Control system based on intelligent multi-screen cooperation

Abstract

The invention relates to the field of screen control, in particular to a control system based on intelligent multi-screen cooperation, and the system comprises a processing unit which comprises a plurality of sub-processing modules and is used for processing each target video; the analysis unit is used for determining the display state of each target screen according to the picture imbalance reference value and the display decline reference value, and determining an optimization mode as load distribution optimization or environment optimization according to the first display state proportion and the first display state aggregation degree; the load optimization unit is used for determining the screen category of the target screen according to the image complexity reference value and the dynamic reference value, and determining an optimization processing mode as task allocation optimization or task sequence optimization based on the balance coefficient; the display optimization unit is used for determining an ambient light influence state corresponding to each target screen according to the illumination intensity coefficient and the illumination distribution coefficient, and determining whether to adjust the brightness of the target screen according to the ambient light influence state; according to the invention, the display effect under the control of the multi-spliced screen is improved.

Description

Technical Field

[0001] The present invention relates to the field of screen control, and particularly to a control system based on intelligent multi-screen collaboration. Background Art

[0002] With the rapid development of information and intelligent technologies, control systems are increasingly widely used in fields such as emergency management, traffic dispatching, and military command. Traditional control systems usually rely on simple multi-screen expansion, and there are many problems such as scattered critical information, insufficient multi-screen collaboration capabilities, and uneven load in a multi-screen environment, which seriously affect decision-making efficiency and resource utilization. Therefore, how to develop a control system based on intelligent multi-screen collaboration to improve the efficiency and intelligence of the control system during multi-screen collaboration is a technical problem that needs to be solved urgently by those skilled in the art.

[0003] Chinese Patent Publication No. CN115361248A discloses a multi-screen interaction method, device, and system. Among them, the multi-screen interaction method includes: establishing a communication link with a receiving terminal under the same network resource; obtaining program parameter information of the multimedia resource being played; and sending the program parameter information and a play command to the receiving terminal through the communication link for the receiving terminal to continue playing the multimedia resource being played according to the program parameter information and the play command. It can be seen that the above technical solution has the following problems: the actual resource utilization requirements of each screen during the multi-screen interaction process are not considered, which easily leads to difficult effective allocation of processing resources, thus affecting the multi-screen display effect. Summary of the Invention

[0004] Therefore, the present invention provides a control system based on intelligent multi-screen collaboration to overcome the problems in the prior art that the actual resource utilization requirements of each screen during the multi-screen interaction process are not considered, which easily leads to difficult effective allocation of processing resources, thus affecting the multi-screen display effect.

[0005] To achieve the above object, the present invention provides a control system based on intelligent multi-screen collaboration, including:

[0006] A processing unit, which includes a number of sub-processing modules for processing each target video;

[0007] An analysis unit, which is connected to the processing unit for determining the display state of each target screen according to a picture imbalance reference value and a display decline reference value, and determining an optimization method as load distribution optimization or environment optimization according to a first display state ratio and a first display state aggregation degree;

[0008] A load optimization unit, which is respectively connected to a processing unit and an analysis unit, is used to determine the screen category of a target screen according to an image complexity reference value and a dynamic reference value, and determine an optimization processing method as task allocation optimization or task sequence optimization based on an equilibrium coefficient;

[0009] A display optimization unit, which is respectively connected to a processing unit, an analysis unit, and a load optimization unit, is used to determine the ambient light influence state corresponding to each target screen according to a light intensity coefficient and a light distribution coefficient, and determine whether to adjust the brightness of the target screen according to the ambient light influence state.

[0010] Further, the analysis unit determines a display state according to a picture misalignment reference value and a display decline reference value, and the display state includes:

[0011] A first display state where the picture misalignment reference value is greater than a preset picture misalignment reference value and the display decline reference value is greater than a preset display decline reference value;

[0012] A second display state where the picture misalignment reference value is less than or equal to the preset picture misalignment reference value or the display decline reference value is less than or equal to the preset display decline reference value.

