Intelligent driving-based LED lamp bead built-in ic control method and system

Abstract

The application discloses an LED lamp bead built-in IC control method and system based on intelligent driving, relates to the technical field of IC control, and obtains the nonlinear response curve of an LED, applies a Gamma correction algorithm to correct a target brightness value, maps the corrected target brightness value to a PWM duty cycle to obtain a duty cycle range, dynamically adjusts a PWM frequency according to the PWM duty cycle value and ambient light conditions, controls the LED to operate based on the dynamically adjusted PWM frequency, and optimizes the PWM signal switching frequency through a power consumption optimization algorithm after the current operating state data of the LED is obtained in the LED operating process. The control system introduces the nonlinear response curve of the LED, adopts the Gamma correction algorithm, nonlinearly corrects the target brightness value, adjusts the brightness into a smooth curve more in line with the human eye perception characteristics, and improves the adjustment effect of low brightness and high brightness.

Description

Technical Field

[0001] This invention relates to the field of IC control technology, and more specifically to a method and system for controlling LED beads with built-in ICs based on intelligent driving. Background Technology

[0002] With the rapid development of LED lighting technology, built-in IC control systems have gradually become one of the core technologies of modern LED chips. Traditional LED chips usually achieve brightness adjustment and color change through external control circuits. However, with the increasing demand for lighting and display applications, the complexity and integration requirements of the system are gradually increasing. Built-in IC technology has emerged to meet this need. In scenarios such as smart homes, urban lighting, stage lighting, and dynamic displays, LED lights need to achieve precise brightness control and colorful dynamic effects. Built-in IC control systems can respond quickly to digital signals to achieve efficient dimming and multi-channel color control.

[0003] The existing technology has the following drawbacks: The relationship between the brightness of LED beads and the driving current is usually non-linear. Traditional control systems do not take into account this non-linear response characteristic, resulting in uneven brightness adjustment, especially in the low and high brightness ranges. This leads to insensitivity to brightness changes in the low brightness range, with almost no noticeable change during adjustment, making it difficult to achieve a soft low-brightness effect. In the high brightness range, the brightness changes are too abrupt, and the brightness changes perceived by the user are unnatural, resulting in a poor visual effect.

[0004] Based on this, the present invention proposes an intelligent driving method and system for controlling LED beads with built-in ICs. By introducing the nonlinear response curve of the LED and using the Gamma correction algorithm, the target brightness value is nonlinearly corrected, and the brightness is adjusted to a smooth curve that is more in line with the human eye's perception characteristics, thereby improving the adjustment effect of low and high brightness. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for controlling LED beads with built-in ICs based on intelligent driving, so as to overcome the shortcomings in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a control method for LED beads with built-in ICs based on intelligent driving, the control method comprising the following steps: After receiving the user's control signal, the control system analyzes the target brightness value based on the control signal, matches the target brightness value with the brightness range of the LED beads, obtains the nonlinear response curve of the LED, and then applies the Gamma correction algorithm to correct the target brightness value. The target brightness value is mapped to the PWM duty cycle to obtain the duty cycle range. The PWM frequency is dynamically adjusted according to the PWM duty cycle value and the ambient light conditions. The LED operation is controlled based on the dynamically adjusted PWM frequency. During LED operation, after acquiring the current operating status data of the LED, the switching frequency of the PWM signal is optimized through a power consumption optimization algorithm to reduce the energy consumption of the LED. When the external control signal is interrupted, the default PWM parameters are automatically restored through the built-in IC.

[0007] In a preferred embodiment, after obtaining the nonlinear response curve of the LED, a Gamma correction algorithm is applied to correct the target brightness value to ensure a smoother visual brightness change, including the following steps: Collect the relationship curve between the actual brightness of LED beads and the current or driving signal, obtain the initial correction coefficient through the nonlinear response curve of the LED, and obtain the corrected correction coefficient through the Gamma algorithm; After obtaining the target brightness value, the target brightness value is normalized. In the formula, The initial target brightness value, The target brightness is normalized to a value that makes the target brightness value range [0,1]. The corrected brightness value is obtained by correcting the normalized value of the target brightness using the corrected correction coefficients. The expression is: In the formula, To correct the brightness value, To correct the correction coefficients, the obtained corrected brightness values ​​are inversely normalized to obtain the corrected target brightness value, expressed as: In the formula, To correct the target brightness value.

