Optimization method and device for beacon positioning

Abstract

The application is suitable for the technical field of positioning processing, and provides a beacon positioning optimization method and device.The beacon positioning optimization method comprises the following steps: acquiring a feature information matching library corresponding to a positioning area; receiving a detection signal sent by a plurality of beacon devices, extracting a current signal strength and a current reaching time of the detection signal; matching target feature information in the feature information matching library according to a plurality of current signal strengths, a plurality of beacon signal strengths, a plurality of current reaching times and a plurality of beacon reaching times; and taking a positioning reference point corresponding to the target feature information as position information of a device to be positioned.The beacon positioning optimization method can provide a high-precision and high-robustness positioning solution in a complex and dynamically changing environment by introducing a feature information matching library and combining multi-dimensional signal feature matching and adaptive adjustment.

Description

Technical Field

[0001] This invention belongs to the technical field of positioning processing, and particularly relates to an optimization method and apparatus for beacon positioning. Background Technology

[0002] With the continuous development of wireless communication technology, beacon-based positioning methods have been widely used in many application fields. Beacon positioning technology utilizes the signal strength, time of arrival, and other characteristic information of multiple beacon devices to perform positioning through certain algorithms. Common beacon positioning methods include signal strength-based positioning methods (such as RSSI positioning), time of arrival-based positioning methods (such as TOA positioning), and time difference of arrival-based positioning methods (such as TDOA positioning).

[0003] Existing beacon positioning methods typically receive signals from multiple beacon devices and calculate the location of the device to be located based on information such as signal strength and time of arrival. However, due to environmental factors such as multipath effects and signal attenuation, signal accuracy is often difficult to guarantee. These errors can lead to reduced positioning accuracy, especially in complex positioning areas such as indoors or underground environments. To improve the accuracy and reliability of positioning, many researchers have attempted to improve the performance of beacon positioning by optimizing algorithms.

[0004] However, current beacon positioning optimization methods mostly focus on signal acquisition and processing, lacking effective solutions for efficiently matching the relationship between positioning area feature information and beacon signals. Especially when the positioning area has high environmental complexity, the optimization results of existing methods often fail to meet the requirements of high-precision positioning. Therefore, how to optimize beacon positioning methods through more accurate feature information matching and improve positioning accuracy remains a challenge in current technology. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide an optimization method and apparatus for beacon positioning to solve the technical problem that the optimization effect of existing methods often fails to meet the requirements of high-precision positioning.

[0006] A first aspect of the present invention provides an optimization method for beacon positioning, the optimization method for beacon positioning comprising:

[0007] Obtain a feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to multiple positioning reference points; the feature information includes the signal strength of multiple beacons and the arrival time of multiple beacons corresponding to the current positioning reference point;

[0008] Receive detection signals sent by multiple beacon devices, and extract the current signal strength and current arrival time of the detection signals;

[0009] Based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times, target feature information is matched in the feature information matching library;

[0010] The positioning reference point corresponding to the target feature information is used as the location information of the device to be positioned.

[0011] Furthermore, the positioning area includes multiple positioning grid areas, and each positioning grid area includes multiple sub-grid areas;

[0012] The step of matching target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times includes:

[0013] Acquire first feature information corresponding to each of multiple positioning grid regions; the first feature information refers to the average value of second feature information corresponding to each of multiple sub-network regions within the positioning grid region; the first feature information includes the signal strength of multiple first beacons and the arrival time of multiple first beacons.

[0014] Based on multiple current signal strengths, multiple first beacon signal strengths, multiple current arrival times, and multiple first beacon arrival times, the maximum similarity feature information is matched among multiple first feature information.

[0015] Match the target localization grid region corresponding to the target feature information with the highest similarity;

[0016] Obtain the second feature information corresponding to each of the multiple sub-network regions under the target positioning grid region; the second feature information includes the signal strength of multiple second beacons and the arrival time of multiple second beacons.

[0017] The target feature information is matched among multiple second feature information based on multiple current signal strengths, multiple second beacon signal strengths, multiple current arrival times, and multiple second beacon arrival times.

[0018] Further, the step of matching the target feature information among multiple second feature information based on multiple current signal strengths, multiple second beacon signal strengths, multiple current arrival times, and multiple second beacon arrival times includes:

[0019] Calculate the similarity between multiple current signal strengths and multiple second beacon signal strengths in each second feature information to obtain the first similarity corresponding to each second feature information;

[0020] Extract the current feature information whose first similarity is greater than the first threshold;

[0021] The similarity between multiple current arrival times and multiple second beacon arrival times in each second current feature information is calculated to obtain the second similarity corresponding to each second feature information; wherein, there is clock synchronization between multiple beacon devices, but there is no clock synchronization between the device to be located and the beacon devices;

[0022] If the second similarity is greater than the first threshold, then the second feature information corresponding to the second similarity is used as the target feature information.

[0023] Further, the step of calculating the similarity between multiple current signal strengths and multiple second beacon signal strengths in each second feature information to obtain the first similarity corresponding to each second feature information includes:

[0024] The error value is obtained by subtracting the current signal strength from the signal strength of the second beacon corresponding to the same beacon device.

