Bluetooth device remote positioning method and system based on internet of things

Abstract

This invention relates to the field of remote positioning technology for small devices, and discloses a method and system for remote positioning of Bluetooth devices based on the Internet of Things (IoT). The method includes: acquiring initial signal data emitted by the Bluetooth device to obtain classified signal strength groups; obtaining a subset of signal data based on the degree of environmental interference and preset screening conditions; extracting relevant feature data to obtain a signal distribution feature set, performing time delay deviation calibration and matrix processing to obtain a signal data matrix; extracting initial feature values ​​to determine the weighted signal data weight distribution; prioritizing the signals to obtain an optimized signal data combination; obtaining the final positioning network topology based on the signal data combination and signal strength threshold; and performing iterative calculations based on the positioning network topology to obtain the final location information of the Bluetooth device. This method enables accurate positioning of Bluetooth devices in dynamic and complex environments.

Description

Technical Field

[0001] This invention relates to the field of remote positioning of small devices, and more particularly to a method and system for remote positioning of Bluetooth devices based on the Internet of Things. Background Technology

[0002] In the field of modern technology, the application of the Internet of Things (IoT) is constantly expanding, demonstrating its significant importance in the management and positioning of personal devices. With the widespread adoption of smart devices, accurately and efficiently locating small devices using intelligent sensing systems has become a key aspect of improving user experience and security. Research in this field not only concerns technological innovation but also directly impacts the convenience and reliability of daily life.

[0003] In existing technologies, positioning methods for small devices rely too heavily on a single signal source or a fixed network environment. In dynamic and complex environments, they are easily affected by interference or misleading information. Positioning accuracy and reliability often fail to meet requirements, making it difficult to establish a stable positioning foundation and affecting the overall positioning effect. Summary of the Invention

[0004] This invention provides a method and system for remote positioning of Bluetooth devices based on the Internet of Things, so as to achieve accurate positioning of Bluetooth devices in dynamic and complex environments.

[0005] In a first aspect, to address the aforementioned technical problems, the present invention provides a method for remotely locating Bluetooth devices based on the Internet of Things, comprising: Acquire initial signal data emitted by Bluetooth devices, and preliminarily group the initial signal data according to the signal strength of different sources to obtain classified signal strength groups; The degree of environmental interference caused by the signal coverage range in each signal strength group is analyzed, and the signal data subset that meets the filtering conditions is obtained by filtering and matching according to the degree of environmental interference and preset filtering conditions. Feature data related to sparsity are extracted from the signal data subset to obtain a signal distribution feature set. Based on the signal distribution feature set and a preset fluctuation threshold, a high fluctuation subset is determined. The high fluctuation subset is then subjected to time delay deviation calibration and matrix processing to obtain an integrated signal data matrix. The initial feature value of each data point is extracted from the signal data matrix, and the adjusted confidence score of the data point is obtained by evaluating the initial feature value. The weighted signal data weight distribution is determined based on the adjusted confidence score. The signals are prioritized according to the signal data weight distribution, and the priority ranking is adjusted again by obtaining the noise information in the current environment to obtain the optimized signal data combination. A blind zone distribution table is determined based on the signal data combination and a preset signal strength threshold. In the blind zone distribution table, the locations where the node communication delay is higher than a preset delay threshold or the node density is lower than a preset density threshold are adjusted to obtain the final positioning network topology. Based on the positioning network topology, the location information accuracy is iteratively calculated to obtain an optimized location information reference value. Then, based on the location information reference value and the distance between the Bluetooth device and the node, a weighted fusion is performed to obtain the final location information of the Bluetooth device.

[0006] In one optional implementation, the step of filtering and matching based on the degree of environmental interference and preset filtering conditions to obtain a subset of signal data that meets the filtering conditions includes: When the degree of environmental interference exceeds a preset threshold, the signal corresponding to the degree of environmental interference is marked as a high interference subset. The signal strength values ​​within the high interference subset are adjusted to obtain a compensated signal strength subset. Based on the compensated signal strength subset, condition matching is performed. If the signal strength value in the compensated signal strength subset meets the preset filtering conditions, it is included in the target data set; otherwise, it is discarded, thus obtaining a signal data subset that meets the filtering conditions.

[0007] In one optional implementation, the step of determining a high-fluctuation subset based on the signal distribution feature set and a preset fluctuation threshold, and performing time delay deviation calibration and matrixing on the high-fluctuation subset to obtain an integrated signal data matrix includes: When the signal fluctuation value in the signal distribution feature set exceeds a preset fluctuation threshold, it is marked as a high fluctuation subset; The time delay deviation within the high-fluctuation subset is calibrated, and the calibrated time delay data is determined as the time delay correction dataset. Adjust the data timestamps in the delay correction dataset to a preset unified benchmark, and calculate the signal-to-noise ratio of each device signal; When the signal-to-noise ratio of any device signal is lower than the preset signal-to-noise ratio threshold, the device signal is smoothed to obtain the corrected signal value. A matrix is ​​constructed based on the corrected signal value to obtain a signal data matrix; each element in the signal data matrix represents the corrected signal value of the device at the corresponding time point.

[0008] In one optional implementation, the step of extracting initial feature values ​​for each data point from the signal data matrix and evaluating the data points based on the initial feature values ​​to obtain an adjusted confidence score includes: Extract the initial feature value of each data point in the signal data matrix, compare it with the average value of the historical performance records of the data point, and obtain the deviation from the average value. When the deviation exceeds the preset signal strength fluctuation range, the data point is evaluated for credibility to obtain an initial credibility score. The fluctuation frequency and outlier ratio are extracted from historical data. When the fluctuation frequency exceeds the fluctuation frequency threshold and the outlier ratio exceeds the preset normal standard, the initial credibility score is reduced to obtain the adjusted credibility score.

[0009] In one optional implementation, determining the weighted signal data weight distribution based on the adjusted confidence score includes: Based on the adjusted confidence score, assign weight ratios to the data points in the signal data matrix, and determine whether the weight ratios meet the preset balance conditions. When the weight ratio does not meet the preset balance condition, the weight ratio is adjusted. When the weight ratio meets the preset balance condition, the weighted signal data weight distribution is determined.

[0010] In one optional implementation, the step of prioritizing the signals according to the signal data weight distribution and then adjusting the priority ranking a second time by obtaining noise information in the current environment to obtain an optimized signal data combination includes: By comparing the weight value of each signal data in the signal data weight distribution with a preset weight threshold, when the weight value is lower than the threshold, its priority ranking is reduced to obtain the adjusted priority ranking data. Obtain noise information in the current environment, perform preliminary filtering on the noise information, and determine the filtered environmental interference information; When the filtered environmental interference information is higher than a preset noise level threshold, the lowest priority signal data in the priority sorting data is removed to obtain an optimized signal data combination.

