Flexible Display Encapsulation Power Optimization Method Based on Ion Beam Surface Modification Technology

Abstract

The present invention discloses a flexible display packaging power optimization method based on ion beam surface modification technology, which relates to the field of ion beam surface modification technology. It is used to solve the problem that different types of flexible batteries have different requirements for the working environment. When a flexible battery shows a pseudo-performance change, the monitoring system will make adjustments according to the performance change of the flexible battery, and the original stability inside the battery will be broken after the battery performance rebounds. The method includes collecting and classifying the feedback data of the matched flexible battery, analyzing the current abnormal state of the flexible battery using the MAD algorithm based on the feedback data of the flexible battery, and marking the current flexible battery as a pseudo-abnormal state battery or a non-pseudo-abnormal state battery; precisely adjusting the battery performance according to the threshold to ensure that the working efficiency of the flexible battery with performance rebound after adjustment is within an acceptable range. Through the matching and classification of the battery and the classified adjustment of the flexible battery, the stability of the flexible battery and the safety guarantee of the application scenario are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ion beam surface modification, and more specifically, to an optimization method for a flexible display packaging power supply based on ion beam surface modification technology. Background Art

[0002] Ion beam surface modification technology is a technology that uses ion beams to perform physical and chemical treatments on the surface of materials. It can adjust the properties and characteristics of materials by changing the surface morphology, chemical composition, and structure of the materials. When ion beam surface modification technology is applied to a flexible display packaging power supply, it can change the conductivity, transmission performance, and stability of battery materials.

[0003] The prior art has the following deficiencies:

[0004] Different types of flexible batteries have different requirements for the working environment. When a flexible battery shows a pseudo-performance change, the monitoring system will make adjustments according to the performance change of the flexible battery. After the battery performance rebounds, the original stability inside the battery will be broken, resulting in problems with the battery and posing a safety hazard to the working environment.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above defects of the prior art, an embodiment of the present invention provides an optimization method for a flexible display packaging power supply based on ion beam surface modification technology to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An optimization method for a flexible display packaging power supply based on ion beam surface modification technology includes the following steps:

[0009] Step S1, collecting flexible battery data and working environment data;

[0010] Step S2, calculating a battery classification threshold and an environment classification threshold using a logistic regression method according to the flexible battery data and the working environment data, and classifying and matching the flexible battery and the environment respectively according to the battery classification threshold and the environment classification threshold;

[0011] Step S3, collecting the feedback data of the flexible battery after classification and matching, analyzing the current abnormal state of the flexible battery using the MAD algorithm according to the feedback data of the flexible battery, and marking the current flexible battery as a pseudo-abnormal state battery or a non-pseudo-abnormal state battery;

[0012] Step S4: Collect the performance change data of the pseudo-abnormal state batteries, calculate the threshold ratio deviation using the random forest method based on the performance change data, and precisely adjust the pseudo-abnormal state batteries through the threshold ratio deviation.

[0013] In a preferred embodiment, in step S1, the flexible battery data includes battery power density, battery charge and discharge rate, battery interface resistance, and battery conductivity, and the working environment data is environmental humidity and environmental population density.

[0014] In a preferred embodiment, in step S2, use the logistic regression algorithm to analyze the flexible battery, collect the battery power density and battery charge and discharge rate of a sufficient number of flexible batteries, and first set the battery classification threshold. The specific steps are as follows:

[0015] Take the average of the battery power density and battery charge and discharge rate of the collected flexible batteries, de-unify them, and record them as a and b respectively. Through the logistic regression formula where z is the calculation result of the logistic regression formula, x is the weighted sum of the average values of the battery power density and battery charge and discharge rate, and its calculation formula can be x = w 1 a + w 2 b, w 1 and w 2 are the weight values of a and b respectively. Take the calculation result z as the battery classification threshold, collect the battery power density and battery charge and discharge rate of the current flexible battery, and compare the calculation result using the logistic regression formula with the battery classification threshold; if the calculation result exceeds the battery classification threshold, classify the current flexible battery as a low-safety battery, otherwise classify the current flexible battery as a high-safety battery.

