System applied to train signal monitoring and self-adaptive intensity adjustment

Abstract

The invention provides a system applied to train signal monitoring and self-adaptive intensity adjustment, which is characterized by comprising a transmission-type RIS unit, a reflection-type RIS unit and a user terminal, the transmission-type RIS unit is arranged on a window of each train carriage; the reflective RIS unit is arranged in a compartment of each train; the electromagnetic wave is enhanced by the transmission-type RIS unit, and the electromagnetic wave enhanced by the transmission-type RIS unit reaches the user terminal through two paths, namely the transmission direct-injection link and the refraction link; the transmission direct-injection link is that the electromagnetic wave enhanced by the transmission-type RIS unit directly reaches the user terminal; according to the refraction link, the electromagnetic waves enhanced by the transmission-type RIS unit are subjected to directional beam forming to the reflection-type RIS unit, and then are reflected to the user terminal through beam forming of the reflection-type RIS unit. According to the system, the wireless signal coverage range and the signal intensity in the compartment are improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a system for adaptively adjusting the intensity of train signal monitoring. Background Art

[0002] With the rapid development of intelligent railway transportation, trains have become one of the important means of transportation in people's daily lives. In modern society, passengers not only expect trains to provide a fast and safe way of travel, but also pay more and more attention to the quality of network connections during the journey. Due to the high-speed characteristics of trains, the wireless signals inside the carriages are often seriously affected, which not only reduces the passengers' network experience, but also affects the security and stability of data communications on the trains.

[0003] Traditional train wireless signal coverage mainly relies on the receiver of the passenger's mobile phone terminal to process the signal. However, in a high-speed moving environment, due to the multipath effect and Doppler effect, signal propagation will attenuate and fluctuate, resulting in uneven signal coverage inside the carriage and poor signal quality at the receiving device terminal such as mobile phones. In addition, the metal structure of the carriage has a large penetration loss on the signal, which makes the problem more complicated. At the same time, there is also the situation where frequent switching on the train leads to unstable signals. Summary of the invention

[0004] In order to solve the above problems, the present invention aims to provide a system for adaptively adjusting the intensity of train signal monitoring.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] The present invention provides a system for adaptively adjusting the intensity of train signal monitoring, characterized in that the system comprises: a transmission RIS unit, a reflection RIS unit, and a user terminal; the transmission RIS unit is arranged on the window of each train compartment; the reflection RIS unit is arranged inside the compartment of each train compartment; the electromagnetic wave is enhanced by the transmission RIS unit, and the electromagnetic wave enhanced by the transmission RIS unit reaches the user terminal through two paths: a transmission direct link and a refraction link: the transmission direct link is that the electromagnetic wave enhanced by the transmission RIS unit is directly emitted to the user terminal; the refraction link is that the electromagnetic wave enhanced by the transmission RIS unit is directionally beamformed to the reflection RIS unit, and then reflected to the user terminal through the reflection RIS unit beamforming.

[0007] Furthermore, in the system for adaptively adjusting the intensity of train signal monitoring provided by the present invention, it can also have the following characteristics: wherein, the system has a signal enhancement mechanism based on the autonomous needs of the user terminal, and the system also includes a positioning tracking sensor, an algorithm calculation module, and a machine learning module; the positioning tracking sensor is arranged in the transmission RIS unit and the reflection RIS unit, and is used to locate the position of the user terminal; the algorithm calculation module calculates the moving path of the user terminal according to the position of the user terminal through a preset algorithm and plans a beamforming path with the minimum electromagnetic wave propagation loss according to the moving path, and this path is the optimal beamforming path; the machine learning module trains the beamforming path formed by previous calculations through a preset model, and synchronously updates the optimal beamforming path using the training results.

[0008] Furthermore, the system for adaptively adjusting the intensity of train signal monitoring provided by the present invention may also have the following features: wherein the workflow of the signal enhancement mechanism based on the request of the user terminal is as follows:

[0009] Step 1: The user places the user terminal close to the transmissive RIS unit or the reflective RIS unit so that it is within the sensing range of the positioning tracking sensor;

[0010] Step 2: The positioning tracking sensor detects the user terminal and tracks the position movement of the user terminal;

[0011] Step 3: The algorithm calculation module calculates the moving path of the user terminal and plans the optimal beamforming path according to the moving path;

[0012] Step 4: The transmissive RIS unit and the reflective RIS unit perform beamforming according to the optimal beamforming path to enhance the electromagnetic wave signal strength.