[0008] In a preferred embodiment, the relationship curve between the actual brightness of the LED chip and the current or driving signal is collected. An initial correction coefficient is obtained through the LED's nonlinear response curve, and a corrected correction coefficient is obtained using the Gamma algorithm. The expression is as follows: In the formula, The corrected coefficients are the adjusted coefficients. These are the initial correction coefficients. Given the current ambient brightness, For maximum ambient brightness, The current LED temperature, Temperature threshold For the maximum allowable temperature, , It is the adjustment coefficient, and , All are greater than 0.

[0009] In a preferred embodiment, the calculation logic for the initial correction coefficient is as follows: The normalized value of the actual brightness and the normalized value of the driving current of the LED are obtained through the nonlinear response curve of the LED, and the nonlinear exponent is calculated, expressed as: In the formula, It is a non-linear exponent. This is the normalized value of the actual brightness of the LED. Given the normalized value of the drive current, the initial correction coefficient is: , These are the initial correction coefficients.

[0010] In a preferred embodiment, mapping the target brightness value to a PWM duty cycle to obtain the duty cycle range includes the following steps: Obtain the maximum and minimum duty cycle range of the PWM, and convert the brightness value into the actual PWM duty cycle according to the mapping formula. The expression is: In the formula, The initial PWM duty cycle value is used to control the brightness output of the LED. Minimum duty cycle, For maximum duty cycle, To correct the target brightness value, the initial PWM duty cycle value is adjusted: In the formula, This indicates the final PWM duty cycle value. Initial PWM duty cycle value, Minimum duty cycle, For maximum duty cycle, This indicates selecting the maximum value. This indicates that the minimum value is selected and the duty cycle range is output. .

[0011] In a preferred embodiment, the PWM frequency is dynamically adjusted based on the PWM duty cycle and ambient light conditions, including the following steps: Obtain the current ambient brightness and the final PWM duty cycle value. Normalize the current ambient brightness and the final PWM duty cycle value to map their values ​​to the range [0,1]. Subtract the final PWM duty cycle value from the normalized current ambient brightness to obtain the adjustment factor. Dynamically adjust the PWM frequency using the adjustment factor. The expression is: In the formula, The dynamically adjusted PWM frequency. This is the initial PWM frequency. It is a regulating factor.

[0012] In a preferred embodiment, during LED operation, after acquiring the current operating status data of the LED, the PWM signal switching frequency is optimized using a power consumption optimization algorithm, including the following steps: Obtain the current operating status data of the LED, including current power consumption, LED temperature, and flicker frequency. When normalizing the current power consumption, LED temperature, and flicker frequency, map their values ​​to the range [0,1]. Obtain the normalized values ​​of the current power consumption, LED temperature, and flicker frequency. Subtract the normalized value of the current power consumption from the normalized value of the flicker frequency and add the normalized value of the LED temperature to obtain the secondary optimization index. Use this secondary optimization index to perform secondary optimization on the dynamically adjusted PWM frequency to obtain the optimized PWM frequency. The expression is: In the formula, To optimize PWM frequency, The dynamically adjusted PWM frequency. This is a secondary optimization index.

