Method and device for detecting abnormal point of indoor positioning Bluetooth beacon

Abstract

The invention provides a method and a device for detecting an abnormal point of an indoor positioning Bluetooth beacon, and relates to the field of indoor positioning. The method comprises the following steps: acquiring a first neighborhood and a second neighborhood of a first beacon point from a beacon point set; calculating a first average value of the coordinates of the beacon points in the first neighborhood, calculating a second average value of the coordinates of the beacon points in the second neighborhood, and calculating an offset score of the first beacon points according to the first average value and the second average value; calculating an average offset score of all beacon points in the beacon point set, and comparing the offset score with the average offset score; and calculating a weighted distance according to a comparison result, calculating a subspace offset factor according to the weighted distance, and marking the beacon point of which the subspace offset factor is greater than a threshold value as an abnormal point. By implementing the technical scheme provided by the invention, the indoor Bluetooth beacon abnormal point can be effectively identified, and the stability and accuracy of an indoor positioning system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of indoor positioning, and in particular to a method and device for detecting abnormal points of indoor positioning Bluetooth beacons. Background Art

[0002] With the rapid development of Internet of Things technology, Bluetooth indoor positioning systems are increasingly being used in logistics, retail, smart home and other fields. Traditional GPS (Global Positioning System) cannot work effectively in indoor environments due to limitations such as signal attenuation and multipath effects. In contrast, indoor positioning systems based on Bluetooth technology have become an ideal solution due to their low power consumption, low cost and high device penetration rate.

[0003] In complex environments such as large factories, Bluetooth beacons may be misplaced. In addition, since the factory environment is dynamic and changeable, some Bluetooth beacons that were originally correctly placed may deviate from their original positions due to environmental interference or equipment movement. Considering the vast factory space and the large number of beacons, relying on manual inspection of the placement of beacons one by one not only requires a lot of manpower and time, but also cannot fully guarantee accuracy.

[0004] Therefore, a detection method that can accurately locate abnormal points of Bluetooth beacons is needed. Summary of the invention

[0005] The present application provides a method and device for detecting abnormal points of indoor positioning Bluetooth beacons. By accurately identifying and processing abnormal beacon points, it can significantly improve the accuracy of indoor positioning and the robustness of the system, optimize the layout and maintenance of beacon points, and thus provide users with more accurate and reliable positioning services.

[0006] In a first aspect of the present application, a method for detecting abnormal points of an indoor positioning Bluetooth beacon is provided, which is applied to an indoor positioning platform, and the method comprises: Acquire a first neighborhood and a second neighborhood of a first beacon point from a beacon point set, wherein the first neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a preset scenario, the second neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a current scenario, and the first beacon point is any one of the beacon point set; Calculating a first average value of the coordinates of the beacon points in the first neighborhood, and calculating a second average value of the coordinates of the beacon points in the second neighborhood, and calculating an offset score of the first beacon point according to the first average value and the second average value; Calculating an average offset score of all beacon points in the beacon point set, and comparing the offset score with the average offset score; A weighted distance is calculated according to the comparison result, a subspace offset factor is calculated according to the weighted distance, and a beacon point whose subspace offset factor is greater than a threshold is marked as an abnormal point. The step of calculating the weighted distance according to the comparison result and calculating the subspace offset factor according to the weighted distance includes: When the offset score is greater than the average offset score, determining a second beacon point that is second closest to the first beacon point, and calculating a target distance between the first beacon point and the second beacon point; Calculating a weighted distance of the first beacon point according to the target distance and a preset weight, and calculating a local density of the first beacon point according to the weighted distance; The average of the local densities of all beacon points in the first neighborhood is calculated to obtain a subspace density, and the subspace offset factor is calculated according to the subspace density and the local density of the first beacon point.

[0007] Optionally, calculating a first average value of the coordinates of the beacon points in the first neighborhood and calculating a second average value of the coordinates of the beacon points in the second neighborhood includes: The first average value and the second average value are calculated by the following formula: ; ; in, is the first average x Axis coordinate mean, is the first average y Axis coordinate mean, P xi is the first neighbor i beacon points x Axis coordinate values, P yi is the first neighbor i beacon points y Axis coordinate values, The second average x Axis coordinate mean, The second average y Axis coordinate mean, P xj is the first j beacon points x Axis coordinate values, P yj is the first j The y-axis coordinate value of the beacon point, K is the first quantity.

[0008] Optionally, calculating the offset score of the first beacon point according to the first average value and the second average value includes: The offset score of the first beacon point is calculated by the following formula: ; Where D is the offset score between the first and second neighbors, is the first average x Axis coordinate mean, is the first average y Axis coordinate mean, The second average x Axis coordinate mean, The second average y Axis coordinates mean.

[0009] Optionally, acquiring the first neighborhood and the second neighborhood of the first beacon point from the beacon point set includes: According to the preset coordinates of the beacon point, the Euclidean distance between other beacon points and the first beacon point is calculated, and according to the Euclidean distance, a first number of beacon points closest to the first beacon point are selected to form a first neighborhood, wherein the other beacon points are beacon points in the beacon point set other than the first beacon point; The actual distance between other beacon points and the first beacon point is determined according to the actually measured signal strength value, and a first number of beacon points closest to the first beacon point are selected according to the actual distance to form a second neighborhood, and the other beacon points are beacon points in the beacon point set except the first beacon point.

