Bluetooth beacon sensor layout optimization method for balancing cost and detection precision

Abstract

The invention discloses a Bluetooth beacon sensor layout optimization method for balancing cost and detection precision. The method comprises the following steps: 1) collecting RSSI data in a real environment, and carrying out analysis and parameter fitting to obtain an RSSI-distance relation model; and 2) designing a beacon layout mode, analyzing the influence of the beacon layout mode on the positioning precision, considering the cost and the positioning precision, and carrying out optimization modeling on the beacon layout mode. According to the method, a Bluetooth beacon sensor layout mode optimization model considering cost and positioning precision factors can be effectively established, guidance is provided for implementation of a positioning method based on the Bluetooth beacon sensors, and therefore the most suitable beacon layout mode can be optimized according to the conditions such as the actual environment, the cost budget and the precision requirement.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensor network node positioning, and in particular to a Bluetooth beacon sensor deployment optimization method that balances cost and detection accuracy. Background Art

[0002] With the rapid development of wireless communication technology, positioning technology based on Bluetooth Low Energy (BLE) has been widely used in the field of positioning 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 that BLE beacons be deployed in the road in advance as anchor nodes for wireless positioning. Different beacon deployment methods will have different positioning accuracy and different implementation costs. At present, there are various deployment methods for BLE beacon sensors, including double-sided layout, single-sided layout and alternating layout, and different deployment methods have different effects on positioning accuracy and cost. However, the existing deployment methods often only consider one of the factors, ignoring the balance between cost and positioning accuracy. Therefore, in practical applications, it is often difficult to select the most suitable beacon deployment method according to specific environments and needs. In order to achieve low-cost vehicle positioning, it is not feasible to simply increase the beacon deployment density. Therefore, it is necessary to design a BLE beacon deployment method that can take into account both the beacon deployment cost and positioning accuracy, while ensuring accuracy, reducing the installation cost of the equipment as much as possible, and reducing the difficulty of promoting the positioning method. However, there is currently no research on optimizing the BLE beacon deployment method that comprehensively considers positioning accuracy and cost. Summary of the invention

[0004] This paper mainly aims at the problem that different BLE beacon deployment methods will affect the frequency, dimension and other attributes of RSSI observation information during the continuous positioning of GNSS / BLE / INS vehicle combination, thereby affecting the positioning accuracy. In the selection of beacon deployment methods, both cost and positioning accuracy are considered, and a Bluetooth beacon sensor deployment optimization method that balances cost and detection accuracy is proposed.

[0005] The present invention considers both cost and positioning accuracy in the selection of beacon deployment methods to guide the practical application of the positioning method, so that the most suitable beacon deployment method can be selected according to the actual environment, cost budget, accuracy requirements and other conditions.

[0006] The present invention is achieved through the following technical solutions:

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

[0008] S1. According to the analysis of the RSSI raw data collected in the real environment, the RSSI-distance relationship model is fitted 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 a simulation environment required for simulation positioning experiments, conduct sensitivity analysis of positioning accuracy under different beacon layouts to beacon deployment parameters through multiple groups of positioning simulation experiments, and obtain positioning accuracy under different beacon deployment methods;

[0011] S4. Establish a relationship model between cost and deployment mode to estimate the beacon deployment cost when using different beacon deployment modes;

[0012] S5. Establish a relationship model between positioning accuracy and deployment mode, so as to estimate the positioning accuracy when using different beacon deployment modes;

[0013] S6. Establish a beacon deployment optimization model that takes into account cost and positioning accuracy, perform dual-objective optimization, and obtain a Pareto optimal solution set to guide beacon deployment in actual environments.

[0014] In the above step S1, based on the analysis of the RSSI raw 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 the relationship between RSSI and distance using a log-normal shadow fading model that takes environmental factors into account:

[0016] RSSI=A-10ηlg(d)+ξ (1)

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

[0018] S102. Perform Gaussian filtering on the collected multiple groups of RSSI values ​​to obtain the Gaussian filtering mean (the RSSI average value after eliminating RSSI values ​​that are one standard deviation away from the mean) as the accurate RSSI value at each location, and use the least squares method 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 the above step S2, 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 according to a spacing D (unit: m), and N beacons can be placed at each position to form a beacon array;

[0021] S202, single-side layout: beacons are arranged at a spacing D on only one side of the road, and N beacons can be placed at each position to form a beacon array;

[0022] S203, alternating layout: beacons are arranged alternately on both sides of the road at a spacing D; and N beacons can be placed at each position to form a beacon array;

[0023] S204. In the single-sided and alternating layout simulation environments, the double-sided beacon layout is still used at the turning point to ensure the positioning accuracy during the turning.

