Intelligent temperature control method and system for organic electroluminescent device
By real-time monitoring and partitioning to calculate the abnormality of the operating data of organic electroluminescent devices, and setting an intelligent cooling strategy, the problem of insufficient accuracy in traditional temperature control methods is solved, and the stability and consistency of the device are improved.
Patent Information
- Application Number
- CN202510507149.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-22
- Publication Date
- 2025-07-18
AI Technical Summary
The temperature control method of organic electroluminescent devices in the prior art relies on a fixed temperature threshold, ignores the performance influence in the low temperature state, resulting in insufficient temperature control accuracy and difficult to ensure the stability and consistency of the device.
By monitoring the device operation data in real time, collecting the abnormality of the operation data based on the data timestamp, compute the abnormality of the sub-running data in partition, map it to the blank curve, calculate the comprehensive abnormality level, and set a cooling strategy for intelligent temperature control.
It significantly improves the speed and accuracy of temperature control, improves the stability and consistency of device display effects, and provides guarantee for the reliable operation of the device.
Smart Images
Figure CN120335519A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of organic electroluminescent devices, and in particular, to an intelligent temperature control method and system for an organic electroluminescent device. Background Art
[0002] As a self-luminous display technology, organic electroluminescent devices have been widely used in fields such as smart phones, televisions, and wearable devices due to their advantages such as high contrast, wide color gamut, and flexibility. However, organic electroluminescent devices generate high heat during operation, resulting in unstable operating performance of the organic electroluminescent devices.
[0003] In the prior art, workers usually set a fixed temperature threshold based on experience. When the real-time temperature exceeds the temperature threshold, a cooling control is initiated for the organic electroluminescent device. However, this control method has obvious defects: on the one hand, it ignores the impact on the device operating performance when the real-time temperature is lower than the temperature threshold; on the other hand, this method completely depends on the temperature threshold and fails to consider the performance impact caused by the operating data during the actual operation of the organic electroluminescent device. This single temperature control method not only has limitations, but also is difficult to ensure the temperature control accuracy of the organic electroluminescent device. Summary of the Invention
[0004] Embodiments of the present invention provide an intelligent temperature control method and system for an organic electroluminescent device. By real-time monitoring the operating data of the organic electroluminescent device and timely responding to the impact of various temperature states on the device performance, the temperature control speed and accuracy of the device are significantly improved, thereby improving the stability and consistency of the display effect.
[0005] To achieve the above object, the present invention provides an intelligent temperature control method for an organic electroluminescent device, including: Pre-setting a plurality of data timestamps, collecting a plurality of real-time operating data corresponding to the organic electroluminescent device based on the data timestamps, and determining the degree of abnormality of the operating data corresponding to each data timestamp; Extracting all the degrees of abnormality of the operating data, determining a representative degree of abnormality of the operating data, partitioning all the degrees of abnormality of the operating data according to the representative degree of abnormality of the operating data, and calculating the sub-degree of abnormality of the operating data of the organic electroluminescent device; Extracting all the sub-degrees of abnormality of the operating data, randomly mapping all the sub-degrees of abnormality of the operating data onto a blank curve to obtain a sub-degree of abnormality curve of the operating data, and calculating the comprehensive degree of abnormality of the operating data of the organic electroluminescent device according to the sub-degree of abnormality curve of the operating data; Obtain the preset abnormal degree of the preset comprehensive operation data, and determine whether there is a risk in the current operating temperature of the organic electroluminescent device according to the abnormal degree of the comprehensive operation data and the preset abnormal degree of the preset comprehensive operation data; When there is a risk in the current operating temperature of the organic electroluminescent device, set the cooling strategy of the cooling device based on the abnormal degree of the comprehensive operation data, and perform intelligent temperature control on the organic electroluminescent device based on the cooling strategy.
[0006] Further, when collecting a plurality of real-time operation data corresponding to the organic electroluminescent device based on the data timestamp and determining the abnormal degree of the operation data of the real-time operation data corresponding to each data timestamp, it includes: Randomly select a data timestamp as the reference data timestamp, and determine the median data timestamp corresponding to all data timestamps; Determine the reference real-time operation data corresponding to the reference data timestamp, and determine the median real-time operation data corresponding to the median data timestamp; Determine the difference between the reference real-time operation data and the median real-time operation data, and use it as the operation data reference difference value of the reference real-time operation data; Preset the preset segmentation quantity, determine the comparison real-time operation data adjacent to the reference real-time data based on the preset segmentation quantity, determine the difference between each comparison real-time operation data and the median real-time operation data, and use it as the operation data comparison difference value of the comparison real-time operation data; Determine the data timestamp difference between the reference data timestamp and the data timestamp corresponding to each comparison real-time operation data; Determine the abnormal degree of the operation data of the reference real-time operation data according to the operation data reference difference value, the operation data comparison difference value, and the data timestamp difference.
[0007] Further, when determining the abnormal degree of the operation data of the reference real-time operation data according to the operation data reference difference value, the operation data comparison difference value, and the data timestamp difference, it includes: Calculate the abnormal degree of the operation data of the reference real-time operation data according to the following formula: ; where q is the abnormal degree of the operation data of the reference real-time operation data, w e is the reference real-time operation data, w1 e is the median real-time operation data, r is the number of comparison real-time operation data, w2 t is the t-th comparison real-time operation data, a tis the difference in data timestamps between the data timestamp corresponding to the t-th comparison real-time operation data and the reference data timestamp.
