within a preset historical period; the newly added PCI is at least one;

[0006] Mapping the user location data of the newly added PCI to a plane rectangular coordinate system;

[0007] Based on the user location data of the newly added PCI, a minimum circumscribed triangle is obtained by fitting in the plane rectangular coordinate system;

[0008] Determine the target base station data based on the minimum circumscribed triangle by a spatial optimization algorithm;

[0009] A suspected shared site is determined based on the target base station data.

[0010] In a possible implementation manner, mapping the user location data of the newly added PCI to a plane rectangular coordinate system includes:

[0011] For each of the newly added PCIs, obtaining user location data of the newly added PCI;

[0012] The user position data of the newly added PCI is mapped to a plane rectangular coordinate system through Gauss-Krüger projection.

[0013] In a possible implementation manner, the user location data based on the newly added PCI is fitted in the plane rectangular coordinate system to obtain a minimum circumscribed triangle, including:

[0014] Based on the scattered data distribution of the user location data of the newly added PCI, a minimum circumscribed triangle is fitted in the plane rectangular coordinate system by means of geometric drawing;

[0015] Output the vertices of the minimum outer triangle.

[0016] In a possible implementation manner, determining the target base station data based on the minimum circumscribed triangle by using a spatial optimization algorithm includes:

[0017] Based on the scattered user location data within the minimum circumscribed triangle area, the minimum circumscribed triangle area is divided into a plurality of clusters by a spatial optimization algorithm combined with the distribution density of the scattered user location data;

[0018] Calculate the signal strength and center point of different coordinate points in each cluster;

[0019] Taking the average of the signal strengths of a preset number of adjacent points inward from the vertex of the minimum circumscribed triangle;

[0020] Determining whether the average values ​​of the preset number of adjacent signal strengths meet a preset signal step condition;

[0021] When the average values ​​of the preset number of adjacent signal strengths meet the preset signal step condition, the vertex is determined as the target base station data.

[0022] In a possible implementation manner, determining a suspected shared site based on the target base station data includes:

[0023] The target base station data is converted into longitude and latitude data by Gauss-Krüger projection inverse operation;

[0024] Calculate the distance between each signal source data and the nearest base station;

[0025] The base station with the shortest distance corresponding to each signal source is selected and determined as the suspected shared site.

[0026] In a possible implementation, it further includes:

[0027] When the mean values ​​of the preset number of adjacent signal strengths do not satisfy the preset signal step condition, the cluster center point with the smallest density is removed, and the minimum circumscribed triangle is refitted.

[0028] In a second aspect, the present disclosure provides a shared site detection device, including:

[0029] An identification module, configured to identify a newly added PCI according to MDT data in a preset area within a preset historical period; the newly added PCI is at least one;

[0030] A mapping module, used for mapping the user location data of the newly added PCI to a plane rectangular coordinate system;

[0031] A fitting module, configured to obtain a minimum circumscribed triangle by fitting in the plane rectangular coordinate system based on the user location data of the newly added PCI;

[0032] A determination module, configured to determine target base station data based on the minimum circumscribed triangle by using a spatial optimization algorithm;

[0033] The shared site determination module is used to determine a suspected shared site based on the target base station data.

[0034] In a third aspect, the present disclosure provides an electronic device, including:

[0035] processor;

[0036] a memory for storing instructions executable by the processor;

[0037] The processor is configured to execute the instructions to implement the method described in the first aspect.

[0038] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect.

[0039] In a fifth aspect, the present disclosure provides a computer program product, comprising a computer program / instructions, characterized in that when the computer program / instructions are executed by a processor, the method described in the first aspect is implemented.

[0040] The technical solution disclosed in this disclosure brings at least the following beneficial effects:

[0041] In an embodiment of the present disclosure, a newly added PCI is identified based on the MDT data in a preset area within a preset historical period; the newly added PCI is at least one; the user location data of the newly added PCI is mapped to a plane rectangular coordinate system; based on the user location data of the newly added PCI, a minimum circumscribed triangle is fitted in the plane rectangular coordinate system; through a spatial optimization algorithm, the target base station data is determined based on the minimum circumscribed triangle; and a suspected shared site is determined based on the target base station data. In this way, the communication discrete signal can be converted into plane coordinates, and the signal source position can be determined by fitting the minimum circumscribed triangle. The signal source position in the area can be more accurately estimated by the spatial source search algorithm to find the site with the highest matching degree and suspected to be a friendly competitor. In this way, not only does it not require additional labor costs or hardware equipment, but the suspected shared site can be automatically identified only by the algorithm, thereby effectively reducing operating costs and improving the intelligent management level of site equipment; at the same time, the accuracy and timeliness of shared site identification can also be improved.

[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0044] Figure 1 A schematic diagram of a flow chart of a shared site detection method provided in an embodiment of the present disclosure;

[0045] Figure 2 is a schematic diagram of a three-straight-line coordinate diagram provided by an embodiment of the present disclosure;

[0046] Figure 3 is a schematic diagram of a circumscribed triangle of the straight line L2 and the point P1 provided in an embodiment of the present disclosure;

[0047] Figure 4 is a schematic diagram of a circumscribed triangle of the straight line L2 and the point P2 provided in an embodiment of the present disclosure;

[0048] Figure 5 is a schematic diagram of a minimum circumscribed triangle provided by an embodiment of the present disclosure;

[0049] Figure 6 This is a schematic diagram of an average value of adjacent signal strength RSRP provided by an embodiment of the present disclosure;

[0050] Figure 7 is a schematic diagram of regional density clustering provided by an embodiment of the present disclosure;

[0051] Figure 8 A flow chart of a shared site detection method provided in an embodiment of the present disclosure

[0052] Fig. 9 is a structural schematic diagram of a shared site detection device provided by an embodiment of the present disclosure;

[0053] Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0054] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0055] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0056] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0057] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of national laws and regulations.

[0058] It should be noted that in the embodiments of the present disclosure, there may be existing solutions in the industry such as certain software, components, models, etc., which should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present disclosure, but it does not mean that the applicant has or will necessarily use the solution.