[0013] Further, the analysis unit determines a corresponding optimization method according to the proportion of the first display state and the aggregation degree of the first display state, including:

[0014] If the proportion of the first display state is greater than a preset proportion of the first display state or the aggregation degree of the first display state is greater than a preset aggregation degree, the optimization method is load distribution optimization;

[0015] If the proportion of the first display state is less than or equal to the preset proportion of the first display state and the aggregation degree of the first display state is less than or equal to the preset aggregation degree, the optimization method is environmental optimization.

[0016] Further, when the load optimization unit performs load distribution optimization, it determines the screen category corresponding to each target screen according to the image complexity reference value and the dynamic reference value corresponding to each target screen, and the screen category includes:

[0017] A first type of screen where the image complexity reference value is greater than a preset image complexity reference value and the dynamic reference value is greater than a preset dynamic reference value;

[0018] A second type of screen where the image complexity reference value is less than or equal to the preset image complexity reference value or the dynamic reference value is less than or equal to the preset dynamic reference value.

[0019] Further, under equilibrium analysis conditions, the load optimization unit detects the comparison result between the equilibrium coefficient and the preset equilibrium coefficient to determine the processing method;

[0020] If the balance coefficient is greater than the preset balance coefficient, the processing method is to optimize the task allocation;

[0021] If the balance coefficient is less than or equal to the preset balance coefficient, the processing method is to optimize the task order;

[0022] The balance analysis condition is that the screen categories of all target screens are determined to be completed.

[0023] Further, when the load optimization unit optimizes the task allocation, it detects the proportion of the number of a certain type of screen corresponding to each target screen, arranges them in descending order according to the proportion of the number of the certain type of screen to obtain a screen sequence, and obtains several screen combinations in the screen sequence according to the preset extraction order, and randomly assigns each screen combination to each sub-processing module.

[0024] Further, when the load optimization unit optimizes the task order, it conducts a task analysis for each sub-processing module, detects the correlation coefficient corresponding to each target screen of the sub-processing module, and determines the corresponding task order priority value according to the correlation coefficient.

[0025] Further, when the display optimization unit performs environment optimization, it detects the ambient light influence state corresponding to each target screen, and when the ambient light influence state is that the light intensity coefficient is greater than the preset light intensity coefficient or the light distribution coefficient is greater than the preset light distribution coefficient, it adjusts the brightness of the target screen.

[0026] Further, when adjusting the overlapping area range, the difference between the light intensity coefficient and the preset light intensity coefficient is recorded as the intensity difference, and the difference between the light distribution coefficient and the preset light distribution coefficient is recorded as the distribution difference;

[0027] If the intensity difference is greater than the preset intensity difference or the distribution difference is greater than the preset distribution difference, then increase the brightness of the target screen in proportion;

[0028] If the intensity difference is less than or equal to the preset intensity difference and the distribution difference is less than or equal to the preset distribution difference, then increase the brightness of the target screen by a fixed value.

[0029] Further, each target screen includes several sub-screens.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows. In the technical solution of the present invention, the analysis unit determines the display status of each target screen according to the picture imbalance reference value and the display decline reference value, and determines the optimization method as load distribution optimization or environment optimization according to the first display status ratio and the first display status aggregation degree. The quality of the video content is reflected by the picture imbalance reference value and the display decline reference value, avoiding the problems of task conflicts or load imbalance caused by improper task allocation when ignoring the video content quality in the prior art. The degree of task difficulty distribution is reflected by the first display status ratio and the first display status aggregation degree, making the selection of the optimization method more in line with the actual application scenario, and thus improving the screen display effect.

[0031] Further, in the technical solution of the present invention, the load optimization unit determines the screen category corresponding to each target screen according to the image complexity reference value and the dynamic reference value corresponding to each target screen. The actual required resources of the target screen are reflected by the image complexity reference value and the dynamic reference value. The analysis of the image complexity reference value and the dynamic reference value is helpful for the reasonable allocation of resources in the subsequent task processing process.

[0032] Further, in the technical solution of the present invention, under the condition of balanced analysis, the load optimization unit determines the processing method as task allocation optimization or task sequence optimization according to the comparison result of the balance coefficient and the preset balance coefficient. The balance degree of the current processing tasks of the sub-processing module is reflected by the balance coefficient, and different optimization methods are selected correspondingly. The selection of the optimization method is beneficial to balancing the load of each sub-processing module, more beneficial to the analysis of task allocation, avoiding unreasonable task allocation caused by a single optimization method, realizing dynamic adjustment of the processor load, and achieving load balance.

