A Bluetooth beacon sensor deployment optimization method that balances cost and detection accuracy

Abstract

This invention discloses a Bluetooth beacon sensor deployment optimization method that balances cost and detection accuracy: 1) RSSI data is collected in a real environment and analyzed and parameter fitted to obtain an RSSI-distance relationship model; 2) The beacon deployment method is designed and its impact on positioning accuracy is analyzed. Considering cost and positioning accuracy, the beacon deployment method is optimized and modeled. This invention can effectively establish a Bluetooth beacon sensor deployment optimization model that considers cost and positioning accuracy factors, providing guidance for the implementation of Bluetooth beacon sensor-based positioning methods. It allows for the selection of the most suitable beacon deployment method based on actual environment, cost budget, accuracy requirements, and other conditions.

Description

Technical Field

[0001] This invention relates to the field of wireless sensor network node positioning technology, and in particular to an optimized method for deploying Bluetooth beacon sensors that balances cost and detection accuracy. Background Technology

[0002] With the rapid development of wireless communication technology, Bluetooth Low Energy (BLE)-based positioning technology has been widely used in the field of location services. BLE beacon sensors communicate with mobile devices or other receivers through broadcast signals to determine their location information, and have been widely used in intelligent transportation, indoor navigation, logistics management and other fields.

[0003] However, BLE-based positioning technology requires the pre-deployment of BLE beacons as anchor nodes for wireless positioning on roads. Different beacon deployment methods result in varying positioning accuracy and implementation costs. Currently, there are various deployment methods for BLE beacon sensors, including bilateral, unilateral, and alternating layouts, each with different impacts on positioning accuracy and cost. However, existing deployment methods often only consider one factor, neglecting the balance between cost and positioning accuracy. Therefore, in practical applications, it is often difficult to select the most suitable beacon deployment method based on specific environments and requirements. Furthermore, simply increasing beacon density is not feasible for achieving low-cost vehicle positioning; therefore, a BLE beacon deployment method that balances deployment cost and positioning accuracy is needed, minimizing equipment installation costs and reducing the difficulty of promoting the positioning method while ensuring accuracy. However, there is currently no research on optimizing BLE beacon deployment methods that comprehensively consider both positioning accuracy and cost. Summary of the Invention

[0004] This invention addresses the issue that different BLE beacon deployment methods can affect the frequency, latitude, and other attributes of RSSI observation information during continuous vehicle positioning using GNSS / BLE / INS, thus impacting positioning accuracy. To balance cost and positioning accuracy in beacon deployment selection, this invention proposes an optimized Bluetooth beacon sensor deployment method.

[0005] This invention considers both cost and positioning accuracy when selecting beacon deployment methods to guide the practical application of positioning methods. It enables the selection of the most suitable beacon deployment method based on actual environment, cost budget, accuracy requirements, and other conditions.

[0006] This invention is achieved through the following technical solution:

[0007] A method for optimizing the deployment of Bluetooth beacon sensors that balances cost and detection accuracy includes the following steps:

[0008] S1. Based on the analysis of the raw RSSI data collected in the real environment, fit the RSSI-distance relationship model to determine the effective coverage radius of the beacon;

[0009] S2. Design different types of beacon layouts and beacon deployment parameters to adapt to different road conditions and cost requirements;

[0010] S3. Establish the simulation environment required for the simulation positioning experiment. Through multiple sets of positioning simulation experiments, conduct sensitivity analysis of positioning accuracy to beacon deployment parameters under different beacon layouts to obtain the positioning accuracy under different beacon deployment methods.

[0011] S4. Establish a model to determine the relationship between cost and deployment method, which is used to estimate the beacon deployment cost when using different beacon deployment methods.

[0012] S5. Establish a model to estimate the positioning accuracy when using different beacon deployment methods.

[0013] S6. Establish an optimization model for beacon deployment considering cost and positioning accuracy, perform bi-objective optimization to obtain the Pareto optimal solution set, and use it to guide beacon deployment in real-world environments.

