Beacon positioning optimization method and device

Abstract

The invention is suitable for the technical field of positioning processing, and provides a beacon positioning optimization method and device, and the method comprises the steps: obtaining a feature information matching library corresponding to a positioning region; receiving detection signals sent by a plurality of beacon devices, and extracting the current signal intensity and the current arrival time of the detection signals; matching target feature information in a feature information matching library according to the plurality of current signal intensities, the plurality of beacon signal intensities, the plurality of current arrival times and the plurality of beacon arrival times; and taking the positioning reference point corresponding to the target feature information as the position information of the to-be-positioned equipment. According to the beacon positioning optimization method, a high-precision and high-robustness positioning solution can be provided in a complex and dynamically changing environment by introducing the feature information matching library and combining multi-dimensional signal feature matching and self-adaptive adjustment.

Description

Technical Field

[0001] The present invention belongs to the technical field of positioning processing, and in particular relates to a beacon positioning optimization method and device. Background Art

[0002] With the continuous development of wireless communication technology, beacon-based positioning methods have been widely used in numerous application fields. Beacon positioning technology uses characteristic information such as signal strength and arrival time of multiple beacon devices to determine the location of the target through a specific algorithm. Common beacon positioning methods include those based on signal strength (such as RSSI positioning), those based on time of arrival (such as TOA positioning), and those based on time difference of arrival (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 arrival time. However, due to environmental factors such as multipath 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 and underground. To improve positioning accuracy and reliability, many researchers have attempted to improve beacon positioning through optimization algorithms.

[0004] However, current beacon positioning optimization methods mostly focus on signal acquisition and processing, lacking effective solutions for efficiently matching the characteristic information of the positioning area with the beacon signal. Especially when the positioning area is environmentally complex, the optimization results of existing methods often fall short of the requirements for high-precision positioning. Therefore, optimizing beacon positioning methods through more precise feature information matching and improving positioning accuracy remains a challenge in current technology. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a beacon positioning optimization method and device to solve the technical problem that the optimization effect of the existing method often cannot meet the requirements of high-precision positioning.

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

[0007] Obtain a feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to each of multiple positioning reference points; the feature information includes multiple beacon signal strengths and multiple beacon arrival times corresponding to the current positioning reference point;

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

[0009] matching target feature information in a feature information matching library according to multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times;

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

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

[0012] The step of matching target feature information in a feature information matching library according to multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times includes:

[0013] Obtaining first characteristic information corresponding to each of the plurality of positioning grid areas; the first characteristic information being an average of second characteristic information corresponding to each of the plurality of sub-network areas within the positioning grid area; the first characteristic information comprising a plurality of first beacon signal strengths and a plurality of first beacon arrival times;

[0014] Matching maximum similarity feature information among the plurality of first feature information according to the plurality of current signal strengths, the plurality of first beacon signal strengths, the plurality of current arrival times, and the plurality of first beacon arrival times;

[0015] Matching the target positioning grid area corresponding to the target feature information with the maximum similarity;

[0016] Obtaining second characteristic information corresponding to each of the plurality of sub-network areas under the target positioning grid area; the second characteristic information including a plurality of second beacon signal strengths and a plurality of second beacon arrival times;

[0017] The target characteristic information is matched in the plurality of second characteristic information according to the plurality of current signal strengths, the plurality of second beacon signal strengths, the plurality of current arrival times and the plurality of second beacon arrival times.

[0018] Furthermore, the step of matching the target characteristic information in the plurality of second characteristic information according to the plurality of current signal strengths, the plurality of second beacon signal strengths, the plurality of current arrival times, and the plurality of second beacon arrival times includes:

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

[0020] Extracting current feature information whose first similarity is greater than a first threshold;

[0021] Calculating similarities between the multiple current arrival times and the multiple second beacon arrival times in each second current feature information respectively, to obtain a second similarity corresponding to each second feature information; wherein clock synchronization exists between the multiple beacon devices, and clock synchronization does not exist between the device to be located and the beacon device;