[0013] The LED lamp bead built-in IC control system based on intelligent drive includes a matching module, a calibration module, a control module, and an optimization module; Matching module: After receiving the user control signal, it analyzes the target brightness based on the control signal and matches the target brightness value with the brightness range of the LED beads; Correction module: After obtaining the nonlinear response curve of the LED, the Gamma correction algorithm is applied to correct the target brightness value, and the corrected target brightness value is mapped to the PWM duty cycle to obtain the duty cycle range; Control module: Dynamically adjusts the PWM frequency based on the PWM duty cycle and ambient light conditions, and controls the LED operation based on the dynamically adjusted PWM frequency; Optimization module: During LED operation, after acquiring the current operating status data of the LED, the PWM signal switching frequency is optimized through a power consumption optimization algorithm to reduce the LED's energy consumption. When the external control signal is interrupted, the built-in IC automatically restores the default PWM parameters.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention obtains the nonlinear response curve of an LED, applies a Gamma correction algorithm to correct the target brightness value, maps the corrected target brightness value to a PWM duty cycle to obtain the duty cycle range, dynamically adjusts the PWM frequency based on the PWM duty cycle value and ambient light conditions, and controls the LED operation based on the dynamically adjusted PWM frequency. During LED operation, after obtaining the current operating status data of the LED, the PWM signal switching frequency is optimized using a power consumption optimization algorithm. The control system, by introducing the nonlinear response curve of the LED and employing a Gamma correction algorithm, performs nonlinear correction on the target brightness value, adjusting the brightness to a smoother curve that better conforms to the characteristics of human eye perception, thus improving the adjustment effect at low and high brightness levels. 2. This invention optimizes the PWM signal switching frequency by acquiring the current operating status data of the LED during operation and then using a power consumption optimization algorithm. The control system dynamically adjusts the PWM signal switching frequency based on the power consumption optimization algorithm by acquiring real-time LED operating status data (such as temperature and current), thereby reducing switching losses and significantly lowering overall energy consumption while meeting brightness requirements. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: Please refer to Figure 1 As shown in this embodiment, the LED bead built-in IC control method based on intelligent driving includes the following steps: After receiving the user control signal, the control system analyzes the target brightness value (such as a digital signal from 0 to 255) based on the control signal and matches the target brightness value with the brightness range of the LED beads to confirm that the output requirement is within the LED's operating range. After obtaining the LED's nonlinear response curve, the system applies a Gamma correction algorithm to correct the target brightness value to ensure a smoother visual brightness change. The corrected target brightness value is mapped to the PWM duty cycle to obtain the duty cycle range. Based on the PWM duty cycle value and ambient light conditions, the system dynamically adjusts the PWM frequency and controls the LED operation based on the dynamically adjusted PWM frequency. During LED operation, after obtaining the LED's current operating status data, the system optimizes the PWM signal switching frequency through a power consumption optimization algorithm to reduce the LED's energy consumption. When the external control signal is interrupted, the system automatically restores the default PWM parameters through the built-in IC.

[0019] This application obtains the nonlinear response curve of the LED, applies a Gamma correction algorithm to correct the target brightness value, maps the corrected target brightness value to the PWM duty cycle to obtain the duty cycle range, and dynamically adjusts the PWM frequency based on the PWM duty cycle value and ambient light conditions. The LED operation is controlled based on the dynamically adjusted PWM frequency. During LED operation, the current operating status data of the LED is acquired, and the PWM signal switching frequency is optimized using a power consumption optimization algorithm. The control system, by introducing the nonlinear response curve of the LED and employing the Gamma correction algorithm, performs nonlinear correction on the target brightness value, adjusting the brightness to a smoother curve that better conforms to the characteristics of human eye perception, thus improving the adjustment effect at low and high brightness levels.

[0020] This application optimizes the PWM signal switching frequency by acquiring the current operating status data of the LED during operation and then using a power consumption optimization algorithm. The control system dynamically adjusts the PWM signal switching frequency based on the power consumption optimization algorithm by acquiring real-time LED operating status data (such as temperature and current), thereby reducing switching losses and significantly lowering overall energy consumption while meeting brightness requirements.

[0021] Example 2: After receiving the user control signal, the control system analyzes the target brightness value (such as a digital signal of 0~255) based on the control signal, matches the target brightness value with the brightness range of the LED beads, and confirms that the output requirement is within the LED operating range. This includes the following steps: The brightness control signal received from the user may take the following forms: Digital brightness values ​​(e.g., digital signals in the range of 0 to 255).

[0022] Analog voltage signal (which needs to be converted to a digital value via an ADC).

[0023] Brightness settings are transmitted via communication protocols (such as Wi-Fi or Bluetooth commands).

[0024] Based on the type of input signal, it is resolved into a specific target brightness value: Direct acquisition: If it is a clear digital signal (such as 0~255), it is directly used as the target brightness value.

[0025] Mapping Conversion: If it is an analog signal, map it from the input range (e.g., 0V to 3.3V) to the 0255 range.

[0026] Protocol Decoding: Parses the brightness setting value in the communication protocol (such as MIDI, DMX protocol). Checks if the parsed target brightness value is within the effective brightness range of the LED beads: if it is less than 0, it is corrected to 0 (lights off). If it is greater than 255, it is corrected to 255 (full brightness).

[0027] The target brightness value is adapted according to the actual brightness operating range of the LED (e.g., 5%~100% brightness): if the actual brightness range of the LED is 12.75~255 (corresponding to 5%~100% brightness), the target brightness value is remapped to this range proportionally.

