A positioning method, device and readable storage medium based on a Bluetooth beacon

Abstract

The application provides a positioning method, device and readable storage medium based on a Bluetooth beacon, and the method comprises the following steps: generating a Bluetooth indoor ranging model in real time based on LORA and the Bluetooth beacon; performing parameter optimization on the Bluetooth indoor ranging model through a filtering algorithm to obtain an optimized ranging model; obtaining a ranging result through the optimized ranging model; and obtaining a positioning result through a multi-parameter fusion received signal strength RSSI weighted centroid positioning algorithm based on the ranging result. The LoRa and Bluetooth multi-mode beacon system provided by the application can achieve good indoor positioning effect, focuses on online generation and improvement of the Bluetooth indoor ranging model, proposes a centroid weighting improvement algorithm integrating the multi-element idea, and improves indoor positioning result testing, improves the accuracy of the generated indoor ranging model, and reduces indoor ranging errors.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a positioning method, apparatus and readable storage medium based on Bluetooth beacons. Background Technology

[0002] In recent years, the demand for new IoT indoor positioning services in location service systems has been growing, and new mobile positioning devices are emerging in the field of indoor positioning. Mobile phones, tablets, and other portable mobile positioning devices are becoming increasingly common in people's daily lives. Among them, Bluetooth beacons have advantages such as low power consumption and low cost, and can effectively provide managers with location information and other services related to personnel through IoT location service systems.

[0003] However, many positioning services on the market that utilize Bluetooth beacons are not perfect. Due to limitations in the area and terrain of the positioning environment, the deployment density of base stations is not high, and the complex distribution of various anchor circles during positioning makes accurate positioning difficult. Summary of the Invention

[0004] The technical problem to be solved by this application is to address the above-mentioned shortcomings of the prior art by providing a positioning method, device and readable storage medium based on Bluetooth beacons to solve the problems existing in the prior art.

[0005] Firstly, this application provides a positioning method based on Bluetooth beacons, wherein...

[0006] The methods include:

[0007] S1. Based on LoRa and Bluetooth beacons, generate Bluetooth indoor ranging models in real time;

[0008] S2. Optimize the parameters of the Bluetooth indoor ranging model using a filtering algorithm to obtain the optimized ranging model;

[0009] S3. Obtain the distance measurement result through the optimized distance measurement model;

[0010] S4. Based on the ranging results, the positioning result is obtained by using the multi-parameter fusion received signal strength RSSI weighted centroid positioning algorithm.

[0011] In some embodiments, S1 includes:

[0012] S11. Based on LoRa communication technology, obtain the RSSI of each of the multiple multi-mode beacon base stations;

[0013] S12. Based on the RSSI of each of the multiple multi-mode beacon base stations, obtain the model parameter values ​​corresponding to each of the multiple multi-mode beacon base stations;

[0014] S13. Obtain the model parameters of the Bluetooth indoor ranging model based on the model parameter values ​​corresponding to each of the multiple multimode beacon base stations.

[0015] In some embodiments, S2, the filtering algorithm includes: a Gaussian mean optimization algorithm and / or a one-dimensional Kalman optimization algorithm.

[0016] In some embodiments, the blue color is optimized using a Gaussian mean optimization algorithm.

[0017] The parameters of the intradental ranging model were optimized, including:

[0018] S31a. Repeatedly generate multiple Bluetooth indoor ranging models to obtain multiple model parameters;

[0019] S31b. The multiple model parameters are weighted and averaged using a Gaussian template and standard deviation to obtain the optimized model parameters.

[0020] In some embodiments, the Bluetooth indoor system is optimized using a one-dimensional Kalman optimization algorithm.

[0021] The ranging model undergoes parameter optimization, including:

[0022] S32a. Repeatedly generate multiple Bluetooth indoor ranging models to obtain multiple model parameters;

[0023] S32b. Based on the multiple model parameters, the parameters are optimized using a one-dimensional Kalman optimization algorithm to obtain the optimized model parameters.

[0024] In some embodiments, S4 includes:

[0025] S41. Based on the distance measurement results, the distance between the anchor node and the point to be measured is obtained;

[0026] S42. Select the three smallest values ​​from the distance between the anchor node and the point to be measured as the radii of the three anchor circles, and take the anchor node as the center of the circle;

[0027] S43. Determine the weighting point based on the positional relationship of the three anchor circles;

[0028] S44. Based on the ranging results and the weighted points, the positioning result of the point to be measured is obtained.

[0029] In some embodiments, S44, the location result of the point to be measured is obtained by the following formula:

[0030]

[0031] Where, d O1 d O2d O3 This is the distance measurement result from the point to be measured to the three anchor nodes, A(X A ,Y A ), B(X) B ,Y B ), C(X) C ,Y C ) represents the weighting point determined based on the positional relationship of the three anchor circles, d A1 d A2 d A3 d represents the distance from weighted point A to the three anchor nodes; B1 d B2 d B3 d represents the distance from weighted point B to the three anchor nodes; C1 d C2 d C3 These are the distances from weighted point C to the three anchor nodes, respectively; w A w B w C These are the weights corresponding to A, B, and C, respectively; (X O ,Y O ) represents the location result of the point to be measured; z represents the weight correction coefficient.

[0032] Secondly, this application provides a positioning device based on Bluetooth beacons, the device comprising:

[0033] The generation module is configured to generate a Bluetooth indoor ranging model in real time based on LoRa and Bluetooth beacons.

[0034] The optimization module is configured to optimize the parameters of the Bluetooth indoor ranging model using a filtering algorithm to obtain an optimized ranging model.

[0035] The ranging module is configured to obtain the ranging result through the optimized ranging model.

[0036] The positioning module is configured to obtain the positioning result based on the ranging result using a multi-parameter fusion received signal strength RSSI weighted centroid positioning algorithm.

[0037] Thirdly, this application provides a Bluetooth beacon-based positioning device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the Bluetooth beacon-based positioning method described in the first aspect above.

[0038] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the Bluetooth beacon-based positioning method described in the first aspect.