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

[0025] S301, establishing a simulation environment based on the IMU and GNSS data collected by the real vehicle, setting up simulated beacons around the real track, inferring RSSI theoretical data through the real track and RSSI-distance relationship, and then adding Gaussian white noise to simulate RSSI simulation data;

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

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

[0028] The above step S4 is to establish a relationship model between the cost and the deployment mode, which is used to estimate the beacon deployment cost when using different beacon deployment modes. The specific steps are as follows:

[0029] S401. Investigate the actual length of the beacon layout section, the number of turns, installation conditions and other specific conditions, use the beacon poles erected vertically on the roadside to install the BLE beacons. Installing the beacons on the top of the poles can reduce the obstruction of the BLE signals by other objects. Select the appropriate beacon layout method according to the actual situation.

[0030] S402: Calculate the deployment cost of the BLE beacon 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 indicates the length of the road section to be laid; the coefficient B indicates the number of turns and is used to calculate the beacons to be added at the turns. When the 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, a=2 when the double-sided layout is selected, and a=1 when other layouts are selected.

[0033] The above step S5 is to establish a relationship model between positioning accuracy and deployment mode, which is used to estimate the positioning accuracy when using different beacon deployment modes. The specific steps are as follows:

[0034] S501. Model the relationship between the layout mode and the positioning accuracy, and use the least squares method to perform polynomial surface fitting on the sensitivity analysis results. Its principle is based on minimizing the sum of square errors (SSE) between the predicted value and the actual value. For a given two-independent variable data set, a polynomial equation of the following form is used to construct a surface model:

[0035]

[0036] In the formula, a ij represents the coefficient, x and y represent the independent variables, n and m represent the polynomial degrees on x and y respectively; these coefficients can be solved by minimizing the SSE between the actual observation value z and the model prediction value. The calculation of SSE is shown in the following formula:

[0037] SSE=∑[z i -f(x i ,y i )] 2 (4)

[0038] S502, finally using a polynomial equation of 3rd and 2nd order combination to establish a relationship model between the average positioning error and 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] Where D is the beacon interval, N is the number of beacons, and p is the coefficient.

[0042] In the above step S6, a beacon deployment optimization model considering cost and positioning accuracy is established, and a dual-objective optimization solution is performed to obtain a Pareto optimal solution set, which is used to guide beacon deployment in an actual environment. The specific steps are as follows:

[0043] S601, the relationship models obtained in steps S402 and S502 are combined into a dual-objective optimization model of beacon deployment considering cost and positioning accuracy as follows:

[0044]

[0045] S602. Finally, a non-dominated sorting genetic algorithm is used to solve the above dual-objective optimization model, and a Pareto optimal solution set of three layout modes under a given road condition can be obtained.

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

[0047] The present invention obtains a more accurate RSSI-distance relationship model by collecting RSSI data in a real environment and conducting in-depth analysis and parameter fitting. This model can more truly reflect the relationship between signal strength and distance, thereby providing a more reliable basis for subsequent positioning and sensor deployment. Three types of beacon layouts are designed to provide references for the deployment of beacons in practical applications. In view of the problem that the relationship between the two factors of cost and positioning accuracy cannot be well balanced during the beacon deployment process, a relationship model between cost, deployment method and positioning accuracy is established to guide the practical application of the positioning method, so that the most suitable beacon deployment method can be selected according to the actual environment, cost budget, accuracy requirements and other conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the principle flow of the Bluetooth beacon sensor deployment optimization method for balancing cost and detection accuracy of the present invention.

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

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

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

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

[0053] Figure 6 It is a sensitivity analysis diagram of positioning accuracy to beacon interval and beacon number under different beacon layouts in the examples of the present invention, including a) bilateral type, b) unilateral type, c) alternating type, and c) alternating type.

