Control system for driving integrated SMD LED lamp beads

Abstract

The invention discloses a control system for driving an integrated SMD LED lamp bead, and relates to the technical field of LED driving control, and the system comprises a data collection and analysis module which is used for obtaining a real-time current signal and temperature sensor data in an LED driving circuit, performing digital sampling processing on the current signal and the temperature signal through a high-precision analog-to-digital converter, generating an original current temperature data sequence containing a timestamp, obtaining an initialized data acquisition result, and determining a fault type classification and severity evaluation result according to the initialized data acquisition result; the control system for driving the integrated SMD LED lamp bead can realize accurate control and abnormal protection of current, improves the stability and reliability of an LED driving circuit, prolongs the service life of an LED, and has important practical value.

Description

Technical Field

[0001] The present invention relates to the technical field of light emitting diode drive control, and in particular to a control system for driving integrated SMD LED lamp beads. Background Art

[0002] As a core technology in modern lighting and display systems, LED driver technology directly determines the performance stability and service life of the equipment, playing an irreplaceable role in key application areas such as energy-saving lighting, automotive electronics, and display screens. With the increasing demand for light source quality, precise and stable current control has become the fundamental guarantee for the reliable operation of LED systems.

[0003] Current LED drive solutions suffer from significant drawbacks. Traditional open-loop control approaches are unable to cope with complex and volatile operating environments. Existing closed-loop control systems are slow to respond and have limited accuracy. Most protection mechanisms are insensitive, making it difficult to maintain output current stability under harsh conditions. These limitations severely restrict the widespread application of LED systems in demanding applications. A key challenge in this field stems from the persistent disruption of drive current stability caused by ambient temperature fluctuations and component aging. Temperature fluctuations can cause semiconductor device parameters to drift, resulting in output current deviations from set values. Component aging during long-term operation further exacerbates the cumulative effect of these deviations. This current instability directly threatens the safe operation of LEDs. When the system is unable to promptly detect and respond to abnormal conditions such as overcurrent or short circuits, the sudden surge of high current can cause permanent damage to the devices. Furthermore, the response delay of traditional protection mechanisms often reaches tens of milliseconds or even longer, a window long enough for fault currents to irreversibly damage sensitive LEDs. This significant gap exists between the demand for fast protection in the millisecond range and the capabilities of current technology. Summary of the Invention

[0004] The purpose of the present invention is to provide a control system for driving integrated SMD LED lamp beads, constructing an intelligent drive control system that can monitor output current changes in real time, automatically compensate for temperature drift and aging effects, and have millisecond-level rapid protection response capabilities, ensuring that the light-emitting diodes can obtain stable and accurate drive current under various harsh working conditions.

[0005] To achieve the above object, the present invention provides the following technical solution: a control system for driving integrated SMD LED lamp beads, the system comprising: A data acquisition and analysis module is used to obtain real-time current signals and temperature sensor data from the LED drive circuit, digitally sample and process the current signals and temperature signals through a high-precision analog-to-digital converter, generate a raw current and temperature data sequence containing a timestamp, obtain initialized data acquisition results, and determine the fault type classification and severity assessment results based on the initialized data acquisition results; The protection decision-making and execution module is used to generate a protection level classification scheme based on the fault type classification and severity assessment results using output decision reasoning technology. If the protection level exceeds the preset threshold, a circuit breaker or current limiting protection operation is executed within milliseconds, cutting off the abnormal current path through the hardware interrupt mechanism, and obtaining a system status feedback signal after the protection action is executed; The control recovery and adaptive adjustment module is used to reinitialize the control parameters of the drive circuit based on the system status feedback signal and the specific requirements of the protection level classification. It determines whether the fault recovery conditions are met through real-time feedback response. If the recovery conditions are met, it reactivates the current monitoring and control loop to determine the stable operating state after recovery.

[0006] Preferably, determining the fault type classification and severity assessment results based on the initialized data collection results includes: Based on the initialized data acquisition results, feature extraction is performed on the correlation between the current signal and the temperature signal, and a mapping relationship between the influence of temperature on current is constructed. The current offset under the current temperature conditions is determined through deviation trend analysis to obtain a preliminary current deviation assessment value.