[0039] This application provides a Bluetooth beacon-based positioning method, apparatus, and readable storage medium. The method includes: generating a Bluetooth indoor ranging model in real time based on LoRa and Bluetooth beacons; optimizing the parameters of the Bluetooth indoor ranging model using a filtering algorithm to obtain an optimized ranging model; obtaining a ranging result using the optimized ranging model; and obtaining a positioning result based on the ranging result using a multi-parameter fusion received signal strength (RSSI) weighted centroid positioning algorithm. The LoRa and Bluetooth multimode beacon system provided in this application can achieve good indoor positioning performance. The focus is on the online generation and improvement of the Bluetooth indoor ranging model, the proposed centroid weighted improvement algorithm incorporating multiple elements, and the improved indoor positioning result testing. First, this application proposes a Bluetooth indoor ranging model optimization method. Currently, Bluetooth indoor ranging models are generated before positioning begins, which results in a failure to perfectly match the attenuation patterns of real-world signals during positioning. By utilizing the Bluetooth system, the indoor ranging model can be generated online and updated in real time during positioning. For the main parameters of the indoor ranging model, an improved scheme combining the Gaussian mean method and the one-dimensional Kalman method is introduced to enhance the accuracy of the generated indoor ranging model and reduce indoor ranging errors. Furthermore, this application analyzes and studies various positioning algorithms, employing a multi-parameter fusion approach to refine the weighted centroid algorithm. It considers the influence of multiple parameters on the positioning results in the actual environment to obtain a suitable weighted point. This point satisfies the condition of minimizing the sum of errors to the three circles while also reducing the area of ​​the possible location of the measurement point. The algorithm's effectiveness was verified using LoRa and Bluetooth network-side positioning systems. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] Figure 1 This is a schematic diagram showing the RSSI test results under different transmit powers in this application;

[0042] Figure 2 This is a schematic diagram illustrating the impact of pedestrian movement on RSSI in this application;

[0043] Figure 3 A schematic diagram illustrating the Bluetooth beacon-based positioning method provided in an embodiment of this application;

[0044] Figure 4 This is a schematic diagram illustrating the real-time generation of an indoor ranging model according to an embodiment of this application;

[0045] Figure 5 This is a schematic diagram illustrating the distance error after filtering optimization obtained using the indoor ranging model and RSSI, respectively, in an embodiment of this application.

[0046] Figure 6This is a schematic diagram illustrating various configurations of the three anchor circles in the embodiments of this application;

[0047] Figure 7 This is a schematic diagram of the intersection of two circles in an embodiment of this application;

[0048] Figure 8 This is a schematic diagram of the two circles separating in an embodiment of this application;

[0049] Figure 9 This is a schematic diagram showing the case where two circles are contained within each other in an embodiment of this application;

[0050] Figure 10 This is an example diagram of three anchor circles in an embodiment of this application;

[0051] Figure 11 This is a schematic diagram of an experiment using the RSSI-weighted centroid localization algorithm with multi-parameter fusion in an embodiment of this application.

[0052] Figure 12 This is a schematic diagram illustrating the positioning errors of the three positioning algorithms obtained according to Table 2-2 in the embodiments of this application;

[0053] Figure 13 This is a schematic diagram showing the positioning errors of the three positioning algorithms obtained according to Table 2-3 in the embodiments of this application;

[0054] Figure 14 This is a schematic diagram illustrating the average positioning error obtained by adjusting the coefficients in an embodiment of this application;

[0055] Figure 15 A schematic diagram of a Bluetooth beacon-based positioning device provided in an embodiment of this application;

[0056] Figure 16 Another schematic diagram of a Bluetooth beacon-based positioning device provided in an embodiment of this application.

[0057] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0058] To enable those skilled in the art to better understand the technical solution of this application, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0059] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining this application and are not intended to limit this application.

[0060] It is understood that, without conflict, the various embodiments and features in the embodiments of this application can be combined with each other.

[0061] It is understood that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, while parts unrelated to this application are not shown in the drawings.

[0062] It is understood that each unit or module involved in the embodiments of this application may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure.

[0063] It is understood that the terms "first," "second," etc., used in the embodiments of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0064] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this application may occur in a different order than those marked in the accompanying drawings.

[0065] It is understood that the flowcharts and block diagrams of this application illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagrams and flowcharts may be implemented using a hardware-based system to implement the specified function, or using a combination of hardware and computer instructions.

[0066] It is understood that the units and modules involved in the embodiments of this application can be implemented by software or by hardware. For example, the units and modules can be located in the processor.

[0067] Currently, there are numerous computational methods for Bluetooth beacon-based positioning, but they all have certain limitations and can be broadly categorized into two types: non-range positioning and range positioning. Geometric centroid positioning does not rely on range data, but it requires the introduction of other data to assist in the calculation. It utilizes the coordinate data of nearby beacon base stations collected from the point to be measured. However, the number of base stations largely determines the final positioning effect; when the number of base stations deployed is insufficient, the positioning area will become larger, leading to a larger positioning error. The main parameter of the range positioning algorithm is the distance from the point to be located to the monitoring Bluetooth device. Currently, range positioning mainly uses RSSI (Received Signal Strength Indicator) ranging.

[0068] Currently, the distance measurement models generated in literature research are cumbersome and computationally complex. Once the offline measurement is completed, no further adjustments or updates are made. They lack real-time performance and environmental adaptability. They do not take into account that when the real environment changes over time, the indoor distance measurement model generated in the previous time period will no longer be of use and cannot obtain accurate and effective distance measurement values.

[0069] Furthermore, RSSI-based ranging and positioning typically begins by acquiring the distance between the target point and the Bluetooth base station. This distance is then used in positioning algorithms such as trilateration, maximum likelihood estimation, and weighted centroid positioning to calculate the location coordinates of the person being located. Among these, the RSSI weighted centroid positioning algorithm is widely used. However, existing weighted centroid algorithms have very strict requirements for RSSI ranging accuracy. Their positioning performance is not only affected by the accuracy of the generated indoor ranging model but also by the instability of the RSSI data collected from the target point. Moreover, due to the presence of various variable factors in the indoor environment, these two influences are difficult to completely eliminate.

