An indoor blind area identification positioning method, system, device and medium

Abstract

This application belongs to the field of communication technology and discloses an indoor blind spot identification and positioning method, system, device, and medium. The positioning method includes: synchronously collecting Bluetooth positioning information and network indicators through a mobile test terminal and fusing them to generate sampling points with location information; calculating the coverage quality score of each sampling point based on the network indicators to determine blind spot sampling points; then using a sliding window to count the number of blind spot sampling points in consecutive sampling points to confirm valid anomalies; determining core points based on the number of anomalies within a preset clustering radius; aggregating core points and neighboring anomalies into blind spot clusters; and summarizing the location information of anomalies within the blind spot clusters to generate blind spot location data. This solution achieves a complete closed loop from multi-source data fusion and acquisition, scoring determination, and spatial domain clustering, and can automatically identify indoor network blind spots and output their precise location, range, and level information, providing reliable data support for the accurate operation and optimization of indoor distributed networks.

Description

Technical Field

[0001] This application belongs to the field of communication technology, and specifically relates to an indoor blind spot identification and positioning method, system, device and medium based on Bluetooth positioning and network testing. Background Technology

[0002] With the large-scale deployment of 5G, IoT, and indoor distributed networks (IDC) in high-density indoor scenarios such as shopping malls, office buildings, underground parking garages, hospitals, and rail transit, users' requirements for the continuity of indoor mobile communication coverage, speed stability, and service reliability are continuously increasing. Complex indoor spatial structures, wall obstructions, multipath interference, uneven equipment deployment density, and untimely maintenance and inspections can easily create weak coverage areas, signal blind spots, and speed dips, directly leading to user experience degradation issues such as access failures, sudden speed drops, dropped calls, and excessive latency.

[0003] Traditional indoor coverage testing relies on manual drive testing, fixed-point data collection, and background log analysis. This method suffers from drawbacks such as low positioning accuracy, discrete data, large instantaneous fluctuations and interference, subjective blind spot determination, inability to automatically cluster areas, and difficulty in quantifying output. As a result, it cannot meet the needs of intelligent, automated, and refined operation and maintenance of indoor distribution networks. Summary of the Invention

[0004] To address the aforementioned issues, this application provides an indoor blind spot identification and positioning method, system, device, and medium based on Bluetooth positioning and network testing. This solution effectively eliminates false alarms caused by instantaneous signal fluctuations, providing reliable data support for the accurate operation and optimization of indoor distributed antenna systems (DAS) networks.

[0005] The first aspect of this disclosure proposes an indoor blind spot identification and positioning method based on Bluetooth positioning and network testing, the positioning method comprising: Indoor network testing is conducted using a mobile testing terminal. The mobile testing terminal collects Bluetooth positioning information and network indicators in real time and fuses them to generate sampling point data with location information. The coverage quality score of each sampling point is calculated based on the network indicators in the data of each sampling point, and the blind sampling points are determined based on the coverage quality score. The sampling points are read continuously based on a sliding window of a specific length, and anomalies are determined among the sampling points according to the number of blind sampling points within the sliding window. Based on the number of other anomalies within a preset clustering radius for each anomaly, a core point is determined among the anomalies, and the core point is combined with the anomalies adjacent to it within the preset clustering radius to generate a blind zone cluster. Blind zone location data is generated by summarizing the location information of each abnormal point in the blind zone cluster.

[0006] According to a preferred embodiment of this disclosure, the step of generating blind zone location data by summarizing the location information of each of the abnormal points in the blind zone cluster includes: The center point of the blind zone is calculated by averaging the location information of each abnormal point in the blind zone cluster. The maximum distance between the center point of the blind zone and each of the abnormal points in the blind zone cluster is taken as the maximum radius of the blind zone. The equivalent area of ​​the blind zone cluster is determined based on the center point of the blind zone and the maximum radius of the blind zone, and is used as the location data of the blind zone.

[0007] According to a preferred embodiment of this disclosure, determining a core point among the outliers based on the number of other outliers within a preset clustering radius for each outlier includes: The spacing between each of the anomalies is calculated based on the location information of each anomaly. For each of the aforementioned outliers, other outliers with a spacing smaller than the preset clustering radius are considered as adjacent outliers. If the number of adjacent abnormal points is greater than or equal to the preset number of cluster points, then the abnormal point corresponding to the adjacent abnormal point is taken as the core point.

[0008] According to a preferred embodiment of this disclosure, the step of reading consecutive sampling points based on a sliding window of a specific length, and determining outliers among the sampling points based on the number of blind sampling points within the sliding window, includes: The sampling points are read continuously based on a sliding window of a specific length; The number of blind sampling points within the sliding window is compared with a preset threshold for the number of abnormal points. If the number of blind sampling points within the sliding window is greater than or equal to a preset threshold for the number of abnormal points, then the blind sampling points within the sliding window are considered as abnormal points.