[0025] Obtain the preset weights for each beacon device;

[0026] The error value corresponding to each beacon device is multiplied by a preset weight to obtain the corrected error value;

[0027] Calculate the average of the multiple corrected error values;

[0028] Obtain multiple pre-stored first numerical ranges and the preset similarity corresponding to the first numerical ranges;

[0029] The preset similarity corresponding to the first numerical range in which the average value falls is taken as the first similarity.

[0030] Further, the step of calculating the similarity between multiple current arrival times and multiple second beacon arrival times in each second current feature information to obtain the second similarity corresponding to each second feature information includes:

[0031] Extract the first maximum value among multiple arrival times of the second beacons;

[0032] Divide the arrival times of multiple second beacons by the first maximum value to obtain the first ratio data corresponding to the arrival times of multiple second beacons;

[0033] Extract the second maximum value from multiple current arrival times;

[0034] Divide the multiple current arrival times by the second maximum value to obtain the second ratio data corresponding to the multiple current arrival times;

[0035] Calculate the Euclidean distance between the first proportional data and the second proportional data;

[0036] Obtain multiple pre-stored second numerical ranges and the preset similarity corresponding to the second numerical ranges;

[0037] The preset similarity corresponding to the second numerical range of the Euclidean distance is taken as the second similarity.

[0038] Furthermore, after the step of matching target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times, the method further includes:

[0039] When no target feature information is matched in the feature information matching library, the location information of the device to be located is calculated based on multiple current arrival times.

[0040] Furthermore, the step of calculating the location information of the device to be located based on multiple current arrival times when no target feature information is matched in the feature information matching library includes:

[0041] When no target feature information is matched in the feature information matching library, the current arrival time and signal transmission speed are multiplied to obtain the transmission distance;

[0042] Obtain the coordinate information of multiple beacon devices;

[0043] The location information of the device to be located is calculated based on the coordinate information and transmission distance of each of the multiple beacon devices.

[0044] A second aspect of the present invention provides an optimization apparatus for beacon positioning, comprising:

[0045] The acquisition unit is used to acquire a feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to multiple positioning reference points; the feature information includes multiple beacon signal strengths and multiple beacon arrival times corresponding to the current positioning reference point.

[0046] The receiving unit is used to receive detection signals sent by multiple beacon devices and extract the current signal strength and current arrival time of the detection signals.

[0047] The matching unit is used to match target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times;

[0048] The determining unit is used to take the positioning reference point corresponding to the target feature information as the position information of the device to be positioned.

[0049] A third aspect of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the beacon positioning optimization method described in the first aspect.

[0050] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the beacon positioning optimization method described in the first aspect.

[0051] The beneficial effects of this invention compared to existing technologies are as follows: Traditional beacon positioning methods rely on directly using signal strength or time of arrival (TOA) for positioning calculations. This information is often affected by environmental factors such as multipath effects and signal attenuation, leading to inaccurate positioning results. This invention, however, constructs a feature information matching library that stores feature information from multiple positioning reference points, including comprehensive data across multiple dimensions such as signal strength and TOA. By performing multi-dimensional information matching within this feature information matching library, the impact of signal errors on positioning accuracy can be effectively reduced, thereby achieving higher-precision positioning. This invention's method combines signal strength and TOA information from multiple beacon devices to construct a matching library containing various feature information. Compared to traditional methods that rely solely on signal strength or TOA for matching, this multi-dimensional matching method better copes with environmental changes and signal interference, improving the robustness of the positioning system in complex environments. Even in environments with severe multipath effects or significant signal attenuation, it can still maintain relatively accurate positioning results. Through the design of the feature information matching library, this invention enables the positioning algorithm to adjust based on the characteristics of different environments. When the environment of the positioning area changes, the feature information library can be dynamically updated to adapt to new positioning requirements, further improving the system's adaptability. This adaptive characteristic enables the method to provide stable positioning performance in different scenarios. The method of this invention, by pre-establishing a feature information matching library, makes the matching operation during the positioning process more efficient. Compared to traditional real-time calculation of positioning errors, this invention can quickly locate reference points using the matching library and directly find the positioning reference point corresponding to the target feature information in the library. This not only improves positioning accuracy but also optimizes computational efficiency and reduces the computational burden on real-time positioning systems. Beacon positioning technology is often affected by environmental factors, such as walls, obstacles, and multipath propagation, which may lead to fluctuations in positioning accuracy. However, through the feature information matching library, the system can automatically adjust the positioning strategy when the environment changes, selecting the positioning reference point that best matches the current environmental characteristics. This method significantly reduces the impact of environmental factors on positioning performance, especially in complex environments such as indoors and underground, significantly improving the stability and accuracy of positioning. In summary, the beacon positioning optimization method of this invention, by introducing a feature information matching library, combining multi-dimensional signal feature matching and adaptive adjustment, can provide a high-precision and robust positioning solution in complex and dynamically changing environments, greatly improving the performance and reliability of beacon positioning technology in practical applications. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A schematic flowchart of an optimized beacon positioning method provided by the present invention is shown;

[0054] Figure 2 A schematic diagram of an optimized beacon positioning device according to an embodiment of the present invention is shown;

[0055] Figure 3 A schematic diagram of a terminal device provided in an embodiment of the present invention is shown. Detailed Implementation

[0056] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0057] This invention provides an optimization method and apparatus for beacon positioning to solve the technical problem that the optimization effect of existing methods often fails to meet the requirements of high-precision positioning.