[0008] Further, when determining the abnormal degree of the operation data and partitioning all the abnormal degrees of the operation data according to the abnormal degree of the representative operation data, it includes: Determine the first abnormal degree of the representative operation data and the second abnormal degree of the representative operation data; Classify all the abnormal degrees of the operation data that are less than the first abnormal degree of the representative operation data into the low abnormal degree area of the operation data; Classify all the abnormal degrees of the operation data that are greater than or equal to the first abnormal degree of the representative operation data and less than the second abnormal degree of the representative operation data into the medium abnormal degree area of the operation data; Classify all the abnormal degrees of the operation data that are greater than or equal to the second abnormal degree of the representative operation data into the high abnormal degree area of the operation data; Determine the low area mean and low area variance of the low abnormal degree area of the operation data; Generate a first interval range according to the low area mean and the low area variance, and count the number of the first abnormal degrees of the operation data falling into the first interval range; Determine the middle area mean and middle area variance of the medium abnormal degree area of the operation data; Generate a second interval range according to the middle area mean and the middle area variance, and count the number of the second abnormal degrees of the operation data falling into the second interval range; Determine the middle area mean and middle area variance of the medium abnormal degree area of the operation data; Generate a third interval range according to the high area mean and the high area variance, and count the number of the third abnormal degrees of the operation data falling into the third interval range; Calculate the abnormal degree of the sub-operation data of the organic electroluminescent device according to the number of the first abnormal degrees of the operation data, the number of the second abnormal degrees of the operation data, and the number of the third abnormal degrees of the operation data.
[0009] Further, when calculating the abnormal degree of the sub-operation data of the organic electroluminescent device according to the number of the first abnormal degrees of the operation data, the number of the second abnormal degrees of the operation data, and the number of the third abnormal degrees of the operation data, it includes: Configure a first calculation weight for the number of the first abnormal degrees of the operation data, configure a second calculation weight for the number of the second abnormal degrees of the operation data, and configure a third calculation weight for the number of the third abnormal degrees of the operation data; Calculate the abnormal degree of the sub-operation data of the organic electroluminescent device according to the following formula: ; Among them, y is the abnormal degree of the sub-operation data of the organic electroluminescent device, u1 is the quantity of the abnormal degree of the first operation data, i1 is the first calculation weight, u2 is the quantity of the abnormal degree of the second operation data, i2 is the second calculation weight, u3 is the quantity of the abnormal degree of the third operation data, and i3 is the third calculation weight.
[0010] Further, when randomly mapping all the abnormal degrees of the sub-operation data onto a blank curve to obtain the curve of the abnormal degree of the sub-operation data, it includes: Randomly mapping all the abnormal degrees of the sub-operation data onto a blank curve to obtain the curve of the abnormal degree of the sub-operation data, where the abscissa of the blank curve shows an arithmetic increase, and randomly mapping the abnormal degree of the sub-operation data onto the ordinate corresponding to each abscissa; Determine the maximum abnormal degree of the sub-operation data from the curve of the abnormal degree of the sub-operation data; Determine the initial abnormal degree of the sub-operation data and the ending abnormal degree of the sub-operation data on the curve of the abnormal degree of the sub-operation data; Count the first quantity between the initial abnormal degree of the sub-operation data and the maximum abnormal degree of the sub-operation data; Count the second quantity between the ending abnormal degree of the sub-operation data and the maximum abnormal degree of the sub-operation data; Judge whether both the first quantity and the second quantity are greater than a preset quantity. If so, calculate the comprehensive abnormal degree of the operation data of the organic electroluminescent device; If not, randomly map all the abnormal degrees of the sub-operation data again until both the first quantity and the second quantity are greater than the preset quantity.
[0011] Further, when calculating the comprehensive abnormal degree of the operation data of the organic electroluminescent device according to the curve of the abnormal degree of the sub-operation data, it includes: Calculate the comprehensive abnormal degree of the operation data of the organic electroluminescent device according to the following formula: ; Among them, p is the comprehensive abnormal degree of the operation data of the organic electroluminescent device, s1 is the initial abnormal degree of the sub-operation data, s2 is the maximum abnormal degree of the sub-operation data, s3 is the ending abnormal degree of the sub-operation data, d1 is the first calculation coefficient, d2 is the second calculation coefficient, d3 is the third calculation coefficient, d1 + d2 + d3 = 1, d1 > 0, d2 > 0, d3 > 0, f is the quantity of the abnormal degree of the sub-operation data, g1 is the variance of the sub-operation data degrees except the initial abnormal degree of the sub-operation data among all the abnormal degrees of the sub-operation data, g2 is the variance of the sub-operation data degrees except the ending abnormal degree of the sub-operation data among all the abnormal degrees of the sub-operation data, and g is the variance of all the abnormal degrees of the sub-operation data.
[0012] Further, when determining whether there is a risk in the current operating temperature of the organic electroluminescent device according to the abnormal degree of the comprehensive operating data and the preset abnormal degree of the comprehensive operating data, it includes: When the abnormal degree of the comprehensive operating data is less than the preset abnormal degree of the comprehensive operating data, it is determined that there is no risk in the current operating temperature of the organic electroluminescent device; When the abnormal degree of the comprehensive operating data is greater than or equal to the preset abnormal degree of the comprehensive operating data, it is determined that there is a risk in the current operating temperature of the organic electroluminescent device.