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

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

[0024] Subtracting the second beacon signal strength corresponding to the same beacon device from the current signal strength to obtain an error value;

[0025] Get the preset weight of each beacon device;

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

[0027] Calculating an average of a plurality of the corrected error values;

[0028] Obtaining a plurality of pre-stored first numerical ranges and preset similarities corresponding to the first numerical ranges;

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

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

[0031] extracting a first maximum value among the plurality of second beacon arrival times;

[0032] Dividing the arrival times of the plurality of second beacons by the first maximum value to obtain first proportional data corresponding to the arrival times of the plurality of second beacons;

[0033] extracting the second maximum value among the multiple current arrival times;

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

[0035] calculating the Euclidean distance between the first-scale data and the second-scale data;

[0036] Obtaining a plurality of pre-stored second numerical ranges and preset similarities corresponding to the second numerical ranges;

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

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

[0039] When the target feature information is not 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, when the target feature information is not 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:

[0041] When the target feature information is not matched in the feature information matching library, the current arrival time and the signal transmission speed are multiplied to obtain the transmission distance;

[0042] Get 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 corresponding to each of the multiple beacon devices.

[0044] A second aspect of an embodiment of the present invention provides a beacon positioning optimization device, including:

[0045] An acquisition unit is configured to acquire a feature information matching library corresponding to a positioning area; the feature information matching library includes feature information corresponding to each of a plurality of positioning reference points; the feature information includes a plurality of beacon signal strengths and a plurality of beacon arrival times corresponding to a current positioning reference point;

[0046] a receiving unit, configured to receive detection signals sent by a plurality of beacon devices, and extract the current signal strength and current arrival time of the detection signals;

[0047] a matching unit, configured to match target feature information in a feature information matching library according to a plurality of current signal strengths, a plurality of beacon signal strengths, a plurality of current arrival times, and a plurality of beacon arrival times;

[0048] The determining unit is configured to use 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 an embodiment of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps in the beacon positioning optimization method described in the first aspect are implemented.

[0050] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps in the beacon positioning optimization method described in the first aspect.

[0051] Compared to the prior art, the embodiments of the present invention offer the following advantages: Traditional beacon positioning methods rely directly on signal strength or arrival time for positioning calculations. This information is often affected by environmental factors such as multipath and signal attenuation, resulting in inaccurate positioning results. The present invention, however, constructs a feature information matching library that stores feature information for multiple positioning reference points, including comprehensive data on multiple dimensions such as signal strength and arrival time. By performing multi-dimensional information matching within the feature information matching library, the impact of signal errors on positioning accuracy can be effectively reduced, thereby achieving higher-precision positioning. The present method combines the signal strength and arrival time information of multiple beacon devices to construct a matching library containing multiple feature information. Compared to traditional methods that rely solely on signal strength or arrival time matching, this multi-dimensional matching approach is more resistant to environmental changes and signal interference, improving the robustness of the positioning system in complex environments. Even in environments with severe multipath or high signal attenuation, it can still maintain relatively accurate positioning results. The design of the feature information matching library enables the positioning algorithm to adapt based on the characteristics of different environments. As the environment of the positioning area changes, the feature information library can be dynamically updated to adapt to new positioning requirements, further improving the adaptability of the system. This adaptive nature enables this method to provide stable positioning performance in various scenarios. By pre-establishing a feature information matching library, the method of the present invention makes the matching operation during the positioning process more efficient. Compared to traditional real-time calculation of positioning errors, the present invention can quickly locate reference points using the matching library and directly find the positioning reference points corresponding to the target feature information in the library. This not only improves positioning accuracy but also optimizes computational efficiency, reducing the computational burden of the real-time positioning system. Beacon positioning technology is often affected by environmental factors such as walls, obstacles, and multipath propagation, which can cause fluctuations in positioning accuracy. However, by using the feature information matching library, the system can automatically adjust the positioning strategy as the environment changes, selecting the positioning reference points that best match 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 positioning stability and accuracy. 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 highly robust positioning solution in complex and dynamically changing environments, greatly improving the performance and reliability of beacon positioning technology in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A schematic flow chart of a beacon positioning optimization method provided by the present invention is shown;

[0054] Figure 2 A schematic diagram of a beacon positioning optimization device provided by an embodiment of the present invention is shown;

[0055] Figure 3 A schematic diagram of a terminal device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0056] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0057] The embodiments of the present invention provide a beacon positioning optimization method and device to solve the technical problem that the optimization effect of existing methods often cannot meet the requirements of high-precision positioning.

