Visible light communication progressive signal enhancement method and system

Abstract

The invention relates to the technical field of visible light communication, in particular to a visible light communication progressive signal enhancement method and system. A receiving end image sensor transmits a shot light source image to a computer; the computer calculates the relative distance between the receiving end and the transmitting end by using an equipotential line construction method based on an average gray model, determines the relative orientation of the receiving end and the transmitting end by using an orientation judgment method based on image fine feature recognition, and estimates the relative position of the receiving end and the transmitting end through multiple iterations in combination with the direction and the distance; and the computer controls the RIS module to gradually adjust the light field according to the position information generated in the iteration process, so that the light source dynamically tracks the position of the receiving terminal. According to the real-time position of the visible light communication receiving end, the light field is adjusted multiple times to be converged around the mobile terminal, the illumination intensity of the area where the receiving terminal is located can be greatly improved on the premise that the mobile attribute of the receiving terminal is met, and the communication requirement for the high signal-to-noise ratio is met.

Description

Technical Field

[0001] The present invention relates to the technical field of visible light communication, and in particular to a method and system for progressive signal enhancement in visible light communication. Background Art

[0002] Industrial Internet has become one of the key digital industries in the country, and has further proposed industrial communication network requirements 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 makes up for the shortcomings of traditional wireless communications in the field of industrial Internet applications, and has become an irreplaceable technology in the industrial Internet environment.

[0003] With the mechanization and intelligence of production equipment in modern industrial plants, the movement and transfer of various production factors have become more frequent. The emergence of intelligent mobile devices such as automatic guided vehicles and autonomous robots has further increased the complexity of the communication environment. They frequently shuttle between production workshops. For such mobile communication terminals, the traditional fixed light source solution is difficult to meet the high signal-to-noise ratio requirements within its entire range of motion due to power consumption limitations. Therefore, high-precision tracking of the communication receiving end becomes very important in mobile scenarios.

[0004] The existing solution is to divide the working space into multiple cells according to the irradiation range of the visible light transmitter, and regularly deploy multiple signal sources in the space as access points for each cell. Each cell has a limited coverage range and communicates independently with each other. Figure 1 However, in this scheme, there are inevitable overlapping areas between cells. The interference of adjacent signal sources in these areas and the delay caused by cell switching during movement seriously affect the performance of the system. Summary of the invention

[0005] The present invention aims to solve the problem that the existing multi-cell visible light communication scheme completely ignores the important information of the receiving terminal position and cannot dynamically adjust the light field coverage according to the real-time position of the mobile terminal. A method and system for progressive signal enhancement of visible light communication is proposed. According to the real-time position of the visible light communication receiving terminal, the light field is adjusted multiple times to converge around the mobile terminal. Under the premise of meeting its mobile properties, the light intensity in the area where the receiving terminal is located is greatly improved to meet the communication needs of high signal-to-noise ratio.

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

[0007] A visible light communication progressive signal enhancement method, comprising:

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

[0009] The computer uses an equipotential line construction method based on an average grayscale model to calculate the relative distance between the transmitter and receiver, and uses an orientation determination method based on image subtle feature recognition to determine the relative orientation of the transmitter and receiver. The relative position of the transmitter and receiver is estimated by combining the direction and distance through multiple iterations.

[0010] The computer controls the RIS module to gradually adjust the light field according to the position information generated by the iterative process, so that the light source dynamically tracks the location of the receiving terminal.

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

[0012] Assume that the regulated light beam is emitted from the transmitting end at an arbitrary deflection angle ψ, the height of the transmitting end is h, the coordinate point directly below it is 0, and the coordinate of the intersection of its central optical axis and the ground is x l , the coordinate of the maximum brightness point is x m , the angle between the light 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 direct line connecting the two ends of the transmitter and the receiver rotate around the central optical axis to form a conical surface. The intersection of the conical surface and the ground is an ellipse, and the geometric center coordinate of the ellipse is x. c , construct elliptical grayscale equipotential lines to provide a basis for the step length of the light spot movement in each iteration process;

[0013] Correct the initial value above, and the correction parameter is the center point coordinate x c The principle of correction is to make any The near focus of the ellipse generated by the value is the same as x m The points coincide with each other to obtain the corrected elliptical grayscale equipotential lines.

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

[0015]

[0016] By taking the partial derivative of the average grayscale of the captured image The X coordinate corresponding to the angle θ at this time is Where m represents the Lambertian order of the light source after RIS control m = 2(f / (fL)) 2 -1, L represents the distance between lens and light source, and f represents the focal length of the lens with 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 the gray value.

