Progressive Signal Enhancement Method and System for Visible Light Communication

Abstract

This invention relates to the field of visible light communication technology, and particularly to a progressive signal enhancement method and system for visible light communication. The receiving end image sensor transmits captured images of the light source to a computer. The computer calculates the relative distance between the transmitting and receiving ends using an equipotential line construction method based on an average grayscale model, determines the relative orientation of the transmitting and receiving ends using an orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends through multiple iterations combining direction and distance. The computer controls a RIS module to progressively adjust the light field based on the position information generated during the iteration process, enabling the light source to dynamically track the location of the receiving terminal. This invention, based on the real-time position of the visible light communication receiver, repeatedly adjusts the light field to converge around the mobile terminal, greatly improving the illumination intensity in the area where the receiving terminal is located while meeting its mobility requirements, thus satisfying the communication requirements for a high signal-to-noise ratio.

Description

Technical Field

[0001] This invention relates to the field of visible light communication technology, and in particular to a progressive signal enhancement method and system for visible light communication. Background Technology

[0002] The Industrial Internet has become one of the key national digital industries, further raising requirements for industrial communication networks such as deep coverage, ultra-low latency, and secure communication. Visible Light Communication (VLC), with its ubiquitous coverage, ultra-wide bandwidth, and inherent security, perfectly compensates for the shortcomings of traditional wireless communication in industrial Internet applications, becoming an irreplaceable technology in the Industrial Internet environment.

[0003] With the mechanization and intelligentization of production equipment in modern industrial plants, the movement and transfer of various production factors are becoming increasingly frequent. The emergence of intelligent mobile devices such as automated guided vehicles (AGVs) and autonomous robots has further increased the complexity of the communication environment, as they frequently move through production workshops. For these mobile communication terminals, traditional fixed-light-emitting solutions are difficult to meet the high signal-to-noise ratio requirements across their entire range of motion due to power consumption limitations. Therefore, high-precision tracking of the communication receiver becomes crucial in mobile scenarios.

[0004] The existing solution divides the workspace into multiple cells based on the irradiance range of the visible light emitter, and regularly deploys multiple signal sources as access points for each cell. Each cell has limited coverage and communicates independently with others. Figure 1 As shown. However, in this scheme, there are unavoidable overlapping areas between cells. Interference from adjacent signal sources in these areas, as well as the time delay caused by cell handover during movement, severely affect the system performance. Summary of the Invention

[0005] This invention aims to solve the problem that existing multi-cell visible light communication schemes completely ignore the important information of the receiver's location and cannot dynamically adjust the light field coverage according to the real-time location of the mobile terminal. It proposes a progressive signal enhancement method and system for visible light communication. Based on the real-time location of the visible light communication receiver, the light field is adjusted multiple times to converge around the mobile terminal. While meeting the mobile attributes, it can greatly improve the light intensity in the area where the receiver is located and meet the communication requirements of high signal-to-noise ratio.

[0006] To achieve the above objectives, the technical solution adopted is:

[0007] A progressive signal enhancement method for visible light communication includes:

[0008] The receiving end image sensor transmits the captured light source image to the computer;

[0009] The computer calculates the relative distance between the transmitting and receiving ends using an equipotential line construction method based on an average grayscale model, determines the relative orientation of the transmitting and receiving ends using an orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends by combining direction and distance through multiple iterations.

[0010] The computer-controlled RIS module gradually adjusts the light field based on the position information generated during the iterative process, enabling the light source to dynamically track the location of the receiving terminal.

[0011] According to the progressive signal enhancement method for visible light communication of the present invention, the equipotential line construction method based on the average grayscale model further includes:

[0012] Assume the controlled light beam exits from the emitter at an arbitrary deflection angle ψ, the emitter's height is h, its direct direction is designated as coordinate 0, and the coordinate of the intersection of its central optical axis and the ground is x. l The coordinates of the spot with maximum brightness are x m The angle between the beam from the transmitter to the receiver and the optical axis is... The transmission distance is d, and the incident angle of the light beam reaching the receiver is θ. Now, let the line connecting the transmitting and receiving ends rotate around the central optical axis to form a cone. The intersection of this cone with the ground is an ellipse, and let the coordinates of the geometric center of the ellipse be x. c Construct elliptical gray-level equipotential lines to provide a basis for the spot movement step size in each iteration process;

[0013] The initial values ​​above are corrected by adjusting the center point coordinate x. c The principle of modification is to make any The near focus of the ellipse generated by the value is related to x. m By aligning the points, we obtain the corrected elliptical grayscale contour lines.

