Multi-screen splicing intelligent control system

Abstract

The invention belongs to the technical field of display methods, and particularly relates to a multi-screen splicing intelligent control system which comprises a brightness analysis module, an aging analysis module, a dynamic compensation control module and a display driving module. And a central screen brightness gain coefficient and an edge screen brightness gain coefficient which are respectively required by approaching a preset reference brightness value are respectively generated by using a preset compensation calculation model according to the central screen aging coefficient and the edge screen aging coefficient. The central screen and the edge screen of the spliced screen are distinguished, so that the brightness gain coefficients of the plurality of screens are adjusted according to different aging degrees of different spliced screens, the display brightness of the plurality of adjusted spliced screens tends to the set reference brightness, and the display brightness of the plurality of screens is improved. The problem of inconsistent brightness attenuation caused by high-frequency use of the central area in the prior art is solved, brightness coordination of multiple spliced screens during display is realized, and the viewing experience of viewers is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of screen dynamic display, and in particular relates to a multi-screen splicing intelligent control system. Background Art

[0002] The screen splicing system is to splice multiple display images together to form an overall large screen, achieving higher information display density and wider display range. For example, the LED sphere screen in Las Vegas presents a stunning visual effect by splicing multiple display screens into a whole. The core of this technology is to divide a complete video or picture into multiple parts, each screen only displays a part of it, and finally realize the complete picture presentation through physical splicing.

[0003] When multiple screens are spliced ​​together, the most important thing is the color coordination and brightness consistency between the multiple screens. However, the difference in usage frequency between the central area and the surrounding area screens and the resulting inconsistency in the aging coefficients of the two are important issues that affect the final display effect:

[0004] Specifically, since the human eye's visual focus is naturally concentrated on the central area, core advertisements, real-time information and other high-frequency updated content in commercial scenarios are usually deployed in the central area, while edge screens are mostly used as static decorations or auxiliary information displays, resulting in the average daily usage time of the central area screen being higher than that of the edge area.

[0005] In this unbalanced usage mode: due to the self-luminous characteristics of the OLED screen, the decay rate of the organic material in the high-brightness area is higher than that in the edge area. Under long-term high-load operation in the central area, the light decay rate of the lamp beads in the central area screen is faster than that in the edge area. As a result, when multiple spliced ​​screens are used for splicing and display of the same data stream, even under the same control command, the display brightness of the multiple screens will be different from that of the screen in the central area and the surrounding screens, resulting in incoordination of the screen display and affecting the viewing experience.

[0006] Here, the screen in the center area is calibrated as the center screen, and the screen in the edge area is calibrated as the edge screen, that is, a certain position in the center of a large display screen formed by splicing multiple spliced ​​screens is calibrated as the center screen, and the remaining sub-screens of the large screen spliced ​​by all the spliced ​​screens except the calibrated center screen are calibrated as edge screens. The determination of the center area position is based on a specific usage scenario, where the usage frequency exceeds specific data, so that multiple screens in the center area and other screens under the control command of the same control terminal have inconsistent brightness caused by inconsistent aging due to the usage frequency, and the center screen and the remaining edge screens in the center area are distinguished.

[0007] Therefore, there is a need for an intelligent control system that coordinates the display brightness of the central area and the edge position screens when performing multi-screen splicing display, so as to achieve consistent brightness of the overall spliced screen. Summary of the Invention

[0008] To solve the above problems existing in the prior art, the present invention provides a multi-screen splicing intelligent control system, which solves the problem of inconsistent brightness caused by different aging degrees of the screens in the central area and the edge position in a multi-screen splicing display system.

[0009] The object of the present invention can be achieved by the following technical solutions: A multi-screen splicing intelligent control system includes:

[0010] A brightness analysis module, configured to respectively obtain the current brightness value of the central screen and the current brightness value of the edge screen;

[0011] An aging analysis module, configured to respectively calculate the aging coefficient of the central screen and the aging coefficient of the edge screen according to the historical usage data and the current brightness value of the central screen and the edge screen, where the historical usage data includes the usage duration, average brightness, screen working temperature, and peak brightness of the central screen and the edge screen;

[0012] A dynamic compensation control module, configured to respectively generate the central screen brightness gain coefficient and the edge screen brightness gain coefficient required to approach the preset reference brightness value according to the central screen aging coefficient and the edge screen aging coefficient by using a preset compensation calculation model;

[0013] The dynamic compensation control module generates control instructions for the central screen brightness gain coefficient and the edge screen brightness gain coefficient respectively and sends them to the display driving module;

[0014] A display driving module, configured to receive and execute the control instructions to respectively drive the central screen and the edge screen for display.

