Video data distribution method and system for multi-screen organic electroluminescent panel

Abstract

The invention relates to the technical field of organic electroluminescent panels, in particular to a video data distribution method and system for a multi-screen organic electroluminescent panel. Comprising the steps of setting a plurality of demand scenes according to historical video data, and constructing a video processing model according to all the demand scenes; generating an initial distribution strategy according to the to-be-distributed video data and the video processing model, and generating a primary distribution strategy according to the initial distribution strategy and the operation deviation value of each receiving point; obtaining feedback data packets of each receiving point according to a preset correction time node, and judging whether a correction instruction is generated or not according to all the feedback data packets; a plurality of demand scenes are established based on different split screen demands, and processing sub-models corresponding to the demand scenes are established, so that a distribution strategy is dynamically adjusted according to different operation demands of a to-be-distributed video. The operation effect of the multi-screen organic electroluminescence panel is improved, and the synchronization precision and color consistency of distribution of multi-screen video data are improved.

Description

Technical Field

[0001] The present application relates to the technical field of organic electroluminescent panels, and particularly to a method and system for video data distribution of a multi-screen organic electroluminescent panel. Background Art

[0002] With the rapid development of display technologies, organic electroluminescent (organic electroluminescent panel) panels have been widely used in the high-end display field due to their excellent characteristics such as self-luminescence, high contrast ratio, wide color gamut, and flexibility. Especially in multi-screen splicing display systems, organic electroluminescent panel technology exhibits technical advantages that traditional LCDs cannot match.

[0003] However, in terms of video data distribution in multi-screen organic electroluminescent panel systems, there are still many key problems to be solved in the existing technologies. The current mainstream multi-screen video distribution technologies are prone to obvious brightness and color deviations between multiple screens. The aging characteristics of organic electroluminescent panels are not considered, and the display performance differences between panels become increasingly significant after long-term use. There are significant deficiencies in aspects such as synchronization accuracy, color consistency, aging compensation, and system scalability. Summary of the Invention

[0004] The purpose of the present application is: to solve the above technical problems, the present application provides a method and system for video data distribution of a multi-screen organic electroluminescent panel, aiming to improve the synchronization accuracy and color consistency of multi-screen video data distribution and improve the operation effect of the multi-screen organic electroluminescent panel.

[0005] In some embodiments of the present application, a method for video data distribution of a multi-screen organic electroluminescent panel is provided, including: Setting multiple demand scenarios according to historical video data, and constructing a video processing model according to all the demand scenarios; Generating an initial distribution strategy according to the video data to be distributed and the video processing model, and generating a primary distribution strategy according to the initial distribution strategy and the operation deviation values of each receiving point; Obtaining feedback data packets of each receiving point according to a preset correction time node, and determining whether to generate a correction instruction according to all the feedback data packets; Among them, when constructing multiple demand scenarios, it includes: Establishing a demand scenario sequence B, B = (b1, b2... b i ... b m ), where b i is the i-th demand scenario; m is the number of demand scenarios.

[0006] In some embodiments of the present application, when constructing the video processing model, it includes: Establishing a pre-recognition model; Set \(b\) in sequence according to the demand scenario sequence \(B\). i as the target demand scenario; Set the processing sub-model of the target demand scenario according to the historical video data; Set the processing sub-models of each demand scenario in sequence; Establish a sequence of processing sub-models \(P\), \(P=(p_1,p_2,\cdots,p i \cdots p m ), where \(p i is the processing sub-model of the \(i\)-th demand scenario, and \(m\) is the number of monitoring indicators; Construct a video processing model according to the sequence of processing sub-models \(P\) and the pre-identification model.

[0007] In some embodiments of the present application, when generating the initial distribution strategy, it includes: Generate a demand feature package according to the video data to be distributed; Set \(b\) in sequence according to the demand scenario sequence \(B\). i as the target demand scenario; Generate a similarity evaluation value \(w\) between the demand feature package and the target demand scenario; \(w = \beta i * (j i - j' i ) 2 ; Wherein, is the number of demand indicators; \(\beta i is the influence factor of the \(i\)-th demand indicator; \(j i is the reference value of the \(i\)-th demand indicator generated based on the demand feature package; \(j' i is the reference value of the \(i\)-th demand indicator in the target demand scenario; Generate similarity evaluation values between the demand feature package and each demand scenario in sequence; Establish a sequence of similarity evaluation values \(W\), \(W=(w_1,w_2,\cdots,w i \cdots w m ), where \(w i is the similarity evaluation value between the demand feature package and the \(i\)-th demand scenario, and \(m\) is the number of demand scenarios; Set the processing sub-model of the demand scenario corresponding to the maximum value \(w max in the similarity evaluation sequence \(W\) as the primary processing model; Generate sub-data packets to be distributed at each receiving point according to the primary processing model and the video data to be distributed; Generate an initial distribution strategy according to all the sub-data packets to be distributed.