[0007] Preferably, determining the fault type classification and severity assessment results based on the initialized data collection results further includes: Through the preliminary current deviation evaluation value, combined with dynamic parameter adjustment technology, an adaptive proportional integral differential control algorithm is used to optimize the control parameters of the drive circuit in real time. Specifically, the output signal is adjusted by optimizing the duty cycle correction value and switching frequency. At the same time, the error integral accumulation and differential prediction compensation are used to reduce the control error and determine the adjusted drive control instructions.

[0008] Preferably, determining the fault type classification and severity assessment results based on the initialized data collection results further includes: The current feedback signal after the adjusted drive control instruction is executed is obtained, and a real-time feedback response is performed on the feedback signal. The control parameters are further corrected by adjusting the control gain to determine whether the current has reached the preset stable range, and the optimized current state information is obtained.

[0009] Preferably, determining the fault type classification and severity assessment results based on the initialized data collection results further includes: Based on the optimized current state information, an abnormal fluctuation detection mechanism is constructed. If a sudden change or irregular fluctuation is detected in the current signal, a rapid response mechanism is triggered. The fuzzy logic control algorithm is used to analyze the signal characteristics through the construction of a fuzzy rule base and the input membership function to determine the fault type classification and severity assessment results.

[0010] Preferably, the determination of the fault type classification and severity assessment result includes scoring the comprehensive fault severity, and the specific formula is: ; in, Indicates the fault severity rating value, Indicates the deviation between the measured current value and the target setting value. Indicates the deviation between the measured temperature and the reference temperature. represents the current deviation weight factor, represents the temperature deviation weight factor, Represents a positive constant, ∈[0.05, 0.8].

[0011] Preferably, the current deviation weight factor The specific calculation formula is: ; in, represents the current deviation weight factor, Indicates the resistance used when obtaining current, represents the standard deviation of current fluctuation, Indicates the deviation between the measured current value and the target setting value. Represents the heat capacity constant, that is, the amount of heat that can be absorbed or released per unit temperature change. Indicates the deviation between the measured temperature and the reference temperature; =1- ; in, Represents the temperature deviation weight factor.

[0012] Preferably, the data acquisition and analysis module includes a sampling resistor, a temperature sensor and an analog-to-digital converter, and the resolution of the analog-to-digital converter is not less than 16 bits.

[0013] Preferably, the original current and temperature data sequence includes current values ​​and temperature values ​​collected at least once every 1 millisecond, and is accompanied by corresponding timestamp information.

[0014] Preferably, the protection decision and execution module adopts a hierarchical response strategy, dividing the protection level into three levels, specifically including: early warning state, current limiting protection state and circuit breaker protection state; among which, the early warning state sends a risk warning signal through the communication interface, the current limiting protection state controls the output power by reducing the pulse width modulation duty cycle, and the circuit breaker protection state immediately disconnects the current path.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: This control system for driving integrated SMD LEDs uses high-precision analog-to-digital conversion to collect current and temperature data, establishes a mapping relationship between temperature and current, and evaluates current deviation. It uses an adaptive PID algorithm to optimize drive parameters in real time, adjusting the output signal based on duty cycle and switching frequency. It also incorporates an abnormal fluctuation detection mechanism, employing fuzzy logic to analyze fault type and severity, and implementing circuit disconnection or current limiting protection based on the protection level. This invention enables precise current control and abnormality protection, improves the stability and reliability of the LED driver circuit, and extends the LED lifespan, possessing significant practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the present invention provides a technical solution: a control system for driving integrated SMD LED lamp beads, the system comprising: A data acquisition and analysis module is used to obtain real-time current signals and temperature sensor data from the LED drive circuit, digitally sample and process the current signals and temperature signals through a high-precision analog-to-digital converter, generate a raw current and temperature data sequence containing a timestamp, obtain initialized data acquisition results, and determine the fault type classification and severity assessment results based on the initialized data acquisition results; The protection decision-making and execution module is used to generate a protection level classification scheme based on the fault type classification and severity assessment results using output decision reasoning technology. If the protection level exceeds the preset threshold, a circuit breaker or current limiting protection operation is executed within milliseconds, cutting off the abnormal current path through the hardware interrupt mechanism, and obtaining a system status feedback signal after the protection action is executed; The control recovery and adaptive adjustment module is used to reinitialize the control parameters of the drive circuit based on the system status feedback signal and the specific requirements of the protection level classification. It determines whether the fault recovery conditions are met through real-time feedback response. If the recovery conditions are met, it reactivates the current monitoring and control loop to determine the stable operating state after recovery.