[0014] According to the progressive signal enhancement method for visible light communication of the present invention, the corrected equation of the elliptical gray-level equipotential line is further expressed as:

[0015]

[0016] By taking the partial derivative of the average gray level of the captured image The X coordinate corresponding to angle θ at this time is obtained. Where m represents the Lambertian order of the light source after RIS modulation, m = 2(f / (fL)). 2 -1, L represents the lens-light source distance, and f represents the focal length of the lens that provides the same focusing effect as RIS;

[0017] By calculating the average gray value of the image captured by the receiver, the receiver position is determined on the equipotential line corresponding to that gray value.

[0018] According to the progressive signal enhancement method for visible light communication of the present invention, the orientation determination method based on image subtle feature recognition is further used as the basis for determining the direction of light spot movement in each iteration process. The orientation determination method based on image subtle feature recognition uses a CNN-based image recognition algorithm to determine the direction of the receiver relative to the peak point of the light spot based on the subtle features of the shape of the captured light source. First, the relative orientation needs to be quantized. The orientation is quantized into four values ​​of 45°, 135°, 225°, and 315° with the imaging direction as the reference and counterclockwise as the positive direction. These values ​​are represented by A1, A2, A3, A4 or B1, B2, B3, B4, and each represents a 90° angle range centered on itself.

[0019] According to the progressive signal enhancement method for visible light communication of the present invention, further, when the transmitted beam is not deflected, i.e. ψ = 0, the position of the receiver is roughly determined with the position of the light source as a reference point; the initially acquired light source image is input into a CNN-based image recognition algorithm to identify the relative orientations A1, A2, A3, and A4 of the transceiver.

[0020] According to the progressive signal enhancement method for visible light communication of the present invention, when the emitted beam is deflected to a certain area, i.e. ψ≠0, the orientation is distinguished by using the peak point of the light spot as a reference point; the real-time acquired light source image is input into a CNN-based image recognition algorithm to identify the relative orientations B1, B2, B3, and B4 of the transceiver.

[0021] According to the progressive signal enhancement method for visible light communication of the present invention, further, estimating the relative positions of the transmitting and receiving ends by combining direction and distance through multiple iterations includes:

[0022] (1) Initialize the transmitter state to beam deflection angle ψ = 0. Based on the image of the light source captured at this time, determine whether the receiver is located in area A1, A2, A3 or A4. Move the peak point of the light spot from the transmitter location O to the center point Q1 of the area.

[0023] (2) Adjust the LED lamp lens-light source spacing L to increase the light spot intensity, so that the light spot only covers the area where the receiver is located; based on the average gray level of the image captured at this time... Construct grayscale equipotential lines with the line containing OQ1 as the axis; and determine the relative orientation as B1, B2, B3 or B4 based on the CNN-based image recognition algorithm, and move the peak point of the light spot from Q1 to Q2.

[0024] (3) Repeat step (2) until the lens-light source distance L is adjusted to the maximum value. If the average gray value of the image reaches the threshold ratio of the theoretical maximum threshold at this time, then the peak point of the light spot is the estimated receiver position. If the average gray value of the image does not reach the threshold ratio of the theoretical maximum threshold at this time, repeat step (2) until the average gray value of the image reaches the threshold ratio of the theoretical maximum threshold.

[0025] Furthermore, the present invention also provides a progressive signal enhancement system for visible light communication, comprising a transmitting LED, a receiving image sensor, a computer, and a RIS module, wherein:

[0026] The receiving end image sensor transmits the captured light source image to the computer;

[0027] The computer calculates the relative distance between the transmitting and receiving ends using an equipotential line construction method based on an average grayscale model, determines the relative orientation of the transmitting and receiving ends using an orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends by combining direction and distance through multiple iterations.

[0028] The RIS module gradually adjusts the light field based on the position information generated during the iteration process, enabling the light source to dynamically track the location of the receiving terminal.