[0016] In a preferred embodiment, in step S2, collect the environmental humidity and environmental population density of a sufficient number of application environments, set the environmental classification threshold, take the average of the collected environmental humidity and environmental population density, de-unify them, and record them as c and d respectively. Obtain the calculation result through the logistic regression formula as the environmental classification threshold; collect the environmental humidity and environmental population density of the current environment, and compare the calculation result using the logistic regression formula with the environmental classification threshold; if the calculation result exceeds the environmental classification threshold, classify the current environment as a high-risk environment, otherwise classify the current environment as a low-risk environment;

[0017] If the current flexible battery is a high-safety battery, match the current flexible battery with a high-risk environment and apply it to the high-risk environment; if the current flexible battery is a low-safety battery, match the current flexible battery with a low-risk environment and apply it to the low-risk environment.

[0018] In a preferred embodiment, in step S3, after classifying the flexible battery according to the environment, the current flexible battery feedback data is collected. The current flexible battery feedback data includes the continuous working duration of the current flexible battery and the current battery working temperature;

[0019] The algorithm can be used to screen the current flexible battery feedback data. The specific steps are as follows:

[0020] Collect the continuous working durations of a sufficient number of flexible batteries and merge them into a duration data set marked as SC;

[0021] Calculate the median: Calculate the median of the duration data set and record it as T;

[0022] Calculate the absolute deviation: For each continuous working duration of the flexible battery, calculate the absolute difference between each continuous working duration of the flexible battery and the median of the duration data set;

[0023] Calculate the median absolute deviation: Take the median of the absolute differences between all the continuous working durations of the flexible batteries and the median of the duration data set to obtain the median absolute deviation MAD. The calculation formula of MAD is as follows: MAD = Median(|SC i -Median(T)|), where SC i is the i-th data point in the duration data set, and i can take values such as 1, 2, 3, etc.; Median(T) is the median of the duration data set;

[0024] According to the calculated median absolute deviation MAD, the continuous working duration threshold t of the flexible battery can be set x = T + MAD; where t x is the continuous working duration threshold of the flexible battery, and T is the median of the duration data set;

[0025] Calculate the flexible battery working temperature threshold marked as c x , and analyze the current battery abnormal situation by comparing the current flexible battery feedback data with the calculated threshold. If the continuous working duration of the current flexible battery exceeds the continuous working duration threshold t of the flexible battery x and the working temperature of the current flexible battery exceeds the flexible battery working temperature c x , then mark the current battery as a pseudo-abnormal battery, otherwise mark the current battery as a non-pseudo-abnormal battery.

[0026] In a preferred embodiment, in step S4, if the current battery is a non-pseudo battery, perform a conventional adjustment on the current battery; if the current battery is a pseudo battery, collect the battery change data and perform a precise adjustment on the current battery; the battery change data is the change amount of the interface resistance and the change amount of the conductivity. The specific operation steps are as follows:

[0027] Collect sufficient battery interface resistance and battery conductivity; obtain the change amount of interface resistance and the change amount of conductivity by subtracting the battery interface resistance and battery conductivity collected before the flexible battery works from the battery interface resistance and battery conductivity respectively; combine the obtained resistance change amounts into a resistance change data set, and combine the obtained conductivity change amounts into a conductivity change data set;

[0028] Screen the battery change data through the ensemble method of random forest.