[0028] After obtaining the nonlinear response curve of the LED, the target brightness value is corrected using a Gamma correction algorithm to ensure a smoother visual brightness change. This includes the following steps: The relationship between the actual brightness of an LED chip and the current or drive signal is typically non-linear: changes in LED brightness have a logarithmic effect on human perception, while changes in the drive signal are linear. Typical response curves may require experimental measurement or consulting a specification sheet. The normalized values ​​of the actual brightness and driving current of the LED are obtained through the nonlinear response curve of the LED, and the nonlinear exponent is calculated. The expression is as follows: In the formula, It is a non-linear exponent. This is the normalized value of the actual brightness of the LED. Given the normalized value of the drive current, the initial correction coefficient is: , These are the initial correction coefficients; The corrected coefficients are obtained using the Gamma algorithm, expressed as follows: In the formula, The corrected coefficients are the adjusted coefficients. These are the initial correction coefficients. Given the current ambient brightness, For maximum ambient brightness, The current LED temperature, Temperature threshold For the maximum allowable temperature, , It is the adjustment coefficient, and , All are greater than 0.

[0029] After obtaining the target brightness value, the target brightness value is normalized. In the formula, The initial target brightness value, The target brightness is normalized to a value that makes the target brightness value range [0,1]. The corrected brightness value is obtained by correcting the normalized value of the target brightness using the corrected correction coefficients. The expression is: In the formula, To correct the brightness value, The target brightness normalized value, To correct the correction coefficients, the obtained corrected brightness values ​​are inversely normalized to obtain the corrected target brightness value, expressed as: In the formula, To correct the target brightness value.

[0030] The brighter the ambient light, the smaller the Gamma correction factor needs to be. The specific reasons are as follows: In bright environments, the human eye is not sensitive to subtle changes in brightness. Reducing the Gamma correction factor can decrease over-dimming in bright areas, enhance the utilization of the dynamic range of brightness, and thus make the display effect more natural. In bright environments, the human eye has a lower demand for detail in dark areas. Lowering the Gamma correction factor reduces the enhancement of low-brightness areas and allocates more resources to high-brightness areas, optimizing the display effect.

[0031] High ambient brightness reduces screen contrast (light causes screen reflections, affecting black level performance). Lowering the Gamma correction factor reduces the emphasis on low-brightness areas, preventing grayscale regions from appearing blurry. Reducing the Gamma value makes the signal's non-linear correction more linear, making the brightness performance more suitable for high-brightness scenarios.

[0032] In bright environments, users typically want a brighter screen to counteract ambient light. Lowering the Gamma correction factor reduces over-driving of dark areas, effectively reducing energy consumption and preventing excessive LED heat generation. With a smaller Gamma value, brightness changes become more linear, consistent with human eye perception in bright environments. Users are more likely to accept gradual changes in monitor brightness without perceiving the screen as "too bright" or "too dark."

[0033] In high ambient light conditions, display devices need to enhance the performance of medium-to-high brightness areas to combat interference from ambient light. Reducing the Gamma correction factor improves resolution in high-brightness areas, making the image appear clearer.

[0034] The higher the LED temperature, the smaller the Gamma correction coefficient needs to be. The specific reasons are as follows: As temperature increases, the luminous efficiency (luminous flux) of LEDs decreases. Maintaining a large Gamma correction factor will over-amplify dark area brightness signals, causing the LED to fail to accurately output the expected brightness, further degrading the display effect. Decreasing the Gamma correction factor reduces signal amplification in dark areas, thereby reducing excessive demands on LED performance. When LED temperature is too high, the emission spectrum may drift, affecting color performance. Lowering the Gamma correction factor reduces the driving burden on low-brightness signals, stabilizes light emission characteristics, and avoids excessive color distortion.

[0035] When an LED temperature is too high, reducing the Gamma correction factor can decrease excessive driving of low-brightness signals, thereby reducing overall power consumption and slowing down the rate of temperature rise. This is a passive temperature control method that can prevent further damage caused by overheating. High temperatures accelerate the light decay and material aging of LEDs. Reducing the Gamma correction factor can decrease the current driving amplitude of the LED in dark areas, thereby reducing the LED's operating heat and extending its lifespan.

