Behavior trajectory location acquisition system based on intelligent communication and its application in subway station halls

Abstract

The present invention discloses a method for acquiring the position of a trajectory of the object to be measured based on intelligent communication. The method comprises the following steps: receiving signals of n Bluetooth beacons through a Bluetooth intelligent communication tool, and obtaining the Bluetooth position of an object to be measured based on signal strength and Bluetooth spatial position information; collecting data of an accelerometer, a geomagnetic sensor, and a gyroscope sensor at fixed time intervals, and using the angular velocity output by the gyroscope to predict the horizontal and vertical orientation angles of the trajectory of the object to be measured; performing Fourier transform on the acceleration data and the angular velocity data to obtain the step frequency of the object to be measured; calculating the step length of the object to be measured based on the acceleration value; and finally, fusing these data based on the Bluetooth position and sensor information using an extended Kalman filter to obtain the position of the object to be measured, thereby providing extended services for the object to be measured and accurately completing the acquisition and positioning of the trajectory position in complex environments at low cost and high efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent technology of rail transportation subways, and specifically relates to a movement trajectory position acquisition system based on intelligent communication and its application in subway station halls. Background Art

[0002] In recent years, subways, as a vital component of urban public transportation, have demonstrated numerous advantages, including high capacity, high speed, high efficiency, and low energy consumption. As more and more passengers choose the subway as their preferred mode of transportation, the demand for underground navigation is growing. To build smart subways, address passengers' practical needs for indoor positioning, and provide personalized user services and location-based services, accurate indoor movement trajectory acquisition and positioning are required.

[0003] Outdoor positioning technology based on the global navigation satellite system is becoming increasingly mature and has broad application prospects in multiple fields. However, in relatively complex indoor environments such as subway stations, indoor positioning cannot be achieved due to signal shielding and other reasons.

[0004] There are many common indoor positioning technologies, but each has its own drawbacks. For example, infrared technology has good positioning effects, but the equipment is expensive and easily affected by the environment. Radio frequency technology (RFID) requires a large number of devices to be deployed in the positioning space. WiFi is a commonly used method for indoor positioning, but each access point requires independent power supply.

[0005] Based on the above issues, combined with the spatial environment of urban subway station halls and the intended user groups, and considering the required positioning accuracy, cost-effectiveness, and engineering effort, the use of Bluetooth low-power positioning technology is more suitable for the goals of indoor positioning and navigation in urban subways. Bluetooth positioning technology is currently widely used in mobile terminals. It uses passive Bluetooth low-power beacons, which offer low deployment costs and high efficiency, making it suitable for various underground environments. Furthermore, considering that Bluetooth positioning alone is susceptible to interference from environmental factors, an inertial navigation positioning method, consisting of sensors such as accelerometers and gyroscopes from smart communication tools, is employed to improve accuracy. This method uses an extended Kalman filter to fuse Bluetooth and sensor information to determine passenger location. This position is then corrected based on the subway station hall environment and the layout of the station facilities. This enables the acquisition and positioning of movement trajectory positions in complex environments, offering simple and convenient operation and low cost, better meeting practical needs. Summary of the Invention

[0006] The present invention is aimed at the problems in the prior art and provides a method for obtaining the position of a trajectory of the object to be measured based on intelligent communication. Through a Bluetooth intelligent communication tool, the signals of n Bluetooth beacons are received, and the Bluetooth position of the object to be measured is obtained based on the signal strength and Bluetooth spatial position information; then, at fixed time intervals, the data of the accelerometer, geomagnetic sensor and gyroscope sensor are collected, and the angular velocity output by the gyroscope is used to predict the horizontal and vertical orientation angles of the trajectory of the object to be measured; Fourier transform is performed on the acceleration data and the angular velocity data to obtain the step frequency of the object to be measured; according to the value of acceleration, the step length of the object to be measured is calculated; finally, according to the Bluetooth position and sensor information, the extended Kalman filter is used to fuse these data to obtain the position of the object to be measured, providing extended services for the object to be measured, and accurately completing the trajectory position acquisition and positioning in complex environments at a low cost and high efficiency.

