Antenna anomaly identification method and device

Abstract

The application relates to the technical field of antennas and provides an antenna anomaly identification method and device. The method comprises the following steps: acquiring reference signal received power of each test point; acquiring antenna pixel points associated with sampling pixel points on a target drawing to obtain target antenna pixel points; mapping the sampling pixel points to a preset coordinate system; clustering the sampling pixel points in the preset coordinate system to obtain a plurality of clustering clusters; and determining whether the target antenna pixel points associated with the sampling pixel points are abnormal according to the cluster center longitudinal coordinates of the plurality of clustering clusters in the preset coordinate system. The antenna anomaly identification method and device provided by the application can accurately find components in hidden engineering and accurately record specific test paths and test results, and the accuracy and efficiency of antenna anomaly identification are effectively improved through subsequent analysis and positioning.

Description

Technical Field

[0001] This application relates to the field of antenna technology, specifically to an antenna anomaly identification method and apparatus. Background Technology

[0002] Indoor distribution systems are a common network architecture used to improve the mobile communication environment within buildings. Their principle is to distribute the mobile communication base station signal evenly throughout the building by deploying indoor antennas in various areas, ensuring ideal signal coverage. Indoor distribution systems mainly consist of active devices, passive devices, antennas, and feeders. Since the system's endpoints are passive devices and antennas, which lack power supply units, their upstream components are typically monitored. However, the antennas themselves cannot be independently monitored, making it difficult to determine whether the antennas in the indoor distribution system are operating normally.

[0003] Currently, anomaly identification for antennas in indoor distribution systems mainly relies on on-site complaint investigations and weak coverage testing, followed by judgment based on the optimization experience of on-site testing personnel. However, because indoor distribution systems may have concealed components, on-site testing personnel may not be able to accurately locate the system's components. Therefore, anomaly identification based solely on on-site investigation results is inaccurate and inefficient. Furthermore, since GPS positioning is unavailable indoors, on-site testing requires manual marking of points, making it difficult to accurately record specific test paths and results, which hinders subsequent analysis and further reduces the accuracy and efficiency of anomaly identification. Summary of the Invention

[0004] This application provides an antenna anomaly identification method and apparatus to solve the technical problem of low accuracy and efficiency in identifying antenna anomalies during on-site investigation and testing.

[0005] In a first aspect, embodiments of this application provide an antenna anomaly identification method, including:

[0006] Obtain the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration;

[0007] Obtain the antenna pixel points associated with the sampling pixel points on the target drawing to obtain the target antenna pixel points; the sampling pixel points are the pixel points mapped from the test points to the target drawing, the antenna pixel points are the pixel points corresponding to the antennas on the target drawing, and the target drawing is the indoor distribution system drawing of the target area;

[0008] The sampled pixels are mapped to a preset coordinate system; the preset coordinate system uses the first distance between the sampled pixels and the target antenna pixels on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis.

[0009] The sampled pixels in the preset coordinate system are clustered to obtain multiple clusters;

[0010] Based on the ordinate of the cluster centers of the multiple clusters in the preset coordinate system, determine whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal.

[0011] In one embodiment, the clustering of sampled pixels in the preset coordinate system to obtain multiple clusters includes:

[0012] Divide the two sampled pixels with the minimum distance among all sampled pixels into a close cluster, and determine the distance between the two sampled pixels as the first distance to be processed;

[0013] Calculate the minimum distance between any sampled pixel outside the nearby cluster and the sampled pixel within the nearby cluster to obtain the second distance to be processed;

[0014] After adding any of the sampled pixels to the nearby cluster, return to the step of calculating the minimum distance between any sampled pixel outside the nearby cluster and the sampled pixel inside the nearby cluster, until all sampled pixels are added to the nearby cluster, resulting in multiple second distances to be processed;

[0015] The first distance to be processed and the plurality of second distances to be processed are determined as the second distances, and the optimal grouping method of the plurality of second distances is determined according to the natural breakpoint method;

[0016] Cluster the sampled pixels corresponding to the second distance within any group in the optimal grouping method to obtain multiple clusters.

[0017] In one embodiment, determining the optimal grouping method for multiple second distances based on the natural breakpoint method includes:

[0018] The sum of squared first deviations of the multiple second distances is obtained based on the mean of the multiple second distances;

[0019] Multiple second distances are grouped according to different grouping methods to obtain multiple groups under any grouping method;

[0020] Based on the mean of the second distance in any group under any grouping method, the second deviation sum of squares of the second distance in any group is obtained;

[0021] Summing the sum of the squared second deviations of all groups under any of the grouping methods yields the total sum of squared deviations.

[0022] The variance fit goodness of fit for any grouping method is obtained by combining the first sum of squared deviations with the total sum of squared deviations.

[0023] The grouping method corresponding to the maximum variance fit under different grouping methods is determined as the optimal grouping method for the second distance.

[0024] In one embodiment, determining whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the ordinate of the cluster center of the plurality of clusters in the preset coordinate system includes:

[0025] If the minimum value of the ordinate of the cluster center in the preset coordinate system is less than the absolute value threshold of the received power, then the number of sampled pixels associated with any target antenna pixel in the first cluster corresponding to the minimum value of the ordinate of the cluster center is obtained, and multiple first quantities are obtained.

[0026] The sampled pixels associated with the target antenna pixel corresponding to the first maximum number among all sampled pixels are determined as the target sampled pixel set;

[0027] The line connecting the sampled pixels in the target sampled pixel set is used as the positive anomaly dividing line;

[0028] If the cluster center corresponding to the minimum ordinate of the cluster center of multiple second clusters is located on the side of the positive anomaly dividing line away from the ordinate in the preset coordinate system, then the second cluster corresponding to the minimum ordinate of the cluster center is determined to be a normal cluster; the second cluster is a cluster other than the first cluster among the multiple clusters.

[0029] The number of sampled pixels associated with any target antenna pixel in the normal cluster is obtained to obtain multiple second quantities;

[0030] Add the sampled pixels associated with the target antenna pixel corresponding to the second maximum number from all sampled pixels to the target sampled pixel set, and take the normal cluster as the first cluster, then return to the step of taking the line connecting the sampled pixels in the target sampled pixel set as the positive anomaly dividing line, until all clusters have completed the positive anomaly judgment;

[0031] If the cluster center corresponding to the minimum ordinate of the cluster center of multiple second clusters is located on the side of the positive anomaly dividing line closer to the ordinate in the preset coordinate system, then the second cluster corresponding to the minimum ordinate of the cluster center is determined to be an anomalous cluster.

[0032] The number of sampled pixels associated with any target antenna pixel in the abnormal cluster is obtained to obtain the third number;

[0033] The fourth quantity is obtained by acquiring the number of sampling pixels associated with any target antenna pixel among all sampling pixels;

[0034] If the ratio of the third quantity to the fourth quantity is greater than the ratio threshold, then the antenna corresponding to any target antenna pixel is determined to be abnormal.

[0035] In one embodiment, determining whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the ordinate of the cluster center of the plurality of clusters in the preset coordinate system includes:

[0036] If the minimum value of the ordinate of the cluster center in the preset coordinate system is greater than or equal to the threshold value of the absolute value of the received power, then it is determined that the antennas corresponding to all target antenna pixels are abnormal.

[0037] In one embodiment, obtaining the antenna pixel point associated with the sampling pixel point on the target drawing, and thus obtaining the target antenna pixel point, includes:

[0038] If the minimum distance between the first sampled pixel and all antenna pixels at the first sampling time is a unique value, then the antenna pixel corresponding to the unique value is determined as the target antenna pixel corresponding to the first sampled pixel.