[0059] In the related technologies, major network operators have successively carried out the decommissioning of old equipment, the construction of new 5G (fifth-generation mobile communication technology), and the transformation of CRAN (Cloud-Radio Access Network) computer rooms. The equipment of major network operators changes frequently, and the number of shared sites continues to increase. In the actual operation process, due to the long financial settlement cycle of the tower company, operators often cannot promptly detect the addition of equipment from other operators to the site, which brings great challenges to the financial cost allocation and audit confirmation. In other words, tower cost expenditure is a rigid cost expenditure for the operator's network line. For shared sites, according to the agreement between the two parties, it is necessary to share the power of the equipment of the sharing party. However, in recent years, the scale and frequency of equipment changes of various operators have been large, and it is difficult to accurately locate the equipment changes of other operators. There are problems such as poor update timeliness, resulting in a waste of financial costs. The following methods are currently commonly used to solve the above problems:

[0060] 1) Manual inspection: The local and municipal companies will regularly arrange their own employees and maintenance personnel to check the sharing status of all sites one by one, and feedback the inspection results to the provincial companies. 2) "Harmony Eye" detection: Select some suitable base stations and install "Harmony Eye" wireless environment monitoring terminals. By detecting changes in friendly competitor signals at the monitoring sites, changes in base station sharing information can be discovered in a timely manner.

[0061] However, the current methods have at least the following technical problems:

[0062] 1. Manual investigation.

[0063] This is a detection method that lacks high precision: the current quality verification accuracy is low and is seriously affected by the experience level of the verifiers, making it difficult to effectively detect shared problems in complex sites; the cost is too high: a full monthly verification requires a large amount of human resources, totaling more than 100,000 person-days of time and personnel costs, and the existing number of maintenance personnel is far from meeting this demand.

[0064] 2. “Harmony Eye” detection.

[0065] High investment costs: Additional hardware equipment needs to be purchased and deployed on tower sites, with an investment of about RMB 1,000 per site. In addition, manpower and costs are required to maintain and test these devices. These inputs are relatively low compared to the outputs, making it difficult to promote them across the province.

[0066] In order to solve the above problems, the embodiments of the present disclosure provide a shared site detection method. This method can convert the communication discrete signal into plane coordinates through Gaussian projection, and fit the minimum circumscribed triangle to determine the signal source position. By introducing a spatial source search algorithm, the signal source position in the area is estimated more accurately using the two dimensions of signal strength and signal density, and the site with the highest matching degree suspected of being a friendly competitor is found. In this way, not only does it not require additional labor costs or hardware equipment, but it can also automatically identify whether a site has newly added friendly equipment through an algorithm, thereby effectively saving the company's operating costs and improving the intelligent management level of site equipment.

[0067] The technical solutions provided by various embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0068] Figure 1 The present invention provides a flowchart of a shared site detection method, which can be applied to a server, such as a single server or a server cluster. Figure 1 As shown, the shared site detection method may include the following steps:

[0069] S101, identifying a newly added PCI according to MDT data in a preset area within a preset historical period.

[0070] Among them, at least one new PCI is added.

[0071] In an embodiment of the present disclosure, MDT (Minimization Drive Test) data within a certain area (i.e., a preset area, for example, an area with a certain location as the center and a preset distance as the radius, the preset distance may be, for example, 5 kilometers, 6 kilometers, etc.) may be obtained within a past period of time (i.e., a preset historical period, for example, the past 3 months, 4 months, etc.). Then, based on the MDT data within the preset area within the preset historical period, the PCI (Physical Cell Identifier) ​​of the newly added cells in the preset area within the preset historical period is identified. It is understandable that the newly added PCI may be one, two or more. As a specific example, an area with a radius of 5 kilometers from a certain location may be selected as the preset area, and the MDT data of users in the area may be collected on a monthly basis. For example, the MDT data may be within a 5-kilometer range around 113.376156375 east longitude and 23.019922675 north latitude in May, June and July 2023. For specific examples, please refer to Table 1. Then, the collected MDT data is compared month by month. By listing all PCIs of each month and comparing and analyzing the PCI data of the previous month, the frequency points and cell physical identifiers (PCIs) newly added by competitors (friendly companies) of the current month are output. For example, by comparing the data of May with the data of June and July, it is found that the newly added PCI of friendly company 1 in June and July is 81; by comparing the data of May and June with the data of July, it is found that the PCIs of two newly added friendly companies in July are 201 and 325. In Table 1, RARP is the reference signal received power (Reference Signal Received Power).

[0072] Table 1

[0073] date User ID Friendly frequency Friendly PCI RSRP longitude latitude May 1 1 1650 99 -105 113.35337 23.011219 May 1 2 3740 311 -110 113.353477 23.011196 …… …… …… …… -109 …… …… July 31 ****** 3740 311 -111 113.355926 23.011207

[0074] S102, mapping the user location data of the newly added PCI to a plane rectangular coordinate system.

[0075] In an embodiment of the present disclosure, after the newly added PCI is identified, the user location data in each newly added PCI can be obtained, and the user location data can be, for example, the longitude and latitude data of the user. Then, for each newly added PCI, the user location data in the newly added PCI can be mapped to a plane rectangular coordinate system, for example, the longitude can be converted to the X axis, and the latitude can be converted to the Y axis, so as to achieve the mapping of the user location data to the plane rectangular coordinate system.

[0076] S103, based on the user position data of the newly added PCI, a minimum circumscribed triangle is obtained by fitting in a plane rectangular coordinate system.

[0077] In the embodiments of the present disclosure, it is considered that the emission shape of the cell signal source is a fan-shaped shape with a vertex characteristic. Therefore, after mapping the user location data of the newly added PCI to the plane rectangular coordinate system, the shape that is most appropriate to the fan and has vertex characteristics, that is, the minimum circumscribed triangle, can be fitted based on the scattered data distribution of the user geographic location coordinates of the newly added PCI, and the vertex of each minimum circumscribed triangle can be limited to a specific area. In this way, the minimum circumscribed triangle of each newly added PCI in the plane rectangular coordinate system can be obtained, and at the same time, the vertices of each minimum circumscribed triangle can also be output.