[0070] To address the problems existing in the current system and improve the accuracy and practicality of Bluetooth beacon positioning, the main improvements in this application include:

[0071] (1) Optimization of Bluetooth Indoor Ranging Model. This application studies the real-time dynamic generation of an indoor ranging model based on Bluetooth beacons. In designing the positioning system of this application, a LoRa communication wireless networking method is additionally introduced for the Bluetooth beacon. Factors that interfere with the Bluetooth beacon signal, such as transmission power, temperature and humidity, air pressure, and obstacles, are analyzed and summarized, and the Bluetooth beacon signal is filtered and optimized. The advantages and disadvantages of current indoor ranging model improvement schemes are analyzed, and methods for real-time periodic acquisition of the indoor ranging model and dynamic parameter tuning are studied. Then, the Gaussian mean method and / or one-dimensional Kalman method are used to optimize the main parameters A and n of the model to ensure the accuracy of the indoor ranging model function. The ranging effect of the improved model is tested through specific experiments.

[0072] (2) Optimization of weighted centroid positioning algorithm based on multi-parameter fusion. In view of the current application status and business characteristics of positioning in China, and in view of the shortcomings of traditional RSSI positioning algorithm in terms of positioning accuracy and practicality, an improved multi-parameter fusion weighted centroid positioning algorithm is introduced. The improved algorithm is compared and analyzed with positioning algorithms proposed in recent years to verify the advantages of the algorithm in terms of accuracy, adaptability and implementation efficiency.

[0073] This application uses LoRa and Bluetooth positioning systems as platforms to compare the algorithm in this application with positioning algorithms used in other literature. The comparison and verification show that the weighted centroid positioning algorithm based on the multi-parameter fusion concept proposed in this application calculates a position that is closer to the actual position of the person being located, and the algorithm can adaptively handle multiple positional distributions of the three anchor circles.

[0074] Before describing the embodiments of this application, the terms and concepts used in the embodiments of this application are explained:

[0075] 1. Bluetooth Indoor Distance Measurement Model

[0076] Signals in indoor environments are subject to obstruction, reflection, and absorption by furniture, pedestrians, and other factors during transmission. Therefore, it's impossible for a signal transmitter to perfectly achieve uniform, dead-angle-free signal transmission in an ideal space. The free-space model is overly idealistic and doesn't thoroughly analyze the impact of indoor obstacles and multipath effects, resulting in significant ranging errors.

[0077] Considering the various changing factors present in real physical environments, the path loss model takes into account the various interference factors that may exist in real-world conditions, making it more suitable for indoor ranging. The attenuation law of the Bluetooth beacon signal conforms to the logarithmic distance path loss model. When the distance is 1m, A represents the signal strength received at one meter. Therefore, the derivation is transformed into equation (1-1) as follows:

[0078] RSSI(d)=A-10nlgd (1-1)

[0079] Where RSSI is the received signal strength, d is the distance, A is the signal strength when the transmitter and receiver are 1m apart, and n is the environmental attenuation factor.

[0080] 2. Research on Bluetooth beacon signals

[0081] Currently, there are three Bluetooth output level classes: Class 1 (100m), Class 2 (approximately 10m), and Class 3 (approximately 2-3m). The experiment used in this application took place in an office building area, which is relatively small, so a long-range signal transmission was not required. Therefore, the Bluetooth beacon integrated on the Bluetooth beacon base station and positioning terminal is of Class 2. To maintain low power consumption during Bluetooth operation, an appropriate transmission power should be set in advance based on the actual environment.

[0082] This experiment uses the Bluetooth WH-BLE103 as the transmitter, with a signal strength range of [-20dBm, -8dBm]. The BLE monitoring module integrated on the beacon base station is used as the receiver, with a receiving sensitivity of -96dBm.

[0083] The received signal strength at the receiver was tested sequentially at different distances from the transmitter and at different power levels. Figure 1 This is a schematic diagram showing the RSSI test results under different transmit powers in this application, as shown below. Figure 1 As shown, it can be concluded that the beacon performs stably at level 7. Furthermore, since Bluetooth is primarily used in small, locally formed physical environments, to better leverage its localization capabilities and provide better subsequent positioning services, the transmission power of the Bluetooth beacon should be neither too high nor too low.

[0084] 3. Impact of signal transmission environment

[0085] Significant fluctuations in Bluetooth signals can occur when the indoor environment changes dramatically. Using a Bluetooth WH-BLE103 as the transmitter with a transmit power of 1dBm, and a BLE monitoring module integrated into a Bluetooth beacon base station as the receiver with a receive sensitivity of -96dBm, the transmitter and receiver were placed 3 meters apart. The experiment first tested the signal strength when no one was moving around, and then measured the signal strength when people were walking back and forth between the devices. The two scenarios were then compared.

[0086] Figure 2 This is a schematic diagram illustrating the impact of pedestrian movement on RSSI in this application, as shown below. Figure 2 As shown, when people are walking in the environment, the signal strength is unstable and fluctuates significantly.

[0087] 4. Online parameter generation for Bluetooth indoor ranging model

[0088] The role of the indoor ranging model is to convert received RSSI values ​​into distance. The more closely the generated indoor ranging model conforms to the ranging patterns observed during actual signal propagation, the more accurate the ranging will be, and the higher the positioning accuracy will be. However, current research methods, after generating the indoor ranging model in advance, do not perform further adjustments. This ignores the crucial issue that indoor ranging models change with the real environment, resulting in a lack of timeliness in the pre-generated models. Although the indoor environment is more stable than the outdoor environment, it is not static. The position and number of indoor objects, temperature changes due to weather, and the movement of objects and people can all cause signal fluctuations, making it difficult for the previously generated model to achieve accurate ranging, thus degrading the positioning service effect. Therefore, methods for generating ideal indoor ranging models online should be studied to improve the reliability of RSSI ranging.

[0089] 5. Weighted Centroid Algorithm

[0090] The main principle of the weighted centroid algorithm is to approximate the coordinates of the test point by obtaining information about multiple known anchor nodes near the test point and using the centroid of the polygon formed by these anchor nodes. While the calculation steps of this algorithm are simple and easy to understand, it does not deeply consider that the influence of anchor nodes at different locations on the test point varies. When the number of anchor nodes deployed near the test point is small, the area they form becomes too large. Therefore, the test point will be considered to be at the centroid regardless of its location within this area, resulting in low positioning accuracy.