[0039] If the minimum distance between the first sampled pixel at the first sampling time and all antenna pixels is not unique, then the target antenna pixel corresponding to the first sampled pixel is determined based on the second sampled pixel at the second sampling time and the third sampled pixel at the third sampling time.

[0040] The second sampling time is the previous sampling time of the first sampling time, and the third sampling time is the next sampling time of the first sampling time.

[0041] In one embodiment, determining the target antenna pixel corresponding to the first sampling pixel based on the second sampling pixel corresponding to the second sampling time and the third sampling pixel corresponding to the third sampling time includes:

[0042] If the target antenna pixel corresponding to the second sampled pixel is any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, then the target antenna pixel corresponding to the second sampled pixel is determined as the target antenna pixel corresponding to the first sampled pixel.

[0043] If the target antenna pixel corresponding to the second sampled pixel is not any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, then the target antenna pixel corresponding to the third sampled pixel is determined to be the target antenna pixel corresponding to the first sampled pixel.

[0044] Secondly, embodiments of this application provide an antenna anomaly identification device, comprising:

[0045] The reference signal received power acquisition module is used to: acquire the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration;

[0046] The target antenna pixel acquisition module is used to: acquire antenna pixels associated with sampling pixels on a target drawing to obtain target antenna pixels; wherein the sampling pixels are pixels mapped from the test points to the target drawing, the antenna pixels are pixels corresponding to antennas on the target drawing, and the target drawing is an indoor distribution system drawing of the target area;

[0047] The coordinate system mapping module is used to: map the sampled pixel point to a preset coordinate system; the preset coordinate system uses the first distance between the sampled pixel point and the target antenna pixel point on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis;

[0048] The clustering module is used to: cluster the sampled pixels in the preset coordinate system to obtain multiple clusters;

[0049] The antenna anomaly identification module is used to: determine whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the ordinate of the cluster center of the multiple clusters in the preset coordinate system.

[0050] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the antenna anomaly identification method described in the first aspect.

[0051] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the antenna anomaly identification method described in the first aspect.

[0052] The antenna anomaly identification method and apparatus provided in this application acquire the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration, acquire the antenna pixel points associated with the sampled pixel points on the target drawing, obtain the target antenna pixel points, and map the sampled pixel points to a preset coordinate system. The preset coordinate system uses the first distance between the sampled pixel point and the target antenna pixel point on the target drawing as the abscissa and the absolute value of the reference signal received power as the ordinate. The sampled pixel points in the preset coordinate system are clustered to obtain multiple clusters. Based on the ordinate of the cluster centers of the multiple clusters in the preset coordinate system, it is determined whether the antenna corresponding to the target antenna pixel point associated with the sampled pixel point is abnormal. Each step in this application is automated by an algorithm. By associating test points and antenna location points with target drawings, it can accurately locate components in concealed works and accurately record specific test paths and test results, which is beneficial for subsequent analysis and localization. The specific analysis and localization method determines whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the correlation between the sampled pixel and the antenna pixel, the relationship between the first distance corresponding to the sampled pixel and the absolute value of the reference signal received power, cluster analysis, and the absolute value of the reference signal received power corresponding to the cluster center of the cluster. This effectively improves the accuracy and efficiency of antenna anomaly identification. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is one of the flowcharts illustrating the antenna anomaly identification method provided in the embodiments of this application;

[0055] Figure 2 This is a schematic diagram of the preset coordinate system in the antenna anomaly identification method provided in the embodiments of this application;

[0056] Figure 3 This is a second schematic flowchart of the antenna anomaly identification method provided in the embodiments of this application;

[0057] Figure 4 This is a schematic diagram of the second distance in the antenna anomaly identification method provided in the embodiments of this application;

[0058] Figure 5 This is the third flowchart illustrating the antenna anomaly identification method provided in this application embodiment;

[0059] Figure 6This is a clustering diagram in the antenna anomaly identification method provided in the embodiments of this application;

[0060] Figure 7 yes Figure 6 The corresponding clustering dendrogram;

[0061] Figure 8 This is the fourth flowchart illustrating the antenna anomaly identification method provided in the embodiments of this application;

[0062] Figure 9 This is a schematic diagram of the positive anomaly segmentation line in the antenna anomaly identification method provided in the embodiments of this application;

[0063] Figure 10 This is the fifth flowchart illustrating the antenna anomaly identification method provided in the embodiments of this application;

[0064] Figure 11 This is a schematic diagram of the antenna anomaly identification device provided in the embodiments of this application;

[0065] Figure 12 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] Figure 1 This is one of the flowcharts illustrating the antenna anomaly identification method provided in the embodiments of this application;

[0068] Figure 2 This is a schematic diagram of the preset coordinate system in the antenna anomaly identification method provided in the embodiments of this application.

[0069] Reference Figure 1 This application provides an antenna anomaly identification method, which may include:

[0070] 101. Obtain the reference signal received power at each test point within the target area at multiple sampling times within any sampling duration;

[0071] 102. Obtain the antenna pixels associated with the sampling pixels on the target drawing to obtain the target antenna pixels;

[0072] Sampling pixels are the test points mapped to the pixels on the target drawing, antenna pixels are the pixels corresponding to the antenna on the target drawing, and the target drawing is the indoor distribution system drawing of the target area.

[0073] 103. Map the sampled pixels to the preset coordinate system;

[0074] The preset coordinate system uses the first distance between the sampling pixel and the target antenna pixel on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis.

[0075] 104. Cluster the sampled pixels in the preset coordinate system to obtain multiple clusters;

[0076] 105. Based on the ordinate of the cluster centers of multiple clusters in the preset coordinate system, determine whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal.

[0077] In step 101, an indoor testing device based on a micro inertial navigation system can be used to obtain the reference signal received power of each test point and map each test point onto the target drawing to obtain the sampled pixel points.

[0078] Micro inertial navigation systems, or "micro inertial navigation" for short, are miniature inertial navigation systems based on microelectromechanical systems (MEMS) sensor technology. They do not rely on external information or radiate energy to the outside. Their basic working principle is based on Newton's laws of motion. By measuring the acceleration of the carrier in the inertial reference frame, integrating it over time, and transforming it into the navigation coordinate system, information such as the carrier's velocity, yaw angle, and position in the navigation coordinate system can be obtained.

[0079] In practical applications, the test point data acquired by indoor testing equipment is not limited to the reference signal received power, as shown in the table below:

[0080] Table 1. Data of test points obtained by indoor testing equipment (taking test points 1 and 2 as examples)

[0081] Timestamp 1667290697756 1667290699768 The x-coordinate of the sampled pixel corresponding to the test point 570.7 574.1 The vertical coordinate of the sampled pixel corresponding to the test point 1318.9 1281 Reference signal received power (dBm) at the test point -101 -103 Signal-to-noise ratio (dB) at test points 7 2 The unique identifier of the community where the test site is located 194544387 194544387 Physical cell identifier of the cell where the test site is located 304 304 downlink carrier frequency number of the cell where the test point is located 38400 38400 Physical community signage of neighboring communities 327 200 Downlink carrier frequency number of neighboring cells 38950 38400 Reference signal received power of neighboring cells -110 -112

[0082] It should be noted that the horizontal and vertical coordinates of the sampled pixels in this table are pixel coordinates on the target drawing.