[0009] According to a preferred embodiment of this disclosure, the step of calculating the coverage quality score of each sampling point based on network indicators in the data of each sampling point, and determining blind spot sampling points based on the coverage quality score, includes: For each of the sampling point data, the network metrics in the sampling point data are normalized. The normalized network metrics are weighted and summed to generate the coverage quality score of the sampling point corresponding to the sampling point data. The coverage quality score is compared with a preset quality score, and blind sampling points are determined from the sampling points based on the comparison result.

[0010] According to a preferred embodiment of this disclosure, the step of conducting network testing indoors using a mobile testing terminal, and using the mobile testing terminal to collect Bluetooth positioning information and network indicators in real time and fuse them to generate sampling point data with location information, includes: The mobile testing terminal is used to conduct network testing indoors to obtain Bluetooth positioning information and network metrics; the Bluetooth positioning information includes location information and time information; the network metrics include time information and network signal strength information. The network metrics and Bluetooth positioning information are aligned according to the time information and fused to generate sampling point data with location information.

[0011] According to a preferred embodiment of this disclosure, the positioning method further includes: The blind zone range is determined based on the blind zone location data, and each sampling point within the blind zone range is used as a blind zone sampling point. The average coverage quality scores of the blind zone sampling points are averaged to obtain the comprehensive score mean. The index degradation degree is calculated based on the network index of the blind zone sampling points; By integrating the average comprehensive score, the blind spot range, and the index deterioration degree, a blind spot level value is generated, and the blind spot level is determined based on the blind spot level value. The blind zone range, blind zone level, index degradation degree, and optimization strategies corresponding to the blind zone level are integrated with the indoor map for annotation, and the blind zone range is visualized.

[0012] To address the aforementioned technical problems, a second aspect of this disclosure proposes an indoor blind spot identification and positioning system based on Bluetooth positioning and network testing, the positioning system comprising: The data acquisition module is used to conduct network testing indoors via a mobile test terminal. The mobile test terminal collects Bluetooth positioning information and network indicators in real time and fuses them to generate sampling point data with location information. The blind zone sampling point determination module is used to calculate the coverage quality score of each sampling point based on the network indicators in the data of each sampling point, and determine the blind zone sampling point based on the coverage quality score. An anomaly detection module is used to read consecutive sampling points based on a sliding window of a specific length, and to determine anomalies among the sampling points according to the number of blind sampling points within the sliding window. The blind zone clustering module is used to determine the core point among the abnormal points based on the number of other abnormal points within a preset clustering radius for each abnormal point, and to combine the core point with the abnormal points adjacent to it within the preset clustering radius to generate a blind zone cluster. The blind spot location module is used to generate blind spot location data by summarizing the location information of each abnormal point in the blind spot cluster.

[0013] To address the aforementioned technical problems, a third aspect of this disclosure provides an electronic device, comprising: Processor; and A memory storing computer-executable instructions, which, when executed, cause the processor to perform the method described in any of the above embodiments.

[0014] To address the aforementioned technical problems, a fourth aspect of this disclosure provides a computer storage medium that stores one or more programs, which, when executed by a processor, implement the method described in any of the above embodiments.

[0015] Compared with existing technologies, this application has the following advantages: It synchronously collects Bluetooth positioning information and network indicators through a mobile testing terminal and fuses them to generate sampling points with location information. Based on the network indicators, it calculates the coverage quality score of each sampling point to determine blind spot sampling points. Then, it uses a sliding window to count the number of blind spot sampling points in consecutive sampling points to filter out instantaneous fluctuations and confirm valid anomalies. Subsequently, it determines core points based on the number of anomalies within a preset clustering radius, aggregating core points and neighboring anomalies into blind spot clusters. Finally, it summarizes the location information of anomalies within the blind spot clusters to generate blind spot location data. This solution achieves a complete closed loop from multi-source data fusion and acquisition, scoring and determination, temporal denoising to spatial clustering. It can automatically identify indoor network blind spots and output their precise location, range, and level information, effectively eliminating false alarms caused by instantaneous signal fluctuations, and providing reliable data support for the accurate operation and optimization of indoor distributed networks.

[0016] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of 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.

[0018] Figure 1 A schematic flowchart of an indoor blind spot identification and positioning method based on Bluetooth positioning and network testing according to an embodiment of the present disclosure is shown. Figure 2A second schematic flowchart of an indoor blind spot identification and positioning method based on Bluetooth positioning and network testing according to an embodiment of the present disclosure is shown. Figure 3 A schematic flowchart of an indoor blind spot identification and positioning method based on Bluetooth positioning and network testing according to an embodiment of the present disclosure is shown in part three. Figure 4 A schematic flowchart of an indoor blind spot identification and positioning method based on Bluetooth positioning and network testing according to an embodiment of the present disclosure is shown in Figure 4. Figure 5 A schematic diagram of an indoor blind spot identification and positioning system based on Bluetooth positioning and network testing, according to an embodiment of the present disclosure, is shown. Figure 6 A schematic diagram of an electronic device structure according to an embodiment of the present disclosure is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. 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.