[0018] According to the progressive signal enhancement method for visible light communication of the present invention, further, an azimuth determination method based on image subtle feature recognition is a basis for determining the direction of movement of the light spot in each iteration process; the azimuth 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 according to the subtle features of the shape of the photographed light source; first, the relative azimuth needs to be quantified, and the direction is quantified into four values ​​of 45°, 135°, 225°, and 315° with the imaging direction as a reference and counterclockwise as the positive direction, which are represented by A1, A2, A3, A4 or B1, B2, B3, B4, respectively, and each represents an angle range of 90° with itself as the center direction.

[0019] According to the progressive signal enhancement method for visible light communication of the present invention, further, when the transmitted light beam is not deflected, that is, ψ=0, the rough position of the receiver is judged with the position of the light source as a reference point; the initially collected light source image is input into the CNN-based image recognition algorithm to identify the relative positions A1, A2, A3, and A4 of the transmitting and receiving ends.

[0020] According to the progressive signal enhancement method for visible light communication of the present invention, further, when the transmitted light beam deviates to a certain area, that is, ψ≠0, the orientation is distinguished by taking the peak point of the light spot as a reference point; the real-time collected light source image is input into the CNN-based image recognition algorithm to identify the relative orientations B1, B2, B3, and B4 of the transmitting and receiving ends.

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

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

[0023] (2) Adjust the distance L between the LED light lens and the light source to increase the intensity of the light spot, so that the light spot only covers the area where the receiver is located; according to the average grayscale of the image captured at this time The grayscale equipotential line is constructed with the straight line where OQ1 is located as the axis; and the relative orientation is judged to be B1, B2, B3 or B4 according to the image recognition algorithm based on CNN, and the peak point of the spot is moved 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 grayscale value of the image reaches the threshold ratio of the theoretical maximum threshold at this time, the peak point of the light spot is the estimated receiver position. If the average grayscale value of the image does not reach the threshold ratio of the theoretical maximum threshold at this time, repeat step (2) until the average grayscale value of the image reaches the threshold ratio of the theoretical maximum threshold.

[0025] Furthermore, the present invention also provides a visible light communication progressive signal enhancement system, comprising a transmitting end LED, a receiving end 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 by using an equipotential line construction method based on an average grayscale model, determines the relative orientation of the transmitting and receiving ends by using an orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends by combining the direction and distance through multiple iterations;

[0028] The RIS module gradually adjusts the light field according to the position information generated by the iterative process, so that the light source dynamically tracks the location of the receiving terminal.

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

[0030] Visible light communication has moving communication terminals in application scenarios such as industrial workshops. For visible light communication terminals that move over a large range, the traditional fixed light source large-scale broadcast illumination coverage solution has low average irradiance under large movement range due to the limitation of total power, and cannot meet the communication requirements of high signal-to-noise ratio. Aiming at the problem of dynamic tracking of visible light communication receiving end in motion scenes, the present invention proposes a progressive signal enhancement method for visible light communication, establishes an iterative visible light signal enhancement technology based on RIS control of the transmitter and CMOS image sensor, and achieves the purpose of simultaneous position tracking and signal enhancement through multiple light beam adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings of the embodiments of the present invention, wherein the drawings are only used to illustrate some embodiments of the present invention, but not to limit all embodiments of the present invention thereto.

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

[0033] Figure 2 is a schematic diagram of geometric relationships when the light beam is tilted in an embodiment of the present invention;

[0034] Figure 3 is a schematic diagram of the rough orientation of the light source as a reference point and the corresponding image features according to an embodiment of the present invention;

[0035] Figure 4 It is the azimuth division based on the peak point of the light spot and the corresponding light spot displacement vector in the embodiment of the present invention;

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

[0037] Figure 6 It is a structural schematic diagram of a visible light communication progressive signal enhancement system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings of specific embodiments of the present invention to clearly and completely describe the exemplary scheme of the embodiment of the present invention. Unless otherwise defined, the technical terms or scientific terms used in the present invention should be the common meanings understood by people with ordinary skills in the field.

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

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

[0041] Step S102: The computer calculates the relative distance between the transmitting and receiving ends by using an equipotential line construction method based on an average grayscale model, determines the relative orientation of the transmitting and receiving ends by 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.