[0015] Preferably, the aging analysis module uses the following calculation formulas to respectively calculate the aging coefficient of the central screen and the aging coefficient of the edge screen:

[0016]

[0017] Among them, α is the aging coefficient, k is the material attenuation constant of the screen, represents a linear correlation function between the aging degree and the cumulative effect of the usage duration and the average brightness, t i is the cumulative usage duration of the screen on the i-th day, L a (i) is the average brightness of the screen on the i-th day, Indicates the impact of the peak brightness of a single spliced screen on the accelerated aging of the screen. β is the peak brightness accelerated decay factor, obtained from multiple experimental data, and L p (i) is the peak brightness duration of the screen on the i-th day, and L max is the maximum brightness displayed on the screen, represents the environmental temperature compensation term, and T env is the environmental temperature, and T 0 is the aging test temperature of the screen, and γ is the temperature influence factor of the screen.

[0018] Preferably, the compensation calculation model is determined by training an artificial neural network model with training data;

[0019] The training data includes the parameters of multiple screens, as well as the central screen brightness gain coefficient and the edge screen brightness gain coefficient after brightness adjustment. The parameters include: the central screen aging coefficient, the edge screen aging coefficient, the current brightness value of the central screen, the current brightness value of the edge screen, and a preset reference value.

[0020] Preferably, the determination of the compensation calculation model includes:

[0021] The central screen brightness gain coefficient and the edge screen brightness gain coefficient required for the neural network model to output a value approaching the preset reference brightness value;

[0022] Calculate the theoretical brightness value of the central screen and the theoretical brightness value of the edge screen corresponding to the central screen brightness gain coefficient and the edge screen brightness gain coefficient respectively;

[0023] Taking the minimum difference between the theoretical brightness value of the central screen and the theoretical brightness value of the edge screen corresponding to the central screen brightness gain coefficient and the edge screen brightness gain coefficient respectively and the preset reference brightness value as the goal, use the backpropagation algorithm for training, and determine the trained neural network model as the compensation calculation model.

[0024] Preferably, the aging analysis module presets an aging coefficient threshold. The aging analysis module monitors the calculated aging coefficient according to the preset aging coefficient threshold, and issues a warning when the calculated aging coefficient is greater than the preset aging coefficient threshold.

[0025] Preferably, the brightness analysis module divides the central screen and the edge screen into multiple grids respectively, and customizes a label for each grid;

[0026] Measure the brightness values of multiple grids in multiple screen areas respectively, and then calculate the average brightness value of the screen based on the brightness values corresponding to the multiple grids within one screen and the number of grids, and calibrate the average brightness value as the current brightness value of the screen.

[0027] Preferably, it further includes a clock control module, which is electrically connected to the display driving module and is used to perform clock synchronization on the driving display of the screen by the display driving module using the PTP protocol.

[0028] Preferably, it further includes an image position analysis module, which is electrically connected to the display driving module. The image position analysis module obtains image edge points at multiple screen seams through an edge detection algorithm and determines whether the images at adjacent screen seams are aligned:

[0029] If it is determined to be aligned, the alignment result is transmitted to the display driving module;

[0030] If it is determined to be misaligned, the misalignment result is transmitted to the terminal.

[0031] Preferably, the edge detection algorithm of the image position analysis module uses the Canny operator and includes the following steps:

[0032] a: Smooth the image of the screen using a Gaussian filter. The formula of the Gaussian filter is:

[0033]

[0034] where (x, y) are the coordinates of the pixels in the image, and σ is the standard deviation of the Gaussian distribution;

[0035] b: Calculate the image gradient value to obtain the gradient values in the horizontal and vertical directions of the image, and use the Sobel operator to calculate the image gradient value;

[0036] c: Convolve the image based on the Sobel operator, and set the gradient components of each pixel point in the X direction and the y direction to G x (x, y), G y (x, y), and calculate the gradient magnitude and gradient direction;

[0037] where the calculation formula of the gradient magnitude G(x, y) is:

[0038]

[0039] The calculation formula of the gradient direction θ(x, y) is:

[0040]

[0041] d: Obtain edge points through non-maximum suppression and double-threshold detection.