[0029] The beneficial effects achieved by adopting the above technical solution are:

[0030] Visible light communication (VLC) applications often involve moving communication terminals in industrial settings. For VLC terminals moving over large areas, traditional fixed-light-source, wide-area broadcast illumination solutions suffer from low average irradiance due to power limitations, failing to meet the high signal-to-noise ratio requirements. This invention addresses the challenge of dynamic tracking at the receiver in moving VLC scenarios by proposing a progressive signal enhancement method. It establishes an iterative VLC signal enhancement technique based on a RIS-controlled transmitter and a CMOS image sensor, achieving simultaneous position tracking and signal enhancement through multiple beam adjustments. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0032] Figure 1 This is a schematic diagram of a conventional multi-cell visible light communication method according to an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the geometric relationship under the condition of beam tilt in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the rough orientation of the light source as a reference point and the corresponding image features of each embodiment of the present invention.

[0035] Figure 4 This is the orientation division with reference to the peak point of the light spot and the corresponding light spot displacement vector in this embodiment of the invention;

[0036] Figure 5 This is an example diagram of position tracking and signal enhancement according to an embodiment of the present invention;

[0037] Figure 6 This is a schematic diagram of the visible light communication progressive signal enhancement system according to an embodiment of the present invention. Detailed Implementation

[0038] The exemplary solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art.

[0039] This embodiment discloses a progressive signal enhancement method for visible light communication, comprising the following steps:

[0040] Step S101: The receiving end image sensor transmits the captured light source image to the computer.

[0041] Step S102: The computer calculates the relative distance between the transmitting and receiving ends using the equipotential line construction method based on the average grayscale model, determines the relative orientation of the transmitting and receiving ends using the orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends by combining direction and distance through multiple iterations.

[0042] Step S103: The computer-controlled RIS module gradually adjusts the light field according to the position information generated during the iteration process, so that the light source dynamically tracks the location of the receiving terminal, and finally realizes wide-area coverage and signal enhancement of visible light communication.

[0043] The method for constructing equipotential lines based on the average grayscale model is as follows:

[0044] The construction of grayscale equipotential lines is the basis for the step size of the light spot movement in each iteration. Assume the controlled light beam exits from the emitter at an arbitrary deflection angle ψ, the emitter height is h, and its direct direction is taken as coordinate 0, with the coordinate of the intersection of its central optical axis and the ground being x. l The coordinates of the brightest spot (peak point) are x m The angle between the beam from the transmitter to the receiver and the optical axis is... The transmission distance is d, and the incident angle of the light beam reaching the receiver is θ. Now, let the line connecting the transmitting and receiving ends rotate around the central optical axis to form a cone. Then, the intersection of this cone with the ground is an ellipse, as shown below. Figure 2 As shown, let the geometric center of the ellipse be x. c It is important to note that the center point x of the ellipse... c Peak point of light spot x m and the optical axis pointing point x l It is in three different positions, and the relationship between the three is x. m <x l <x c The three will coincide only when ψ = 0.

[0045] When the transmission distance is long, the off-axis angle formed at the transmitting and receiving ends Since the change in θ caused by the receiver moving along the ellipse is very small, it can be assumed that the power or gray value received by the receiver on the ellipse is approximately constant when the transmission distance is long. Therefore, the equipotential line can be constructed as an ellipse.

[0046] Furthermore, considering the actual situation, all equipotential lines should have their peak points x... m A closed curve enclosed within, but when enough hours, x m Since the initial values ​​will be outside the ellipse, they need to be adjusted. The adjustment parameter is the center point coordinate x. c The principle of modification is to make any The near focus of the ellipse generated by the value is related to x. m When the points coincide, the corrected equation of the elliptical gray-level equipotential line is expressed as:

[0047]

[0048] By taking the partial derivative of the average gray level of the captured image The X coordinate corresponding to angle θ at this time is obtained. Where m represents the Lambertian order of the light source after RIS modulation, m = 2(f / (fL)). 2 -1, L represents the lens-light source distance, and f represents the focal length of the lens that provides the same focusing effect as RIS.