[0033] Further, in the technical solution of the present invention, the display optimization unit dynamically adjusts the brightness of the target screen by detecting the light intensity coefficient and the light distribution coefficient corresponding to each target screen. The ambient light state is reflected by the light intensity coefficient and the light distribution coefficient, and dynamically adjusting the brightness of the target screen is beneficial to reducing the visual impact of the ambient light on the content of the target screen.

[0034] Further, in the technical solution of the present invention, the image complexity reference value corresponding to the target screen is determined according to the image complexity of each splicing area, and the splicing area is determined according to the actual situation of the target quantity reference value and the target stability reference value, making the selection of the splicing area more accurate, and thus improving the determination accuracy of the subsequent image complexity reference value. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a unit connection diagram of the control system based on intelligent multi-screen collaboration of the present invention;

[0036] Figure 2 This is a flow chart of the present invention for determining a display state according to a picture imbalance reference value and a display degradation reference value;

[0037] Figure 3 A flowchart of determining a screen category according to an image complexity reference value and a dynamic reference value in a picture according to the present invention;

[0038] Figure 4 This is a flow chart of the present invention for comparing the detection result of the equalization coefficient with the preset equalization coefficient to determine the processing method. DETAILED DESCRIPTION

[0039] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0040] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0041] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0042] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0043] See also Figures 1 to 4 As shown, the present invention provides a control system based on intelligent multi-screen collaboration, including:

[0044] A processing unit, comprising a plurality of sub-processing modules for processing each target video;

[0045] an analysis unit connected to the processing unit, configured to determine the display state of each target screen according to the image imbalance reference value and the display degradation reference value, and determine whether the optimization mode is load distribution optimization or environment optimization according to the first display state proportion and the first display state concentration;

[0046] A load optimization unit, which is respectively connected to the processing unit and the analysis unit, is used to determine the screen category of the target screen according to the image complexity reference value and the dynamic reference value, and determine the optimization processing method as task assignment optimization or task sequence optimization based on the balance coefficient;

[0047] A display optimization unit, which is respectively connected to the processing unit, the analysis unit and the load optimization unit, is used to determine the ambient light influence state corresponding to each target screen according to the light intensity coefficient and the light distribution coefficient, and determine whether to adjust the brightness of the target screen according to the ambient light influence state.

[0048] The present invention is applied to municipal remote command. The target video is the monitoring video of the area that needs to be monitored. The monitoring video can be obtained by means of a fixed monitoring device or drone shooting, etc. The monitoring area is the total area of the area photographed by the monitoring video. If drone shooting is used, the monitoring area is the area of the smallest circle on the horizontal plane that can include the projection of the drone movement trajectory. Each sub-processing module of the processing unit has the data processing ability to process each target video, including but not limited to image enhancement, denoising, color correction and backlight compensation.

[0049] The present invention has historical records. Any historical record records the picture imbalance reference value, display decline reference value, first display state ratio, first display state aggregation degree, image complexity reference value, dynamic reference value, balance coefficient, △Vt, light intensity coefficient and light distribution coefficient in a multi-screen collaborative control process, and each historical record corresponds to a qualified mark. The qualified mark records whether the corresponding multi-screen collaborative control process meets the user requirements, and the qualified mark is manually recorded.

[0050] Specifically, the analysis unit determines the display state according to the picture imbalance reference value and the display decline reference value. The display state includes:

[0051] A first display state where the picture imbalance reference value is greater than the preset picture imbalance reference value and the display decline reference value is greater than the preset display decline reference value;

[0052] A second display state where the picture imbalance reference value is less than or equal to the preset picture imbalance reference value or the display decline reference value is less than or equal to the preset display decline reference value.

[0053] Denote the picture imbalance reference value as V. V is the average value of the reference difference Vi corresponding to the monitoring periods of the nearest preset extraction quantity.