[0014] In step S1 above, based on the analysis of the raw RSSI data collected in the real environment, the RSSI-distance relationship model is fitted to determine the effective coverage radius of the beacon. The specific steps are as follows:

[0015] S101. Determine whether to use a log-normal shadowing fading model that considers environmental factors to fit the relationship between RSSI and distance:

[0016] RSSI=A-10ηlg(d)+ξ (1)

[0017] In the formula, A = P t -PL(d0)+ξ, where PL(d) is the path loss between the receiver and transmitter, d0 is a reference distance, typically set to 1m, η is the path loss exponent, which is affected by the surrounding environment, and ξ is a variable with a mean of zero and a standard deviation of σ. R Gaussian random variables;

[0018] S102. Gaussian filtering is applied to the collected RSSI values ​​to obtain the mean value of the Gaussian filter (the average RSSI value after removing RSSI values ​​that are more than one standard deviation away from the mean) as the accurate RSSI value at each location. The least squares method is used to obtain the parameters A and η in the above formula to fit the relationship between RSSI and distance and determine the effective coverage radius of the beacon.

[0019] In step S2 above, different types of beacon layouts are designed to adapt to different road conditions and cost requirements. The specific steps are as follows:

[0020] S201, Double-sided layout: Beacons are symmetrically arranged on both sides of the road at a spacing of D (in meters), and N beacons can be placed at each location to form a beacon array;

[0021] S202, Single-sided layout: Beacons are placed only on one side of the road at intervals D, and N beacons can be placed at each location to form a beacon array;

[0022] S203, Alternating Layout: Beacons are alternately placed on both sides of the road at intervals D; and N beacons can be placed at each location to form a beacon array;

[0023] S204. In simulation environments with single-sided and alternating layouts, a double-sided beacon layout is still used at turns to ensure positioning accuracy during turns.

[0024] In step S3 above, the simulation environment required for the simulation positioning experiment is established. Sensitivity analysis of positioning accuracy to beacon deployment parameters is conducted through multiple sets of positioning simulation experiments under different beacon layouts to obtain the positioning accuracy under different beacon deployment methods. The specific steps are as follows:

[0025] S301. Based on the IMU and GNSS data collected from the actual vehicle, establish a simulation environment, set up simulation beacons around the actual trajectory, deduce the theoretical RSSI data through the relationship between the actual trajectory and RSSI-distance, and then add Gaussian white noise to simulate RSSI simulation data.

[0026] S302. Set the parameter range [D1, D2] for the beacon spacing D and the simulation step size D. s Set the parameter range [N1, N2] for the number of beacons N and the simulation step size N. s ;

[0027] S303. Conduct multiple sets of simulation positioning experiments according to the above simulation parameters to obtain the positioning accuracy under different beacon layouts and beacon deployment parameters.

[0028] Step S4 above establishes a model relating cost and deployment method, used to estimate the beacon deployment cost when using different beacon deployment methods. The specific steps are as follows:

[0029] S401. Investigate the specific details of the actual beacon deployment section, including its length, number of turns, and installation conditions. Use vertically erected beacon poles on the roadside to install BLE beacons. Installing the beacons on the upper part of the poles reduces obstruction of the BLE signal by other objects. Select a suitable beacon layout method based on the actual situation.

[0030] S402. Calculate the deployment cost of BLE beacons using the following formula:

[0031]

[0032] In the formula, the dependent variable C represents the deployment cost, the independent variable D represents the beacon interval, and the independent variable N represents the number of beacons. The symbol indicates rounding up; the coefficient L represents the length of the road section to be laid; the coefficient B represents the number of turns, used to calculate the number of beacons to be added at the turns, B=0 when a double-sided layout is selected, p is the cost of a single beacon pole, b is the cost of a single BLE beacon; the coefficient a is the layout coefficient, a=2 when a double-sided layout is selected, and a=1 when other layouts are selected.