[0029] The technical effects and advantages of the flexible display packaging power supply optimization method based on the ion beam surface modification technology of the present invention:

[0030] The present invention collects flexible battery data and working environment data, classifies flexible batteries into high - security batteries and low - security batteries according to the flexible battery data, divides the working environment into high - risk environments and low - risk environments according to the working environment data, matches the flexible batteries with the working environment, collects the feedback data of the classified and matched flexible batteries, analyzes the abnormal conditions of the flexible batteries according to the feedback data of the flexible batteries, judges whether the flexible batteries have pseudo - performance changes according to the analysis results. If there is no pseudo - performance change, perform routine performance adjustment on the flexible batteries; if there is a pseudo - performance change, collect the battery change data, calculate the performance rebound control threshold, and accurately adjust the battery performance according to the threshold to ensure that the working efficiency of the flexible battery with performance rebound after adjustment is within an acceptable range. By classifying and matching the batteries and classifying and adjusting the flexible batteries, the stability of the flexible batteries and the safety guarantee of the application scenarios are improved. Brief Description of the Drawings

[0031] Figure 1 It is the first logical schematic diagram of the flexible display packaging power supply optimization method based on the ion beam surface modification technology of the present invention;

[0032] Figure 2 It is the second logical schematic diagram of the flexible display packaging power supply optimization method based on the ion beam surface modification technology of the present invention;

[0033] Figure 3 It is the process schematic diagram of the flexible display packaging power supply optimization method based on the ion beam surface modification technology of the present invention. Detailed Embodiments

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] The present invention classifies flexible batteries into high - safety batteries and low - safety batteries according to the flexible battery data by collecting the flexible battery data and the working environment data. The working environment is divided into high - risk environment and low - risk environment according to the working environment data. The flexible battery is matched with the working environment. The feedback data of the classified and matched flexible battery is collected, and the abnormal conditions of the flexible battery are analyzed according to the flexible battery feedback data. According to the analysis result, it is judged whether the flexible battery has a pseudo - performance change. If there is no pseudo - performance change, the conventional performance adjustment is carried out on the flexible battery; if there is a pseudo - performance change, the battery change data is collected, the performance rebound control threshold is calculated, and the battery performance is accurately adjusted according to the threshold, so as to ensure that the working efficiency of the flexible battery with performance rebound after adjustment is within an acceptable range. By classifying and matching the batteries and adjusting the flexible batteries, the stability of the flexible battery and the safety guarantee of the application scenario are improved.

[0036] Embodiment, the present invention discloses an optimization method for a flexible display package power supply based on an ion beam surface modification technology, as Figure 1 、 Figure 2 、 Figure 3 shown, specifically including the following steps:

[0037] Step S1, collect flexible battery data and working environment data.

[0038] Step S2, use the logistic regression method to calculate the battery classification threshold and the environment classification threshold according to the flexible battery data and the working environment data, and classify and match the flexible battery and the environment respectively according to the battery classification threshold and the environment classification threshold.

[0039] Step S3, collect the feedback data of the classified and matched flexible battery, analyze the abnormal state of the current flexible battery by using the MAD algorithm according to the flexible battery feedback data, and mark the current flexible battery as a pseudo - abnormal state battery or a non - pseudo - abnormal state battery.

[0040] Step S4, collect the performance change data of the pseudo - abnormal state battery, calculate the threshold ratio deviation by using the random forest method according to the performance change data, and accurately adjust the pseudo - abnormal state battery through the threshold ratio deviation.

[0041] The specific implementation is as follows:

[0042] In step S1, the flexible battery data includes battery power density, battery charge and discharge rate, battery interface resistance, and battery conductivity. The battery power density can be calculated by using an infrared thermal imager to detect the battery output power during battery charging and discharging. The battery charge and discharge rate can be calculated by using a multifunctional tester to detect the charge and discharge amount of the battery within the measurement time. The battery can be preliminarily classified according to the battery power density and the battery discharge rate; the battery interface resistance and the battery conductivity can be used as consideration parameters for subsequent performance adjustment. The interface resistance of the flexible battery before operation can be detected by a four-terminal measuring instrument, and the conductivity of the flexible battery before operation can be detected by a conductivity measuring instrument.

[0043] The battery power density refers to the power that the battery can provide per unit volume or per unit mass. If the battery power density is too high, the battery will face overcharging and over-discharging problems, which will affect the stability of the battery and reduce the safety of the battery.