[0083] In step 102, the relevant data for the antenna pixels can be shown in the table below:

[0084] Table 2 Antenna Pixel Data Table (Taking antenna pixels 1 and 2 as examples)

[0085] The unique identifier of a cell corresponding to an antenna pixel. 194544387 194544387 Antenna pixels correspond to the floor where the antenna is located 10F 10F Antenna pixels correspond to antenna identifiers. ANT6-10F ANT13-10F Antenna pixel x-coordinate 31314.99249 42262.75114 Antenna pixel ordinate 55824.10982 59692.96711

[0086] It should be noted that the horizontal and vertical coordinates of the antenna pixels in this table are the pixel coordinates on the target drawing.

[0087] In step 103, the correlation data between the sampling pixels corresponding to each test point and the target antenna pixels associated with them can be shown in the following table:

[0088] Table 3 shows the correlation data between sampled pixels and target antenna pixels (taking test points 1 and 2 as examples).

[0089] Timestamp 1667290697756 1667290699768 The x-coordinate of the sampled pixel corresponding to the test point 570.7 574.1 The vertical coordinate of the sampled pixel corresponding to the test point 1318.9 1281 Reference signal received power (dBm) at the test point -101 -103 Signal-to-noise ratio (dB) at test points 7 2 The unique identifier of the community where the test site is located 194544387 194544387 The identifier of the target antenna pixel. ANT6-10F ANT6-10F x-coordinate of target antenna pixel 471 471 Target antenna pixel ordinate 1200 1200 First distance 155.2 131.1

[0090] The first distance and reference signal received power corresponding to each sampled pixel can be obtained from Table 3, and the two can be correlated to generate a preset coordinate system. Then, each sampled pixel can be mapped to the preset coordinate system.

[0091] Reference Figure 2 The x-axis, D, represents the first distance, and the y-axis, |RSRP|, represents the absolute value of the received reference signal power. Because electromagnetic waves experience losses during propagation through media such as air, the relationship between the first distance and the received reference signal power exhibits a characteristic where the received reference signal power gradually decreases as the first distance increases. Furthermore, since the received reference signal power is typically represented by a negative number with a maximum value of 0, this is reflected in… Figure 2 In the preset coordinate system, as the first distance D of the sampled pixel increases, the absolute value of its reference signal received power |RSRP| also gradually increases.

[0092] Figure 2 The preset coordinate system includes three sampling pixel points P i (X i ,Y i ), P j (X j ,Y j ) and P k (X k ,Y k ), D ij Characterizing P i With P j The distance between them, D ik Characterizing P i With P k The distance between two sampled pixels characterizes their similarity. The closer the distance between two sampled pixels, the more similar the wireless environments of the test points corresponding to the two sampled pixels. If one sampled pixel is associated with a target antenna pixel corresponding to a normal antenna, then the other sampled pixel is also associated with a target antenna pixel corresponding to a normal antenna. Figure 2 In the middle, D ijSignificantly smaller than D ik If we assume P i If P is a sampled pixel associated with the target antenna pixel corresponding to the normal antenna, then P j It is also the sampling pixel associated with the target antenna pixel corresponding to the normal antenna, while P k Due to relative to P i The deviation distance is large, so P can be determined. k These are the sampled pixels associated with the target antenna pixels corresponding to the abnormal antenna.

[0093] In step 105, the absolute value of the received power of the reference signal corresponding to the cluster center of multiple clusters is used to determine whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal.

[0094] In indoor distribution systems, an abnormal antenna refers to a weak mobile phone signal received within the antenna's coverage area. Possible causes for this phenomenon include:

[0095] 1. There is a hardware problem with the antenna, passive components connected to the remote RF unit, or the feeder.

[0096] 2. The actual location of the antenna differs significantly from the planned location on the indoor distribution drawings.

[0097] Once the antenna malfunction corresponding to a specific target antenna pixel is identified, further investigation and repair can be carried out to improve mobile phone signal reception.

[0098] The antenna anomaly identification method provided in this embodiment obtains the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration, obtains the antenna pixel points associated with the sampled pixel points on the target drawing, obtains the target antenna pixel points, and maps the sampled pixel points to a preset coordinate system. The preset coordinate system uses the first distance between the sampled pixel point and the target antenna pixel point on the target drawing as the abscissa and the absolute value of the reference signal received power as the ordinate. The sampled pixel points in the preset coordinate system are clustered to obtain multiple clusters. Based on the ordinate of the cluster center of the multiple clusters in the preset coordinate system, it is determined whether the antenna corresponding to the target antenna pixel point associated with the sampled pixel point is abnormal. In this embodiment, each step is automated by the algorithm. By associating test points and antenna locations with target drawings, components in concealed works can be accurately located, and specific test paths and results can be accurately recorded, which is beneficial for subsequent analysis and localization. The specific analysis and localization method determines whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the correlation between the sampled pixel and the antenna pixel, the relationship between the first distance corresponding to the sampled pixel and the absolute value of the reference signal received power, cluster analysis, and the absolute value of the reference signal received power corresponding to the cluster center of the cluster. This effectively improves the accuracy and efficiency of antenna anomaly identification.

[0099] In addition, this embodiment is not limited to different operators or equipment manufacturers, and has a wide range of applications.

[0100] Figure 3 This is a second schematic flowchart of the antenna anomaly identification method provided in the embodiments of this application;

[0101] Figure 4 This is a schematic diagram of the second distance in the antenna anomaly identification method provided in the embodiments of this application.

[0102] Reference Figure 3 In one embodiment, clustering the sampled pixels in a preset coordinate system to obtain multiple clusters may include:

[0103] 301. Divide the two sampled pixels with the minimum distance among all sampled pixels into a close cluster, and determine the distance between the two sampled pixels as the first distance to be processed;

[0104] 302. Calculate the minimum distance between any sampled pixel outside the nearest cluster and the sampled pixel inside the nearest cluster to obtain the second distance to be processed;

[0105] 303. After adding any sampled pixel to the nearest cluster, return to step 302;

[0106] 304. If all sampled pixels are added to the nearest cluster, multiple second distances to be processed are obtained;

[0107] 305. Determine the first distance to be processed and multiple second distances to be processed as the second distance, and determine the optimal grouping method for the multiple second distances according to the natural breakpoint method;

[0108] 306. Cluster the sampled pixels corresponding to the second distance within any group in the optimal grouping method to obtain multiple clusters.

[0109] Reference Figure 4 Now, assuming the preset coordinate system includes four sampled pixels a, b, c, and d, the distance between any two sampled pixels can be calculated. For example, if the distance between a and b is the smallest, then the distance between a and b is the first distance to be processed. a and b are divided into a cluster of similar pixels. Then, the distance between c and a and between c and b are calculated. If the distance between c and a is the smallest, then the distance between c and a is a second distance to be processed. c is added to the cluster of similar pixels between a and b. Then, the distance between d and a, d and b, and d and c are calculated. If the distance between d and b is the smallest, then the distance between d and b is the second distance to be processed. d is added to the cluster of similar pixels between a, b, and c. At this point, all sampled pixels have been added to the cluster of similar pixels. The first distance to be processed and these two second distances to be processed are determined as the second distance.

[0110] It should be noted that while the methods in steps 301 to 305 can yield multiple second distances, they ultimately divide all sampled pixels into a single cluster of similar pixels, without achieving any classification effect. Therefore, the above methods are only for obtaining the second distance based on the correlation between sampled pixels outside the cluster and the current cluster of similar pixels, rather than for classifying and clustering the sampled pixels.

[0111] In this embodiment, the nearest clusters are obtained by minimizing the distance between each pair of sampled pixels. Then, multiple second distances are obtained by utilizing the correlation between the sampled pixels outside the cluster and the current nearest cluster. These second distances can characterize the maximum correlation between the sampled pixels outside the cluster and the current nearest cluster. The sampled pixels are clustered according to the optimal grouping method of these second distances, which can make the sampled pixels in the same cluster have a high degree of correlation.