[0007] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a method for obtaining a trajectory position based on intelligent communication, comprising the following steps:

[0008] S1, through the Bluetooth smart communication tool, receives the signals of n Bluetooth beacons, and obtains the Bluetooth position of the object to be measured based on the signal strength and Bluetooth spatial position information ;

[0009] S2, at a fixed time interval, collects data from the accelerometer, geomagnetic sensor and gyroscope sensor, and uses the angular velocity output by the gyroscope to realize the horizontal and vertical orientation angle of the trajectory of the object to be measured. Prediction; perform Fourier transform on the acceleration data and angular velocity data to obtain the step frequency of the measured object ; According to the value of acceleration, the step length of the object to be measured is realized l Calculation of

[0010] S3, based on the Bluetooth location and sensor information, the extended Kalman filter is used to fuse these data to obtain the position of the object to be measured , the steps further comprising:

[0011] S31, model the position of the object to be measured, and its state matrix x k for:

[0012]

[0013] in, Indicates the position coordinates of the object to be measured after k steps; Indicates the direction of the object after k steps; represents the step length of the kth step; Indicates the change in the horizontal direction angle of the object when it moves the kth step; Indicates the change in the vertical direction angle of the object when it moves through the kth step; is the system process noise variable;

[0014] The measurement equation is:

[0015]

[0016] in, Indicates the spatial coordinates of the object to be measured obtained through the Bluetooth smart communication tool; Indicates the step length of the object to be measured estimated by the sensor; Indicates the orientation angle data obtained from the sensor; Indicates the change in the heading angle; Represents the system observation noise variable, and sets the system state equation and measurement equation as:

[0017]

[0018]

[0019] S32, the linearization of the nominal trajectory equation, mainly consists of two parts:

[0020] The nonlinear function in the state equation exist Perform a first-order Taylor series expansion in the neighborhood of to obtain the linearized state equation:

[0021]

[0022] in, for Time has come The state transition matrix at time .

[0023]

[0024]

[0025] Substituting the state equation into the state transfer matrix:

[0026]

[0027] Similarly, for the nonlinear function in the observation equation exist Perform a first-order Taylor series expansion in the neighborhood of to obtain the linearized measurement equation:

[0028]

[0029] in, for The measurement matrix at the moment:

[0030]

[0031]

[0032] Substituting the measurement equation into the equation yields the measurement matrix:

[0033]

[0034] S33, optimal estimation of the state is performed according to the classical Kalman filter method:

[0035] The first thing to do for optimal state estimation is to predict the state one step ahead:

[0036]

[0037] in, for The state value of the object under test at the moment, its initial value is , It will Substituting into the linearized state equation we get The predicted value of the state of the object under test at the moment, that is, the coordinate information of the passenger;

[0038] Next, we need to calculate several intermediate values:

[0039]

[0040]

[0041]

[0042] in, is the predicted value of mean square error, is the optimal estimate of the mean square error, and the initial value is , is the Kalman filter gain, is the system process noise The covariance matrix of represents the system observation noise The covariance matrix of Represents the identity matrix.

[0043] Finally, based on The state value of the object under test at the moment and Kalman filter gain The optimal state estimate can be obtained:

[0044]

[0045] As an improvement of this case, step S4 is also included to filter the position after the Kalman filter in step S3. Compare with the feasible path area, if the location If it is in an inaccessible area, its position will be corrected to the distance within the feasible area. The nearest point .

[0046] As an improvement to this case, step S1 further includes:

[0047] S11, receiving signals of n Bluetooth beacons through the Bluetooth smart communication tool, the strength of which is ;

[0048] S12, select the four strongest Bluetooth signals from n

[0049] S13, according to the attenuation model of the Bluetooth signal, use the signal strength value A and the path loss factor n to calculate the distance corresponding to the four signal strengths respectively , the calculation method is:

[0050]

[0051] S14, according to the MAC addresses of the four Bluetooth beacons, obtain the spatial positions of the corresponding four beacons ;

[0052] S15, divide the four Bluetooth beacons into a group of three, forming a total of 4 groups. The positions and distances of the three Bluetooth beacons in each group determine the position of the object to be measured, and obtain four spatial position coordinates. ;

[0053] S16, weight the coordinates according to the four distances to obtain the Bluetooth position of the object to be measured , the weighting method is:

[0054] .