[0019] This embodiment uses a data acquisition and analysis module to implement high-frequency, real-time monitoring of the current signal and ambient temperature in the LED driver circuit. A high-precision analog-to-digital converter is used to digitize the analog signal, and each set of sampled data is timestamped to form a time-series data sequence. After preprocessing, the data is used to identify abnormal patterns, thereby classifying possible fault types (such as short circuit, overcurrent, and overheating) and, combined with an evaluation algorithm, determining their severity. Subsequently, the protection decision and execution module, based on the classification results, utilizes a decision-making inference model to generate a protection level that matches the fault severity. If the determined protection level exceeds the system tolerance threshold, the module immediately initiates circuit breaker or current limiting protection. A hardware interrupt mechanism ensures that the protection operation is completed within milliseconds, minimizing further damage to the LED module. After the protection is executed, the control recovery and adaptive adjustment module receives system status feedback, reconfigures the driver circuit control parameters, and determines whether fault resolving conditions, such as temperature drop and current stabilization, have been met. If so, normal operation resumes.

[0020] The control system first uses a data acquisition and analysis module to monitor the current and temperature in the LED driver circuit in real time. Current signals are collected by connecting a small sampling resistor (e.g., between 10 milliohms and 100 milliohms) in series with the LED current path. During circuit operation, the current is calculated by measuring the voltage difference across the sampling resistor. The current is calculated by dividing the voltage by the resistance of the resistor. For example, if the resistor has a resistance of 50 milliohms and the measured voltage is 0.5 volts, the actual current is 10 amperes.

[0021] Temperature is typically detected using an analog or digital temperature sensor, such as a thermistor or integrated temperature chip. These sensors output a voltage signal or digital value that has a linear or quasi-linear relationship with temperature. For example, if the output voltage of a temperature sensor increases by 10 millivolts for every 1°C increase, the current temperature can be calculated by reading the voltage. If the voltage read is 0.75 volts and the reference zero temperature is 25°C, the current temperature is 100°C.

[0022] The system collects current and temperature values ​​every 1 millisecond and adds a time tag to each set of data to form a data sequence. The system uses a preset threshold to determine whether the current collected data is abnormal. For example: If the current value is higher than the preset maximum allowable current value (such as 1.5 amps), it is judged as overcurrent; If the temperature is higher than the preset maximum operating temperature (e.g. 85 degrees Celsius), it is judged as overheating; If the current change between two consecutive samples exceeds a certain set value (such as more than 0.3 amperes per millisecond), it may be a short circuit or strong fluctuation fault.

[0023] Based on the fault diagnosis, the system further quantifies the severity of the fault. If this value exceeds the severity threshold set by the system (such as 2.0), it is considered a high-risk fault.

[0024] Based on the scoring results, the system determines which level of protection measures to implement. For example: When the score is low, only the drive current is limited, and the power supply can be reduced by adjusting the PWM duty cycle; When the score is medium, the data sampling interval is further shortened based on the current limit; If the score is higher than the set protection threshold, the hardware interrupt mechanism is immediately triggered to turn off the main switch MOS tube, thereby cutting off the current.

[0025] After the power outage or current limiting operation is completed, the system determines whether the conditions for resuming operation are met through status sampling. The judgment basis is: Whether the current has returned to the set safety range, such as less than 1.2 amps; Whether the current temperature drops below a safe level, such as below 75 degrees Celsius; Whether the above state is maintained for a set period of time, such as more than 5 seconds.

[0026] Only when all recovery conditions are met will the system restart the PWM signal, re-enable the drive path, and enter the normal current control and feedback closed-loop state.

[0027] All judgment thresholds, such as maximum allowable current, maximum temperature, and rate of change thresholds, are obtained through experimental calibration. The specific method involves simulating various faults under different environmental conditions. By analyzing the electrical characteristic waveforms and the damage thresholds to the LED module, a safety margin is ultimately established that ensures timely protection without causing false trips. For example, the maximum current threshold is set at 90% of the LED package's upper limit, and the temperature is set at 85% of the safety margin before thermal failure of the chip package. The rate of change is determined by the inflection point speed determined by the transient test curve as the limit.