[0049] Therefore, by calculating the average grayscale value of the image captured by the receiver, the receiver's position can be determined on the equipotential line corresponding to that grayscale value. If the receiver's position relative to point (x) can be further confirmed... m By determining the direction of ,0), the accurate position of the receiver can be obtained by combining it with the equipotential lines.

[0050] The location determination method based on image subtle feature recognition is as follows:

[0051] The orientation determination method based on image subtle feature recognition is used to determine the direction of light spot movement in each iteration. This method employs an image recognition algorithm based on a Convolutional Neural Network (CNN) to determine the receiver's orientation relative to the peak point of the light spot based on subtle features of the captured light source shape. First, the relative orientation needs to be quantized. The orientation is quantized into four values: 45°, 135°, 225°, and 315°, with the imaging direction as a reference and counterclockwise as positive. These values ​​are represented by A1, A2, A3, A4 or B1, B2, B3, B4, each representing a 90° angle range centered on itself.

[0052] When the emitted beam is not deflected (ψ = 0), using the location of the light source as the dividing point, the CNN-based image recognition algorithm can effectively distinguish the relative orientation of the receiver based on the collinearity of the object and image in the imaging system, accurately achieving orientation recognition in four zones. However, this identification method relying on the collinearity of the object and image cannot further subdivide the identified area to lock the receiver position into a smaller range. Therefore, this method is used in the first stage for the rough orientation determination of A1, A2, A3, and A4 using the location of the light source as the reference point, such as... Figure 3 As shown.

[0053] In subsequent stages, when the emitted beam is deflected towards a certain region (ψ≠0), the peak point of the beam (x) is used as the reference point. m Distinguishing orientations using 0 as a reference point requires more feature information from the image. This solution proposes to distinguish orientations based on subtle features of the captured light source image, such as shape, contour, and grayscale distribution. The division method is as follows: Figure 4 As shown, the characteristics of the captured light source images change accordingly with the change in the receiver's position, and images captured from the same location should also exhibit certain similarities and patterns. A large number of light source images are pre-captured at different locations, and these images are then compared to (x...) m The orientation of points B1, B2, B3, and B4 is divided, and then features are extracted from them using a CNN to train a model that can effectively identify orientations. The weights of the convolutional kernels are iteratively updated based on the error between the true and predicted values ​​until a set of recognizable features is extracted to obtain the trained model. Then, real-time acquired light source images are input into the trained model to identify orientations B1, B2, B3, and B4.

[0054] A method for receiver autonomous tracking and signal enhancement is proposed based on the idea of ​​incremental iteration. The following is a specific example to illustrate how the spot range is gradually reduced during the iteration process and the spot is gradually locked to the location of the receiver.

[0055] (1) Initialize receiver tracking

[0056] First, establish a coordinate system on the receiving plane with the location of the transmitter as the origin O. Then, denote the peak value of the light spot during the k-th iteration as Q. k The light spot displacement vector is denoted as Average gray level of the captured image The goal of this method is to make Q equal to K iterations. K The location coincides with that of the receiver.

[0057] Taking receiver position point K as an example, the transmitter state is initialized to a beam deflection angle ψ = 0 and a lens-light source distance to ensure the light source covers the entire test area. Based on the captured image of the light source, the receiver's location is determined to be area A1. The peak point of the light spot is then moved from point O along the vector... Move to the center point Q1 of region A1.

[0058] (2) Initially reduce the area of ​​the light spot

[0059] The LED light consists of a lens and a light source. Adjusting the lens-light source distance L increases the light spot intensity, ensuring the light spot only covers area A1. This is based on the average grayscale of the image captured at this time. A grayscale equipotential line 1 is constructed using the line containing OQ1 as the axis. Based on a CNN-based image recognition algorithm, the relative orientation is determined to be B4. The peak point of the light spot is then moved from Q1 along... Figure 5 middle Move to Q2.

[0060] (3) Reduce the spot size again.

[0061] Adjusting the lens-light source spacing L, the light spot only covers the area B4 from step (2), further reducing the coverage area while increasing the intensity of the light spot. This is based on the average grayscale of the image captured at this point. A grayscale equipotential line 2 is constructed using the line containing OQ2 as the axis. Based on a CNN-based image recognition algorithm, the relative orientation is determined to be B3. The peak point of the light spot is then moved from Q2 along... Figure 5 middle Move to Q3.