[0058] First, the present invention provides an optimization method for beacon positioning. Figure 1 , Figure 1 FIG. 1 shows a schematic flow chart of a beacon positioning optimization method provided by the present invention. Figure 1 As shown, the beacon positioning optimization method may include the following steps:

[0059] Step 101: Obtain a feature information matching library corresponding to a positioning area; the feature information matching library includes feature information corresponding to each of multiple positioning reference points; the feature information includes multiple beacon signal strengths and multiple beacon arrival times corresponding to the current positioning reference point;

[0060] Before implementing this embodiment, it is necessary to collect feature information for multiple positioning reference points within the positioning area and store this information in a "feature information matching library" in advance as the basis for subsequent matching. The "feature information matching library" for 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 device's location.

[0061] Characteristic 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, and ultra-wideband (UWB) beacons) emit signals at a specific location, the characteristic information will record the signal strength of these beacons and the time it takes for the signals from the beacons to reach the 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 to be located extracts two key features of these signals: the current signal strength and the current signal arrival time.

[0064] It's worth noting that signal strength is used to match the corresponding positioning reference point, while arrival time is used to verify the accuracy of the matched positioning reference point. The reason for using both feature information and arrival time to match positioning reference points is that in actual application environments, obstacles may change or crowds may gather, causing signal strength to change, thereby affecting the matching accuracy of the positioning reference point. However, signal arrival time has a smaller impact on obstacle changes or crowds. Therefore, this embodiment uses a redundant mechanism (arrival time) to verify whether the signal strength corresponding to the positioning reference point is normal, thereby improving positioning accuracy.

[0065] Step 103: Match target feature information in a 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 being located, the system searches for the most similar signature information in the signature information matching library. In other words, the system searches for the reference point signature information that is closest to the signature information of the current detection signal.

[0067] Among them, in order to improve the matching efficiency of feature information, this application divides the positioning area into multiple positioning grid areas. Each positioning grid area is further divided into multiple sub-grid areas. The positioning grid area and the sub-grid area each correspond to their own feature information. During the matching process, the first matching is performed based on the positioning grid area, and then the second matching is performed based on the multiple sub-grid areas under the positioning grid area, so that the matching process does not need to traverse all feature information, thereby improving matching efficiency. The specific matching logic is as follows:

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

[0069] Step 1031: Obtain first characteristic information corresponding to each of a plurality of positioning grid areas; the first characteristic information is an average value of second characteristic information corresponding to each of a plurality of sub-network areas within the positioning grid area; the first characteristic information includes a plurality of first beacon signal strengths and a plurality of first beacon arrival times;

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

[0071] Step 1032: matching maximum similarity feature information among the plurality of first feature information based on the plurality of current signal strengths, the plurality of first beacon signal strengths, the plurality of current arrival times, and the plurality of first beacon arrival times;

[0072] This step compares the current signal strength and arrival time received by the device being located with the first feature information stored in the feature information database. Specifically, the system calculates the similarity between the current signal's characteristics and the grid area's features. By comparing these similarities, it finds the "maximum similarity feature information" that most closely matches the current signal's characteristics, thereby identifying the grid area with the most accurate match. The purpose of this step is to determine the likely location of the device in the positioning grid area.

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

[0074] Step 1033: matching the target positioning grid area corresponding to the target feature information with the maximum similarity;

[0075] The device's target positioning grid area, i.e., the grid area where the device's possible location lies, is determined using the maximum similarity feature information obtained in the previous step. The matching process determines the approximate location area of the device to be located, which is determined by matching the first feature information of the grid area.