[0058] First, this invention provides an optimized method for beacon positioning. Please refer to [link / reference]. Figure 1 , Figure 1 A schematic flowchart of an optimized beacon positioning method provided by the present invention is shown. Figure 1 As shown, the optimization method for beacon positioning may include the following steps:

[0059] Step 101: Obtain the feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to multiple positioning reference points; the feature information includes the signal strength of multiple beacons and the arrival time of multiple beacons corresponding to the current positioning reference point;

[0060] Before executing this embodiment, it is necessary to collect feature information of multiple positioning reference points within the positioning area and store the feature information of all positioning reference points within the positioning area in a "feature information matching library" as the basis for subsequent matching. The "feature information matching library" of the positioning area contains multiple positioning reference points and their corresponding feature information. This feature information is used to compare with the actual detection signal to determine the location of the device.

[0061] The feature information includes, but is not limited to, the strength and arrival time of the beacon signals collected at each positioning reference point. For example, if multiple beacon devices (such as Wi-Fi signals, Bluetooth beacons, UWB beacons, etc.) emit signals at a specific location, the feature information will record the signal strength of these beacons and the time it takes for the signals emitted from the beacons to arrive at that reference point.

[0062] Step 102: Receive detection signals sent by multiple beacon devices, and extract the current signal strength and current arrival time of the detection signals;

[0063] During the positioning process, the device to be located receives detection signals from multiple beacon devices. The device extracts two key features from these signals: the current signal strength and the current signal arrival time.

[0064] It is worth noting that signal strength is used to match the corresponding positioning reference point, while the time of arrival (TOA) is used to verify whether the matched reference point is correct. The reason for using both feature information and TOA to match the positioning reference point is that in real-world applications, changes in obstacles or crowds may occur, causing variations in signal strength and affecting the accuracy of the positioning reference point matching. TOA has a relatively small impact on obstacle changes or crowds. Therefore, this embodiment uses a redundancy mechanism (TOA) to verify the signal strength of the positioning reference point, thereby improving positioning accuracy.

[0065] Step 103: Match target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times;

[0066] By comparing the signal strength and arrival time of multiple beacon signals received from the device to be located, the system searches for the most similar feature information in a feature information matching database. In other words, the system looks for reference point feature information that is closest to the feature information of the currently detected signal.

[0067] To improve the matching efficiency of feature information, this application divides the positioning region into multiple positioning grid regions. Each positioning grid region is further divided into multiple sub-grid regions. Both the positioning grid region and the sub-grid regions correspond to their respective feature information. During the matching process, a first matching is performed based on the positioning grid region, followed by a second matching based on the multiple sub-grid regions within the positioning grid region. This eliminates the need to traverse all feature information during the matching process, thus improving matching efficiency. The specific matching logic is as follows:

[0068] Specifically, step 103 includes steps 1031 to 1035:

[0069] Step 1031: Obtain the first feature information corresponding to each of the multiple positioning grid regions; the first feature information refers to the average value of the second feature information corresponding to each of the multiple sub-network regions in the positioning grid region; the first feature information includes the signal strength of multiple first beacons and the arrival time of multiple first beacons;

[0070] The first feature information is obtained by averaging the second feature information of all sub-grid regions within the grid area. The second feature information includes the beacon signal strength and signal arrival time of each sub-grid. By calculating the average of the second feature information of multiple sub-grid regions, the first feature information of the entire grid area is obtained.

[0071] Step 1032: Based on multiple current signal strengths, multiple first beacon signal strengths, multiple current arrival times, and multiple first beacon arrival times, match the feature information with the maximum similarity among multiple first feature information;

[0072] This step compares the current signal strength and arrival time received by the device to be located with the first feature information stored in the feature information database. Specifically, the system calculates the similarity between the features of the current signal and the feature information of the grid area. By comparing these similarities, the "maximum similarity feature information," that is, the grid area with the most accurate match, is found. The purpose of this step is to determine the possible location grid area of ​​the device.

[0073] The matching logic for the maximum similarity feature information is the same as that in steps A1 to A4, and will not be repeated here.

[0074] Step 1033: Match the target localization grid region corresponding to the target feature information with the maximum similarity;

[0075] The target location grid region of the device is determined using the maximum similarity feature information obtained in the previous step, i.e., the grid region where the device may be located. Through the matching process, the approximate location region of the device to be located is determined, which is obtained by matching with the first feature information of the grid region.