[0013] Furthermore, in the system for adaptively adjusting the intensity of train signal monitoring provided by the present invention, it can also have the following characteristics: wherein, the positioning tracking sensor detects and tracks the position of the user terminal in the following manner: the positioning tracking sensor includes a photoelectric sensor and an NFC sensor; the photoelectric sensor determines whether the user terminal is close to the RIS unit by detecting changes in light intensity, thereby determining the position of the user terminal according to the position of the RIS unit; the NFC sensor recognizes and exchanges data with the NFC sensing module of the user terminal, thereby realizing the position tracking and locking of the user terminal.

[0014] Furthermore, in the system for adaptively adjusting the intensity of train signal monitoring provided by the present invention, it can also have the following characteristics: wherein the system also has a signal enhancement mechanism based on the real-time monitoring signal of the carriage, and the system also includes a carriage signal strength detection unit and a control unit; the carriage signal strength detection unit is arranged in the carriage, and includes an antenna, a signal receiver and a data processor, the antenna is used to receive the radio electromagnetic wave signal inside the carriage, the signal receiver is used to perform preliminary processing on the received signal, and the data processor is used to analyze the signal strength and perform signal conversion; the central control unit is used to analyze the signal strength data and determine whether the transmission phase coefficient of the RIS needs to be adjusted according to a preset algorithm and a threshold value, and the central control unit includes multiple microprocessors and a data storage module, and the data storage module stores a preset algorithm and a judgment rule.

[0015] Furthermore, the system for adaptively adjusting the intensity of train signal monitoring provided by the present invention may also have the following features: wherein the workflow of the signal enhancement mechanism based on the real-time monitoring signal of the carriage is as follows:

[0016] Step S1: The carriage signal strength detection unit monitors the wireless signal strength in the carriage in real time and transmits the monitoring data to the central control unit;

[0017] Step S2: The central control unit receives data from the carriage signal strength detection unit, analyzes the signal strength of each area in the carriage, and determines whether the signal strength meets a preset signal strength threshold;

[0018] Step S3: Generate a corresponding transmission phase coefficient adjustment decision based on the judgment result, and the transmission RIS unit is adaptively adjusted:

[0019] When the signal strength in a certain area is lower than the signal strength threshold, the central control unit generates a transmission phase coefficient adjustment decision instruction of the RIS. The RIS unit adjusts the transmission phase coefficient to enhance the signal strength in the area so that it meets the signal strength threshold.

[0020] When the signal strength in a certain area meets the signal strength threshold, the central control unit generates a transmission phase coefficient of the RIS without adjusting the decision, and the transmission phase coefficient of the RIS unit remains unchanged;

[0021] When the signal strength in a certain area exceeds the signal strength threshold, the central control unit generates a transmission phase coefficient adjustment decision instruction for the RIS. The RIS unit adjusts the transmission phase coefficient to reduce the signal strength in the area so that it meets the signal strength threshold.

[0022] Furthermore, the system for adaptively adjusting the strength of train signal monitoring provided by the present invention may also have the following characteristics: wherein the signal strength analysis and judgment in step S2 is performed by the following algorithm:

[0023] The learning model is:

[0024] F DLM (X,θ) (1),

[0025] In formula (1), X is the current signal strength; θ is the model parameter; F DLM is the output of the model, used to predict the signal strength distribution;

[0026] The channel model is:

[0027]

[0028] Where G represents the channel model from the base station to the RIS; N BS is the number of base station antennas; M is the number of antennas at the receiving end; L g Represents the number of paths in the channel; G l represents the path gain or complex gain coefficient of the lth path; is the transmission direction vector, is the transmission direction response vector of the base station array; is the conjugate transposed vector of the receiving direction, and is the receiving direction response vector of the receiving antenna array;

[0029] The update rules are:

[0030] Speed ​​update: v i (t+1)=ω·v i (t)+c1·r1·(p i -x i (t))+c2·r2·(gx i (t)) (3),

[0031] Position update: x i (t+1)=x i (t)+v i (t+1)(4),

[0032] Among them, ω is the inertia factor, c1 and c2 are learning factors; r1 and r2 are random numbers in the range [0,1]; v i and x i Represent the speed and position of the i-dimensional particle respectively; v i (t) is the current position of the ith particle in the tth iteration; v i (t+1) is the velocity of the ith particle in the t+1th iteration; x i(t) represents the current position of the i-th particle at the t-th iteration, which is the specific coordinate of the particle in the current search space; x i (t+1) represents the updated position of the i-th particle at the t+1th iteration; g represents the global optimal position of the entire group; p i is the best historical position of the ith particle.