[0008] In some embodiments of the present application, when generating the primary distribution strategy, it includes: Establish a sequence of receiving points \(A\), \(A=(a_1,a_2,\cdots,ai …a n )), where a i is the i-th receiving point; n is the number of receiving points; Generate an association mapping table according to the primary processing model and the receiving point sequence A; Set a i as the target receiving point in sequence according to the receiving point sequence A; Generate the operation deviation value c of the target receiving point; Generate the operation deviation values of each receiving point in sequence; Establish an operation deviation value sequence C, C = (c1, c2... c i …c n ), where c i is the operation deviation value of the i-th receiving point, and n is the number of receiving points; Preset an operation deviation value threshold C1; If c i > C1, generate a primary compensation instruction for the i-th receiving point; Generate a primary distribution strategy according to all the primary compensation instructions and the initial distribution strategy.

[0009] In some embodiments of the present application, when generating the operation deviation value c of the target receiving point, it includes: c = e1 * Q1 * Y1(i) * η i * (k i - k' i ) 2 + e2 * Q2 * µ i * d i where e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; is the number of device evaluation indicators generated based on the primary processing model; η i is the influence factor of the i-th device evaluation indicator; k i is the reference value of the i-th device evaluation indicator of the target receiving point; k' i is the standard reference value of the i-th device evaluation indicator; 3 is the number of associated perturbation indicators; µ i is the influence factor of the i-th associated perturbation indicator; d i is the reference value of the i-th associated perturbation indicator generated based on all the associated receiving points of the target receiving point in the association mapping table; Y1(i) is a selection coefficient; if (k i - k' i ) < 0, Y1(i) = 1; if (k i - k'​i ) > 0, Y1(i) = 0.

[0010] In some embodiments of the present application, when determining whether to generate a correction instruction based on all feedback data packets, it includes: Setting a plurality of first-level monitoring indicators and a plurality of second-level monitoring indicators according to the first-level processing model; Setting a i as the receiving point to be evaluated; Generating an operation evaluation value f of the receiving point to be evaluated based on all feedback data packets; f = (g i * h i )]; Where θ4 is the number of first-level monitoring indicators; g i is the influence factor of the i-th first-level monitoring indicator; h i is the reference value of the i-th first-level monitoring indicator of the receiving point to be evaluated; Generating the operation evaluation values of each receiving point in sequence; Establishing an operation evaluation value sequence F, F = (f1, f2... f i ... f n ), where f i is the operation evaluation value of the i-th receiving point, and n is the number of receiving points; Generating a correction evaluation value t based on the operation evaluation value sequence F; Presetting a correction evaluation value threshold T1, if t > T1, a first-level correction instruction is generated at the current correction time node.

[0011] In some embodiments of the present application, when generating a correction evaluation value t based on the operation evaluation value sequence F, it includes: t = e3 * Q3 * Y2(i) * (f i - f')2] + e4 * Q4 * r i * v i Where e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; Y2(i) is a selection coefficient; if (f i - f') > 0; Y2(i) = 0; if (f i - f') < 0; Y2(i) = 1; θ5 is the number of second-level monitoring indicators; r i is the influence factor of the i-th second-level monitoring indicator; v i is the reference value of the i-th second-level monitoring indicator at the current correction time node.

[0012] ​In some embodiments of the present application, a video data distribution system for a multi-screen organic electroluminescent panel is provided, including: A central control unit, configured to set multiple demand scenarios according to historical video data, and construct a video processing model according to all demand scenarios; A distribution unit, configured to generate an initial distribution strategy according to the video data to be distributed and the video processing model, and generate a first-level distribution strategy according to the initial distribution strategy and the operation deviation values of each receiving point; The central control unit includes: A first processing module, configured to establish a demand scenario sequence B, B = (b1, b2…b i …b m ), where b i is the i-th demand scenario; m is the number of demand scenarios; The first processing module is further configured to establish a pre-identification model; Set b i as the target demand scenario in sequence according to the demand scenario sequence B; Set a processing sub-model for the target demand scenario according to historical video data; Set the processing sub-models of each demand scenario in sequence; Establish a processing sub-model sequence P, P = (p1, p2…p i …p m ), where p i is the processing sub-model of the i-th demand scenario, and m is the number of monitoring indicators; Construct a video processing model according to the processing sub-model sequence P and the pre-identification model; A second processing module, configured to obtain feedback data packets of each receiving point according to a preset correction time node, and determine whether to generate a correction instruction according to all feedback data packets.