[0013] Further, when setting the cooling strategy of the cooling device based on the abnormal degree of the comprehensive operating data and performing intelligent temperature control on the organic electroluminescent device based on the cooling strategy, it includes: Preset multiple preset abnormal degrees of the comprehensive operating data in advance; Preset multiple preset cooling strategies in advance; According to the relationship between the abnormal degree of the comprehensive operating data and multiple preset abnormal degrees of the comprehensive operating data, select the corresponding preset cooling strategy to perform intelligent temperature control on the organic electroluminescent device, wherein the abnormal degree of the comprehensive operating data and the preset cooling strategy show a proportional relationship.
[0014] To achieve the above object, the present invention also provides an intelligent temperature control system for an organic electroluminescent device, including: A data processing module, configured to preset multiple data timestamps in advance, collect multiple real-time operating data corresponding to the organic electroluminescent device based on the data timestamps, and determine the abnormal degree of the operating data corresponding to each data timestamp; A first calculation module, configured to extract all the abnormal degrees of the operating data, determine the representative abnormal degree of the operating data, partition all the abnormal degrees of the operating data according to the representative abnormal degree of the operating data, and calculate the sub-abnormal degree of the operating data of the organic electroluminescent device; A second calculation module, configured to extract all the sub-abnormal degrees of the operating data, randomly map all the sub-abnormal degrees of the operating data onto a blank curve to obtain a sub-abnormal degree curve of the operating data, and calculate the abnormal degree of the comprehensive operating data of the organic electroluminescent device according to the sub-abnormal degree curve of the operating data; A risk judgment module, configured to obtain the preset abnormal degree of the comprehensive operating data preset in advance, and determine whether there is a risk in the current operating temperature of the organic electroluminescent device according to the abnormal degree of the comprehensive operating data and the preset abnormal degree of the comprehensive operating data; A temperature control module is configured to, when there is a risk in the current operating temperature of the organic electroluminescent device, set a cooling strategy for a cooling device based on the degree of abnormality of the comprehensive operating data, and perform intelligent temperature control on the organic electroluminescent device based on the cooling strategy.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention discloses an intelligent temperature control method and system for an organic electroluminescent device, which collects real-time operating data based on a data timestamp, determines the corresponding degree of abnormality of the operating data; divides the degree of abnormality of the operating data according to the representative degree of abnormality of the operating data, and calculates the sub-degree of abnormality of the operating data; randomly maps the sub-degree of abnormality of the operating data onto a blank curve to obtain a sub-degree of abnormality curve of the operating data, and calculates the comprehensive degree of abnormality of the operating data; determines whether there is a risk in the current operating temperature according to the comprehensive degree of abnormality of the operating data; sets a cooling strategy based on the comprehensive degree of abnormality of the operating data, and performs intelligent temperature control, and through real-time operating data, timely responds to the influence of various temperature states on the device performance, significantly improves the temperature control speed and accuracy of the device, and further improves the stability and consistency of the device display effect, providing a strong guarantee for the reliable operation of the device. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 shows a schematic flow chart of an intelligent temperature control method for an organic electroluminescent device in an embodiment of the present invention; Figure 2 shows a schematic structural diagram of an intelligent temperature control system for an organic electroluminescent device in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0017] The following will further describe in detail the specific embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
[0018] 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.
[0019] The terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, 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 stated, the meaning of "a plurality of" is two or more.
[0020] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "coupled" 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.
[0021] The following is a description of the preferred embodiments of the present invention in conjunction with the drawings.
[0022] As Figure 1 shown, an intelligent temperature control method for an organic electroluminescent device according to an embodiment of the present invention includes: S110: Preset a plurality of data timestamps, collect a plurality of real-time operation data corresponding to the organic electroluminescent device based on the data timestamps, and determine the degree of abnormality of the operation data corresponding to each data timestamp; In this embodiment, the data timestamp refers to a specific time point. In order to avoid performance fluctuations of the organic electroluminescent device, the number of data timestamps is preferably 12, preferably the 10th second, the 20th second, the 30th second, the 40th second, the 50th second, the 60th second, the 70th second, the 80th second, the 90th second, the 100th second, the 110th second, and the 120th second.
[0023] In this embodiment, the real-time operation data includes static power consumption, dynamic power consumption, operating voltage, operating current, resistivity, and power generation rate, etc.
[0024] In this embodiment, the real-time operation data of the same type corresponding to each data timestamp is extracted as the basis for determining the abnormal degree of the operation data. For example, the static power consumption corresponding to each data timestamp is extracted to obtain a set of static power consumptions, and the abnormal degree of the operation data corresponding to each static power consumption in this set of static power consumptions is determined.
[0025] In this embodiment, if the number of real-time operation data corresponding to each data timestamp is 14, then 14 sets of real-time operation data of the same type can be obtained, and the number of abnormal degrees of the operation data of each set of real-time operation data of the same type is 12.
[0026] In some embodiments of the present application, when collecting a plurality of real-time operation data corresponding to an organic electroluminescent device based on the data timestamp and determining the abnormal degree of the operation data of the real-time operation data corresponding to each data timestamp, it includes: Randomly select a data timestamp as the reference data timestamp, and determine the median data timestamp corresponding to all data timestamps; Determine the reference real-time operation data corresponding to the reference data timestamp, and determine the median real-time operation data corresponding to the median data timestamp; Determine the difference between the reference real-time operation data and the median real-time operation data, and use it as the operation data reference difference value of the reference real-time operation data; Preset a preset segmentation number in advance, determine the comparison real-time operation data adjacent to the reference real-time data based on the preset segmentation number, determine the difference between each comparison real-time operation data and the median real-time operation data, and use it as the operation data comparison difference value of the comparison real-time operation data; Determine the data timestamp difference between the reference data timestamp and the data timestamp corresponding to each comparison real-time operation data; Determine the abnormal degree of the operation data of the reference real-time operation data according to the operation data reference difference value, the operation data comparison difference value, and the data timestamp difference.