[0036] At high temperatures, reducing the Gamma correction factor decreases the overdrive of dark areas and makes the mid-to-high brightness areas closer to linear performance. Although the overall brightness will decrease slightly, it maintains a visual balance in brightness distribution, ensuring acceptable visual performance even at high temperatures. If a large Gamma value is maintained at excessively high temperatures, overly bright dark areas will further increase the thermal burden and reduce contrast. Reducing the Gamma value suppresses brightness in dark areas, making the display contrast more stable under high-temperature conditions. When the LED temperature rises close to the safe threshold, reducing the Gamma correction factor helps reduce the LED workload, preventing safety issues or automatic shutdown due to overheating.

[0037] Mapping the target brightness value to the PWM duty cycle to obtain the duty cycle range includes the following steps: Obtain the maximum and minimum duty cycle range of the PWM, the maximum duty cycle of the PWM (usually 100%), and the minimum duty cycle of the PWM (usually 0% or a fixed value less than 100% to prevent the LED from turning off completely). Based on the mapping formula, convert the brightness value into the actual PWM duty cycle. The expression is: In the formula, The initial PWM duty cycle value is used to directly control the brightness output of the LED. Minimum duty cycle, For maximum duty cycle, To correct for the target brightness value, the initial PWM duty cycle is adjusted to ensure that the final duty cycle is within the allowable range. In the formula, This indicates the final PWM duty cycle value. Initial PWM duty cycle value, Minimum duty cycle, For maximum duty cycle, This indicates selecting the maximum value. This indicates that the minimum value is selected and the duty cycle range is output. .

[0038] Based on the PWM duty cycle and ambient light conditions, the PWM frequency is dynamically adjusted, and the LED operation is controlled based on the dynamically adjusted PWM frequency, including the following steps: Obtain the current ambient brightness and the final PWM duty cycle value. Normalize the current ambient brightness and the final PWM duty cycle value to map their values ​​to the range [0,1]. Subtract the final PWM duty cycle value from the normalized current ambient brightness to obtain the adjustment factor. Dynamically adjust the PWM frequency using the adjustment factor. The expression is: In the formula, The dynamically adjusted PWM frequency. This is the initial PWM frequency. As a regulating factor; The relationships between current ambient brightness and PWM frequency adjustment are as follows: 1) Low ambient light: In low-light conditions, the human eye is highly sensitive to flicker, so it is necessary to increase the PWM frequency to reduce flicker. This is because, in low-light environments, lower PWM frequencies (e.g., below several hundred hertz) make flicker more noticeable and affect the visual experience.

[0039] Adjustment strategy: Increase the PWM frequency (for example, use a higher frequency such as 1kHz to several kHz) to reduce the human eye's perception of flicker and ensure a smoother display.

[0040] 2) High ambient light: In high ambient light conditions, the human eye is less sensitive to flicker because the strong surrounding light makes the flicker effect less noticeable. Therefore, in high-brightness environments, a lower PWM frequency can reduce power consumption and heat generation, while also saving driver resources.

[0041] Adjustment strategy: The PWM frequency can be appropriately reduced (for example, using a frequency in the range of 100Hz to 1kHz) to optimize energy efficiency and reduce heat.

[0042] The relationships between PWM duty cycle and PWM frequency adjustment are as follows: High duty cycle scenarios: Increase the frequency to reduce visible flicker; Low duty cycle scenarios: Maintain low to medium frequencies to optimize electromagnetic interference performance.

[0043] During LED operation, after acquiring the current operating status data of the LED, the switching frequency of the PWM signal is optimized through a power consumption optimization algorithm to reduce the energy consumption of the LED. This includes the following steps: Obtain the current operating status data of the LED, including current power consumption, LED temperature, and flicker frequency. When normalizing the current power consumption, LED temperature, and flicker frequency, map their values ​​to the range [0,1]. Obtain the normalized values ​​of the current power consumption, LED temperature, and flicker frequency. Subtract the normalized value of the current power consumption from the normalized value of the flicker frequency and add the normalized value of the LED temperature to obtain the secondary optimization index. Use this secondary optimization index to perform secondary optimization on the dynamically adjusted PWM frequency to obtain the optimized PWM frequency. The expression is: In the formula, To optimize PWM frequency, The dynamically adjusted PWM frequency. This is a secondary optimization index.