[0033] Furthermore, the system for adaptively adjusting the strength of train signal monitoring provided by the present invention may also have the following characteristics: wherein the adaptive adjustment process of the RIS unit in step S3 is a real-time dynamic and continuous adjustment.

[0034] Furthermore, in the system for adaptively adjusting the intensity of train signal monitoring provided by the present invention, it can also have the following characteristics: wherein, the system's signal enhancement mechanism based on the autonomous needs of the user terminal and the signal enhancement mechanism based on the real-time monitoring signal of the carriage are carried out simultaneously.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The system for adaptively adjusting the intensity of train signal monitoring applied in the present invention significantly improves the wireless signal coverage and quality inside the carriage by dynamically adjusting RIS, especially in areas where traditional technologies are difficult to cover or the signal is weak. The present invention reduces interference by accurately controlling the transmitted signal and the reflected signal, and avoids the additional interference disadvantages that may be introduced by traditional methods of enhancing signals (such as using amplifiers). At the same time, the present invention reduces the demand for complex hardware and can adapt to complex and changing environments. The system of the present invention is both efficient and energy-saving, improving the quality of wireless signals in train carriages, not only limited to improving the network experience of passengers, but also able to provide more stable and reliable wireless communication support for train operation management, safety monitoring and data transmission, etc. It has important significance and broad application prospects for the construction of intelligent railway systems, especially for the advancement of communication technology for high-speed railways. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 , Figure 2 is a schematic diagram of a transmission direct link and a refraction link in the system of the present invention, Figure 1 and Figure 2 Different situations are indicated;

[0038] Figure 3 It is a structural schematic diagram of the RIS unit in the system of the present invention;

[0039] Figure 4 A schematic diagram of the installation of a transmissive RIS unit on a vehicle window in the system of the present invention;

[0040] Figure 5 The figure is a working principle diagram of the signal enhancement mechanism based on the autonomous demand of the user terminal in the system of the present invention.

[0041] Figure 6 This is a schematic diagram of the installation location of the algorithm calculation module;

[0042] Figure 7 It is a working flow chart of the signal enhancement mechanism based on the real-time monitoring signal of the carriage in the system of the present invention;

[0043] Figure 8 A flow chart of signal strength monitoring in the system of the present invention;

[0044] Fig. 9 The figure is a flow chart of signal strength analysis and decision making in the system of the present invention.

[0045] Markings in the figure: 1. Transmissive RIS unit; 2. Reflective RIS unit; 3. User terminal; 4. Seat; 5. Window; 6. Front wall of the car; 7. Algorithm calculation module; 101. Sensor part; 102. RIS material. DETAILED DESCRIPTION

[0046] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the following embodiments are combined with the accompanying drawings to specifically illustrate the technical solutions of the present invention.

[0047] The embodiment of the present invention provides a system for adaptively adjusting the intensity of train signal monitoring. The system uses a prior art transmissive intelligent surface (RIS), which is a plane composed of a large number of passive components. The RIS can change the phase and amplitude of the signal to achieve active control of signal propagation.

[0048] See also Figure 1 and Figure 2 The system for adaptively adjusting the intensity of train signal monitoring according to the embodiment of the present invention includes: a transmission RIS unit 1, a reflection RIS unit 2, and a user terminal 3.

[0049] The transmissive RIS unit 1 is arranged on the window of each train compartment. For the arrangement of the transmissive RIS unit 1 on the window, see Figure 4 The reflective RIS unit 2 is arranged inside the carriage of each train, for example, at the front wall of the carriage. The structure of the transmissive RIS unit is similar to that of the reflective RIS unit, see Figure 2 , Figure 2101 is the sensor part, 102 is the RIS material, the RIS material 102 in the transmissive RIS unit is the transmissive RIS material, and the RIS material 102 in the reflective RIS unit is the reflective RIS material. The electromagnetic wave emitted by the electromagnetic wave base station outside the vehicle has a relatively small loss when passing through the vehicle window, and the transmissive RIS unit 1 can directly receive the electromagnetic wave. The electromagnetic wave is enhanced by the transmissive RIS unit 1, and the electromagnetic wave enhanced by the transmissive RIS unit 1 reaches the user terminal through two paths: the transmission direct link and the refraction link. The transmission direct link is that the electromagnetic wave enhanced by the transmissive RIS unit 1 directly reaches the user terminal 3. The refraction link is that the electromagnetic wave enhanced by the transmissive RIS unit 1 is directionally beamformed to the reflective RIS unit 2, and then reflected to the user terminal 3 through the beamforming of the reflective RIS unit 2.