[0044] The battery discharge rate refers to the rate at which the battery releases electrical energy per unit time. The faster the battery discharge rate, the more chemical reactions will occur inside the battery, increasing the battery heat and voltage fluctuations, thus increasing the risk of battery runaway and reducing the safety of the battery.

[0045] The working environment data is environmental humidity and environmental population density. The environmental humidity can be detected by using a humidity sensor, and the environmental population density can be obtained by accessing a commercial database.

[0046] Too high environmental humidity will accelerate the corrosion of the metal components of the battery, thereby affecting the performance and safety of the battery, making it prone to battery leakage risk. Moreover, when the environmental humidity is too high, the conductivity increases, making it easier to cause harm to things in the environment. The greater the environmental population density, the more people will be endangered and greater losses will be caused when the battery has problems.

[0047] To calculate the battery power density, the output power of the battery can be collected by using an infrared thermal imager first. After determining the size of the flexible battery, according to the battery power density formula ρ = W / V, where ρ is the battery power density, W is the output power of the battery, and V is the battery volume.

[0048] The battery charge and discharge rate can be obtained by dividing the battery charge amount or the battery discharge amount measured by the multifunctional tester by the charging time or the discharging time, and taking the average value of the battery charging rate and the battery discharging rate as the battery charge and discharge rate, marked as σ.

[0049] It should be noted that a thermal imager is an instrument used to measure the surface temperature distribution of an object. It can convert the infrared radiation on the object's surface into a thermal image and display it on a monitor. The thermal imager can be used to measure the temperature distribution of a flexible battery during charging and discharging, thereby calculating the battery power density. A multi-functional tester is an instrument integrated with multiple measurement functions and is usually used to test and measure various parameters and characteristics in aspects such as electro-mechanics. These instruments can simultaneously measure the measurement time of the battery and the charge and discharge amount during the measurement time.

[0050] A four-terminal measuring instrument is an instrument specifically used to measure resistance. It uses four electrodes to eliminate the influence of test lead resistance on the measurement result and improve the measurement accuracy. It can be used to measure the interface resistance of a flexible battery. A conductivity measuring instrument is an instrument used to measure the conductivity of a substance and can detect the conductivity of a flexible battery and transmit the data to a monitor.

[0051] A humidity sensor is a sensor used to measure the relative humidity in the air. It can sense the water vapor content in the air and convert it into a corresponding electrical signal or digital signal for output.

[0052] A commercial database is a database provided by commercial institutions for obtaining commercial information and data, including company databases, industry databases, etc. The corresponding environmental population density can be obtained from the commercial database.

[0053] In step S2, a flexible battery is analyzed using a logistic regression algorithm, and the battery power density and battery charge and discharge rate of a sufficient number of flexible batteries are collected. First, set the battery classification threshold, and the specific steps are as follows:

[0054] Take the average value of the battery power density and battery charge and discharge rate of the collected flexible batteries and de-unitize them, and record them as a and b respectively. Through the logistic regression formula where z is the calculation result of the logistic regression formula, and x is the weighted sum of the average values of the battery power density and battery charge and discharge rate. Its calculation formula can be x = w 1 a + w 2 b, w 1 and w 2 are the weight values of a and b respectively, and their specific values can be set according to the actual situation. For example, w 1 = w 2 = 0.5. Take the calculation result z as the battery classification threshold. Collect the battery power density and battery charge and discharge rate of the current flexible battery, and compare the calculation result of the logistic regression formula with the battery classification threshold. If the calculation result exceeds the battery classification threshold, classify the current flexible battery as a low-safety battery; otherwise, classify the current flexible battery as a high-safety battery.