[0020] The same reference numerals in the accompanying drawings denote the same or similar elements, components, or parts, and therefore repeated descriptions of the same or similar elements, components, or parts may be omitted below. It should also be understood that although qualifiers such as first, second, third, etc., indicating numbers may be used herein to describe various devices, elements, components, or parts, these devices, elements, components, or parts should not be limited by these qualifiers. That is, these qualifiers are only used to distinguish one from another. For example, a first device may also be referred to as a second device, without departing from the essence of the technical solution of this disclosure. Furthermore, the terms "and / or" and "and / or" refer to all combinations including the first or more of the listed items.

[0021] Please see Figure 1 , Figure 1 This is one of the flowcharts of an indoor blind spot identification and positioning method based on Bluetooth positioning and network testing provided in this disclosure, such as... Figure 1 As shown, the positioning methods include: S11. Conduct network testing indoors using a mobile testing terminal. Collect Bluetooth positioning information and network metrics in real time using the mobile testing terminal and fuse them to generate sampling point data with location information.

[0022] In this embodiment, a mobile testing terminal is used to collect Bluetooth positioning information and multi-dimensional network indicators in real time during indoor walking tests. The two types of heterogeneous data are precisely aligned using timestamps as a basis and fused to generate unified sampling point data with spatial location tags. This solves the problem of disconnect between network data and spatial location in traditional network testing, ensuring that each network indicator has traceable geographical coordinates. Furthermore, by replacing manual annotation with Bluetooth positioning, data collection is automated and continuous, providing a location-related data foundation for subsequent coverage quality scoring, blind spot determination, and clustering. This serves as the data source and a necessary prerequisite for the entire blind spot identification method.

[0023] In this embodiment, network testing is conducted indoors using a mobile testing terminal to obtain Bluetooth positioning information and network metrics. The Bluetooth positioning information includes location information and time information; the network metrics include time information and network signal strength information. The network metrics and Bluetooth positioning information are aligned according to the time information and fused to generate sampling point data with location information.

[0024] Specifically, when the test terminal is used indoors for testing and movement, the following data is collected simultaneously: a) Bluetooth positioning information: location coordinates (x, y), floor level, timestamp t; b) network metrics: timestamp t, SS-RSRP, SS-SINR, uplink / downlink speeds, Ping success rate, Ping latency, access success rate, retransmission rate, drop rate, etc. The network metrics and location information are precisely aligned according to the timestamps to form sampling points for various metric parameters with coordinate information.

[0025] S12. Calculate the coverage quality score of each sampling point based on the network indicators in the data of each sampling point, and determine the blind area sampling points based on the coverage quality score.

[0026] In this embodiment, for each sampling point with location information, a coverage quality score is calculated based on its network indicators, and then blind zone sampling points are selected based on the score results. By converting multi-dimensional network indicators into a unified score value, the blind zone determination is simplified from "comparing each indicator one by one" to "comparing a single threshold," reducing the complexity of the determination. At the same time, it ensures that the selection of blind zone sampling points is entirely based on network quality data, realizing the objectivity and automation of blind zone identification.

[0027] Furthermore, for each sampling point data, the network indicators in the sampling point data can be normalized; the normalized network indicators can be weighted and summed to generate the coverage quality score of the sampling point corresponding to the sampling point data; the coverage quality score can be compared with the preset quality score, and blind sampling points can be determined in the sampling points based on the comparison results.

[0028] In this embodiment, the following algorithm is used to normalize the various key network metrics to the [0,1] interval, thereby unifying the evaluation scale.

[0029]

[0030] range:

[0031] in: : Current measured value of the indicator; : Normalized measured values ​​of the current indicator; : Worst-case threshold for indicators (blind zone threshold); : Optimal threshold for indicators (full coverage threshold).

[0032] In this embodiment, the parameters in the coverage quality comprehensive scoring model are set according to actual needs, using four indicators—SS-RSRP, SS-SINR, downlink rate (Throughput DL), and uplink rate (Throughput UL)—as comprehensive coverage quality scoring factors. We construct a weighted comprehensive scoring model using the weights:

[0033] The constraints are as follows: ; ; Implementation Example Weighting: ; The judgment criteria are as follows: : Identified as a weak coverage sampling point ( ); : Determined as a blind sampling point ( ). The preset quality score for weak coverage. The pre-set quality score corresponds to the blind zone. In this scheme, the pre-set quality score corresponding to the blind zone is compared with the coverage quality score to determine the blind zone sampling point. If the pre-set quality score corresponding to weak coverage is compared with the coverage quality score, the weak coverage sampling point can be determined. The weak coverage sampling point can further determine the indoor areas with weak communication, and then process them.