[0091] To address the issue of poor localization accuracy in centroid algorithms, this application assigns weights to nearby anchor nodes involved in the localization process. The smaller the distance between an anchor node and the point to be measured, the greater its role in localization, and therefore the greater the weight assigned to the anchor node's coordinates. Conversely, the greater the distance, the smaller its role, and the smaller the assigned weight. The expression for calculating the coordinates (xo, yo) of the point to be measured is as follows:

[0092]

[0093] In the above formula, λ i =1 / d i Represents the weight of the i-th anchor node, (x i ,y i ) represents the coordinates of the i-th anchor node, d i This represents the distance between the i-th anchor node and the point to be measured.

[0094] Analysis of the algorithm reveals that the positioning effect is relatively ideal only when a certain number of anchor nodes are deployed, with the anchor node coordinates serving as the weighting factor. However, in actual applications, the complexity of the environment may lead to a smaller number of anchor nodes being deployed. When only three anchor nodes participate in positioning, the resulting weighting area becomes larger, and the improvement in positioning accuracy is not significant.

[0095] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0096] This application provides a positioning method based on Bluetooth beacons. The working process of this method can be implemented by electronic devices, such as computers, handheld smart terminals, etc. For ease of explanation, the embodiments of this application are described with computers as the main body for method execution.

[0097] Figure 3 A schematic diagram of the Bluetooth beacon-based positioning method provided in the embodiments of this application is shown below. Figure 3As shown, this application provides a positioning method based on Bluetooth beacons, the method comprising S1-S4, as follows:

[0098] S1. Based on LoRa and Bluetooth beacons, generate Bluetooth indoor ranging models in real time;

[0099] In this step, considering that current indoor ranging model generation schemes cannot meet the requirement of timely acquisition of ideal indoor ranging models for personnel positioning, this application realizes online real-time generation of Bluetooth indoor ranging models through LoRa and Bluetooth positioning systems. Since the indoor environment is relatively stable in a short period, the indoor ranging model is fitted at regular intervals, and the model update time can be reasonably adjusted according to the actual positioning situation.

[0100] In some embodiments, S1 includes:

[0101] S11. Based on LoRa communication technology, obtain the RSSI of each of the multiple multi-mode beacon base stations;

[0102] S12. Based on the RSSI of each of the multiple multi-mode beacon base stations, obtain the model parameter values ​​corresponding to each of the multiple multi-mode beacon base stations;

[0103] S13. Obtain the model parameters of the Bluetooth indoor ranging model based on the model parameter values ​​corresponding to each of the multiple multimode beacon base stations.

[0104] Specifically, Figure 4 This is a schematic diagram illustrating the real-time generation of an indoor ranging model according to an embodiment of this application, such as... Figure 4 As shown, several multi-mode beacon base stations are additionally placed within the positioning environment based on the size of the indoor area. These base stations do not participate in the positioning service of the positioning terminal. Following the principle that signal strength decreases with distance and the ranging model becomes less effective, taking multi-mode beacon base stations A, B, C, and D as an example, monitoring base station A is configured to only monitor the signal strength and ID values ​​broadcast by the three nearest multi-mode beacon base stations B, C, and D. The server performs Gaussian mean filtering on the received RSSI from the base stations based on the base station IDs to obtain three sets of relatively ideal RSSI. Then, based on the base station deployment location information, dAB, dAC, and dAD are obtained. The distance and RSSI are combined pairwise and, according to expression (1-1), inverse calculations are performed to obtain three sets of A and n. Their average value is the initial parameter of the indoor ranging model. Simultaneously, as mentioned above, the RSSI value is prone to fluctuation during acquisition due to factors such as transmission power, temperature, humidity, and indoor obstacles, resulting in deviations compared to the actual data and thus reducing the reliability of the indoor ranging model. To improve the accuracy and reliability of the indoor ranging model, after the indoor ranging model is updated, other algorithms are used to optimize the main parameters A and n. The optimized A and n are then stored in the database as the final parameters of the model.

[0105] S2. Optimize the parameters of the Bluetooth indoor ranging model using a filtering algorithm to obtain the optimized ranging model;

[0106] In this step, the latest RSSI values ​​can be used to generate a high-performance indoor ranging model that conforms to environmental patterns, and the main parameters A and n can be calculated. However, due to the randomness of RSSI value collection, even with multiple optimization algorithms, errors are inevitable, indicating room for further research improvement. To reduce the error of RSSI-based ranging, a correlation filtering algorithm is considered to optimize the main parameters of the model based on the online generation of the Bluetooth indoor ranging model.

[0107] Since indoor ranging models can be repeatedly generated over a period of time, the main parameters of the model can be obtained. Real-life environments are constantly changing, with many disruptive factors that change in real time, such as temperature, humidity, and surrounding pedestrians and objects. Because the generation process of indoor ranging models can be continuous over a period of time, and the indoor environment is relatively stable for a short period, the indoor ranging model at the previous moment and the current moment in the positioning process will not differ significantly. Algorithms can be used to process the parameters to reduce these differences.

[0108] In some embodiments, in S2, the filtering algorithm includes: Algorithm I: Gaussian mean optimization algorithm, and / or, Algorithm II: one-dimensional Kalman optimization algorithm.

[0109] In Algorithm I, Gaussian mean optimization, the acquisition of statistically necessary data is susceptible to bias due to limitations in equipment accuracy, changes in ambient temperature and humidity, ground vibrations, and other unforeseen circumstances. While this bias is negligible and has a small impact on the final statistical data, its causes are varied and difficult to quickly and accurately identify and eliminate. By repeatedly testing the required physical quantity while keeping other physical quantities constant, and obtaining a set of data, the average of these data points can be used as the final test result for that physical quantity. This approach effectively eliminates some random errors.