[0112] Figure 5 This is the third flowchart illustrating the antenna anomaly identification method provided in this application embodiment;

[0113] Figure 6 This is a clustering diagram in the antenna anomaly identification method provided in the embodiments of this application;

[0114] Figure 7 yes Figure 6 The corresponding clustering dendrogram.

[0115] Reference Figure 5 In one embodiment, determining the optimal grouping method for multiple second distances based on the natural breakpoint method may include:

[0116] 501. Based on the mean of multiple second distances, obtain the sum of squared first deviations of the multiple second distances;

[0117] 502. Group multiple second distances according to different grouping methods to obtain multiple groups under any grouping method;

[0118] 503. Based on the mean of the second distance in any group under this grouping method, obtain the sum of squared deviations of the second distance within that group;

[0119] 504. Summing the sum of the squared second deviations of all groups under this grouping method yields the total sum of squared deviations;

[0120] 505. Based on the sum of squares of the first deviation and the sum of squares of the deviations, the variance fit of this grouping method is obtained;

[0121] 506. The grouping method corresponding to the maximum variance fit under different grouping methods is determined as the optimal grouping method for the second distance.

[0122] In step 501, it is assumed that the multiple second distances are D. T1 D T2 ,…,D Tn Where n is an integer greater than or equal to 2, the mean of the multiple second distances is:

[0123]

[0124] The sum of squares of the first deviations of the multiple second distances is:

[0125]

[0126] In step 502, these multiple second distances are divided into different types of groups, such as two groups, three groups, four groups, etc. This is not limited here; assuming the multiple second distances are divided into two groups, the first grouping method can divide D... T1 D T2 ,…,D Tn Divided into [D] T1 ] and [D T2 ,…,D Tn Two groups, the second method can be used to... T1 D T2 ,…,D Tn Divided into [D] T1 D T2 ] and [D T3 ,…,D TnIn addition to the two groups, there are various other grouping methods when divided into two groups, as well as when divided into three or four groups, which will not be elaborated here.

[0127] In step 503, under the first grouping method [D] T1 The group mean is:

[0128]

[0129] In the first grouping method [D] T2 ,…,D Tn The group mean is:

[0130]

[0131] Then, under the first grouping method, [D] T1 The sum of squares of the second deviation of the second distance within the group is:

[0132] SDAM 11 =(D T1 -V avr_gp11 ) 2 (5-5)

[0133] The first grouping method [D T2 ,…,D Tn The sum of squares of the second deviation of the second distance within the group is:

[0134]

[0135] Similarly, we can obtain the second grouping method [D] T1 D T2 The group mean is:

[0136]

[0137] In the second grouping method [D T3 ,…,D Tn The group mean is:

[0138]

[0139] Then, under the second grouping method, [D] T1 D T2 The sum of squares of the second deviation of the second distance within the group is:

[0140] SDAM 21 =(D T1 -V avr_gp21 ) 2 +(D T2 -V avr_gp21 ) 2(5-9)

[0141] The second grouping method [D] T3 ,…,D Tn The sum of squares of the second deviation of the second distance within the group is:

[0142]

[0143] Similarly, the sum of squared deviations of the second distance within each group under other grouping methods is calculated.

[0144] In step 504, the sum of squared deviations under the first grouping method is:

[0145] SDAM_ALL1 = SDAM 11 +SDAM 12 (5-11)

[0146] The sum of squared deviations under the second grouping method is:

[0147] SDAM_ALL2 = SDAM 21 +SDAM 22 (5-12)

[0148] Similarly, the sum of squared deviations under other grouping methods is calculated.

[0149] In step 505, the goodness of fit of the variance under the first grouping method, obtained according to formulas (5-2) and (5-11), is:

[0150] GVF1=(SDAM-SDCM_ALL1) / SDAM; (5-13)

[0151] The variance fit goodness of fit for the second grouping method, obtained from formulas (5-2) and (5-12), is as follows:

[0152] GVF2=(SDAM-SDCM_ALL2) / SDAM; (5-14)

[0153] Similarly, the variance fit for other grouping methods is calculated.

[0154] In step 506, the GVF value is between 0 and 1, where 1 indicates a very good fit and 0 indicates a very poor fit. The GVF value closest to 1 is the optimal grouping method.

[0155] It should be noted that here, the second distance is divided into two groups, three groups, four groups, etc., and all grouping methods are compared using GVF to select the optimal grouping method.

[0156] Assuming the second grouping method is the optimal grouping method, that is, dividing the second distance into [D]... T1 D T2 ] and [D T3 ,…,D Tn If there are two groups, then during clustering, D will be... T1 The corresponding sampling pixels and D T2 The corresponding sampled pixels are divided into one category, and D T3 To D Tn The corresponding sampled pixels are classified into one category, thus dividing all sampled pixels into two clusters.

[0157] Specifically, assuming there are four sampled pixels a, b, c, d, and e, these correspond to four second distances. The optimal grouping method is to divide these four second distances into [D...]. T1 D T2 ] and [D T3 D T4 Two groups, of which D T1 Let D be the second distance between a and b. T2 Let D be the second distance between c and a. T3 Let D be the second distance between d and b. T4 If the second distance between e and c is given, then a, b, and c are grouped into one class, and d and e are grouped into another class.

[0158] Reference Figure 6 There are six sampled pixels P1, P2, P3, P4, P5 and P6 in the preset coordinate system. According to the method of this embodiment, these six sampled pixels are clustered. The most reasonable clustering method is to cluster these six sampled pixels into two clusters, namely cluster C1 composed of P1, P2 and P3 and cluster C2 composed of P4, P5 and P6.

[0159] Figure 7 yes Figure 6 A dendrogram of the second distance between mid-sampled pixels, from Figure 7 As can be seen from this, the multiple second distances, from smallest to largest, are the second distance D between P1 and P2. 12 The second distance D between P3 and either P1 or P2 C13 The second distance D between P4 and P5 45 The second distance D between P6 and either P4 or P5 C26 The second distance D between any sampled pixel in P1, P2, and P3 and any sampled pixel in P4, P5, and P6 C1C2 Because of D C1C2The maximum correlation indicates that the correlation between any sampled pixel in P1, P2, and P3 and any sampled pixel in P4, P5, and P6 is relatively weak, while the correlation between P1, P2, and P3 and the correlation between P4, P5, and P6 is relatively strong. Therefore, the most reasonable clustering method is to form cluster C1 with P1, P2, and P3 and cluster C2 with P4, P5, and P6, forming two clusters in total, which is consistent with the clusters obtained by applying this embodiment.

[0160] Traditional clustering methods do not impose any restrictions on the second distance, so the number of clusters formed ultimately needs to be determined manually.

[0161] The natural breakpoint method in this embodiment obtains the variance fit goodness under each grouping method based on the second distance, and selects the optimal grouping method of the second distance based on the variance fit goodness. This can fully explore the similarity between sampled pixels. When clustering sampled pixels according to the optimal grouping method, it can cluster the sampled pixels with the highest similarity, automatically obtain the number of clusters and form the corresponding clusters, thereby improving the accuracy and efficiency of clustering.

[0162] Figure 8 This is the fourth flowchart illustrating the antenna anomaly identification method provided in the embodiments of this application;

[0163] Figure 9 This is a schematic diagram of the positive anomaly segmentation line in the antenna anomaly identification method provided in the embodiments of this application.