[0078] S104, determining target base station data based on the minimum circumscribed triangle through a spatial optimization algorithm.

[0079] In the embodiment of the present disclosure, after fitting the minimum circumscribed triangle of each newly added PCI, the possible shared base station data, that is, the target base station data, can be determined based on the user geographic location information in the minimum circumscribed triangle through a spatial source finding algorithm (SSFA). Exemplarily, a density partitioning module that clusters the spatial data set according to density features can be implemented in the spatial source finding algorithm to determine the target base station data based on the user geographic location information in the minimum circumscribed triangle.

[0080] S105: Determine a suspected shared site based on the target base station data.

[0081] In an embodiment of the present disclosure, after the target base station data is determined based on the minimum circumscribed triangle, the suspected shared site can be determined based on the target base station data. Exemplarily, the suspected shared site can be determined by calculating the distance between the signal source and the target base station data. For example, the distance between the suspected signal source and the longitude and latitude of the base station can be calculated, and the base station with the shortest distance corresponding to each signal source is taken out, which is the suspected friendly shared site, that is, the site with the highest matching degree suspected to have a friendly site, as the suspected shared site. In this way, the detection of suspected shared sites can be achieved.

[0082] In an embodiment of the present disclosure, a newly added PCI is identified based on the MDT data in a preset area within a preset historical period; the newly added PCI is at least one; the user location data of the newly added PCI is mapped to a plane rectangular coordinate system; based on the user location data of the newly added PCI, a minimum circumscribed triangle is fitted in the plane rectangular coordinate system; through a spatial optimization algorithm, the target base station data is determined based on the minimum circumscribed triangle; and a suspected shared site is determined based on the target base station data. In this way, the communication discrete signal can be converted into plane coordinates, and the signal source position can be determined by fitting the minimum circumscribed triangle. The signal source position in the area can be more accurately estimated by the spatial source search algorithm to find the site with the highest matching degree and suspected to be a friendly competitor. In this way, not only does it not require additional labor costs or hardware equipment, but the suspected shared site can be automatically identified only by the algorithm, thereby effectively reducing operating costs and improving the intelligent management level of site equipment; at the same time, the accuracy and timeliness of shared site identification can also be improved.

[0083] In some possible implementations, mapping the user location data of the newly added PCI to a plane rectangular coordinate system includes:

[0084] For each newly added PCI, obtain user location data of the newly added PCI;

[0085] The user location data of the newly added PCI is mapped to the plane rectangular coordinate system through Gauss-Krüger projection.

[0086] In an embodiment of the present disclosure, the mapping of the user location data of the newly added PCI to the plane rectangular coordinate system can be realized by Gauss-Kruger projection. Exemplarily, for each newly added PCI, the user location data of the newly added PCI can be obtained. For example, for each newly added PCI, the location information of all users related to it is found by tracing back and combining the corresponding monthly data table. The specific example of the user's location information can be shown in Table 2. Then, the Gauss-Kruger projection can be used to convert the user's longitude and latitude data into a plane rectangular coordinate system, the longitude is converted to the X-axis, and the latitude is converted to the Y-axis. Since the starting point of the longitude and latitude does not pass through the prime meridian, the origin is not set, and the value retains the accuracy of the longitude and latitude to avoid deformation and distortion. As a specific example, the data after conversion to the plane rectangular coordinate system can be shown in Table 3. In this way, not only can the conversion of the user location data to the plane rectangular coordinate system be realized, but also the accuracy of the position coordinates can be guaranteed.

[0087] Table 2

[0088] user RSRP Friendly frequency Friendly 1PCI longitude latitude 1 -105 1650 81 113.***236 23.***345 2 -110 3740 81 113.***732 23.***353 3 -108 1650 81 113.***742 23.***737 4 -102 3740 81 113.***263 23.***184 …… …… …… …… …… ……

[0089] Table 3

[0090] user Friendly 1PCI x-axis y-axis 1 81 2561762.282 37819218.44 2 81 2561760.446 37819229.4 3 81 2561753.547 37819251.18 4 81 2561750.876 37819228.97 …… …… …… ……

[0091] In some possible implementations, based on the user location data of the newly added PCI, a minimum circumscribed triangle is obtained by fitting in a plane rectangular coordinate system, including:

[0092] Based on the scattered data distribution of the newly added PCI user location data, the minimum circumscribed triangle is fitted in the plane rectangular coordinate system by geometric drawing.

[0093] Output the vertices of the smallest outer triangle.

[0094] In an embodiment of the present disclosure, based on the user location data of the newly added PCI, the minimum circumscribed triangle is fitted in a plane rectangular coordinate system. Exemplarily, based on the distribution of scattered data of the user location data of the newly added PCI, a shape that is most appropriate to the fan and has vertex characteristics, that is, a minimum circumscribed triangle, can be fitted by geometric drawing, and the position of the signal source (fixed point) is limited to a specific area, and the vertices of the minimum circumscribed triangle are output at the same time. The base station of the friendly company is determined based on the vertex method, so as to more accurately grasp the coverage shape of the cell, user distribution, signal coverage strength and other information. It can be understood that each newly added PCI corresponds to a minimum circumscribed triangle.