[0042] Step S103: The computer controls the RIS module to gradually adjust the light field according to the position information generated in the iterative process, so that the light source dynamically tracks the position of the receiving terminal, and finally realizes wide-area coverage and signal enhancement of visible light communication at the same time.

[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 length of the spot movement in each iteration process. Assume that the regulated light beam is emitted from the transmitting end at an arbitrary deflection angle ψ, the height of the transmitting end is h, and the coordinate point directly below it is 0, and the coordinate of the intersection of its central optical axis and the ground is x l , the coordinate of the maximum brightness point (peak point) is x m , the angle between the light 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 direct line connecting the two ends of the transmitter and receiver rotate around the central optical axis to form a conical surface. The intersection of the conical surface and the ground is an ellipse, such as Figure 2 As shown, let the geometric center coordinate of the ellipse be x c It should be noted that the center point x of the ellipse c , spot peak point x m and the optical axis points to point x l 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 by the transmitting and receiving ends is very small, and it can be considered that the change of θ caused by the movement of the receiver along the ellipse is negligible. Then it can be considered that when the transmission distance is long, the power or grayscale value received by the receiver on the ellipse is approximately unchanged, so the equipotential line can be constructed as an ellipse.

[0046] Further combined with the actual situation, all equipotential lines should have the peak point x m Wrapped in a closed curve, but when Enough hours, x m The initial value will be outside the ellipse, so it is necessary to make some corrections to the initial value. The correction parameter is the center point coordinate x c The principle of correction is to make any The near focus of the ellipse generated by the value is the same as x m The points coincide with each other, and the corrected elliptical grayscale equipotential line equation is expressed as:

[0047]

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

[0049] Then, by calculating the average gray value of the image captured by the receiver, the receiver position can be determined on the equipotential line corresponding to the gray value. m ,0) direction, the exact position of the receiver can be obtained in combination with the equipotential lines.

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

[0051] The azimuth determination method based on image subtle feature recognition is the basis for determining the direction of the light spot movement in each iteration process. The azimuth determination method based on image subtle feature recognition uses an image recognition algorithm based on a convolutional neural network (CNN) to determine the direction of the receiver relative to the peak point of the light spot according to the subtle features of the shape of the captured light source. First, the relative azimuth needs to be quantified. The direction is quantified into four values ​​of 45°, 135°, 225°, and 315° with the imaging direction as the reference and counterclockwise as the positive direction. They are represented by A1, A2, A3, A4 or B1, B2, B3, and B4, respectively, each representing an angle range of 90° with itself as the center direction.

[0052] When the emission beam is not deflected (ψ=0), the image recognition algorithm based on CNN can effectively distinguish the relative position of the receiver according to the collinear relationship of the imaging system, and can accurately realize the position recognition of the four partitions. However, this recognition method based on the collinear relationship of the image cannot further divide the recognized area and lock the receiver position in a smaller range. Therefore, this method is used for the rough position A1, A2, A3, A4 determination with the light source position as the reference point in the first stage, such as Figure 3 shown.

[0053] In the subsequent stage, when the emission beam deviates to a certain area (ψ≠0), the peak point of the spot (x m ,0) is the reference point to distinguish the orientation, which requires more feature information in the image. This scheme proposes to distinguish the orientation based on the subtle features of the light source image in terms of shape, contour, and grayscale distribution. The division method is as follows Figure 4 As the receiver position changes, the characteristics of the light source image will also change accordingly, and the images taken at the same position should also have certain similarities and regularities. Pre-take a large number of light source images at different positions and compare them relative to (x m ,0) points are divided into different directions, and then CNN is used to extract features and train a model that can effectively identify directions. The weight of the convolution kernel is iteratively updated through the error between the true value and the predicted value until an identifiable effective feature set is extracted to obtain a trained model. Then the light source image collected in real time is input into the trained model to identify directions B1, B2, B3, and B4.

[0054] A method for autonomous tracking and signal enhancement at the receiving end is proposed through the idea of ​​progressive iteration. The following is a specific example to illustrate how to gradually reduce the range of the light spot and gradually lock the light spot to the location of the receiver during the iteration process.

[0055] (1) Initialize receiver tracking

[0056] First, a coordinate system is established on the receiving plane with the location of the transmitter as the origin O, and the peak point of the spot in the kth iteration is recorded as Q k , the spot displacement vector is recorded as The average grayscale of the captured image The purpose of this method is to make Q K Coincident with the location of the receiver.