[0042] The beneficial effects of the present invention are:

[0043] Through the above solution, by distinguishing the central screen and the edge screens of the tiled screen, the aging degrees of different tiled screens are made different, and the brightness gain coefficients of multiple screens are adjusted, so that the display brightness of the multiple tiled screens after adjustment tends to the set reference brightness, solving the problem of inconsistent brightness attenuation caused by high-frequency use in the central area in the prior art, achieving brightness coordination when multiple tiled screens are displayed, and improving the viewing experience of viewers. The aging analysis module integrates multi-dimensional historical data and real-time measurements, establishes a multi-dimensional aging model, and realizes the accurate calculation of the aging coefficients of multiple tiled screens, thereby improving the accuracy of the compensation calculation of the subsequent compensation calculation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0045] Figure 1 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will describe in detail the specific embodiments, structures, features, and effects of the present invention with reference to the accompanying drawings and preferred embodiments.

[0047] Please refer to Figure 1 , in a multi-screen tiled display system, the screens in the central area age faster due to high-frequency use, resulting in a brightness difference from the edge screens. The prior art fails to effectively combine historical usage data and real-time brightness values for dynamic compensation, resulting in poor display consistency. This embodiment provides a multi-screen tiled intelligent control system, including:

[0048] A brightness analysis module that uses brightness sensors to respectively obtain the current brightness value of the central screen and the current brightness value of the edge screens, and transmits the obtained brightness value data to the aging analysis unit through the CAN bus;

[0049] An aging analysis module for respectively calculating the aging coefficient of the central screen and the aging coefficient of the edge screens according to the historical usage data and the current brightness values of the central screen and the edge screens, where the historical usage data includes the usage duration, average brightness, screen working temperature, and brightness peak of the central screen and the edge screens;

[0050] A dynamic compensation control module for respectively generating the brightness gain coefficient of the central screen and the brightness gain coefficient of the edge screens that are required to approach the preset reference brightness value according to the aging coefficient of the central screen and the aging coefficient of the edge screens using a preset compensation calculation model;

[0051] The dynamic compensation control module generates control instructions for the central screen brightness gain coefficient and the edge screen brightness gain coefficient respectively and sends them to the display driving module;

[0052] A clock control module, which is electrically connected to the display driving module, is used to perform clock synchronization on the driving display of multiple tiled screens by the display driving module using the PTP protocol to ensure that the multiple screens are displayed under the same clock;

[0053] The display driving module is used to receive and execute control instructions to drive the central screen and the edge screen to display respectively, and precisely adjust the driving circuits of each screen according to the time-sharing synchronization instructions to adjust the display brightness of each screen.

[0054] The dynamic compensation control module calculates the central screen brightness gain coefficient and the edge screen brightness gain coefficient using a preset compensation calculation model. The preset reference brightness value of the dynamic compensation module is the reference brightness required when the data stream to be played is displayed through the multi-screen splicing system, and it is set according to the specific playback scenario and requirements.

[0055] Among them, the compensation calculation model is determined by training an artificial neural network model using the following training data;

[0056] Training data: parameters of multiple screens and the central screen brightness gain coefficient and the edge screen brightness gain coefficient after brightness adjustment. The parameters include: central screen aging coefficient, edge screen aging coefficient, current brightness value of the central screen, current brightness value of the edge screen, and the preset reference value.

[0057] Through the collaborative work of each module of the system, a closed-loop control of the display brightness consistency of multiple tiled screens is achieved. The central screen and the edge screen of the tiled screen are distinguished, so as to adjust the brightness gain coefficients of multiple screens according to the different aging degrees of different tiled screens, making the display brightness of the adjusted multiple tiled screens tend to the set reference brightness, solving the problem of inconsistent brightness attenuation caused by high-frequency use in the central area in the prior art, realizing the brightness coordination of multiple tiled screens during display, and improving the viewing experience of viewers.

[0058] The aging analysis module integrates historical data (duration, brightness, temperature) with real-time measurement to establish a multi-dimensional aging model, realizing the accurate calculation of the aging coefficients of multiple tiled screens, thereby improving the accuracy of the compensation calculation of the subsequent compensation calculation model.