[0027] In this embodiment, the median data timestamp refers to the median corresponding to all data timestamps.
[0028] In this embodiment, calculate the difference between the reference real-time operation data and the median real-time operation data, and then take the absolute value as the operation data reference difference value.
[0029] In this embodiment, the preset segmentation number is set in advance, and here it is preferably 6, that is, three real-time operation data are extracted from the left side of the reference real-time operation data, and three real-time operation data are extracted from the right side of the reference real-time operation data. It should be noted here that if the number of real-time operation data on the left or right side is less than 3, the actual number shall prevail.
[0030] In this embodiment, the difference between the real-time operation data and the median real-time operation data is calculated, and then the absolute value is taken as the operation data comparison difference value.
[0031] In this embodiment, the difference between the reference data timestamp and the data timestamp corresponding to each comparison real-time operation data is calculated, and then the absolute value is taken as the data timestamp difference.
[0032] The beneficial effects of the above technical solutions are as follows: The present invention determines the operation data abnormality degree of the reference real-time operation data according to the operation data reference difference value, the operation data comparison difference value, and the data timestamp difference, ensuring the determination accuracy of the operation data abnormality degree of each item. The operation data abnormality degree can reflect the influence degree of a real-time operation data in a group of real-time operation data, providing a basis for the intelligent temperature control of the organic electroluminescent device.
[0033] In some embodiments of the present application, when determining the operation data abnormality degree of the reference real-time operation data according to the operation data reference difference value, the operation data comparison difference value, and the data timestamp difference, it includes: Calculating the operation data abnormality degree of the reference real-time operation data according to the following formula: ; where q is the operation data abnormality degree of the reference real-time operation data, w e is the reference real-time operation data, w1 e is the median real-time operation data, r is the number of comparison real-time operation data, w2 t is the t-th comparison real-time operation data, a t is the data timestamp difference between the data timestamp corresponding to the t-th comparison real-time operation data and the reference data timestamp.
[0034] S120: Extract all the operation data abnormality degrees, determine the representative operation data abnormality degree, partition all the operation data abnormality degrees according to the representative operation data abnormality degree, and calculate the sub-operation data abnormality degree of the organic electroluminescent device; In some embodiments of the present application, when determining the representative operation data abnormality degree and partitioning all the operation data abnormality degrees according to the representative operation data abnormality degree, it includes: Determining the first representative operation data abnormality degree and the second representative operation data abnormality degree; Classifying all the operation data abnormality degrees less than the first representative operation data abnormality degree into the low operation data abnormality degree area; Classify all degrees of abnormal operation data that are greater than or equal to the abnormal degree of the first representative operation data and less than the abnormal degree of the second representative operation data into the medium abnormal degree area of operation data; Classify all degrees of abnormal operation data that are greater than or equal to the abnormal degree of the second representative operation data into the high abnormal degree area of operation data; Determine the low area mean and low area variance of the low abnormal degree area of operation data; Generate a first interval range based on the low area mean and the low area variance, and count the number of the first degrees of abnormal operation data falling within the first interval range; Determine the middle area mean and middle area variance of the medium abnormal degree area of operation data; Generate a second interval range based on the middle area mean and the middle area variance, and count the number of the second degrees of abnormal operation data falling within the second interval range; Determine the middle area mean and middle area variance of the medium abnormal degree area of operation data; Generate a third interval range based on the high area mean and the high area variance, and count the number of the third degrees of abnormal operation data falling within the third interval range; Calculate the sub - abnormal degree of operation data of the organic electroluminescent device according to the number of the first degrees of abnormal operation data, the number of the second degrees of abnormal operation data, and the number of the third degrees of abnormal operation data.
[0035] In this embodiment, as described above, 14 groups of real - time operation data can be obtained, with 12 real - time operation data in each group, and thus 12 degrees of abnormal operation data.
[0036] In this embodiment, calculate the mean and variance of all degrees of abnormal operation data, take the smaller value as the abnormal degree of the first representative operation data, and take the larger value as the abnormal degree of the second representative operation data.
[0037] In this embodiment, when counting the number of the first degrees of abnormal operation data, do not count the degrees of abnormal operation data equal to the low area mean and the low area variance.
[0038] In this embodiment, when counting the number of the second degrees of abnormal operation data, do not count the degrees of abnormal operation data equal to the middle area mean and the middle area variance.
[0039] In this embodiment, when counting the number of the third degrees of abnormal operation data, do not count the degrees of abnormal operation data equal to the high area mean and the high area variance.
[0040] The beneficial effects of the above technical solution are as follows: According to the number of abnormal degrees of the first operating data, the number of abnormal degrees of the second operating data, and the number of abnormal degrees of the third operating data, the present invention calculates the abnormal degree of the sub-operating data of the organic electroluminescent device. By calculating the abnormal degree of the sub-operating data, the comprehensive influence brought by a set of real-time operating data can be reflected, providing reliable technical support for the analysis of the abnormal operation of the organic electroluminescent device.