[0011] In one optional implementation, determining the blind zone distribution table based on the signal data combination and a preset signal strength threshold includes: The coverage area of ​​the positioning network is divided into sub-regions, and the signal strength value in each sub-region is determined based on the combination of signal data. By comparing the signal strength value with a preset signal strength threshold, if the signal strength value is lower than the preset signal strength threshold, the area where the signal strength value is located is marked as a potential coverage blind spot, and a blind spot distribution map is obtained.

[0012] In one optional implementation, adjusting the locations in the blind zone distribution table where node communication latency is higher than a preset latency threshold or node density is lower than a preset density threshold to obtain the final positioning network topology includes: Based on the blind zone distribution chart, the communication quality between nodes is analyzed to obtain node communication delay and regional node density. The connection paths of nodes whose communication latency is higher than a preset latency threshold are adjusted to obtain an optimized set of node communication paths. When the density of nodes in the region is lower than a preset density threshold, virtual nodes are added in the region to obtain the adjusted region nodes. The final positioning network topology is determined based on the set of node communication paths and the adjusted regional nodes.

[0013] In one optional implementation, the step of iteratively calculating the location information accuracy based on the positioning network topology to obtain an optimized location information reference value, and then performing weighted fusion based on the location information reference value and the distance between the Bluetooth device and the node to obtain the final location information of the Bluetooth device, includes: Based on the positioning network topology, the multi-point signal strength distribution of the Bluetooth device is obtained, and the multi-point signal strength distribution is weighted and fused to obtain the initial location information. By comparing the deviation data between the initial location information and the reference point location in the actual environment, when the deviation data exceeds a preset deviation threshold, the deviation is gradually reduced based on the average value of historical location data to obtain a corrected location information reference value. The location weight is set according to the distance between the Bluetooth device and the node, and the final location information of the Bluetooth device is obtained by weighting the location weight and the location information reference value.

[0014] Secondly, the present invention provides a Bluetooth device remote positioning system based on the Internet of Things, comprising: The data acquisition module is used to acquire the initial signal data emitted by the Bluetooth device, and to perform preliminary grouping of the initial signal data according to the signal strength of different sources to obtain classified signal strength groups. The filtering and matching module is used to analyze the degree of environmental interference caused by the signal coverage range in each signal strength group, and to perform filtering and matching based on the degree of environmental interference and preset filtering conditions to obtain a subset of signal data that meets the filtering conditions. The matrix processing module is used to extract feature data related to sparsity from the signal data subset to obtain a signal distribution feature set, and to determine a high-fluctuation subset based on the signal distribution feature set and a preset fluctuation threshold. The high-fluctuation subset is then subjected to time delay deviation calibration and matrix processing to obtain an integrated signal data matrix. The weight distribution module is used to extract the initial feature value of each data point from the signal data matrix, evaluate the data point based on the initial feature value to obtain the adjusted confidence score, and determine the weighted signal data weight distribution based on the adjusted confidence score. The signal optimization module is used to prioritize the signals according to the signal data weight distribution, and to adjust the priority ranking again by acquiring noise information in the current environment, so as to obtain an optimized signal data combination. The position adjustment module is used to determine a blind zone distribution table based on the signal data combination and a preset signal strength threshold, and to adjust the positions in the blind zone distribution table where the node communication delay is higher than a preset delay threshold or the node density is lower than a preset density threshold, so as to obtain the final positioning network topology. The location determination module is used to iteratively calculate the accuracy of the location information according to the positioning network topology to obtain an optimized location information reference value, and to perform weighted fusion based on the location information reference value and the distance between the Bluetooth device and the node to obtain the final location information of the Bluetooth device.

[0015] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the Internet of Things-based Bluetooth device remote positioning method described in any one of the above.

[0016] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the Bluetooth device remote positioning method based on the Internet of Things as described above.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention pre-groups the initial signal data according to the signal strength of different sources. Since different signal strengths may represent different signal sources and propagation conditions, grouping allows for more targeted processing of subsequent data. This results in the technical effect of orderly classifying the initial signal data, facilitating subsequent analysis. This technical effect is based on the principle that classifying signal features can improve data processing efficiency and accuracy.

[0018] (2) This invention analyzes and filters the signal coverage area in each signal strength group to determine the degree of environmental interference. Because it considers the impact of environmental interference on the signal, it can remove highly interfered signals. This results in obtaining a subset of signal data that meets the filtering criteria, thus improving the reliability of the signal data. This technical effect is achieved based on the principle that eliminating interfering signals can improve data quality.

[0019] (3) This invention prioritizes signals based on the weight distribution of signal data and makes secondary adjustments based on noise information in the current environment. Because it comprehensively considers both signal weights and environmental noise, it can more rationally arrange signal priorities. This results in an optimized combination of signal data, improving the accuracy of signal positioning. The underlying technical basis is the principle that reasonable signal priority sorting and adjustment can reduce the impact of noise interference on positioning. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of a Bluetooth device remote positioning method based on the Internet of Things provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of a Bluetooth device remote positioning system based on the Internet of Things provided in the second embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Reference Figure 1 The first embodiment of the present invention provides a method for remote positioning of Bluetooth devices based on the Internet of Things, including the following steps: S11, acquire the initial signal data emitted by the Bluetooth device, and preliminarily group the initial signal data according to the signal strength of different sources to obtain the classified signal strength groups; S12, Analyze the degree of environmental interference caused by the signal coverage range in each signal strength group, and perform screening and matching according to the degree of environmental interference and preset screening conditions to obtain a subset of signal data that meets the screening conditions; S13, extract feature data related to sparsity from the signal data subset to obtain a signal distribution feature set, and determine a high-fluctuation subset based on the signal distribution feature set and a preset fluctuation threshold. Perform time delay deviation calibration and matrix processing on the high-fluctuation subset to obtain an integrated signal data matrix. S14, extract the initial feature value of each data point from the signal data matrix, evaluate the data point based on the initial feature value to obtain the adjusted confidence score, and determine the weighted signal data weight distribution based on the adjusted confidence score; S15, prioritize the signals according to the signal data weight distribution, and adjust the priority ranking a second time by obtaining noise information in the current environment to obtain an optimized signal data combination. S16. Determine a blind zone distribution table based on the signal data combination and a preset signal strength threshold. Adjust the positions in the blind zone distribution table where the node communication delay is higher than a preset delay threshold or the node density is lower than a preset density threshold to obtain the final positioning network topology. S17. Based on the positioning network topology, the location information accuracy is iteratively calculated to obtain an optimized location information reference value. Then, the location information reference value and the distance between the Bluetooth device and the node are weighted and fused to obtain the final location information of the Bluetooth device.