[0110] In some embodiments, the Bluetooth indoor ranging model is optimized using a Gaussian mean optimization algorithm, including:

[0111] S31a. Repeatedly generate multiple Bluetooth indoor ranging models to obtain multiple model parameters;

[0112] S31b. The multiple model parameters are weighted and averaged using a Gaussian template and standard deviation to obtain the optimized model parameters.

[0113] Specifically, the Gaussian mean of parameters A and n in the indoor ranging model is calculated. The calculation methods are the same for both. Taking parameter A as an example, the calculation method is as follows:

[0114] (1) First, the indoor ranging model was generated repeatedly, and a set of A data was collected. i and n i ;

[0115] (2) The selection weight is w i The Gaussian template and standard deviation U, and the template size m are selected based on the actual situation. The weights are expressed as shown in equation (1-2):

[0116]

[0117] (3) Collect m A i Weighted average, calculated The optimized xA is expressed as shown in (1-3):

[0118]

[0119] Furthermore, for Algorithm II one-dimensional Kalman optimization, the acquisition of experimental data is often hampered by relevant noise, thus the acquired data inevitably contains errors. Since the causes of these errors are uncertain, they cannot be completely eliminated. However, by using prediction methods to select appropriate performance metrics and then identifying the correlation factors between them from the observed data, it is possible to estimate approximately the true data.

[0120] In some embodiments, the Bluetooth indoor ranging model is optimized using a one-dimensional Kalman optimization algorithm, including:

[0121] S32a. Repeatedly generate multiple Bluetooth indoor ranging models to obtain multiple model parameters;

[0122] S32b. Based on the multiple model parameters, the parameters are optimized using a one-dimensional Kalman optimization algorithm to obtain the optimized model parameters.

[0123] Specifically, the parameters A and n of the indoor ranging model are optimized using the one-dimensional Kalman method. The calculation methods are the same for both. Taking parameter A as an example, the calculation method is as follows:

[0124] (1) First, the indoor ranging model was generated repeatedly, and a set of A data was collected. i and n i ;

[0125] (2) Define the appropriate measurement noise error and process noise error for the Kalman model as R0 and R1, respectively. i and Q i And based on A0 obtained at the initial time point and the uncertainty P estimated at the initial time point. 0,0 Let them equal the estimated values ​​at the next time point. and P 1,0 The expression is shown in (1-4);

[0126]

[0127] (3) The estimated uncertainty P of the current time point based on the previous time point. i,i-1 and R i The Kalman gain K is obtained. i The expression is shown in (1-5);

[0128]

[0129] (4) Estimated value of the current time point based on the previous time point. Determine the input A of the prediction model i To obtain the estimated value at the current point in time. The optimized output xA is expressed as shown in (1-6);

[0130]

[0131] (5) The uncertainty P of the current time point prediction i,i The expression is shown in (1-7);

[0132] P i,i =P i,i-1 *(1-K i (1-7)

[0133] (6) Estimated value of the current time point to the next time point The expression is shown in (1-8);

[0134]

[0135] (7) Calculate the uncertainty P of the current time point to the next time point. i+1,i The expression is shown in (1-9).

[0136] P i+1,i =P i,i +Q i (1-9)

[0137] The following is a test performance analysis of the two algorithms mentioned above in this application.

[0138] First, the Bluetooth beacon base station used in this application was selected to continuously monitor broadcasts from other nearby base stations. Its BLE monitoring module sensitivity was -96dBm. Then, an indoor ranging model was generated from the acquired multiple sets of RSSI values. Ten sets of A and n identified within the continuous time interval S1-S10 were extracted and processed using Algorithm I and Algorithm II respectively. All data obtained from the indoor ranging model parameter algorithm optimization experiment are shown in Table 1-1.

[0139]

[0140] Table 1-1

[0141] Simultaneously, a Bluetooth beacon base station was deployed in the experimental environment. Another set of Bluetooth base stations A1, A1, ..., A10 were fixed at distances of 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, and 5.0 m from the base station. These base stations were only used to monitor RSSI values ​​and did not participate in the generation of the indoor ranging model. The RSSI values ​​acquired at time S4 ​​are shown in Table 1-2.

[0142] d(m) 0.5 1 1.5 2 2.5 RSSI (dBm) -49 -60 -66 -70 -73 d(m) 3 3.5 4 4.5 5 RSSI (dBm) -76 -78 -80 -82 -84

[0143] Table 1-2

[0144] Figure 5 This is a schematic diagram illustrating the distance error after filtering optimization obtained using the indoor ranging model and RSSI, respectively, in an embodiment of this application. Figure 5 As shown, the average distance error obtained using the original method is 1.32m, the average distance error obtained after optimization using Algorithm I is 0.76m, and the average distance error obtained after optimization using Algorithm II is 0.93m. This application generates an indoor ranging model and obtains the main parameters A and n in a relatively short time, and then optimizes them using algorithms. It can be seen that both algorithms can significantly reduce the ranging error, and their algorithms are easy to program and implement. Among them, Algorithm I (Gaussian mean) achieves better ranging accuracy.

[0145] S3. Obtain the distance measurement result through the optimized distance measurement model;

[0146] In this step, based on the online generation of the Bluetooth indoor ranging model, the main parameters of the model are optimized by adding a correlation filtering algorithm to obtain an optimized ranging model. Then, the distance to the point to be measured is measured using the optimized ranging model to obtain the ranging result.

[0147] S4. Based on the ranging results, the positioning result is obtained by using the multi-parameter fusion received signal strength RSSI weighted centroid positioning algorithm.

[0148] In this step, for the RSSI weighted centroid localization algorithm with multi-parameter fusion, since the weighted centroid localization algorithm is easily limited by the area of ​​the weighted region and the number of anchor nodes, the intersection point formed by the intersection of the anchor circles can be regarded as the weighted point, which narrows down the possible area where the measured point is located. However, even if RSSI is filtered and optimized, the distance measured based on RSSI still deviates from the true distance. Therefore, in practical applications, there are multiple situations where the three anchor circles are composed.

[0149] For example, Figure 6 This is a schematic diagram illustrating various configurations of the three anchor circles in the embodiments of this application.