[0041] In some embodiments of the present application, when calculating the abnormal degree of the sub-operating data of the organic electroluminescent device according to the number of abnormal degrees of the first operating data, the number of abnormal degrees of the second operating data, and the number of abnormal degrees of the third operating data, it includes: Configuring a first calculation weight for the number of abnormal degrees of the first operating data, a second calculation weight for the number of abnormal degrees of the second operating data, and a third calculation weight for the number of abnormal degrees of the third operating data; Calculating the abnormal degree of the sub-operating data of the organic electroluminescent device according to the following formula: ; where y is the abnormal degree of the sub-operating data of the organic electroluminescent device, u1 is the number of abnormal degrees of the first operating data, i1 is the first calculation weight, u2 is the number of abnormal degrees of the second operating data, i2 is the second calculation weight, u3 is the number of abnormal degrees of the third operating data, and i3 is the third calculation weight.
[0042] In this embodiment, the first calculation weight is less than the second calculation weight and less than the third calculation weight, and the first calculation weight is preferably 0.15, the second calculation weight is preferably 0.35, and the third calculation weight is preferably 0.5. Specifically, it can also be adaptively adjusted according to the actual situation.
[0043] S130: Extract all the abnormal degrees of the sub-operating data, randomly map all the abnormal degrees of the sub-operating data onto a blank curve to obtain a curve of the abnormal degree of the sub-operating data, and calculate the comprehensive abnormal degree of the operating data of the organic electroluminescent device according to the curve of the abnormal degree of the sub-operating data; In some embodiments of the present application, all the abnormal degrees of the sub-operating data are randomly mapped onto a blank curve to obtain a curve of the abnormal degree of the sub-operating data. Among them, the abscissa of the blank curve shows an arithmetic progression increase, and the abnormal degree of the sub-operating data is randomly mapped onto the ordinate corresponding to each abscissa; Determine the maximum abnormal degree of the sub-operating data from the curve of the abnormal degree of the sub-operating data; Determine the initial abnormal degree of the sub-operating data and the final abnormal degree of the sub-operating data on the curve of the abnormal degree of the sub-operating data; Count a first quantity between the abnormal degree of the initial sub - operation data and the abnormal degree of the maximum sub - operation data; Count a second quantity between the abnormal degree of the end - point sub - operation data and the abnormal degree of the maximum sub - operation data; Determine whether both the first quantity and the second quantity are greater than a preset quantity. If so, calculate the abnormal degree of the comprehensive operation data of the organic electroluminescent device; If not, randomly remap the abnormal degrees of all sub - operation data until both the first quantity and the second quantity are greater than the preset quantity.
[0044] In this embodiment, as described above, one abnormal degree of sub - operation data can be obtained for each group of the same type of real - time operation data, so 14 abnormal degrees of sub - operation data can be obtained.
[0045] In this embodiment, the abscissa is 1, 2, 3, 4, 5, 6, etc., which is an arithmetic sequence, and the specific quantity corresponds to the abnormal degree of sub - operation data.
[0046] In this embodiment, if the maximum abnormal degree of sub - operation data on the abnormal degree curve of sub - operation data is not unique, randomly select one.
[0047] In this embodiment, when counting the first quantity, do not count the quantity of the abnormal degree of the initial sub - operation data and the abnormal degree of the maximum sub - operation data. When counting the second quantity, do not count the quantity of the abnormal degree of the end - point sub - operation data and the abnormal degree of the maximum sub - operation data.
[0048] In this embodiment, the preset quantity is preferably 4, and can be adjusted according to the actual situation.
[0049] The beneficial effects of the above - mentioned technical solution are as follows: The present invention determines whether both the first quantity and the second quantity are greater than the preset quantity, which can ensure the determination accuracy of the abnormal degree of comprehensive operation data, ensure the comprehensiveness of calculation, and avoid calculation errors caused by uneven data distribution. At the same time, according to the abnormal degree of comprehensive operation data, different real - time operation data and data at different times can be jointly processed, comprehensively reflecting the operation situation of the organic electroluminescent device, intuitively judging whether there are performance instability problems in the organic electroluminescent device, and also avoiding the limitations and singularity of single - data judgment.
[0050] In some embodiments of the present application, when calculating the abnormal degree of the comprehensive operation data of the organic electroluminescent device according to the abnormal degree curve of sub - operation data, it includes: Calculate the abnormal degree of the comprehensive operation data of the organic electroluminescent device according to the following formula: ; Wherein, p is the abnormal degree of the comprehensive operation data of the organic electroluminescent device, s1 is the abnormal degree of the initial sub-operation data, s2 is the abnormal degree of the maximum sub-operation data, s3 is the abnormal degree of the end sub-operation data, d1 is the first calculation coefficient, d2 is the second calculation coefficient, d3 is the third calculation coefficient, d1 + d2 + d3 = 1, d1 > 0, d2 > 0, d3 > 0, f is the number of abnormal degrees of the sub-operation data, g1 is the variance of the sub-operation data abnormal degrees except the abnormal degree of the initial sub-operation data among all the sub-operation data abnormal degrees, g2 is the variance of the sub-operation data abnormal degrees except the abnormal degree of the end sub-operation data among all the sub-operation data abnormal degrees, and g is the variance of all the sub-operation data abnormal degrees.