[0023] In step S11, the initial signal data emitted by the Bluetooth device is acquired, and the initial signal data is initially grouped according to the signal strength of different sources to obtain the classified signal strength groups.

[0024] It should be noted that the initial signal data is a collection of Bluetooth signal data emitted by Bluetooth devices in the surrounding environment. This data may include core parameters such as the device ID of one or more devices, broadcast packet content, signal strength, and timestamp. Bluetooth devices include headphones, mobile phones, computers, and other devices capable of emitting Bluetooth signals. Different sources refer to the physical and protocol layer differences of the signal transmitting devices, serving as a classification dimension. Specifically, the source of the Bluetooth signal can be headphones, mobile phones, or computers. Signal strength refers to the received signal strength indication value, characterizing the degree of attenuation of electromagnetic waves during spatial transmission. The classified signal strength grouping is mainly obtained through signal source separation, signal strength grouping, and feature extraction. In this embodiment, subsequent operations prioritize the signal strength grouping related to headphone devices.

[0025] For example, a Bluetooth acquisition device uses a built-in antenna and signal processor to scan surrounding Bluetooth signal frequency bands in real time, recording the source identifier and basic information of each signal. Preliminary differentiation of signals from different sources can be achieved by classifying the signals based on their device identifier and broadcast frequency. In this way, signals are initially categorized into different source groups, allowing for the filtering of non-headphone devices and the allocation of high-priority processing channels for headphone signals.

[0026] In this embodiment, signals from different sources are initially distinguished. Signal strength values ​​are recorded for each source using signal strength detection, resulting in a signal strength data set. Next, signal stability analysis is performed on the signal source fluctuations within the signal strength data set. If the signal strength fluctuation exceeds a preset threshold, it is marked as an unstable signal group; otherwise, it is marked as a stable signal group, thus determining the classified signal stability groups. A secondary comparison of the signal strength data from the stable and unstable signal groups is performed, further subdividing the signal strength values ​​into high and low intervals. Signal sources within each interval are then grouped and identified, resulting in a refined signal strength group set. Based on this refined signal strength group set, feature extraction is performed to obtain the classified signal strength groups.

[0027] For example, with a preset intensity fluctuation threshold of 5dBm, if the signal strength of device A fluctuates within a range of 3dBm over a period of time, it is marked as a stable signal group; while the fluctuation range of device B is 8dBm, it is marked as an unstable signal group. This classification method helps to quickly screen out reliable signal sources and improve the accuracy of subsequent analysis. When performing a secondary comparison between stable and unstable signal groups, the signal strength values ​​can be further subdivided into high and low intervals through data grouping. The intensity values ​​are divided into three intervals: above -60dBm, -60 to -80dBm, and below -80dBm. Device A falls into the first interval, device B falls into the third interval, and the signal sources within each interval are grouped and identified. This refined grouping can more clearly show the signal distribution characteristics, facilitating further analysis.

[0028] Furthermore, features such as signal duration and frequency stability are extracted from stable signal groups, while features such as fluctuation frequency and intensity variation trends are extracted from unstable signal groups. After these features are categorized and organized, the final classification result can be determined. For example, device A might be classified as a high-intensity stable signal, suitable for location tracking, while device B might be classified as a low-intensity unstable signal, only suitable for auxiliary reference. This classification result helps optimize the application effect of Bluetooth signals.

[0029] In step S12, the degree of environmental interference caused by the signal coverage range in each signal strength group is analyzed, and the signal data subset that meets the filtering conditions is obtained by filtering and matching according to the degree of environmental interference and the preset filtering conditions.

[0030] It should be noted that signal coverage range refers to the geographical or spatial area within which the signal emitted by the Bluetooth device can be effectively transmitted and maintain a certain strength. In this embodiment, it specifically refers to the effective signal coverage area corresponding to each group after grouping by signal strength in step S11, such as a strong signal group with a coverage radius of 5 meters and a weak signal group with a coverage radius of 10 meters. The degree of environmental interference refers to the degree of signal quality degradation caused by co-frequency devices, multipath reflections, and other electromagnetic noise in the environment during Bluetooth signal propagation. It is usually expressed by quantitative indicators, such as SNR < 15dB or packet loss rate > 10%, which is considered a high-interference environment. By quantifying the degree of environmental interference, severely interfered inferior signals in each signal group can be identified, thereby improving the signal reliability of subsequent processing. Preset filtering conditions refer to the filtering rules pre-set to achieve signal data purification. The filtering and matching is performed through a progressive operation of marking high-interference subsets, adjusting signal strength, and conditional matching filtering. The signal data subset refers to the set of signal data that meets the conditions after this filtering and matching step; it is a high-confidence subset of the original signal data.

[0031] In one implementation, the step of filtering and matching based on the degree of environmental interference and preset filtering conditions to obtain a subset of signal data that meets the filtering conditions includes: When the degree of environmental interference exceeds a preset threshold, the signal corresponding to the degree of environmental interference is marked as a high interference subset. The signal strength values ​​within the high interference subset are adjusted to obtain a compensated signal strength subset. Based on the compensated signal strength subset, condition matching is performed. If the signal strength value in the compensated signal strength subset meets the preset filtering conditions, it is included in the target data set; otherwise, it is discarded, thus obtaining a signal data subset that meets the filtering conditions.

[0032] It should be noted that the preset threshold is a pre-established critical value used to determine whether the degree of environmental interference exceeds the limit. It is usually an empirical value or an experimentally optimized value, derived from statistical analysis of a large amount of measured data. When the interference level exceeds this threshold, the error in the signal-distance mapping relationship will increase significantly, failing to meet the positioning accuracy requirements. The high-interference subset is the set of signals in the original signal strength group whose environmental interference level exceeds the threshold.

[0033] In this step and subsequent steps, the marking methods can be metadata marking, adding fields to the signal data header; physical layer marking, by adjusting the signal modulation method or adding a preamble identifier; or database recording, storing the ID, timestamp, and interference index value of the high interference signal in the database for subsequent steps to call.

[0034] In this embodiment, adjusting the signal strength values ​​within the high-interference subset refers to addressing the signal strength distortion caused by interference within this subset by using a compensation algorithm to correct the signal strength values, making them closer to the theoretical strength corresponding to the actual propagation loss. The compensation algorithm can be a mean filtering, Kalman filtering, or interference cancellation algorithm based on historical data. In interference environments, the signal strength detected by the receiver in a Bluetooth signal is superimposed with environmental noise, causing the measured value to deviate from the theoretical value of the free-space path loss model. The goal of the adjustment process is to restore the true signal strength.