[0164] Reference Figure 8 In one embodiment, determining whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the ordinate of the cluster centers of multiple clusters in a preset coordinate system may include:

[0165] 801. If the minimum value of the ordinate of the cluster center in the preset coordinate system of multiple clusters is less than the absolute value threshold of the received power, then obtain the number of sampled pixels associated with any target antenna pixel in the first cluster corresponding to the minimum value of the ordinate of the cluster center, and obtain multiple first quantities.

[0166] In addition, if the minimum value of the ordinate of the cluster center in the preset coordinate system of multiple clusters is greater than or equal to the threshold of the absolute value of the received power, then it is determined that the antennas corresponding to all target antenna pixels are abnormal.

[0167] 802. Among all the sampled pixels, the sampled pixels associated with the target antenna pixel corresponding to the first maximum number are determined as the target sampled pixel set;

[0168] 803. Use the line connecting the sampled pixels in the target sampled pixel set as the positive anomaly dividing line;

[0169] 804. If the cluster center corresponding to the minimum ordinate of the cluster center of multiple second clusters is located on the side of the positive anomaly dividing line away from the ordinate in the preset coordinate system, then the second cluster corresponding to the minimum ordinate of the cluster center is determined to be a normal cluster.

[0170] The second cluster is the cluster other than the first cluster among multiple clusters;

[0171] 805. Obtain the number of sampled pixels associated with any target antenna pixel in the normal cluster, and obtain multiple second quantities;

[0172] 806. Add the sampled pixels associated with the target antenna pixel corresponding to the second maximum number of all sampled pixels to the target sampled pixel set, and take the normal cluster as the first cluster, then return to step 803;

[0173] 807. If all clusters have completed the positive anomaly detection, then stop the process;

[0174] 808. If the cluster center corresponding to the minimum ordinate of the cluster center of multiple second clusters is located on the side of the positive anomaly dividing line closer to the ordinate in the preset coordinate system, then the second cluster corresponding to the minimum ordinate of the cluster center is determined to be an anomaly cluster.

[0175] 809. Obtain the number of sampled pixels associated with any target antenna pixel in the abnormal cluster to obtain the third quantity;

[0176] 810. Obtain the number of sampling pixels associated with any target antenna pixel among all sampling pixels, and obtain the fourth quantity;

[0177] 811. If the ratio of the third quantity to the fourth quantity is greater than the ratio threshold, then the antenna corresponding to any target antenna pixel is determined to be abnormal.

[0178] In step 801, the absolute value threshold of the received power can be set according to the actual situation. In this embodiment, the absolute value threshold of the received power can be 100 dBm.

[0179] In addition, the cluster center coordinates of any cluster can be calculated using the following formula;

[0180]

[0181] Among them, X center Y is the x-coordinate of the cluster center of any cluster. center Let P be the ordinate of the cluster center, h be the number of sampled pixels in the cluster, and P be the number of samples in the cluster. Q For any sampled pixel in this cluster, For sampling pixel point P Qx-coordinate For sampling pixel point P Q The ordinate.

[0182] If the minimum value of the ordinate of the cluster center in the preset coordinate system is greater than or equal to the threshold value of the absolute value of the received power, it indicates that the antennas corresponding to all target antenna pixels have a weak coverage problem, that is, the antennas corresponding to all target antenna pixels are abnormal.

[0183] If the minimum value of the ordinate of the cluster center of all clusters in the preset coordinate system is less than the absolute value threshold of the received power, assuming that the ordinate of the cluster center of cluster C3 is the smallest among all clusters, obtain the target antenna pixels corresponding to the sampling pixels P7, P8, P9 and P10 in C3. Assuming that the target antenna pixel corresponding to P7 is G1, the target antenna pixel corresponding to P8 is G2, the target antenna pixel corresponding to P9 is G2, and the target antenna pixel corresponding to P10 is G3, then the sampling pixels associated with the target antenna pixel G1 are P7 and P8, that is, the first quantity is 2; the sampling pixel associated with the target antenna pixel G2 is P9, that is, the first quantity is 1; and the sampling pixel associated with the target antenna pixel G3 is P10, that is, the first quantity is 1. Finally, three first quantities are obtained.

[0184] In step 802, the first maximum quantity obtained from step 801 is 2, and the corresponding target antenna pixel is G1.

[0185] It should be noted that step 802 does not involve obtaining the sampled pixels associated with G1 in cluster C3, but rather obtaining the sampled pixels associated with G1 in all clusters, i.e., all sampled pixels, and determining these sampled pixels as the target sampled pixels.

[0186] Reference Figure 9 As can be seen, a positive anomaly dividing line exists. It should be noted that the sampled pixels on this positive anomaly dividing line all belong to a specific cluster. Figure 9 For ease of display, the clusters to which some sampled pixels on the positive anomaly dividing line belong are not drawn.

[0187] In step 804, the cluster corresponding to the minimum ordinate of the cluster center is obtained from the remaining clusters other than cluster C3, referring to... Figure 9 In each cluster, x represents the cluster center, assuming it is C5. If the cluster center of C5 is located on the side of the positive anomaly dividing line away from the vertical axis, that is, on the right side of the positive anomaly dividing line, then C5 is determined to be a normal cluster.

[0188] In steps 805 to 806, according to the methods in steps 801 and 802, the number of sampled pixels associated with the same target antenna pixel in C5 is obtained, the target antenna pixel corresponding to the maximum number is determined, and then the sampled pixels associated with the target antenna pixel are obtained from all sampled pixels and added to the target sampled pixel set to form part of the positive anomaly dividing line, thereby updating and extending the positive anomaly dividing line.

[0189] It should be noted that for sampled pixels in the target sampled pixel set, if the horizontal coordinates are the same in the preset coordinate system, only the sampled pixel with the largest vertical coordinate is retained.

[0190] Once all clusters have completed the positive anomaly detection, the corresponding positive anomaly dividing lines will also be drawn.

[0191] In step 808, the cluster corresponding to the minimum ordinate of the cluster center is obtained from the remaining clusters other than cluster C3, referring to... Figure 9 If the cluster center of C6 is located on the side of the positive anomaly dividing line closer to the vertical axis, that is, on the left side of the positive anomaly dividing line, then C6 is determined to be an anomalous cluster.

[0192] In step 809, the number of sampled pixels associated with a certain target antenna pixel in C6 is obtained to obtain the third quantity;

[0193] In step 810, the number of sampling pixels associated with the target antenna pixel is obtained out of all sampling pixels, thus obtaining the fourth number;

[0194] In step 811, the ratio threshold can be set according to the actual situation, and is not limited here. In this embodiment, the ratio threshold can be 50%.

[0195] This embodiment first compares the minimum value of the ordinate of each cluster center with the absolute value threshold of the received power. Since a smaller absolute value of the received power indicates a stronger antenna coverage capability, if the minimum value of the absolute value of the received power corresponding to the cluster center is greater than or equal to the absolute value threshold of the received power, it indicates that the received power of all clusters is too low, thus identifying all associated antenna anomalies. When the minimum value of the absolute value of the received power corresponding to the cluster center is less than the absolute value threshold of the received power, all clusters are classified by drawing a positive anomaly dividing line and the positive anomaly dividing line is updated to complete the positive anomaly classification of all clusters. Finally, based on the ratio of the number of sampled pixels associated with the target antenna pixel in the abnormal cluster to the number of sampled pixels associated with the target antenna pixel in all sampled pixels, it is determined whether the antenna corresponding to the target antenna pixel is abnormal. The method in this embodiment fully explores the relationship between the cluster center and the reference signal received power, as well as the relationship between the sampled pixels and the target antenna pixels in the positive and abnormal clusters, and between all sampled pixels and the target antenna pixels. It can find the target antenna pixels that are most closely related to the sampled pixels and whose reference signal received power is questionable, and then determine whether the antenna corresponding to the target antenna pixel is abnormal, thereby improving the accuracy and efficiency of antenna anomaly identification.