[0028] Through the above mechanism, the system realizes real-time monitoring, efficient diagnosis and rapid protection of the LED lamp driving process, and has the ability to automatically judge and safely restore operation.

[0029] This implementation enables efficient monitoring, autonomous diagnosis, and rapid protection response for the LED driver control system. This significantly improves the timeliness and accuracy of system fault handling while effectively reducing the risk of LED module damage due to fault expansion. Protection execution latency is as low as milliseconds, helping to prevent chip overheating or degradation caused by the continued flow of abnormal current. Furthermore, an adaptive adjustment mechanism automatically restores system operation based on recovery conditions, improving system reliability and intelligence, reducing the need for manual intervention, and enhancing the robustness of the LED lighting system in complex electrical environments.

[0030] Based on the initialized data acquisition results, the fault type classification and severity assessment results are determined, including extracting features based on the correlation between the current signal and the temperature signal, constructing a mapping relationship between the impact of temperature on current, and judging the current offset under the current temperature conditions through deviation trend analysis to obtain a preliminary current deviation assessment value.

[0031] This embodiment performs precise feature modeling and deviation analysis based on the operating data of the LED drive system, especially the changing relationship between the current signal and the temperature signal, to assist in achieving more accurate fault judgment and severity assessment.

[0032] First, during the system's initial operation, the control system continuously collects temperature and current values ​​over a period of time. For example, the data may be collected every millisecond, with a total number of collections ranging from 100 to 1000. This generates multiple temperature-current data points. The system pairs each temperature value with the corresponding current value at the time of collection, forming a data sample set.

[0033] Next, the system performs statistical modeling on these paired temperature and current data. The preferred modeling method is quadratic polynomial fitting, which is to establish a mathematical model that can predict current values ​​based on temperature values. The process of building this model includes trying to approximate each set of observed current values ​​using a linear combination of "squared temperature term", "linear temperature term" and "a constant term". The system uses the principle of minimum error to automatically adjust the above three coefficients so that the sum of the squared errors between the predicted values ​​and the actual sampled values ​​of all data points is minimized, thereby obtaining the prediction model with the highest fitting accuracy.

[0034] Once the model is established, the system can use the current temperature value collected in real time during subsequent real-time operation to calculate the theoretical current value under the temperature conditions through the above prediction model. The system then compares this predicted current value with the actual current value collected, and the difference between the two is the "current deviation value."

[0035] For example, if the current temperature is 70 degrees Celsius, after substituting it into the fitting model, the system calculates that the theoretical current should be 1.2 amperes. However, the actual current sampled at this time is 1.6 amperes. The current deviation is positive 0.4 amperes, indicating that the system currently has an overcurrent phenomenon that exceeds the model's expectations.

[0036] To enhance judgment stability, the system also averages current deviations over multiple consecutive cycles. This average, called the sliding average deviation, is recorded over the last five sampling cycles using a sliding window. If this average consistently exceeds a set safety range (e.g., 0.3 amps), the system identifies a stable abnormal trend.

[0037] To more comprehensively assess the significance of deviations, the system also calculates the percentage of the current deviation relative to the predicted value. For example, if the predicted current is 1.2 amps and the actual current is 1.6 amps, the deviation is 0.4 amps, representing 33.3% of the predicted value. The system sets a threshold for the deviation (for example, 25%); any deviation exceeding this threshold is considered a potential fault signal.

[0038] The above threshold parameter settings have clear basis: The average deviation threshold (e.g., 0.3 amps) is the optimal compromise point obtained through experiments in various LED driver scenarios. It can effectively distinguish normal fluctuations from true faults. The offset rate threshold (e.g., 25%) is used to improve the system's adaptability to different current operating ranges, allowing it to maintain consistent sensitivity regardless of low or high current operation. The size of the sliding window (e.g., 5 to 10 cycles) is set based on the LED driver response characteristics to avoid the impact of short-term fluctuations while not introducing excessive delays.