[0010] Optionally, determining the actual distance between the other beacon points and the first beacon point according to the actually measured signal strength value includes: The actual distance between other beacon points and the first beacon point is determined by the following formula: RSSI=RSSI 0 -10nlog 10 (d) +Xs ; in, RSSI is the signal strength value received by the first beacon point, RSSI 0 is the signal strength value emitted by the other beacon points, n is the path loss exponent, d is the actual distance between the other beacon points and the first beacon point, Xs is the shadow attenuation, which is a random variable following a Gaussian distribution.

[0011] Optionally, the calculating a weighted distance according to the comparison result, and the calculating a subspace offset factor according to the weighted distance includes: The weighted distance is calculated by the following formula: SWD ( q ) =ω·SD ( q ); in, SWD ( q ) is the first beacon point q The weighted distance of ω is the weight, SD ( q ) is the target distance from the first beacon point to the second beacon point, The local density of the first beacon point is calculated by the following formula: ; in, ρ ( q ) is the first beacon point q The local density of The subspace offset factor of the first beacon point is calculated by the following formula: ; in, The first beacon point q The subspace shift factor, is the subspace density of the first beacon point.

[0012] In a second aspect of the present application, a detection system for an indoor positioning Bluetooth beacon outlier point is provided, which is used to implement the indoor positioning Bluetooth beacon outlier point detection method as described in any one of the above, including a neighborhood module, a calculation module, a comparison module and an execution module, wherein: a neighborhood module, configured to obtain a first neighborhood and a second neighborhood of a first beacon point from a beacon point set, wherein the first neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a preset scenario, and the second neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a current scenario, and the first beacon point is any one of the beacon point set; a calculation module configured to calculate a first average value of the coordinates of the beacon points in the first neighborhood, calculate a second average value of the coordinates of the beacon points in the second neighborhood, and calculate an offset score of the first beacon point based on the first average value and the second average value; a comparison module configured to calculate an average offset score of all beacon points in the beacon point set, and compare the offset score with the average offset score; an execution module configured to calculate a weighted distance according to the comparison result, calculate a subspace offset factor according to the weighted distance, and mark a beacon point whose subspace offset factor is greater than a threshold as an abnormal point, The step of calculating the weighted distance according to the comparison result and calculating the subspace offset factor according to the weighted distance includes: When the offset score is greater than the average offset score, determining a second beacon point that is second closest to the first beacon point, and calculating a target distance between the first beacon point and the second beacon point; Calculating a weighted distance of the first beacon point according to the target distance and a preset weight, and calculating a local density of the first beacon point according to the weighted distance; The average of the local densities of all beacon points in the first neighborhood is calculated to obtain a subspace density, and the subspace offset factor is calculated according to the subspace density and the local density of the first beacon point.

[0013] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0014] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By calculating the first and second neighborhoods of the beacon points in the preset scene and the current scene, and combining the offset score, weighted distance, subspace offset factor and other indicators, it is possible to accurately identify the beacon points with offset or abnormal positions in the actual scene. After excluding these beacon points, the accuracy of indoor positioning can be significantly improved; 2. Indoor environments are often complex, and the layout of beacon points may change due to various factors, such as furniture placement, personnel movement, etc. The embodiment of the present application can flexibly respond to these environmental changes by comparing the neighborhood information of the beacon points in the preset scene and the current scene. Even if the position of the beacon point is offset, the abnormal point can be accurately detected to ensure the stable operation of the positioning system; 3. In practical applications, beacon points may be affected by electromagnetic interference, signal attenuation and other factors, resulting in abnormal signal strength or location information. The embodiment of the present application can effectively filter out the influence of these interference factors by comprehensively considering the offset score, weighted distance and other indicators of the beacon points, thereby improving the anti-interference ability of the positioning system in complex environments; 4. By detecting abnormal beacon points, a basis can be provided for optimizing the layout of beacon points. For those beacon points with offset or abnormal positions, their layout can be readjusted to make them more in line with the needs of actual application scenarios, thereby improving the rationality of the layout of the entire beacon point network and further improving the performance of indoor positioning; 5. For users, the accuracy of indoor positioning directly affects their usage experience. The embodiments of the present application can effectively improve the accuracy of indoor positioning and provide users with more accurate positioning services, such as quickly finding the target store in a shopping mall, accurately navigating in an airport, etc., thereby improving user satisfaction and usage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for detecting abnormal points of indoor positioning Bluetooth beacons disclosed in an embodiment of the present application; Figure 2 It is a schematic diagram of the simulation experiment effect of the method for detecting abnormal points of indoor positioning Bluetooth beacons disclosed in the embodiment of the present application; Figure 3 It is another flow chart of the method for detecting abnormal points of indoor positioning Bluetooth beacons disclosed in the embodiment of the present application; Figure 4 It is a module schematic diagram of the indoor positioning Bluetooth beacon abnormal point detection system disclosed in the embodiment of the present application; Figure 5 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0017] Explanation of the reference numerals: 401, neighborhood module; 402, calculation module; 403, comparison module; 404, execution module; 501, processor; 502, communication bus; 503, user interface; 504, network interface; 505, memory. DETAILED DESCRIPTION