[0054] Figure 7 Schematic diagram of the relationship between the deployment parameters and positioning accuracy of different beacon layouts in the examples of the present invention, where a) is a double-sided type, b) is a single-sided type,

[0055] Figure 8 It is the Pareto optimal solution under the three layout modes in the example of the present invention. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work 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 that balances cost and detection accuracy includes the following steps:

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

[0059] The beacon was raised at the experimental site, and the Bluetooth receiver was gradually moved from 1m away from the beacon to 20m away from the beacon, 1m at a time, and 200 RSSI values ​​were collected at each position, for a total of 20 groups. According to statistics, the average standard deviation of the 20 groups of RSSI values ​​was 3.72, which can reflect the RSSI noise level in a similar indoor environment;

[0060] Gaussian filtering is performed on 20 groups of RSSI values ​​to obtain the Gaussian filtering mean (the RSSI average value after removing RSSI values ​​that are one standard deviation away from the mean) as the accurate RSSI value at each location, and finally the least squares method is used to obtain the parameters A and η. Among them, A = -41.59, η = 1.57, then the logarithmic normal shadow fading model of the BLE beacon in this environment can be expressed as follows:

[0061] RSSI=-41.59-10×1.57×lg(d) (1)

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

[0063]

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

[0065] It can be seen that RSSI gradually decays as the distance increases, but the decay speed changes from fast to slow. For RSSI ranging, the more obvious the attenuation change, the more beneficial it is to the ranging accuracy. Therefore, as the distance increases, the use value of RSSI also changes from optimal to suboptimal and then to unusable. Figure 2 , the effective coverage radius of the beacon is limited to 25m, and it is considered that the RSSI of the BLE beacon within 25m of the vehicle is of reference value. When solving the positioning algorithm, the RSSI value of the beacon outside 25m can be excluded by calculating the distance between the filter estimated position and the beacon position, thereby 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 follows:

[0067] 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; Single-sided layout: beacons are arranged only on one side of the road at a spacing of D, and N beacons can be placed at each position to form a beacon array; Alternating layout: beacons are alternately 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. In the simulation environment of single-sided and alternating layouts, double-sided beacon layout is still used at the turning point to ensure positioning accuracy during turning.

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

[0069] Carry out real-world driving on an open road, collect 1Hz GNSS positioning results, 200Hz IMU raw data and high-precision reference true values. Based on the above data, build a simulation environment. Figure 3 As shown in the figure, the red color represents the real trajectory of the vehicle, with a total length of about 1800m. In order to simulate the interruption of GNSS signals in a complex road environment, two sections are selected as GNSS denial areas (the green part in the figure, about 230m and 850m long) in the vehicle path, and the GNSS positioning data therein are deleted. Then, a GNSS interference area is assumed at the entrance and exit of the GNSS denial area (the blue part in the figure, each section is about 30m long), and Gaussian white noise with zero mean, σ=3m is added to the GNSS positioning data therein. At the same time, simulated BLE beacons (taking the yellow dots in the figure as an example) are deployed on one or both sides of the road (road width 20m) in different ways in the GNSS denial and interference areas, broadcasting Bluetooth signals at a frequency of 5Hz, with an effective broadcast range of 25m. The RSSI data received by the vehicle in real time can be obtained from the distance between the real position of the vehicle and the beacon, and according to the RSSI noise level analysis in the previous step, Gaussian white noise with zero mean, σ=4dBm is added to it to simulate the RSSI simulation data in a similar indoor environment.

[0070] In order to establish the relationship between the three variables of beacon interval, number of beacons, and beacon layout and positioning accuracy, the sensitivity analysis of positioning accuracy to beacon interval and number of beacons under different beacon layouts was carried out. The parameter range of beacon interval D was set to [20, 60] and the simulation step was 5m. The parameter range of beacon number N was set to [1, 5] and the simulation step was 1. Multiple groups of simulation positioning experiments were carried out. The results are shown in Figure 2. Figure 6 shown.

[0071] Step S4: According to the designed double-sided, single-sided and alternating beacon layouts (such as Figure 4 (a, b and c in the figure), based on the simulation experiment, a beacon deployment optimization model considering cost and positioning accuracy is built.