[0196] Figure 10 This is the fifth flowchart illustrating the antenna anomaly identification method provided in this application; see also... Figure 10 In one embodiment, obtaining the antenna pixel point associated with the sampling pixel point on the target drawing to obtain the target antenna pixel point may include:

[0197] 1001. If the minimum distance between the first sampled pixel and all antenna pixels at the first sampling time is not unique, then determine whether the target antenna pixel corresponding to the second sampled pixel is any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels.

[0198] If the minimum distance between the first sampled pixel and all antenna pixels at the first sampling time is a unique value, then the antenna pixel corresponding to the unique value is determined as the target antenna pixel corresponding to the first sampled pixel.

[0199] 1002. If the target antenna pixel corresponding to the second sampled pixel is any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, then the target antenna pixel corresponding to the second sampled pixel is determined as the target antenna pixel corresponding to the first sampled pixel.

[0200] 1003. If the target antenna pixel corresponding to the second sampled pixel is not any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, then the target antenna pixel corresponding to the third sampled pixel is determined to be the target antenna pixel corresponding to the first sampled pixel.

[0201] The second sampling time is the previous sampling time of the first sampling time, and the third sampling time is the next sampling time of the first sampling time.

[0202] Since test point sampling is usually performed on a fixed sampling path rather than random sampling, each sampled pixel moves closer to and then further away from a certain antenna pixel as time progresses along the sampling path.

[0203] If the minimum distance between the first sampled pixel and all antenna pixels at the first sampling time is a unique value, it means that the first sampled pixel has found the antenna pixel that is closest to it, and the antenna pixel can be determined as the corresponding target antenna pixel.

[0204] If the minimum distance between the first sampled pixel and all antenna pixels at the first sampling time is not unique, it means that the distance between the first sampled pixel and multiple antenna pixels is the minimum. In this case, if the target antenna pixel corresponding to the second sampled pixel is any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, it means that the second sampled pixel and the first sampled pixel have a high similarity to the target antenna pixel on the sampling path. Therefore, the target antenna pixel corresponding to the second sampled pixel can be determined as the target antenna pixel corresponding to the first sampled pixel.

[0205] If the minimum distance between the first sampled pixel and all antenna pixels at the first sampling time is not unique, it means that the distance between the first sampled pixel and multiple antenna pixels is minimized. In this case, if the target antenna pixel corresponding to the second sampled pixel is not any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, it means that the similarity between the second and first sampled pixels relative to the target antenna pixel on the sampling path is low. Therefore, it can be determined that the first sampled pixel has a high similarity to the third sampled pixel at the next time step. Thus, the target antenna pixel corresponding to the third sampled pixel can be identified as the target antenna pixel corresponding to the first sampled pixel.

[0206] This embodiment determines the position of a sampled pixel relative to the antenna pixel corresponding to the minimum distance at a given moment by using the uniqueness of the minimum distance between the sampled pixel and all antenna pixels. When the minimum distance is unique, the antenna pixel corresponding to the minimum distance is used as the target antenna pixel. When the minimum distance is not unique, the similarity between the sampled pixel at this moment and the sampled pixel at the previous or next moment is determined, and the target antenna pixel corresponding to the adjacent moment with higher similarity is used. This allows the most accurate target antenna pixel to be found by combining the target antenna pixels at the previous or next moment.

[0207] The antenna anomaly identification device provided in the embodiments of this application is described below. The antenna anomaly identification device described below can be referred to in correspondence with the antenna anomaly identification method described above.

[0208] Figure 11 This is a schematic diagram of the antenna anomaly identification device provided in an embodiment of this application. (Refer to...) Figure 11 This application provides an antenna anomaly identification device, which may include:

[0209] The reference signal received power acquisition module 1101 is used to: acquire the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration;

[0210] The target antenna pixel acquisition module 1102 is used to: acquire antenna pixels associated with sampling pixels on a target drawing to obtain target antenna pixels; wherein the sampling pixels are pixels mapped from the test points to the target drawing, the antenna pixels are pixels corresponding to the antenna on the target drawing, and the target drawing is an indoor distribution system drawing of the target area;

[0211] The coordinate system mapping module 1103 is used to: map the sampled pixel point to a preset coordinate system; the preset coordinate system uses the first distance between the sampled pixel point and the target antenna pixel point on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis;

[0212] Clustering module 1104 is used to: cluster the sampled pixels in the preset coordinate system to obtain multiple clusters;

[0213] The antenna anomaly identification module 1105 is used to: determine whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the ordinate of the cluster center of the multiple clusters in the preset coordinate system.

[0214] The antenna anomaly identification device provided in this embodiment acquires the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration, acquires the antenna pixel points associated with the sampled pixel points on the target drawing, obtains the target antenna pixel points, and maps the sampled pixel points to a preset coordinate system. The preset coordinate system uses the first distance between the sampled pixel point and the target antenna pixel point on the target drawing as the abscissa and the absolute value of the reference signal received power as the ordinate. The sampled pixel points in the preset coordinate system are clustered to obtain multiple clusters. Based on the ordinate of the cluster center of the multiple clusters in the preset coordinate system, it is determined whether the antenna corresponding to the target antenna pixel point associated with the sampled pixel point is abnormal. In this embodiment, each step is automated by the algorithm. By associating test points and antenna locations with target drawings, components in concealed works can be accurately located, and specific test paths and results can be accurately recorded, which is beneficial for subsequent analysis and localization. The specific analysis and localization method determines whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the correlation between the sampled pixel and the antenna pixel, the relationship between the first distance corresponding to the sampled pixel and the absolute value of the reference signal received power, cluster analysis, and the absolute value of the reference signal received power corresponding to the cluster center of the cluster. This effectively improves the accuracy and efficiency of antenna anomaly identification.

[0215] In addition, this embodiment is not limited to different operators or equipment manufacturers, and has a wide range of applications.

[0216] In one embodiment, clustering module 1104 is specifically used for:

[0217] Divide the two sampled pixels with the minimum distance among all sampled pixels into a close cluster, and determine the distance between the two sampled pixels as the first distance to be processed;

[0218] Calculate the minimum distance between any sampled pixel outside the nearby cluster and the sampled pixel within the nearby cluster to obtain the second distance to be processed;

[0219] After adding any of the sampled pixels to the nearby cluster, return to the step of calculating the minimum distance between any sampled pixel outside the nearby cluster and the sampled pixel inside the nearby cluster, until all sampled pixels are added to the nearby cluster, resulting in multiple second distances to be processed;

[0220] The first distance to be processed and the plurality of second distances to be processed are determined as the second distances, and the optimal grouping method of the plurality of second distances is determined according to the natural breakpoint method;

[0221] Cluster the sampled pixels corresponding to the second distance within any group in the optimal grouping method to obtain multiple clusters.

[0222] In one embodiment, clustering module 1104 is specifically used for:

[0223] The sum of squared first deviations of the multiple second distances is obtained based on the mean of the multiple second distances;

[0224] Multiple second distances are grouped according to different grouping methods to obtain multiple groups under any grouping method;

[0225] Based on the mean of the second distance in any group under any grouping method, the second deviation sum of squares of the second distance in any group is obtained;

[0226] Summing the sum of the squared second deviations of all groups under any of the grouping methods yields the total sum of squared deviations.

[0227] The variance fit goodness of fit for any grouping method is obtained by combining the first sum of squared deviations with the total sum of squared deviations.

[0228] The grouping method corresponding to the maximum variance fit under different grouping methods is determined as the optimal grouping method for the second distance.