[0055] Collect the environmental humidity and environmental population density of a sufficient amount of application environments. Similarly, set the environmental classification threshold. After averaging and de-unifying the collected environmental humidity and environmental population density, record them as c and d respectively. Use the above logistic regression formula to obtain the calculation result as the environmental classification threshold. Collect the environmental humidity and environmental population density of the current environment, and compare the calculation result obtained using the logistic regression formula with the environmental classification threshold. If the calculation result exceeds the environmental classification threshold, classify the current environment as a high-risk environment; otherwise, classify the current environment as a low-risk environment.

[0056] If the current flexible battery is a high-safety battery, match the current flexible battery with a high-risk environment and apply it to a high-risk environment; if the current flexible battery is a low-safety battery, match the current flexible battery with a low-risk environment and apply it to a low-risk environment.

[0057] It should be noted that the methods for setting the battery classification threshold and the environmental classification threshold are not unique, and the logistic regression formula can be adjusted according to the actual situation.

[0058] In step S3, after classifying and matching the flexible battery with the environment, collect the feedback data of the current flexible battery. The feedback data of the current flexible battery includes the continuous working duration of the current flexible battery and the current battery working temperature.

[0059] The longer the continuous working duration of the current flexible battery, the more likely its battery negative electrode is to undergo polarization. The negative electrode polarization phenomenon usually refers to a phenomenon that occurs during the electron transfer process on the electrode surface, resulting in hindrance to the electron transfer on the electrode surface, thereby affecting the electrochemical reaction. However, after stopping working for a period of time, it will return to normal or recover, which easily leads to false abnormal problems in the battery.

[0060] The higher the current battery working temperature, it will cause an increase in the internal resistance of the battery, thereby affecting the battery conductivity. However, when the battery is working at a low load or the battery stops working and the temperature of the battery itself decreases, the internal resistance of the battery will decrease or even recover, which easily leads to false abnormal problems in the battery.

[0061] The MAD algorithm can be used to screen the feedback data of the current flexible battery. The specific steps are as follows:

[0062] Since the feedback data of the current flexible battery contains two types of data, the present invention only takes one of them as an example. Collect a sufficient amount of continuous working durations of flexible batteries and combine them into a duration data set labeled as SC.

[0063] Calculate the median: First, calculate the median of the duration data set and record it as T.

[0064] Calculate the absolute deviation: For the continuous working duration of each flexible battery, calculate the absolute difference between the continuous working duration of each flexible battery and the median of the duration dataset, that is, take the absolute value of the difference between the continuous working duration of each flexible battery and the median of the duration dataset.

[0065] Calculate the median absolute deviation: Take the median of the absolute differences between the continuous working durations of all flexible batteries and the median of the duration dataset to obtain the median absolute deviation MAD. The calculation formula of MAD is as follows: MAD = Median(|SC i - Median(T)|), where SC i is the i-th data point in the duration dataset, and i can take values such as 1, 2, 3, etc. Median(T) is the median of the duration dataset.

[0066] According to the calculated median absolute deviation MAD, the continuous working duration threshold t of the flexible battery can be set x = T + MAD. Where t x is the continuous working duration threshold of the flexible battery, and T is the median of the duration dataset.

[0067] Similarly, the working temperature threshold of the flexible battery can be calculated and marked as c x , and the abnormal situation of the current battery can be analyzed by comparing the feedback data of the current flexible battery with the calculated threshold. If the continuous working duration of the current flexible battery exceeds the continuous working duration threshold t of the flexible battery x and the working temperature of the current flexible battery exceeds the working temperature c of the flexible battery x , then mark the current battery as a pseudo-abnormal battery, otherwise mark the current battery as a non-pseudo-abnormal battery.

[0068] It should be noted that the MAD algorithm is a statistical method for detecting outliers. This method uses the median and the median absolute deviation to identify outliers in the dataset and can also be used for threshold setting. The method of setting the threshold in the above steps is not unique and can be adjusted according to the actual situation.