[0039] Ultimately, the current deviation value and its changing trend will be sent to the fault classification and severity assessment module, and will be used together with other indicators such as temperature change rate and current mutation amplitude to participate in comprehensive judgment, so as to support the system to make accurate and efficient protection decisions.

[0040] This implementation introduces a dynamic feature modeling mechanism by analyzing the correlation between current and temperature. This enables fault diagnosis to go beyond a single threshold and incorporates model prediction and deviation identification capabilities, enhancing the system's environmental adaptability and diagnostic accuracy. This significantly reduces false positives and improves fault identification reliability, particularly in scenarios with large temperature fluctuations or frequent load changes.

[0041] Based on the initialized data acquisition results, the fault type classification and severity assessment results are determined, which also includes using preliminary current deviation assessment values, combined with dynamic parameter adjustment technology, and using an adaptive proportional integral differential control algorithm to optimize the control parameters of the drive circuit in real time. Specifically, the output signal is adjusted through duty cycle correction values ​​and switching frequency optimization, and the control error is reduced by using error integral accumulation and differential prediction compensation to determine the adjusted drive control instructions.

[0042] In this embodiment, after obtaining the current deviation evaluation value, the system further introduces a dynamic adaptive proportional-integral-differential control strategy to adjust the control parameters of the LED drive circuit in real time, thereby achieving precise control and improving system stability and response speed.

[0043] This control process uses the difference between the current "target current" and the "actual measured current" as the input error. The controller first responds based on the immediate magnitude of this error, which is the proportional control component. It then accumulates the error values ​​over time to determine the error accumulation trend, which constitutes the integral control component. Furthermore, the controller calculates the rate of change of the error over time, the error rate of change, which is used to predict future trends, forming the differential control component.

[0044] The controller multiplies each of these three results by the corresponding adjustment coefficients (proportional, integral, and differential), then adds them together to produce a comprehensive correction value for controlling the output. This correction value determines how to adjust key control parameters in the LED driver circuit, primarily the PWM duty cycle and the driver chip's switching frequency.

[0045] The three coefficients of the controller are dynamic and not fixed values, but are adjusted in real time according to the current state of the system. For example: When a large absolute value of the current deviation is detected (e.g., exceeding 0.3 amps), the system increases the proportional coefficient to enhance the response; If the error changes rapidly (for example, the difference between the two sampling errors exceeds 0.1 amperes), increase the differential coefficient to enhance the prediction and compensation capabilities; When the system enters a stable state and the error is below 0.1 amps for multiple cycles, reduce the integral coefficient to avoid overcompensation.

[0046] The correction signal output by the controller is mainly used in two aspects: Adjust the PWM duty cycle: For example, if the current setting is 80%, the controller outputs a correction value of 5% based on the error calculation, and the new duty cycle is set to 85%, thereby increasing the drive current; Adjusting the switching frequency: The controller fine-tunes the drive frequency within an allowable range (e.g., ±10%) based on the system load fluctuation. For example, a frequency of 500 kHz can be adjusted down to 480 kHz to optimize conversion efficiency and reduce system noise.

[0047] In order to further improve the control accuracy and stability, the system introduces two compensation mechanisms: Integral compensation mechanism: The error values ​​within multiple sampling periods are added one by one and incorporated into the control operation to eliminate small deviations that persist for a long time; Differential prediction mechanism: Calculate the difference between the current error value and the error value of the previous cycle. If the difference is large, it means that the system state has changed dramatically, and a quick response and increased correction intensity should be made.

[0048] The determination process of all threshold parameters is based on actual system testing and operational data statistics: For example, the absolute value of the current deviation is used as the fast adjustment threshold, which is obtained by counting the maximum tolerable offset of the LED chip under different environments; The bound on the rate of error change is determined by evaluating the energy response time in the drive loop; The duty cycle correction range is controlled within ±20%, which is a compromise between output power and electrical safety. The switching frequency adjustment range is set to ±10%, which is calculated based on EMI (electromagnetic interference) control requirements and efficiency optimization.

[0049] Ultimately, the system uses this control strategy to generate a comprehensive drive control command containing PWM signal parameters and frequency control instructions, which is sent by the microcontroller to the LED driver chip to achieve dynamic and real-time closed-loop control, effectively suppressing disturbances, reducing flicker and improving system reliability.