[0013] In some embodiments of the present application, the distribution unit includes: A first control module, configured to generate a demand feature packet according to the video data to be distributed; Set b i as the target demand scenario in sequence according to the demand scenario sequence B; Generate a similarity evaluation value w between the demand feature packet and the target demand scenario; w = β i * (j i - j' i ) 2 ; Wherein, is the number of demand indicators; β i is the influence factor of the i-th demand indicator; j iGenerate the reference value of the i-th requirement indicator based on the requirement feature package; j' i Is the reference value of the i-th requirement indicator in the target requirement scenario; Generate similarity evaluation values for each requirement scenario in sequence according to the requirement feature package; Establish a similarity evaluation value sequence W, W = (w1, w2…w i …w m ), where w i Is the similarity evaluation value of the requirement feature package and the i-th requirement scenario, and m is the number of requirement scenarios; Set the processing sub-model of the requirement scenario corresponding to the maximum value w max In the similarity evaluation sequence W as the first-level processing model; Generate the sub-data packets to be distributed at each receiving point according to the first-level processing model and the video data to be distributed; Generate an initial distribution strategy according to all the sub-data packets to be distributed; The second control module is used to establish a receiving point sequence A, A = (a1, a2…a i …a n ), where a i Is the i-th receiving point; n is the number of receiving points; Generate an association mapping table according to the first-level processing model and the receiving point sequence A; Set a i As the target receiving point in sequence according to the receiving point sequence A; Generate the operation deviation value c of the target receiving point; Generate the operation deviation values of each receiving point in sequence; Establish an operation deviation value sequence C, C = (c1, c2…c i …c n ), where c i Is the operation deviation value of the i-th receiving point, and n is the number of receiving points; Preset the operation deviation value threshold C1; If c i > C1, generate the first-level compensation instruction for the i-th receiving point; Generate the first-level distribution strategy according to all the first-level compensation instructions and the initial distribution strategy.

[0014] In some embodiments of the present application, the second processing module is further used for: Set multiple first-level monitoring indicators and multiple second-level monitoring indicators according to the first-level processing model; Set a i As the receiving point to be evaluated in sequence according to the receiving point sequence A; Generate the operation evaluation value f of the receiving point to be evaluated according to all the feedback data packets; f = (g i *h i ) where θ4 is the number of primary monitoring indicators; g i is the influence factor of the i-th primary monitoring indicator; h i is the reference value of the i-th primary monitoring indicator of the receiving point to be evaluated; Generate the operation evaluation values of each receiving point in sequence; Establish an operation evaluation value sequence F, F = (f1, f2... f i ... f n ), where f i is the operation evaluation value of the i-th receiving point, and n is the number of receiving points; Generate a corrected evaluation value t based on the operation evaluation value sequence F; Preset a corrected evaluation value threshold T1. If t > T1, generate a primary correction instruction at the current correction time node.

[0015] Compared with the prior art, the video data distribution method and system of a multi-screen organic electroluminescent panel in an embodiment of the present application have the following beneficial effects: Based on different multi-screen requirements, multiple demand scenarios are established, and corresponding processing sub-models for each demand scenario are established, so as to dynamically adjust the distribution strategy according to the different operation requirements of the video to be distributed, and improve the operation effect of the multi-screen organic electroluminescent panel.

[0016] By collecting the feedback data of each multi-screen, the overall operation state of the multi-screen organic electroluminescent panel is diagnosed in a timely manner, and the refresh rate and pixel displacement strategy of each multi-screen are adjusted in a timely manner to ensure the display consistency between each multi-screen, and improve the synchronization accuracy and color consistency of the distribution of multi-screen video data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic flowchart of a video data distribution method for a multi-screen organic electroluminescent panel in a preferred embodiment of an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following further describes in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0019] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0020] The terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0021] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0022] As Figure 1 shown, a video data distribution method for a multi-screen organic electroluminescent panel according to a preferred embodiment of an embodiment of the present application includes: S101: Set a plurality of demand scenarios according to historical video data, and construct a video processing model according to all the demand scenarios; S102: Generate an initial distribution strategy according to the video data to be distributed and the video processing model, and generate a primary distribution strategy according to the initial distribution strategy and the operation deviation values of each receiving point; S103: Obtain the feedback data packets of each receiving point according to the preset correction time node, and determine whether to generate a correction instruction according to all the feedback data packets; Among them, when constructing a plurality of demand scenarios, it includes: Establish a demand scenario sequence B, B = (b1, b2... b i ... b m ), where b i is the i-th demand scenario; m is the number of demand scenarios.

[0023] Specifically, multiple demand metrics are set according to historical video data, and the demand metrics include, but are not limited to, multiple parameters such as video length, video content complexity, display type (single video segmented into multiple screens, single video synchronously displayed on multiple screens, single video asynchronously displayed on multiple screens), etc. By quantifying each demand metric, multiple value ranges for each demand metric are generated, and multiple demand scenarios are constructed based on the random combination of each value range.

[0024] Specifically, when constructing a video processing model, it includes: Establish a pre-recognition model; Set b in sequence according to the demand scenario sequence B i As the target demand scenario; Set the processing sub-model of the target demand scenario according to historical video data; Set the processing sub-models of each demand scenario in sequence; Establish a processing sub-model sequence P, P = (p1, p2…p i …p m ), where p i Is the processing sub-model of the i-th demand scenario, and m is the number of monitoring metrics; Construct a video processing model according to the processing sub-model sequence P and the pre-recognition model.

[0025] Specifically, the pre-recognition model is used to extract the demand metrics in the video data to be distributed, and perform signal recognition and format conversion on the video data to be distributed, so that it can be smoothly transmitted to each receiving point.