[0033] Step S5 above establishes a model relating positioning accuracy and deployment method, used to estimate positioning accuracy when using different beacon deployment methods. The specific steps are as follows:

[0034] S501. Model the relationship between deployment method and positioning accuracy. Use the least squares method to fit the sensitivity analysis results to a polynomial surface. Its principle is based on minimizing the sum of squared errors (SSE) between the predicted and actual values. For a given bivariate dataset, use a polynomial equation of the following form to construct a surface model:

[0035]

[0036] In the formula, a ij Let represent coefficients, x and y represent independent variables, and n and m represent the polynomial degrees of x and y, respectively. These coefficients can be solved by minimizing the sum of squared errors (SSE) between the actual observed value z and the model's predicted value. The SSE is calculated as follows:

[0037] SSE=∑[z i -f(x i ,y i )] 2 (4)

[0038] S502. Finally, a polynomial equation combining 3rd and 2nd degree equations was used to establish a model relating the average positioning error to the beacon interval and the number of beacons:

[0039] APE = f2(D,N) = p00 +p 10 D+p 01 N+p 20 D 2 +p 11 D·N+p 02 N 2

[0040] +p 30 D 3 +p 21 D 2 ·N+p 12 D·N 2 (5)

[0041] In the formula, D represents the beacon interval, N represents the number of beacons, and p represents the coefficient.

[0042] In step S6 above, an optimization model for beacon deployment considering cost and positioning accuracy is established. A bi-objective optimization solution is then performed to obtain the Pareto optimal solution set, which is used to guide beacon deployment in real-world environments. The specific steps are as follows:

[0043] S601. The relationship models obtained in steps S402 and S502 are combined to form a dual-objective optimization model for beacon deployment considering both cost and positioning accuracy, as follows:

[0044]

[0045] S602. Finally, the non-dominated sorting genetic algorithm is used to solve the above bi-objective optimization model, which yields the Pareto optimal solution set for the three layout modes under a given road condition.

[0046] Compared with the prior art, the present invention has the following advantages and effects:

[0047] This invention, through collecting RSSI data in a real environment and performing in-depth analysis and parameter fitting, obtains a more accurate RSSI-distance relationship model. This model more realistically reflects the relationship between signal strength and distance, thus providing a more reliable foundation for subsequent positioning and sensor deployment. Three types of beacon layouts are designed to provide a reference for beacon deployment in practical applications. Addressing the issue of not being able to effectively balance cost and positioning accuracy during beacon deployment, a relationship model among cost, deployment method, and positioning accuracy is established to guide the practical application of positioning methods. This allows for the selection of the most suitable beacon deployment method based on actual environment, cost budget, and accuracy requirements. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating the principle and flow of the Bluetooth beacon sensor deployment optimization method that balances cost and detection accuracy according to the present invention.

[0049] Figure 2 This is a schematic diagram of the RSSI-distance relationship in an example of the present invention.

[0050] Figure 3 This is a schematic diagram of the simulation environment in an example of the present invention.

[0051] Figure 4 These are schematic diagrams of three beacon layout methods in the embodiments of the present invention, wherein (a) is a schematic diagram of a double-sided beacon layout, (b) is a schematic diagram of a single-sided beacon layout, and (c) is a schematic diagram of an alternating beacon layout.

[0052] Figure 5 This is a schematic diagram of the installation of the beacon and beacon pole in an example of the present invention.

[0053] Figure 6 This is a sensitivity analysis diagram of positioning accuracy to beacon spacing and number of beacons under different beacon layouts in the examples of the present invention, where a) double-sided, b) single-sided, c) alternating, and c) alternating.

[0054] Figure 7 This is a schematic diagram illustrating the relationship between deployment parameters and positioning accuracy under different beacon layouts in this invention, where a) is a double-sided layout and b) is a single-sided layout.