[0229] In one embodiment, the antenna anomaly identification module 1105 is specifically used for:

[0230] If the minimum value of the ordinate of the cluster center in the preset coordinate system is less than the absolute value threshold of the received power, then the number of sampled pixels associated with any target antenna pixel in the first cluster corresponding to the minimum value of the ordinate of the cluster center is obtained, and multiple first quantities are obtained.

[0231] The sampled pixels associated with the target antenna pixel corresponding to the first maximum number among all sampled pixels are determined as the target sampled pixel set;

[0232] The line connecting the sampled pixels in the target sampled pixel set is used as the positive anomaly dividing line;

[0233] If the cluster center corresponding to the minimum ordinate of the cluster center of multiple second clusters is located on the side of the positive anomaly dividing line away from the ordinate in the preset coordinate system, then the second cluster corresponding to the minimum ordinate of the cluster center is determined to be a normal cluster; the second cluster is a cluster other than the first cluster among the multiple clusters.

[0234] The number of sampled pixels associated with any target antenna pixel in the normal cluster is obtained to obtain multiple second quantities;

[0235] Add the sampled pixels associated with the target antenna pixel corresponding to the second maximum number from all sampled pixels to the target sampled pixel set, and take the normal cluster as the first cluster, then return to the step of taking the line connecting the sampled pixels in the target sampled pixel set as the positive anomaly dividing line, until all clusters have completed the positive anomaly judgment;

[0236] If the cluster center corresponding to the minimum ordinate of the cluster center of multiple second clusters is located on the side of the positive anomaly dividing line closer to the ordinate in the preset coordinate system, then the second cluster corresponding to the minimum ordinate of the cluster center is determined to be an anomalous cluster.

[0237] The number of sampled pixels associated with any target antenna pixel in the abnormal cluster is obtained to obtain the third number;

[0238] The fourth quantity is obtained by acquiring the number of sampling pixels associated with any target antenna pixel among all sampling pixels;

[0239] If the ratio of the third quantity to the fourth quantity is greater than the ratio threshold, then the antenna corresponding to any target antenna pixel is determined to be abnormal.

[0240] In one embodiment, the antenna anomaly identification module 1105 is specifically used for:

[0241] If the minimum value of the ordinate of the cluster center in the preset coordinate system is greater than or equal to the threshold value of the absolute value of the received power, then it is determined that the antennas corresponding to all target antenna pixels are abnormal.

[0242] In one embodiment, the target antenna pixel acquisition module 1102 is specifically used for:

[0243] If the minimum distance between the first sampled pixel and all antenna pixels at the first sampling time is a unique value, then the antenna pixel corresponding to the unique value is determined as the target antenna pixel corresponding to the first sampled pixel.

[0244] If the minimum distance between the first sampled pixel at the first sampling time and all antenna pixels is not unique, then the target antenna pixel corresponding to the first sampled pixel is determined based on the second sampled pixel at the second sampling time and the third sampled pixel at the third sampling time.

[0245] The second sampling time is the previous sampling time of the first sampling time, and the third sampling time is the next sampling time of the first sampling time.

[0246] In one embodiment, the target antenna pixel acquisition module 1102 is specifically used for:

[0247] If the target antenna pixel corresponding to the second sampled pixel is any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, then the target antenna pixel corresponding to the second sampled pixel is determined as the target antenna pixel corresponding to the first sampled pixel.

[0248] If the target antenna pixel corresponding to the second sampled pixel is not any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, then the target antenna pixel corresponding to the third sampled pixel is determined to be the target antenna pixel corresponding to the first sampled pixel.

[0249] Figure 12 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 12 As shown, the electronic device may include: a processor 1210, a communication interface 1220, a memory 1230, and a communication bus 1240, wherein the processor 1210, the communication interface 1220, and the memory 1230 communicate with each other via the communication bus 1240. The processor 1210 can call a computer program in the memory 1230 to execute the steps of the antenna anomaly identification method, such as including:

[0250] Obtain the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration;

[0251] Obtain the antenna pixel points associated with the sampling pixel points on the target drawing to obtain the target antenna pixel points; the sampling pixel points are the pixel points mapped from the test points to the target drawing, the antenna pixel points are the pixel points corresponding to the antennas on the target drawing, and the target drawing is the indoor distribution system drawing of the target area;

[0252] The sampled pixels are mapped to a preset coordinate system; the preset coordinate system uses the first distance between the sampled pixels and the target antenna pixels on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis.

[0253] The sampled pixels in the preset coordinate system are clustered to obtain multiple clusters;

[0254] Based on the ordinate of the cluster centers of the multiple clusters in the preset coordinate system, determine whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal.

[0255] Furthermore, the logical instructions in the aforementioned memory 1230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0256] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the antenna anomaly identification method provided in the above embodiments, such as including:

[0257] Obtain the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration;

[0258] Obtain the antenna pixel points associated with the sampling pixel points on the target drawing to obtain the target antenna pixel points; the sampling pixel points are the pixel points mapped from the test points to the target drawing, the antenna pixel points are the pixel points corresponding to the antennas on the target drawing, and the target drawing is the indoor distribution system drawing of the target area;

[0259] The sampled pixels are mapped to a preset coordinate system; the preset coordinate system uses the first distance between the sampled pixels and the target antenna pixels on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis.

[0260] The sampled pixels in the preset coordinate system are clustered to obtain multiple clusters;

[0261] Based on the ordinate of the cluster centers of the multiple clusters in the preset coordinate system, it is determined whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal.

[0262] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the methods provided in the above embodiments, such as including:

[0263] Obtain the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration;

[0264] Obtain the antenna pixel points associated with the sampling pixel points on the target drawing to obtain the target antenna pixel points; the sampling pixel points are the pixel points mapped from the test points to the target drawing, the antenna pixel points are the pixel points corresponding to the antennas on the target drawing, and the target drawing is the indoor distribution system drawing of the target area;

[0265] The sampled pixels are mapped to a preset coordinate system; the preset coordinate system uses the first distance between the sampled pixels and the target antenna pixels on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis.

[0266] The sampled pixels in the preset coordinate system are clustered to obtain multiple clusters;

[0267] Based on the ordinate of the cluster centers of the multiple clusters in the preset coordinate system, it is determined whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal.

[0268] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0269] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0270] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0271] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for identifying antenna anomalies, characterized in that, include: Obtain the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration; Obtain the antenna pixel points associated with the sampling pixel points on the target drawing to obtain the target antenna pixel points; the sampling pixel points are the pixel points mapped from the test points to the target drawing, the antenna pixel points are the pixel points corresponding to the antennas on the target drawing, and the target drawing is the indoor distribution system drawing of the target area; The sampled pixels are mapped to a preset coordinate system; the preset coordinate system uses the first distance between the sampled pixels and the target antenna pixels on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis. Clustering of sampled pixels in the preset coordinate system yields multiple clusters, including: The nearest clusters are obtained by minimizing the distance between each pair of sampled pixels. Then, multiple second distances are obtained by utilizing the correlation between the sampled pixels outside the cluster and the current nearest cluster. The sampled pixels are clustered according to the optimal grouping method of the second distances to obtain multiple clusters. Based on the ordinate of the cluster centers of the multiple clusters in the preset coordinate system, determine whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal, including: If the minimum value of the ordinate of the cluster center in the preset coordinate system is less than the absolute value threshold of the received power, then all clusters are classified as positive anomalies by drawing a positive anomaly dividing line and the positive anomaly dividing line is updated. Based on the ratio of the number of sampled pixels associated with the target antenna pixel in the abnormal cluster to the number of sampled pixels associated with the target antenna pixel in all sampled pixels, it is determined whether the antenna corresponding to the target antenna pixel is abnormal.