[0018] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0019] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0020] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0021] This embodiment discloses a method for detecting abnormal points of indoor positioning Bluetooth beacons, which is applied to indoor positioning platforms. Figure 1 is a flow chart of a method for detecting abnormal points of indoor positioning Bluetooth beacons disclosed in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: S101. Acquire a first neighborhood and a second neighborhood of a first beacon point from a beacon point set, where the first neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a preset scenario, and the second neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a current scenario, and the first beacon point is any one of the beacon point set; S102, calculating a first average value of the coordinates of the beacon points in the first neighborhood, and calculating a second average value of the coordinates of the beacon points in the second neighborhood, and calculating an offset score of the first beacon point according to the first average value and the second average value; S103, calculating an average offset score of all beacon points in the beacon point set, and comparing the offset score with the average offset score; S104: Calculate a weighted distance according to the comparison result, calculate a subspace offset factor according to the weighted distance, and mark a beacon point whose subspace offset factor is greater than a threshold as an abnormal point.

[0022] Figure 2It is a schematic diagram of the simulation experiment effect of the detection method of the indoor positioning Bluetooth beacon abnormal point disclosed in the embodiment of the present application. The experimental map of the present invention is a 120m*100m plane. Beacons are arranged on both sides of the U-shaped corridor channel with a length of 90m and a width of 70m. The corridor width is 5m. The preset scene is to place 14 Bluetooth beacons on the west side of the west corridor and the east side of the east corridor, 12 Bluetooth beacons on the east side of the west corridor and the west side of the east corridor, 12 Bluetooth beacons on the south side of the south corridor, and 9 Bluetooth beacons on the north side of the south corridor. Each beacon takes itself as the origin and transmits its own number (ID) information and position-related signals that can be used to calculate the received signal strength value (RSSI) to the surrounding area. All the above beacon points constitute a beacon point set.

[0023] The first neighborhood refers to a set of a certain number (first number) of beacon points that are closest to the first beacon point in a preset scene. According to the fixed layout and known coordinates of the beacon points in the preset scene, the distance between the first beacon point and other beacon points is calculated, and then the first first number of beacon points closest to the first beacon point are selected as the first neighborhood. For example, if the first number is 4, then the 4 beacon points closest to the first beacon point are selected as the first neighborhood. The first neighborhood reflects the local environment and relative position relationship of the beacon point in an ideal state, and provides a benchmark for the subsequent offset score calculation. The second neighborhood refers to a set of a certain number (first number) of beacon points that are closest to the first beacon point in the current scene (ie, the actual scene). According to the actual measured signal strength (such as RSSI value) or the real-time coordinates of the beacon point, the distance between the first beacon point and other beacon points is calculated, and then the first first number of beacon points closest to the first beacon point are selected as the second neighborhood. For example, in an actual scene, due to environmental changes or beacon point position offset, the beacon point closest to the first beacon point may be different from the preset scene. The second neighborhood reflects the local environment and relative position relationship of the beacon point in the actual scene. By comparing with the first neighborhood, the position deviation of the beacon point can be found. The first average value of the coordinates of the beacon points in the first neighborhood is calculated, including the average values ​​of the x-axis and y-axis coordinates. This can be obtained by adding the x-axis coordinates of all beacon points in the first neighborhood and dividing them by the first number, and adding the y-axis coordinates of all beacon points and dividing them by the first number. Similarly, the second average value of the coordinates of the beacon points in the second neighborhood is calculated, including the average values ​​of the x-axis and y-axis coordinates, in the same way as the calculation of the first average value. The offset score is used to measure the degree of position deviation of the first beacon point in the preset scene and the current scene. The offset score is calculated based on the difference between the first average value and the second average value. For example, the Euclidean distance between the two average values ​​can be calculated. The larger the Euclidean distance, the larger the position deviation of the first beacon point, and the higher the offset score. The average offset score refers to the average value of the offset scores of all beacon points in the beacon point set. The offset scores of all beacon points are added and divided by the total number of beacon points, and the result is the average offset score. By comparing the offset score of the first beacon point with the average offset score, it can be determined whether the position deviation of the first beacon point is significantly higher than the overall average level. If the offset score of the first beacon point is greater than the average offset score, it means that the beacon point may have a large position deviation or anomaly and needs to be weighted; if the offset score is less than or equal to the average offset score, the position deviation of the beacon point is considered to be within the normal range. The weighted distance refers to the weighted processing of the K nearest neighbor distance based on the offset score of the beacon point. The neighboring points of all neighboring points of each target beacon point are regarded as the subspace of the beacon point. The local density is the inverse of the weighted distance of the beacon point, and the subspace density is the average of the local density of all points in the first neighborhood of the beacon point. The subspace offset factor is the ratio of the subspace density of the beacon point to the local density.A threshold of the subspace offset factor is set to determine whether the beacon point is an outlier. When the subspace offset factor of the beacon point is greater than the threshold, the beacon point is considered an outlier and marked. These outliers may be caused by position offset, signal interference or other reasons. They need to be processed or excluded in the subsequent positioning process to improve the accuracy and reliability of positioning.