[0055] As another improvement of this case, step S2 further includes:

[0056] S21, collecting raw data from the accelerometer, geomagnetic sensor, and gyroscope sensor at fixed time intervals through an intelligent communication tool;

[0057] S22, filtering the raw data collected in step S21;

[0058] S23, using the angular velocity output by the gyroscope, calculates the posture of the intelligent communication tool and converts its coordinate system into a real-world coordinate system;

[0059] S24, using the angular velocity output by the gyroscope to realize the horizontal and vertical orientation angles of the object to be measured when it moves prediction;

[0060] S25, performing Fourier transform on the acceleration data and the angular velocity data to obtain the passenger's walking frequency f;

[0061] S26: Estimating the passenger's step length l based on the acceleration value.

[0062] In order to achieve the above-mentioned purpose, the present invention also adopts a technical solution: a system for acquiring a trajectory position based on intelligent communication, comprising:

[0063] A Bluetooth beacon module, which is used to send and receive Bluetooth beacon signals and provide the Bluetooth location of the object to be measured;

[0064] A user intelligent communication module, which includes at least an accelerometer, a geomagnetic sensor, and a gyroscope sensor, and analyzes and calculates the movement information of the object to be measured;

[0065] The server module uses an extended Kalman filter to fuse the data based on the Bluetooth location provided by the Bluetooth beacon module and the sensor information learned by the user intelligent communication module to obtain the location of the object to be measured and provide extended services.

[0066] In order to achieve the above-mentioned purpose, the present invention also adopts a technical solution: the application of the intelligent communication-based movement trajectory location acquisition system in the subway station hall includes the following steps:

[0067] S1, deploy passive Bluetooth beacon modules in the subway station hall;

[0068] S2, establish a three-dimensional spatial coordinate system in the subway station, and build a Bluetooth beacon database and a subway station hall layout database based on the location of the Bluetooth beacon module and the layout of the station hall passages and equipment, and store them in the server module;

[0069] S3: When a passenger enters a subway station with a smart communication tool, the smart communication tool automatically communicates with the Bluetooth beacon installed in the station hall to obtain the signal strength and the passenger's Bluetooth location. ;

[0070] S4, the intelligent communication tool obtains data from at least the acceleration, geomagnetic, and gyroscope sensors at fixed time intervals, and analyzes and calculates the direction of the passenger's movement , cadence and step length information;

[0071] S5, combining the Bluetooth location and sensor information, using the extended Kalman filter to fuse the data obtained in step S4 to obtain the passenger's location ;

[0072] S6, combining the subway station hall layout data stored by the server in step S2, correcting the passenger position to obtain the final passenger position ;

[0073] S7, the server module provides personalized services to passengers based on their locations.

[0074] As a further improvement of the present invention, the Bluetooth beacon database in step S2 includes at least the MAC address of the Bluetooth beacon, the spatial coordinates of the Bluetooth beacon, the station hall area and floor data where the Bluetooth beacon is located; the established subway station hall layout database includes at least the starting point, end point, direction, width of the feasible range, the scope of the station hall area and the position of the railings.

[0075] Compared with the existing technology, the present invention proposes a motion trajectory position acquisition system based on intelligent communication and its application in subway station halls. When traditional outdoor positioning technology cannot be used in complex indoor environments, this case proposes a motion trajectory position acquisition system and method based on intelligent communication in subway station halls. Bluetooth beacons are arranged in the subway station hall, and the Bluetooth signals are scanned by passengers' intelligent communication tools to calculate the rough position; the passenger's current step frequency, step length, walking direction, etc. are obtained by using acceleration sensors, geomagnetic sensors, etc.; the Bluetooth information and sensor information are integrated through the extended Kalman filter technology to improve the positioning accuracy and calculate the passenger's position coordinates; the passenger's position coordinates are corrected in combination with the layout of the station hall equipment to achieve precise positioning in complex indoor environments, meet the navigation needs of subway passengers in complex indoor environments, and meet the needs of subway operators to provide personalized public services to the outside world. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a schematic diagram of a location acquisition scheme for the application of the intelligent communication-based movement trajectory location acquisition system in a subway station hall;

[0077] Figure 2 Schematic diagram of Bluetooth three-point positioning in step S15 of the method for obtaining a movement trajectory position based on intelligent communication of the present invention. DETAILED DESCRIPTION

[0078] The present invention will be described in more detail below with reference to the accompanying drawings and embodiments.