[0035] For example, if the signal strength value within a high-interference subset drops to -85dBm due to interference, it can be improved to -75dBm after compensation adjustment, resulting in a compensated signal strength subset. This adjustment effectively mitigates the impact of interference on the signal, laying the foundation for subsequent screening. The compensated signal strength subset is the subset of high-interference subsets whose signal strength values ​​have been corrected to be closer to the actual propagation loss after adjustment.

[0036] Furthermore, conditional matching refers to the process of comparing each signal strength value in the compensated signal strength subset with preset screening criteria to determine whether it meets quality requirements. The preset screening criteria are a set of rules pre-defined to ensure the high reliability of the selected signals, used to determine whether conditional matching is valid. These criteria may include strength thresholds, stability requirements, and some environmental parameters. The subset of signal data that meets the screening criteria is equivalent to the target data set; it is the set of signal data that meets the criteria after conditional matching and is a high-quality subset of the compensated signal strength subset that satisfies the preset screening criteria. For example, if the preset screening criterion is a signal strength value higher than -80dBm, then signals meeting the criterion are included in the target data set, while signals below this value are discarded. Through this conditional matching, a subset of signal data that better meets application requirements can be selected.

[0037] In step S13, feature data related to sparsity are extracted from the signal data subset to obtain a signal distribution feature set. Based on the signal distribution feature set and a preset fluctuation threshold, a high fluctuation subset is determined. The high fluctuation subset is then subjected to time delay deviation calibration and matrix processing to obtain an integrated signal data matrix.

[0038] It should be noted that this step addresses the core issues of uneven Bluetooth signal distribution in complex environments leading to coverage blind spots and multipath propagation causing time asynchrony through progressive operations including sparse feature extraction, fluctuation subset identification, delay calibration, and matrix processing. In sparse feature extraction, sparsity-related feature data refers to quantitative indicators describing the spatial distribution density of Bluetooth devices, including but not limited to device density, coverage overlap rate, average coverage radius, signal strength, delay, and noise power.

[0039] In one implementation, the step of determining a high-fluctuation subset based on the signal distribution feature set and a preset fluctuation threshold, and performing time delay deviation calibration and matrix processing on the high-fluctuation subset to obtain an integrated signal data matrix includes: When the signal fluctuation value in the signal distribution feature set exceeds a preset fluctuation threshold, it is marked as a high fluctuation subset; The time delay deviation within the high-fluctuation subset is calibrated, and the calibrated time delay data is determined as the time delay correction dataset. Adjust the data timestamps in the delay correction dataset to a preset unified benchmark, and calculate the signal-to-noise ratio of each device signal; When the signal-to-noise ratio of any device signal is lower than the preset signal-to-noise ratio threshold, the device signal is smoothed to obtain the corrected signal value. A matrix is ​​constructed based on the corrected signal value to obtain a signal data matrix; each element in the signal data matrix represents the corrected signal value of the device at the corresponding time point.

[0040] It's important to note that signal fluctuation value is the amplitude of change in Bluetooth signal strength per unit time, used to quantify signal stability. A larger fluctuation value indicates more drastic changes in signal strength over time, resulting in lower stability; conversely, a smaller fluctuation value indicates a more stable signal. The fluctuation threshold is a statistical critical value set to determine whether a signal is highly volatile. A high-fluctuation subset is a subset of the signal distribution feature set where fluctuation values ​​exceed a preset threshold. This typically involves traversing the signal distribution feature set to filter out continuous time windows where fluctuation values ​​exceed the threshold.

[0041] In this embodiment, delay deviation calibration corrects the delay of signals from each device in the high-fluctuation subset, eliminating delay errors caused by clock asynchrony and multipath reflection. The delay correction dataset is a set of delay data after delay deviation calibration, whose delay values ​​have been corrected to unbiased estimates close to the true values, providing an accurate timing alignment basis for subsequent matrix construction. Data timestamps record the specific time points at which Bluetooth devices receive signals from each device, used to identify the time sequence of the signals. The preset unified benchmark is the same time standard to which all device signal timestamps need to be adjusted, used to eliminate timestamp deviations caused by device clock drift. Signal-to-noise ratio (SNR) is the ratio of device signal power to noise power, reflecting signal quality. The preset SNR threshold is a critical value set to determine whether the signal needs smoothing. Smoothing is an operation that suppresses signal noise and improves signal reliability through algorithms; the algorithms can be moving average, Kalman filtering, or median filtering. The corrected signal value is the signal data with suppressed noise after smoothing. Matrix construction is the operation of arranging the corrected signal values ​​according to the time dimension and the device dimension to generate a two-dimensional structured matrix. The signal data matrix is ​​a two-dimensional matrix with time as the row and device as the column. Each element is the corrected signal value of the corresponding device at the corresponding time point.

[0042] For example, in an indoor Bluetooth signal monitoring scenario, the selected subset of signal data includes signal records from 10 devices, with a device distribution density of 0.5 devices per square meter. First, the average distance between devices is calculated using a density analysis algorithm, yielding a result of 2.2 meters. Then, signal characteristic fluctuation analysis is performed to extract the frequency offset of each device's signal. For example, the frequency offset of a certain device's signal is 3.5 Hz. Combining device distribution density and frequency offset, a weighted average is used to construct a multi-source signal integration framework. The signal data is categorized and integrated according to device distance and frequency offset to form an initial signal matrix. The matrix dimension is 10x5, representing the signal value distribution of the 10 devices at 5 time points. Next, to address the signal delay deviation issue, delay correction is performed. The signal propagation delay of each device is calculated. For example, the delay of a certain device is 2.3 milliseconds, and the standard delay benchmark is 2.0 milliseconds, resulting in a deviation of 0.3 milliseconds. Linear interpolation is used to correct the delay deviation, adjusting the timestamps of the signal data to a unified benchmark to ensure the temporal consistency of signal values ​​within the matrix. To further optimize the integration effect, signal quality assessment is performed, calculating the signal-to-noise ratio (SNR) of each device. If the SNR of a device is 15.2 dB and falls below the preset threshold of 18.0 dB, its signal value is smoothed using a moving average algorithm with a window size of three time points to generate corrected signal values. This results in an integrated signal data matrix, where each element represents the optimized signal value of the device at a specific time point, providing a reliable data foundation for subsequent analysis. This method, through density analysis, fluctuation characteristic extraction, time delay correction, and quality assessment, forms a complete logical chain, ensuring the accuracy and consistency of signal integration.

[0043] In step S14, the initial feature value of each data point is extracted from the signal data matrix, the adjusted confidence score of the data point is obtained by evaluating the initial feature value, and the weighted signal data weight distribution is determined based on the adjusted confidence score.