[0072] Model the relationship between cost and deployment method. The cost of beacon deployment mainly consists of two parts: beacon poles and BLE beacons. Beacon poles are columnar objects erected vertically on the side of the road, similar to traffic sign poles, with a cost of about 1,500 yuan (refer to the purchase and installation price of traffic sign poles). Installing beacons on poles can reduce the obstruction of BLE signals by other objects; BLE beacons are Bluetooth signal transmission modules made of BLE chips, with a cost of about 50 yuan. The installation diagram of beacons and beacon poles is shown in the figure below. Figure 5 shown.

[0073] Therefore, the deployment cost of BLE beacons is calculated as follows:

[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 indicates the length of the road section to be laid; the coefficient B indicates the number of turns and is used to calculate the beacons to be added at the turns. When the double-sided layout is selected, B=0; the coefficient a is the layout coefficient. When the double-sided layout is selected, a=2, and when other layouts are selected, a=1.

[0076] Step S5: In order 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, and its principle is based on minimizing the sum of square errors (SSE) between the predicted value and the actual value. For a given two-independent variable data set, we can use the following form of polynomial equation to construct a surface model:

[0077]

[0078] where a ij represents the coefficients, x and y represent the independent variables, and n and m represent the polynomial degrees on x and y respectively. These coefficients can be solved by minimizing the SSE between the actual observation z and the model prediction. The calculation of SSE is shown in the following formula:

[0079] SSE=∑[z i -f(x i ,y i )] 2 (5)

[0080] The relationship model between the average positioning error, beacon interval and beacon number established by using a polynomial equation of cubic and quadratic combination is:

[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] Where 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 The coefficients are shown in Table 1. The models corresponding to the three beacon layouts are as follows Figure 7 shown.

[0084] Table 1 Model coefficients under different beacon layouts

[0085]

[0086]

[0087] Step S6: The formulas in the above steps can form a beacon deployment optimization model that takes into account cost and positioning accuracy:

[0088]

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

[0090] The non-dominated sorting genetic algorithm is used to solve the Pareto optimal solution set of the three layout modes under this road condition. The results are as follows: Figure 8 As shown. It can be seen that when the cost limit is small and the accuracy requirement is high, the double-sided layout is a better choice; when the cost limit is large and the accuracy requirement is low, the alternating layout is a better choice. The cost and accuracy of the unilateral layout are generally not as good as the other two layouts, but when only one side of the road is suitable for laying beacons, the unilateral layout becomes the best choice.

[0091] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made based on the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can better understand and use the present invention. The present invention is limited only by the claims and their full scope and equivalents.

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

[0093] The implementation methods of the present invention are not limited to the above-mentioned embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principles of the present invention shall be equivalent replacement methods and shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing the deployment of Bluetooth beacon sensors that balances cost and detection accuracy, characterized in that The steps include: S1. According to the analysis of the RSSI raw data collected in the real environment, the RSSI-distance relationship model is fitted 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 a simulation environment required for simulation positioning experiments, conduct sensitivity analysis of positioning accuracy under different beacon layouts to beacon deployment parameters through multiple groups of positioning simulation experiments, and obtain positioning accuracy under different beacon deployment methods; S4. Establish a relationship model between cost and deployment mode to estimate the beacon deployment cost when using different beacon deployment modes; S5. Establish a relationship model between positioning accuracy and deployment mode, so as to estimate the positioning accuracy when using different beacon deployment modes; S6. Establish a beacon deployment optimization model that takes into account cost and positioning accuracy, perform dual-objective optimization, and obtain a Pareto optimal solution set to guide beacon deployment in actual environments.