[0150] Image, as shown Figure 6 As shown, the positions of the three anchor circles include:

[0151] (a) The three anchor circles intersect at a single point;

[0152] (b) The intersection of the three anchor circles forms a region;

[0153] (c) They intersect at three regions;

[0154] (d) Any two circles intersect;

[0155] (e) Any two circles are separated;

[0156] (f) Contains any two circles.

[0157] In response to the above-mentioned situations, in some embodiments, S4 includes:

[0158] S41. Based on the distance measurement results, the distance between the anchor node and the point to be measured is obtained;

[0159] S42. Select the three smallest values ​​from the distance between the anchor node and the point to be measured as the radii of the three anchor circles, and take the anchor node as the center of the circle;

[0160] S43. Determine the weighting point based on the positional relationship of the three anchor circles;

[0161] S44. Based on the ranging results and the weighted points, the positioning result of the point to be measured is obtained.

[0162] Specifically, the multi-parameter fusion centroid localization algorithm proposed in this application follows this approach: The initial RSSI values ​​are combined with filtering and ranging models to determine the distance between the anchor node and the point to be measured. The three smallest values ​​are then selected by sorting; these values ​​form the radii of the three anchor circles, with the anchor node acting as the center. The handling methods for various scenarios are explained by arbitrarily selecting two of these circles.

[0163] Figure 7 This is a schematic diagram of the intersection of two circles in an embodiment of this application, as shown below. Figure 7As shown, when the two circles intersect, since the intersection points P1 and P2 of circles A and B lie on both circles, it can be clearly deduced that the distances from P1 to circles A and B, and the distances from P2 to circles A and B, are both minimized, as the distances we are looking for are all zero. r1 and r2 are the radii of circles A and B, respectively. The multi-parameter fusion approach considers both minimizing the sum of the distances from the weighted point to the two circles and minimizing the distance from the weighted point to the other circle. It can be clearly concluded that regardless of how the position of circle C changes, one of P1 or P2 will definitely have a shorter distance to circle C. The coordinates of this point are considered as a new parameter participating in the positioning algorithm, i.e., a new weighted object.

[0164] Figure 8 This is a schematic diagram of the two circles being separated in an embodiment of this application, as shown below. Figure 8 As shown, when the two circles are separated, the lines connecting the centers of circles A and B intersect at P1 and P2. Therefore, any point P3 on the line segment from P1 to P2 will have the shortest distance to both circles A and B. The multi-parameter fusion approach considers both minimizing the sum of distances from the weighted point to the two circles and minimizing the distance from the weighted point to the other circle. It is clear that regardless of the position of circle C, there will always be a point on the line segment from P1 to P2 with the shortest distance to circle C. The coordinates of this point are considered as a new parameter in the positioning algorithm, i.e., a new weighted object.

[0165] Figure 9 This is a schematic diagram of the case where two circles are contained within each other in an embodiment of this application, as shown below. Figure 9 As shown, when two circles are contained within each other, the extensions of the lines connecting center A and center B intersect at points P1 and P2. Therefore, any point P3 on the line segment from P1 to P2 has the shortest distance to both circles A and B. The multi-parameter fusion approach considers both minimizing the sum of distances from the weighted point to the two circles and minimizing the distance from the weighted point to the other circle. There must be a point on the line segment from P1 to P2 with the shortest distance to circle C. The coordinates of this point are considered as a new parameter in the positioning algorithm, i.e., a new weighted object.

[0166] Therefore, it can be concluded that any two circles can be selected to calculate suitable weighting points under the multi-parameter fusion approach. After pairwise combination operations, three new suitable weighting points can be obtained respectively. Since the distance between the weighting point and the point to be measured is inversely proportional to the weight, that is, the closer a weighting point is to the point to be measured, the greater its weight in the calculation. When the distance between the weighting point and the point to be measured is large, its proportion in the weight should be reduced. In addition, since the weighting point is determined by the three anchor circles, the selection of the weight should not only consider the distance from a single anchor circle to the weighting point, but also use the sum of the reciprocals of the distances from the weighting point to the three circles as the new weighting parameter to obtain the coordinate value of the point to be measured, and add a weight correction coefficient z to adjust the weight. This is both realistic and can effectively allow secondary parameters that are far apart to play a dominant role in the calculation, enhancing the correlation between the weighting point and the point to be measured.

[0167] Specifically, Figure 10 This is an example diagram of three anchor circles in an embodiment of this application, such as... Figure 10 As shown, in some embodiments, in S44, the location result of the point to be measured is obtained by the following formula:

[0168]

[0169] Where, d O1 d O2 d O3 This is the distance measurement result from the point to be measured to the three anchor nodes (O1, O2, O3), A(X A ,Y A ), B(X) B ,Y B ), C(X) C ,Y C ) represents the weighting point determined based on the positional relationship of the three anchor circles, where A(X) A ,Y A B(X) is the weighted point obtained by combining circles O1 and O2. B ,Y B C(X) is the weighted point obtained by combining circles O1 and O3. c ,Y c ) is the weighted point obtained by combining circles O2 and O3.

[0170] In addition, there is a distance error from the weighted object to each circle. Therefore, we need to consider the distance parameter d from the weighted point to each circle. A1 d A2 d A3 d represents the distance from weighted point A to the three anchor nodes; B1 d B2 d B3 d represents the distance from weighted point B to the three anchor nodes; C1 d C2 d C3 These are the distances from weighted point C to the three anchor nodes, respectively; w A w B w C These are the weights corresponding to A, B, and C, respectively; (X O ,Y O ) represents the location result of the point to be measured; z is the weight correction coefficient, used to increase the power value of the weight and adjust the degree of weight correction.

[0171] The following is an experiment using the RSSI-weighted centroid localization algorithm with multi-parameter fusion:

[0172] Figure 11 This is a schematic diagram of an experiment using the multi-parameter fusion RSSI-weighted centroid localization algorithm in an embodiment of this application, as shown below. Figure 11 As shown, an office measuring 9m long and 6m wide was designated as the experimental area Q. Four multi-mode beacon base stations, P_1, P_2, P_3, and P_4, were deployed as anchor nodes. The building height at the experimental location was measured to be approximately 3m. Due to the height and safety constraints, deployment on the ceiling was not considered. Furthermore, to prevent obstruction from the ground, walls, and ceiling, and to ensure the signal strength and uniformity of the Bluetooth beacons integrated on the base stations, the multi-mode beacon base stations were placed on tripods 1.2m above the ground, ensuring that the base stations and the positioning terminal were on the same horizontal line during the experiment. A coordinate system was established with the lower left corner of the experimental area Q as the origin, and P1, ..., P10 were appropriately selected as the test points within the laboratory.