[0044] In one implementation, the step of extracting initial feature values ​​for each data point from the signal data matrix and evaluating the data points based on the initial feature values ​​to obtain an adjusted confidence score includes: Extract the initial feature value of each data point in the signal data matrix, compare it with the average value of the historical performance records of the data point, and obtain the deviation from the average value. When the deviation exceeds the preset signal strength fluctuation range, the data point is evaluated for credibility to obtain an initial credibility score. The fluctuation frequency and outlier ratio are extracted from historical data. When the fluctuation frequency exceeds the fluctuation frequency threshold and the outlier ratio exceeds the preset normal standard, the initial credibility score is reduced to obtain the adjusted credibility score.

[0045] It should be noted that a data point is the smallest independent unit of the corrected signal value and its associated attributes for each device at a specific point in time. It is the fundamental object for multi-source signal integration, reliability assessment, and weight allocation, generated from the signal data matrix in step S13. Initial feature values ​​are basic indicators extracted from the signal data matrix for preliminary data reliability assessment, and may include signal strength, signal-to-noise ratio, and timestamp stability. The average value of historical performance records is the statistical mean of signal characteristics of the same device or similar devices in historical scenarios, obtained through long-term data accumulation or a pre-stored historical database. The deviation from the average value is the absolute value of the difference between the feature value of the current data point and the average value of historical performance records.

[0046] The preset signal strength fluctuation range is a threshold value set to determine whether the signal strength fluctuates abnormally. When the deviation from the relative average exceeds this threshold, the data point is determined to be an abnormal fluctuation signal. Reliability assessment is the process of quantifying the reliable contribution of a data point to the positioning result by analyzing its characteristics and its matching degree with historical normal states. The initial reliability score is a preliminary reliability score calculated based on the deviation from the relative average, reflecting the reliability of the data point relative to historical normal states. For example, if the signal strength of a data point is -75dBm and its frequency distribution is concentrated in a specific range, and a comparison with historical records shows a large deviation from the average, it can be preliminarily determined that its characteristic value is abnormal. For a preset threshold range, such as signal strength fluctuation within ±5dBm being considered normal, if it exceeds this range, a preliminary reliability assessment is performed, resulting in an initial reliability score. For example, a score of 0.6 is lower than the normal range of 0.8.

[0047] Furthermore, the fluctuation frequency is the number of times the signal strength exceeds the preset fluctuation range per unit time, and the outlier ratio is the proportion of sampling points whose signal strength exceeds the preset fluctuation range out of the total sampling points. The fluctuation frequency threshold refers to the upper limit of the allowed number of abnormal signal strength fluctuations per unit time, and the preset normal standard refers to the upper limit of the proportion of sampling points whose signal strength exceeds the preset fluctuation range out of the total sampling points. The adjusted confidence score is the final confidence score adjusted based on the initial confidence score, combined with the fluctuation frequency and outlier ratio, to more accurately reflect the overall reliability of the data points.

[0048] For example, a data point fluctuates 3 times per hour over the past 24 hours, far exceeding the preset threshold of 1 time per hour, and the outlier rate reaches 30%, exceeding the normal standard of 10%. In this case, its reliability score can be lowered from 0.6 to 0.4. This dynamic adjustment helps to more accurately reflect the reliability of the data point and avoid misjudgments due to short-term fluctuations. To address the performance differences of the data point at different time periods, stability indices for the morning, noon, and evening periods can be compared. The morning index is 0.7, noon is 0.5, and evening is 0.3, all below the preset stability threshold of 0.6, requiring further correction. By increasing the sampling density of evening data or adjusting the time weight, the overall stability index can be raised to above 0.6, meeting the weighted distribution requirements. This method effectively balances the differences across time periods and improves the comprehensiveness of the evaluation.

[0049] In another implementation, determining the weighted signal data weight distribution based on the adjusted confidence score includes: Based on the adjusted confidence score, assign weight ratios to the data points in the signal data matrix, and determine whether the weight ratios meet the preset balance conditions. When the weight ratio does not meet the preset balance condition, the weight ratio is adjusted. When the weight ratio meets the preset balance condition, the weighted signal data weight distribution is determined.

[0050] It should be noted that this step, based on the adjusted confidence score, involves weighting the data points in the signal data matrix. This weighting can be done according to the confidence score, ensuring that high-confidence data points receive higher weights while avoiding imbalances in weight distribution, thus improving the accuracy and stability of the positioning results. The preset equilibrium condition is a set of mathematical constraints or logical rules to ensure that the weight distribution meets the multi-point collaboration principle.

[0051] For example, in a conference room with densely distributed data points, the scenario is characterized by dense data point distribution, high signal overlap, and susceptibility of single base station signals to multipath interference. The preset equalization conditions could be: weight of a single data point ≤ 0.3; total weight = 1; minimum weight = 0.1. In this embodiment, the maximum weight of a single data point is 0.3, while the weight of a certain data point is 0.4. In this case, the weight of that data point needs to be proportionally reduced to 0.3. If a data point has a final score of 0.8, its weight is assigned as 0.3, while the weight of a data point with a score of 0.4 is only 0.1. Then, it is determined whether the weight allocation meets the equalization conditions, such as whether the total weight sum is close to 1.0. If so, the final weighted distribution result is formed. This method can reasonably allocate resources, ensuring that the contribution and reliability of each data point in the signal data matrix match, providing a more stable foundation for subsequent signal processing.

[0052] In step S15, the signals are prioritized according to the signal data weight distribution, and noise information in the current environment is obtained to adjust the priority ranking a second time, resulting in an optimized signal data combination, including: By comparing the weight value of each signal data in the signal data weight distribution with a preset weight threshold, when the weight value is lower than the threshold, its priority ranking is reduced to obtain the adjusted priority ranking data. Obtain noise information in the current environment, perform preliminary filtering on the noise information, and determine the filtered environmental interference information; When the filtered environmental interference information is higher than a preset noise level threshold, the lowest priority signal data in the priority sorting data is removed to obtain an optimized signal data combination.

[0053] It should be noted that in this step, based on the weighted signal data weight distribution, the weight value of each signal data is obtained from the distribution. This weight value is then compared with a preset threshold. If the weight value is lower than the preset threshold, the priority of the signal data is reduced, resulting in adjusted priority ranking data. The preset weight threshold is a critical value set to determine whether a signal has low weight, based on scenario requirements or algorithmic experience. For example, if the weight value of a signal data is 0.2, and the preset threshold is 0.5, since the weight value is lower than the threshold, its priority can be adjusted from high to medium or low, forming adjusted priority ranking data. This adjustment helps to prioritize the allocation of processing resources to more reliable data points.