[0050] The system for adaptively adjusting the intensity of train signal monitoring applied in the embodiment of the present invention has a signal enhancement mechanism based on the autonomous demand of the user terminal and a signal enhancement mechanism based on the real-time monitoring signal of the carriage, and the two signal enhancement mechanisms are performed simultaneously.

[0051] (1) Signal enhancement mechanism based on the autonomous needs of user terminals:

[0052] When the signal of the user terminal 3 is not strong, the network is not smooth and unstable, the user can hold the user terminal 3 close to the RIS unit to trigger the mechanism to enhance the signal strength through beamforming.

[0053] The system is equipped with positioning tracking sensors, algorithm calculation modules and machine learning modules.

[0054] The positioning tracking sensor is set in the transmissive RIS unit and the reflective RIS unit to locate the position of the user terminal. It is specifically implemented in the following way: the positioning tracking sensor includes a photoelectric sensor and an NFC sensor. The photoelectric sensor determines whether the user terminal is close to the RIS unit by detecting the change in light intensity, thereby determining the position of the user terminal according to the position of the RIS unit. The NFC sensor recognizes and exchanges data with the NFC sensing module of the user terminal, thereby realizing the position tracking and locking of the user terminal.

[0055] The algorithm calculation module calculates the moving path of the user terminal according to the location of the user terminal through a preset algorithm and plans a beamforming path with the minimum electromagnetic wave propagation loss based on the moving path. This path is the optimal beamforming path. Figure 6 , Figure 6The positional relationship between the algorithm calculation module 7 and the RIS unit is shown in the figure. The algorithm calculation module 7 is set at the seat, for example, it can be set on the side wall of the train compartment, and can be appropriately adjusted according to the windows of different vehicle models, so that the RIS unit on the window is as close to the algorithm module as possible. The algorithm calculation module should have an inspection port to facilitate subsequent maintenance and support work. The hardware of the algorithm module includes the algorithm unit, the CPU main frequency is Inter Pentium43.00GHz, and the memory is 128GB.

[0056] The machine learning module trains the beamforming path formed by previous calculations through a preset model (deep learning + particle swarm algorithm), and uses the training results to synchronously update the optimal beamforming path.

[0057] See also Figure 5 ,The workflow of the signal enhancement mechanism based on the user terminal’s request is as follows:

[0058] Step 1: The user places the user terminal close to the transmissive RIS unit or the reflective RIS unit so that it is within the sensing range of the positioning tracking sensor.

[0059] Step 2: The positioning tracking sensor detects the user terminal and tracks the position movement of the user terminal.

[0060] Step 3: The algorithm calculation module calculates the moving path of the user terminal and plans the optimal beamforming path according to the moving path.

[0061] Step 4: The transmissive RIS unit and the reflective RIS unit perform beamforming according to the optimal beamforming path to enhance the electromagnetic wave signal strength. Figure 5 , when the relative position of the user terminal 3 changes, the best link is updated according to the algorithm to achieve link switching.

[0062] (2) Signal enhancement mechanism based on the real-time monitoring signal of the carriage:

[0063] The system is provided with a carriage signal strength detection unit and a control unit.

[0064] The carriage signal strength detection unit is arranged in the carriage, and includes an antenna, a signal receiver and a data processor. The antenna is used to receive the radio electromagnetic wave signal inside the carriage. The signal receiver is used to perform preliminary processing on the received signal. The data processor is used to analyze the signal strength and perform signal conversion.

[0065] See also Fig. 9The central control unit is used to analyze the signal strength data and determine whether the transmission phase coefficient of the transmission RIS unit 1 needs to be adjusted according to the preset algorithm and threshold, and instruct the transmission RIS unit 1 to make dynamic adjustments. The central control unit includes multiple microprocessors and a data storage module, and the data storage module stores the preset algorithm and judgment rules.