[0173] Using multi-mode base station P_1 as a reference, P_1 base station was placed in experimental area Q. At a distance of 0.5m from P_1, the positioning terminal of this project continuously acquired 50 sets of RSSI values. These values ​​were then optimized using a Gaussian filtering algorithm to obtain the ideal signal strength at this distance. The same processing method was then applied to receive the corresponding signal strength values ​​at distances of 1m, 1.5m…5m from the base station to generate a reliable indoor ranging model. Based on the acquired indoor ranging model and the real-time received signal strength, the distances from each test point (P1, P2, …, P10) to each anchor node (P_1, P_2, P_3, P_4) in experimental area Q were calculated, as shown in Table 2-1.

[0174]

[0175] Table 2-1

[0176] The coordinates of the test point were obtained using the first and second existing positioning algorithms, as well as the optimized positioning algorithm of this application. During testing and comparison, it was found that the positioning accuracy varied depending on the weight correction coefficient z used to calculate the coordinates of the test point; a suitable weight correction coefficient z improved the positioning results. The positioning coordinates obtained using the three positioning algorithms when the weight correction coefficient z = 1 are shown in Table 2-2.

[0177]

[0178] Table 2-2

[0179] Figure 12 This is a schematic diagram showing the positioning errors of the three positioning algorithms obtained according to Table 2-2 in the embodiments of this application. Figure 12As shown, the maximum positioning error of the prior art 1 positioning algorithm is 1.17m, and the average positioning error is 0.72m; the maximum positioning error of the prior art 2 multilateral positioning algorithm is 1.04m, and the average positioning error is 0.61m; the improved multi-parameter fusion positioning algorithm of this application has a maximum positioning error of 1.02m and an average positioning error of 0.56m. A comprehensive comparison shows that the algorithm with the improved multi-parameter fusion concept of this application has the best positioning effect.

[0180] Furthermore, the error can be improved by dynamically adjusting the weight correction coefficient z. For example, Table 2-3 shows the positioning coordinates obtained by using three positioning algorithms when the weight correction coefficient z = 2 in the algorithm of this application. Figure 13 This is a schematic diagram showing the positioning errors of the three positioning algorithms obtained according to Table 2-3 in the embodiments of this application. Figure 13 As shown, when the weight correction coefficient z = 2 in the algorithm of this application, the maximum positioning error of the optimized multi-parameter fusion positioning algorithm is 0.96m and the average positioning error is 0.39m, and the positioning accuracy of the algorithm of this application is further improved.

[0181]

[0182] Table 2-3

[0183] Based on the above experimental method, by adjusting the value of the weight correction coefficient z, the average value of the coordinate error of the test point is taken for each test, and the positioning error change curve is obtained.

[0184] Figure 14 This is a schematic diagram illustrating the process of adjusting coefficients to obtain the average positioning error in an embodiment of this application, as shown below. Figure 14 As shown, the optimization algorithm of this application has the highest positioning accuracy when the weight correction coefficient z = 2 is dynamically adjusted. In actual positioning in indoor environments, the positioning effect can be well achieved by dynamically adjusting the weight correction coefficient z ∈ (2,8).

[0185] Based on the above embodiments, the LoRa and Bluetooth multimode beacon system provided in this application can achieve good indoor positioning results. The focus is on the online generation and improvement of the Bluetooth indoor ranging model, the proposal of a centroid weighted improvement algorithm that integrates multiple elements, and the improvement of indoor positioning result testing.

[0186] First, this application proposes an optimization method for Bluetooth indoor ranging models. Currently, Bluetooth indoor ranging models are generated before positioning begins, which means they cannot perfectly match the attenuation patterns of real-world signals during positioning. By leveraging the Bluetooth system, the indoor ranging model can be generated and updated online during positioning. For the main parameters of the indoor ranging model, improved schemes using the Gaussian mean method and one-dimensional Kalman method are introduced to enhance the accuracy of the generated indoor ranging model and reduce indoor ranging errors.

[0187] In addition, this application analyzes and studies various positioning algorithms, and improves the weighted centroid algorithm by adopting the idea of ​​multi-parameter fusion. It considers the influence of various parameters on the positioning results in the actual environment to obtain a suitable weighted point. This point satisfies the requirement that the sum of the errors of the three circles is minimized, and also reduces the area of ​​the possible location of the measurement point. The algorithm effect was verified by the network-side positioning system of LoRa and Bluetooth.

[0188] The positioning algorithm provided in this application has applications including, but not limited to: cloud positioning and real-time trajectory querying of employees for organizations / enterprises, facilitating risk prevention and management of employees leaving their workplaces during work hours. This application uses Bluetooth signals for positioning, offering advantages such as portability, no need for app installation, low power consumption, zero operation, and real-time trajectory tracking.

[0189] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0190] Figure 15 A schematic diagram of a Bluetooth beacon-based positioning device provided in an embodiment of this application is shown below. Figure 15 As shown, this application provides a positioning device based on Bluetooth beacons, the device comprising:

[0191] The generation module 11 is configured to generate a Bluetooth indoor ranging model in real time based on LoRa and Bluetooth beacons.

[0192] Optimization module 12 is configured to optimize the parameters of the Bluetooth indoor ranging model using a filtering algorithm to obtain an optimized ranging model;

[0193] The ranging module 13 is configured to obtain the ranging result through the optimized ranging model;

[0194] The positioning module 14 is configured to obtain the positioning result based on the ranging result by using a multi-parameter fusion received signal strength RSSI weighted centroid positioning algorithm.

[0195] Regarding the limitations on the Bluetooth beacon-based positioning device, please refer to the limitations on the Bluetooth beacon-based positioning method in the above embodiments of this application, which will not be repeated here.