[0054] In this embodiment, the noise information in the current environment can be electromagnetic noise or multipath reflection noise that interferes with the Bluetooth signal. Preliminary filtering refers to the technical operation of denoising the noise information and extracting effective interference features, which include, but are not limited to, noise frequency, intensity, and direction. The filtered environmental interference information refers to the effective interference features, used to quantify the interference intensity of the current environment. Its core is to remove redundant information from the noise and retain interference features directly related to Bluetooth signal transmission. For example, if the detected environmental noise level is -60dBm, after preliminary filtering through noise suppression, the noise level drops to -80dBm. If the preset threshold is -85dBm, it indicates that the noise level after filtering still does not meet the standard. At this point, it can be determined that the environmental interference information is too high and further processing is required. This method can effectively identify the degree of interference and provide a basis for subsequent adjustments.

[0055] Furthermore, the preset noise level threshold is a critical value for interference intensity set to determine whether the environment is highly interfered with; if the filtered environmental interference information exceeds this threshold, the current environment is determined to be a highly interfered environment. Removing the lowest priority signal refers to sorting the signal data by weight value from low to high based on the weight distribution, identifying the signal with the lowest weight value, marking it as the lowest priority signal, and removing it from the priority-sorted data. The optimized signal data combination is obtained by grouping the remaining high-priority signal data set through combination filtering, ensuring that each group of signal data meets preset balance conditions. For example, if 3 out of 10 signal data have low priority, they are temporarily removed. The data is divided into 3 groups, and the intensity distribution of each group of signal data must meet balance conditions, such as an intensity difference not exceeding 10 dBm. If the difference in a group of data reaches 15 dBm, the grouping needs to be readjusted to finally determine the optimized signal data combination result. This grouping method helps improve the targeting of data processing.

[0056] In step S16, a blind zone distribution table is determined based on the signal data combination and a preset signal strength threshold. In the blind zone distribution table, the locations where the node communication delay is higher than a preset delay threshold or the node density is lower than a preset density threshold are adjusted to obtain the final positioning network topology.

[0057] In one implementation, determining the blind zone distribution table based on the signal data combination and a preset signal strength threshold includes: The coverage area of ​​the positioning network is divided into sub-regions, and the signal strength value in each sub-region is determined based on the combination of signal data. By comparing the signal strength value with a preset signal strength threshold, if the signal strength value is lower than the preset signal strength threshold, the area where the signal strength value is located is marked as a potential coverage blind spot, and a blind spot distribution map is obtained.

[0058] It should be noted that the coverage area of ​​a positioning network refers to the continuous geographical area formed by the deployed positioning nodes where the signal strength meets the positioning requirements. Region segmentation is a technical operation that divides the coverage area of ​​the positioning network into multiple non-overlapping sub-regions according to certain rules to refine the analysis of signal strength distribution. Sub-regions are the basic analytical units after region segmentation, typically with regular or irregular geometric shapes, serving as sample points for signal strength analysis. The signal strength value in this step is the average strength of the Bluetooth signal within the sub-region, calculated from the signal values ​​in the signal data combination. A weighted average is usually used to improve accuracy, reflecting the signal strength of the area.

[0059] Furthermore, the preset signal strength threshold is a critical value set to determine whether a sub-region is a signal blind zone; if the average signal strength of a sub-region is lower than this threshold, it is determined to be a blind zone. Potential coverage blind zones are sub-regions with signal strength values ​​lower than the preset threshold, i.e., areas with insufficient signal coverage that cannot support reliable positioning. The blind zone distribution chart is a structured data table or visualization chart that records the location, area, extreme signal strength values, and other attributes of potential coverage blind zones.

[0060] In one implementation, adjusting the locations in the blind zone distribution table where node communication latency is higher than a preset latency threshold or node density is lower than a preset density threshold to obtain the final positioning network topology includes: Based on the blind zone distribution chart, the communication quality between nodes is analyzed to obtain node communication delay and regional node density. The connection paths of nodes whose communication latency is higher than a preset latency threshold are adjusted to obtain an optimized set of node communication paths. When the density of nodes in the region is lower than a preset density threshold, virtual nodes are added in the region to obtain the adjusted region nodes. The final positioning network topology is determined based on the set of node communication paths and the adjusted regional nodes.

[0061] It should be noted that the positioning network topology adjustment is a core component of optimizing the positioning network based on the blind zone distribution table. This involves analyzing node communication delays and regional node density, specifically adjusting the connection paths of high-latency nodes or supplementing with virtual nodes, ultimately generating a positioning network topology with more balanced coverage and more efficient communication. Nodes, specifically physical devices or virtual entities in the positioning network that perform signal transmission, reception, or data processing, are the basic units of the positioning network topology. Communication quality analysis refers to the technical process of evaluating the reliability and real-time performance of communication links by detecting communication indicators between nodes in the positioning network, and identifying communication bottlenecks affecting positioning accuracy. Communication indicators include, but are not limited to, inter-node communication delay, signal strength, stability, and the impact of multipath effects. Node communication delay is the time difference between data transmissions between adjacent nodes in the positioning network, reflecting the real-time performance of inter-node communication. Regional node density is the number of positioning nodes per unit area, reflecting the density of node coverage within the area.

[0062] In this embodiment, the preset delay threshold is the maximum allowable inter-node communication delay set to ensure real-time positioning. When the inter-node communication delay exceeds the preset delay threshold, the communication links between nodes are replanned by switching neighboring nodes, adding multi-path transmission, or optimizing protocol parameters to reduce latency and improve reliability. The optimized node communication path set is a set containing the optimal communication links between all nodes after adjusting the connection paths of high-latency nodes. The preset density threshold is the minimum node density set to ensure the integrity of positioning coverage. Virtual nodes can be virtual beacons simulated by software algorithms, without physical hardware, only providing virtual signal parameters to supplement areas with insufficient physical node coverage. The adjusted area nodes are the set of nodes in the area that meet the density threshold after supplementing virtual nodes or adjusting physical nodes. Further, the adjusted nodes are integrated with the optimized communication paths, and the network resources are reallocated through load balancing based on resource allocation needs to determine whether the signal coverage balance requirements are met, resulting in the final positioning network topology.

[0063] In step S17, based on the positioning network topology, the location information accuracy is iteratively calculated to obtain an optimized location information reference value. Then, based on the location information reference value and the distance between the Bluetooth device and the node, a weighted fusion is performed to obtain the final location information of the Bluetooth device, including: Based on the positioning network topology, the multi-point signal strength distribution of the Bluetooth device is obtained, and the multi-point signal strength distribution is weighted and fused to obtain the initial location information. By comparing the deviation data between the initial location information and the reference point location in the actual environment, when the deviation data exceeds a preset deviation threshold, the deviation is gradually reduced based on the average value of historical location data to obtain a corrected location information reference value. The location weight is set according to the distance between the Bluetooth device and the node, and the final location information of the Bluetooth device is obtained by weighting the location weight and the location information reference value.

