An operation monitoring method for an organic light emitting display system

Abstract

The present application relates to the technical field of organic light-emitting display systems, and in particular to an operation monitoring method for an organic light-emitting display system. The method comprises: establishing multiple monitoring sub-areas according to the equipment parameters of the organic light-emitting display system, and establishing a correlation model according to all the monitoring sub-areas; acquiring monitoring data packets of each monitoring sub-area according to a preset monitoring time node, and sequentially generating operation evaluation values ​​of each monitoring sub-area; judging whether to generate an early warning instruction according to all the operation evaluation values; establishing multiple monitoring sub-areas according to the display panel of the organic light-emitting display system, and monitoring the operation status of each monitoring sub-area in real time according to the preset monitoring time node, and correcting the monitoring data of each monitoring sub-area according to the preset correlation model, and timely screening the monitoring sub-areas with display deviations, so as to accurately correct the control parameters of the organic light-emitting display system and ensure the stability of the display effect.

Description

Technical Field

[0001] The present application relates to the technical field of organic light-emitting display systems, and in particular to an operation monitoring method of an organic light-emitting display system. Background Art

[0002] In recent years, organic light-emitting display systems, as an advanced display technology, have been widely used in various fields, including high-end TVs, smart phones, wearable devices, automotive displays, virtual reality (VR) and augmented reality (AR) devices, etc.

[0003] However, with the continuous use of display devices, factors such as aging of display devices and working environment conditions have led to a decline in display quality of display devices, low display energy efficiency and impact on user use. The stability of the display effect of the display device cannot be guaranteed. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides an operation monitoring method of an organic light-emitting display system, aiming to improve the control efficiency of the organic light-emitting display system and ensure the stability of the display effect.

[0005] In some embodiments of the present application, multiple monitoring sub-areas are established based on the display panel of the organic light-emitting display system, and the operating status of each monitoring sub-area is monitored in real time according to preset monitoring time nodes, and display deviations in each monitoring sub-area are warned and corrected in time.

[0006] In some embodiments of the present application, a monitoring model is constructed by extracting features from historical parameters, and the monitoring data of each monitoring sub-area is corrected according to a preset correlation model, so that the real-time display deviation of each monitoring sub-area is accurately monitored, and the monitoring sub-areas with display deviations are timely screened, so that the control parameters of the organic light-emitting display system are accurately corrected to ensure the stability of the display effect.

[0007] In some embodiments of the present application, a method for monitoring the operation of an organic light-emitting display system is provided, comprising:

[0008] Establishing a plurality of monitoring sub-areas according to the device parameters of the organic light emitting display system, and establishing a correlation model according to all the monitoring sub-areas;

[0009] Acquire monitoring data packets of each monitoring sub-area according to preset monitoring time nodes, and generate operation evaluation values ​​of each monitoring sub-area in turn;

[0010] Determine whether to generate an early warning instruction based on all operation evaluation values;

[0011] The establishment of multiple monitoring sub-areas includes:

[0012] Establish a monitoring sub-area sequence A, A=(a1, a2…a i …a n ), where a i is the ith monitoring sub-area; n is the number of monitoring sub-areas.

[0013] In some embodiments of the present application, the establishment of the association model according to all monitoring sub-areas includes:

[0014] Create multiple display scenes and display scene sequence B, B=(b1, b2…b c …b r ), where b c is the cth display scene; r is the number of display scenes;

[0015] Set b in sequence i Present the scenario to the target;

[0016] Generate a first-level association model of the target display scene according to the historical display parameters of the target display scene and the position parameters of all monitored sub-areas;

[0017] Generate the first-level association model of each display scene in turn, and establish the first-level association model sequence K, K = (k1, k2…k c …k r ), where k c It is the first-level association model of the c-th display scene.