[0076] Step 1034: Obtain second characteristic information corresponding to each of the multiple sub-network areas under the target positioning grid area; the second characteristic information includes multiple second beacon signal strengths and multiple second beacon arrival times;

[0077] After the target grid area is determined, the second characteristic information of the multiple sub-grid areas under the area is further obtained. The second characteristic information includes multiple second beacon signal strengths and multiple second beacon arrival times in the sub-grid area.

[0078] Step 1035: Match the target characteristic information in the multiple second characteristic information according to the multiple current signal strengths, the multiple second beacon signal strengths, the multiple current arrival times and the multiple second beacon arrival times.

[0079] The system again compares the current signal strength and arrival time of the device being located with the second signature information within the target grid area. This step also calculates the similarity between signal signatures and selects the second signature information that best matches the current signal. This matching process yields more accurate location information.

[0080] In the embodiment corresponding to steps 1031 to 1035, the positioning area is divided into multiple grid areas and sub-grid areas, starting with a coarse positioning grid area match and then refining the signal feature match to the sub-grid area, ultimately obtaining high-precision device location information. The first feature information and the second feature information represent the signal feature information of the grid area and the sub-grid area, respectively. This layer-by-layer matching method can significantly reduce the amount of computation while ensuring high-precision positioning results.

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

[0082] Step A1: calculating similarities between multiple current signal strengths and multiple second beacon signal strengths in each second feature information respectively, to obtain a 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 being located. The second beacon signal strength is the reference signal strength data collected and stored in advance in each sub-grid area. The essence of similarity calculation is to evaluate the "closeness" of the current measured data with the reference data in the database. The first similarity value reflects the degree of match in the signal strength dimension. The specific matching logic is as follows:

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

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

[0086] The current signal strength is the signal strength received by the device being located. The second beacon signal strength is the pre-stored signal strength data for that beacon 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 distance deviation between the device being located and the beacon, or the impact of environmental influences (such as obstacles and interference) on signal strength. The magnitude of the error value represents the difference between the current device's signal strength and the beacon signal.

[0087] Step A12: Obtain the preset weight of each beacon device;

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

[0089] In real-world applications, beacon signals may be affected by environmental factors such as walls, metal objects, or other wireless signals. Giving higher weights to beacons that are less susceptible to interference can reduce the impact of noise on positioning results and ensure more stable positioning. Different beacons may have different hardware quality and signal propagation characteristics. For example, some beacons may have higher battery levels, stronger signals, or more stable frequencies. Weighting can account for these factors and improve positioning system reliability. In some cases, individual beacons may fail or experience signal anomalies. Giving higher weights to beacons with normal signals can compensate for the impact of failed beacons to a certain extent and enhance the system's fault tolerance.

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

[0091] The corrected error value is calculated by combining the error value with the beacon device weight. This is to account for the varying reliability or importance of beacons. If a beacon device has a higher weight, the impact of its error value will be amplified; conversely, the impact of a beacon device with a lower weight will be suppressed.

[0092] Step A14: calculating an average of the plurality of corrected error values;

[0093] To further determine the overall matching degree, the system calculates the average of these corrected error values. The average represents the combined error of all beacon devices, taking into account both the errors of different beacon devices and their weights, thus obtaining an overall deviation metric.

[0094] Step A15: Acquire a plurality of pre-stored first numerical ranges and preset similarities corresponding to the first numerical ranges;

[0095] The first numerical range refers to a set of predefined error value intervals, each corresponding to a specific preset similarity level. These intervals are set based on experimental or actual measurement data, representing different levels of error values and the corresponding degree of signal matching. The preset similarity level is a predefined similarity score that typically corresponds to a numerical range. When the error value falls within a specific numerical range, it is associated with a specific similarity value.

[0096] Step A16: taking 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, the corresponding preset similarity can be found based on its numerical range. This method establishes a mapping relationship between the error value and the similarity, resulting in the final first similarity. A small average error value may indicate a good match in signal strength, resulting in a high similarity; a large average error value may indicate a large difference in signal strength, resulting in a low similarity.