[0064] It should be noted that the multi-point signal strength distribution is a set of signal strength data received by the Bluetooth device from multiple nodes in the positioning network. It reflects the signal reception strength of the earphone in different directions, providing spatial distribution characteristics of the signal for initial location estimation and serving as the basic input for weighted fusion. Secondly, based on the topological characteristics of the positioning network, the multi-point data is processed through weighted fusion to obtain initial location information. For example, the core of weighted fusion lies in assigning weights based on the signal strength and reliability of different nodes. In an indoor environment, three positioning nodes detect signal strengths of -60dBm, -70dBm, and -50dBm from the Bluetooth device, respectively. The node with the strongest signal is assigned a higher weight, thus providing an initial estimate of the earphone's location.

[0065] In this embodiment, deviation data is obtained by comparing the estimated value with the location of a reference point in the actual environment. The deviation threshold is the maximum allowable deviation value set to determine the accuracy of the initial location information. The average value of historical location data is the average value of the Bluetooth device's positioning results at historical moments, used to smooth the random error of the current initial location. For example, if the deviation between the initial estimated location and the reference point is 2.5 meters, exceeding the preset 1.0-meter threshold, a smoothing algorithm can be used to adjust based on the average value of historical location data, gradually reducing the deviation, and finally obtaining the corrected intermediate value of the location information. In particular, the influence of the interference source can be weakened by signal filtering, and it can be determined whether there is residual interference. If so, the spatial coordinates are repeatedly adjusted to obtain an optimized location information reference value. This correction method can smooth position jumps and improve the continuity of location information.

[0066] Furthermore, the location weights are dynamically assigned weight coefficients based on the distance between the Bluetooth device and the nodes, used to weight and fuse the location information reference values. The final location information, after deviation correction and weighting, is the Bluetooth device coordinates, which is the final output of the positioning method. For example, in the final fusion calculation of the Bluetooth device location, distance weighting is used, taking into account the node distribution characteristics in the topology. The distances between the earphone and the three nodes are 3 meters, 5 meters, and 2 meters, respectively. Different weights are assigned according to the distance, with closer nodes receiving higher weights, ultimately fusing the device location result. This method fully utilizes the spatial characteristics of node distribution, making the final location result closer to reality.

[0067] Reference Figure 2 The second embodiment of the present invention provides a Bluetooth remote positioning system based on the Internet of Things, comprising: The data acquisition module is used to acquire the initial signal data emitted by the Bluetooth device, and to perform preliminary grouping of the initial signal data according to the signal strength of different sources to obtain classified signal strength groups. The filtering and matching module is used to analyze the degree of environmental interference caused by the signal coverage range in each signal strength group, and to perform filtering and matching based on the degree of environmental interference and preset filtering conditions to obtain a subset of signal data that meets the filtering conditions. The matrix processing module is used to extract feature data related to sparsity from the signal data subset to obtain a signal distribution feature set, and to determine a high-fluctuation subset based on the signal distribution feature set and a preset fluctuation threshold. The high-fluctuation subset is then subjected to time delay deviation calibration and matrix processing to obtain an integrated signal data matrix. The weight distribution module is used to extract the initial feature value of each data point from the signal data matrix, evaluate the data point based on the initial feature value to obtain the adjusted confidence score, and determine the weighted signal data weight distribution based on the adjusted confidence score. The signal optimization module is used to prioritize the signals according to the signal data weight distribution, and to adjust the priority ranking again by acquiring noise information in the current environment, so as to obtain an optimized signal data combination. The position adjustment module is used to determine a blind zone distribution table based on the signal data combination and a preset signal strength threshold, and to adjust the positions in the blind zone distribution table where the node communication delay is higher than a preset delay threshold or the node density is lower than a preset density threshold, so as to obtain the final positioning network topology. The location determination module is used to iteratively calculate the accuracy of the location information according to the positioning network topology to obtain an optimized location information reference value, and to perform weighted fusion based on the location information reference value and the distance between the Bluetooth device and the node to obtain the final location information of the Bluetooth device.

[0068] It should be noted that the IoT-based Bluetooth device remote positioning device provided in this embodiment of the invention is used to execute all the process steps of the IoT-based Bluetooth device remote positioning method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0069] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, it implements the steps described in the various embodiments of the IoT-based Bluetooth device remote positioning method, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the data acquisition module.

[0070] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0071] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0072] The processor 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0073] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0074] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they 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 also 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: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0075] It should be noted that the device embodiments described above are merely illustrative. 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; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0076] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A Bluetooth device remote positioning method based on Internet of Things, characterized in that, include: Acquire initial signal data emitted by Bluetooth devices, and preliminarily group the initial signal data according to the signal strength of different sources to obtain classified signal strength groups; The degree of environmental interference caused by the signal coverage range in each signal strength group is analyzed, and the signal data subset that meets the filtering conditions is obtained by filtering and matching according to the degree of environmental interference and preset filtering conditions. Feature data related to sparsity are extracted from the signal data subset to obtain a signal distribution feature set. Based on the signal distribution feature set and a preset fluctuation threshold, a high fluctuation subset is determined. The high fluctuation subset is then subjected to time delay deviation calibration and matrix processing to obtain an integrated signal data matrix. The initial feature value of each data point is extracted from the signal data matrix, and the adjusted confidence score of the data point is obtained by evaluating the initial feature value. The weighted signal data weight distribution is determined based on the adjusted confidence score. The signals are prioritized according to the signal data weight distribution, and the priority ranking is adjusted again by acquiring noise information in the current environment to obtain an optimized signal data combination. This includes: comparing the weight value of each signal data in the signal data weight distribution with a preset weight threshold; when the weight value is lower than the weight threshold, its priority ranking is reduced to obtain adjusted priority ranking data; acquiring noise information in the current environment, performing preliminary filtering on the noise information to determine the filtered environmental interference information; when the filtered environmental interference information is higher than a preset noise level threshold, the lowest priority signal data in the priority ranking data is removed to obtain the optimized signal data combination. A blind zone distribution table is determined based on the signal data combination and a preset signal strength threshold. In the blind zone distribution table, the locations where the node communication delay is higher than a preset delay threshold or the node density is lower than a preset density threshold are adjusted to obtain the final positioning network topology. Based on the positioning network topology, the location information accuracy is iteratively calculated to obtain an optimized location information reference value. Then, a weighted fusion is performed based on the location information reference value and the distance between the Bluetooth device and the node to obtain the final location information of the Bluetooth device. This includes: acquiring the multi-point signal strength distribution of the Bluetooth device based on the positioning network topology; performing weighted fusion on the multi-point signal strength distribution to obtain initial location information; comparing the deviation data between the initial location information and the reference point position in the actual environment; when the deviation data exceeds a preset deviation threshold, adjusting based on the average value of historical location data to gradually reduce the deviation and obtain a corrected location information reference value; setting location weights based on the distance between the Bluetooth device and the node; and performing a weighted fusion based on the location weights and the location information reference value to obtain the final location information of the Bluetooth device.