[0034] In this embodiment, for each location-labeled sampling point, a normalization formula is used to map multi-dimensional network indicators such as SS-RSRP (Synchronization Signal Received Power), SS-SINR (Synchronization Signal Interference-to-Noise Ratio), and uplink / downlink rates to the [0,1] interval. Then, a weighted comprehensive scoring model is used to calculate the coverage quality score S for each sampling point. The blind zone sampling points are determined based on the comparison between S and a preset threshold (e.g., S<0.3 for weak coverage, S<0.15 for blind zones). This unifies the multi-dimensional and heterogeneous network indicators to the same evaluation scale, avoiding the one-sidedness of single-indicator judgment. At the same time, the weighting mechanism highlights the core weights of signal strength and quality, making blind zone determination more in line with actual coverage experience. This provides a reliable basis for anomaly point screening for subsequent sliding window denoising and spatial clustering.

[0035] S13. Read continuous sampling points based on a sliding window of a specific length, and determine outliers among the sampling points according to the number of blind sampling points in the sliding window.

[0036] In this embodiment, a fixed-length sliding window is used to read data from a continuous sequence of sampling points, counting the number of blind zone sampling points within the window to determine which points are outliers. By introducing a continuity constraint based on the time dimension, only blind zone sampling points with a certain density within the window are identified as outliers. This filters out isolated, sporadic blind zone sampling points, effectively distinguishing between persistent coverage degradation and instantaneous signal fluctuations, improving the accuracy of outlier detection, and reducing the false alarm rate.

[0037] S14. Based on the number of other anomalies within the preset clustering radius for each anomaly, determine the core point among the anomalies, and combine the core point with the adjacent anomalies within the preset clustering radius to generate a blind zone cluster.

[0038] In this embodiment, anomalies are considered as objects. The number of adjacent anomalies within a preset clustering radius for each anomaly is counted. Anomalies reaching a certain number are identified as core points. Then, the core points and their adjacent anomalies within the radius are combined and aggregated to generate blind zone clusters. Through spatial density constraints, discrete anomalies are automatically aggregated into contiguous blind zone clusters based on geographical proximity. This avoids misidentifying isolated anomalies as independent blind zones, making the blind zone identification results more consistent with the spatial continuity characteristics of actual coverage degradation, and improving the accuracy and engineering feasibility of blind zone localization.

[0039] S15. Generate blind zone location data by summarizing the location information of each abnormal point in the blind zone cluster.

[0040] In this embodiment, based on the generated blind zone clusters, the location information carried by each anomaly point within the cluster is extracted and summarized to finally generate blind zone location data. By aggregating the location information of discrete anomaly points within the cluster into a unified blind zone location output, the information dimensionality is upgraded from "point-level anomaly" to "region-level blind zone," enabling the blind zone identification results to have a clear spatial location expression, which can be directly used for subsequent visualization and engineering positioning and handling.

[0041] In this embodiment, after determining the blind zone location data, the blind zone range can be determined based on the blind zone location data, and each sampling point within the blind zone range can be used as a blind zone sampling point. The coverage quality scores of the blind zone sampling points are averaged to obtain the comprehensive score mean. The index degradation degree is calculated based on the network index of the blind zone sampling points. The comprehensive score mean, blind zone range, and index degradation degree are fused to generate a blind zone level value, and the blind zone level is determined based on the blind zone level value. The blind zone range, blind zone level, index degradation degree, and the optimization strategy corresponding to the blind zone level are connected and labeled with the indoor map to perform visual labeling of the blind zone range.

[0042] In this embodiment, based on blind zone location data, the blind zone range is first determined, and all sampling points within the range are extracted as blind zone sampling points. The coverage quality scores of these sampling points are averaged to obtain the comprehensive score mean. Then, the index degradation degree is calculated based on the network indicators of the blind zone sampling points. Subsequently, the comprehensive score mean, blind zone range, and index degradation degree are fused to generate a blind zone level value, and the blind zone level is determined accordingly. Finally, the blind zone range, blind zone level, index degradation degree, and corresponding optimization strategies are visualized and annotated by connecting with the indoor map. Through multi-dimensional fusion evaluation, a quantitative level and degradation degree are output for each blind zone, avoiding the one-sidedness of single-indicator grading. At the same time, the blind zone information is bound to the indoor map and associated with recommended optimization strategies, realizing a closed-loop chain from blind zone identification to location, grading, and policy determination. This allows maintenance personnel to intuitively view the location and severity of blind zones and directly obtain handling suggestions, significantly improving the efficiency and accuracy of indoor network optimization.