[0044] When the external control signal is interrupted, the built-in IC automatically restores the default PWM parameters, including the following steps: The built-in IC continuously monitors external control signals. When an interruption or loss of an external control signal is detected, the system automatically recognizes the signal interruption and initiates a recovery process. The built-in IC has a preset set of default PWM parameters, including default duty cycle, frequency, and brightness settings. Common default parameters might be medium brightness and a stable frequency, ensuring that the LED can operate normally without an external control signal.

[0045] After a signal interruption, the built-in IC immediately restores the PWM duty cycle and frequency to their default values. The default duty cycle controls the LED brightness, while the frequency ensures stable LED operation, preventing flickering or instability. After restoring to the default parameters, the built-in IC regenerates the PWM signal and controls the LED brightness through the driver circuit. The generated PWM signal remains stable, ensuring the LED emits light normally.

[0046] After the recovery process is complete, the built-in IC will report the current recovery status to the system or external device, ensuring that the PWM parameters have been restored to their default values. If there is an external indicator device (such as an indicator light or display screen), it will also display a signal indicating successful recovery. The restored PWM signal will continue to run, and the built-in IC will continuously monitor the LED's operating status, power consumption, temperature, and other indicators to ensure that the LED can operate stably under default parameters.

[0047] Once the external control signal is restored, the built-in IC adjusts the PWM parameters according to the new control signal, smoothly transitioning to the new operating state and avoiding the impact of sudden parameter changes on the LED. Through the above steps, the built-in IC can ensure that the LED system continues to operate normally when the external control signal is interrupted, and can smoothly switch back to the new control mode when the control signal is restored.

[0048] Example 3: The LED lamp bead built-in IC control system based on intelligent driving described in this example includes a matching module, a calibration module, a control module, and an optimization module; Matching module: After receiving the user control signal, it analyzes the target brightness value (such as a digital signal of 0~255) based on the control signal, matches the target brightness value with the brightness range of the LED beads, confirms that the output requirement is within the LED working range, and sends the target brightness value to the calibration module; The calibration module: After obtaining the nonlinear response curve of the LED, it applies the Gamma correction algorithm to correct the target brightness value to ensure that the visual brightness change is smoother. The corrected target brightness value is mapped to the PWM duty cycle to obtain the duty cycle range, and the duty cycle range is sent to the control module. Control module: Based on the PWM duty cycle and ambient light conditions, dynamically adjust the PWM frequency, control the LED operation based on the dynamically adjusted PWM frequency, and send the LED operation status data to the optimization module; Optimization module: During LED operation, after acquiring the current operating status data of the LED, the PWM signal switching frequency is optimized through a power consumption optimization algorithm to reduce the LED's energy consumption. When the external control signal is interrupted, the built-in IC automatically restores the default PWM parameters.

[0049] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0050] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0051] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for controlling LED beads with built-in ICs based on intelligent driving, characterized in that: The control method includes the following steps: After receiving the user's control signal, the control system analyzes the target brightness value based on the control signal, matches the target brightness value with the brightness range of the LED beads, obtains the nonlinear response curve of the LED, and then applies the Gamma correction algorithm to correct the target brightness value. The target brightness value is mapped to the PWM duty cycle to obtain the duty cycle range. The PWM frequency is dynamically adjusted according to the PWM duty cycle value and the ambient light conditions. The LED operation is controlled based on the dynamically adjusted PWM frequency. During LED operation, after acquiring the current operating status data of the LED, the switching frequency of the PWM signal is optimized through a power consumption optimization algorithm to reduce the energy consumption of the LED. When the external control signal is interrupted, the default PWM parameters are automatically restored through the built-in IC.

2. The LED bead built-in IC control method based on intelligent driving according to claim 1, characterized in that: After obtaining the nonlinear response curve of the LED, the target brightness value is corrected using a Gamma correction algorithm to ensure a smoother visual brightness change. This includes the following steps: Collect the relationship curve between the actual brightness of LED beads and the current or driving signal, obtain the initial correction coefficient through the nonlinear response curve of the LED, and obtain the corrected correction coefficient through the Gamma algorithm; After obtaining the target brightness value, the target brightness value is normalized. In the formula, The initial target brightness value, The target brightness is normalized to a value that makes the target brightness value range [0,1]. The corrected brightness value is obtained by correcting the normalized value of the target brightness using the corrected correction coefficients. The expression is: In the formula, To correct the brightness value, To correct the correction coefficients, the obtained corrected brightness values ​​are inversely normalized to obtain the corrected target brightness value, expressed as: In the formula, To correct the target brightness value.