[0050] This implementation effectively addresses the issue of reduced control accuracy in LED driver systems due to power supply fluctuations, temperature variations, or load disturbances by introducing an adaptive PID control algorithm. This allows for rapid and stable optimization of driver output parameters, improving system response speed, stability, and robustness. In particular, under transient or boundary conditions, current control remains within a reasonable range, preventing flicker or electrical shock, and extending LED device life.

[0051] Based on the initialized data acquisition results, determining the fault type classification and severity assessment results also includes obtaining the current feedback signal after the adjusted drive control instruction is executed, performing real-time feedback response to the feedback signal, further correcting the control parameters through control gain adjustment, judging whether the current has reached the preset stable range, and obtaining optimized current state information.

[0052] In this embodiment, after executing a control command to the driver circuit, the system immediately collects the latest current feedback value as a basis for evaluating the control effect. This feedback value represents the actual operating current of the LED lamp after executing the control command. The system compares this feedback current with the preset target current value and calculates the current error, also known as the "current error value", which is equal to the target current minus the feedback current.

[0053] If the absolute value of the current error is large, for example, exceeding 0.3 amps, it indicates that the system still has significant control deviation, so further optimization of the controller's internal parameters is required. The system then enters a "feedback response" phase.

[0054] In the feedback response phase, the system will dynamically adjust the gain coefficient in the controller according to the current error size and change trend. The specific method is as follows: If the current error value is large, for example, the current feedback current is 0.5 amperes lower than the target value, the system automatically increases the proportional coefficient, that is, the controller's response intensity to the current error will be greater, thereby speeding up the adjustment speed; If the current error value is in a medium range, for example, between 0.1 amps and 0.3 amps, the system will increase the integral coefficient so that the previously accumulated small errors can also be corrected; If the error grows rapidly between two consecutive sampling cycles, for example, the error increases from 0.1 amps in the previous cycle to 0.25 amps in the current cycle, the system will increase the differential coefficient to predict future trends in advance, thereby making control compensation faster.

[0055] Adjustments to these three gain parameters are based on a proportional gain mechanism. For example, if the basic proportional coefficient is 1 and the system determines the current error is too large and sets the adjustment factor to 0.2, the new proportional coefficient will be amplified to 1 times 1 plus 0.2, or 1 times 1.2, resulting in a new proportional gain value of 1.2. The integral and differential parameters are adjusted in the same way.

[0056] Subsequently, the system recalculates the control instructions based on the optimized gain parameters, executes the new duty cycle and frequency settings, and monitors the changes in the feedback current again. To determine whether the system has entered a stable state, the system uses the following three criteria: The absolute value of the current error must be less than 0.1 ampere; The rate of change of the current error relative to the error of the previous sampling period must be less than 0.05 amperes per millisecond; The above two conditions need to be met within five consecutive sampling periods to determine that the system is in a stable state.

[0057] The sampling period is typically set to 1 millisecond, so the system observes current trends for at least five milliseconds. If the system determines that the current is stable, it records the current value, ambient temperature, current control parameters, and other information as a set of "optimized current state information" for subsequent control model reference or long-term trend optimization.

[0058] These threshold parameters are determined through actual system experiments. For example, the 0.1 ampere stability error threshold is based on the visually imperceptible standard of less than 5% change in LED light output. The error change rate is controlled at 0.05 amperes per millisecond to prevent rapid fluctuations in error within a very short period of time, which could lead to system misjudgment. The stability duration is set at five sampling periods, determined after fully considering the balance between noise immunity and response speed.

[0059] Through the above mechanism, the system can automatically identify the control effect after executing the control, dynamically optimize the controller parameters according to the feedback results, and form a high-precision and high-robustness adaptive closed-loop control process, thereby significantly improving the reliability and performance stability of the LED drive system in complex operating environments.

[0060] This implementation incorporates a closed-loop feedback control mechanism, enabling the controller to self-correct in real time based on performance, further improving control accuracy and robustness. By dynamically adjusting the control gain, the system demonstrates enhanced stability in complex operating conditions, such as environmental disturbances and load variations, ensuring the LED operating current remains precisely within the target setting, minimizing overshoot, undershoot, and oscillation.