[0054] V i = ω1C i × ω2B i ;

[0055] where \(i = 1, 2, 3, \cdots, n\), \(C\) i is the tearing frequency of the target screen detected in the \(i\)-th monitoring period, \(B\) i is the video frame drop rate of the target screen detected in the \(i\)-th monitoring period, \(V\) i is the reference difference corresponding to the \(i\)-th monitoring period, and \(n\) is the preset extraction quantity; the value of the preset extraction quantity can be set by the user according to the actual application scenario. The greater the user's requirement for the confirmation accuracy of the picture misalignment reference value, the larger the value of the preset extraction quantity. The present invention applies a cyclic monitoring period. Since the preset extraction quantity is \(n\), the analysis unit makes a determination of the display state and the optimization method at the end of every \(n\) monitoring periods; a value of the monitoring period and the preset extraction quantity is provided, the monitoring period duration is \(10s\), and the preset extraction quantity is \(10\); for a single monitoring period, its corresponding tearing frequency = the number of tears detected in this monitoring period / the duration of the monitoring period. In the present invention, the number of tears corresponding to each monitoring period can be statistically obtained by FCAT, but is not limited thereto. The video frame drop rate corresponding to a single monitoring period = the number of lost frames detected in this monitoring period / the total number of frames that should be displayed in this monitoring period. \(\omega_1\) is the first weight coefficient, and \(\omega_2\) is the second weight coefficient. The values of \(\omega_1\) and \(\omega_2\) can be obtained by the user through a deep learning network and historical records. It can be understood that if the tearing frequency has a greater impact on the video display effect, the value of \(\omega_1\) is larger, and the principle of the value of \(\omega_2\) is the same. How to obtain training samples through historical records and learn the weight coefficients is easy for those skilled in the art to understand and will not be elaborated here. A preferred value in the implementation of the present invention is provided, \(\omega_1 = 0.6\), \(\omega_2 = 0.4\).

[0056] The confirmation method of the display decreasing reference value is to extract \(V_i\) corresponding to each monitoring period and calculate \(\Delta V_t\) corresponding to each adjacent period. For example, \(\Delta V_1 = V_2 - V_1\), \(\Delta V_2 = V_3 - V_2\), \(\Delta V_t = V_{t + 1} - V_t\), \(t = 1, 2, 3, \cdots, n - 1\). Calculate the comparison result between each \(\Delta V_t\) and the preset difference. If \(\Delta V_t\) is less than the preset difference, then record this \(\Delta V_t\) as the first category difference. If \(\Delta V_t\) is greater than or equal to the preset difference, then record this \(\Delta V_t\) as the second category difference. The display decreasing reference value = the number of the second category differences / (the number of the first category differences + the number of the second category differences). The value of the preset difference can be set by the user according to the actual application scenario. The greater the user's requirement for the display effect of the target screen, the smaller the value of the preset difference. A value setting method is provided. Statistically analyze \(\Delta V_t\) in the historical records that meet the user's requirements, eliminate the outliers among them, and record the average value of the \(\Delta V_t\) after eliminating the outliers as the preset difference.

[0057] In the present invention, the screen display effect is reflected by the screen misalignment reference value. The larger the screen misalignment reference value is, the worse the display effect is. The display decline reference value is used to characterize the change degree of the screen misalignment reference value, that is, the change trend of the display effect. The larger the display decline reference value is, the worse the change trend of the display effect is. The higher the user's requirement for the display effect of the target screen is, the smaller the values of the preset screen misalignment reference value and the preset display decline reference value are. The values of the preset screen misalignment reference value and the preset display decline reference value can be adaptively set by the user according to the actual application scenario. A method for obtaining the values of the preset screen misalignment reference value and the preset display decline reference value is provided. The corresponding screen misalignment reference value and display decline reference value in the historical record that meet the user's needs are extracted, the outliers are removed, and the average values of the screen misalignment reference value and the display decline reference value after removing the outliers are respectively recorded as the preset screen misalignment reference value and the preset display decline reference value. The method for removing outliers can be, but is not limited to, the 3σ criterion method or the IQR method.

[0058] Specifically, the analysis unit determines the corresponding optimization method according to the first display state ratio and the first display state aggregation degree. The optimization methods include:

[0059] If the first display state ratio is greater than the preset first display state ratio or the first display state aggregation degree is greater than the preset aggregation degree, the optimization method is load distribution optimization;

[0060] If the first display state ratio is less than or equal to the preset first display state ratio and the first display state aggregation degree is less than or equal to the preset aggregation degree, the optimization method is environment optimization.