[0043] In this embodiment, the blind zone level is determined as follows: a hierarchical model is constructed based on the average comprehensive score, blind zone area, and index degradation degree. The threshold is determined according to 3GPP specifications, operator acceptance standards, and actual network measurements, and is divided into 4 levels: Level 1 (slight weak coverage): Business is generally normal; Level 2 (generally weak coverage): The rate drops significantly; Level 3 (severe blind spot): Business is difficult to establish; Level 4 (Blocking blind spot): or Business operations were completely interrupted; the level of disruption was directly linked to optimization priority and project handling methods. This represents the blind spot level value.

[0044] In this embodiment, the output results are integrated with visualization, including: a) blind zone floor and center point coordinates; b) blind zone radius, equivalent area, and level; c) prediction of coverage degradation causes; and d) optimization strategies: adding nodes, power adjustment, position adjustment, and interference suppression. It also supports integration with indoor map systems to achieve visual labeling of blind zone locations.

[0045] In this embodiment, Bluetooth positioning information and network metrics are synchronously collected by a mobile testing terminal and fused to generate sampling points with location information. Based on the network metrics, the coverage quality score of each sampling point is calculated to determine blind spot sampling points. Then, a sliding window is used to count the number of blind spot sampling points in consecutive sampling points to filter out instantaneous fluctuations and confirm valid anomalies. Subsequently, core points are determined based on the number of anomalies within a preset clustering radius. Core points and neighboring anomalies are aggregated into blind spot clusters. Finally, the location information of anomalies within the blind spot clusters is summarized to generate blind spot location data. This solution achieves a complete closed loop from multi-source data fusion and acquisition, scoring and determination, temporal denoising to spatial clustering. It can automatically identify indoor network blind spots and output their precise location, range, and level information, effectively eliminating false alarms caused by instantaneous signal fluctuations, and providing reliable data support for the accurate operation and optimization of indoor distributed antenna systems (DAS).

[0046] Please see Figure 2 , Figure 2 This is a schematic diagram of the second part of the indoor blind spot identification and positioning method based on Bluetooth positioning and network testing provided in this disclosure. Figure 2 As shown, the positioning methods include: S21. Take the average value of the location information of each abnormal point in the blind zone cluster to calculate the center point of the blind zone.

[0047] In this embodiment, the arithmetic mean of the location information of all anomaly points within the blind zone cluster is taken to calculate the center point of the blind zone. The locations of discrete anomaly points within the cluster are aggregated into a deterministic coordinate point, realizing spatial compression from a "multi-point set" to a "single point". This makes the expression of the blind zone location more concise and clear, facilitating subsequent accurate labeling on the map, navigation and positioning, and correlation analysis with surrounding facilities.

[0048] In this embodiment, specifically, the center point of the blind zone It is obtained by averaging the coordinates of all anomaly points within the blind zone cluster.

[0049]

[0050]

[0051] : Number of valid outliers within the cluster Let x be the x-coordinate of the i-th anomaly point. Let be the ordinate of the i-th anomaly point; S22. The maximum distance between the center point of the blind zone and each abnormal point in the blind zone cluster is taken as the maximum radius of the blind zone.

[0052] In this embodiment, using the calculated center point of the blind zone as a reference, the distances between the center point and each anomaly point within the blind zone cluster are calculated, and the maximum distance value is taken as the maximum radius of the blind zone. With the center point as the origin, the boundary of the coverage area is automatically determined by the distance to the farthest anomaly point, eliminating the need for a preset fixed radius. This allows the blind zone range to adapt to the actual distribution of anomalies within the cluster, ensuring that all anomalies are included within the blind zone. Simultaneously, it provides clear geometric boundary parameters for the blind zone, facilitating subsequent circular or near-circular area labeling and visualization on the map.

[0053] In this embodiment, the maximum radius of the blind zone That is, the center coordinates And the farthest anomaly Distance at coordinates Indicates the center and the i-th outlier The spacing between them.

[0054]

[0055] S23. Determine the equivalent area of ​​the blind zone cluster based on the center point and maximum radius of the blind zone, and use it as the location data of the blind zone.

[0056] In this embodiment, the equivalent area of ​​the blind zone cluster is calculated using the circle's center point and maximum radius as the radius, according to the circular area formula (π×r²). This equivalent area is then output as the blind zone location data. By abstracting the irregular blind zone cluster composed of discrete outliers into a standard circular region with defined geometric parameters (center, radius, and area), a quantitative expression of the blind zone's spatial extent is achieved. This provides a comparable area dimension between different blind zones, facilitating subsequent ranking of blind zone severity, priority assessment, and resource allocation decisions.

[0057] In this embodiment, the equivalent area of ​​the blind zone cluster is: .

[0058] Please see Figure 3 , Figure 3 This is the third flowchart of an indoor blind spot identification and positioning method based on Bluetooth positioning and network testing provided in this disclosure, as shown below. Figure 3 As shown, the positioning methods include: S31. Calculate the distance between each anomaly point based on the location information of each anomaly point.