[0026] Specifically, the processing sub-model is used to perform spatial segmentation, time series segmentation and compression processing on the video data to be distributed, so as to improve the transmission efficiency of the video data to be distributed. For example, when a single video is segmented into multiple screens, the original video data to be distributed is spatially segmented according to the device parameters assigned to each, so as to generate the sub-packets to be distributed corresponding to each receiving point.

[0027] Specifically, when the video data to be distributed is too long, the processing sub-model performs time series segmentation on the video data to be distributed, so as to split the overall video data into multiple parts, reduce the single transmission pressure, and improve the synchronization of each receiving point.

[0028] Specifically, according to the differences of each demand scenario, when each processing sub-model processes the video data to be distributed, the focus is also different. For example, for a demand scenario with high synchronization, by reducing the duration of the video content in a single sub-packet to be distributed, the transmission efficiency is improved and the overall latency is reduced.

[0029] It can be understood that in the above embodiments, multiple demand scenarios are established based on different split-screen requirements, and corresponding processing sub-models for each demand scenario are established, so as to dynamically adjust the distribution strategy according to different operation requirements of the video to be distributed, and improve the operation effect of the multi-screen organic electroluminescent panel.

[0030] In a preferred embodiment of the present application, when generating the initial distribution strategy, it includes: Generating a demand feature packet according to the video data to be distributed; Setting b i as the target demand scenario in sequence according to the demand scenario sequence B; Generating a similarity evaluation value w between the demand feature packet and the target demand scenario; w = β i * (j i - j' i ) 2 ; Wherein, is the number of demand indicators; β i is the influence factor of the i-th demand indicator; j i is the reference value of the i-th demand indicator generated based on the demand feature packet; j' i is the reference value of the i-th demand indicator in the target demand scenario; Generating similarity evaluation values between the demand feature packet and each demand scenario in sequence; Establishing a similarity evaluation value sequence W, W = (w1, w2... w i ... w m ), where w i is the similarity evaluation value between the demand feature packet and the i-th demand scenario, and m is the number of demand scenarios; Setting the processing sub-model of the demand scenario corresponding to the maximum value w max in the similarity evaluation sequence W as the primary processing model; Generating sub-packets to be distributed for each receiving point according to the primary processing model and the video data to be distributed; Generating an initial distribution strategy according to all the sub-packets to be distributed.

[0031] Specifically, the larger the similarity evaluation value, the better the processing effect of the processing sub-model in the corresponding demand scenario on the current video data to be distributed.

[0032] Specifically, when generating the primary distribution strategy, it includes: Establishing a receiving point sequence A, A = (a1, a2... a i ... a n ), where a i is the i-th receiving point; n is the number of receiving points; Generate an association mapping table according to the primary processing model and the received point sequence A; Set a according to the received point sequence A in turn i as the target receiving point; Generate the operation deviation value c of the target receiving point; Generate the operation deviation values of each receiving point in turn; Establish an operation deviation value sequence C, C = (c1, c2…c i …c n ), where c i is the operation deviation value of the i-th receiving point, and n is the number of receiving points; Preset the operation deviation value threshold C1; If c i > C1, generate the primary compensation instruction for the i-th receiving point; Generate the primary distribution strategy according to all the primary compensation instructions and the initial distribution strategy.

[0033] Specifically, the operation deviation value threshold can be set according to historical parameters.

[0034] Specifically, set each sub-screen as a single receiving point according to the device parameters of the organic electroluminescent panel, so as to establish a receiving point sequence.

[0035] Specifically, construct an association mapping table according to the position parameters of the sub-screen corresponding to each receiving point in combination with the current demand scenario. If the sub-screen corresponding to the current receiving point affects the sub-screen corresponding to the target receiving point during video display, then the current receiving point is the associated receiving point of the target receiving point.

[0036] Specifically, the initial distribution strategy includes multiple parameters such as the distribution order and distribution time of each receiving point.

[0037] Specifically, the primary compensation instruction refers to making targeted adjustments according to the actual state of the target receiving point. For example, for a sub-screen with a high delay, correct the corresponding distribution time so that the time when each receiving point receives the sub-packet to be distributed is consistent. For a sub-screen with a large degree of pixel aging, correct the corresponding driving parameters to ensure the color consistency of each sub-screen.

[0038] Specifically, generate the operation state of the standard sub-screen according to the primary processing model, so as to analyze and evaluate the actual operation state of each receiving point, and thus generate the operation deviation value of each receiving point.

[0039] Specifically, the operation state of the standard sub-screen refers to the optimal operation state of the sub-screen in the current demand scenario.