[0061] The first display state ratio = M1 / (M1 + M2), where M1 is the number of target screens in the first display state detected in the monitoring period of the most recent preset extraction quantity, and M2 is the number of target screens in the second display state detected in the monitoring period of the most recent preset extraction quantity. The method for confirming the first display state aggregation degree is that within the monitoring period of the most recent preset extraction quantity, the time duration from the first appearance of the target screen in the first display state to the last appearance of the target screen in the first display state is recorded as the first time duration. The first display state aggregation degree = the first time duration / the total time duration of the monitoring period of the preset extraction quantity.

[0062] The value of the preset first display state ratio can be set by the user according to the actual application scenario. In the present invention, the first display state ratio can effectively reflect the display effect of all target screens. The higher the user's requirement for the display effect, the smaller the preset value of the first display state ratio. A value-taking method is provided to detect the first display state ratio of each monitoring period in the historical records that meet the user's requirements, and filter out the abnormal values therein. The average value of the first display state ratio after removing the abnormal values is recorded as the preset first display state ratio; a value of the preset first display state ratio is provided, and the preset first display state ratio is 30%. The present invention reflects the temporal distribution of the target screens in the first display state through the first display state aggregation degree. The value of the preset first display state aggregation degree can be set by the user according to the actual application scenario. In the present invention, the preset first display state aggregation degree can effectively reflect the influence degree of the first display state aggregation degree on the display effect. Therefore, the higher the user's requirement for the display effect, the smaller the preset first display state aggregation degree. The present invention provides a value, and the preset first display state aggregation degree is 0.3.

[0063] Specifically, when the load optimization unit performs load distribution optimization, it determines the screen category corresponding to each target screen according to the image complexity reference value and the dynamic reference value corresponding to each target screen. The screen categories include:

[0064] A type of screen where the image complexity reference value is greater than the preset image complexity reference value and the dynamic reference value is greater than the preset dynamic reference value;

[0065] A type of screen where the image complexity reference value is less than or equal to the preset image complexity reference value or the dynamic reference value is less than or equal to the preset dynamic reference value.

[0066] Among them, for a single target screen, the image complexity corresponding to each splicing area is determined, and the average value of the image complexity is recorded as the image complexity reference value corresponding to the target screen;

[0067] In the present invention, the target screen is composed of a plurality of sub - screens spliced together. The gap between any two spliced sub - screens is recorded as a splicing seam, which is already known to those skilled in the art and will not be elaborated here. Each splicing seam of the target screen corresponds to a splicing area. The splicing area is a rectangle. The connection line of the center points of the short sides of the rectangle completely coincides with the splicing seam and has the same length. And any short side has an overlapping part with the edges of the two relevant sub - screens of the splicing seam, and the lengths of the overlapping parts are the same. The method for determining the area of the rectangle is as follows: extract a number of basic video frames within the preset detection duration of the target screen, and obtain a target quantity reference value and a target stability reference value. If the target quantity reference value is greater than the preset target quantity reference value or the target stability reference value is less than or equal to the preset target stability reference value, the areas of the rectangles of each splicing area are the same, and the rectangle area has a positive correlation with the target fluctuation degree; where the target fluctuation degree is the maximum value of the target quantity difference and the target stability difference. The target quantity difference = target quantity reference value - preset target quantity reference value, the target stability difference = target stability reference value - preset target stability reference value. The relevant sub - screens are the two sub - screens corresponding to the splicing seam. The number of basic video frames is set by the user himself. The greater the user's accuracy requirement for the target quantity reference value and the target stability reference value, the greater the number of basic video frames.

[0068] If the target quantity reference value is less than or equal to the preset target quantity reference value and the target stability reference value is greater than the preset target quantity reference value, the area of the splicing area of the first splicing seam has a positive correlation with the target fluctuation degree, and the area of the splicing area of the second splicing seam adopts the initial area.