[0018] In some embodiments of the present application, when obtaining monitoring data packets of each monitoring sub-area according to a preset monitoring time node, it includes:

[0019] Preset multiple monitoring cycles;

[0020] The user display demand parameters of the current monitoring period and establish multiple time intervals:

[0021] Generate the time interval sequence T of the current monitoring period, T= (t1, t2…t x …t m ), where t x is the xth time interval in the current monitoring cycle; m is the number of time intervals in the current monitoring cycle;

[0022] Set the end time node of each time interval as the monitoring time node;

[0023] The operating parameters of each monitoring sub-area in the corresponding time interval are obtained according to the preset monitoring time node, and a monitoring data packet of each monitoring sub-area is generated.

[0024] In some embodiments of the present application, when the operation evaluation value of each monitoring sub-area is generated in sequence, it includes:

[0025] Set multiple characteristic monitoring indicators according to the preset monitoring model;

[0026] According to the monitoring sub-area sequence A, set a i To monitor a sub-area for a target:

[0027] Get the monitoring data packet of the target monitoring sub-area at the current monitoring time node,

[0028] Generate the actual operation curve of each characteristic monitoring indicator in the time interval corresponding to the current monitoring point according to the monitoring data packet;

[0029] Generate the expected operation curve of each characteristic monitoring indicator in the time interval corresponding to the current monitoring point according to the user display demand parameters of the current monitoring period;

[0030] Select the target association model of the current monitoring time node, and generate the corresponding correction operation curve of each characteristic monitoring indicator at the current monitoring point according to the target association model;

[0031] Generate the operation evaluation value f of the target monitoring sub-area at the current monitoring time node;

[0032] Generate the operation evaluation value of each monitoring sub-area at the current monitoring time node in sequence;

[0033] Establish the operation evaluation value series F of the current monitoring time node, F=(f1,f2…f i …f n ), where f i It is the operation evaluation value of the i-th monitoring sub-area at the current monitoring time node.

[0034] In some embodiments of the present application, when selecting the target association model of the current monitoring time node, it includes:

[0035] Obtain the user display demand parameters within the time interval corresponding to the current monitoring time node, and generate similarity evaluation values ​​for each display scenario in turn;

[0036] Establish a similar evaluation value sequence J, J = (j1, j2…j c …j r ), where j c The similarity evaluation value between the user display demand parameter and the cth display scenario in the time interval corresponding to the current monitoring time node;

[0037] Set the maximum value j in the similarity evaluation value sequence J max The first-level association model of the corresponding display scene is the target association model of the current monitoring time node.

[0038] In some embodiments of the present application, when the similarity evaluation values ​​of each display scene are generated in sequence, it includes:

[0039] Set b in sequence c Present the scenario to the target;

[0040] Generate a similarity evaluation value j with the target display scene;

[0041] j= µ v ×(g v -g' v ) 2;

[0042] Among them, r1 is the number of display evaluation indicators; µ v is the impact factor of the vth display evaluation index; g v is the real-time reference value of the vth display evaluation index generated based on the user display demand parameters in the time interval corresponding to the current monitoring time node; g' v is the reference value of the vth display evaluation indicator in the target display scenario.

[0043] In some embodiments of the present application, when generating the operation evaluation value f of the target monitoring sub-area at the current monitoring time node, it includes:

[0044] Establish the first-level deviation value series D1 of the target monitoring sub-area at the current monitoring time node, D1=(d 11 , d 12 …d 1w …d 1θ ), where d 1w It is the first-level deviation value generated based on the deviation between the actual operation curve and the expected operation curve of the wth characteristic monitoring indicator; θ is the number of characteristic monitoring indicators;

[0045] Establish the secondary deviation value series D2 of the target monitoring sub-area at the current monitoring time node, D2=(d 21 , d 22 …d 2w …d 2θ ), where d 2w It is a secondary deviation value generated based on the deviation between the actual operation curve and the modified operation curve of the wth characteristic monitoring indicator;

[0046] The operation evaluation value f of the target monitoring sub-area is generated according to the primary deviation value sequence D1 and the secondary deviation value sequence D2.