[0040] Specifically, when generating the operation deviation value c of the target receiving point, it includes: c = e1 * Q1 * Y1(i) * η i * (k i - k' i ) 2 + e2 * Q2 * µ i * d i ) Where, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of device evaluation indicators generated based on the primary processing model; η i is the influence factor of the i-th device evaluation indicator; k i is the reference value of the i-th device evaluation indicator at the target receiving point; k' i is the standard reference value of the i-th device evaluation indicator; 3 is the number of associated perturbation indicators; µ i is the influence factor of the i-th associated perturbation indicator; d i is the reference value of the i-th associated perturbation indicator generated based on all associated receiving points of the target receiving point in the associated mapping table; Y1(i) is the selection coefficient; if (k i - k' i ) < 0, Y1(i) = 1; if (k i - k' i ) > 0, Y1(i) = 0.

[0041] Specifically, the larger the operation deviation value is, it indicates that the deviation between the device states of the current receiving points and the standard operation state is larger, and it is necessary to correct the initial distribution strategy in time, so as to improve the synchronization accuracy and color consistency of the multi-screen video data distribution.

[0042] Specifically, the operation states of the standard split screens have different corresponding parameters according to different demand scenarios. For example, when the video synchronization is displayed on multiple split screens, the data reception delay time is an important reference indicator. When the video segmentation is displayed on multiple split screens, the operation color difference at the edges of each split screen is an important reference indicator. Dynamically adjust the influence factors of each device evaluation indicator and management perturbation indicator according to the primary processing model.

[0043] Specifically, the standard reference values of each device evaluation indicator are the values of each device evaluation indicator under the operation state of the standard split screen.

[0044] Specifically, the device evaluation indicators include, but are not limited to, multiple parameters such as regional color difference, pixel aging degree, data reception delay duration, etc. The associated perturbation indicators include, but are not limited to, multiple parameters such as video color complexity, edge color difference between each associated reception point and the target reception point, difference in data reception delay duration, etc. By quantifying all the above indicators, a comprehensive evaluation is carried out on the split screens corresponding to each reception point, so as to dynamically adjust the initial distribution strategy.

[0045] Specifically, all the parameters in the model are normalized by presetting a first fixed coefficient and a second fixed coefficient, so that each parameter in the model is within the same value range.

[0046] It can be understood that in the above embodiments, according to the actual display requirements and the actual operating status corresponding to the allocation of each reception point, the distribution parameters of the video data to be distributed are dynamically adjusted to ensure the display consistency between each split screen and improve the synchronization accuracy and color consistency of the multi-screen video data distribution.

[0047] In the preferred embodiment of the present application, when judging whether to generate a correction instruction according to all the feedback data packets, it includes: Setting a plurality of first-level monitoring indicators and a plurality of second-level monitoring indicators according to the first-level processing model; Setting a i as the reception point to be evaluated according to the reception point sequence A; Generating an operation evaluation value f of the reception point to be evaluated according to all the feedback data packets; f= (g i * h i )]; Wherein, θ4 is the number of first-level monitoring indicators; g i is the influence factor of the i-th first-level monitoring indicator; h i is the reference value of the i-th first-level monitoring indicator of the reception point to be evaluated; Generating the operation evaluation value of each reception point in turn; Establishing an operation evaluation value sequence F, F = (f1, f2... f i ... f n ), wherein, f i is the operation evaluation value of the i-th reception point, and n is the number of reception points; Generating a correction evaluation value t according to the operation evaluation value sequence F; Presetting a correction evaluation value threshold T1, if t > T1, a first-level correction instruction is generated at the current correction time node.

[0048] Specifically, the correction evaluation value threshold can be set according to historical parameters.

[0049] Specifically, by adding multiple optical sensors to each sub-screen, feedback data packets at each receiving point are collected.

[0050] Specifically, corresponding primary monitoring indicators and secondary monitoring indicators are set according to the current demand scenario.

[0051] Specifically, the primary monitoring indicators include, but are not limited to, parameters such as the operating brightness deviation, chromaticity deviation, and operating uniformity of a single sub-screen. By quantifying each primary monitoring indicator, the actual operating status of each sub-screen is analyzed. The larger the operating evaluation value, the better the actual operating status of the sub-screen corresponding to the receiving point to be evaluated.

[0052] Specifically, when generating the corrected evaluation value t according to the sequence F of operating evaluation values, it includes: t = e3 * Q3 * Y2(i) * (f i - f')2] + e4 * Q4 * r i * v i Wherein, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; Y2(i) is a selection coefficient; if (f i - f') > 0; Y2(i) = 0; if (f i - f') < 0; Y2(i) = 1; θ5 is the number of secondary monitoring indicators; r i is the influence factor of the i-th secondary monitoring indicator; v i is the reference value of the i-th secondary monitoring indicator at the current calibration time node.

[0053] Specifically, the secondary monitoring indicators include, but are not limited to, multiple parameters such as the brightness consistency, chromaticity difference, and synchronization error between each receiving point.

[0054] Specifically, the larger the corrected evaluation value, the worse the overall display effect between each sub-screen of the current organic electroluminescent panel.

[0055] Specifically, the primary correction instruction refers to adjusting the sub-data packets to be distributed at each receiving point in a timely manner according to all the feedback data packets, and timely adjusting the refresh rate and pixel displacement strategy of each sub-screen to ensure the display consistency between each sub-screen, and improving the synchronization accuracy and color consistency of the distribution of multi-screen video data Specifically, all parameters in the model are normalized by presetting the third fixed coefficient and the fourth fixed coefficient, so that each parameter in the model is within the same value range.