[0057] Taking the receiver position point K as an example, the transmitter state is initialized to the beam deflection angle ψ = 0 and the lens-light source spacing, so that the light source covers the entire test area. According to the light source image taken at this time, it is determined that the receiver is located in area A1, and the peak point of the spot is moved from point O along the vector Move to the center point Q1 of area A1.

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

[0059] The LED lamp includes a lens and a light source. The distance L between the lens and the light source is adjusted to increase the intensity of the light spot. The light spot only covers area A1. According to the average grayscale of the image captured at this time The grayscale equipotential line 1 is constructed with the straight line where OQ1 is located as the axis, and the relative orientation is determined to be B4 according to the image recognition algorithm based on CNN. The peak point of the spot is moved from Q1 along Figure 5 middle Move to Q2.

[0060] (3) Reduce the light spot range again

[0061] Adjust the lens-light source distance L, the spot only covers the area B4 of step (2), the spot coverage area is further reduced and the spot intensity is further increased. The grayscale equipotential line 2 is constructed with the straight line where OQ2 is located as the axis, and the relative orientation is determined to be B3 according to the image recognition algorithm based on CNN. The peak point of the spot is moved from Q2 along Figure 5 middle Move to Q3.

[0062] (4) Iteratively determine the exact position of the receiver

[0063] Set the ratio threshold η th , if the average gray value of the image reaches the threshold ratio of the theoretical maximum value Then Q3 is the estimated receiver position, the process ends, and the total number of iterations K = 3. If it is not reached, that is Then repeat step (3) until the kth iteration satisfies Then K = k, Q k is the estimated receiver position.

[0064] In general, this tracking and enhancement process is more like a process of aiming and correcting the transmitter to the receiver. First, a rough regional judgment is made, and then the locking range is continuously adjusted and narrowed according to the grayscale of the captured image through iterations until a satisfactory accuracy is achieved. In this process, as the position of the receiver is gradually determined, the energy of the light spot is gradually concentrated and aimed at the receiver, so that the signal strength is significantly improved. As the signal strength increases, the calculation error caused by noise is reduced, thereby further improving the accuracy of the position estimation. As a result, a virtuous cycle of mutual gain is formed between the signal and the positioning, and the tracking of the receiver position and the enhancement of the received signal are simultaneously completed under the idea of ​​mutual benefit.

[0065] It should also be noted that the method process starts threshold determination only when k=3 iterations. This is because the spot range and intensity are gradually adjusted during the first three iterations, and are adjusted to the minimum range and maximum intensity when k=3. Under the same proportional threshold, the position accuracy obtained by setting different lens-light source spacings is different. Only when L is adjusted to the maximum value, the energy at the transmitting end is most concentrated, and the obtained position has the highest credibility. Therefore, in order to ensure that the obtained position has a high credibility, this method does not perform threshold determination before L reaches the maximum value.

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

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

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

[0069] The RIS module gradually adjusts the light field according to the position information generated by the iterative process, so that the light source dynamically tracks the location of the receiving terminal.

[0070] The present invention uses a configurable intelligent surface (RIS) to assist in the automatic tracking of visible light communication receiving terminals. In the initial capture stage, the light source achieves wide-area coverage. Then, combined with the relative position estimation of the transceiver, the light source coverage is continuously reduced through iteration, which can simultaneously achieve wide-area coverage and signal enhancement of visible light communication. In order to improve the accuracy of azimuth determination, the present invention adopts a coarse division of only 4 azimuths. The azimuth accuracy cannot meet the needs of precise positioning, so the present invention proposes a solution to gradually improve the positioning accuracy through multiple iterations.

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

[0072] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0073] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0074] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, 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 embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0075] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for progressive signal enhancement in visible light communication, characterized in that: include: The image sensor at the receiving end transmits the captured light source image to the computer; The computer uses an equipotential line construction method based on an average grayscale model to calculate the relative distance between the transmitter and receiver, and uses an orientation determination method based on image subtle feature recognition to determine the relative orientation of the transmitter and receiver. The relative position of the transmitter and receiver is estimated by combining the direction and distance through multiple iterations. The computer controls the RIS module to gradually adjust the light field according to the position information generated by the iterative process, so that the light source dynamically tracks the location of the receiving terminal.