[0024] By comparing the first neighborhood and the second neighborhood of the beacon point in the preset scene and the current scene, the calculated offset score can effectively reflect the change of the beacon point position. It can accurately identify those beacon points whose positions are offset or abnormal in the actual scene, and avoid these abnormal points from causing large errors in the positioning results, thereby significantly improving the accuracy of indoor positioning. The indoor environment is often more complex and changeable, and the layout of the beacon points may be affected by various factors and change, such as furniture placement, personnel movement, etc. The embodiment of the present application can flexibly respond to these environmental changes by comparing the neighborhood information of the beacon points in different scenes. Even if the beacon point position is offset, it can accurately detect the abnormal point and ensure the stable operation of the positioning system. In practical applications, the beacon point may be affected by factors such as electromagnetic interference and signal attenuation, resulting in abnormal signal strength or position information. The embodiment of the present application can effectively filter out the influence of these interference factors by comprehensively considering the offset score, weighted distance and other indicators of the beacon point, and improve the anti-interference ability of the positioning system in complex environments. By detecting abnormal beacon points, a basis can be provided for the optimization of the layout of beacon points. For those beacon points whose positions are offset or abnormal, their layout can be readjusted to make them more in line with the needs of actual application scenarios, thereby improving the rationality of the layout of the entire beacon point network and further improving the performance of indoor positioning.

[0025] Optionally, acquiring the first neighborhood and the second neighborhood of the first beacon point from the beacon point set includes: According to the preset coordinates of the beacon point, the Euclidean distance between other beacon points and the first beacon point is calculated, and according to the Euclidean distance, a first number of beacon points closest to the first beacon point are selected to form a first neighborhood, wherein the other beacon points are beacon points in the beacon point set other than the first beacon point; The actual distance between other beacon points and the first beacon point is determined according to the actually measured signal strength value, and a first number of beacon points closest to the first beacon point are selected according to the actual distance to form a second neighborhood, and the other beacon points are beacon points in the beacon point set except the first beacon point.

[0026] According to the coordinate position of the beacon point in the preset scene, the Euclidean distance between other beacon points and the first beacon point is calculated. Euclidean distance is a common method for measuring the straight-line distance between two points. In the calculated Euclidean distance, the first first number of beacon points closest to the first beacon point are selected, and these beacon points together constitute the first neighborhood of the first beacon point. The first neighborhood reflects the distribution of beacon points around the first beacon point in the preset scene, and is a neighborhood in an ideal state. In the actual scene, by measuring the signal strength value between the beacon points, the actual distance between the beacon points can be estimated. There is usually a certain relationship between signal strength and distance, and the signal strength can be converted into distance through a signal propagation model. According to the estimated actual distance, the first first number of beacon points closest to the first beacon point are selected, and these beacon points together constitute the second neighborhood of the first beacon point. The second neighborhood reflects the distribution of beacon points around the first beacon point in the current actual scene, and is a neighborhood in a real environment. The first neighborhood is calculated based on the preset coordinates of the beacon point and represents the neighborhood under ideal conditions; the second neighborhood is estimated based on the actual measured signal strength value and represents the neighborhood in the actual scene. By comparing these two neighborhoods, it is possible to find out the position change of the beacon point in the actual scene relative to the preset scene. The first number is pre-set and represents the number of nearest beacon points that need to be selected when calculating the neighborhood. For example, if the first number is set to 4, then in the first neighborhood and the second neighborhood, the 4 beacon points closest to the first beacon point will be selected. Other beacon points refer to all beacon points in the beacon point set except the first beacon point. They all participate in the calculation of the Euclidean distance and the actual distance to the first beacon point to determine whether to be selected into the neighborhood.

[0027] Using the preset coordinates of the beacon points to calculate the Euclidean distance can accurately reflect the spatial position relationship of the beacon points under the ideal layout. By selecting the first number of beacon points closest to the first beacon point to form the first neighborhood, the proximity of the beacon points in the first neighborhood to the first beacon point in the preset scene is ensured, providing a reliable basis for the subsequent offset score calculation. The actual measured signal strength value contains the environmental information of the beacon point in the current scene, such as signal propagation loss, reflection, refraction, etc. The actual distance between the beacon points determined according to these actual signal strength values ​​can more accurately reflect the relative position relationship of the beacon points in the actual environment. Selecting the first number of beacon points closest to the first beacon point to form the second neighborhood ensures the proximity of the beacon points in the second neighborhood to the first beacon point in the current scene, providing a key basis for detecting the position offset of the beacon point. Obtaining the neighborhood information of the first beacon point from two dimensions, the preset scene and the current scene, can effectively respond to changes in the indoor environment. Even if the layout of the beacon points changes or environmental factors cause changes in the signal propagation characteristics, by comparing the differences between the first neighborhood and the second neighborhood, the abnormal situation of the beacon point can still be accurately detected, which improves the robustness of the algorithm in different scenarios. By comprehensively considering the preset coordinates of the beacon point and the actual signal strength value, the neighborhood is selected from two perspectives, which can more comprehensively evaluate the location status of the beacon point. This multi-dimensional analysis method helps to reduce misjudgments and missed judgments caused by a single factor and improves the accuracy of abnormal beacon point detection.