[0055] Figure 8 This is the Pareto optimal solution among the three layout modes in the examples of this invention. Detailed Implementation

[0056] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, a method for optimizing the deployment of Bluetooth beacon sensors to balance cost and detection accuracy includes the following steps:

[0058] Step S1: Collect BLE beacon data in an underground parking lot, analyze the raw RSSI data, and finally fit the RSSI-distance relationship model. The specific steps are as follows:

[0059] In the experimental setting, the beacon was elevated, and the Bluetooth receiver was gradually moved from a distance of 1m to a distance of 20m, moving 1m at a time. 200 RSSI values ​​were collected at each location, for a total of 20 sets. Statistical analysis showed that the average standard deviation of the 20 sets of RSSI values ​​was 3.72, which can reflect the RSSI noise level in an indoor-like environment.

[0060] Gaussian filtering was applied to the 20 sets of RSSI values ​​to obtain the Gaussian filtered mean (the average RSSI value after removing RSSI values ​​that deviate from the mean by one standard deviation) as the accurate RSSI value at each location. Finally, the least squares method was used to obtain the parameters A and η. Where A = -41.59 and η = 1.57, the log-normal shadowing fading model of the BLE beacon under this environment can be expressed as follows:

[0061] RSSI=-41.59-10×1.57×lg(d) (1)

[0062] The RSSI-distance relationship at this point can be expressed as:

[0063]

[0064] As one embodiment, the RSSI-distance relationship is as follows: Figure 2 As shown.

[0065] This shows that RSSI gradually decreases with increasing distance, but the rate of decrease slows down. For RSSI ranging, a more significant decrease in attenuation is more beneficial to ranging accuracy. Therefore, as distance increases, the value of RSSI decreases from optimal to suboptimal and then to unusable. Figure 2 The effective coverage radius of the beacon is limited to 25m, and it is assumed that only the RSSI of BLE beacons within 25m of the vehicle is of reference value. During the positioning algorithm calculation, the distance between the estimated location and the beacon location can be filtered to exclude RSSI values ​​of beacons beyond 25m, thus improving the reliability of RSSI ranging.

[0066] Step S2: Design different types of beacon layouts to adapt to different road conditions and cost requirements, as detailed below:

[0067] Double-sided layout: Beacons are symmetrically placed on both sides of the road at intervals D, and N beacons can be placed at each location to form a beacon array. Single-sided layout: Beacons are placed only on one side of the road at intervals D, and N beacons can be placed at each location to form a beacon array. Alternating layout: Beacons are alternately placed on both sides of the road at intervals D, and N beacons can be placed at each location to form a beacon array. In the simulation environments of single-sided and alternating layouts, the double-sided beacon layout is still used at turns to ensure positioning accuracy during turns.

[0068] Step S3: Collect GNSS positioning results and IMU raw data during the actual vehicle driving process, and use them to build a simulation environment to test the impact of BLE beacon deployment method on positioning accuracy.

[0069] A real-world driving test was conducted on an open road to collect 1Hz GNSS positioning results, 200Hz IMU raw data, and high-precision reference values. Based on this data, a simulation environment was built. Figure 3 As shown, the red line represents the vehicle's actual trajectory, approximately 1800m in length. To simulate GNSS signal interruption in a complex road environment, two segments were selected as GNSS denial zones (green areas in the figure, approximately 230m and 850m long), and GNSS positioning data within these zones were removed. Next, GNSS interference zones were assumed at the entrances and exits of the GNSS denial zones (blue areas in the figure, each segment approximately 30m long), and zero-mean Gaussian white noise with σ = 3m was added to the GNSS positioning data within these zones. Simultaneously, simulated BLE beacons (using the yellow dots in the figure as an example) were deployed on one or both sides of the road (20m wide) within the GNSS denial and interference zones in different ways, broadcasting Bluetooth signals at a frequency of 5Hz with an effective broadcast range of 25m. The real-time RSSI data received by the vehicle was obtained from the distance between the vehicle's actual position and the beacons. Based on the RSSI noise level analysis from the previous step, zero-mean Gaussian white noise with σ = 4dBm was added to simulate RSSI data in an indoor-like environment.