[0095] As an example, the specific implementation of this embodiment may be as follows:

[0096] For any newly added PCI, determine the set of boundary projection coordinates based on the user geographic location plane projection coordinate data in step 3, and mark the set as a convex point set (X n , Y n ), and randomly select 6 points (X a1 ,Y a1 ),(X a2 ,Y a2 ), (X b1 ,Y b1 ), (X b2 ,Y b2 ) and (X c1 ,Y c1 ), (X c2 ,Y c2 ); According to the given point coordinates, calculate the slope and intercept of each line, determine the positions of the three lines in the rectangular coordinate system, and record them as line 1, line 2, and line 3. Secondly, calculate the intersection points between line 1 and line 2, and line 1 and line 3, and record them as p1 and p2. And determine the straight-line distances h1 and h2 between p1 and p2 and line 3 according to the distance formula. And the distance between p1 and p2. Calculate the minimum circumscribed triangle according to the triangle area formula. After traversing all six point combinations, take the triangle with the smallest area as the minimum circumscribed triangle. The detailed calculation process is as follows:

[0097] From the convex point set (X n , Y n ) and record them as (X a1 ,Y a1 ),(X a2 ,Y a2 ), (X b1 ,Y b1 ), (X b2 ,Y b2 ) and (X c1 ,Y c1 ), (X c2 ,Y c2 ). Calculate the slope and intercept of each straight line based on the given point coordinates, where (X a1 ,Y a1 ),(X a2 ,Y a2 ) are connected to form a straight line 1, (X b1 ,Y b1 ), (X b2 ,Y b2 ) are connected to form straight line 2, (X c1 ,Y c1 ), (X c2 ,Y c2 ) to form a straight line 3. On a two-dimensional plane, a point set (x, y) can be fitted by a straight line. The slope (m) and intercept (b) of this line are calculated by the least squares method. The following is an example:

[0098] 1. For line 1: calculate the slope and intercept b1 = Y a1 -m1*X a1 .

[0099] 2. For line 2: calculate the slope and intercept b2 = Y b1 -m2*X b1 .

[0100] 3. For line 3: calculate the slope and intercept b3 = Y c1 -m3*X c1 .

[0101] 4. Continue to calculate the coordinates of the intersection of the two lines, calculate the X and Y coordinate formulas of the intersection, find the intersection of line 1 and line 2, and record it as point P1. The example is as follows:

[0102] Calculate the horizontal coordinate X of the intersection point p1 : Calculate the ordinate Y of the intersection point p1 :

[0103] 5. Continue to calculate the coordinates of the intersection of the two straight lines, calculate the X-coordinate and Y-coordinate formulas of the intersection, find the intersection of line 1 and line 3, and record it as point P2.

[0104] Calculate the horizontal coordinate X of the intersection point p2 : Calculate the ordinate Y of the intersection point p2 : Y P2 =m1*X P2 +b1.

[0105] 6. Calculate the height of the triangle formed by points P1, P2 and line 2. Calculate the distance h1 from point P1 to line 2: Calculate the distance h2 from point P2 to line 2:

[0106] 7. Calculate the length of the base of the triangle base. The length of the base is the horizontal distance between the two intersection points: base = abs(X p2 -X p1 ).

[0107] 8. Based on the length of the base of the enclosed triangle and the distances h1 and h2 from point P1 to line 2, calculate the area of ​​the triangle: S Δ =0.5*base*(h1+h2).

[0108] The data calculation details are as follows:

[0109] ① Take a set of six boundary points of the plane landmarks, as shown in Table 4 below:

[0110] Table 4

[0111] point X coordinate Y coordinate a1 2562777.26338 37818524.403181 a2 2562787.103669 37817886.962362 b1 2562791.46113 37818496.66445 b2 2562789.72681 37817880.78548 c1 2562838.88668 37817433.29877 c2 2562843.05825 37817444.53341

[0112] Combination Figure 2 , input area partition module to calculate slope and intercept, then

[0113] L1 is: b1=Y a1 -m1*X a1 =128194774.362506

[0114] L2 is: b2=Y b1 -m2*X b1 =872261168.647452L

[0115] L3 is: b3=Y c1 -m3*X c1=30915338.3421732

[0116] ②Based on the slope and intercept calculated in step ①, substitute them into the formula to determine the coordinates of the intersection of the two lines and record them as points P1 and P2, see Figure 3 and Figure 4 .

[0117] Coordinates of P1: Y p1 =m1*X p1 +b1=294209204.232475

[0118] Coordinates of P2: Y p2 =m1*X p2 +b1=229693995.311917

[0119] ③ Based on the output of P1, P2 and line 2 calculated above, calculate the height of the enclosed triangle.

[0120] The distance h1 from point P1 to line 2:

[0121] The distance h2 from point P2 to line 2:

[0122] ④ Calculate the length of the base of the enclosed triangle and record it as base; the length of the base is the horizontal distance between the two intersection points.

[0123] base = abs(X p2 -X p1 )=995932.92701915

[0124] ⑤Finally, based on the above triangle-related data, and, calculate the area of ​​the triangle.

[0125] S Δ =0.5*base*(h1+h2)=4583897716457.64

[0126] Repeat the above process, combine all the points, and calculate the area of ​​the triangle formed by all six points, recorded as S ALLΔ ={S1, S2, ..., S m}, where m is the number of combinations. Finally, take the triangle with the smallest area as the minimum circumscribed triangle, i.e. S minΔ =minS ALLΔ The communication discrete signal of the collected sample is transformed by Gauss-Krüger projection, and the coordinate set is as follows Figure 5 As shown, the values ​​of the three vertices in the plane coordinate system can be found in Table 5.

[0127] Table 5

[0128] vertex x-axis y-axis Vertex 1 2562438.128 37815431.47 Vertex 2 2562724.248 37815908.23 Vertex 3 2562932.93 37815238.96

[0129] In this way, by drawing the minimum circumscribed triangle, the position and shape of the geographical location in the plane coordinate system can be intuitively presented, which facilitates the search for the vertex emission point of the signal source and visualization. This method can help better understand the signal coverage range and direction, and provide an effective analysis tool for the emission area shape and vertex characteristics of the base station cell signal source.

[0130] In a possible implementation, determining target base station data based on a minimum circumscribed triangle using a spatial optimization algorithm includes:

[0131] Based on the scattered user location data within the minimum circumscribed triangle area, the minimum circumscribed triangle area is divided into multiple clusters through a spatial optimization algorithm combined with the distribution density of the scattered user location data;

[0132] Calculate the signal strength and center point of different coordinate points in each cluster;

[0133] Take the average signal strength of a preset number of adjacent points from the vertex of the smallest circumscribed triangle inward;

[0134] Determine whether the average values ​​of a preset number of adjacent signal strengths meet a preset signal step condition;

[0135] When the average values ​​of the signal strengths of a preset number of adjacent ones meet the preset signal step condition, the vertex is determined as the target base station data.