[0059] In this embodiment, based on the location information of each anomaly point within the blind zone cluster, the spatial distance between each anomaly point is calculated pairwise to obtain the spacing data between each anomaly point. By quantifying the spatial distribution density between anomaly points, a basic metric is provided for subsequently judging the morphological compactness or dispersion of the blind zone cluster, enabling the spatial structural characteristics of the blind zone to be numerically described, supporting further analysis of whether the blind zone is concentrated or dispersed.

[0060] In this embodiment, a distance-based density clustering method is used to aggregate discrete outliers into blind zone clusters.

[0061] Planar distance between two points: .

[0062] S32. For each outlier, other outliers with a spacing smaller than the preset clustering radius are taken as adjacent outliers.

[0063] In this embodiment, for each outlier, the distance between it and other outliers is compared with a preset clustering radius. Outliers with a distance smaller than this radius are identified as neighboring outliers. Using the relationship between the distance and the preset radius as the criterion, a set of neighboring points within the spatial neighborhood of each outlier is defined. This provides a reliable basis for subsequent identification of core points based on the number of neighbors and the generation of blind zone clusters, ensuring that the clustering process strictly follows the preset spatial density constraints.

[0064] S33. When the number of adjacent anomalies is greater than or equal to the preset number of cluster points, the anomaly points corresponding to the adjacent anomalies are taken as core points.

[0065] In this embodiment, the number of neighboring anomalies is used as the criterion for determination. When the number reaches or exceeds a preset cluster point threshold, the anomaly is identified as a core point. By setting a minimum neighbor number threshold, it is ensured that only points located in the center of a dense anomaly area are selected as core points. This effectively filters out anomalies located at the edge of the blind zone or in isolated positions, ensuring that the determination of core points has spatial density protection. This guarantees that the blind zone clusters subsequently generated based on core points can accurately reflect the true concentrated areas of coverage degradation, thus improving the robustness of blind zone identification.

[0066] In this embodiment, cluster generation, cluster radius (e.g., 1-3 meters), minimum number of cluster points If a point is within the radius Within the range, there are greater than or equal to A valid outlier is a core point, and blind zone clusters are formed by expanding from the core points.

[0067] Please see Figure 4 , Figure 4This is the fourth flowchart of an indoor blind spot identification and positioning method based on Bluetooth positioning and network testing provided in this disclosure, as shown below. Figure 4 As shown, the positioning methods include: S41. Read continuous sampling points based on a sliding window of a specific length.

[0068] In this embodiment, a fixed-length sliding window is used, sliding sequentially along the sampling point sequence. Each time, consecutive sampling points within the window's coverage area are read as a data unit. This sliding window mechanism divides the continuous sampling point sequence into locally continuous data segments, preserving the spatial or temporal adjacency of the sampling points while achieving segmented processing of the long sequence. This provides structured data input for subsequent segmented evaluation of the coverage quality of local areas, avoiding the smooth masking of local degradation features by global statistics.

[0069] S42. Compare the number of blind sampling points in the sliding window with the preset threshold for the number of abnormal points.

[0070] In this embodiment, the sliding window length is set. Continuous reading Each network test sampling point is used to set a threshold for the number of abnormal data points. The preset threshold for the number of outliers is the limit on the number of outlier data points.

[0071] S43. If the number of blind sampling points in the sliding window is greater than or equal to the preset threshold for the number of abnormal points, then the blind sampling points in the sliding window will be regarded as abnormal points.

[0072] In this embodiment, within the sliding window, statistics satisfying... Number of outliers ,pass Is it greater than or equal to? This is used to determine whether an outlier is a valid anomaly or a normal fluctuation in the indicator, thereby filtering out instantaneous fluctuations. Specifically:

[0073] Judgment criteria: Confirmed as a valid outlier Determined to be an instantaneous fluctuation, it is excluded. The nth sampling point : Coverage quality score for this sampling point The blind zone detection threshold is ( ), The total number of outliers in the window. : Threshold for the number of outliers (configurable, usually 5 to 10).

[0074] Please see Figure 5 , Figure 5 This disclosure provides an indoor blind spot identification and positioning system based on Bluetooth positioning and network testing. The positioning system includes: a data acquisition module 11, a blind spot sampling point determination module 12, an anomaly point determination module 13, a blind spot clustering module 14, and a blind spot location positioning module 15.

[0075] In this embodiment, the data acquisition module 11 is used to conduct network testing indoors via a mobile test terminal, and to collect Bluetooth positioning information and network indicators in real time via the mobile test terminal and fuse them to generate sampling point data with location information.

[0076] In this embodiment, the blind zone sampling point determination module 12 is used to calculate the coverage quality score of each sampling point based on the network indicators in the data of each sampling point, and determine the blind zone sampling point based on the coverage quality score.

[0077] In this embodiment, the anomaly point determination module 13 is used to read continuous sampling points based on a sliding window of a specific length, and determine anomalies in the sampling points according to the number of blind sampling points in the sliding window.