[0098] In the embodiments corresponding to steps A11 through A16, errors are corrected by combining the preset weights of the beacon devices, thereby avoiding the potential bias caused by a single beacon device and improving overall matching accuracy. By adjusting the weights of the beacon devices, the stability and reliability of different beacon devices are taken into account, avoiding the excessive impact of certain unstable beacons on the matching results. By calculating the average of multiple corrected error values, the excessive impact 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 the preset similarity effectively converts the original error data into an intuitive similarity value, facilitating subsequent operations and analysis.

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

[0100]

[0101] Among them, S represents the first similarity, n represents the number of beacon devices, and w i represents the preset weight of the i-th beacon device, w jrepresents the preset weight of the jth beacon device, a i Indicates the current signal strength of the i-th signal, b i Indicates the signal strength of the second beacon of the i-th order, a1, a2, ..., a n Indicates multiple current signal strengths, b1, b2, ..., b n Indicates multiple second beacon signal strengths.

[0102] The purpose of this mathematical model is to calculate two data sequences a1, a2, ..., a n and b1,b2,…,b n The similarity between the two datasets is important, especially when the error at each data point may be different. This mathematical model takes into account multiple factors to ensure that the similarity assessment is not based solely on the error size, but also includes normalization, weighting, and nonlinear error sensitivity.

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

[0104] To eliminate the impact of different data ranges on similarity calculations, different data series may have different dimensions and scales. All data points and errors are converted to a unified range so that error values can be directly compared. Comparing the error value to the maximum value in the data set ensures that the influence of the error is not distorted by the range of a data set being too large or too small.

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

[0106] This mathematical model can effectively capture small differences in the data and quickly respond to larger errors. For tasks that require highly accurate similarity assessment (such as exact matching), this nonlinear processing can effectively prevent the accumulation of errors that will lead to distortion of the final similarity.

[0107] Step A2: extracting current feature information with a first similarity greater than a first threshold;

[0108] Steps A1 to A2 are the screening process for the signal strength dimension. The first threshold is a preset value. If the first similarity of a sub-grid area exceeds this value (indicating a high degree of match), it is considered a "candidate area" and enters the time matching process. The advantage of this is that it first filters out areas with "relatively consistent signal strength" from "all sub-grids" and then makes the next judgment within these candidate areas.

[0109] Step A3: Calculating similarities between the multiple current arrival times and the multiple second beacon arrival times in each second current feature information, respectively, to obtain a second similarity corresponding to each second feature information; wherein clock synchronization exists between the multiple beacon devices, and clock synchronization does not exist between the device to be located and the beacon device;

[0110] This stage is a matching calculation in the time dimension. The arrival time of the second beacon is the arrival time of the original collection of the area in the feature library. The specific calculation logic of the second similarity is as follows:

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

[0112] Step A31: extracting a first maximum value among a plurality of second beacon arrival times;

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

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

[0115] It is worth noting that, since clock synchronization exists between multiple beacon devices in an actual positioning environment, clock synchronization often does not exist between the device to be positioned and the beacon device. This may result in clock deviation between the device used by the feature information matching library and the device to be positioned during the actual positioning process, which in turn leads to errors in the arrival times collected by the two. To solve this problem, this embodiment determines whether there is a deviation in the beacon arrival time by calculating the ratio data between the beacon arrival times. Since there is no need to compare the actual arrival time data, but rather to focus on the ratio difference in arrival time between beacon devices, there is no need to synchronize the clocks of the device used by the feature information matching library and the device to be positioned during the actual positioning process.

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

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

[0118] Step A35: calculating the Euclidean distance between the first-scale data and the second-scale data;

[0119] Step A36: Acquire a plurality of pre-stored second numerical ranges and preset similarities corresponding to the second numerical ranges;

[0120] The second numerical range refers to a set of predefined error value intervals, each corresponding to a specific preset similarity level. These intervals are set based on experimental or actual measurement data, representing different levels of error values and the corresponding degree of signal matching. The preset similarity level is a predefined similarity score that typically corresponds to a numerical range. When the error value falls within a specific numerical range, it is associated with a specific similarity value.