[0056] ​Another preferred embodiment of a video data distribution method for a multi-screen organic electroluminescent panel in any of the above preferred embodiments. In this preferred embodiment, a video data distribution system for a multi-screen organic electroluminescent panel is provided, including: A central control unit, configured to set multiple demand scenarios according to historical video data, and construct a video processing model according to all demand scenarios; A distribution unit, configured to generate an initial distribution strategy according to the video data to be distributed and the video processing model, and generate a first-level distribution strategy according to the initial distribution strategy and the operation deviation values of each receiving point; The central control unit includes: A first processing module, configured to establish a demand scenario sequence B, B = (b1, b2... b i …b m ), where b i is the i-th demand scenario; m is the number of demand scenarios; The first processing module is further configured to establish a pre-identification model; According to the demand scenario sequence B, b i is sequentially set as the target demand scenario; Set a processing sub-model for the target demand scenario according to historical video data; Sequentially set the processing sub-models for each demand scenario; Establish a processing sub-model sequence P, P = (p1, p2... p i …p m ), where p i is the processing sub-model for the i-th demand scenario, and m is the number of monitoring indicators; Construct a video processing model according to the processing sub-model sequence P and the pre-identification model; A second processing module, configured to obtain the feedback data packets of each receiving point according to the preset correction time node, and determine whether to generate a correction instruction according to all the feedback data packets.

[0057] In the preferred embodiment of the present application, the distribution unit includes: A first control module, configured to generate a demand feature packet according to the video data to be distributed; According to the demand scenario sequence B, b i is sequentially set as the target demand scenario; Generate a similarity evaluation value w between the demand feature packet and the target demand scenario; w = β i * (j i - j' i ) 2 ; Wherein, is the number of demand indicators; β iis the influence factor of the i-th requirement index; j i is the reference value for generating the i-th requirement index based on the requirement feature package; j' i is the reference value of the i-th requirement index in the target requirement scenario; Generate similarity evaluation values for each requirement scenario in sequence according to the requirement feature package; Establish a similarity evaluation value sequence W, W = (w1, w2…w i …w m ), where w i is the similarity evaluation value of the requirement feature package and the i-th requirement scenario, and m is the number of requirement scenarios; Set the processing sub-model of the requirement scenario corresponding to the maximum value w max in the similarity evaluation sequence W as the primary processing model; Generate sub-packets to be distributed for each receiving point according to the primary processing model and the video data to be distributed; Generate an initial distribution strategy based on all the sub-packets to be distributed; The second control module is used to establish a receiving point sequence A, A = (a1, a2…a i …a n ), where a i is the i-th receiving point; n is the number of receiving points; Generate an association mapping table according to the primary processing model and the receiving point sequence A; Set a i as the target receiving point in sequence according to the receiving point sequence A; Generate the operation deviation value c of the target receiving point; Generate the operation deviation values of each receiving point in sequence; Establish an operation deviation value sequence C, C = (c1, c2…c i …c n ), where c i is the operation deviation value of the i-th receiving point, and n is the number of receiving points; Preset the operation deviation value threshold C1; If c i > C1, generate a primary compensation instruction for the i-th receiving point; Generate a primary distribution strategy according to all the primary compensation instructions and the initial distribution strategy.

[0058] In the preferred embodiment of the present application, the second processing module is further used for: Set multiple primary monitoring indicators and multiple secondary monitoring indicators according to the primary processing model; Set a i as the receiving point to be evaluated in sequence according to the receiving point sequence A; Generate the operation evaluation value f of the receiving point to be evaluated based on all feedback data packets; f = (g i * h i ); Among them, θ4 is the number of primary monitoring indicators; g i is the influence factor of the i-th primary monitoring indicator; h i is the reference value of the i-th primary monitoring indicator of the receiving point to be evaluated; Generate the operation evaluation values of each receiving point in sequence; Establish an operation evaluation value sequence F, F = (f1, f2…f i …f n ), where f i is the operation evaluation value of the i-th receiving point, and n is the number of receiving points; Generate the corrected evaluation value t based on the operation evaluation value sequence F; Preset the corrected evaluation value threshold T1. If t > T1, generate a primary correction instruction at the current correction time node.

[0059] According to the first concept of the present application, establish multiple demand scenarios based on different split-screen requirements, and establish corresponding processing sub-models for each demand scenario, so as to dynamically adjust the distribution strategy according to different operation requirements of the video to be distributed, and improve the operation effect of the multi-screen organic electroluminescent panel.

[0060] According to the second concept of the present application, by collecting the feedback data of each split screen, diagnose the overall operation state of the multi-screen organic electroluminescent panel in a timely manner, and adjust the refresh rate and pixel displacement strategy of each split screen in a timely manner to ensure the display consistency between each split screen, and improve the synchronization accuracy and color consistency of the distribution of multi-screen video data.