[0028] Optionally, determining the actual distance between the other beacon points and the first beacon point according to the actually measured signal strength value includes: The actual distance between other beacon points and the first beacon point is determined by the following formula: RSSI=RSSI 0 -10nlog 10 (d) +Xs ; in, RSSI is the signal strength value received by the first beacon point, RSSI 0 is the signal strength value emitted by the other beacon points, n is the path loss exponent, d is the actual distance between the other beacon points and the first beacon point, Xs is the shadow attenuation, which is a random variable following a Gaussian distribution.

[0029] The above formula is based on the signal propagation model. By measuring the received signal strength (RSSI) and the known reference signal strength (RSSI0, the signal strength value should be known data), combined with the path loss index (n), the actual distance (d) between the beacon points is estimated. This method takes advantage of the relationship between signal strength and distance, that is, the signal strength gradually weakens with the increase of distance. The shadow attenuation (Xs) in the formula takes into account the impact of environmental factors on signal propagation, making the distance estimation more in line with the actual scenario. In an indoor environment, the signal will be blocked and reflected by a variety of obstacles, resulting in random fluctuations in signal strength. The introduction of Xs can effectively compensate for this fluctuation and improve the accuracy of distance estimation. Through this formula, the actual distance between beacon points can be accurately determined, providing reliable data support for subsequent steps such as neighborhood selection, offset score calculation, and weighted distance calculation. This helps to improve the accuracy of indoor positioning Bluetooth beacon anomaly detection, optimize the layout and maintenance of beacon points, and improve the overall performance of indoor positioning systems.

[0030] Optionally, calculating a first average value of the coordinates of the beacon points in the first neighborhood and calculating a second average value of the coordinates of the beacon points in the second neighborhood includes: The first average value and the second average value are calculated by the following formula: ; ; in, is the first average x Axis coordinate mean, is the first average y Axis coordinate mean, P xi is the first neighbor i beacon points x Axis coordinate values, P yi is the first neighbor i beacon points y Axis coordinate values, The second average x Axis coordinate mean, The second average y Axis coordinate mean, P xj is the first j beacon points x Axis coordinate values, P yj is the first j The y-axis coordinate value of the beacon point, K is the first quantity.

[0031] By summing up the x-axis coordinates and y-axis coordinates of all beacon points in the first neighborhood and the second neighborhood, respectively, and then dividing by the number of beacon points in the corresponding neighborhood, the coordinate average of each neighborhood can be obtained. These averages reflect the center position of the beacon points in the neighborhood, providing a basis for the subsequent calculation of the offset score. By comparing the coordinate averages of the first neighborhood and the second neighborhood, the offset score of the first beacon point can be calculated. The size of the offset score reflects the position deviation of the first beacon point in the preset scene and the current scene, and is an important indicator for judging whether the beacon point is abnormal. Accurately calculating the first average and the second average is of great significance for the detection of abnormal points of indoor positioning Bluetooth beacons. By comparing the coordinate averages of the two neighborhoods, beacon points with offset or abnormal positions can be effectively identified, providing reliable data support for the optimization and maintenance of indoor positioning systems.

[0032] Optionally, calculating the offset score of the first beacon point according to the first average value and the second average value includes: The offset score of the first beacon point is calculated by the following formula: ; Where D is the offset score between the first and second neighbors, is the first average x Axis coordinate mean, is the first average y Axis coordinate mean, The second average x Axis coordinate mean, The second average y Axis coordinates mean.

[0033] By calculating the distance between the average coordinates of the first neighborhood and the second neighborhood, the offset score of the first beacon point can be obtained. The size of the offset score reflects the position deviation of the first beacon point in the preset scene and the current scene. If the offset score is large, it means that the position of the first beacon point has changed significantly in the actual scene, and there may be anomalies; if the offset score is small, it means that the position of the first beacon point remains basically unchanged in the actual scene, and there is no obvious anomaly. By calculating the offset score, beacon points with offset or abnormal positions can be effectively identified, providing reliable data support for the optimization and maintenance of indoor positioning systems. The offset score is an important indicator for judging whether a beacon point is abnormal, and is of great significance for improving indoor positioning accuracy and system stability.

[0034] Optionally, the calculating a weighted distance according to the comparison result, and the calculating a subspace offset factor according to the weighted distance includes: When the offset score is greater than the average offset score, determining a second beacon point that is second closest to the first beacon point, and calculating a target distance between the first beacon point and the second beacon point; Calculating a weighted distance of the first beacon point according to the target distance and a preset weight, and calculating a local density of the first beacon point according to the weighted distance; The average of the local densities of all beacon points in the first neighborhood is calculated to obtain a subspace density, and the subspace offset factor is calculated according to the subspace density and the local density of the first beacon point.

[0035] When the offset score of the first beacon point is greater than the average offset score of all beacon points, it indicates that the first beacon point may have a position offset or anomaly. In this case, it is necessary to determine the second beacon point that is the second closest to the first beacon point. This can be achieved by comparing the distance between the first beacon point and other beacon points, and selecting the beacon point with the second smallest distance as the second beacon point. Calculate the target distance between the first beacon point and the second beacon point, that is, the actual distance between them. This distance can be determined by signal strength value or other methods. According to the target distance and the preset weight, calculate the weighted distance of the first beacon point. The setting of the weight can be based on the offset score or other relevant factors to reflect the importance or influence of the beacon point. The local density is defined as the inverse of the weighted distance of the beacon point, and the local density reflects the "crowding degree" of the beacon point in its weighted neighborhood. Calculate the mean of the local density of all beacon points in the first neighborhood to obtain the subspace density. The subspace density represents the average value of the "crowding degree" of all beacon points in the first neighborhood. The subspace offset factor is the ratio of the subspace density of the first beacon point to the local density. When the subspace offset factor is greater than a preset threshold, it indicates that the first beacon point may have a position offset or anomaly, so it can be marked as an abnormal point. In the embodiment of the present application, the preset threshold may be 1.4.