[0136] In an embodiment of the present disclosure, when the target base station data is determined based on the minimum circumscribed triangle by a spatial optimization algorithm, for each minimum circumscribed triangle, the minimum circumscribed triangle area can be divided into multiple clusters based on the scattered user location data in the minimum circumscribed triangle area by combining the distribution density of the scattered user location data through a spatial optimization algorithm. Among them, the distribution density can be represented by the regional density, that is, the minimum circumscribed triangle area can be divided into multiple clusters by combining the regional density of the scattered user location data distribution through a spatial optimization algorithm. Then, the signal strength and cluster center point of different coordinate points in each cluster can be calculated. In this way, the density can be used as the affiliation of the cluster to examine the cluster, and the number of clusters can be automatically determined and clusters of any shape can be found without retaining noise. At the same time, the object discarding caused by improper threshold selection is also avoided, which is more suitable for finding the cluster center with the highest density and the dense area where it is located. Afterwards, the regional division module in the spatial source search algorithm can be used to determine the target base station data based on the vertices of the minimum circumscribed triangle, the signal strength and center point of different coordinate points in each cluster. Exemplarily, the RSRP average of the signal strength of a preset number of adjacent points can be taken from the vertex of the smallest circumscribed triangle inward, for example, the preset number can be 20. The preset number of adjacent signal strength averages are compared with the preset signal step condition to determine whether the preset number of adjacent signal strength averages meet the preset signal step condition. The preset signal step condition can be, for example, a step-by-step decreasing trend, or a proportion of the step-by-step decreasing trend is greater than a preset proportion, and the preset proportion can be, for example, 70%. If the preset number of adjacent signal strength averages meet the preset signal step condition, the vertex can be determined as suspected signal source data, that is, target base station data.

[0137] As a specific example, the process of determining the target base station data by using the spatial fast-moving algorithm may include the following processing:

[0138] 1. Divide the regional density module.

[0139] The spatial source finding algorithm (SSFA) is an algorithm used in this embodiment to infer and estimate the target location. The regional division module of the spatial source finding algorithm can distinguish the coverage shape and distribution of the cell by the regional division module. The SSFA algorithm uses effective algorithm strategies and technical means in the process of finding the target or transmitting signal source, and can complete the source finding task in a shorter time. This helps to improve the real-time performance and work efficiency of the algorithm. Through the collaborative work of the regional division module and the transmitting source positioning module, the SSFA algorithm can provide a more accurate target or transmitting source positioning result. It can make full use of available information and data to infer and estimate the target location. Exemplarily, based on the scattered user geographic location data of the minimum circumscribed triangle area after fitting, the regional division module in the spatial source finding algorithm can be used to classify it into different clusters by combining the user geographic location information with the density of information distribution, and determine the cluster center point. In this way, density can be used as the attribution of the cluster, and the number of clusters can be automatically determined and clusters of any shape can be found without retaining noise. At the same time, it also avoids the object discarding caused by improper threshold selection, and is more suitable for finding the cluster center with the highest density and the dense area where it is located. The density calculation formula can be as follows:

[0140]

[0141] Among them, x represents the x-coordinate of the point where the user receives the signal, and y represents the y-coordinate of the point where the user receives the signal. Ordered sequence: Reorder the data according to the distance between all objects and:

[0142] S pmax ={x1,x2,...,x n dis(x max ,x1)≤dis(x max ,x2)≤……≤dis(x max ,x n )}

[0143] Through the above formula, the signal strength of different coordinate points in different clusters and the cluster center point are calculated. The calculation results can be shown in Table 6.

[0144] Table 6

[0145]

[0146] 2. Find signal source modules from friendly companies

[0147] After calculating the center point, you can use the area division module in the spatial source search algorithm. Take the average signal strength RSRP of 20 adjacent vertex step by step to compare, and if it shows a step-by-step decreasing trend, then the vertex is the most suspected location point shared by the friendly competitor. If the conditions are not met, it means it is not the most suspected point, then remove the cluster center point with the smallest density and refit the smallest circumscribed triangle until the most suspected signal source location is found. This method can effectively determine the range of a geographic location in a plane coordinate system, and provide visual support for finding the signal source emission point, helping analysts to better understand the signal coverage range. The signal strength RSRP average value example is as follows Figure 6 , a schematic diagram of regional density clustering can be found in Figure 7 The specific processing flow can be as follows:

[0148] 1) Calculate the distance between the vertex and all the points around the center with the highest density. This step is to determine a center point and then calculate the distance between the center point and all the points in the nearest cluster.

[0149] 2) Compare the RSRP values ​​of each point. If the proportion of signal strength gradually weakening is greater than 70%, it is considered that the point may be a shared site of a friendly company.

[0150] 3) If the conditions are met, the vertex can be defined as the most suspected friendly vendor shared site. If the RSRP values ​​of the points around a vertex show a clear trend of signal strength weakening, then the vertex is defined as the most suspected friendly vendor shared site.

[0151] 4) If the conditions are not met, the cluster with the smallest center point density is eliminated, and the smallest circumscribed triangle is refitted until the most suspected friendly shared site (that is, the target base station data) is selected. The coordinate examples of suspected friendly shared sites can be found in Table 7.

[0152] Table 7

[0153] PCI Friendly signal source x-axis y-axis 81 Source 1 2562438.128** 37815431.47** 82 Source 2 2562724.248** 37815908.23** 83 Signal Source 3 2256243.923** 37815238.96** 84 Signal Source 4 2562438.128** 37815431.47** ... ... ... ...