[0070] To establish the relationship between beacon spacing, number of beacons, beacon layout, and positioning accuracy, a sensitivity analysis of positioning accuracy to beacon spacing and number of beacons under different beacon layouts was conducted. The parameter range for beacon spacing D was set to [20, 60] and the simulation step size to 5m; the parameter range for the number of beacons N was set to [1, 5] and the simulation step size to 1m. Multiple sets of simulated positioning experiments were performed, and the results are as follows: Figure 6 As shown.

[0071] Step S4: Based on the designed beacon layouts of two-sided, one-sided, and alternating types (e.g., ... Figure 4 As shown in a, b, and c), a beacon deployment optimization model considering cost and positioning accuracy is modeled based on simulation experiments.

[0072] A model was created to illustrate the relationship between cost and deployment method. The cost of beacon deployment mainly consists of two parts: the beacon pole and the BLE beacon. The beacon pole is a vertically erected post on the roadside, similar to a traffic sign pole, costing approximately 1500 yuan (referencing the purchase and installation price of traffic sign poles). Mounting the beacon on the pole reduces the obstruction of the BLE signal by other objects. The BLE beacon is a Bluetooth signal transmitting module made using a BLE chip, costing approximately 50 yuan. A schematic diagram of the beacon and beacon pole installation is shown below. Figure 5 As shown.

[0073] Therefore, the deployment cost of BLE beacons is calculated using the following formula:

[0074]

[0075] In the formula, the dependent variable C represents the deployment cost, the independent variable D represents the beacon interval, and the independent variable N represents the number of beacons. The symbol indicates rounding up; the coefficient L represents the length of the road section to be laid; the coefficient B represents the number of turns, used to calculate the beacons to be added at the turns, B=0 when a double-sided layout is selected; the coefficient a is the layout coefficient, a=2 when a double-sided layout is selected, and a=1 when other layouts are selected.

[0076] Step S5: To fit the relationship model between the layout parameters and the positioning accuracy, the least squares method is used to perform polynomial surface fitting on the simulation results. The least squares method is a commonly used data fitting method, based on minimizing the sum of squared errors (SSE) between the predicted and actual values. For a given bivariate dataset, we can use a polynomial equation of the following form to construct a surface model:

[0077]

[0078] Where a ij Let represent coefficients, x and y represent independent variables, and n and m represent the polynomial degrees of x and y, respectively. These coefficients can be solved by minimizing the SSE between the actual observed value z and the model prediction. The SSE is calculated as follows:

[0079] SSE=∑[z i -f(x i ,y i )] 2 (5)

[0080] The model establishing the relationship between the average positioning error and the beacon interval and the number of beacons, using a combination of cubic and quadratic polynomial equations, is as follows:

[0081] APE = f2(D,N) = p 00 +p 10 D+p 01 N+p 20 D 2 +p 11 D·N+p 02 N 2

[0082] +p 30 D 3 +p 21 D 2 ·N+p12 D·N 2 (6)

[0083] In the formula, D represents the beacon interval, N represents the number of beacons, and p represents the coefficient. The models corresponding to the three beacon layouts are as follows: Figure 6 As shown, the coefficients are shown in Table 1. The models corresponding to the three beacon layouts are as follows: Figure 7 As shown.

[0084] Table 1 Model coefficients under different beacon layouts

[0085]

[0086]

[0087] Step S6: The formulas in the above steps can be used to construct an optimization model for beacon deployment that considers both cost and positioning accuracy.

[0088]

[0089] This model is a bi-objective optimization model. After selecting the beacon layout pattern and setting the road length and number of turns, the model is solved to obtain the Pareto optimal solution set under the given layout pattern and road conditions. This solution set contains a series of optimal solutions that balance cost and accuracy. The scheme designer can refer to this result and the actual road environment to select the most suitable beacon layout and deployment parameters.