[0047] In some embodiments of the present application, when generating the operation evaluation value f of the target monitoring sub-area, it includes:

[0048] f=e1×Q1×[ βw ×d 1w +e2×Q2× β w ×d 2w +e3×Q3×U;

[0049] Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; β w is the influence factor of the w-th feature monitoring index; U is the historical evaluation value generated based on all historical monitoring data packets between the start time node and the current monitoring time node of the target monitoring sub-region in the current monitoring cycle.

[0050] In some embodiments of the present application, when judging whether to generate a warning instruction according to all operation evaluation values, it includes:

[0051] Obtain the operation evaluation value sequence F at the current monitoring time node;

[0052] Preset the first operation evaluation value threshold F1;

[0053] If f i > F1, the i-th monitoring sub-region generates a first-level warning instruction at the current monitoring time node;

[0054] If f i < F1, the i-th monitoring sub-region does not generate a warning instruction at the current monitoring time node;

[0055] Generate the risk evaluation value h at the current monitoring time node according to the operation evaluation value sequence F;

[0056] Preset the first risk evaluation value threshold H1;

[0057] If h > H1, a second-level warning instruction is generated at the current monitoring time node.

[0058] In some embodiments of the present application, when generating the risk evaluation value h at the current monitoring time node, it includes:

[0059] h = e4×Q4× S i ×Y1(i)×(f i - F1)] + e5×Q5× Y2(i)×(f i - F1)];

[0060] Wherein, e4 is a preset fourth weight coefficient; e5 is a preset fifth weight coefficient; Q4 is a preset fourth fixed coefficient; Q5 is a preset fifth fixed coefficient; S iis the influencing factor of the i-th monitoring sub-area; Y1(i) is the selection coefficient. If (f i -F1)>0,Y1(i)=1; if (f i -F1)<0,Y1(i)=0;Y2(i) is the selection coefficient. If (f i -F1)>0,Y2(i)=1 / ((f i -F1); if (f i -F1)<0,Y2(i)=0.

[0061] Compared with the prior art, the operation monitoring method of an organic light-emitting display system in the embodiment of the present application has the following beneficial effects:

[0062] A plurality of monitoring sub-areas are established according to the display panel of the organic light-emitting display system, and the operation status of each monitoring sub-area is monitored in real time according to the preset monitoring time nodes, so as to promptly warn and correct the display deviations in each monitoring sub-area.

[0063] By extracting features from historical parameters to construct a monitoring model, and correcting the monitoring data of each monitoring sub-area according to a preset correlation model, the real-time display deviation of each monitoring sub-area can be accurately monitored, and the monitoring sub-areas with display deviations can be screened in time, so as to accurately correct the control parameters of the organic light-emitting display system and ensure the stability of the display effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flow chart of an operation monitoring method of an organic light-emitting display system in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0065] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0066] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply 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 understood as a limitation on the present application.

[0067] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0068] In the description of this 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 can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0069] like Figure 1 As shown, a method for monitoring the operation of an organic light-emitting display system in a preferred embodiment of the present application includes:

[0070] S101: establishing a plurality of monitoring sub-areas according to device parameters of the organic light emitting display system, and establishing a correlation model according to all the monitoring sub-areas;

[0071] S102: Acquire monitoring data packets of each monitoring sub-area according to a preset monitoring time node, and sequentially generate operation evaluation values ​​of each monitoring sub-area;

[0072] S103: judging whether to generate a warning instruction according to all operation evaluation values;

[0073] When establishing multiple monitoring sub-areas, include:

[0074] Establish a monitoring sub-area sequence A, A=(a1, a2…a i …a n ), where a i is the ith monitoring sub-area; n is the number of monitoring sub-areas.