[0154] In this way, a comprehensive judgment is made by introducing the two factors of concentration density and signal strength. First, the distance of all points in each cluster category is calculated to obtain a distance matrix; then, according to the distance matrix, the minimum distance is selected as the basis for judging the position coordinates of the suspected friendly signal source. This method takes into account the distance between all points in each cluster category, and can more accurately determine the position of the signal source, while avoiding the errors and deficiencies that may occur in traditional methods. Through this method, the position of the suspected friendly signal source can be determined quickly and accurately, providing a useful reference for research and application in related fields. This step uses the concentration density and signal strength tracking method to determine the most likely position of the real suspected friendly signal source. Considering the distance between all points in each cluster category, the minimum distance is selected as the basis for judging the position coordinates of the suspected friendly signal source.

[0155] In a possible implementation, determining a suspected shared site based on target base station data includes:

[0156] The target base station data is converted into longitude and latitude data through the inverse operation of Gauss-Krüger projection;

[0157] Calculate the distance between each signal source data and the nearest base station;

[0158] The base station with the shortest distance corresponding to each signal source is selected and determined as the suspected shared site.

[0159] In an embodiment of the present disclosure, when determining a suspected shared site based on target base station data, the corresponding coordinate point (i.e., target base station data) can be converted into longitude and latitude data by Gauss-Kruger projection inverse operation. The result after Gauss-Kruger projection inverse operation is shown in Table 8 below. The Haversine formula is then used to calculate the distance between the signal source and the operating base station. The data of each signal source and the nearest base station are output by calculation, as shown in Table 9. The Haversine formula is shown below:

[0160]

[0161] Among them, Lngl Lat1 represents point A, which represents the longitude and latitude of the friendly company, Lng2 Lat2 represents point B, the longitude and latitude of the local company; a=Lat1-Lat2 is the difference between the latitudes of the two points and b=Lng1-Lng2 is the difference between the longitudes of the two points; 3.6378.137 is the equatorial radius of the earth, in kilometers.

[0162] Table 8

[0163] Friendly signal source longitude latitude Source 1 113.***490 23.***987 Source 2 113.***982 23.***748 Signal Source 3 113.***406 23.***345

[0164] Table 9

[0165]

[0166] Finally, the base station with the shortest distance corresponding to each signal source can be selected as the suspected shared site. The specific base station data examples of the suspected shared site can be shown in Table 10. By sending the subsequent output results for further verification and validation, those shared overpayment problems that may be relatively hidden on site can be effectively discovered, while minimizing the expenditure of financial costs to achieve more efficient cost control. The verification and validation process of this method not only helps to improve work efficiency, but also provides strong guarantees for the robustness of billing for friendly shared sites.

[0167] Table 10

[0168] Signal Source Base station ID longitude latitude Distance between signal source and base station Source 1 **0 113.***075 23.***693 12.2 m Source 2 **1 113.***075 23.***693 312.2 m Signal Source 3 **2 113.***075 23.***693 193.6 m

[0169] In a further possible implementation, it also includes:

[0170] When the mean values ​​of the signal strengths of a preset number of adjacent ones do not satisfy the preset signal step condition, the cluster center point with the smallest density is removed, and the minimum circumscribed triangle is refitted.

[0171] In the embodiment of the present disclosure, if the mean values ​​of the signal strengths of a preset number of adjacent signals do not meet the preset signal step condition, the cluster where the center point with the smallest density is located can be eliminated, that is, the center point of the cluster with the smallest density can be eliminated. Then, the minimum circumscribed triangle can be refitted, and then the steps of shared site detection can be repeated until the most suspected shared site is selected. It can be understood that the number of repetitions can also be limited, for example, set to 3 times, 5 times, etc.

[0172] In order to make the shared site detection method provided by the embodiment of the present disclosure clearer, the following is described in conjunction with a specific embodiment. As follows:

[0173] The disclosed embodiment can collect communication discrete signals from different sites, identify the newly added PCI friendly cells by comparing and analyzing the MDT data in the same area for multiple months, perform Gauss-Krüger projection on the user geographic information data of the identified friendly cells, and map them into a plane rectangular coordinate system. By referring to the means of geometric drawing graphics technology, the shape that is most appropriate to the fan and has vertex characteristics - the minimum circumscribed triangle is fitted, the position of the signal source is limited to a specific area, and the minimum circumscribed triangle vertex is output at the same time. The method of determining the friendly base station (shared site) based on the vertex method can more accurately grasp the coverage shape and distribution of the cell, and can also more accurately grasp the user distribution and signal coverage of the friendly site, which facilitates the search for the vertex emission point of the signal source and visualization, and greatly improves the efficiency of shared site data collection. For the identified data, we use the spatial source search algorithm, combined with the two dimensions of density and signal strength, and adopt the regional division module to divide the signal area to improve the real-time performance and work efficiency of the algorithm; we deeply mine the signal information, and determine the location of the emission source through the collaborative work of the regional division module and the emission source positioning module, thereby improving the precise positioning of the location of suspected friendly sites. After determining the most suspicious vertex coordinates, we convert these vertex coordinates into longitude and latitude again through the inverse operation of the Gauss-Kruger projection. And use the Haversine formula to calculate the distance between the signal source and the operating base station.

[0174] In actual use, after the data is input, the data characteristics of the site's geographic location information and the conversion of longitude and latitude are analyzed through Gauss-Krüger projection and SSFA algorithm, and the coordinates of the suspicious shared site are output and displayed in text form. The staff can follow the prompts to quickly locate the shared site, and conduct verification, processing, and cost expenditure backtracking of the shared site. In this way, the accuracy and timeliness of friendly site identification can be greatly improved, and the operator's labor cost expenditure on shared sites can be better reduced.

[0175] Combination Figure 8 , a specific example implementation process of the embodiment of the present disclosure may include the following process:

[0176] Step 1: Identify the newly added PCI

[0177] Select an area with a radius of 5 kilometers from a certain location, collect the MDT data of users in the area on a monthly basis, compare the collected MDT data month by month, list all PCIs of each month, and compare and analyze the PCI data of the previous month, and output the frequency points and cell physical identifiers (PCIs) newly added by competitors (friendly companies) in the current month. For example, collect a set of MDT data within a 5-kilometer range around 113.376156375 east longitude and 23.019922675 north latitude in May, June and July 2023. See Table 1 above.