[0079] Example 1

[0080] The application of the trajectory location acquisition system based on intelligent communication in the subway station hall, the solution for the object to be measured to obtain the location information in the subway is as follows Figure 1 As shown, the specific steps include:

[0081] S1. Deploy passive Bluetooth beacon modules in the station hall. The beacon spacing in general areas is controlled at 5-8m. Ensure that the distance between each point in the exhibition hall and at least three beacons is no more than 8m. The beacon spacing in special areas, such as near automatic ticket gates, can be reduced to about 2-3m.

[0082] S2, establish a three-dimensional spatial coordinate system in the station, and establish a Bluetooth beacon database and a station hall layout database based on the location of the Bluetooth beacon module and the layout of the station hall passages, equipment, etc., and store them in the server module; the Bluetooth beacon database includes: the MAC address of the Bluetooth beacon, the spatial coordinates of the Bluetooth beacon, the station hall area and floor where the Bluetooth beacon is located, etc.; the station hall layout database includes the starting point, end point, direction, width of the passage, the scope of the station hall area, the location of the railings, etc.

[0083] S3: When a passenger enters the station with a smartphone, the app automatically communicates with the Bluetooth beacon installed in the station hall to obtain the signal strength and the passenger's Bluetooth location. ;

[0084] S31, the mobile phone receives signals from n Bluetooth beacons in the station hall, with a strength of ;

[0085] S32, select the four strongest Bluetooth signals from n Bluetooth signals ;

[0086] S33, according to the attenuation model of the Bluetooth signal, using the received signal strength value when the reference distance is 1 meter and path loss factor Calculate the distances corresponding to the four signal strengths respectively , the calculation method is:

[0087]

[0088] S34, according to the MAC addresses of the four Bluetooth beacons, obtain the spatial positions of the corresponding four beacons ;

[0089] S35, divide the four Bluetooth beacons into groups of three, forming a total of 4 groups. The positions and distances of the three Bluetooth beacons in each group can determine the position of a passenger, so that a total of four spatial position coordinates can be calculated. ,like Figure 2 As shown;

[0090] S36, weight the coordinates according to the four distances to obtain the passenger's Bluetooth location , the weighting method is:

[0091]

[0092] S4: When a passenger enters the station with a smartphone, the app collects data from sensors such as acceleration, geomagnetism, and gyroscope at fixed time intervals, and analyzes and calculates the passenger's walking direction. , cadence , step length and other information;

[0093] S41, the mobile phone app regularly collects data from the accelerometer, geomagnetic sensor, and gyroscope sensor;

[0094] S42, filtering the collected raw sensor data;

[0095] S43, using the angular velocity output by the gyroscope, calculates the posture of the smartphone and converts the coordinate system of the smartphone into a real-world coordinate system;

[0096] S44, using the angular velocity output by the gyroscope to realize the horizontal and vertical orientation angle of the passenger estimates;

[0097] S45, perform Fourier transform on the acceleration and angular velocity data to obtain the passenger's walking frequency ;

[0098] S46, under the pre-set pedestrian movement speed level regulations, select the speed level according to the acceleration value, and then realize the passenger step length Estimates.

[0099] S5 combines Bluetooth location and sensor information and uses an extended Kalman filter to fuse these data to obtain the passenger's location ;

[0100] S51, model the system, state matrix:

[0101]

[0102] in, Indicates that the passenger has left Position coordinates after step, Indicates that the passenger has left The direction after the step, Indicates the Step length, Indicates that the passenger has left The change in the horizontal direction angle when taking a step, Indicates that the passenger has left The change in the vertical direction angle during the step, is the system process noise variable;

[0103] The measurement equation is:

[0104]

[0105] in Represents the spatial coordinates of the passenger obtained through Bluetooth positioning, represents the passenger’s step length estimated by the sensor, It is also the estimated heading angle from the sensor data, Then the change in the direction angle is, is the system observation noise variable. Assume that the state equation and measurement equation of the system are:

[0106]

[0107]

[0108] S52, the linearization of the nominal trajectory equation, mainly consists of two parts:

[0109] The nonlinear function in the state equation exist Perform a first-order Taylor series expansion in the neighborhood of to obtain the linearized state equation:

[0110]

[0111] in, for Time has come The state transition matrix at time .