[0078] In this embodiment, the blind zone clustering module 14 is used to determine the core point among the abnormal points based on the number of other abnormal points within the preset clustering radius of each abnormal point, and to combine the core point with the adjacent abnormal points within the preset clustering radius to generate a blind zone cluster.

[0079] In this embodiment, the blind spot location module 15 is used to generate blind spot location data based on the location information of each abnormal point in the blind spot cluster.

[0080] In this embodiment, the blind spot location module 15 is specifically used to calculate the blind spot center point by averaging the location information of each abnormal point in the blind spot cluster; to take the maximum value of the distance between the blind spot center point and each abnormal point in the blind spot cluster as the maximum radius of the blind spot; and to determine the equivalent area of ​​the blind spot cluster based on the blind spot center point and the maximum radius of the blind spot as the blind spot location data.

[0081] In this embodiment, the blind zone clustering module 14 is specifically used to calculate the distance between each abnormal point based on the location information of each abnormal point; for each abnormal point, other abnormal points with a distance smaller than the preset clustering radius are taken as adjacent abnormal points; when the number of adjacent abnormal points is greater than or equal to the preset number of cluster points, the abnormal point corresponding to the adjacent abnormal point is taken as the core point.

[0082] In this embodiment, the outlier determination module 13 is specifically used to read continuous sampling points based on a sliding window of a specific length; compare the number of blind sampling points in the sliding window with a preset outlier number threshold; if the number of blind sampling points in the sliding window is greater than or equal to the preset outlier number threshold, then the blind sampling points in the sliding window are regarded as outliers.

[0083] In this embodiment, the blind sampling point determination module 12 is specifically used to normalize the network indicators in the sampling point data for each sampling point; to perform weighted summation on each normalized network indicator to generate a coverage quality score for the sampling point corresponding to the sampling point data; to compare the coverage quality score with a preset quality score, and to determine the blind sampling point in the sampling point based on the comparison result.

[0084] In this embodiment, the data acquisition module 11 is specifically used to conduct network testing indoors via a mobile testing terminal to acquire Bluetooth positioning information and network metrics; the Bluetooth positioning information includes location information and time information; the network metrics include time information and network signal strength information; the network metrics and Bluetooth positioning information are aligned according to time information and fused to generate sampling point data with location information.

[0085] In this embodiment, the positioning system further includes: a blind zone level visualization module, used to determine the blind zone range based on blind zone location data, and to use each sampling point within the blind zone range as a blind zone sampling point; to average the coverage quality scores of the blind zone sampling points to obtain a comprehensive score mean; to calculate the index degradation degree based on the network index of the blind zone sampling points; to fuse the comprehensive score mean, the blind zone range, and the index degradation degree to generate a blind zone level value, and to determine the blind zone level based on the blind zone level value; and to interface and annotate the blind zone range, blind zone level, index degradation degree, and the optimization strategy corresponding to the blind zone level with the indoor map to perform blind zone range visualization annotation.

[0086] like Figure 6 As shown, this embodiment of the present disclosure provides an electronic device, including a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140. Memory 1130 is used to store computer programs; When the processor 1110 executes the program stored in the memory 1130, it implements any of the above methods.

[0087] The electronic device provided in this embodiment includes a processor 1110 that executes a program stored in a memory 1130 to conduct network testing indoors via a mobile testing terminal. The mobile testing terminal collects Bluetooth positioning information and network metrics in real time and fuses them to generate sampling point data with location information. Based on the network metrics in each sampling point data, a coverage quality score is calculated for each sampling point. Blind spot sampling points are determined based on the coverage quality score. Continuous sampling points are read using a sliding window of a specific length. Anomalies are identified among the sampling points based on the number of blind spot sampling points within the sliding window. Core points are identified among the anomalies based on the number of other anomalies within a preset clustering radius for each anomaly. The core points are then combined with adjacent anomalies within the preset clustering radius to generate blind spot clusters. Blind spot location data is generated by summarizing the location information of each anomaly point in the blind spot cluster.

[0088] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, and a component bus, etc. For ease of illustration, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of bus.

[0089] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0090] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 1130 may also be at least one storage device located remotely from the aforementioned processor 1110.

[0091] The processor 1110 mentioned above can be a general-purpose processor 1110, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0092] This disclosure provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors 1110 to implement the methods of any of the above embodiments.

[0093] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this disclosure is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0094] 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; and these 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. An indoor blind spot identification and positioning method based on Bluetooth positioning and network testing, characterized in that, The positioning method includes: Indoor network testing is conducted using a mobile testing terminal. The mobile testing terminal collects Bluetooth positioning information and network indicators in real time and fuses them to generate sampling point data with location information. The coverage quality score of each sampling point is calculated based on the network indicators in the data of each sampling point, and the blind sampling points are determined based on the coverage quality score. The sampling points are read continuously based on a sliding window of a specific length, and anomalies are determined among the sampling points according to the number of blind sampling points within the sliding window. Based on the number of other anomalies within a preset clustering radius for each anomaly, a core point is determined among the anomalies, and the core point is combined with the anomalies adjacent to it within the preset clustering radius to generate a blind zone cluster. Blind zone location data is generated by summarizing the location information of each abnormal point in the blind zone cluster.