[0076] Step 1034: Obtain the second feature information corresponding to each of the multiple sub-network regions under the target positioning grid region; the second feature information includes the signal strength of multiple second beacons and the arrival time of multiple second beacons;

[0077] After the target grid region is determined, the next step is to obtain the second feature information of multiple sub-grid regions within that region. The second feature information includes the signal strength and arrival time of multiple second beacons within each sub-grid region.

[0078] Step 1035: Match the target feature information among multiple second feature information based on multiple current signal strengths, multiple second beacon signal strengths, multiple current arrival times, and multiple second beacon arrival times.

[0079] The system then compares the current signal strength and arrival time of the device to be located with the second feature information within the target grid area. This step also involves calculating the similarity between signal features and selecting the second feature information that best matches the current signal. Through this matching process, more accurate location information can be obtained.

[0080] In the embodiments corresponding to steps 1031 to 1035, the positioning area is divided into multiple grid areas and sub-grid areas. The matching process begins with coarse matching of the positioning grid areas and progresses to refined signal feature matching of the sub-grid areas, ultimately yielding high-precision device location information. The first feature information and the second feature information represent the signal feature information of the grid areas and sub-grid areas, respectively. This layer-by-layer matching method significantly reduces computational load while ensuring high-precision positioning results.

[0081] Specifically, step 1035 includes steps A1 to A4:

[0082] Step A1: Calculate the similarity between multiple current signal strengths and multiple second beacon signal strengths in each second feature information to obtain the first similarity corresponding to each second feature information;

[0083] The current signal strength is the RSSI value of each beacon signal received in real time by the device to be located. The second beacon signal strength is the reference signal strength data pre-collected and stored for each sub-grid region. The essence of similarity calculation is to assess the "closeness" between the currently measured data and the reference data in the database. The first similarity value reflects the degree of matching in the signal strength dimension. The specific matching logic is as follows:

[0084] Specifically, step A1 includes steps A11 to A16:

[0085] Step A11: Subtract the current signal strength from the signal strength of the second beacon corresponding to the same beacon device to obtain the error value;

[0086] The current signal strength is the signal strength received by the device to be located. The second beacon signal strength is the pre-stored signal strength data of that beacon device in the database. The error value is calculated by subtracting the current signal strength from the second beacon signal strength. This error value reflects the impact of distance deviation between the device to be located and the beacon device, or environmental influences (such as obstacles or interference), on the signal strength. The magnitude of the error value signifies the difference between the current device's signal strength and the beacon signal strength.

[0087] Step A12: Obtain the preset weights for each beacon device;

[0088] Preset weights refer to the importance or reliability of each beacon device in a specific application scenario. Because different beacon devices may have varying signal coverage or stability in different environments or geographical locations, each beacon device is assigned a different weight. These weights can be set based on factors such as device quality and signal stability.

[0089] In practical applications, beacon signals may be interfered with by environmental factors, such as walls, metal objects, or other wireless signals. Giving higher weights to beacons less susceptible to interference can reduce the impact of noise on positioning results, making positioning more stable. Different beacons may have different hardware qualities and signal propagation characteristics. For example, some beacons may have higher battery levels, stronger signals, and more stable frequencies; weighting can take these factors into account, improving the reliability of the positioning system. In some cases, individual beacons may fail or have abnormal signals. By giving higher weights to other beacons with normal signals, the impact of failed beacons can be compensated for to some extent, enhancing the system's fault tolerance.

[0090] Step A13: Multiply the error value corresponding to each beacon device by a preset weight to obtain the corrected error value;

[0091] The corrected error value is calculated by combining the error value with the weights of the beacon devices. This is to account for the different levels of reliability or importance among the beacon devices. If a beacon device has a higher weight, the impact of its error value will be amplified; conversely, the impact of the error of a beacon device with a lower weight on the final result will be suppressed.

[0092] Step A14: Calculate the average of the multiple corrected error values;

[0093] To obtain a comprehensive matching degree, the system calculates the average of these corrected error values. The average represents the synthesized error result of all beacon devices, taking into account both the errors of different beacon devices and their weights, thus obtaining an overall deviation measure.

[0094] Step A15: Obtain multiple pre-stored first numerical ranges and the preset similarity corresponding to the first numerical ranges;

[0095] The first numerical range refers to a predefined set of error value intervals, each interval corresponding to a specific preset similarity. These intervals are set based on experimental or actual measurement data, representing different degrees of error and their corresponding signal matching degree. The preset similarity is a predefined similarity score, which usually corresponds one-to-one with the numerical range. When an error value falls within a specific numerical range, it corresponds to a specific similarity value.

[0096] Step A16: Take the preset similarity corresponding to the first numerical range in which the average value is located as the first similarity.

[0097] Once the average error value is calculated, a corresponding preset similarity can be found based on its numerical range. This method establishes a mapping relationship between the error value and the similarity, yielding the final first similarity score. A smaller average error value may indicate a better match in signal strength, resulting in a higher similarity score; conversely, a larger average error value may indicate a greater difference in signal strength, leading to a lower similarity score.