[0062] (4) Iteratively determine the accurate location of the receiver

[0063] Let the proportional threshold η th If the average grayscale value of the image reaches the threshold proportion of the theoretical maximum value at this time... Then Q3 is the estimated receiver position, the process ends, and the total number of iterations K = 3. If it is not reached, i.e. Then repeat step (3) until the condition is met in the k-th iteration. Then K = k, Q k This represents the estimated receiver location.

[0064] In summary, this tracking and enhancement process is more like aiming and correcting the transmitter towards the receiver. First, a rough area is determined. Then, through iterative adjustments based on the captured image grayscale, the locking range is continuously narrowed until satisfactory accuracy is achieved. During this process, as the receiver's position is gradually determined, the light spot energy gradually concentrates and aligns with the receiver, thus significantly improving signal strength. As the signal strength increases, calculation errors caused by noise are reduced, further improving the accuracy of position estimation. Therefore, a virtuous cycle of mutual benefit is formed between signal and positioning, simultaneously achieving receiver position tracking and received signal enhancement based on the principle of synesthesia and mutual benefit.

[0065] It's also important to note that this method only begins threshold determination after k=3 iterations. This is because the spot size and intensity are gradually adjusted during the first three iterations, reaching their minimum and maximum at k=3. Under the same proportional threshold, different lens-source spacing settings yield different positional accuracies. Only when L is adjusted to its maximum value does the energy at the transmitting end become most concentrated, resulting in the highest reliability of the obtained position. Therefore, to ensure high reliability of the obtained position, this method does not perform threshold determination until L reaches its maximum value.

[0066] Corresponding to the above method, this embodiment also proposes a progressive signal enhancement system for visible light communication, including a transmitting LED, a receiving image sensor, a computer, and a RIS module, wherein:

[0067] The receiving end image sensor transmits the captured light source image to the computer.

[0068] The computer calculates the relative distance between the transmitting and receiving ends using an equipotential line construction method based on an average grayscale model, determines the relative orientation of the transmitting and receiving ends using an orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends by combining direction and distance through multiple iterations.

[0069] The RIS module gradually adjusts the light field based on the position information generated during the iteration process, enabling the light source to dynamically track the location of the receiving terminal.

[0070] This invention utilizes a configurable smart surface (RIS) to assist in the automatic tracking of a visible light communication receiving terminal, achieving wide-area coverage of the light source during the initial acquisition phase. Then, by combining the relative position estimation of the transceiver, the coverage area of ​​the light source is continuously narrowed through an iterative process, simultaneously achieving wide-area coverage and signal enhancement for visible light communication. To improve the accuracy of orientation determination, this invention employs a coarse division with only four orientations. This orientation accuracy is insufficient for precise positioning; therefore, this invention proposes a scheme to gradually improve positioning accuracy through multiple iterations.

[0071] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0072] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0073] The units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations are not considered to be beyond the scope of this invention.

[0074] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiments can be implemented in hardware or as a software functional module. This invention is not limited to any particular combination of hardware and software.