[0036] By comparing the offset score with the average offset score, beacon points with large position changes can be identified. Further, by calculating the target distance between these beacon points and their second nearest neighbor beacon points, the actual position deviation of the beacon points can be quantified more accurately. For those beacon points with high offset scores, introducing preset weights to calculate the weighted distance can more reasonably reflect the importance of the beacon points and their possible impact on the positioning system, thereby improving the accuracy of anomaly detection. By calculating the local density of the first beacon point and the mean local density of all beacon points in the first neighborhood (subspace density), the local distribution of the beacon points can be evaluated, which is crucial to maintaining the stability of the positioning system in complex environments. The calculation of the subspace offset factor helps to identify those beacon points that behave abnormally in the local area. Even if their absolute offset scores are not particularly high, they appear abnormal relative to the distribution of surrounding beacon points. Such beacon points may also be marked as abnormal. By identifying abnormal beacon points and their position deviations, guidance can be provided for the re-layout of beacon points, optimizing the distribution of beacon points, and improving the coverage uniformity and positioning accuracy of the entire positioning system.

[0037] Optionally, the calculating a weighted distance according to the comparison result, and the calculating a subspace offset factor according to the weighted distance includes: The weighted distance is calculated by the following formula: SWD ( q ) =ω·SD ( q ); in, SWD ( q ) is the first beacon point q The weighted distance of ω is the weight, SD ( q ) is the target distance from the first beacon point to the second beacon point, The local density of the first beacon point is calculated by the following formula: ; in, ρ ( q ) is the first beacon point q The local density of The subspace offset factor of the first beacon point is calculated by the following formula: ; in, The first beacon point q The subspace shift factor, is the subspace density of the first beacon point.

[0038] By introducing weights ω, can more reasonably reflect the importance of the actual distance between beacon points in anomaly detection. In the embodiment of the present application ω is 1.2. For those beacon points with larger offset scores, their influence in the weighted distance calculation can be increased by adjusting the weights, thereby improving the accuracy of anomaly detection. The calculation of local density and subspace density provides a basis for the calculation of the subspace offset factor. All neighboring points of the first number of neighboring points of the first beacon point are taken as the subspace of the first beacon point. The subspace density reflects the overall situation of the relative position relationship of the beacon points in the entire first neighborhood. The subspace offset factor is a key indicator for determining whether a beacon point is abnormal. When the subspace offset factor is greater than a preset threshold, it means that the relative "crowding degree" of the beacon point in its subspace is much higher than other points, and there may be position offset or anomaly, so it can be marked as an abnormal point. If Figure 2 As shown, the points with square frames on the outside are abnormal points, the hollow ones are points that have produced displacement, and the solid ones are points that have produced exchange.

[0039] Figure 3 is another flow chart of the method for detecting abnormal points of indoor positioning Bluetooth beacons disclosed in the embodiment of the present application, such as Figure 3 As shown, the detection method of indoor positioning Bluetooth beacon abnormal points includes the following steps: S301, calculating the distance between Bluetooth beacons by measuring RSSI values; S302, finding the target number of nearest neighbor beacon points of the target beacon point in the ideal beacon layout scenario, and calculating the first average value of the target number of nearest neighbor beacon points; S303, finding the target number of nearest neighbor beacon points of the target beacon point in the actual beacon layout scenario, and calculating the second average value of the target number of nearest neighbor beacon points; S304, determining the offset score according to the first average value and the second average value; S305, judging whether the offset score exceeds the average value; if so, executing S306, weighted calculation of distance, otherwise executing S307, unweighted calculation of distance; S308, calculating the subspace offset factor according to the distance; S309, judging whether the subspace offset factor exceeds the threshold, if so, executing S310, marking it as an abnormal point, otherwise executing S311, marking it as a normal point.

[0040] This embodiment also discloses a detection system for abnormal points of indoor positioning Bluetooth beacons. Figure 4 is a module schematic diagram of the indoor positioning Bluetooth beacon abnormal point detection system disclosed in the embodiment of the present application, such as Figure 4 As shown, the system includes a neighborhood module 401, a calculation module 402, a comparison module 403 and an execution module 404, wherein: A neighborhood module 401 is configured to obtain a first neighborhood and a second neighborhood of a first beacon point from a beacon point set, wherein the first neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a preset scenario, and the second neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a current scenario, and the first beacon point is any one of the beacon point set; A calculation module 402 is configured to calculate a first average value of the coordinates of the beacon points in the first neighborhood, calculate a second average value of the coordinates of the beacon points in the second neighborhood, and calculate an offset score of the first beacon point based on the first average value and the second average value; A comparison module 403 is configured to calculate an average offset score of all beacon points in the beacon point set, and compare the offset score with the average offset score; The execution module 404 is configured to calculate a weighted distance according to the comparison result, calculate a subspace offset factor according to the weighted distance, and mark a beacon point whose subspace offset factor is greater than a threshold as an abnormal point. The step of calculating the weighted distance according to the comparison result and calculating the subspace offset factor according to the weighted distance includes: When the offset score is greater than the average offset score, determining a second beacon point that is second closest to the first beacon point, and calculating a target distance between the first beacon point and the second beacon point; Calculating a weighted distance of the first beacon point according to the target distance and a preset weight, and calculating a local density of the first beacon point according to the weighted distance; The average of the local densities of all beacon points in the first neighborhood is calculated to obtain a subspace density, and the subspace offset factor is calculated according to the subspace density and the local density of the first beacon point.