[0059] The determination of the compensation calculation model includes:

[0060] The central screen brightness gain coefficient and the edge screen brightness gain coefficient required for the neural network model output to approach the preset reference brightness value;

[0061] Calculate the theoretical brightness values of the central screen and the edge screen corresponding to the central screen brightness gain coefficient and the edge screen brightness gain coefficient respectively;

[0062] Aiming at minimizing the differences between the theoretical brightness values of the central screen and the edge screen corresponding to the central screen brightness gain coefficient and the edge screen brightness gain coefficient respectively and the preset reference brightness value, use the backpropagation algorithm for training, and determine the trained neural network model as the compensation calculation model. The backpropagation algorithm can effectively minimize the error between the predicted output and the actual target (i.e., the preset reference brightness value).

[0063] Among them, the aging analysis module uses the following calculation formulas to calculate the central screen aging coefficient and the edge screen aging coefficient respectively. Of course, the calculation of the screen aging coefficient here is for the calculation of a specific sub-screen. That is, there can be multiple sub-screens for the central screen or the edge screen. Therefore, the following is the aging coefficient calculation formula for a single sub-screen:

[0064]

[0065] Among them, α is the aging coefficient, and k is the specific material attenuation constant of a single screen, which is obtained from the collection parameters of the screen;

[0066] Represents a linear correlation function between the aging degree and the cumulative effect of the usage duration and the average brightness, t i Is the cumulative usage duration of the screen on the i-th day, L a (i) is the average brightness of the screen on the i-th day;

[0067] Represents the influence of the peak brightness within a single spliced screen on the screen's accelerated aging. Among them, β is the peak brightness acceleration decay factor, which is obtained through multiple experimental data, L p (i) is the peak brightness duration of the screen on the i-th day, L max Is the maximum brightness displayed by the screen;

[0068] Since the environmental temperature will have a certain impact on the working temperature of the screen itself; use Represents the environmental temperature compensation term, T env Is the environmental temperature, T 0 Is the aging test temperature of the screen, and γ is the temperature influence factor of the screen, used to characterize the influence of the increase in the screen working temperature on the screen's accelerated aging, which is obtained through a large number of experimental test data.

[0069] Among them, the determination of the material attenuation constant k includes setting the experimental environmental temperature to T in the experimental environment 0Under this condition, control a single screen to have a fixed brightness L a Continuously work, and record the time required for the brightness of this screen to decay to 90% and calibrate it as t a Then, the material decay constant k is calculated using the following formula:

[0070]

[0071] where α 1 is the preset aging threshold of the screen, and α 1 = 0.1

[0072] When the aging of the screen reaches a certain degree, it means that the parts inside the screen have reached the service life. The aging analysis module also presets an aging coefficient threshold. The aging analysis module monitors the calculated aging coefficient according to the preset aging coefficient threshold. When the calculated aging coefficient is greater than the preset aging coefficient threshold, a warning is issued. Here, the aging coefficient threshold is limited by the material of the specific screen

[0073] Since the light emitted by the screen is captured by a camera or other image acquisition device, so as to realize the detection of the brightness value of a single sub-screen. However, when the size of a single sub-screen is relatively large, there will be shooting dead angles or shadow positions when the camera or other image acquisition devices capture it. Especially for a curved screen. Therefore, in one embodiment, the brightness analysis module divides the central screen and the edge screen into multiple grids respectively, and customizes a label for each grid

[0074] Measure the brightness values of multiple grids in multiple screen areas respectively, and then calculate the average brightness value of the screen based on the brightness values corresponding to the multiple grids in a said screen and the number of grids, and calibrate the average brightness value as the current brightness value of the screen

[0075] By dividing the screen into multiple grids and measuring and analyzing the brightness of each grid separately, a refined evaluation of the screen brightness distribution can be realized. This method can not only provide the average brightness value of the entire screen, but also reveal the brightness change situation in the local area, so as to more accurately reflect the true performance of the screen

[0076] In order to check whether the pixels between multiple tiled screens are aligned, so as to check whether the frame data on the edges between multiple tiled screens is synchronized, and whether the brightness transition between multiple tiled screens is smooth. In one embodiment, an image position analysis module is further included. The image position analysis module is electrically connected to the display driving module. The image position analysis module obtains the image edge points at the seams of multiple screens through an edge detection algorithm, and determines whether the images at the adjacent screen seams are aligned. If they are aligned, the alignment result is transmitted to the display driving module. If they are not aligned, the result of image misalignment is uploaded to the terminal, and the terminal is the operator's mobile phone or PC. The operator can perform further maintenance and adjustment.