[0112]

[0113]

[0114] Substituting the state equation into the state transfer matrix:

[0115]

[0116] Similarly, for the nonlinear function in the observation equation exist Perform a first-order Taylor series expansion in the neighborhood of to obtain the linearized measurement equation:

[0117]

[0118] in, for The measurement matrix at the moment:

[0119]

[0120]

[0121] Substituting the measurement equation into the equation yields the measurement matrix:

[0122]

[0123] S53, optimal estimation of the state is performed according to the classical Kalman filter method:

[0124] The first thing to do for optimal state estimation is to predict the state one step ahead:

[0125]

[0126] in, for The state value of the object under test at the moment, its initial value is , It will Substituting into the linearized state equation we get The predicted value of the state of the object to be measured at the moment, that is, the coordinate information of the passenger;

[0127] Next, we need to calculate several intermediate values:

[0128]

[0129]

[0130]

[0131] in, is the predicted value of mean square error, is the optimal estimate of the mean square error, and the initial value is , is the Kalman filter gain, is the system process noise The covariance matrix of represents the system observation noise The covariance matrix of Represents the identity matrix.

[0132] Finally, based on The state value of the object under test at the moment and Kalman filter gain The optimal state estimate can be obtained:

[0133]

[0134] S54, return to S52, and continue the calculation at the next moment.

[0135] S6, combined with the station hall layout information stored in the server, the passenger position obtained in S5 Compare with the walkable area in the station hall. If the location is found to be in an inaccessible area (such as passing through a wall to the other side of the wall), the location will be corrected to the distance within the accessible area. The nearest point .

[0136] S7, the server module provides personalized services to passengers based on their location, such as navigation within complex station halls, the arrival time of the next train, and the congestion situation of each carriage.

[0137] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above examples. The above examples and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications are possible without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for obtaining a trajectory position based on intelligent communication, characterized in that: The steps include: S1, through the Bluetooth smart communication tool, receives the signals of n Bluetooth beacons, and obtains the Bluetooth position of the object to be measured based on the signal strength and Bluetooth spatial position information ; S2, at a fixed time interval, collects data from the accelerometer, geomagnetic sensor and gyroscope sensor, and uses the angular velocity output by the gyroscope to realize the horizontal and vertical orientation angle of the trajectory of the object to be measured. Prediction; perform Fourier transform on the acceleration data and angular velocity data to obtain the step frequency of the measured object ; According to the value of acceleration, the step length of the object to be measured is realized l Calculation of S3, based on the Bluetooth location and sensor information, the extended Kalman filter is used to fuse these data to obtain the position of the object to be measured , the steps further comprising: S31, model the position of the object to be measured, and its state matrix x k for: in, Indicates the position coordinates of the object to be measured after k steps; Indicates the direction of the object after k steps; represents the step length of the kth step; Indicates the change in the horizontal direction angle of the object when it moves the kth step; Indicates the change in the vertical direction angle of the object when it moves through the kth step; is the system process noise variable; The measurement equation is: in, Indicates the spatial coordinates of the object to be measured obtained through the Bluetooth smart communication tool; Indicates the step length of the object to be measured estimated by the sensor; Indicates the orientation angle data obtained from the sensor; Indicates the change in the heading angle; Represents the system observation noise variable, and sets the system state equation and measurement equation as: S32, linearization of the nominal trajectory equation: S321, the nonlinear function in the state equation exist Perform a first-order Taylor series expansion in the neighborhood of to obtain the linearized state equation: in, for Time has come The state transition matrix at time t; Substituting the state equation into the state transfer matrix: S322, the nonlinear function in the observation equation exist Perform a first-order Taylor series expansion in the neighborhood of to obtain the linearized measurement equation: in, for The measurement matrix at the moment: Substituting the measurement equation into the equation yields the measurement matrix: S33, optimal estimation of the state is performed according to the classical Kalman filter method: S331, one-step state prediction: in, for The state value of the object under test at the moment, its initial value is , It will Substituting into the linearized state equation we get The predicted value of the state of the object under test at the moment, that is, the coordinate information of the passenger; S332, intermediate value calculation: in, is the predicted value of mean square error, is the optimal estimate of the mean square error, and the initial value is , is the Kalman filter gain, is the system process noise The covariance matrix of represents the system observation noise The covariance matrix of represents the identity matrix; S333, based on The state value of the object under test at the moment and Kalman filter gain The optimal state estimate can be obtained: 。 2. The method for obtaining a trajectory position based on intelligent communication according to claim 1, characterized in that The step S4 is also included, the position after the Kalman filter in step S3 is Compare with the feasible path area, if the location If it is in an inaccessible area, its position will be corrected to the distance within the feasible area. The nearest point .