[0041] Optionally, the calculation module 402 is configured to: The first average value and the second average value are calculated by the following formula: ; ; in, is the first average x Axis coordinate mean, is the first average y Axis coordinate mean, P xi is the first neighbor i beacon points x Axis coordinate values, P yi is the first neighbor i beacon pointsy Axis coordinate values, The second average x Axis coordinate mean, The second average y Axis coordinate mean, P xj is the first j beacon points x Axis coordinate values, P yj is the first j The y-axis coordinate value of the beacon point, K is the first quantity.

[0042] Optionally, the calculation module 402 is configured to: The offset score of the first beacon point is calculated by the following formula: ; Where D is the offset score between the first and second neighbors, is the first average x Axis coordinate mean, is the first average y Axis coordinate mean, The second average x Axis coordinate mean, The second average y Axis coordinates mean.

[0043] Optionally, the neighborhood module 401 is configured to: According to the preset coordinates of the beacon point, the Euclidean distance between other beacon points and the first beacon point is calculated, and according to the Euclidean distance, a first number of beacon points closest to the first beacon point are selected to form a first neighborhood, wherein the other beacon points are beacon points in the beacon point set other than the first beacon point; The actual distance between other beacon points and the first beacon point is determined according to the actually measured signal strength value, and a first number of beacon points closest to the first beacon point are selected according to the actual distance to form a second neighborhood, and the other beacon points are beacon points in the beacon point set except the first beacon point.

[0044] Optionally, the neighborhood module 401 is configured to: The actual distance between other beacon points and the first beacon point is determined by the following formula: RSSI=RSSI 0 -10nlog 10 (d) +Xs ; in, RSSIis the signal strength value received by the first beacon point, RSSI 0 is the signal strength value emitted by the other beacon points, n is the path loss exponent, d is the actual distance between the other beacon points and the first beacon point, Xs is the shadow attenuation, which is a random variable following a Gaussian distribution.

[0045] Optionally, the execution module 404 is configured to: The weighted distance is calculated by the following formula: SWD ( q ) =ω·SD ( q ); in, SWD ( q ) is the first beacon point q The weighted distance of ω is the weight, SD ( q ) is the target distance from the first beacon point to the second beacon point, The local density of the first beacon point is calculated by the following formula: ; in, ρ ( q ) is the first beacon point q The local density of The subspace offset factor of the first beacon point is calculated by the following formula: ; in, The first beacon point q The subspace shift factor, is the subspace density of the first beacon point.

[0046] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0047] This embodiment also discloses an electronic device, referring to Figure 5The electronic device may include: at least one processor 501 , at least one communication bus 502 , a user interface 503 , a network interface 504 , and at least one memory 505 .

[0048] The communication bus 502 is used to realize the connection and communication between these components.

[0049] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0050] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0051] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 501 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501, and it can be implemented separately through a chip.

[0052] Among them, the memory 505 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 505 may also be optionally at least one storage device located away from the aforementioned processor 501. As Figure 5 As shown, the memory 505 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a method for detecting abnormal points of indoor positioning Bluetooth beacons.

[0053] exist Figure 5 In the electronic device shown, the user interface 503 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 501 can be used to call the application program of the detection method of the indoor positioning Bluetooth beacon anomaly point stored in the memory 505. When executed by one or more processors 501, the electronic device executes one or more methods in the above-mentioned embodiments.

[0054] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0055] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0057] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0058] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0059] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory 505, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 505 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0060] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for detecting abnormal points of indoor positioning Bluetooth beacons, characterized in that: Applied to an indoor positioning platform, the method comprises: Acquire a first neighborhood and a second neighborhood of a first beacon point from a beacon point set, wherein the first neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a preset scenario, the second neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a current scenario, and the first beacon point is any one of the beacon point set; Calculating a first average value of the coordinates of the beacon points in the first neighborhood, and calculating a second average value of the coordinates of the beacon points in the second neighborhood, and calculating an offset score of the first beacon point according to the first average value and the second average value; Calculating an average offset score of all beacon points in the beacon point set, and comparing the offset score with the average offset score; A weighted distance is calculated according to the comparison result, a subspace offset factor is calculated according to the weighted distance, and a beacon point whose subspace offset factor is greater than a threshold is marked as an abnormal point. The step of calculating the weighted distance according to the comparison result and calculating the subspace offset factor according to the weighted distance includes: When the offset score is greater than the average offset score, determining a second beacon point that is second closest to the first beacon point, and calculating a target distance between the first beacon point and the second beacon point; Calculating a weighted distance of the first beacon point according to the target distance and a preset weight, and calculating a local density of the first beacon point according to the weighted distance; The average of the local densities of all beacon points in the first neighborhood is calculated to obtain a subspace density, and the subspace offset factor is calculated according to the subspace density and the local density of the first beacon point.