[0066] See also Figure 7 ,The workflow of the signal enhancement mechanism based on the real-time monitoring signal of the carriage is as follows:

[0067] Step S1: The carriage signal strength detection unit monitors the wireless signal strength in the carriage in real time and transmits the monitoring data to the central control unit. Figure 8 .

[0068] Step S2: The central control unit receives data from the carriage signal strength detection unit, analyzes the signal strength of each area in the carriage, and determines whether the signal strength meets a preset signal strength threshold.

[0069] The signal strength analysis and judgment in step S2 is performed by the following algorithm:

[0070] The learning model is:

[0071] F DLM (X,θ) (1),

[0072] In formula (1), X is the current signal strength; θ is the model parameter; F DLM is the output of the model, used to predict the signal strength distribution;

[0073] The channel model is:

[0074]

[0075] Where G represents the channel model from the base station (BS) to the reconfigurable smart surface (RIS), N BS is the number of base station antennas. M is the number of antennas at the receiving end. L g G represents the number of paths in the channel. l Represents the path gain or complex gain coefficient of the lth path. is the transmission direction vector, and the transmission direction response vector of the base station array. is the conjugate transposed vector of the receiving direction, representing the receiving direction response vector of the receiving antenna array.

[0076] The update rules are:

[0077] Speed ​​update: v i (t+1)=ω·v i (t)+c1·r1·(pi -x i (t))+c2·r2·(gx i (t)) (3),

[0078] Position update: x i (t+1)=x i (t)+v i (t+1) (4),

[0079] Among them, ω is the inertia factor, c1 and c2 are learning factors. r1 and r2 are random numbers in the range [0,1], which are used to increase the randomness of the search. i and x i Represent the speed and position of the i-dimensional particle, v i (t) is the current position of the ith particle in the tth iteration. i (t+1) is the velocity of the ith particle at the t+1th iteration. i (t) represents the current position of the i-th particle at the t-th iteration, which is the specific coordinate of the particle in the current search space. i (t+1) represents the updated position of the i-th particle at the t+1th iteration. g represents the global optimal position of the entire group. i is the historical best position (local optimal position) of the ith particle.

[0080] Step S3: Generate a corresponding transmission phase coefficient adjustment decision based on the judgment result, and the transmission RIS unit is adaptively adjusted. The adaptive adjustment process is a real-time dynamic and continuous adjustment:

[0081] When the signal strength in a certain area is lower than the signal strength threshold, the central control unit generates a RIS transmission phase coefficient adjustment decision instruction. The transmission RIS unit 1 adjusts the transmission phase coefficient to enhance the signal strength in the area so that it meets the signal strength threshold. This adjustment method realizes the dynamic adaptive RIS transmission phase coefficient.

[0082] When the signal strength in a certain area meets the signal strength threshold, the central control unit generates a decision not to adjust the transmission phase coefficient of the RIS, and the transmission phase coefficient of the transmission RIS unit 1 remains unchanged.

[0083] When the signal strength in a certain area exceeds the signal strength threshold, the central control unit generates a transmission phase coefficient adjustment decision instruction for the RIS. The transmission RIS unit 1 adjusts the transmission phase coefficient to reduce the signal strength in the area so that it meets the signal strength threshold.

[0084] The above adjustment method when the signal strength meets or exceeds the signal strength threshold achieves optimization and energy saving effects, and the transmission phase coefficient is reduced or adjusted or remains unchanged, which can avoid unnecessary energy consumption and signal interference.

[0085] After calculating the optimal transmission surface adjustment strategy based on the signal enhancement mechanism of the real-time monitoring signal of the carriage, the optimal transmission phase and amplitude settings are determined for each unit to maximize the signal strength in a specific area and achieve a specific beamforming effect.

[0086] Since the signal enhancement mechanism based on the autonomous needs of the user terminal and the signal enhancement mechanism based on the real-time monitoring signal of the carriage are carried out simultaneously, the passengers in the seats are mainly enhanced through the transmission direct link. In the case of high passenger flow, the passengers standing in the empty space in front and behind the carriage can rely on the transmission direct link and the refraction link to enhance at the same time.

[0087] The system used for adaptive intensity adjustment of train signal monitoring is powered by AC power. It is powered by the train's own power supply system. Since the charging port on the high-speed train is under the seat, the power supply method of this system can be used to supply power under the high-speed train seat, which is independent of the power supply that the user can use and access.