3. The LED bead built-in IC control method based on intelligent driving according to claim 2, characterized in that: Collect the relationship curve between the actual brightness of the LED beads and the current or driving signal. Obtain the initial correction coefficient through the nonlinear response curve of the LED, and obtain the corrected correction coefficient through the Gamma algorithm. The expression is: In the formula, The correction factor is the adjusted value. These are the initial correction coefficients. Given the current ambient brightness, For maximum ambient brightness, The current LED temperature, Temperature threshold For the maximum allowable temperature, , It is the adjustment coefficient, and , All are greater than 0.

4. The LED bead built-in IC control method based on intelligent driving according to claim 3, characterized in that: The calculation logic for the initial correction coefficient is as follows: The normalized values ​​of the actual brightness and driving current of the LED are obtained through the LED's nonlinear response curve, and the nonlinear exponent is calculated, expressed as: In the formula, It is a non-linear exponent. This is the normalized value of the actual brightness of the LED. Given the normalized value of the drive current, the initial correction coefficient is: , These are the initial correction coefficients.

5. The LED bead built-in IC control method based on intelligent driving according to claim 4, characterized in that: Mapping the target brightness value to the PWM duty cycle to obtain the duty cycle range includes the following steps: Obtain the maximum and minimum duty cycle range of the PWM, and convert the brightness value into the actual PWM duty cycle according to the mapping formula. The expression is: In the formula, The initial PWM duty cycle value is used to control the brightness output of the LED. Minimum duty cycle, For maximum duty cycle, To correct the target brightness value, the initial PWM duty cycle value is adjusted: In the formula, This indicates the final PWM duty cycle value. Initial PWM duty cycle value, Minimum duty cycle, For maximum duty cycle, This indicates selecting the maximum value. This indicates that the minimum value is selected and the duty cycle range is output. .

6. The LED bead built-in IC control method based on intelligent driving according to claim 5, characterized in that: The PWM frequency is dynamically adjusted based on the PWM duty cycle and ambient light conditions, including the following steps: Obtain the current ambient brightness and the final PWM duty cycle value. Normalize the current ambient brightness and the final PWM duty cycle value to map their values ​​to the range [0,1]. Subtract the final PWM duty cycle value from the normalized current ambient brightness to obtain the adjustment factor. Dynamically adjust the PWM frequency using the adjustment factor. The expression is: In the formula, The dynamically adjusted PWM frequency. The initial PWM frequency, It is a regulating factor.

7. The LED bead built-in IC control method based on intelligent driving according to claim 6, characterized in that: During LED operation, after acquiring the current operating status data of the LED, the PWM signal switching frequency is optimized using a power consumption optimization algorithm, including the following steps: Obtain the current operating status data of the LED, including current power consumption, LED temperature, and flicker frequency. When normalizing the current power consumption, LED temperature, and flicker frequency, map their values ​​to the range [0,1]. Obtain the normalized values ​​of the current power consumption, LED temperature, and flicker frequency. Subtract the normalized value of the current power consumption from the normalized value of the flicker frequency and add the normalized value of the LED temperature to obtain the secondary optimization index. Use this secondary optimization index to perform secondary optimization on the dynamically adjusted PWM frequency to obtain the optimized PWM frequency. The expression is: In the formula, To optimize the PWM frequency, The dynamically adjusted PWM frequency. This is a secondary optimization index.

8. A smart-driven LED bead built-in IC control system for implementing the control method according to any one of claims 1-7, characterized in that: It includes a matching module, a calibration module, a control module, and an optimization module; Matching module: After receiving the user control signal, it analyzes the target brightness based on the control signal and matches the target brightness value with the brightness range of the LED beads; Correction module: After obtaining the nonlinear response curve of the LED, the Gamma correction algorithm is applied to correct the target brightness value, and the corrected target brightness value is mapped to the PWM duty cycle to obtain the duty cycle range; Control module: Dynamically adjusts the PWM frequency based on the PWM duty cycle and ambient light conditions, and controls the LED operation based on the dynamically adjusted PWM frequency; Optimization module: During LED operation, after acquiring the current operating status data of the LED, the PWM signal switching frequency is optimized through a power consumption optimization algorithm to reduce the LED's energy consumption. When the external control signal is interrupted, the built-in IC automatically restores the default PWM parameters.

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