[0061] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application.

Claims

1. A video data distribution method for a multi-screen organic electroluminescent panel, characterized in that: include: Set multiple demand scenarios based on historical video data, and build a video processing model based on all demand scenarios; Generate an initial distribution strategy based on the video data to be distributed and the video processing model, and generate a primary distribution strategy based on the initial distribution strategy and the running deviation value of each receiving point; Obtain feedback data packets from each receiving point according to a preset correction time node, and determine whether to generate a correction instruction based on all feedback data packets; Among them, when building multiple demand scenarios, it includes: Establish a demand scenario sequence B, B=(b1, b2…b i …b m ), where b i is the i-th demand scenario; m is the number of demand scenarios.

2. The video data distribution method for a multi-screen organic electroluminescent panel according to claim 1, characterized in that: When building a video processing model, include: Establish a pre-recognition model; According to the required scene sequence B, set b in sequence i Target demand scenario; A processing sub-model that sets target demand scenarios based on historical video data; Set the processing sub-model for each demand scenario in turn; Establish the processing sub-model sequence P, P=(p1,p2…p i …p m ), where p i is the processing sub-model of the i-th demand scenario, and m is the number of monitoring indicators; A video processing model is constructed according to the processing sub-model sequence P and the pre-recognition model.

3. The video data distribution method of a multi-screen organic electroluminescent panel as claimed in claim 2, characterized in that: When generating an initial distribution strategy, include: Generate a demand feature package according to the video data to be distributed; Set b in sequence according to the required scenario sequence B i Target demand scenario; Generate a similarity evaluation value w between the demand feature package and the target demand scenario; w=[ β i *(j i -j' i ) 2 ]; in, is the quantity of demand indicators; β i is the influencing factor of the i-th demand indicator; j i Generates the reference value of the i-th demand index based on the demand feature package; j' i is the reference value of the i-th demand indicator in the target demand scenario; The requirement feature packages are sequentially generated with similar evaluation values ​​for each requirement scenario; Establish a similar evaluation value sequence W, W=(w1,w2…w i …w m ), where w i is the similarity evaluation value between the demand feature package and the i-th demand scenario, and m is the number of demand scenarios; Set the maximum value w in the similar evaluation sequence W max The processing sub-model of the corresponding demand scenario is a first-level processing model; Generate sub-data packets to be distributed at each receiving point according to the primary processing model and the video data to be distributed; Generate an initial distribution strategy based on all sub-data packets to be distributed.

4. The video data distribution method of a multi-screen organic electroluminescent panel as claimed in claim 3, characterized in that: When generating a primary distribution strategy, include: Establish a receiving point sequence A, A=(a1,a2…a i …a n ), where a i is the i-th receiving point; n is the number of receiving points; Generate an association mapping table according to the primary processing model and the receiving point number column A; Set a in sequence according to the receiving point number A i is the target receiving point; Generate the running deviation value c of the target receiving point; Generate the running deviation value of each receiving point in turn; Establish a running deviation value sequence C, C=(c1,c2…c i …c n ), where c i is the running deviation value of the i-th receiving point, and n is the number of receiving points; Preset operation deviation threshold C1; If c i >C1, generates the first-level compensation instruction for the i-th receiving point; A primary distribution strategy is generated based on all primary compensation instructions and the initial distribution strategy.

5. The video data distribution method of a multi-screen organic electroluminescent panel as claimed in claim 4, characterized in that: When generating the running deviation value c of the target receiving point, it includes: c=e1*Q1*[ Y1(i)*η i *(k i -k' i ) 2 ]+e2*Q2*[ µ i *d i ] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; is the number of equipment evaluation indicators generated based on the primary processing model; η i is the influencing factor of the i-th equipment evaluation index; k i is the reference value of the evaluation index of the i-th device at the target receiving point; k' i is the standard reference value of the i-th equipment evaluation index; 3 is the number of associated disturbance indicators; µ i is the impact factor of the i-th associated disturbance index; d i is the reference value of the i-th associated disturbance index generated based on all associated receiving points of the target receiving point in the associated mapping table; Y1(i) is the selection coefficient; if (k i -k' i )<0, Y1(i)=1; if (k i -k' i )>0, Y1(i)=0.

6. The video data distribution method of a multi-screen organic electroluminescent panel as claimed in claim 5, characterized in that: When judging whether to generate a correction instruction based on all feedback data packets, it includes: Set multiple first-level monitoring indicators and multiple second-level monitoring indicators according to the first-level processing model; Set a in sequence according to the receiving point number A i It is the receiving point to be evaluated; Generate an operation evaluation value f of the receiving point to be evaluated according to all feedback data packets; f=[ (g i *h i )]; Among them, θ4 is the number of first-level monitoring indicators; g i is the influencing factor of the i-th primary monitoring indicator; h i is the reference value of the first-level monitoring indicator of the i-th receiving point to be evaluated; Generate the operation evaluation value of each receiving point in turn; Establish the running evaluation value series F, F=(f1, f2…f i …f n ), where f i is the operation evaluation value of the i-th receiving point, and n is the number of receiving points; Generate a modified evaluation value t according to the running evaluation value sequence F; The correction evaluation value threshold T1 is preset. If t>T1, the current correction time node generates a first-level correction instruction.