[0088] The specific application scenarios of the system for adaptively adjusting the intensity of train signal monitoring are as follows:

[0089] Application to high-speed railway trains: High-speed railway trains often pass through suburbs, tunnels and urban areas. The high-speed movement and different geographical environments cause unstable signals, especially in tunnels and areas far from cities, where the signal attenuation is serious. The intelligent metasurface material placed on the windows contains hundreds or thousands of micro-transmission units, each of which can independently control the phase and intensity of the transmission signal. High-precision signal detectors are installed at key locations in the carriage, close to the doors, the center of the carriage and the seating area. These detectors can capture signal strength and quality in real time. The central control unit is equipped with a high-performance processor and advanced signal processing algorithms, which can analyze signal data in real time and calculate the best transmission strategy based on passenger distribution and changes in the external environment. When entering a tunnel or away from a city base station, the control unit directs the intelligent metasurface to centrally enhance the signal to ensure that the communication quality inside the carriage is not affected.

[0090] Application in urban subways (including underground and ground sections): High-density passengers use mobile devices in subway cars, especially during peak hours, resulting in concentrated signal demand and network congestion. Install smart metasurfaces on the windows of the cars. These transmission surfaces are specially designed to adapt to the narrow space of subway cars and effectively enhance signal coverage in the horizontal direction. Due to the large number of passengers, the system uses more signal detection units, evenly distributed in the car, to ensure accurate capture of signal conditions in each area. The control unit runs a fast response algorithm to instantly adjust the settings of the smart metasurface to adapt to the rapid changes in the ground and underground environment. During peak hours, the system adjusts the smart metasurface according to passenger distribution and mobility patterns to optimize signal coverage and reduce network congestion. The system also integrates user experience monitoring functions to evaluate and further optimize signal coverage strategies by analyzing network usage data.

[0091] The above embodiments are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the scope of protection of the present invention.

Claims

1. A system for adaptively adjusting the intensity of train signal monitoring, characterized in that: include: Transmissive RIS unit, reflective RIS unit, user terminal; The transmissive RIS unit is arranged on the window of each train carriage; The reflective RIS unit is arranged inside the carriage of each train; The electromagnetic wave is enhanced by the transmission RIS unit, and the electromagnetic wave enhanced by the transmission RIS unit reaches the user terminal through two paths: a transmission direct link and a refraction link: The transmission direct link is the electromagnetic wave enhanced by the transmission RIS unit directly reaching the user terminal; The refraction link is that the electromagnetic wave enhanced by the transmission RIS unit is directionally beamformed to the reflection RIS unit, and then reflected to the user terminal through the reflection RIS unit beamforming.

2. The system for adaptively adjusting the intensity of train signal monitoring according to claim 1, characterized in that: The system has a signal enhancement mechanism based on the autonomous needs of the user terminal, and the system also includes a positioning tracking sensor, an algorithm calculation module, and a machine learning module; The positioning tracking sensor is arranged in the transmissive RIS unit and the reflective RIS unit, and is used to locate the position of the user terminal; The algorithm calculation module calculates the moving path of the user terminal according to the position of the user terminal through a preset algorithm and plans a beamforming path with the minimum electromagnetic wave propagation loss according to the moving path, and the path is the optimal beamforming path; The machine learning module trains the beamforming path formed by previous calculations through a preset model, and synchronously updates the optimal beamforming path using the training results.

3. The system for adaptively adjusting the intensity of train signal monitoring according to claim 2, characterized in that: The workflow of the signal enhancement mechanism based on the request of the user terminal is as follows: Step 1: The user places the user terminal close to the transmissive RIS unit or the reflective RIS unit so that the terminal is within the sensing range of the positioning tracking sensor; Step 2: The positioning tracking sensor detects the user terminal and tracks the position movement of the user terminal; Step 3: The algorithm calculation module calculates the moving path of the user terminal and plans the optimal beamforming path according to the moving path; Step 4: The transmissive RIS unit and the reflective RIS unit perform beamforming according to the optimal beamforming path to enhance the electromagnetic wave signal strength.

4. The system for adaptively adjusting the intensity of train signal monitoring according to claim 3, characterized in that: in, The location tracking sensor detects and tracks the location of the user terminal in the following manner: The positioning tracking sensor includes a photoelectric sensor and an NFC sensor; The photoelectric sensor determines whether the user terminal is close to the RIS unit by detecting changes in light intensity, thereby determining the position of the user terminal according to the position of the RIS unit; The NFC sensor recognizes and exchanges data with the NFC sensing module of the user terminal, thereby tracking and locking the position of the user terminal.