[0075] Specifically, the display panel and the driving circuit of the organic light emitting display system are divided to construct a plurality of monitoring sub-areas, wherein a single monitoring sub-area is a partial area of ​​a single driving circuit or a display panel.

[0076] Specifically, when establishing a correlation model based on all monitoring sub-areas, it includes:

[0077] Create multiple display scenes and display scene sequence B, B=(b1, b2…b c …b r ), where b cis the cth display scene; r is the number of display scenes;

[0078] Set b in sequence c Present the scenario to the target;

[0079] Generate a first-level association model of the target display scene according to the historical display parameters of the target display scene and the position parameters of all monitored sub-areas;

[0080] Generate the first-level association model of each display scene in turn, and establish the first-level association model sequence K, K = (k1, k2…k c …k r ), where k c It is the first-level association model of the c-th display scene.

[0081] Specifically, multiple display evaluation indicators are set according to historical display parameters, and the display evaluation indicators include but are not limited to multiple parameters such as average brightness of the picture, peak brightness of the picture, color saturation of the picture, color proportion and color area.

[0082] Each display evaluation index is quantified, and multiple value intervals of each display evaluation index are generated in turn. All value intervals are randomly combined, and multiple display scenes are constructed according to the combination results. A single display scene contains a single value interval of each feature evaluation index.

[0083] Specifically, when displaying different data, the disturbance effects between the various monitoring sub-areas will change. By constructing multiple display scenarios, the correlation between the various monitoring sub-areas can be quickly determined according to real-time user display needs, thereby improving the subsequent data processing efficiency.

[0084] Specifically, the historical parameters of a single display scene are analyzed and processed. If in the current display scene, if the monitoring parameters of the ith monitoring sub-area fluctuate, it will cause the monitoring parameters of the target monitoring sub-area to fluctuate. Then the ith monitoring sub-area is an associated sub-area of ​​the target monitoring sub-area in the current display scene. According to all the association relationships in the current display scene, a corresponding first-level association model is constructed.

[0085] It can be understood that in the above embodiment, multiple monitoring sub-areas are established according to the display panel of the organic light-emitting display system, and the operating status of each monitoring sub-area is monitored in real time according to the preset monitoring time nodes, and the display deviations in each monitoring sub-area are warned and corrected in time.

[0086] In a preferred embodiment of the present application, when obtaining monitoring data packets of each monitoring sub-area according to a preset monitoring time node, it includes:

[0087] Preset multiple monitoring cycles;

[0088] The user display demand parameters of the current monitoring period and establish multiple time intervals:

[0089] Generate the time interval sequence T of the current monitoring period, T= (t1, t2…t x …t m ), where t x is the xth time interval in the current monitoring cycle; m is the number of time intervals in the current monitoring cycle;

[0090] Set the end time node of each time interval as the monitoring time node;

[0091] The operating parameters of each monitoring sub-area in the corresponding time interval are obtained according to the preset monitoring time node, and a monitoring data packet of each monitoring sub-area is generated.

[0092] Specifically, the user display demands in the current monitoring period are analyzed and processed, so as to construct multiple time intervals, and the fluctuation of the display evaluation index corresponding to the display content in a single time interval is small.

[0093] Specifically, when generating the operation evaluation value of each monitoring sub-area in turn, it includes:

[0094] Set multiple characteristic monitoring indicators according to the preset monitoring model;

[0095] According to the monitoring sub-area sequence A, set a i To monitor a sub-area for a target:

[0096] Get the monitoring data packet of the target monitoring sub-area at the current monitoring time node,

[0097] Generate the actual operation curve of each characteristic monitoring indicator in the time interval corresponding to the current monitoring point according to the monitoring data packet;

[0098] Generate the expected operation curve of each characteristic monitoring indicator in the time interval corresponding to the current monitoring point according to the user display demand parameters of the current monitoring period;

[0099] Select the target association model of the current monitoring time node, and generate the corresponding correction operation curve of each characteristic monitoring indicator at the current monitoring point according to the target association model;

[0100] Generate the operation evaluation value f of the target monitoring sub-area at the current monitoring time node;

[0101] Generate the operation evaluation value of each monitoring sub-area at the current monitoring time node in sequence;

[0102] Establish the operation evaluation value series F of the current monitoring time node, F=(f1,f2…f i …fn ), where f i It is the operation evaluation value of the i-th monitoring sub-area at the current monitoring time node.