[0061] Based on the initialized data acquisition results, determining the fault type classification and severity assessment results also includes constructing an abnormal fluctuation detection mechanism based on the optimized current state information. If a sudden change or irregular fluctuation is detected in the current signal, a rapid response mechanism is triggered. The fuzzy logic control algorithm is used to analyze the signal characteristics through the construction of a fuzzy rule base and the input membership function to determine the fault type classification and severity assessment results.

[0062] In this implementation, the system uses the previously acquired optimized current state information as a stable reference and continuously monitors subsequent real-time current signals to identify sudden changes or irregular fluctuations. If the system determines that the current is changing abnormally, it triggers a rapid response mechanism, entering fuzzy logic control mode to intelligently determine the fault type and severity.

[0063] Abnormal fluctuation detection logic, first, the system calculates the difference between the current value and the current value of the previous sampling cycle in each sampling cycle, and evaluates the speed of change of the current in combination with the sampling time interval. If the change rate exceeds 0.5 amperes per millisecond, the system determines it as a mutation behavior. In addition, the system uses every five to ten sampling points as a monitoring window, calculates the average value and standard deviation of the current value during this period, and divides the standard deviation by the average value to obtain a "coefficient of variation". If the coefficient of variation exceeds 15%, the current fluctuation is determined to be abnormal. If the system identifies a current mutation or fluctuation behavior simultaneously or individually, the fuzzy logic control module is immediately started and the conventional PID closed-loop path is temporarily suspended.

[0064] Fuzzy control input modeling: The fuzzy logic control module uses three core indicators as input variables: the mutation amplitude, which is the difference between the current at the current sampling point and the previous sampling point; the fluctuation frequency, which is the number of sampling points judged to be abnormal within a time window; and the fluctuation duration, which is the total duration of the continuous occurrence of abnormalities. These three indicators are mapped into fuzzy linguistic variables by defining membership functions. Taking the mutation amplitude as an example, its value range is divided into three levels: "small," "medium," and "large." For example, if the mutation amplitude is less than 0.2 amperes, it is considered "small"; between 0.2 and 0.6 amperes, the membership gradually increases to 1, which is "medium"; and above 0.6 amperes, it is classified as "large." The fuzzy levels of fluctuation frequency and duration are similarly divided.

[0065] The system pre-sets a set of fuzzy rule bases for inference logic. Each rule defines the output fault type and severity level corresponding to a specific input combination. For example, if the current mutation amplitude is "large", the fluctuation frequency is "high", and the duration is "long", it is judged as a "serious short circuit"; if the mutation amplitude is "medium", the frequency is "medium", and the duration is "medium", it may be "abnormal driver stability"; if the mutation amplitude is "small", but the fluctuation frequency is "high", and the duration is "short", it may be "poor contact".

[0066] The membership degree corresponding to each input variable is used to match rules. When multiple rules are activated simultaneously, the system assigns different weights based on the degree of activation. For example, if the membership values ​​of the three input variables of a rule are 0.8, 0.6, and 0.7, the rule's weight can be the minimum of the three values, 0.6, as the activation degree. The corresponding severity values ​​of multiple activated rules are multiplied by their weights and averaged to obtain a quantitative fault severity judgment result, such as moderate or severe fault.

[0067] The output fault results include the specific fault type (such as short circuit, poor contact, control loop instability, etc.) and its severity level, which are used to drive protection modules or trigger higher-level control strategies.

[0068] The judgment of a current mutation amplitude greater than 0.5 amperes is based on the critical data of normal startup and overcurrent protection triggering of LED lamp beads; the judgment of a coefficient of variation greater than 15% is based on the statistical maximum boundary of current fluctuations in normal steady state of different models of drivers; the membership function interval is optimized through regression analysis of hundreds of sets of fault data; the setting of fuzzy rules is formulated by experienced engineers and can be continuously optimized and upgraded through system self-learning in actual operation.

[0069] Through the above mechanism, this implementation can not only quickly identify and judge abnormal current changes within milliseconds, but also perform fault-tolerant processing of complex abnormal situations through intelligent fuzzy inference models, thereby improving the system's ability to respond to unforeseen problems and enhancing overall fault handling efficiency and accuracy.