[0069] In step S4, if the current battery is a non-pseudo battery, perform a conventional adjustment on the current battery; if the current battery is a pseudo battery, collect the battery change data and perform a precise adjustment on the current battery. The battery change data is the change amount of the interface resistance and the change amount of the conductivity. The specific operation steps are as follows:

[0070] Collect sufficient battery interface resistance and battery conductivity. Obtain the change amount of the interface resistance and the change amount of the conductivity by subtracting the battery interface resistance and the battery conductivity collected before the flexible battery works from the current battery interface resistance and battery conductivity respectively. Combine the obtained resistance change amounts into a resistance change dataset, and combine the obtained conductivity change amounts into a conductivity change dataset.

[0071] The battery change data can be screened by the ensemble method of random forest to improve the accuracy of the data. Since the processing steps for the change amount of interface resistance and the change amount of conductivity are similar, the present invention only takes the change amount of interface resistance as an example as follows:

[0072] Mark the resistance change data set as Dz, and select a certain proportion of samples in the resistance change data set as a small sample data set marked as Dz 1 , and divide Dz into N small data sets Dz according to the ratio of Dz 1 The ratio is divided into N small data sets Dz 2 , Dz 3 , Dz 4 etc. This can ensure that the samples used in the training of each decision tree in the random forest are different. Use these small data sets as nodes to construct a decision tree. Randomly select a sample in the decision tree node. Specifically, randomly select a change amount of interface resistance in the small data set Dz 1 , and randomly select a change amount of interface resistance in the small data set Dz 2 . According to this method, randomly select a data in each small data set.

[0073] Take the average value of these randomly selected data and denote it as Select a different ratio from Dz to divide the resistance change data set Dz to obtain a new small data set as a new node to reconstruct the decision tree. Obtain the average value according to the random data extraction method and denote it as 1 Repeat the operation to construct multiple decision trees to form a random forest and obtain several average value data etc. Arrange these data in ascending order and take out the median denoted as d. Perform normalization processing on the median. The normalization formula can be where d norm is the result after normalization of d, d min is the minimum value in the average value data, d max is the maximum value in the average value data. Take d norm norm as the interface resistance change coefficient. norm as the interface resistance change coefficient.

[0074] Similarly, obtain the conductivity change coefficient for the conductivity change data set and mark it as l norm , and set the threshold ratio with d norm and l norm to calculate the change ratio coefficient by weighted calculation. Set the performance change interval threshold according to the change ratio coefficient. The calculation formula can be X = (d norm + l norm)*c, where X is the change ratio coefficient and c is the threshold ratio. Set the performance change interval threshold according to the change ratio coefficient. For example, when the threshold ratio is set to 120%, the formula for calculating the upper interval threshold of performance change is X = (d norm +l norm )*120%; when the threshold ratio is set to 80%, the formula for calculating the lower interval threshold of performance change can be X = (d norm +l norm )*80%. The setting of the threshold ratio is not unique and is set by professional researchers according to the actual situation, which will not be elaborated here.

[0075] Collect the current battery interface resistance and battery conductivity, calculate the change ratio coefficient through the above method and mark it as Y. When the threshold ratio is 1, obtain the standard change threshold and mark it as

[0076] If the change ratio coefficient Y of the current battery exceeds the standard change threshold, the formula for calculating the threshold deviation ratio can be: where p is the threshold deviation ratio, Y is the change ratio coefficient of the current battery, and c is the preset threshold ratio.

[0077] If the change ratio coefficient Y of the current battery does not exceed the standard change threshold, the formula for calculating the threshold deviation ratio can be:

[0078] Precisely adjust the current battery using the ion beam surface modification technology according to the threshold deviation ratio.

[0079] It should be noted that non-false batteries are adjusted conventionally, that is, adjusted according to the degree of performance degradation of the flexible battery. When the battery conductivity drops by M%, use an ion beam injector to inject ion beams into the battery to increase the battery conductivity by M% and make the battery performance stable again. For example, if the initial battery conductivity is 100% and the current battery conductivity is only 70%, through the ion beam surface modification technology, perform modification treatment on the surface of carbon nanotubes, use an ion beam injector to inject ion beams into the battery, and by controlling the ion beam energy and flow rate, adjust the current battery conductivity back to around 100%.