2. The method for optimizing the deployment of Bluetooth beacon sensors with balanced cost and detection accuracy according to claim 1, characterized in that: In step S1, based on the analysis of the RSSI raw 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 relationship between RSSI and distance using a log-normal shadow fading model that takes environmental factors into account: RSSI=A-10ηlg(d)+ξ (1) Where A=P t -PL(d0)+ξ, PL(d) is the path loss between the receiver and the transmitter, d0 is a reference distance, generally set to 1m, η is the path loss exponent, which is affected by the surrounding environment, ξ is zero mean, and the standard deviation is σ R A Gaussian random variable of S102, Gaussian filtering is performed on the collected multiple groups of RSSI values ​​to obtain the Gaussian filtering mean as the accurate RSSI value at each position, and 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 method for optimizing the deployment of Bluetooth beacon sensors with balanced cost and detection accuracy according to claim 1, characterized in that: In step S2, different types of beacon layouts 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 according to a spacing D, and N beacons can be placed at each position to form a beacon array; S202, single-side layout: beacons are arranged at a spacing D on only one side of the road, and N beacons can be placed at each position to form a beacon array; S203, alternating layout: beacons are arranged alternately on both sides of the road at a spacing D; and N beacons can be placed at each position to form a beacon array; S204. In the single-sided and alternating layout simulation environments, the double-sided beacon layout is still used at the turning point to ensure the positioning accuracy during the turning.

4. The method for optimizing the deployment of Bluetooth beacon sensors with balanced cost and detection accuracy according to claim 1, characterized in that: In step S3, a simulation environment required for the simulation positioning experiment is established, and sensitivity analysis of positioning accuracy under different beacon layouts to beacon deployment parameters is performed through multiple groups of positioning simulation experiments to obtain positioning accuracy under different beacon deployment methods. The specific steps are as follows: S301, establishing a simulation environment based on the IMU and GNSS data collected by the real vehicle, setting up simulated beacons around the real track, inferring RSSI theoretical data through the real track and RSSI-distance relationship, and then adding Gaussian white noise to simulate RSSI simulation data; S302, setting the parameter range [D1, D2] of the beacon spacing D and the simulation step length D s ; Set the parameter range [N1, N2] of the number of beacons N and the simulation step size N s ; S303, performing multiple groups of simulation positioning experiments according to the above simulation parameters to obtain positioning accuracy under different beacon layouts and beacon deployment parameters.

5. The method for optimizing the deployment of Bluetooth beacon sensors with balanced cost and detection accuracy according to claim 1, characterized in that: Step S4, establishing a relationship model between cost and deployment mode, for estimating the beacon deployment cost when using different beacon deployment modes, the specific steps are as follows: S401. Investigate the actual beacon layout section length, number of turns, installation conditions and other specific conditions, use beacon poles erected vertically on the roadside to install BLE beacons. Installing beacons on the upper part of the poles can reduce the obstruction of BLE signals by other objects, and select a suitable beacon layout method according to the actual situation. S402: Calculate the deployment cost of the BLE beacon 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 beacons added at the turns. When the 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 the double-sided layout is selected, a=2, and when other layouts are selected, a=1.

6. The method for optimizing the deployment of Bluetooth beacon sensors with balanced cost and detection accuracy according to claim 1, characterized in that: Step S5, establishing a relationship model between positioning accuracy and deployment mode, for estimating the positioning accuracy when using different beacon deployment modes, the specific steps are as follows: S501. Model the relationship between the layout mode and the positioning accuracy, and use the least square method to perform polynomial surface fitting on the sensitivity analysis results. Its principle is based on minimizing the sum of square errors (SSE) between the predicted value and the actual value. For a given two-independent variable data set, a polynomial equation of the following form is used to construct a surface model: In the formula, a ij represents the coefficient, x and y represent the independent variables, n and m represent the polynomial degrees on x and y respectively; these coefficients can be solved by minimizing the SSE between the actual observation value z and the model prediction value; the calculation of SSE is shown in the following formula: SSE=∑[z i -f(x i ,y i )] 2 (4) S502, finally using a polynomial equation of 3rd and 2nd order combination to establish a relationship model between the average positioning error and 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) Where D is the beacon interval, N is the number of beacons, and p is the coefficient.

7. The method for optimizing the deployment of Bluetooth beacon sensors with balanced cost and detection accuracy according to claim 1, characterized in that: Step S6, establish a beacon deployment optimization model that takes into account cost and positioning accuracy, perform dual-objective optimization, and obtain a Pareto optimal solution set to guide beacon deployment in an actual environment. The specific steps are as follows: S601, the relationship models obtained in steps S402 and S502 are combined into a dual-objective optimization model of beacon deployment considering cost and positioning accuracy as follows: S602. Finally, a non-dominated sorting genetic algorithm is used to solve the above dual-objective optimization model, and a Pareto optimal solution set of three layout modes under a given road condition can be obtained.

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