2. The positioning method according to claim 1, characterized in that, The process of generating blind zone location data by summarizing the location information of each abnormal point in the blind zone cluster includes: The center point of the blind zone is calculated by averaging the location information of each abnormal point in the blind zone cluster. The maximum distance between the center point of the blind zone and each of the abnormal points in the blind zone cluster is taken as the maximum radius of the blind zone. The equivalent area of ​​the blind zone cluster is determined based on the center point of the blind zone and the maximum radius of the blind zone, and is used as the location data of the blind zone.

3. The positioning method according to claim 1, characterized in that, The step of determining the core point among the anomalies based on the number of other anomalies within a preset clustering radius for each anomaly includes: The spacing between each of the anomalies is calculated based on the location information of each anomaly. For each of the aforementioned outliers, other outliers with a spacing smaller than the preset clustering radius are considered as adjacent outliers. If the number of adjacent abnormal points is greater than or equal to the preset number of cluster points, then the abnormal point corresponding to the adjacent abnormal point is taken as the core point.

4. The positioning method according to claim 1, characterized in that, The method of reading consecutive sampling points based on a sliding window of a specific length, and determining outliers among the sampling points according to the number of blind sampling points within the sliding window, includes: The sampling points are read continuously based on a sliding window of a specific length; The number of blind sampling points within the sliding window is compared with a preset threshold for the number of abnormal points. If the number of blind sampling points within the sliding window is greater than or equal to a preset threshold for the number of abnormal points, then the blind sampling points within the sliding window are considered as abnormal points.

5. The positioning method according to claim 1, characterized in that, The step of calculating the coverage quality score for each sampling point based on network metrics from the data at each sampling point, and determining blind spot sampling points based on the coverage quality score, includes: For each of the sampling point data, the network metrics in the sampling point data are normalized. The normalized network metrics are weighted and summed to generate the coverage quality score of the sampling point corresponding to the sampling point data. The coverage quality score is compared with a preset quality score, and blind sampling points are determined from the sampling points based on the comparison result.

6. The positioning method according to claim 1, characterized in that, The process of conducting network testing indoors using a mobile testing terminal, and using the mobile testing terminal to collect Bluetooth positioning information and network metrics in real time and fuse them to generate sampling point data with location information, includes: The mobile testing terminal is used to conduct network testing indoors to obtain Bluetooth positioning information and network metrics; the Bluetooth positioning information includes location information and time information; the network metrics include time information and network signal strength information. The network metrics and Bluetooth positioning information are aligned according to the time information and fused to generate sampling point data with location information.

7. The positioning method according to any one of claims 1 to 6, characterized in that, The positioning method further includes: The blind zone range is determined based on the blind zone location data, and each sampling point within the blind zone range is used as a blind zone sampling point. The average coverage quality scores of the blind zone sampling points are averaged to obtain the comprehensive score mean. The index degradation degree is calculated based on the network index of the blind zone sampling points; By integrating the average comprehensive score, the blind spot range, and the index deterioration degree, a blind spot level value is generated, and the blind spot level is determined based on the blind spot level value. The blind zone range, blind zone level, index degradation degree, and optimization strategies corresponding to the blind zone level are integrated with the indoor map for annotation, and the blind zone range is visualized.

8. An indoor blind spot identification and positioning system based on Bluetooth positioning and network testing, characterized in that, The positioning system includes: The data acquisition module is used to conduct network testing indoors via a mobile test terminal. The mobile test terminal collects Bluetooth positioning information and network indicators in real time and fuses them to generate sampling point data with location information. The blind zone sampling point determination module is used to calculate the coverage quality score of each sampling point based on the network indicators in the data of each sampling point, and determine the blind zone sampling point based on the coverage quality score. An anomaly detection module is used to read consecutive sampling points based on a sliding window of a specific length, and to determine anomalies among the sampling points according to the number of blind sampling points within the sliding window. The blind zone clustering module is used to determine the core point among the abnormal points based on the number of other abnormal points within a preset clustering radius for each abnormal point, and to combine the core point with the abnormal points adjacent to it within the preset clustering radius to generate a blind zone cluster. The blind spot location module is used to generate blind spot location data by summarizing the location information of each abnormal point in the blind spot cluster.

9. An electronic device, characterized in that, include: processor; as well as A memory storing computer-executable instructions, which, when executed, cause the processor to perform the positioning method according to any one of claims 1-7.

10. A computer storage medium, characterized in that, in, The computer storage medium stores one or more programs, which, when executed by a processor, implement the positioning method according to any one of claims 1-7.

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