[0080] The dummy battery is precisely adjusted, that is, the threshold deviation ratio of the current battery is calculated, and precise adjustment is carried out through the threshold deviation ratio. When the conductivity of the battery decreases by M%, an ion beam is injected into the battery using an ion beam injector. The decrease in conductivity by M% is compared with the threshold deviation ratio. If the threshold deviation ratio is large, the conductivity is increased by M%; if the threshold deviation ratio is small, only the conductivity of the battery is increased by the magnitude of the threshold deviation ratio to prevent the stability imbalance when the battery performance rebounds. For example, if the initial conductivity of the battery is 100% and the current conductivity of the battery becomes 70% due to false performance degradation, when the threshold deviation ratio p >= 30%, during the modification process, the conductivity of the current battery is adjusted back to near 100% by controlling the ion beam energy and flow rate; if the threshold deviation ratio p < 30%, during the modification process, the conductivity of the current battery is adjusted to near 70% + p, which can ensure that when the performance of the current battery rebounds, the conductivity remains within an acceptable range. Carbon nanotubes are a kind of nanostructure composed of carbon atoms and have a tubular morphology. The ion beam surface modification technology can improve the battery conductivity and other properties by adjusting the surface chemical composition and physical structure of carbon nanotubes. The methods and formulas involved in the above steps are not unique and can be adjusted according to the actual situation.

[0081] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0082] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0083] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and invention constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0084] In addition, in each embodiment of the present application, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0085] As described above, it is only the specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

[0086] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A flexible display packaging power optimization method based on ion beam surface modification technology, characterized in that: The steps include: Step S1, collecting flexible battery data and working environment data; Step S2, calculating a battery classification threshold and an environment classification threshold using a logistic regression method according to the flexible battery data and the working environment data, and classifying and matching the flexible battery and the environment according to the battery classification threshold and the environment classification threshold; Step S3, collect the classified and matched flexible battery feedback data, and use the flexible battery feedback data to The algorithm analyzes the abnormal state of the current flexible battery and marks the current flexible battery as a false abnormal state battery or a non-false abnormal state battery; Step S4, if the current battery is not a false abnormal battery, then perform regular adjustments on the current battery; if the current battery is a false abnormal battery, then collect battery change data, calculate the threshold ratio deviation based on the battery change data, and accurately adjust the false abnormal battery through the threshold ratio deviation; Calculating the threshold ratio deviation based on the battery change data specifically includes: The battery change data includes the interface resistance change and the conductivity change. The interface resistance change coefficient is calculated based on the interface resistance change. , the conductivity change coefficient is calculated based on the conductivity change ; by and Set the threshold ratio to calculate the weighted change ratio coefficient, and set the performance change interval threshold according to the change ratio coefficient. The calculation formula can be: , where Y is the change ratio coefficient and c is the preset threshold ratio; When the budget threshold ratio is 1, the standard change threshold is marked as ; If the current battery change ratio coefficient Y exceeds the standard change threshold, the formula for calculating the threshold deviation ratio is: , where p is the threshold deviation ratio, is the current battery change ratio coefficient; If the current battery change ratio coefficient Y does not exceed the standard change threshold, the formula for calculating the threshold deviation ratio is: ; Accurately adjust the false abnormal state battery through the threshold ratio deviation, including: When the battery conductivity drops by M%, an ion beam is injected into the battery using an ion beam implanter to compare the conductivity drop of M% with the threshold deviation ratio. If the threshold deviation ratio is large, the conductivity is increased by M%; if the threshold deviation ratio is small, the battery conductivity is only increased by the threshold deviation ratio.

2. The flexible display package power optimization method based on ion beam surface modification technology according to claim 1, characterized in that: In step S1, the flexible battery data includes battery power density, battery charge and discharge rate, battery interface resistance and battery conductivity, and the working environment data includes environmental humidity and environmental population density.