[0069] The method for determining the target quantity reference value is to extract a number of basic video frames within the preset detection duration of the target screen and detect the target quantity in each sub - screen area of each basic video frame. c max is the number of extracted basic video frames, and p c is the target quantity of the sub - screen in the c - th extracted basic video frame. The target quantity of the sub - screen is the number of people in the sub - screen.

[0070] The method for determining the target stability reference value is: target stability reference value = target quantity reference value / preset detection duration;

[0071] The initial area can be set by the user according to the actual application scenario. The higher the user's detection accuracy requirement for the image complexity reference value, the larger the area of the sub - screen, the larger the value of the initial area. In the specific implementation of the present invention, the value of the initial area is 5% of the area of the sub - screen.

[0072] The first splicing seam is the corresponding splicing seam between the target sub-screen and other sub-screens. The target sub-screen is the sub-screen whose target quantity reference value is less than or equal to the preset target quantity reference value and whose target stability reference value is greater than the preset target quantity reference value; the confirmation method of the second splicing seam is the other splicing seams except the first splicing seam.

[0073] For a single splicing area, the confirmation method of the image complexity is to establish a two-dimensional coordinate system for the splicing area. The abscissa and ordinate of the pixel block at any point in the splicing area are both greater than or equal to 0, and the image complexity is L. Wherein, I(x, y) is the pixel gray value at the position (x, y) in the image, I0 is the average value of the pixel gray values at all positions, Q×W is the resolution of the image, Q is the abscissa length of the splicing area, and W is the ordinate length of the splicing area.

[0074] For a single target screen, the confirmation method of its corresponding dynamic reference value is to calculate the pixel difference between adjacent basic video frames. The pixel difference is the absolute value obtained by subtracting the respective pixel averages of the two basic video frames, and the average value of the pixel differences is recorded as the dynamic reference value of the target screen.

[0075] For the values of the preset image complexity reference value and the preset dynamic reference value, the user can set them according to the actual application scenario. The present invention can effectively reflect the influence degree of the color interval and the texture reference value on the display effect of the splicing area through the preset image complexity, and effectively reflect the pixel difference between adjacent frames through the preset dynamic reference value, providing a value-taking method. Statistically analyze the image complexity reference value and the dynamic reference value in the historical records that meet the user's needs, eliminate the outliers among them, and record the respective averages of the image complexity reference value and the dynamic reference value after eliminating the outliers as the current preset image complexity reference value and the preset dynamic reference value.

[0076] Specifically, under the balanced analysis condition, the load optimization unit detects the comparison result between the balance coefficient and the preset balance coefficient to determine the processing method.

[0077] If the balance coefficient is greater than the preset balance coefficient, the processing method is to optimize the task allocation.

[0078] If the balance coefficient is less than or equal to the preset balance coefficient, the processing method is to optimize the task sequence.

[0079] The balanced analysis condition is that the screen categories of all target screens are determined.

[0080] The confirmation method of the balance coefficient is to detect the proportion of the number of a certain type of screen corresponding to each sub - processing module currently, detect the number of processing modules whose proportion of the number of a certain type of screen is greater than the preset proportion of the number of a certain type of screen, record this number as the balance coefficient, and the preset proportion of the number of a certain type of screen is 130% of the average value of the proportion of the number of a certain type of screen corresponding to each processing module currently.

[0081] For the value of the preset balance coefficient, the user can set it according to the actual display state of the target screen. A value - setting method is provided, which is to detect the average value of the balance coefficients in the historical records that meet the user's needs and record it as the preset balance coefficient.

[0082] Specifically, when the load optimization unit optimizes the task allocation, it detects the proportion of the number of a certain type of screen corresponding to each target screen, arranges them in descending order of the proportion of the number of a certain type of screen to obtain a screen sequence, and obtains several screen combinations within the screen sequence according to the preset extraction order, and randomly assigns each screen combination to each sub - processing module.

[0083] The preset extraction order is to sequentially extract the two target screens with the largest and smallest proportion of the number of a certain type of screen that have not been included in the screen combination currently, and record them as a screen combination. The number of target screens in the present invention is an even number. If the total number of target screens in the actual application scenario is an odd number, then the target screen with the intermediate value of the proportion of the number of a certain type of screen is randomly assigned to any one sub - processing module.