2. The method for progressive signal enhancement of visible light communication according to claim 1, characterized in that: The methods for constructing equipotential lines based on the average grayscale model include: Assume that the regulated light beam is emitted from the transmitting end at an arbitrary deflection angle ψ, the height of the transmitting end is h, the coordinate point directly below it is 0, and the coordinate of the intersection of its central optical axis and the ground is x l , the coordinate of the maximum brightness point is x m , the angle between the light 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 direct line connecting the two ends of the transmitter and the receiver rotate around the central optical axis to form a conical surface. The intersection of the conical surface and the ground is an ellipse, and the geometric center coordinate of the ellipse is x. c , construct elliptical grayscale equipotential lines to provide a basis for the step length of the light spot movement in each iteration process; Correct the initial value above, and the correction 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 the same as x m The points coincide with each other to obtain the corrected elliptical grayscale equipotential lines.

3. The method for progressive signal enhancement of visible light communication according to claim 2, characterized in that: The corrected elliptical grayscale equipotential line equation is expressed as: By taking the partial derivative of the average grayscale of the captured image The X coordinate corresponding to the angle θ at this time is Where m represents the Lambertian order of the light source after RIS control m = 2(f / (fL)) 2 -1, L represents the distance between lens and light source, and f represents the focal length of the lens with equivalent RIS focusing effect; By calculating the average gray value of the image captured by the receiver, the receiver position is determined on the equipotential line corresponding to the gray value.

4. The method for progressive signal enhancement of visible light communication according to claim 1, characterized in that: The azimuth determination method based on image subtle feature recognition is the basis for determining the direction of light spot movement in each iteration process; the azimuth 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 according to the subtle features of the shape of the captured light source; first, the relative azimuth needs to be quantified, and the direction is quantified into four values ​​of 45°, 135°, 225°, and 315° with the imaging direction as the reference and counterclockwise as the positive direction, respectively represented by A1, A2, A3, A4 or B1, B2, B3, B4, and each represents an angle range of 90° with itself as the center direction.

5. The method for progressive signal enhancement of visible light communication according to claim 4, characterized in that: When the transmitted light beam is not deflected, that is, ψ = 0, the position of the receiver is roughly determined with the location of the light source as the reference point; the initially collected light source image is input into the CNN-based image recognition algorithm to identify the relative positions A1, A2, A3, and A4 of the transmitting and receiving ends.

6. The method for progressive signal enhancement in visible light communication according to claim 5, characterized in that: When the emitted light beam deviates to a certain area, that is, ψ≠0, the peak point of the light spot is used as a reference point to distinguish the orientation; the real-time collected light source image is input into the CNN-based image recognition algorithm to identify the relative orientations B1, B2, B3, and B4 of the transmitting and receiving ends.

7. The method for progressive signal enhancement in visible light communication according to claim 6, characterized in that: The relative positions of the transmitter and receiver are estimated by combining the direction and distance through multiple iterations, including: (1) Initialize the state of the transmitter to a beam deflection angle ψ = 0, determine whether the receiver is located in area A1, A2, A3 or A4 based on the light source image captured at this time, and move the peak point of the light spot from point O where the transmitter is located to the center point Q1 of the area; (2) The distance L between the LED lamp lens and the light source is adjusted to increase the intensity of the light spot, so that the light spot only covers the area where the above-mentioned receiver is located; according to the average grayscale G1 of the image captured at this time, a grayscale equipotential line is constructed with the straight line where OQ1 is located as the axis; and according to the image recognition algorithm based on CNN, the relative orientation is judged to be B1, B2, B3 or B4, and the peak point of the light spot is moved from Q1 to Q2; (3) Repeat step (2) until the lens-light source distance L is adjusted to the maximum value. If the average grayscale value of the image reaches the threshold ratio of the theoretical maximum threshold at this time, the peak point of the light spot is the estimated receiver position. If the average grayscale value of the image does not reach the threshold ratio of the theoretical maximum threshold at this time, repeat step (2) until the average grayscale value of the image reaches the threshold ratio of the theoretical maximum threshold.

8. A visible light communication progressive signal enhancement system, characterized in that: It includes a transmitting end LED, a receiving end 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 by using an equipotential line construction method based on an average grayscale model, determines the relative orientation of the transmitting and receiving ends by using an orientation determination method based on image subtle feature recognition, and estimates the relative position of the transmitting and receiving ends by combining the direction and distance through multiple iterations; The RIS module gradually adjusts the light field according to the position information generated by the iterative process, so that the light source dynamically tracks the location of the receiving terminal.

9. 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 according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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