[0051] S140: Obtain the preset abnormal degree of the preset comprehensive operation data, and determine whether there is a risk in the current operating temperature of the organic electroluminescent device according to the abnormal degree of the comprehensive operation data and the preset abnormal degree of the preset comprehensive operation data; In some embodiments of the present application, when determining whether there is a risk in the current operating temperature of the organic electroluminescent device according to the abnormal degree of the comprehensive operation data and the preset abnormal degree of the preset comprehensive operation data, it includes: When the abnormal degree of the comprehensive operation data is less than the preset abnormal degree of the preset comprehensive operation data, it is determined that there is no risk in the current operating temperature of the organic electroluminescent device; When the abnormal degree of the comprehensive operation data is greater than or equal to the preset abnormal degree of the preset comprehensive operation data, it is determined that there is a risk in the current operating temperature of the organic electroluminescent device.
[0052] In this embodiment, the preset abnormal degree of the comprehensive operation data is preferably 8, and it can also be adjusted according to the actual situation specifically.
[0053] The beneficial effects of the above technical solution are: The present invention effectively avoids the performance fluctuations caused by traditional single-threshold control, and does not solely rely on the temperature threshold. In the low-temperature state, risks can also be discovered, and then the temperature of the organic electroluminescent device can be intelligently controlled, significantly improving the temperature control speed and accuracy of the device, and further improving the stability and consistency of the display effect of the device, providing a strong guarantee for the reliable operation of the device.
[0054] S150: When there is a risk in the current operating temperature of the organic electroluminescent device, set the cooling strategy of the cooling device based on the abnormal degree of the comprehensive operation data, and perform intelligent temperature control on the organic electroluminescent device based on the cooling strategy.
[0055] In some embodiments of the present application, when setting the cooling strategy of the cooling device based on the abnormal degree of the comprehensive operation data and performing intelligent temperature control on the organic electroluminescent device based on the cooling strategy, it includes: Preset multiple preset abnormal degrees of comprehensive operation data; Preset multiple preset cooling strategies; According to the relationship between the abnormal degree of the comprehensive operation data and multiple preset abnormal degrees of comprehensive operation data, select the corresponding preset cooling strategy to perform intelligent temperature control on the organic electroluminescent device, wherein the abnormal degree of the comprehensive operation data and the preset cooling strategy show a proportional relationship.
[0056] In this embodiment, the cooling device is preferably a micro fan.
[0057] In this embodiment, the number of preset abnormal degrees of comprehensive operation data is preferably 2, including the first preset abnormal degree of comprehensive operation data, preferably 12, and the second preset abnormal degree of comprehensive operation data, preferably 16.
[0058] In this embodiment, the number of preset cooling strategies is preferably 3, including the first preset cooling strategy, preferably the working power and working time, the working power is preferably 7W, where W is the power unit, the working time is preferably 8 minutes, the second preset cooling strategy, the working power is preferably 10W, the working time is preferably 12 minutes, and the third preset cooling strategy, the working power is preferably 13W, the working time is preferably 16 minutes.
[0059] In this embodiment, when the abnormal degree of the comprehensive operation data is greater than or equal to the preset abnormal degree of the comprehensive operation data and less than the first preset abnormal degree of the comprehensive operation data, the first preset cooling strategy is selected to perform intelligent temperature control on the organic electroluminescent device. When the abnormal degree of the comprehensive operation data is greater than or equal to the first preset abnormal degree of the comprehensive operation data and less than the second preset abnormal degree of the comprehensive operation data, the second preset cooling strategy is selected to perform intelligent temperature control on the organic electroluminescent device. When the abnormal degree of the comprehensive operation data is greater than or equal to the second preset abnormal degree of the comprehensive operation data, the third preset cooling strategy is selected to perform intelligent temperature control on the organic electroluminescent device.
[0060] The beneficial effects of the above technical solution are: The present invention sets the cooling strategy of the cooling device based on the abnormal degree of the comprehensive operation data and performs intelligent temperature control on the organic electroluminescent device based on the cooling strategy, ensuring the accurate selection of the cooling strategy, avoiding errors caused by manual participation, realizing the intelligent temperature control of the organic electroluminescent device, and ensuring the control accuracy and efficiency.
[0061] To further elaborate on the technical concept of the present invention, the technical solution of the present invention will be described in combination with specific application scenarios.
[0062] Correspondingly, as Figure 2 shown, the present application also provides an intelligent temperature control system for an organic electroluminescent device, including: A data processing module, configured to preset a plurality of data timestamps, collect a plurality of real-time operation data corresponding to the organic electroluminescent device based on the data timestamps, and determine the degree of abnormality of the operation data corresponding to each data timestamp; A first calculation module, configured to extract all the degrees of abnormality of the operation data, determine a representative degree of abnormality of the operation data, partition all the degrees of abnormality of the operation data according to the representative degree of abnormality of the operation data, and calculate the sub-operation data abnormality degree of the organic electroluminescent device; A second calculation module, configured to extract all the sub-operation data abnormality degrees, randomly map all the sub-operation data abnormality degrees onto a blank curve to obtain a sub-operation data abnormality degree curve, and calculate the comprehensive operation data abnormality degree of the organic electroluminescent device according to the sub-operation data abnormality degree curve; A risk judgment module, configured to obtain a preset comprehensive operation data abnormality degree, and judge whether there is a risk in the current operating temperature of the organic electroluminescent device according to the comprehensive operation data abnormality degree and the preset comprehensive operation data abnormality degree; A temperature control module, configured to, when there is a risk in the current operating temperature of the organic electroluminescent device, set a cooling strategy for the cooling device based on the comprehensive operation data abnormality degree, and perform intelligent temperature control on the organic electroluminescent device based on the cooling strategy.
[0063] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.
[0064] Although the present invention has been described above with reference to embodiments, various improvements can be made to it and components therein can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention can be combined with each other in any way, and the situations of these combinations are not all described in this specification only for the sake of saving space and resources.