[0084] Specifically, when the load optimization unit optimizes the task order, it conducts a task analysis for each sub - processing module, detects the correlation coefficient corresponding to each target screen corresponding to the sub - processing module, and determines the corresponding task order priority value according to the correlation coefficient.

[0085] For a target screen, extract the other target screen with the largest correlation coefficient corresponding to this target screen. If the correlation coefficient between the two target screens is greater than the preset correlation coefficient, then the task order priority value corresponding to the target screen with the largest reference value of image complexity among them = the reference value of image complexity corresponding to the target screen + the correlation coefficient, and the task order priority value corresponding to the target screen with the smallest reference value of image complexity among them = - the correlation coefficient.

[0086] If the correlation coefficient between the two target screens is less than the preset correlation coefficient, then the task order priority values corresponding to the two target screens are their corresponding reference values of image complexity respectively; in the present invention, the sub - processing module preferentially processes the target screen with a larger task order priority value. If there are target screens with the same task order priority value, then randomly select one target screen to be preferentially processed.

[0087] For two target screens, the correlation coefficient is the overlapping area of the monitoring areas corresponding to the two target screens, and the preset correlation coefficient is 40% of the average value of the monitoring areas corresponding to the two target screens.

[0088] Specifically, when the display optimization unit performs environment optimization, it detects the ambient light influence state corresponding to each target screen, and when the ambient light influence state is that the light intensity coefficient is greater than the preset light intensity coefficient or the light distribution coefficient is greater than the preset light distribution coefficient, it adjusts the brightness of the target screen.

[0089] When the ambient light influence state is that the light intensity coefficient is less than or equal to the preset light intensity coefficient and the light distribution coefficient is less than or equal to the preset light distribution coefficient, there is no need to adjust the brightness of the target screen.

[0090] The light intensity coefficient is the light intensity in the current environment, which is detected by a photometer and the unit is Lux. The light distribution coefficient is the area of the region where there is light on the target screen currently.

[0091] For the values of the preset light intensity coefficient and the preset light distribution coefficient, the user can set them according to the actual application scenario. The present invention can effectively reflect the light intensity in the environment through the light intensity and reflect the light area of the target screen through the light distribution coefficient, and provides a method for obtaining the values of the preset light intensity coefficient and the preset light distribution coefficient. It extracts the corresponding light intensity coefficient and light distribution coefficient in the historical records that meet the user's needs, screens out the outliers among them, and respectively records the average values of the light intensity coefficient and the light distribution coefficient after removing the outliers as the preset light intensity coefficient and the preset light distribution coefficient.

[0092] Specifically, when adjusting the overlapping area range, the difference between the light intensity coefficient and the preset light intensity coefficient is recorded as the intensity difference, and the difference between the light distribution coefficient and the preset light distribution coefficient is recorded as the distribution difference.

[0093] If the intensity difference is greater than the preset intensity difference or the distribution difference is greater than the preset distribution difference, then increase the brightness of the target screen proportionally.

[0094] If the intensity difference is less than or equal to the preset intensity difference and the distribution difference is less than or equal to the preset distribution difference, then increase the brightness of the target screen by a fixed value.

[0095] Intensity difference = light intensity coefficient - preset light intensity coefficient;

[0096] Distribution difference = light distribution coefficient - preset light distribution coefficient;

[0097] When increasing the brightness of the target screen proportionally, the increase value of the target screen brightness is positively correlated with the maximum value of the intensity difference and the distribution difference.

[0098] When increasing the brightness of the target screen by a fixed value, the increase value of the target screen brightness is 10% of the current target screen brightness.

[0099] Specifically, each target screen includes a number of sub-screens.

[0100] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A control system based on intelligent multi-screen collaboration, characterized in that, Including: A processing unit, which includes a number of sub - processing modules for processing each target video; An analysis unit, which is connected to the processing unit and is used to determine the display status of each target screen according to the picture misalignment reference value and the display decline reference value, and determine the optimization method as load distribution optimization or environment optimization according to the first display status proportion and the first display status aggregation degree; A load optimization unit, which is respectively connected to the processing unit and the analysis unit, and is used to determine the screen category of the target screen according to the image complexity reference value and the dynamic reference value, and determine the optimization processing method as task assignment optimization or task order optimization based on the balance coefficient; A display optimization unit, which is respectively connected to the processing unit, the analysis unit and the load optimization unit, and is used to determine the ambient light influence status corresponding to each target screen according to the light intensity coefficient and the light distribution coefficient, and determine whether to adjust the brightness of the target screen according to the ambient light influence status.