[0098] In the embodiments corresponding to steps A11 to A16, by combining the preset weights of the beacon devices to correct the error, the deviation that may be caused by a single beacon device is avoided, thus improving the overall matching accuracy. By adjusting the weights of the beacon devices, the stability and reliability of different beacon devices are considered, avoiding excessive influence of certain unstable beacons on the matching results. By calculating the average of multiple corrected error values, the excessive influence of the error of a single beacon device on the final result is reduced, ensuring a more stable positioning result. Matching the error value with a preset similarity effectively transforms the original error data into an intuitive similarity value, facilitating subsequent operations and analysis.

[0099] As an optional embodiment of this application, the first similarity can also be calculated in the following manner:

[0100]

[0101] Where S represents the first similarity, n represents the number of beacon devices, and w i w represents the preset weight of the i-th beacon device. ja represents the preset weight of the j-th beacon device. i b represents the current signal strength of the i-th signal. i Let a1, a2, ..., a represent the signal strength of the i-th second beacon. n Represents multiple current signal strengths, b1, b2, ..., b n This indicates the signal strength of multiple second beacons.

[0102] The purpose of this mathematical model is to calculate the values ​​of two data sequences a1, a2, ..., a3. n and b1, b2, ..., b n The similarity between data points is considered, especially when the error at each data point may differ. This mathematical model takes into account multiple factors to ensure that the similarity assessment is not only based on the magnitude of the error, but also includes normalization, weighting, and non-linear error sensitivity.

[0103] a i -b i This step directly measures the difference between two data points at the same location. Error is a fundamental way to assess the bias between data.

[0104] To eliminate the impact of varying data ranges on similarity calculations, different data sequences may have different dimensions and scales. All data points and errors are transformed to a uniform range, allowing for direct comparison of error values. The error values ​​are then compared to the maximum value in the dataset, ensuring that the impact of the error is not distorted by a dataset's range being too large or too small.

[0105] Weighting is a method of considering the importance of different data points in the overall calculation. In some cases, certain data points are more critical than others.

[0106] This mathematical model can effectively capture subtle differences in the data and react quickly to larger errors. For tasks requiring highly accurate similarity assessment (such as exact matching), this non-linear processing can effectively prevent the accumulation of errors that could lead to distortion in the final similarity score.

[0107] Step A2: Extract the current feature information where the first similarity is greater than the first threshold;

[0108] Steps A1 and A2 are the screening process for the signal strength dimension. The first threshold is a preset value. If the first similarity of a certain sub-grid region exceeds this value (representing a high matching degree), it is considered a "candidate region" and enters the time matching process. The advantage of this approach is that it first filters out regions with "relatively similar signal strengths" from "all sub-grids" and then proceeds to the next step of judgment within these candidate regions.

[0109] Step A3: Calculate the similarity between multiple current arrival times and multiple second beacon arrival times in each second current feature information to obtain the second similarity corresponding to each second feature information; wherein, multiple beacon devices are synchronized with each other, but there is no clock synchronization between the device to be located and the beacon devices;

[0110] This stage involves matching calculations over time. The arrival time of the second beacon is the original arrival time of the feature library for that region. The specific calculation logic for the second similarity is as follows:

[0111] Specifically, step A3 includes steps A31 to A37:

[0112] Step A31: Extract the first maximum value among the arrival times of multiple second beacons;

[0113] Step A32: Divide the arrival times of the multiple second beacons by the first maximum value to obtain the first ratio data corresponding to the arrival times of the multiple second beacons;

[0114] By dividing the arrival time of each second beacon by the extracted maximum arrival time, a scaled value is obtained, which represents the relationship between the arrival time of each beacon and the maximum arrival time.

[0115] It is worth noting that in actual positioning environments, multiple beacon devices synchronize their clocks, while the device to be located and the beacon devices often lack clock synchronization. This can lead to clock discrepancies between the device used in the feature information matching library and the device to be located during the actual positioning process, resulting in errors in the arrival times collected by both. To address this issue, this embodiment calculates the proportional data between beacon arrival times to determine if a discrepancy has occurred. Since it is not necessary to compare actual arrival time data, but rather to focus on the proportional difference in arrival times between beacon devices, clock synchronization between the device used in the feature information matching library and the device to be located during the actual positioning process is unnecessary.

[0116] Step A33: Extract the second maximum value among multiple current arrival times;

[0117] Step A34: Divide the multiple current arrival times by the second maximum value to obtain the second proportional data corresponding to the multiple current arrival times;

[0118] Step A35: Calculate the Euclidean distance between the first proportional data and the second proportional data;

[0119] Step A36: Obtain multiple pre-stored second numerical ranges and the preset similarity corresponding to the second numerical ranges;

[0120] The second numerical range refers to a predefined set of error value intervals, each interval corresponding to a specific preset similarity. These intervals are set based on experimental or actual measurement data, representing different degrees of error and their corresponding signal matching degree. The preset similarity is a predefined similarity score, which usually corresponds one-to-one with the numerical range. When the error value falls within a specific numerical range, it corresponds to a specific similarity value.

[0121] Step A37: Use the preset similarity corresponding to the second numerical range of the Euclidean distance as the second similarity.