[0090] The Pareto optimal solution sets for the three layout patterns under this road condition were obtained using a non-dominated sorting genetic algorithm. The results are as follows: Figure 8 As shown, when cost constraints are low and accuracy requirements are high, the double-sided layout is the better choice; when cost constraints are high and accuracy requirements are low, the alternating layout is the better choice. The single-sided layout is generally inferior to the other two layouts in terms of cost and accuracy, but it becomes the optimal choice when only one side of the road is suitable for beacon placement.

[0091] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

[0092] As described above, the present invention can be implemented well.

[0093] The implementation of the present invention is not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the deployment of Bluetooth beacon sensors to balance cost and detection accuracy, characterized in that... Includes the following steps: S1. Based on the analysis of the raw RSSI data collected in the real environment, fit the RSSI-distance relationship model to determine the effective coverage radius of the beacon; S2. Design different types of beacon layouts and beacon deployment parameters to adapt to different road conditions and cost requirements; S3. Establish the simulation environment required for the simulation positioning experiment. Through multiple sets of simulation positioning experiments, conduct sensitivity analysis of positioning accuracy to beacon deployment parameters under different types of beacon layouts to obtain the positioning accuracy under different beacon deployment methods. S4. Establish a model to show the relationship between cost and beacon deployment method, in order to estimate the beacon deployment cost when using different beacon deployment methods; S5. Establish a relationship model between positioning accuracy and beacon deployment method to estimate positioning accuracy when using different beacon deployment methods; S6. Establish an optimization model for beacon deployment considering cost and positioning accuracy, perform bi-objective optimization to obtain the Pareto optimal solution set, and use it to guide beacon deployment in real-world environments.

2. The Bluetooth beacon sensor deployment optimization method balancing cost and detection accuracy according to claim 1, characterized in that, In step S1, based on the analysis of the raw RSSI data collected in the real environment, the RSSI-distance relationship model is fitted to determine the effective coverage radius of the beacon. The specific steps are as follows: S101. Determine the appropriate model to fit the relationship between RSSI and distance d using a log-normal shadowing fading model that considers environmental factors: RSSI=A-10ηlg(d)+ξ (1) In the formula, A = P t -PL(d0)+ξ, where PL(d) is the path loss between the receiver and transmitter, d0 is a reference distance, typically set to 1m, η is the path loss exponent, which is affected by the surrounding environment, and ξ is a variable with a mean of zero and a standard deviation of σ. R Gaussian random variables; S102. Gaussian filtering is applied to the collected RSSI values ​​to obtain the mean value of the Gaussian filter as the accurate RSSI value at each location. The least squares method is used to obtain the parameters A and η in the above formula to fit the relationship between RSSI and distance and determine the effective coverage radius of the beacon.

3. The Bluetooth beacon sensor deployment optimization method balancing cost and detection accuracy according to claim 1, characterized in that, In step S2, different types of beacon layouts and beacon deployment parameters are designed to adapt to different road conditions and cost requirements. The specific steps are as follows: S201, Double-sided layout: Beacons are symmetrically arranged on both sides of the road at a spacing of D, and N beacons can be placed at each position to form a beacon array; S202, Single-sided layout: Beacons are placed only on one side of the road at intervals D, and N beacons can be placed at each location to form a beacon array; S203, Alternating Layout: Beacons are alternately placed on both sides of the road at intervals D; and N beacons can be placed at each location to form a beacon array; S204. In simulation environments with single-sided and alternating layouts, a double-sided beacon layout is still used at turns to ensure positioning accuracy during turns.