[0065] Those of ordinary skill in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.
Claims
1. An intelligent temperature control method for an organic electroluminescent device, characterized in that, Including: Preset multiple data timestamps, collect multiple real-time operation data corresponding to the organic electroluminescent device based on the data timestamps, and determine the degree of abnormality of the operation data corresponding to each data timestamp; Extract all the degrees of abnormality of the operation data, determine the representative degree of abnormality of the operation data, partition all the degrees of abnormality of the operation data according to the representative degree of abnormality of the operation data, and calculate the degree of abnormality of the sub-operation data of the organic electroluminescent device; Extract all the degrees of abnormality of the sub-operation data, randomly map all the degrees of abnormality of the sub-operation data onto a blank curve to obtain a curve of the degree of abnormality of the sub-operation data, and calculate the comprehensive degree of abnormality of the operation data of the organic electroluminescent device according to the curve of the degree of abnormality of the sub-operation data; Obtain a preset comprehensive degree of abnormality of the operation data, and judge whether there is a risk in the current operating temperature of the organic electroluminescent device according to the comprehensive degree of abnormality of the operation data and the preset comprehensive degree of abnormality of the operation data; When there is a risk in the current operating temperature of the organic electroluminescent device, set a cooling strategy for the cooling device based on the comprehensive degree of abnormality of the operation data, and perform intelligent temperature control on the organic electroluminescent device based on the cooling strategy.
2. The intelligent temperature control method for the organic electroluminescent device according to claim 1, wherein When collecting multiple real-time operation data corresponding to the organic electroluminescent device based on the data timestamps and determining the degree of abnormality of the operation data corresponding to each data timestamp, it includes: Randomly select a data timestamp as the reference data timestamp, and determine the median data timestamp corresponding to all data timestamps; Determine the reference real-time operation data corresponding to the reference data timestamp, and determine the median real-time operation data corresponding to the median data timestamp; Determine the difference between the reference real-time operation data and the median real-time operation data, and use it as the operation data reference difference value of the reference real-time operation data; Preset a preset number of partitions, determine the comparison real-time operation data adjacent to the reference real-time data based on the preset number of partitions, determine the difference between each comparison real-time operation data and the median real-time operation data, and use it as the operation data comparison difference value of the comparison real-time operation data; Determine the data timestamp difference between the reference data timestamp and the data timestamp corresponding to each comparison real-time operation data; Determine the degree of abnormality of the reference real-time operation data according to the operation data reference difference value, operation data comparison difference value, and data timestamp difference.
3. The intelligent temperature control method for an organic electroluminescent device according to claim 2, wherein When determining the degree of abnormality of the reference real-time operation data according to the operation data reference difference value, operation data comparison difference value, and data timestamp difference, it includes: Calculate the degree of abnormality of the reference real-time operation data according to the following formula: ； Among them, q is the degree of abnormality of the operating data of the reference real-time operating data, w e is the reference real-time operating data, w1 e is the median real-time operating data, r is the number of comparison real-time operating data, w2 t is the t-th comparison real-time operating data, a t is the difference in data timestamps between the data timestamp corresponding to the t-th comparison real-time operating data and the reference data timestamp.
4. The intelligent temperature control method for the organic electroluminescent device according to claim 1, wherein When determining the representative degree of abnormality of the operation data and partitioning all the degrees of abnormality of the operation data according to the representative degree of abnormality of the operation data, it includes: Determine the first representative degree of abnormality of the operation data and the second representative degree of abnormality of the operation data; Classify all the degrees of abnormal operation data that are less than the degree of abnormal operation data of the first representative into the low-degree abnormal operation data area; Classify all the degrees of abnormal operation data that are greater than or equal to the degree of abnormal operation data of the first representative and less than the degree of abnormal operation data of the second representative into the medium-degree abnormal operation data area; Classify all the degrees of abnormal operation data that are greater than or equal to the degree of abnormal operation data of the second representative into the high-degree abnormal operation data area; Determine the low-area mean and low-area variance of the low-degree abnormal operation data area; Generate a first interval range according to the low-area mean and the low-area variance, and count the number of the first degrees of abnormal operation data falling into the first interval range; Determine the middle-area mean and middle-area variance of the medium-degree abnormal operation data area; Generate a second interval range according to the middle-area mean and the middle-area variance, and count the number of the second degrees of abnormal operation data falling into the second interval range; Determine the middle-area mean and middle-area variance of the medium-degree abnormal operation data area; Generate a third interval range according to the high-area mean and the high-area variance, and count the number of the third degrees of abnormal operation data falling into the third interval range; Calculate the sub-degree of abnormal operation data of the organic electroluminescent device according to the number of the first degrees of abnormal operation data, the number of the second degrees of abnormal operation data, and the number of the third degrees of abnormal operation data.
5. The intelligent temperature control method for the organic electroluminescent device according to claim 4, wherein When calculating the sub-degree of abnormal operation data of the organic electroluminescent device according to the number of the first degrees of abnormal operation data, the number of the second degrees of abnormal operation data, and the number of the third degrees of abnormal operation data, it includes: Configure a first calculation weight for the number of the first degrees of abnormal operation data, configure a second calculation weight for the number of the second degrees of abnormal operation data, and configure a third calculation weight for the number of the third degrees of abnormal operation data; Calculate the sub-degree of abnormal operation data of the organic electroluminescent device according to the following formula: ； where y is the sub-degree of abnormal operation data of the organic electroluminescent device, u1 is the number of the first degrees of abnormal operation data, i1 is the first calculation weight, u2 is the number of the second degrees of abnormal operation data, i2 is the second calculation weight, u3 is the number of the third degrees of abnormal operation data, and i3 is the third calculation weight.