[0122] In the embodiments corresponding to steps A31 to A37, by scaling the beacon and current arrival time to the maximum value, data consistency is ensured, and interference from clock deviation errors is avoided. Using Euclidean distance to calculate the difference between two scaled time data points can intuitively reflect the similarity between the signal and the arrival time. By matching the calculated Euclidean distance to a preset numerical range, a similarity value can be easily obtained, and the matching effect of different beacons can be compared.

[0123] Step A4: If the second similarity is greater than the first threshold, then the second feature information corresponding to the second similarity is taken as the target feature information.

[0124] Steps A1 to A4 constitute a dual verification mechanism: the first layer uses signal strength for pre-screening, and the second layer uses signal arrival time for correctness verification.

[0125] In the embodiments corresponding to steps A1 to A4, signal strength is matched first, followed by arrival time, resulting in clear logic and high efficiency. Threshold control of the number of candidate regions reduces computation and improves real-time performance. Dual similarity control enhances the accuracy and robustness of positioning.

[0126] Optionally, after step 103, step B1 is also included: when no target feature information is matched in the feature information matching library, the location information of the device to be located is calculated based on multiple current arrival times.

[0127] If no target feature information that perfectly matches the feature information obtained by the current device is found in the feature information matching library (this could be due to changes in the environment, signal interference, changes in beacon equipment, etc.), the system will enter this alternative processing flow. The feature information matching library usually contains a lot of historical data and feature sets, but due to dynamic changes in the environment, some features may not be able to match perfectly. In this case, the system needs an alternative solution to ensure that the positioning task can continue.

[0128] Specifically, step B1 includes steps B11 to B13:

[0129] Step B11: If no target feature information is matched in the feature information matching library, multiply the current arrival time and the signal transmission speed to obtain the transmission distance;

[0130] Step B12: Obtain the coordinate information of multiple beacon devices;

[0131] Step B13: Calculate the location information of the device to be located based on the coordinate information and transmission distance of each of the multiple beacon devices.

[0132] Based on the arrival times of multiple beacons, the distance (time × propagation speed) from each beacon to the device to be located can be obtained. Using this distance information, the location of the device can be determined through geometric calculations (such as triangulation or polygonal positioning).

[0133] Step 104: Use the positioning reference point corresponding to the target feature information as the location information of the device to be located.

[0134] Once the system finds the reference point feature information that best matches the current detection signal, it will use the location of that reference point as the location information of the device to be located. This means that the location of the device to be located has been determined, and the precise location of the device can be estimated or directly obtained based on the location of that reference point.

[0135] Through this process, the system can accurately calculate the specific location of the device to be located by combining the differences in signal strength and arrival time with the help of multiple beacons, thus achieving the goal of high-precision positioning.

[0136] It is worth noting that because the feature matching database is high-precision data measured using high-precision instruments, the positioning results from the feature matching database have a high priority. The current positioning information is only calculated using the current arrival time when no similar feature information can be found in the feature matching database.

[0137] In the embodiments corresponding to steps 101 to 104, traditional beacon positioning methods rely on directly using signal strength or time of arrival (TOA) for positioning calculations. This information is often affected by environmental factors such as multipath effects and signal attenuation, leading to inaccurate positioning results. This invention, however, constructs a feature information matching library that stores feature information from multiple positioning reference points, including comprehensive data across multiple dimensions such as signal strength and TOA. By performing multi-dimensional information matching in the feature information matching library, the impact of signal errors on positioning accuracy can be effectively reduced, thereby achieving higher-precision positioning. This invention's method combines signal strength and TOA information from multiple beacon devices to construct a matching library containing various feature information. Compared to traditional methods that solely rely on signal strength or TOA matching, this multi-dimensional matching method better copes with environmental changes and signal interference, improving the robustness of the positioning system in complex environments. Even in environments with severe multipath effects or significant signal attenuation, it can still maintain relatively accurate positioning results. Through the design of the feature information matching library, this invention enables the positioning algorithm to be adjusted based on the characteristics of different environments. When the environment of the positioning area changes, the feature information database can be dynamically updated to adapt to new positioning requirements, further improving the system's adaptability. This adaptive characteristic enables the method to provide stable positioning performance in different scenarios. The method of this invention, by pre-establishing a feature information matching database, makes the matching operation during the positioning process more efficient. Compared to traditional real-time calculation of positioning errors, this invention can quickly locate reference points using the matching database and directly find the positioning reference point corresponding to the target feature information in the database. This not only improves positioning accuracy but also optimizes computational efficiency and reduces the computational burden on real-time positioning systems. Beacon positioning technology is often affected by environmental factors, such as walls, obstacles, and multipath propagation, which may lead to fluctuations in positioning accuracy. However, through the feature information matching database, the system can automatically adjust its positioning strategy when the environment changes, selecting the positioning reference point that best matches the current environmental characteristics. This method significantly reduces the impact of environmental factors on positioning performance, especially in complex environments such as indoors and underground, significantly improving the stability and accuracy of positioning. In summary, the beacon positioning optimization method of the present invention, by introducing a feature information matching library, combining multi-dimensional signal feature matching and adaptive adjustment, can provide a high-precision and robust positioning solution in complex and dynamically changing environments, greatly improving the performance and reliability of beacon positioning technology in practical applications.