3. The method for obtaining a trajectory position based on intelligent communication according to claim 1 or 2, characterized in that Step S1 further comprises: S11, receiving signals of n Bluetooth beacons through the Bluetooth smart communication tool, the strength of which is ; S12, select the four strongest Bluetooth signals from n ; S13, according to the attenuation model of the Bluetooth signal, use the signal strength value A and the path loss factor n to calculate the distance corresponding to the four signal strengths respectively , the calculation method is: ； S14, according to the MAC addresses of the four Bluetooth beacons, obtain the spatial positions of the corresponding four beacons ; S15, divide the four Bluetooth beacons into a group of three, forming a total of 4 groups. The positions and distances of the three Bluetooth beacons in each group determine the position of the object to be measured, and obtain four spatial position coordinates. ; S16, weight the coordinates according to the four distances to obtain the Bluetooth position of the object to be measured , the weighting method is: 。 4. The method for obtaining a trajectory position based on intelligent communication according to claim 1 or 2, characterized in that The step S2 further comprises: S21, collecting raw data from the accelerometer, geomagnetic sensor, and gyroscope sensor at fixed time intervals through an intelligent communication tool; S22, filtering the raw data collected in step S21; S23, using the angular velocity output by the gyroscope, calculates the posture of the intelligent communication tool and converts its coordinate system into a real-world coordinate system; S24, using the angular velocity output by the gyroscope to realize the horizontal and vertical orientation angles of the object to be measured when it moves prediction; S25, performing Fourier transform on the acceleration data and the angular velocity data to obtain the passenger's walking frequency f; S26, according to the value of acceleration, realize the passenger's step length l Estimates.

5. A location acquisition system using the method for acquiring a trajectory location based on intelligent communication as claimed in claim 1 or 2, characterized in that include: A Bluetooth beacon module, which is used to send and receive Bluetooth beacon signals and provide the Bluetooth location of the object to be measured; A user intelligent communication module, which includes at least an accelerometer, a geomagnetic sensor, and a gyroscope sensor, and analyzes and calculates the movement information of the object to be measured; The server module uses an extended Kalman filter to fuse the data based on the Bluetooth location provided by the Bluetooth beacon module and the sensor information learned by the user intelligent communication module to obtain the location of the object to be measured and provide extended services.

6. The application of the intelligent communication-based trajectory location acquisition system in a subway station hall as claimed in claim 5 is characterized in that The steps include: S1, deploy passive Bluetooth beacon modules in the subway station hall; S2, establish a three-dimensional spatial coordinate system in the subway station, and build a Bluetooth beacon database and a subway station hall layout database based on the location of the Bluetooth beacon module and the layout of the station hall passages and equipment, and store them in the server module; S3: When a passenger enters a subway station with a smart communication tool, the smart communication tool automatically communicates with the Bluetooth beacon installed in the station hall to obtain the signal strength and the passenger's Bluetooth location. ; S4, the intelligent communication tool obtains data from at least the acceleration, geomagnetic, and gyroscope sensors at fixed time intervals, and analyzes and calculates the direction of the passenger's movement , cadence and step length information; S5, combining the Bluetooth location and sensor information, using the extended Kalman filter to fuse the data obtained in step S4 to obtain the passenger's location ; S6, combining the subway station hall layout data stored by the server in step S2, correcting the passenger position to obtain the final passenger position ; S7, the server module provides personalized services to passengers based on their locations.

7. The application of the intelligent communication-based trajectory location acquisition system in a subway station hall as claimed in claim 6 is characterized in that The Bluetooth beacon database in step S2 includes at least the MAC address of the Bluetooth beacon, the spatial coordinates of the Bluetooth beacon, the station hall area and floor data where the Bluetooth beacon is located; the established subway station hall layout database includes at least the starting point, end point, direction, width of the feasible range, the scope of the station hall area and the position of the railings.

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