2. The method of Claim 1, wherein, The step of filtering and matching based on the degree of environmental interference and preset filtering conditions to obtain a subset of signal data that meets the filtering conditions includes: When the degree of environmental interference exceeds a preset threshold, the signal corresponding to the degree of environmental interference is marked as a high interference subset. The signal strength values ​​within the high interference subset are adjusted to obtain a compensated signal strength subset. Based on the compensated signal strength subset, condition matching is performed. If the signal strength value in the compensated signal strength subset meets the preset filtering conditions, it is included in the target data set; otherwise, it is discarded, thus obtaining a signal data subset that meets the filtering conditions.

3. The method of Claim 1, wherein, The process involves determining a high-fluctuation subset based on the signal distribution feature set and a preset fluctuation threshold, performing time delay deviation calibration and matrix processing on the high-fluctuation subset, and obtaining an integrated signal data matrix, including: When the signal fluctuation value in the signal distribution feature set exceeds a preset fluctuation threshold, it is marked as a high fluctuation subset; The time delay deviation within the high-fluctuation subset is calibrated, and the calibrated time delay data is determined as the time delay correction dataset. Adjust the data timestamps in the delay correction dataset to a preset unified benchmark, and calculate the signal-to-noise ratio of each device signal; When the signal-to-noise ratio of any device signal is lower than the preset signal-to-noise ratio threshold, the device signal is smoothed to obtain the corrected signal value. A matrix is ​​constructed based on the corrected signal value to obtain a signal data matrix; each element in the signal data matrix represents the corrected signal value of the device at the corresponding time point.

4. The Bluetooth device remote positioning method according to claim 1, characterized in that, The step of extracting initial feature values ​​for each data point from the signal data matrix and evaluating the data points based on these initial feature values ​​to obtain adjusted confidence scores includes: Extract the initial feature value of each data point in the signal data matrix, compare it with the average value of the historical performance records of the data point, and obtain the deviation from the average value. When the deviation exceeds the preset signal strength fluctuation range, the data point is evaluated for credibility to obtain an initial credibility score. The fluctuation frequency and outlier ratio are extracted from historical data. When the fluctuation frequency exceeds the fluctuation frequency threshold and the outlier ratio exceeds the preset normal standard, the initial credibility score is reduced to obtain the adjusted credibility score.

5. The Bluetooth device remote positioning method according to claim 4, characterized in that, The step of determining the weighted signal data weight distribution based on the adjusted confidence score includes: Based on the adjusted confidence score, assign weight ratios to the data points in the signal data matrix, and determine whether the weight ratios meet the preset balance conditions. When the weight ratio does not meet the preset balance condition, the weight ratio is adjusted. When the weight ratio meets the preset balance condition, the weighted signal data weight distribution is determined.

6. The Bluetooth device remote positioning method according to claim 1, characterized in that, The step of determining the blind zone distribution table based on the signal data combination and a preset signal strength threshold includes: The coverage area of ​​the positioning network is divided into sub-regions, and the signal strength value in each sub-region is determined based on the combination of signal data. The signal strength value is compared with a preset signal strength threshold. When the signal strength value is lower than the preset signal strength threshold, the area where the signal strength value is located is marked as a potential coverage blind spot, and a blind spot distribution table is obtained.

7. The Bluetooth device remote positioning method according to claim 1, characterized in that, The step involves adjusting the locations in the blind zone distribution table where the node communication delay is higher than a preset delay threshold or the node density is lower than a preset density threshold to obtain the final positioning network topology, including: Based on the blind zone distribution table, the communication quality between nodes is analyzed to obtain the node communication delay and the regional node density. The connection paths of nodes whose communication latency is higher than a preset latency threshold are adjusted to obtain an optimized set of node communication paths. When the density of nodes in the region is lower than a preset density threshold, virtual nodes are added in the region to obtain the adjusted region nodes. The final positioning network topology is determined based on the set of node communication paths and the adjusted regional nodes.

8. A Bluetooth device remote positioning system based on the Internet of Things, characterized in that, include: The data acquisition module is used to acquire the initial signal data emitted by the Bluetooth device, and to perform preliminary grouping of the initial signal data according to the signal strength of different sources to obtain classified signal strength groups. The filtering and matching module is used to analyze the degree of environmental interference caused by the signal coverage range in each signal strength group, and to perform filtering and matching based on the degree of environmental interference and preset filtering conditions to obtain a subset of signal data that meets the filtering conditions. The matrix processing module is used to extract feature data related to sparsity from the signal data subset to obtain a signal distribution feature set, and to determine a high-fluctuation subset based on the signal distribution feature set and a preset fluctuation threshold. The high-fluctuation subset is then subjected to time delay deviation calibration and matrix processing to obtain an integrated signal data matrix. The weight distribution module is used to extract the initial feature value of each data point from the signal data matrix, evaluate the data point based on the initial feature value to obtain the adjusted confidence score, and determine the weighted signal data weight distribution based on the adjusted confidence score. The signal optimization module is used to prioritize signals according to the signal data weight distribution, and to further adjust the priority ranking by acquiring noise information in the current environment to obtain an optimized signal data combination. Specifically, it compares the weight value of each signal data in the signal data weight distribution with a preset weight threshold. When the weight value is lower than the weight threshold, its priority ranking is reduced to obtain adjusted priority ranking data. It also acquires noise information in the current environment, performs preliminary filtering on the noise information, and determines the filtered environmental interference information. When the filtered environmental interference information is higher than a preset noise level threshold, it removes the lowest priority signal data from the priority ranking data to obtain the optimized signal data combination. The position adjustment module is used to determine a blind zone distribution table based on the signal data combination and a preset signal strength threshold, and to adjust the positions in the blind zone distribution table where the node communication delay is higher than a preset delay threshold or the node density is lower than a preset density threshold, so as to obtain the final positioning network topology. The location determination module is used to iteratively calculate the accuracy of location information based on the positioning network topology to obtain an optimized location information reference value, and then perform weighted fusion based on the location information reference value and the distance between the Bluetooth device and the node to obtain the final location information of the Bluetooth device. Specifically, based on the positioning network topology, the module obtains the multi-point signal strength distribution of the Bluetooth device, performs weighted fusion on the multi-point signal strength distribution to obtain initial location information, and compares the deviation data between the initial location information and the reference point position in the actual environment. When the deviation data exceeds a preset deviation threshold, the module adjusts based on the average value of historical location data to gradually reduce the deviation and obtain a corrected location information reference value. The location weight is set according to the distance between the Bluetooth device and the node, and the final location information of the Bluetooth device is obtained by weighting the location weight and the location information reference value.

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