[0138] like Figure 2 This invention provides an optimized beacon positioning device; please refer to [link / reference]. Figure 2 , Figure 2 A schematic diagram of an optimized beacon positioning device provided by the present invention is shown, as follows: Figure 2 An optimized beacon positioning device shown includes:

[0139] The acquisition unit 21 is used to acquire a feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to multiple positioning reference points; the feature information includes multiple beacon signal strengths and multiple beacon arrival times corresponding to the current positioning reference point;

[0140] The receiving unit 22 is used to receive detection signals sent by multiple beacon devices and extract the current signal strength and current arrival time of the detection signals;

[0141] Matching unit 23 is used to match target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times;

[0142] The determining unit 24 is used to use the positioning reference point corresponding to the target feature information as the position information of the device to be positioned.

[0143] This invention provides an optimized beacon positioning device. Traditional beacon positioning methods rely on directly using signal strength or time of arrival (TOA) for positioning calculations. This information is often affected by environmental factors such as multipath effects and signal attenuation, leading to inaccurate positioning results. This invention constructs a feature information matching library, which stores feature information from multiple positioning reference points, including comprehensive data across multiple dimensions such as signal strength and TOA. By performing multi-dimensional information matching in the feature information matching library, the impact of signal errors on positioning accuracy can be effectively reduced, thereby achieving higher-precision positioning. This invention's method combines signal strength and TOA information from multiple beacon devices to construct a matching library containing various feature information. Compared to traditional methods that solely rely on signal strength or TOA matching, this multi-dimensional matching method better copes with environmental changes and signal interference, improving the robustness of the positioning system in complex environments. Even in environments with severe multipath effects or significant signal attenuation, it can still maintain relatively accurate positioning results. Through the design of the feature information matching library, this invention enables the positioning algorithm to adjust based on the characteristics of different environments. When the environment of the positioning area changes, the feature information library can be dynamically updated to adapt to new positioning requirements, further improving the system's adaptability. This adaptive characteristic enables the method to provide stable positioning performance in different scenarios. The method of this invention, by pre-establishing a feature information matching library, makes the matching operation during the positioning process more efficient. Compared to traditional real-time calculation of positioning errors, this invention can quickly locate reference points using the matching library and directly find the positioning reference point corresponding to the target feature information in the library. This not only improves positioning accuracy but also optimizes computational efficiency and reduces the computational burden on real-time positioning systems. Beacon positioning technology is often affected by environmental factors, such as walls, obstacles, and multipath propagation, which may lead to fluctuations in positioning accuracy. However, through the feature information matching library, the system can automatically adjust the positioning strategy when the environment changes, selecting the positioning reference point that best matches the current environmental characteristics. This method significantly reduces the impact of environmental factors on positioning performance, especially in complex environments such as indoors and underground, significantly improving the stability and accuracy of positioning. In summary, the beacon positioning optimization method of this invention, by introducing a feature information matching library, combining multi-dimensional signal feature matching and adaptive adjustment, can provide a high-precision and robust positioning solution in complex and dynamically changing environments, greatly improving the performance and reliability of beacon positioning technology in practical applications.

[0144] Figure 3 This is a schematic diagram of a terminal device provided in an embodiment of the present invention. Figure 3As shown, a terminal device 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a beacon positioning optimization program. When the processor 30 executes the computer program 32, it implements the steps in the various beacon positioning optimization method embodiments described above, for example... Figure 1 Steps 101 to 104 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each unit in the above-described device embodiments, for example... Figure 2 The function of the unit shown.

[0145] For example, the computer program 32 can be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 32 in the terminal device 3. For example, the specific functions of each unit of the computer program 32 can be divided as follows:

[0146] The acquisition unit is used to acquire a feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to multiple positioning reference points; the feature information includes multiple beacon signal strengths and multiple beacon arrival times corresponding to the current positioning reference point.

[0147] The receiving unit is used to receive detection signals sent by multiple beacon devices and extract the current signal strength and current arrival time of the detection signals.

[0148] The matching unit is used to match target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times;

[0149] The determining unit is used to take the positioning reference point corresponding to the target feature information as the position information of the device to be positioned.

[0150] The terminal device includes, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of a terminal device 3 and does not constitute a limitation on a terminal device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0151] The processor 30 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0152] The memory 31 can be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 can also be an external storage device of the terminal device 3, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device 3. Furthermore, the memory 31 can include both internal and external storage units of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0153] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0154] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0155] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0156] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0157] This invention provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0158] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0159] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0160] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0161] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0162] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units.