[0077] The edge detection algorithm of the image position analysis module uses the Canny operator, which includes the following steps:

[0078] Step a: Gaussian filtering is a linear smoothing filter, which is suitable for eliminating Gaussian noise and is widely used in image denoising. It makes the image smoother by performing a convolution operation on the image, reducing the influence of noise on subsequent edge detection. The formula of the two-dimensional Gaussian function is:

[0079]

[0080] where (x, y) are the coordinates of the pixels in the image, and σ is the standard deviation of the Gaussian distribution;

[0081] In practical applications, we usually use a discrete Gaussian kernel for convolution operations. For example, a 3×3 Gaussian kernel can be expressed as:

[0082]

[0083] Let the original image be I(x, y), and the image after Gaussian filtering be S(x, y). Then the convolution operation can be expressed as:

[0084]

[0085] where m and n are the radii of the Gaussian kernel;

[0086] Step b: Calculate the image gradient values to obtain the gradient values in the horizontal and vertical directions of the image. Use the Sobel operator to calculate the image gradient values. The Sobel operator is a commonly used edge detection operator. It combines Gaussian smoothing and differential differentiation to calculate the approximate gradient of the image gray function:

[0087] The convolution kernels of the Sobel operator in the x direction and y direction are respectively:

[0088]

[0089] If the image after Gaussian filtering is S(x, y), the gradient components in the x and y directions are respectively:

[0090]

[0091] Step c: Convolve the image based on the Sobel operator, and set the gradient components of each pixel in the X direction and y direction to G x (x, y), G y (x, y), and calculate the gradient magnitude and gradient direction;

[0092] Among them, the calculation formula for the gradient magnitude θ(x, y) is:

[0093]

[0094] The calculation formula for the gradient direction θ(x, y) is:

[0095]

[0096] Step d: Obtain edge points through non-maximum suppression and double-threshold detection.

[0097] Non-maximum suppression is a method for edge refinement. It compares the gradient magnitude of the current pixel with the gradient magnitudes of adjacent pixels in its gradient direction, and only retains the local maximum, thereby eliminating edge blurring;

[0098] And double-threshold detection is used to determine which edges are real edges and which are false edges. Two thresholds need to be set: the low threshold T L and the high threshold T H . Pixel points with a gradient magnitude greater than T H are considered strong edge points, pixel points with a gradient magnitude less than T L are considered non-edge points, and pixel points with a gradient magnitude between T L and T H are considered weak edge points. Weak edge points are considered real edge points only when they are connected to strong edge points.

[0099] Using the image position analysis module combined with an edge detection algorithm (such as the Canny operator) to evaluate the alignment of multiple spliced screens, it can be known whether the multiple screens are aligned under the current display. Through the edge detection algorithm, an estimate of the color alignment or the smoothness of the brightness transition can be achieved, and then the situation where the multiple screens are misaligned at the seams can be transmitted to the operator for the operator to understand.

[0100] The above are only the preferred embodiments of the present invention and do not impose any formal limitations on the present invention. Although the present invention has been disclosed above in the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A multi-screen splicing intelligent control system, characterized by: include: A brightness analysis module is used to obtain the current brightness value of the center screen and the current brightness value of the edge screen respectively; An aging analysis module, used to calculate a center screen aging coefficient and an edge screen aging coefficient respectively according to historical usage data and current brightness values ​​of the center screen and the edge screen, wherein the historical usage data includes usage time, average brightness, screen operating temperature and peak brightness of the center screen and the edge screen; A dynamic compensation control module, used to generate a center screen brightness gain coefficient and an edge screen brightness gain coefficient respectively required to approach a preset reference brightness value according to the center screen aging coefficient and the edge screen aging coefficient using a preset compensation calculation model; The dynamic compensation control module generates control instructions from the central screen brightness gain coefficient and the edge screen brightness gain coefficient respectively and sends them to the display driving module; The display driving module is used to receive and execute the control instructions to respectively drive the center screen and the edge screens to display.