[0103] Specifically, the characteristic monitoring indicators include but are not limited to temperature, screen brightness, screen color, current value at both ends of the driving circuit, voltage value and other parameters.

[0104] Specifically, if the target monitoring sub-area is a part of the display panel, the real-time display data of the target monitoring sub-area is collected, so as to generate the temperature change curve, the actual change curve of the screen brightness, and the real-time change curve of the screen color of the current target monitoring sub-area. If the target monitoring sub-area is a single drive circuit, the real-time current curve, real-time voltage curve, real-time temperature curve and other parameters of the target monitoring sub-area are collected.

[0105] Specifically, the user display demand parameters in the current time interval are analyzed to generate the safe operation interval of each characteristic monitoring indicator in a single monitoring sub-area in turn, and construct the corresponding expected operation curve. According to the current monitoring time node, the monitoring data packets of each monitoring sub-area in the time interval that just ended are obtained, and the monitoring data packets are preprocessed to generate the actual operation curve of each characteristic monitoring indicator.

[0106] Specifically, according to the target association model, the real-time monitoring data of the associated sub-area corresponding to the target monitoring sub-area is obtained, so as to correct the expected operation curve of the target monitoring sub-area.

[0107] Specifically, when generating the operation evaluation value f of the target monitoring sub-area at the current monitoring time node, it includes:

[0108] Establish the first-level deviation value series D1 of the target monitoring sub-area at the current monitoring time node, D1=(d 11 , d 12 …d 1w …d 1θ ), where d 1w It is the first-level deviation value generated based on the deviation between the actual operation curve and the expected operation curve of the wth characteristic monitoring indicator; θ is the number of characteristic monitoring indicators;

[0109] Establish the secondary deviation value series D2 of the target monitoring sub-area at the current monitoring time node, D2=(d 21 , d 22 …d 2w …d 2θ ), where d 2w It is a secondary deviation value generated based on the deviation between the actual operation curve and the modified operation curve of the wth characteristic monitoring indicator;

[0110] Specifically, the lower the overlap between the expected operating curve and the actual operating curve of a single characteristic monitoring indicator, the larger the corresponding first-level deviation value; the lower the overlap between the actual operating curve and the corrected operating curve of a single characteristic monitoring indicator, the larger the corresponding second-level deviation value.

[0111] The operation evaluation value f of the target monitoring sub-area is generated according to the primary deviation value sequence D1 and the secondary deviation value sequence D2.

[0112] f=e1×Q1×[ β w ×d 1w ]+e2×Q2×[ β w ×d 2w ]+e3×Q3×U;

[0113] Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; β w is the influencing factor of the wth characteristic monitoring indicator; U is the historical evaluation value generated based on all historical monitoring data packets of the target monitoring sub-area between the start time node of the current monitoring cycle and the current monitoring time node.

[0114] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient, the second fixed coefficient and the third fixed coefficient, so that each parameter in the model is within the same value range.

[0115] Specifically, β w The larger it is, the more important the corresponding characteristic monitoring indicator is in the target monitoring sub-area.

[0116] Specifically, the larger the operation evaluation value corresponding to the monitoring time node between the start time node of the current monitoring cycle of the target monitoring sub-area, the larger the corresponding historical evaluation value.

[0117] Specifically, the larger the operation evaluation value, the greater the degree of operation deviation in the current monitoring sub-area.