[0178] Comparing the data in May with those in June and July in Table 1, we find that the new PCI of Friendly Business 1 in June and July is 81;

[0179] Comparing the data of May and June with the data of July in Table 1, we found that the PCIs of two new competitors in July were 201 and 325.

[0180] Step 2: Geolocation Conversion

[0181] Based on the newly added PCI identified in step 1, trace back and combine with the corresponding monthly data table to find all user geographic data related to it. The user's location information is shown in Table 2. In order to improve the accuracy of geographic coordinates, the Gauss-Kruger projection can be used to convert the user's longitude and latitude data into a plane rectangular coordinate system, with longitude converted to the X axis and latitude converted to the Y axis. Since the starting point of longitude and latitude does not pass through the prime meridian, there is no origin, and the value retains the longitude and latitude accuracy to avoid deformation and distortion. The converted data is shown in Table 3.

[0182] Step 3: Fitting the circumscribed triangle

[0183] Since the emission shape of the cell signal source is fan-shaped and has the characteristic of one vertex, this proposal is based on the distribution of scattered data of the user's geographic location coordinates, and uses the means of geometric drawing technology to fit the shape that is most appropriate to the fan and has vertex characteristics - the minimum circumscribed triangle, which limits the location of the signal source to a specific area and outputs the vertex of the minimum circumscribed triangle. The vertex method is used as a basis to determine the base station of the friendly company, so as to more accurately grasp the coverage shape of the cell, user distribution, signal coverage strength and other information.

[0184] The specific implementation process of this process is similar to that of the above method embodiment and will not be repeated here.

[0185] Step 4: Divide the regional density module

[0186] The regional division module of the spatial source search algorithm can distinguish that the regional division module is mainly to grasp the coverage shape and distribution of the cell. In the process of searching for the target or emission source, the SSFA algorithm adopts effective algorithm strategies and technical means, and can complete the source search task in a shorter time. This helps to improve the real-time performance and work efficiency of the algorithm. Through the collaborative work of the regional division module and the emission source positioning module, the SSFA algorithm can provide more accurate target or emission source positioning results. It can make full use of available information and data to infer and estimate the target location. Based on the scattered user geographic location data of the minimum circumscribed triangle area after fitting in step 3, the regional division module in the spatial source search algorithm can be introduced. The algorithm can classify it into different clusters and determine the cluster center point by combining the user geographic location information with the density of information distribution. Its advantage is that it uses density as the attribution of the cluster, can automatically determine the number of clusters and find clusters of any shape, does not retain noise, and also avoids the object discarding caused by improper threshold selection, which is more suitable for finding the cluster center with the highest density and the dense area where it is located.

[0187] The specific implementation process of this process is similar to that of the above method embodiment and will not be repeated here.

[0188] Step 5: Find a friendly signal source module

[0189] After calculating the center point in step 4, this proposal uses the area division module in the spatial source search algorithm. Take the average signal strength RSRP of 20 adjacent vertex step by step for comparison. If it shows a step-by-step decreasing trend, then the vertex is the most suspected location point shared by friendly companies. The specific implementation process of this process is similar to the above method embodiment, and will not be repeated here.

[0190] Step 6: Match the most suitable base station

[0191] After determining the coordinates of the suspected friendly signal source, the corresponding coordinate points can be converted into longitude and latitude through the inverse operation of the Gauss-Kruger projection. And the distance between the signal source and the operating base station is calculated using the semi-vector formula. The specific implementation process of this process is similar to the above method embodiment and will not be repeated here. In this way, by sending the subsequent output results for further verification and validation, those shared overpayment problems that may be more hidden on site can be effectively discovered, while minimizing the expenditure of financial costs to achieve more efficient cost control. The verification and validation process of this method not only helps to improve work efficiency, but also provides a strong guarantee for the robustness of the billing of friendly shared stations.

[0192] As a specific example, this embodiment, by citing the site friendly business intelligent detection method based on communication discrete signal area fitting, combined with information support means into actual production applications, has found a total of 5,336 shared problem sites, verified and changed 4,249 sites from exclusive to shared, traced costs of about 2.8116 million yuan, and the sharing improvement rate was 79.63%, as shown in the following Table 11. In this way, by enhancing the intelligent detection and positioning capabilities of site friendly business sharing, promoting the establishment of digital and intelligent management and control means, achieving the reduction of tower service fees, and providing data support for base station site selection decisions, improving the precise management and control capabilities of the rationality, compliance, and accuracy of wireless operating cost payments.

[0193] Table 11

[0194]

[0195] It can be seen that the shared site detection method provided by the embodiment of the present disclosure has at least the following advantages:

[0196] Low cost: Compared with manual inspection, there is no need to deploy a large number of personnel to conduct unified inspection of shared sites, which greatly saves manpower and time costs. At the same time, compared with monitoring methods such as "Heyan" detection that require additional hardware investment, the method adopted in this proposal does not require additional hardware equipment, reducing hardware investment costs.

[0197] Good timeliness: Make full use of MDT data to monitor user data in the area. This method can timely discover and monitor new PCIs added within a time period, so as to discover new friendly shared stations as early as possible.

[0198] The specific implementation and technical effects of each step of this embodiment are similar to those of the above method embodiments, and will not be repeated here.

[0199] Based on the same inventive concept, the embodiment of the present disclosure also provides a shared site detection device. Fig. 9 As shown, the shared site detection device 900 includes:

[0200] An identification module 910, configured to identify a newly added PCI according to MDT data in a preset area within a preset historical period; the newly added PCI is at least one;

[0201] A mapping module 920, configured to map the user location data of the newly added PCI to a plane rectangular coordinate system;

[0202] A fitting module 930, configured to obtain a minimum circumscribed triangle by fitting in the plane rectangular coordinate system based on the user location data of the newly added PCI;

[0203] A determination module 940 is used to determine the target base station data based on the minimum circumscribed triangle by using a spatial optimization algorithm;

[0204] The shared site determination module 950 is configured to determine a suspected shared site based on the target base station data.