2. The antenna anomaly identification method according to claim 1, characterized in that, The sampling pixels in the preset coordinate system are clustered to obtain multiple clusters, including: Divide the two sampled pixels with the minimum distance among all sampled pixels into a close cluster, and determine the distance between the two sampled pixels as the first distance to be processed; Calculate the minimum distance between any sampled pixel outside the nearby cluster and the sampled pixel within the nearby cluster to obtain the second distance to be processed; After adding any of the sampled pixels to the nearby cluster, return to the step of calculating the minimum distance between any sampled pixel outside the nearby cluster and the sampled pixel inside the nearby cluster, until all sampled pixels are added to the nearby cluster, resulting in multiple second distances to be processed; The first distance to be processed and the plurality of second distances to be processed are determined as the second distances, and the optimal grouping method of the plurality of second distances is determined according to the natural breakpoint method; Cluster the sampled pixels corresponding to the second distance within any group in the optimal grouping method to obtain multiple clusters.

3. The antenna anomaly identification method according to claim 2, characterized in that, The optimal grouping method for determining multiple second distances based on the natural breakpoint method includes: The sum of squared first deviations of the multiple second distances is obtained based on the mean of the multiple second distances; Multiple second distances are grouped according to different grouping methods to obtain multiple groups under any grouping method; Based on the mean of the second distance in any group under any grouping method, the second deviation sum of squares of the second distance in any group is obtained; Summing the sum of the squared second deviations of all groups under any of the grouping methods yields the total sum of squared deviations. The variance fit goodness of fit for any grouping method is obtained by combining the first sum of squared deviations with the total sum of squared deviations. The grouping method corresponding to the maximum variance fit under different grouping methods is determined as the optimal grouping method for the second distance.

4. The antenna anomaly identification method according to claim 1, characterized in that, The step of determining whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the ordinate of the cluster center of the multiple clusters in the preset coordinate system includes: If the minimum value of the ordinate of the cluster center in the preset coordinate system is less than the absolute value threshold of the received power, then the number of sampled pixels associated with any target antenna pixel in the first cluster corresponding to the minimum value of the ordinate of the cluster center is obtained, and multiple first quantities are obtained. The sampled pixels associated with the target antenna pixel corresponding to the first maximum number among all sampled pixels are determined as the target sampled pixel set; The line connecting the sampled pixels in the target sampled pixel set is used as the positive anomaly dividing line; If the cluster center corresponding to the minimum ordinate of the cluster center of multiple second clusters is located on the side of the positive anomaly dividing line away from the ordinate in the preset coordinate system, then the second cluster corresponding to the minimum ordinate of the cluster center is determined to be a normal cluster; the second cluster is a cluster other than the first cluster among the multiple clusters. The number of sampled pixels associated with any target antenna pixel in the normal cluster is obtained to obtain multiple second quantities; Add the sampled pixels associated with the target antenna pixel corresponding to the second maximum number from all sampled pixels to the target sampled pixel set, and take the normal cluster as the first cluster, then return to the step of taking the line connecting the sampled pixels in the target sampled pixel set as the positive anomaly dividing line, until all clusters have completed the positive anomaly judgment; If the cluster center corresponding to the minimum ordinate of the cluster center of multiple second clusters is located on the side of the positive anomaly dividing line closer to the ordinate in the preset coordinate system, then the second cluster corresponding to the minimum ordinate of the cluster center is determined to be an anomalous cluster. The number of sampled pixels associated with any target antenna pixel in the abnormal cluster is obtained to obtain the third number; The fourth quantity is obtained by acquiring the number of sampling pixels associated with any target antenna pixel among all sampling pixels; If the ratio of the third quantity to the fourth quantity is greater than the ratio threshold, then the antenna corresponding to any target antenna pixel is determined to be abnormal.

5. The antenna anomaly identification method according to claim 1, characterized in that, The step of determining whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the ordinate of the cluster center of the multiple clusters in the preset coordinate system includes: If the minimum value of the ordinate of the cluster center in the preset coordinate system is greater than or equal to the threshold value of the absolute value of the received power, then it is determined that the antennas corresponding to all target antenna pixels are abnormal.

6. The antenna anomaly identification method according to claim 1, characterized in that, The step of obtaining the antenna pixel points associated with the sampling pixel points on the target drawing, and thus obtaining the target antenna pixel points, includes: If the minimum distance between the first sampled pixel and all antenna pixels at the first sampling time is a unique value, then the antenna pixel corresponding to the unique value is determined as the target antenna pixel corresponding to the first sampled pixel. If the minimum distance between the first sampled pixel at the first sampling time and all antenna pixels is not unique, then the target antenna pixel corresponding to the first sampled pixel is determined based on the second sampled pixel at the second sampling time and the third sampled pixel at the third sampling time. The second sampling time is the previous sampling time of the first sampling time, and the third sampling time is the next sampling time of the first sampling time.

7. The antenna anomaly identification method according to claim 6, characterized in that, The step of determining the target antenna pixel corresponding to the first sampling pixel based on the second sampling pixel corresponding to the second sampling time and the third sampling pixel corresponding to the third sampling time includes: If the target antenna pixel corresponding to the second sampled pixel is any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, then the target antenna pixel corresponding to the second sampled pixel is determined as the target antenna pixel corresponding to the first sampled pixel. If the target antenna pixel corresponding to the second sampled pixel is not any antenna pixel corresponding to the minimum distance between the first sampled pixel and all antenna pixels, then the target antenna pixel corresponding to the third sampled pixel is determined to be the target antenna pixel corresponding to the first sampled pixel.

8. An antenna anomaly identification device, characterized in that, include: The reference signal received power acquisition module is used to acquire the reference signal received power of each test point in the target area collected at multiple sampling times within any sampling duration. The target antenna pixel acquisition module is used to: acquire antenna pixels associated with sampling pixels on a target drawing to obtain target antenna pixels; wherein the sampling pixels are pixels mapped from the test points to the target drawing, the antenna pixels are pixels corresponding to antennas on the target drawing, and the target drawing is an indoor distribution system drawing of the target area; The coordinate system mapping module is used to: map the sampled pixel point to a preset coordinate system; the preset coordinate system uses the first distance between the sampled pixel point and the target antenna pixel point on the target drawing as the horizontal axis and the absolute value of the reference signal received power as the vertical axis; The clustering module is used to: cluster the sampled pixels in the preset coordinate system to obtain multiple clusters, including: The nearest clusters are obtained by minimizing the distance between each pair of sampled pixels. Then, multiple second distances are obtained by utilizing the correlation between the sampled pixels outside the cluster and the current nearest cluster. The sampled pixels are clustered according to the optimal grouping method of the second distances to obtain multiple clusters. The antenna anomaly identification module is used to: determine whether the antenna corresponding to the target antenna pixel associated with the sampled pixel is abnormal based on the ordinate of the cluster center of the multiple clusters in the preset coordinate system, including: If the minimum value of the ordinate of the cluster center in the preset coordinate system is less than the absolute value threshold of the received power, then all clusters are classified as positive anomalies by drawing a positive anomaly dividing line and the positive anomaly dividing line is updated. Based on the ratio of the number of sampled pixels associated with the target antenna pixel in the abnormal cluster to the number of sampled pixels associated with the target antenna pixel in all sampled pixels, it is determined whether the antenna corresponding to the target antenna pixel is abnormal.

9. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the antenna anomaly identification method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the antenna anomaly identification method according to any one of claims 1 to 7.

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