3. The flexible display package power optimization method based on ion beam surface modification technology according to claim 2, characterized in that: In step S2, the flexible battery is analyzed using a logistic regression algorithm to collect the battery power density and battery charge and discharge rate of a sufficient number of flexible batteries. The battery classification threshold is first set. The specific steps are as follows: The battery power density and battery charge and discharge rate of the collected flexible batteries were averaged and denormalized and recorded as and , through the logistic regression formula , where z is the result of the logistic regression formula, and x is the weighted sum of the battery power density and the average value of the battery charge and discharge rate. The calculation formula can be , and They are and The weight value is calculated, and the calculated result z is used as the battery classification threshold. The battery power density and battery charge and discharge rate of the current flexible battery are collected, and the calculated result is compared with the battery classification threshold using the logistic regression formula; If the calculation result exceeds the battery classification threshold, the current flexible battery is classified as a low-safety battery; otherwise, the current flexible battery is classified as a high-safety battery.

4. The flexible display package power optimization method based on ion beam surface modification technology according to claim 2, characterized in that: In step S2, sufficient environmental humidity and environmental population density of the application environment are collected, and an environmental classification threshold is set. The collected environmental humidity and environmental population density are averaged and de-unitized and recorded as c and d respectively. The calculation result is obtained by a logistic regression formula as the environmental classification threshold; Collect the environmental humidity and environmental population density of the current environment, and use the logistic regression formula to calculate the result and compare it with the environmental classification threshold; if the calculation result exceeds the environmental classification threshold, the current environment is classified as a high-risk environment, otherwise the current environment is classified as a low-risk environment; If the current flexible battery is a high-safety battery, the current flexible battery is matched with a high-risk environment and applied to the high-risk environment; if the current flexible battery is a low-safety battery, the current flexible battery is matched with a low-risk environment and applied to the low-risk environment.

5. The flexible display package power optimization method based on ion beam surface modification technology according to claim 4, characterized in that: In step S3, after the flexible battery is matched with the environment and classified, current flexible battery feedback data is collected, where the current flexible battery feedback data includes the current continuous working time of the flexible battery and the current battery working temperature; The current flexible battery feedback data can be filtered using an algorithm. The specific steps are as follows: Collect enough flexible batteries to work continuously for a certain period of time and merge them into a duration dataset labeled ; Calculate the median: Calculate the median of the duration data set and record it as ; Calculate the absolute deviation: for each flexible battery continuous working time, calculate the absolute difference between each flexible battery continuous working time and the median of the time data set; Calculate the median absolute deviation: take the median of the absolute difference between the continuous working time of all flexible batteries and the median of the time data set to get the median absolute deviation , The calculation formula is as follows: ,in, It is the first data points, You can choose 1, 2, 3, etc. The median absolute deviation is calculated from You can set the flexible battery continuous working time threshold = ;in, is the continuous working time threshold of the flexible battery; Calculate the flexible battery operating temperature threshold marked as By comparing the current flexible battery feedback data with the calculated threshold, the current battery abnormality is analyzed. If the current flexible battery continuous working time exceeds the flexible battery continuous working time threshold And the current flexible battery operating temperature exceeds the flexible battery operating temperature , then the current battery is marked as a false abnormal battery, otherwise the current battery is marked as a non-false abnormal battery.

6. The flexible display package power optimization method based on ion beam surface modification technology according to claim 5, characterized in that: In step S4, the interface resistance change and conductivity change are obtained. The specific operation steps are as follows: Collect sufficient battery interface resistance and battery conductivity; obtain interface resistance change and conductivity change by subtracting the battery interface resistance and battery conductivity from the battery interface resistance and battery conductivity collected before the flexible battery works; The obtained resistance changes are combined into a resistance change data set, and the obtained conductivity changes are combined into a conductivity change data set; The battery change data were screened using a random forest ensemble approach.

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