5. The system for adaptively adjusting the intensity of train signal monitoring according to any one of claims 2 to 4, characterized in that: The system also has a signal enhancement mechanism based on the real-time monitoring signal of the carriage, and the system also includes a carriage signal strength detection unit and a control unit; The carriage signal strength detection unit is arranged in the carriage, and comprises an antenna, a signal receiver and a data processor, wherein the antenna is used to receive the radio electromagnetic wave signal inside the carriage, the signal receiver is used to perform preliminary processing on the received signal, and the data processor is used to analyze the signal strength and perform signal conversion; The central control unit is used to analyze the signal strength data and determine whether the transmission phase coefficient of the RIS needs to be adjusted according to the preset algorithm and threshold. The central control unit includes multiple microprocessors and a data storage module. The data storage module stores the preset algorithm and judgment rules.

6. The system for adaptively adjusting the intensity of train signal monitoring according to claim 5, characterized in that: The workflow of the signal enhancement mechanism based on the real-time monitoring signal of the carriage is as follows: Step S1: the carriage signal strength detection unit monitors the wireless signal strength in the carriage in real time and transmits the monitoring data to the central control unit; Step S2: The central control unit receives data from the carriage signal strength detection unit, analyzes the signal strength of each area in the carriage, and determines whether the signal strength meets a preset signal strength threshold; Step S3: Generate a corresponding transmission phase coefficient adjustment decision according to the judgment result, and the transmission RIS unit adaptively adjusts: When the signal strength in a certain area is lower than the signal strength threshold, the central control unit generates a transmission phase coefficient adjustment decision instruction of the RIS, and the RIS unit adjusts the transmission phase coefficient to enhance the signal strength in the area so that it meets the signal strength threshold; When the signal strength in a certain area meets the signal strength threshold, the central control unit generates a decision not to adjust the transmission phase coefficient of the RIS, and the transmission phase coefficient of the RIS unit remains unchanged; When the signal strength in a certain area exceeds the signal strength threshold, the central control unit generates a transmission phase coefficient adjustment decision instruction for the RIS. The RIS unit adjusts the transmission phase coefficient to reduce the signal strength in the area so that it meets the signal strength threshold.

7. The system for adaptively adjusting the intensity of train signal monitoring according to claim 6, characterized in that: in, The signal strength analysis and judgment in step S2 is performed by the following algorithm: The learning model is: F DLM (X,θ) (1), In formula (1), X is the current signal strength; θ is the model parameter; F DLM is the output of the model, used to predict the signal strength distribution; The channel model is: Where G represents the channel model from the base station to the RIS; N BS is the number of base station antennas; M is the number of antennas at the receiving end; L g Represents the number of paths in the channel; G l represents the path gain or complex gain coefficient of the lth path; is the transmission direction vector, is the transmission direction response vector of the base station array; is the conjugate transposed vector of the receiving direction, and is the receiving direction response vector of the receiving antenna array; The update rules are: Speed ​​update: υ i (t+1)=ω·υ i (t)+c1·r1·(p i -x i (t))+c2·r2·(gx i (t)) (3), Position update: x i (t+1)=x i (t)+υ i (t+1) (4), Among them, ω is the inertia factor, c1 and c2 are learning factors; r1 and r2 are random numbers ranging from [0,1]; υ i and x i Respectively represent the speed and position of the i-dimensional particle; υ i (t) is the current position of the ith particle in the tth iteration; υ i (t+1) is the velocity of the ith particle in the t+1th iteration; x i (t) represents the current position of the i-th particle at the t-th iteration, which is the specific coordinate of the particle in the current search space; x i (t+1) represents the updated position of the i-th particle at the t+1th iteration; g represents the global optimal position of the entire group; p i is the best historical position of the ith particle.

8. The system for adaptively adjusting the intensity of train signal monitoring according to claim 6, characterized in that: in, The adaptive adjustment process of the RIS unit in step S3 is a real-time dynamic and continuous adjustment.

9. The system for adaptively adjusting the intensity of train signal monitoring according to claim 6, characterized in that: The system's signal enhancement mechanism based on the autonomous needs of the user terminal and the signal enhancement mechanism based on the real-time monitoring signal of the carriage are performed simultaneously.

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