4. The Bluetooth beacon sensor deployment optimization method balancing cost and detection accuracy according to claim 1, characterized in that, In step S3, the simulation environment required for the simulation positioning experiment is established. Through multiple sets of simulation positioning experiments, the sensitivity analysis of positioning accuracy to beacon deployment parameters under different beacon layouts is conducted to obtain the positioning accuracy under different beacon deployment methods. The specific steps are as follows: S301. Based on the IMU and GNSS data collected from the actual vehicle, establish a simulation environment, set up simulation beacons around the actual trajectory, deduce the theoretical RSSI data through the relationship between the actual trajectory and RSSI-distance, and then add Gaussian white noise to simulate RSSI simulation data. S302. Set the parameter range [D1, D2] for the beacon spacing D and the simulation step size D. s Set the parameter range [N1, N2] for the number of beacons N and the simulation step size N. s ; S303. Conduct multiple sets of simulation positioning experiments according to the above simulation parameters to obtain the positioning accuracy under different types of beacon layouts and beacon deployment parameters.

5. The Bluetooth beacon sensor deployment optimization method balancing cost and detection accuracy according to claim 1, characterized in that, Step S4: Establish a relationship model between cost and beacon deployment method to estimate the beacon deployment cost when using different beacon deployment methods. The specific steps are as follows: S401. Investigate the actual length of the road section where beacons are deployed, the number of turns, and the specific conditions of the installation. Use beacon poles erected vertically on the side of the road to install BLE beacons. Installing the beacons on the upper part of the poles can reduce the obstruction of BLE signals by other objects. Select a suitable beacon layout method according to the actual situation. S402. Calculate the deployment cost of BLE beacons using the following formula: In the formula, the dependent variable C represents the deployment cost, the independent variable D represents the beacon interval, and the independent variable N represents the number of beacons. The symbol indicates rounding up; The coefficient L represents the length of the road section to be laid; The coefficient B represents the number of turns and is used to calculate the number of beacons added at the turns. When a double-sided layout is selected, B = 0. p is the cost of a single beacon pole and b is the cost of a single BLE beacon. The coefficient a is the layout coefficient. When a double-sided layout is selected, a = 2, and when other layouts are selected, a = 1.

6. The Bluetooth beacon sensor deployment optimization method balancing cost and detection accuracy according to claim 5, characterized in that, Step S5: Establish a relationship model between positioning accuracy and beacon deployment method to estimate positioning accuracy when using different beacon deployment methods. The specific steps are as follows: S501. Model the relationship between deployment method and positioning accuracy. Use the least squares method to fit the sensitivity analysis results to a polynomial surface. Its principle is based on minimizing the sum of squared errors (SSE) between the predicted and actual values. For a given bivariate dataset, use a polynomial equation of the following form to construct a surface model: In the formula, a ij Let represent coefficients, x and y represent independent variables, and n and m represent the polynomial degrees of x and y, respectively. These coefficients can be solved by minimizing the SSE between the actual observed value z and the model's predicted value. The SSE is calculated as follows: SSE=∑[z i -f(x i ,y i )] 2 (4) S502. Finally, a polynomial equation combining 3rd and 2nd degree equations was used to establish a model relating the average positioning error to the beacon interval and the number of beacons: APE=f2(D,N)=p 00 +p 10 D+p 01 N+p 20 D 2 +p 11 D·N+p 02 N 2 +p 30 D 3 +p 21 D 2 ·N+p 12 D·N 2 (5) In the formula, D represents the beacon interval, N represents the number of beacons, and p 00 p 10 p 01 p 20 p 11 p 02 p 30 p 21 and p 12 All represent coefficients.

7. The Bluetooth beacon sensor deployment optimization method balancing cost and detection accuracy according to claim 6, characterized in that, Step S6: Establish an optimization model for beacon deployment considering cost and positioning accuracy, perform bi-objective optimization to obtain the Pareto optimal solution set, which is used to guide beacon deployment in a real-world environment. Specific steps are as follows: S601. The relationship models obtained in steps S402 and S502 are combined to form a dual-objective optimization model for beacon deployment considering both cost and positioning accuracy, as follows: S602. Finally, the non-dominated sorting genetic algorithm is used to solve the above bi-objective optimization model, which yields the Pareto optimal solution set for the three layout modes under a given road condition.

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