[0121] Step A37: taking 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 through A37, by scaling the beacon and the current arrival time to a maximum value, data consistency is ensured and interference from clock deviation errors is avoided. Using the Euclidean distance to calculate the difference between the two scaled time data 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 results of different beacons can be compared.

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

[0124] Steps A1 to A4 are 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 through A4, signal strength is matched first, followed by arrival time, resulting in clear logic and high efficiency. Controlling the number of candidate regions using a threshold reduces computation and improves real-time performance. Dual similarity control enhances positioning accuracy and robustness.

[0126] Optionally, after step 103 , the method further includes step B1 : when target feature information is not matched in the feature information matching library, calculating the location information of the device to be located based on multiple current arrival times.

[0127] If the feature information matching library doesn't find target feature information that fully matches the feature information currently being acquired by the device (possibly due to environmental changes, signal interference, beacon device changes, and other reasons), the system will enter this alternative processing flow. The feature information matching library typically contains a wealth of historical data and feature sets, but due to dynamic environmental changes, some features may not be fully matched. In this case, the system requires a fallback solution to ensure the positioning task can continue.

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

[0129] Step B11: When the target feature information is not matched in the feature information matching library, the current arrival time and the signal transmission speed are multiplied to obtain the transmission distance;

[0130] Step B12: Obtaining 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 corresponding to each of the multiple beacon devices.

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

[0133] Step 104: Using the positioning reference point corresponding to the target feature information as the position information of the device to be positioned.

[0134] Once the system finds the reference point feature information that best matches the current detection signal, it uses the reference point's location as the device's position. This means the device's position is now known, and the precise location of the device can be estimated or directly derived based on the reference point's location.

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

[0136] It's worth noting that because the feature matching library uses high-precision data measured by high-precision instruments, its positioning results have higher priority. The current arrival time is used to calculate the current positioning information only when similar feature information cannot be matched in the feature matching library.

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

[0138] like Figure 2 The present invention provides a beacon positioning optimization device, see Figure 2 , Figure 2 A schematic diagram of a beacon positioning optimization device provided by the present invention is shown in FIG. Figure 2 The optimization device for beacon positioning shown includes:

[0139] An acquisition unit 21 is configured to acquire a feature information matching library corresponding to a positioning area; the feature information matching library includes feature information corresponding to each of a plurality of positioning reference points; the feature information includes a plurality of beacon signal strengths and a plurality of beacon arrival times corresponding to a current positioning reference point;

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

[0141] A matching unit 23 is configured to match target feature information in a 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 configured to use the positioning reference point corresponding to the target feature information as the position information of the device to be positioned.

[0143] The present invention provides a beacon positioning optimization device. Traditional beacon positioning methods rely directly on signal strength or arrival time for positioning calculations. This information is often affected by environmental factors such as multipath and signal attenuation, resulting in inaccurate positioning results. The present invention, however, constructs a feature information matching library that stores feature information for multiple positioning reference points, including comprehensive data on multiple dimensions such as signal strength and arrival time. By performing multi-dimensional information matching within the feature information matching library, the impact of signal errors on positioning accuracy can be effectively reduced, thereby achieving higher-precision positioning. The present invention combines the signal strength and arrival time information of multiple beacon devices to construct a matching library containing multiple feature information. Compared to traditional matching methods that rely solely on signal strength or arrival time, this multi-dimensional matching approach is more resistant to environmental changes and signal interference, improving the robustness of the positioning system in complex environments. Even in environments with severe multipath or high signal attenuation, it can still maintain relatively accurate positioning results. The design of the feature information matching library enables the positioning algorithm to adjust based on the characteristics of different environments. As the environment of the positioning area changes, the feature information library can be dynamically updated to adapt to new positioning requirements, further improving the adaptability of the system. This adaptive nature enables this method to provide stable positioning performance in various scenarios. By pre-establishing a feature information matching library, the method of the present invention makes the matching operation during the positioning process more efficient. Compared to traditional real-time calculation of positioning errors, the present invention can quickly locate reference points using the matching library and directly find the positioning reference points corresponding to the target feature information in the library. This not only improves positioning accuracy but also optimizes computational efficiency, reducing the computational burden of the real-time positioning system. Beacon positioning technology is often affected by environmental factors such as walls, obstacles, and multipath propagation, which can cause fluctuations in positioning accuracy. However, by using the feature information matching library, the system can automatically adjust the positioning strategy as the environment changes, selecting the positioning reference points that best match 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 positioning stability and accuracy. 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 highly 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 FIG. 1 is a schematic diagram of a terminal device provided by an embodiment of the present invention. Figure 3As shown, a terminal device 3 of 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, the steps of each of the above-mentioned beacon positioning optimization method embodiments are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are realized, for example Figure 2 Function of the unit shown.