[0163] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0164] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0165] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0166] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0167] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0168] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An optimized method for beacon positioning, characterized in that, The optimization method for beacon positioning includes: Obtain a feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to multiple positioning reference points; the feature information includes the signal strength of multiple beacons and the arrival time of multiple beacons corresponding to the current positioning reference point; Receive detection signals sent by multiple beacon devices, and extract the current signal strength and current arrival time of the detection signals; Based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times, target feature information is matched in the feature information matching library; The positioning reference point corresponding to the target feature information is used as the location information of the device to be positioned. The positioning area includes multiple positioning grid areas, and each positioning grid area includes multiple sub-grid areas; The step of matching target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times includes: Acquire first feature information corresponding to each of multiple positioning grid regions; the first feature information refers to the average value of second feature information corresponding to each of multiple sub-network regions within the positioning grid region; the first feature information includes the signal strength of multiple first beacons and the arrival time of multiple first beacons. Based on multiple current signal strengths, multiple first beacon signal strengths, multiple current arrival times, and multiple first beacon arrival times, the maximum similarity feature information is matched among multiple first feature information. Match the target localization grid region corresponding to the target feature information with the highest similarity; Obtain the second feature information corresponding to each of the multiple sub-network regions under the target positioning grid region; the second feature information includes the signal strength of multiple second beacons and the arrival time of multiple second beacons. Based on multiple current signal strengths, multiple second beacon signal strengths, multiple current arrival times, and multiple second beacon arrival times, the target feature information is matched among multiple second feature information. The step of matching the target feature information among multiple second feature information based on multiple current signal strengths, multiple second beacon signal strengths, multiple current arrival times, and multiple second beacon arrival times includes: Calculate the similarity between multiple current signal strengths and multiple second beacon signal strengths in each second feature information to obtain the first similarity corresponding to each second feature information; Extract the current feature information whose first similarity is greater than the first threshold; The similarity between multiple current arrival times and multiple second beacon arrival times in each second current feature information is calculated to obtain the second similarity corresponding to each second feature information; wherein, there is clock synchronization between multiple beacon devices, but there is no clock synchronization between the device to be located and the beacon devices; If the second similarity is greater than the first threshold, then the second feature information corresponding to the second similarity is used as the target feature information.

2. The optimized beacon positioning method as described in claim 1, characterized in that, The step of calculating the similarity between multiple current signal strengths and multiple second beacon signal strengths in each second feature information to obtain the first similarity corresponding to each second feature information includes: The error value is obtained by subtracting the current signal strength from the signal strength of the second beacon corresponding to the same beacon device. Obtain the preset weights for each beacon device; The error value corresponding to each beacon device is multiplied by a preset weight to obtain the corrected error value; Calculate the average of the multiple corrected error values; Obtain multiple pre-stored first numerical ranges and the preset similarity corresponding to the first numerical ranges; The preset similarity corresponding to the first numerical range in which the average value falls is taken as the first similarity.

3. The optimized beacon positioning method as described in claim 1, characterized in that, The step of calculating the similarity between multiple current arrival times and multiple second beacon arrival times in each second current feature information to obtain the second similarity corresponding to each second feature information includes: Extract the first maximum value among multiple arrival times of the second beacons; Divide the arrival times of multiple second beacons by the first maximum value to obtain the first ratio data corresponding to the arrival times of multiple second beacons; Extract the second maximum value from multiple current arrival times; Divide the multiple current arrival times by the second maximum value to obtain the second ratio data corresponding to the multiple current arrival times; Calculate the Euclidean distance between the first proportional data and the second proportional data; Obtain multiple pre-stored second numerical ranges and the preset similarity corresponding to the second numerical ranges; The preset similarity corresponding to the second numerical range of the Euclidean distance is taken as the second similarity.

4. The optimized beacon positioning method as described in claim 1, characterized in that, After the step of matching target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times, the method further includes: When no target feature information is matched in the feature information matching library, the location information of the device to be located is calculated based on multiple current arrival times.

5. The optimized beacon positioning method as described in claim 1, characterized in that, When no target feature information is matched in the feature information matching library, the step of calculating the location information of the device to be located based on multiple current arrival times includes: When no target feature information is matched in the feature information matching library, the current arrival time and signal transmission speed are multiplied to obtain the transmission distance; Obtain the coordinate information of multiple beacon devices; The location information of the device to be located is calculated based on the coordinate information and transmission distance of each of the multiple beacon devices.

6. A beacon positioning optimization device based on the beacon positioning optimization method according to any one of claims 1 to 5, characterized in that, The beacon positioning optimization device includes: The acquisition unit is used to acquire a feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to multiple positioning reference points; the feature information includes multiple beacon signal strengths and multiple beacon arrival times corresponding to the current positioning reference point. The receiving unit is used to receive detection signals sent by multiple beacon devices and extract the current signal strength and current arrival time of the detection signals. The matching unit is used to match target feature information in the feature information matching library based on multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times; The determining unit is used to take the positioning reference point corresponding to the target feature information as the position information of the device to be positioned.

7. A terminal device, characterized in that, The terminal device includes: a memory, a processor, and a beacon positioning optimization program stored in the memory and executable on the processor, the beacon positioning optimization program being configured to implement the steps of the beacon positioning optimization method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps in the beacon positioning optimization method as described in any one of claims 1 to 5.

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