7. The video data distribution method of a multi-screen organic electroluminescent panel according to claim 6, characterized in that: When generating the modified evaluation value t according to the running evaluation value sequence F, it includes: t=e3*Q3*[ Y2(i)*(f i -f')2]+e4*Q4*[ r i *v i ] Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; Y2(i) is the selection coefficient; if (f i -f')>0;Y2(i)=0; if (f i -f')<0;Y2(i)=1;θ5 is the number of secondary monitoring indicators; r i is the impact factor of the i-th secondary monitoring indicator; v i It is the reference value of the i-th secondary monitoring indicator at the current correction time node.

8. A video data distribution system for a multi-screen organic electroluminescent panel, using the video data distribution method for a multi-screen organic electroluminescent panel according to any one of claims 1 to 7, characterized in that: include: The central control unit is used to set multiple demand scenarios based on historical video data and build a video processing model based on all demand scenarios; A distribution unit, used to generate an initial distribution strategy according to the video data to be distributed and the video processing model, and to generate a primary distribution strategy according to the initial distribution strategy and the running deviation value of each receiving point; The central control unit comprises: The first processing module is used to establish a demand scenario sequence B, B = (b1, b2...b i …b m ), where b i is the i-th demand scenario; m is the number of demand scenarios; The first processing module is also used to establish a pre-recognition model; According to the required scene sequence B, set b in sequence i Target demand scenario; A processing sub-model that sets target demand scenarios based on historical video data; Set the processing sub-model for each demand scenario in turn; Establish the processing sub-model sequence P, P=(p1,p2…p i …p m ), where p i is the processing sub-model of the i-th demand scenario, and m is the number of monitoring indicators; Construct a video processing model according to the processing sub-model sequence P and the pre-recognition model; The second processing module is used to obtain feedback data packets from each receiving point according to a preset correction time node, and determine whether to generate a correction instruction according to all feedback data packets.

9. The video data distribution system for multiple organic electroluminescent panels as claimed in claim 8, characterized in that: The distribution unit comprises: A first control module, used for generating a demand feature package according to the video data to be distributed; Set b in sequence according to the required scenario sequence B i Target demand scenario; Generate a similarity evaluation value w between the demand feature package and the target demand scenario; w=[ β i *(j i -j' i ) 2 ]; in, is the quantity of demand indicators; β i is the influencing factor of the i-th demand indicator; j i Generates the reference value of the i-th demand index based on the demand feature package; j' i is the reference value of the i-th demand indicator in the target demand scenario; The requirement feature packages are sequentially generated with similar evaluation values ​​for each requirement scenario; Establish a similar evaluation value sequence W, W=(w1,w2…w i …w m ), where w i is the similarity evaluation value between the demand feature package and the i-th demand scenario, and m is the number of demand scenarios; Set the maximum value w in the similar evaluation sequence W max The processing sub-model of the corresponding demand scenario is a first-level processing model; Generate sub-data packets to be distributed at each receiving point according to the primary processing model and the video data to be distributed; Generate an initial distribution strategy according to all sub-data packets to be distributed; The second control module is used to establish a receiving point sequence A, A=(a1, a2…a i …a n ), where a i is the i-th receiving point; n is the number of receiving points; Generate an association mapping table according to the primary processing model and the receiving point number column A; Set a in sequence according to the receiving point number A i is the target receiving point; Generate the running deviation value c of the target receiving point; Generate the running deviation value of each receiving point in turn; Establish a running deviation value sequence C, C=(c1,c2…c i …c n ), where c i is the running deviation value of the i-th receiving point, and n is the number of receiving points; Preset operation deviation threshold C1; If c i >C1, generates the first-level compensation instruction for the i-th receiving point; A primary distribution strategy is generated based on all primary compensation instructions and the initial distribution strategy.

10. The video data distribution system for multiple organic electroluminescent panels according to claim 9, characterized in that: The second processing module is also used for: Set multiple first-level monitoring indicators and multiple second-level monitoring indicators according to the first-level processing model; Set a in sequence according to the receiving point number A i It is the receiving point to be evaluated; Generate an operation evaluation value f of the receiving point to be evaluated according to all feedback data packets; f=[ (g i *h i )]; Among them, θ4 is the number of first-level monitoring indicators; g i is the influencing factor of the i-th primary monitoring indicator; h i is the reference value of the first-level monitoring indicator of the i-th receiving point to be evaluated; Generate the operation evaluation value of each receiving point in turn; Establish the running evaluation value series F, F=(f1, f2…f i …f n ), where f i is the operation evaluation value of the i-th receiving point, and n is the number of receiving points; Generate a modified evaluation value t according to the running evaluation value sequence F; The correction evaluation value threshold T1 is preset. If t>T1, the current correction time node generates a first-level correction instruction.

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