2. The method for detecting abnormal points of indoor positioning Bluetooth beacons according to claim 1, characterized in that: The calculating a first average value of the coordinates of the beacon points in the first neighborhood and calculating a second average value of the coordinates of the beacon points in the second neighborhood comprises: The first average value and the second average value are calculated by the following formula: ； ； in, is the first average x Axis coordinate mean, is the first average y Axis coordinate mean, P xi is the first neighbor i beacon points x Axis coordinate values, P yi is the first neighbor i beacon points y Axis coordinate values, The second average x Axis coordinate mean, The second average y Axis coordinate mean, P xj is the first j beacon points x Axis coordinate values, P yj is the first j The y-axis coordinate value of the beacon point, K is the first quantity.

3. The method for detecting abnormal points of indoor positioning Bluetooth beacons according to claim 2, characterized in that: Calculating the offset score of the first beacon point according to the first average value and the second average value comprises: The offset score of the first beacon point is calculated by the following formula: ； Where D is the offset score between the first and second neighbors, is the first average x Axis coordinate mean, is the first average y Axis coordinate mean, The second average x Axis coordinate mean, The second average y Axis coordinate mean.

4. The method for detecting abnormal points of indoor positioning Bluetooth beacons according to claim 1, characterized in that: The acquiring the first neighborhood and the second neighborhood of the first beacon point from the beacon point set comprises: According to the preset coordinates of the beacon point, the Euclidean distance between other beacon points and the first beacon point is calculated, and according to the Euclidean distance, a first number of beacon points closest to the first beacon point are selected to form a first neighborhood, wherein the other beacon points are beacon points in the beacon point set other than the first beacon point; The actual distance between other beacon points and the first beacon point is determined according to the actually measured signal strength value, and a first number of beacon points closest to the first beacon point are selected according to the actual distance to form a second neighborhood, and the other beacon points are beacon points in the beacon point set except the first beacon point.

5. The method for detecting abnormal points of indoor positioning Bluetooth beacons according to claim 4, characterized in that: The determining the actual distance between the other beacon points and the first beacon point according to the actually measured signal strength value comprises: The actual distance between other beacon points and the first beacon point is determined by the following formula: RSSI=RSSI 0 -10nlog 10 (d) +Xs ； in, RSSI is the signal strength value received by the first beacon point, RSSI 0 is the signal strength value emitted by the other beacon points, n is the path loss exponent, d is the actual distance between the other beacon points and the first beacon point, Xs is the shadow attenuation, which is a random variable following a Gaussian distribution.

6. The method for detecting abnormal points of indoor positioning Bluetooth beacons according to claim 1, characterized in that: The step of calculating the weighted distance according to the comparison result and calculating the subspace offset factor according to the weighted distance includes: The weighted distance is calculated by the following formula: SWD （ q ） =ω·SD （ q ）； in, SWD ( q ) is the first beacon point q The weighted distance of ω is the weight, SD ( q ) is the target distance from the first beacon point to the second beacon point, The local density of the first beacon point is calculated by the following formula: ； in, ρ ( q ) is the first beacon point q The local density of The subspace offset factor of the first beacon point is calculated by the following formula: ； in, The first beacon point q The subspace shift factor, is the subspace density of the first beacon point.

7. A detection system for abnormal points of indoor positioning Bluetooth beacons, used to implement the detection method for abnormal points of indoor positioning Bluetooth beacons as described in any one of claims 1 to 6, characterized in that: It includes a neighborhood module, a calculation module, a comparison module and an execution module, wherein: a neighborhood module, configured to obtain a first neighborhood and a second neighborhood of a first beacon point from a beacon point set, wherein the first neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a preset scenario, and the second neighborhood is a set of a first number of nearest neighboring beacon points of the first beacon point in a current scenario, and the first beacon point is any one of the beacon point set; a calculation module configured to calculate a first average value of the coordinates of the beacon points in the first neighborhood, calculate a second average value of the coordinates of the beacon points in the second neighborhood, and calculate an offset score of the first beacon point based on the first average value and the second average value; a comparison module configured to calculate an average offset score of all beacon points in the beacon point set, and compare the offset score with the average offset score; an execution module configured to calculate a weighted distance according to the comparison result, calculate a subspace offset factor according to the weighted distance, and mark a beacon point whose subspace offset factor is greater than a threshold as an abnormal point, The step of calculating the weighted distance according to the comparison result and calculating the subspace offset factor according to the weighted distance includes: When the offset score is greater than the average offset score, determining a second beacon point that is second closest to the first beacon point, and calculating a target distance between the first beacon point and the second beacon point; Calculating a weighted distance of the first beacon point according to the target distance and a preset weight, and calculating a local density of the first beacon point according to the weighted distance; The average of the local densities of all beacon points in the first neighborhood is calculated to obtain a subspace density, and the subspace offset factor is calculated according to the subspace density and the local density of the first beacon point.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 6 is performed.

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