[0075] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A progressive signal enhancement method for visible light communication, characterized in that, include: Step 1: The image sensor at the receiving end transmits the captured image of the light source to the computer; Step 2: The computer calculates the relative distance between the transmitting and receiving ends using the equipotential line construction method based on the average grayscale model, determines the relative orientation of the transmitting and receiving ends using the orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends by combining direction and distance through multiple iterations. Methods for constructing equipotential lines based on the average grayscale model include: Assume the controlled light beam exits from the emitter at an arbitrary deflection angle ψ, the emitter's height is h, its direct direction is designated as coordinate 0, and the coordinate of the intersection of its central optical axis and the ground is x. l The coordinates of the spot with maximum brightness are x m The angle between the beam from the transmitter to the receiver and the optical axis is... The transmission distance is d, and the incident angle of the light beam reaching the receiver is θ. Now, let the line connecting the transmitting and receiving ends rotate around the central optical axis to form a cone. The intersection of this cone with the ground is an ellipse, and let the coordinates of the geometric center of the ellipse be x. c Construct elliptical gray-level equipotential lines to provide a basis for the spot movement step size in each iteration process; The initial values ​​above are corrected by adjusting the center point coordinate x. c The principle of modification is to make any The near focus of the ellipse generated by the value is related to x. m By aligning the points, the corrected elliptical grayscale contour lines are obtained. By calculating the average gray value of the image captured by the receiver, the position of the receiver is determined on the equipotential line corresponding to that gray value; Position determination methods based on image subtle feature recognition include: The orientation determination method based on image subtle feature recognition is the basis for determining the direction of light spot movement in each iteration. The orientation determination method based on image subtle feature recognition uses a CNN-based image recognition algorithm to determine the direction of the receiver relative to the peak point of the light spot based on the subtle features of the shape of the captured light source. First, the relative orientation needs to be quantized. The orientation is quantized into four values ​​of 45°, 135°, 225°, and 315° with the imaging direction as the reference and counterclockwise as the positive direction. These values ​​are represented by A1, A2, A3, A4 or B1, B2, B3, B4, and each represents a 90° angle range centered on itself. The relative positions of the transmitter and receiver are estimated through multiple iterations, combining direction and distance, including: (1) Initialize the transmitter state to beam deflection angle ψ = 0. Based on the image of the light source captured at this time, determine whether the receiver is located in area A1, A2, A3 or A4. Move the peak point of the light spot from the transmitter location O to the center point Q1 of the area. (2) Adjust the LED lamp lens-light source spacing L to increase the light spot intensity, so that the light spot only covers the area where the receiver is located; based on the average gray level of the image captured at this time... Construct grayscale equipotential lines with the line containing OQ1 as the axis; and determine the relative orientation as B1, B2, B3 or B4 based on the CNN-based image recognition algorithm, and move the peak point of the light spot from Q1 to Q2. (3) Repeat step (2) until the lens-light source distance L is adjusted to the maximum value. If the average gray value of the image reaches the threshold ratio of the theoretical maximum threshold at this time, then the peak point of the light spot is the estimated receiver position. If the average gray value of the image does not reach the threshold ratio of the theoretical maximum threshold at this time, repeat step (2) until the average gray value of the image reaches the threshold ratio of the theoretical maximum threshold. Step 3: The computer-controlled RIS module gradually adjusts the light field based on the position information generated during the iteration process, so that the light source dynamically tracks the location of the receiving terminal.

2. The progressive signal enhancement method for visible light communication according to claim 1, characterized in that, The corrected equation for the elliptical grayscale equipotential lines is expressed as: By taking the partial derivative of the average gray level of the captured image The X coordinate corresponding to angle θ at this time is obtained. Where m represents the Lambertian order of the light source after RIS modulation, m = 2(f / (fL)). 2 -1, L represents the lens-light source distance, and f represents the focal length of the lens that provides the same focusing effect as RIS.

3. The progressive signal enhancement method for visible light communication according to claim 1, characterized in that, When the transmitted beam is not deflected, i.e., ψ = 0, the receiver's approximate position is determined using the location of the light source as a reference point; the initially acquired light source image is input into a CNN-based image recognition algorithm to identify the relative orientations A1, A2, A3, and A4 of the transceiver.

4. The progressive signal enhancement method for visible light communication according to claim 3, characterized in that, When the emitted beam is deflected to a certain area, i.e. ψ≠0, the orientation is distinguished by using the peak point of the light spot as a reference point; the real-time acquired light source image is input into a CNN-based image recognition algorithm to identify the relative orientations B1, B2, B3, and B4 of the transmitting and receiving ends.

5. A progressive signal enhancement system for visible light communication, characterized in that, To implement the progressive signal enhancement method for visible light communication as described in claim 1, the system includes a transmitting LED, a receiving image sensor, a computer, and a RIS module, wherein: The receiving end image sensor transmits the captured light source image to the computer; The computer calculates the relative distance between the transmitting and receiving ends using an equipotential line construction method based on an average grayscale model, determines the relative orientation of the transmitting and receiving ends using an orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends by combining direction and distance through multiple iterations. The RIS module gradually adjusts the light field based on the position information generated during the iteration process, enabling the light source to dynamically track the location of the receiving terminal.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method as described in any one of claims 1-4.

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