[0145] Exemplarily, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used to 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 are as follows:

[0146] An acquisition unit is configured to acquire a feature information matching library corresponding to a positioning area; the feature information matching library includes feature information corresponding to each of a plurality of positioning reference points; the feature information includes a plurality of beacon signal strengths and a plurality of beacon arrival times corresponding to a current positioning reference point;

[0147] a receiving unit, configured to receive detection signals sent by a plurality of beacon devices, and extract the current signal strength and current arrival time of the detection signals;

[0148] a matching unit, configured to match target feature information in a feature information matching library according to a plurality of current signal strengths, a plurality of beacon signal strengths, a plurality of current arrival times, and a plurality of beacon arrival times;

[0149] The determining unit is configured to use 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 It is only 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 in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0151] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

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

[0153] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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, execution process, etc. between the above-mentioned devices / units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by 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 embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0156] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0157] An embodiment of the present invention provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0158] 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 storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0159] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0160] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0161] In the embodiments provided by the present invention, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

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

[0164] It will also be understood that the term "and / or" used in the present description and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0165] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to monitoring," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is monitored" may be interpreted as meaning "upon determination" or "in response to determining" or "upon monitoring [described condition or event]" or "in response to monitoring [described condition or event]," depending on the context.

[0166] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0167] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in 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 "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0168] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A beacon positioning optimization method, characterized in that: The beacon positioning optimization method includes: Obtain a feature information matching library corresponding to the positioning area; the feature information matching library includes feature information corresponding to each of multiple positioning reference points; the feature information includes multiple beacon signal strengths and multiple beacon arrival times corresponding to the current positioning reference point; Receive detection signals sent by multiple beacon devices, and extract current signal strength and current arrival time of the detection signals; matching target feature information in a feature information matching library according to multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times; The positioning reference point corresponding to the target feature information is used as the position information of the device to be positioned.

2. The beacon positioning optimization method according to claim 1, wherein: The positioning area includes a plurality of positioning grid areas, and each positioning grid area includes a plurality of sub-grid areas; The step of matching target feature information in a feature information matching library according to multiple current signal strengths, multiple beacon signal strengths, multiple current arrival times, and multiple beacon arrival times includes: Obtaining first characteristic information corresponding to each of the plurality of positioning grid areas; the first characteristic information being an average of second characteristic information corresponding to each of the plurality of sub-network areas within the positioning grid area; the first characteristic information comprising a plurality of first beacon signal strengths and a plurality of first beacon arrival times; Matching maximum similarity feature information among the plurality of first feature information according to the plurality of current signal strengths, the plurality of first beacon signal strengths, the plurality of current arrival times, and the plurality of first beacon arrival times; Matching the target positioning grid area corresponding to the target feature information with the maximum similarity; Obtaining second characteristic information corresponding to each of the plurality of sub-network areas under the target positioning grid area; the second characteristic information including a plurality of second beacon signal strengths and a plurality of second beacon arrival times; The target characteristic information is matched in the plurality of second characteristic information according to the plurality of current signal strengths, the plurality of second beacon signal strengths, the plurality of current arrival times and the plurality of second beacon arrival times.