[0205] In a possible implementation, the mapping module 920 is configured to:

[0206] For each of the newly added PCIs, obtaining user location data of the newly added PCI;

[0207] The user position data of the newly added PCI is mapped to a plane rectangular coordinate system through Gauss-Krüger projection.

[0208] In a possible implementation, the fitting module 930 is used to:

[0209] Based on the scattered data distribution of the user location data of the newly added PCI, a minimum circumscribed triangle is fitted in the plane rectangular coordinate system by means of geometric drawing;

[0210] Output the vertices of the minimum outer triangle.

[0211] In a possible implementation, the determining module 940 is configured to:

[0212] Based on the scattered user location data within the minimum circumscribed triangle area, the minimum circumscribed triangle area is divided into a plurality of clusters by a spatial optimization algorithm combined with the distribution density of the scattered user location data;

[0213] Calculate the signal strength and center point of different coordinate points in each cluster;

[0214] Taking the average of the signal strengths of a preset number of adjacent points inward from the vertex of the minimum circumscribed triangle;

[0215] Determining whether the average values ​​of the preset number of adjacent signal strengths meet a preset signal step condition;

[0216] When the average values ​​of the preset number of adjacent signal strengths meet the preset signal step condition, the vertex is determined as the target base station data.

[0217] In a possible implementation manner, the shared site determination module 950 is used to:

[0218] The target base station data is converted into longitude and latitude data by Gauss-Krüger projection inverse operation;

[0219] Calculate the distance between each signal source data and the nearest base station;

[0220] The base station with the shortest distance corresponding to each signal source is selected and determined as the suspected shared site.

[0221] In a possible implementation, it further includes:

[0222] The elimination module is used to eliminate the cluster center point with the smallest density and refit the smallest circumscribed triangle when the mean values ​​of the preset number of adjacent signal strengths do not meet the preset signal step condition.

[0223] The specific implementation method and technical effect of the device provided in the embodiment of the present disclosure are similar to those of the above-mentioned method embodiment, and will not be repeated here.

[0224] According to an embodiment of the present disclosure, the present disclosure also discloses an electronic device, a computer-readable storage medium, and a computer program product.

[0225] Fig.10 A schematic block diagram of an example electronic device 1000 that can be used to implement an embodiment of the present disclosure is shown. The electronic device 1000 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0226] like Fig.10 As shown, the electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0227] Multiple components in the electronic device 1000 are connected to the I / O interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a disk, an optical disk, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0228] The computing unit 1001 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above, such as the shared site detection method. For example, in some embodiments, the shared site detection method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the shared site detection method described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the shared site detection method in any other appropriate manner (eg, by means of firmware).

[0229] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0230] The program code of the computer program product for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0231] In the context of the present disclosure, a computer-readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0232] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0233] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0234] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0235] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0236] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A shared site detection method, characterized in that: include: Identify newly added PCIs based on MDT data in a preset area during a preset historical period; The number of newly added PCI is at least one; Mapping the user location data of the newly added PCI to a plane rectangular coordinate system; Based on the user location data of the newly added PCI, a minimum circumscribed triangle is obtained by fitting in the plane rectangular coordinate system; Determine the target base station data based on the minimum circumscribed triangle by a spatial optimization algorithm; A suspected shared site is determined based on the target base station data.

2. The shared site detection method according to claim 1, characterized in that: Mapping the user location data of the newly added PCI to a plane rectangular coordinate system includes: For each of the newly added PCIs, obtaining user location data of the newly added PCI; The user position data of the newly added PCI is mapped to a plane rectangular coordinate system through Gauss-Krüger projection.

3. The shared site detection method according to claim 1, characterized in that: The user location data based on the newly added PCI is fitted in the plane rectangular coordinate system to obtain a minimum circumscribed triangle, including: Based on the scattered data distribution of the user location data of the newly added PCI, a minimum circumscribed triangle is fitted in the plane rectangular coordinate system by means of geometric drawing; Output the vertices of the minimum outer triangle.

4. The shared site detection method according to claim 1, characterized in that: The determining the target base station data based on the minimum circumscribed triangle by using a spatial optimization algorithm includes: Based on the scattered user location data within the minimum circumscribed triangle area, the minimum circumscribed triangle area is divided into a plurality of clusters by a spatial optimization algorithm combined with the distribution density of the scattered user location data; Calculate the signal strength and center point of different coordinate points in each cluster; Taking the average of the signal strengths of a preset number of adjacent points inward from the vertex of the minimum circumscribed triangle; Determining whether the average values ​​of the preset number of adjacent signal strengths meet a preset signal step condition; When the average values ​​of the preset number of adjacent signal strengths meet the preset signal step condition, the vertex is determined as the target base station data.

5. The shared site detection method according to claim 4, characterized in that: The determining the suspected shared site based on the target base station data includes: The target base station data is converted into longitude and latitude data by Gauss-Krüger projection inverse operation; Calculate the distance between each signal source data and the nearest base station; The base station with the shortest distance corresponding to each signal source is selected and determined as the suspected shared site.

6. The shared site detection method according to claim 4, characterized in that: Also includes: When the mean values ​​of the preset number of adjacent signal strengths do not satisfy the preset signal step condition, the cluster center point with the smallest density is removed, and the minimum circumscribed triangle is refitted.

7. A shared site detection device, characterized in that: include: An identification module, used to identify a newly added PCI based on MDT data in a preset area within a preset historical period; The number of newly added PCI is at least one; A mapping module, used for mapping the user location data of the newly added PCI to a plane rectangular coordinate system; A fitting module, configured to obtain a minimum circumscribed triangle by fitting in the plane rectangular coordinate system based on the user location data of the newly added PCI; A determination module, configured to determine target base station data based on the minimum circumscribed triangle by using a spatial optimization algorithm; The shared site determination module is used to determine a suspected shared site based on the target base station data.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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