2. The control system based on intelligent multi-screen collaboration according to claim 1, characterized in that The analysis unit determines the display status according to the picture misalignment reference value and the display decline reference value, and the display status includes: A first display status where the picture misalignment reference value is greater than the preset picture misalignment reference value and the display decline reference value is greater than the preset display decline reference value; A second display status where the picture misalignment reference value is less than or equal to the preset picture misalignment reference value or the display decline reference value is less than or equal to the preset display decline reference value.

3. The control system based on intelligent multi-screen collaboration according to claim 2, characterized in that, The analysis unit determines the corresponding optimization method according to the first display status proportion and the first display status aggregation degree, including: If the first display status proportion is greater than the preset first display status proportion or the first display status aggregation degree is greater than the preset aggregation degree, the optimization method is load distribution optimization; If the first display status proportion is less than or equal to the preset first display status proportion and the first display status aggregation degree is less than or equal to the preset aggregation degree, the optimization method is environment optimization.

4. The control system based on intelligent multi-screen collaboration according to claim 3, characterized in that When the load optimization unit performs load distribution optimization, it determines the screen category corresponding to each target screen according to the image complexity reference value and the dynamic reference value corresponding to each target screen, and the screen category includes: A first - type screen where the image complexity reference value is greater than the preset image complexity reference value and the dynamic reference value is greater than the preset dynamic reference value; A second - type screen where the image complexity reference value is less than or equal to the preset image complexity reference value or the dynamic reference value is less than or equal to the preset dynamic reference value.

5. The control system based on intelligent multi-screen collaboration according to claim 4, wherein The load optimization unit detects the comparison result between the balance coefficient and the preset balance coefficient under the balance analysis condition to determine the processing method; If the balance coefficient is greater than the preset balance coefficient, the processing method is to optimize for task assignment; If the balance coefficient is less than or equal to the preset balance coefficient, the processing method is to optimize for task order; The balance analysis condition is that the screen categories of all target screens are determined.

6. The control system based on intelligent multi-screen collaboration according to claim 5, wherein When the load optimization unit optimizes for task assignment, it detects the proportion of the number of first - type screens corresponding to each target screen, arranges them in descending order according to the proportion of the number of first - type screens to obtain a screen sequence, and obtains several screen combinations in the screen sequence according to the preset extraction order, and randomly assigns each screen combination to each sub - processing module.

7. The control system based on intelligent multi-screen collaboration according to claim 5, characterized in that, When the load optimization unit optimizes according to the task order, it analyzes the tasks for each sub - processing module, detects the correlation coefficients corresponding to the target screens of each sub - processing module, and determines the corresponding task order priority values according to the correlation coefficients.

8. The control system based on intelligent multi-screen collaboration according to claim 3, wherein When the display optimization unit performs environmental optimization, it detects the environmental light influence status corresponding to each target screen, and when the environmental light influence status is that the light intensity coefficient is greater than the preset light intensity coefficient or the light distribution coefficient is greater than the preset light distribution coefficient, it adjusts the brightness of the target screen.

9. The control system based on intelligent multi-screen collaboration according to claim 8, wherein When adjusting the overlapping area range, the difference between the detected light intensity coefficient and the preset light intensity coefficient is recorded as the intensity difference, and the difference between the light distribution coefficient and the preset light distribution coefficient is recorded as the distribution difference; If the intensity difference is greater than the preset intensity difference or the distribution difference is greater than the preset distribution difference, then increase the brightness of the target screen proportionally; If the intensity difference is less than or equal to the preset intensity difference and the distribution difference is less than or equal to the preset distribution difference, then increase the brightness of the target screen by a fixed value.

10. The control system based on intelligent multi-screen collaboration according to claim 1, characterized in that, Each target screen includes several sub - screens.

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