[0118] It can be understood that in the above embodiment, the monitoring model is constructed by extracting features from historical parameters, and the monitoring data of each monitoring sub-area is corrected according to a preset correlation model, so that the real-time display deviation of each monitoring sub-area is accurately monitored, and the monitoring sub-areas with display deviations are timely screened, so as to accurately correct the control parameters of the organic light-emitting display system and ensure the stability of the display effect.

[0119] In a preferred embodiment of the present application, when selecting the target association model of the current monitoring time node, it includes:

[0120] Obtain the user display demand parameters within the time interval corresponding to the current monitoring time node, and generate similarity evaluation values ​​for each display scenario in turn;

[0121] Establish a similar evaluation value sequence J, J = (j1, j2…j c …j r ), where j c The similarity evaluation value between the user display demand parameter and the cth display scenario in the time interval corresponding to the current monitoring time node;

[0122] Set the maximum value j in the similarity evaluation value sequence J max The first-level association model of the corresponding display scene is the target association model of the current monitoring time node.

[0123] Specifically, when generating similar evaluation values ​​for each display scene in sequence, it includes:

[0124] Set b in sequence i Present the scenario to the target;

[0125] Generate a similarity evaluation value j with the target display scene;

[0126] j= µ v ×(g v -g' v ) 2;

[0127] Among them, r1 is the number of display evaluation indicators; µ v is the impact factor of the vth display evaluation index; g v is the real-time reference value of the vth display evaluation index generated based on the user display demand parameters in the time interval corresponding to the current monitoring time node; g' v is the reference value of the vth display evaluation indicator in the target display scenario.

[0128] Specifically, by analyzing the user display demand parameters within the current time interval, the corresponding display evaluation indicators are extracted, the corresponding target display scene is quickly selected, the correction efficiency of the real-time monitoring data packet is improved, and the monitoring accuracy of the operating status of the target monitoring sub-area is guaranteed.

[0129] In a preferred embodiment of the present application, when judging whether to generate a warning instruction according to all operation evaluation values, it includes:

[0130] Get the running evaluation value sequence F of the current monitoring time node;

[0131] Preset the first running evaluation value threshold F1;

[0132] If f i > F1, then the i-th monitoring sub-region generates a first-level warning instruction at the current monitoring time node;

[0133] If f i < F1, then the i-th monitoring sub-region does not generate a warning instruction at the current monitoring time node;

[0134] Generate the risk evaluation value h at the current monitoring time node according to the running evaluation value sequence F;

[0135] Preset the first risk evaluation value threshold H1;

[0136] If h > H1, a second-level warning instruction is generated at the current monitoring time node.

[0137] Specifically, the first risk evaluation value threshold and the first running evaluation value threshold can be set according to historical parameters. If the running evaluation value of a single monitoring sub-region exceeds the first running evaluation value threshold, it indicates that there is a running deviation in this monitoring sub-region, and the control parameters of this monitoring sub-region need to be corrected in a timely manner.

[0138] Specifically, when generating the risk evaluation value h at the current monitoring time node, it includes:

[0139] h = e4 × Q4 × S i × Y1(i) × (f i - F1)] + e5 × Q5 × Y2(i) × (f i - F1)];

[0140] Among them, e4 is the preset fourth weight coefficient; e5 is the preset fifth weight coefficient; Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; S i is the influence factor of the i-th monitoring sub-region; Y1(i) is the selection coefficient. If (f i - F1) > 0, Y1(i) = 1; if (f i - F1) < 0, Y1(i) = 0; Y2(i) is the selection coefficient. If (f i - F1) > 0, Y2(i) = 1 / (f i - F1); if (f i - F1) < 0, Y2(i) = 0.

[0141] Specifically, normalize all the parameters in the model through the preset fourth fixed coefficient and fifth fixed coefficient, so that each parameter is within the same value range.

[0142] Specifically, the first-level warning instruction means that there is an operating deviation in the current monitoring sub-area, and the corresponding control strategy needs to be adjusted in time to ensure the stable operation of the organic light-emitting display system and the stability of the display effect.