2. According to claim 1, a multi-screen splicing intelligent control system is characterized by: The aging analysis module uses the following calculation formulas to calculate the center screen aging coefficient and the edge screen aging coefficient respectively: Among them, α is the aging coefficient, k is the material attenuation constant of the screen, The linear correlation function of the aging degree, usage time and average brightness, t i is the cumulative screen usage time on the i-th day, L a (i) is the average brightness of the screen on the i-th day, It represents the influence of the peak brightness of a single spliced ​​screen on the accelerated aging of the screen. β is the peak brightness accelerated attenuation factor, which is obtained through multiple experimental data. L p (i) is the duration of the peak brightness of the screen on the i-th day, L max The maximum brightness of the screen display. represents the ambient temperature compensation term, T env is the ambient temperature, T0 is the aging test temperature of the screen, and γ is the temperature influencing factor of the screen.

3. The multi-screen splicing intelligent control system according to claim 1 is characterized in that: The compensation calculation model uses training data to train and determine the artificial neural network model; The training data includes parameters of multiple screens and a center screen brightness gain coefficient and an edge screen brightness gain coefficient after brightness adjustment. The parameters include: a center screen aging coefficient, an edge screen aging coefficient, a center screen current brightness value, an edge screen current brightness value, and a preset reference value.

4. The multi-screen splicing intelligent control system according to claim 3 is characterized by: Determination of the compensation calculation model includes: The central screen brightness gain coefficient and the edge screen brightness gain coefficient required to approach the preset reference brightness value output by the neural network model; Calculate the theoretical brightness value of the center screen and the theoretical brightness value of the edge screen corresponding to the brightness gain coefficient of the center screen and the brightness gain coefficient of the edge screen respectively; With the goal of minimizing the differences between the theoretical brightness value of the center screen and the theoretical brightness value of the edge screen corresponding to the center screen brightness gain coefficient and the edge screen brightness gain coefficient and the preset reference brightness value, a back propagation algorithm is used for training, and the trained neural network model is determined as a compensation calculation model.

5. The multi-screen splicing intelligent control system according to claim 4 is characterized in that: The aging analysis module is preset with an aging coefficient threshold. The aging analysis module monitors the calculated aging coefficient according to the preset aging coefficient threshold, and issues an early warning when the calculated aging coefficient is greater than the preset aging coefficient threshold.

6. The multi-screen splicing intelligent control system according to claim 1, characterized in that: The brightness analysis module divides the center screen and the edge screen into multiple grids, and customizes a label for each grid; The brightness values ​​of multiple grids in multiple screen areas are measured respectively, and then the average brightness value of the screen is calculated based on the brightness values ​​corresponding to the multiple grids in the screen and the number of grids, and the average brightness value is calibrated as the current brightness value of the screen.

7. The multi-screen splicing intelligent control system according to claim 6, characterized in that: It also includes a clock control module, which is electrically connected to the display driving module and is used to use the PTP protocol to synchronize the clock of the display driving module for driving the screen.

8. The multi-screen splicing intelligent control system according to claim 6, characterized in that: It also includes an image position analysis module, which is electrically connected to the display driving module. The image position analysis module obtains image edge points at multiple screen seams through an edge detection algorithm, and determines whether images at adjacent screen seams are aligned: Determine alignment, and transmit the alignment determination result to the display driver module; The misalignment is determined, and a result of the determination of the misalignment is transmitted to the terminal.

9. The multi-screen splicing intelligent control system according to claim 8, characterized in that: The edge detection algorithm of the image position analysis module adopts the Canny operator. The following steps are involved: a: Use a Gaussian filter to smooth the image on the screen. The formula of the Gaussian filter is: Where (x, y) is the coordinate of the pixel in the image, and σ is the standard deviation of the Gaussian distribution; b: Calculate the image gradient value to obtain the gradient value in the horizontal and vertical directions of the image, and use the Sobel operator to calculate the image gradient value; c: Convolve the image based on the Sobel operator and obtain the gradient components of each pixel in the x-direction and y-direction and set them as G x (x,y),G y (x, y), and calculate the gradient magnitude and gradient direction; Among them, the calculation formula of the gradient amplitude G(x, y) is: The calculation formula for the gradient direction θ(x, y) is: d: Obtain edge points through non-maximum suppression and double threshold detection.

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