3. The beacon positioning optimization method according to claim 2, wherein: The step of matching the target characteristic information in the plurality of second characteristic information according to the plurality of current signal strengths, the plurality of second beacon signal strengths, the plurality of current arrival times, and the plurality of second beacon arrival times comprises: Calculating similarities between the multiple current signal strengths and the multiple second beacon signal strengths in each second feature information respectively to obtain a first similarity corresponding to each second feature information; Extracting current feature information whose first similarity is greater than a first threshold; Calculating similarities between the multiple current arrival times and the multiple second beacon arrival times in each second current feature information respectively, to obtain a second similarity corresponding to each second feature information; wherein clock synchronization exists between the multiple beacon devices, and clock synchronization does not exist between the device to be located and the beacon device; If the second similarity is greater than the first threshold, the second feature information corresponding to the second similarity is used as the target feature information.

4. The beacon positioning optimization method according to claim 3, wherein: The step of respectively calculating the similarities between the multiple current signal strengths and the multiple second beacon signal strengths in each second feature information to obtain the first similarity corresponding to each second feature information includes: Subtracting the second beacon signal strength corresponding to the same beacon device from the current signal strength to obtain an error value; Get the preset weight of each beacon device; Multiplying the error value corresponding to each beacon device by a preset weight to obtain a corrected error value; Calculating an average of a plurality of the corrected error values; Obtaining a plurality of pre-stored first numerical ranges and preset similarities corresponding to the first numerical ranges; The preset similarity corresponding to the first numerical range in which the average value is located is used as the first similarity.

5. The beacon positioning optimization method according to claim 3, wherein: The step of respectively calculating the similarities between the multiple current arrival times and the multiple second beacon arrival times in each second current feature information to obtain the second similarity corresponding to each second feature information includes: extracting a first maximum value among the plurality of second beacon arrival times; Dividing the arrival times of the plurality of second beacons by the first maximum value to obtain first proportional data corresponding to the arrival times of the plurality of second beacons; extracting the second maximum value among the multiple current arrival times; Dividing the multiple current arrival times by the second maximum value to obtain second proportional data corresponding to the multiple current arrival times; calculating the Euclidean distance between the first-scale data and the second-scale data; Obtaining a plurality of pre-stored second numerical ranges and preset similarities corresponding to the second numerical ranges; The preset similarity corresponding to the second numerical range of the Euclidean distance is used as the second similarity.

6. The beacon positioning optimization method according to claim 1, wherein: After the step of matching target feature information in a feature information matching library according to the multiple current signal strengths, the multiple beacon signal strengths, the multiple current arrival times, and the multiple beacon arrival times, the method further includes: When the target feature information is not matched in the feature information matching library, the location information of the device to be located is calculated based on multiple current arrival times.

7. The beacon positioning optimization method according to claim 1, wherein: When the target feature information is not 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 the target feature information is not matched in the feature information matching library, the current arrival time and the signal transmission speed are multiplied to obtain the transmission distance; Get 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 corresponding to each of the multiple beacon devices.

8. A beacon positioning optimization device, characterized in that: The beacon positioning optimization device includes: An acquisition unit is configured to acquire a feature information matching library corresponding to a positioning area; the feature information matching library includes feature information corresponding to each of a plurality of positioning reference points; the feature information includes a plurality of beacon signal strengths and a plurality of beacon arrival times corresponding to a current positioning reference point; a receiving unit, configured to receive detection signals sent by a plurality of beacon devices, and extract the current signal strength and current arrival time of the detection signals; a matching unit, configured to match target feature information in a feature information matching library according to a plurality of current signal strengths, a plurality of beacon signal strengths, a plurality of current arrival times, and a plurality of beacon arrival times; The determining unit is configured to use the positioning reference point corresponding to the target feature information as the position information of the device to be positioned.

9. 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, wherein the beacon positioning optimization program is configured to implement the steps in the beacon positioning optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the beacon positioning optimization method according to any one of claims 1 to 7 are implemented.

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