[0143] Specifically, the second-level warning instruction refers to the large-scale operating deviations of the current organic light-emitting display system, which requires timely maintenance to eliminate the risk of failure.

[0144] According to the first concept of the present application, multiple monitoring sub-areas are established according to the display panel of the organic light-emitting display system, and the operating status of each monitoring sub-area is monitored in real time according to preset monitoring time nodes, and display deviations in each monitoring sub-area are warned and corrected in time.

[0145] According to the second concept of the present application, a monitoring model is constructed by extracting features from historical parameters, and the monitoring data of each monitoring sub-area is corrected according to a preset correlation model, so that the real-time display deviation of each monitoring sub-area can be accurately monitored, and the monitoring sub-areas with display deviations can be timely screened, so as to accurately correct the control parameters of the organic light-emitting display system and ensure the stability of the display effect.

[0146] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A method for monitoring the operation of an organic light-emitting display system, characterized in that: include: Establishing a plurality of monitoring sub-areas according to the device parameters of the organic light emitting display system, and establishing a correlation model according to all the monitoring sub-areas; Acquire monitoring data packets of each monitoring sub-area according to preset monitoring time nodes, and generate operation evaluation values ​​of each monitoring sub-area in turn; Determine whether to generate an early warning instruction based on all operation evaluation values; The establishment of multiple monitoring sub-areas includes: Establish a monitoring sub-area sequence A, A=(a1, a2…a i …a n ), where a i is the ith monitoring sub-area; n is the number of monitoring sub-areas; The establishment of the association model based on all monitoring sub-areas includes: Create multiple display scenes and display scene sequence B, B=(b1, b2…b c …b r ), where b c is the cth display scene; r is the number of display scenes; Set b in sequence i Present the scenario to the target; Generate a first-level association model of the target display scene according to the historical display parameters of the target display scene and the position parameters of all monitored sub-areas; Generate the first-level association model of each display scene in turn, and establish the first-level association model sequence K, K = (k1, k2…k c …k r ), where k c is the first-level association model of the c-th display scene; When obtaining monitoring data packets of each monitoring sub-area according to the preset monitoring time node, it includes: Preset multiple monitoring cycles; The user display demand parameters of the current monitoring period and establish multiple time intervals: Generate the time interval sequence T of the current monitoring period, T= (t1, t2…t x …t m ), where t x is the xth time interval in the current monitoring cycle; m is the number of time intervals in the current monitoring cycle; Set the end time node of each time interval as the monitoring time node; Obtaining the operating parameters of each monitoring sub-area within the corresponding time interval according to the preset monitoring time node, and generating a monitoring data packet for each monitoring sub-area; When generating the operation evaluation value of each monitoring sub-area in turn, it includes: Set multiple characteristic monitoring indicators according to the preset monitoring model; According to the monitoring sub-area sequence A, set a i To monitor a sub-area for a target: Get the monitoring data packet of the target monitoring sub-area at the current monitoring time node, Generate the actual operation curve of each characteristic monitoring indicator in the time interval corresponding to the current monitoring point according to the monitoring data packet; Generate the expected operation curve of each characteristic monitoring indicator in the time interval corresponding to the current monitoring point according to the user display demand parameters of the current monitoring period; Select the target association model of the current monitoring time node, and generate the corresponding correction operation curve of each characteristic monitoring indicator at the current monitoring point according to the target association model; Generate the operation evaluation value f of the target monitoring sub-area at the current monitoring time node; Generate the operation evaluation value of each monitoring sub-area at the current monitoring time node in sequence; Establish the operation evaluation value series F of the current monitoring time node, F=(f1,f2…f i …f n ), where f i is the operation evaluation value of the i-th monitoring sub-area at the current monitoring time node; When selecting the target association model for the current monitoring time node, it includes: Obtain the user display demand parameters within the time interval corresponding to the current monitoring time node, and generate similarity evaluation values ​​for each display scenario in turn; Establish a similar evaluation value sequence J, J = (j1, j2…j c …j r ), where j c The similarity evaluation value between the user display demand parameter and the cth display scenario in the time interval corresponding to the current monitoring time node; Set the maximum value j in the similarity evaluation value sequence J max The first-level association model of the corresponding display scene is the target association model of the current monitoring time node.