[0196] Figure 16 Another schematic diagram of the Bluetooth beacon-based positioning device provided in the embodiments of this application is shown below. Figure 16 As shown, in some embodiments, this application provides a Bluetooth beacon-based positioning device, including a memory 22 and a processor 21. The memory stores a computer program, and the processor is configured to run the computer program to execute the Bluetooth beacon-based positioning method in the above embodiments of this application.

[0197] The memory is connected to the processor. The memory can be flash memory, read-only memory or other types of memory. The processor can be a central processing unit or a microcontroller.

[0198] In some embodiments, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the Bluetooth beacon-based positioning method in the above embodiments of this application.

[0199] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules, or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0200] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A positioning method based on Bluetooth beacons, characterized in that, The method includes: S1. Based on LoRa and Bluetooth beacons, generate Bluetooth indoor ranging models in real time; S2. Optimize the parameters of the Bluetooth indoor ranging model using a filtering algorithm to obtain the optimized ranging model; S3. Obtain the distance measurement result through the optimized distance measurement model; S4. Based on the ranging results, the positioning result is obtained by using the multi-parameter fusion received signal strength RSSI weighted centroid positioning algorithm; S4 includes: S41. Based on the distance measurement results, the distance between the anchor node and the point to be measured is obtained; S42. Select the three smallest values ​​from the distance between the anchor node and the point to be measured as the radii of the three anchor circles, and take the anchor node as the center of the circle; S43. Determine the weighting point based on the positional relationship of the three anchor circles; The positions of the three anchor circles include: the three anchor circles intersect at one point, the three anchor circles intersect to form one region, they intersect in three regions, any two circles intersect, any two circles are separate, and any two circles are contained within each other. S44. Based on the ranging results and the weighted points, the positioning result of the point to be measured is obtained; In S44, the location result of the point to be measured is obtained by the following formula: Where, d O1 d O2 d O3 This is the distance measurement result from the point to be measured to the three anchor nodes, A (X A ,Y A ), B (X B ,Y B ), C (X C ,Y C ) represents the weighting point determined based on the positional relationship of the three anchor circles, d A1 d A2 d A3 d represents the distance from weighted point A to the three anchor nodes; B1 d B2 d B3 d represents the distance from weighted point B to the three anchor nodes; C1 d C2 d C3 These are the distances from weighted point C to the three anchor nodes, respectively; w A w B w C These are the weights corresponding to A, B, and C, respectively; (X O ,Y O ) represents the location result of the point to be measured; z is the weight correction coefficient, and the value of z ranges from 2 to 8.

2. The positioning method based on Bluetooth beacons according to claim 1, characterized in that, S1 includes: S11. Based on LoRa communication technology, obtain the RSSI of each of the multiple multi-mode beacon base stations; S12. Based on the RSSI of each of the multiple multi-mode beacon base stations, obtain the model parameter values ​​corresponding to each of the multiple multi-mode beacon base stations; S13. Obtain the model parameters of the Bluetooth indoor ranging model based on the model parameter values ​​corresponding to each of the multiple multimode beacon base stations.

3. The positioning method based on Bluetooth beacons according to claim 1, characterized in that, In S2, the filtering algorithm includes: Gaussian mean optimization algorithm and / or one-dimensional Kalman optimization algorithm.

4. The positioning method based on Bluetooth beacons according to claim 3, characterized in that, The parameters of the Bluetooth indoor ranging model are optimized using the Gaussian mean optimization algorithm, including: S31a. Repeatedly generate multiple Bluetooth indoor ranging models to obtain multiple model parameters; S31b. The multiple model parameters are weighted and averaged using a Gaussian template and standard deviation to obtain the optimized model parameters.

5. The positioning method based on Bluetooth beacons according to claim 3, characterized in that, The parameters of the Bluetooth indoor ranging model are optimized using a one-dimensional Kalman optimization algorithm, including: S32a. Repeatedly generate multiple Bluetooth indoor ranging models to obtain multiple model parameters; S32b. Based on the multiple model parameters, the parameters are optimized using a one-dimensional Kalman optimization algorithm to obtain the optimized model parameters.

6. A positioning device based on Bluetooth beacons, characterized in that, The device includes: The generation module is configured to generate a Bluetooth indoor ranging model in real time based on LoRa and Bluetooth beacons. The optimization module is configured to optimize the parameters of the Bluetooth indoor ranging model using a filtering algorithm to obtain an optimized ranging model. The ranging module is configured to obtain the ranging result through the optimized ranging model. The positioning module is configured to obtain the positioning result based on the ranging result using a multi-parameter fusion received signal strength RSSI weighted centroid positioning algorithm; The positioning module is specifically configured as follows: Based on the distance measurement results, the distance between the anchor node and the point to be measured is obtained; Select the three smallest distances between the anchor nodes and the points to be measured as the radii of the three anchor circles, and take the anchor nodes as the centers of the circles; The weighting point is determined based on the positional relationship of the three anchor circles; the positional situations of the three anchor circles include: the three anchor circles intersect at one point, the three anchor circles intersect to form one region, they intersect in three regions, any two circles intersect, any two circles are separate, and any two circles are contained within each other; Based on the ranging results and the weighted points, the positioning result of the point to be measured is obtained; wherein, the positioning result of the point to be measured is obtained through the following formula: Where, d O1 d O2 d O3 This is the distance measurement result from the point to be measured to the three anchor nodes, A (X A ,Y A ), B (X B ,Y B ), C (X C ,Y C ) represents the weighting point determined based on the positional relationship of the three anchor circles, d A1 d A2 d A3 d represents the distance from weighted point A to the three anchor nodes; B1 d B2 d B3 d represents the distance from weighted point B to the three anchor nodes; C1 d C2 d C3 These are the distances from weighted point C to the three anchor nodes, respectively; w A w B w C These are the weights corresponding to A, B, and C, respectively; (X O ,Y O ) represents the location result of the point to be measured; z is the weight correction coefficient, and the value of z ranges from 2 to 8.

7. A positioning device based on Bluetooth beacons, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the Bluetooth beacon-based positioning method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the Bluetooth beacon-based positioning method as described in any one of claims 1-5.

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