6. The intelligent temperature control method for an organic electroluminescent device according to claim 1, wherein When randomly mapping all the sub-degrees of abnormal operation data onto a blank curve to obtain a sub-degree of abnormal operation data curve, it includes: Randomly map all the sub-degrees of abnormal operation data onto a blank curve to obtain a sub-degree of abnormal operation data curve, where the abscissa of the blank curve shows an equal difference increase, and randomly map the sub-degrees of abnormal operation data onto the ordinates corresponding to each abscissa; Determine the maximum sub-degree of abnormal operation data from the sub-degree of abnormal operation data curve; Determine the initial sub-degree of abnormal operation data and the end sub-degree of abnormal operation data on the sub-degree of abnormal operation data curve; Count the first quantity between the initial sub-degree of abnormal operation data and the maximum sub-degree of abnormal operation data; Count the second quantity between the end sub-degree of abnormal operation data and the maximum sub-degree of abnormal operation data; Determine whether both the first quantity and the second quantity are greater than a preset quantity. If so, calculate the abnormal degree of the comprehensive operation data of the organic electroluminescent device; If not, randomly map the abnormal degrees of all sub-operation data again until both the first quantity and the second quantity are greater than the preset quantity.
7. The intelligent temperature control method for an organic electroluminescent device according to claim 6, wherein When calculating the abnormal degree of the comprehensive operation data of the organic electroluminescent device according to the abnormal degree curve of the sub-operation data, it includes: Calculate the abnormal degree of the comprehensive operation data of the organic electroluminescent device according to the following formula: ； Where p is the abnormal degree of the comprehensive operation data of the organic electroluminescent device, s1 is the abnormal degree of the initial sub-operation data, s2 is the maximum abnormal degree of the sub-operation data, s3 is the abnormal degree of the end sub-operation data, d1 is the first calculation coefficient, d2 is the second calculation coefficient, d3 is the third calculation coefficient, d1 + d2 + d3 = 1, d1 > 0, d2 > 0, d3 > 0, f is the number of abnormal degrees of the sub-operation data, g1 is the variance of the sub-operation data abnormal degrees except the initial sub-operation data abnormal degree among all sub-operation data abnormal degrees, g2 is the variance of the sub-operation data abnormal degrees except the end sub-operation data abnormal degree among all sub-operation data abnormal degrees, and g is the variance of all sub-operation data abnormal degrees.
8. The intelligent temperature control method for an organic electroluminescent device according to claim 1, wherein When determining whether there is a risk in the current operating temperature of the organic electroluminescent device according to the abnormal degree of the comprehensive operation data and the preset abnormal degree of the comprehensive operation data, it includes: When the abnormal degree of the comprehensive operation data is less than the preset abnormal degree of the comprehensive operation data, it is determined that there is no risk in the current operating temperature of the organic electroluminescent device; When the abnormal degree of the comprehensive operation data is greater than or equal to the preset abnormal degree of the comprehensive operation data, it is determined that there is a risk in the current operating temperature of the organic electroluminescent device.
9. The intelligent temperature control method for an organic electroluminescent device according to claim 1, characterized in that, When setting the cooling strategy of the cooling device based on the abnormal degree of the comprehensive operation data and performing intelligent temperature control on the organic electroluminescent device based on the cooling strategy, it includes: Preset multiple preset abnormal degrees of the comprehensive operation data in advance; Preset multiple preset cooling strategies in advance; According to the relationship between the abnormal degree of the comprehensive operation data and multiple preset abnormal degrees of the comprehensive operation data, select the corresponding preset cooling strategy to perform intelligent temperature control on the organic electroluminescent device, where the abnormal degree of the comprehensive operation data and the preset cooling strategy show a proportional relationship.
10. An intelligent temperature control system for an organic electroluminescent device, which is applied to the intelligent temperature control method of the organic electroluminescent device according to any one of claims 1-9, and is characterized in that, It includes: A data processing module, which is used to preset multiple data timestamps in advance, collect multiple real-time operation data corresponding to the organic electroluminescent device based on the data timestamps, and determine the abnormal degree of the operation data corresponding to each data timestamp; A first calculation module, which is used to extract all abnormal degrees of the operation data, determine the representative abnormal degree of the operation data, partition all abnormal degrees of the operation data according to the representative abnormal degree of the operation data, and calculate the abnormal degree of the sub-operation data of the organic electroluminescent device; A second calculation module, configured to extract the abnormal degrees of all sub-operation data, randomly map the abnormal degrees of all sub-operation data onto a blank curve to obtain a sub-operation data abnormal degree curve, and calculate the comprehensive operation data abnormal degree of the organic electroluminescent device according to the sub-operation data abnormal degree curve; A risk judgment module, configured to obtain a preset comprehensive operation data abnormal degree, and judge whether there is a risk in the current operating temperature of the organic electroluminescent device according to the comprehensive operation data abnormal degree and the preset comprehensive operation data abnormal degree; A temperature control module, configured to, when there is a risk in the current operating temperature of the organic electroluminescent device, set a cooling strategy for a cooling device based on the comprehensive operation data abnormal degree, and perform intelligent temperature control on the organic electroluminescent device based on the cooling strategy.