2. The operation monitoring method of the organic light emitting display system according to claim 1, characterized in that: When generating similar evaluation values ​​for each display scene in sequence, it includes: Set b in sequence c Present the scenario to the target; Generate a similarity evaluation value j with the target display scene; j= µ v ×(g v -g' v ) 2; Among them, r1 is the number of display evaluation indicators; µ v is the impact factor of the vth display evaluation index; g v is the real-time reference value of the vth display evaluation index generated based on the user display demand parameters in the time interval corresponding to the current monitoring time node; g' v is the reference value of the vth display evaluation indicator in the target display scenario.

3. The operation monitoring method of the organic light emitting display system according to claim 2, characterized in that: When generating the operation evaluation value f of the target monitoring sub-area at the current monitoring time node, it includes: Establish the first-level deviation value series D1 of the target monitoring sub-area at the current monitoring time node, D1=(d 11 , d 12 …d 1w …d 1θ ), where d 1w It is the first-level deviation value generated based on the deviation between the actual operation curve and the expected operation curve of the wth characteristic monitoring indicator; θ is the number of characteristic monitoring indicators; Establish the secondary deviation value series D2 of the target monitoring sub-area at the current monitoring time node, D2=(d 21 , d 22 …d 2w …d 2θ ), where d 2w It is a secondary deviation value generated based on the deviation between the actual operation curve and the modified operation curve of the wth characteristic monitoring indicator; The operation evaluation value f of the target monitoring sub-area is generated according to the primary deviation value sequence D1 and the secondary deviation value sequence D2.

4. The operation monitoring method of the organic light emitting display system according to claim 3, characterized in that: When generating the operation evaluation value f of the target monitoring sub-area, it includes: f=e1×Q1×[ b w ×d 1w ]+e2×Q2×[ b w ×d 2w ]+e3×Q3×U; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; β w is the influencing factor of the wth characteristic monitoring indicator; U is the historical evaluation value generated based on all historical monitoring data packets of the target monitoring sub-area between the start time node of the current monitoring cycle and the current monitoring time node.

5. The operation monitoring method of the organic light emitting display system according to claim 4, characterized in that: When judging whether to generate an early warning instruction based on all operation evaluation values, it includes: Get the running evaluation value sequence F of the current monitoring time node; Preset the first operation evaluation value threshold F1; If f i >F1, the i-th monitoring sub-area generates a first-level warning instruction at the current monitoring time node; If f i <F1, then the i-th monitoring sub-region does not generate a warning instruction at the current monitoring time node; Generate the risk assessment value h of the current monitoring time node according to the operation assessment value series F; Preset the first risk assessment value threshold H1; If h>H1, the current monitoring time node generates a second-level warning instruction.

6. The operation monitoring method of the organic light emitting display system according to claim 5, characterized in that: When generating the risk assessment value h of the current monitoring time node, it includes: h=e4×Q4×[ S i ×Y1(i)×(f i -F1) ]+e5×Q5×[ Y2(i)×(f i -F1) ]; Among them, e4 is the preset fourth weight coefficient; e5 is the preset fifth weight coefficient; Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; S i is the influencing factor of the ith monitoring sub-area; Y1(i) is the selection coefficient. If (f i -F1)>0,Y1(i)=1; if (f i -F1)<0,Y1(i)=0;Y2(i) is the selection coefficient. If (f i -F1)>0,Y2(i)=1 / (f i -F1); if (f i -F1)<0,Y2(i)=0.

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