[0070] Determining the fault type classification and severity assessment results includes scoring the comprehensive fault severity. The specific formula is: ; in, Indicates the fault severity rating value, Indicates the deviation between the measured current value and the target setting value. Indicates the deviation between the measured temperature and the reference temperature. represents the current deviation weight factor, represents the temperature deviation weight factor, Represents a positive constant, ∈[0.05, 0.8].

[0071] In this embodiment, in order to further enhance the ability to identify fault conditions, the system introduces a comprehensive fault severity scoring mechanism on the original control framework to assist in judging the urgency of abnormal events and the degree of impact on system stability.

[0072] This scoring mechanism uses two key deviations as input: current deviation, which refers to the difference between the actual current value collected and the target current value set by the system; and temperature deviation, which refers to the difference between the current temperature value and the standard reference temperature. These two deviations reflect whether the current circuit is experiencing abnormal operating trends in terms of power output and ambient heat.

[0073] The system calculates the normalized scoring results for these two deviations respectively, assigns corresponding weight factors, and then performs weighted combination to obtain the final fault severity score. The specific scoring method is as follows: First, the system calculates the current deviation score. This calculation uses the absolute value of the current deviation as the numerator and the denominator as the current deviation plus a positive constant to ensure the result does not approach infinity or become unstable. The current score is multiplied by the first weighting factor to control the influence of current in the overall score.

[0074] Next, the system performs the same treatment on the temperature deviation. The temperature deviation is used as the numerator, and the denominator is the temperature deviation plus a positive constant. The temperature score is then multiplied by a second weighting factor, which complements the current weighting to ensure that the total weight is 1. For example, if the current weight is 0.6, the temperature weight should be 0.4.

[0075] The weighted results are added together to form the final fault severity score. The higher the score, the more severe the deviation of the current system operating status from the normal range, and the more urgent the need for protective measures.

[0076] The positive constant k used here typically ranges from 0.05 to 0.8, with the specific value set based on the system's sensitivity to sudden changes. Smaller k values ​​make the system more sensitive to small deviations, and more likely to trigger high scores. Larger k values ​​smooth the scoring results, making it more suitable for tolerating some stable deviations.

[0077] Taking a practical application as an example: if the current deviation is 0.5 amperes and the temperature deviation is 10 degrees Celsius; the positive constant is 0.1; the current score is calculated as 0.5 divided by 0.5 plus 0.1, that is, divided by 0.6, and the result is about 0.83; the temperature score is calculated as 10 divided by 10.1, and the result is about 0.99; if the current deviation weight is 0.6 and the temperature deviation weight is 0.4, then the final score is the former multiplied by 0.6 plus the latter multiplied by 0.4, which is about 0.83 multiplied by 0.6 plus 0.99 multiplied by 0.4, that is, 0.499 plus 0.3996, for a total of about 0.899.

[0078] This score is compared to the severity thresholds set in the system. A score above 0.75 indicates a moderate anomaly, while a score above 0.9 indicates a severe anomaly. Based on the score, the system decides whether to enter a protection state or switch control strategies, thereby improving the overall control system's responsiveness to complex faults and enhancing decision-making accuracy.

[0079] Under a typical operating condition, the LED system's target current is set at 300mA, and the reference operating temperature is 25°C. When the ambient temperature rises, the current increases, reaching 360mA. When the temperature rises to 40°C, the corresponding current deviation is 60mA, and the temperature deviation is 15°C. Setting the current deviation weighting factor to 0.6, the temperature deviation weighting factor to 0.4, and the positive constant k to 0.1, the calculated fault score is approximately 0.996. This score is very close to 1, indicating that the system is facing a serious anomaly and should trigger a high-priority protection action, verifying the high sensitivity and accuracy of the scoring formula under extreme deviations.

[0080] Considering a medium-deviation scenario, for example, where the measured current is 320mA, the measured temperature is 32°C, the current deviation is 20mA, and the temperature deviation is 7°C, with the same weight and constant configuration, the score is 0.888, indicating a medium-to-high risk level and suitable for intermediate protection mechanisms such as current limiting and load reduction. In the case of minor fluctuations, such as a measured current of 305mA and a temperature of 26°C, with deviations of 5mA and 1°C, respectively, the score drops to 0.368, indicating a minor abnormality. Forced protection is not currently required, but the system can enter the early warning monitoring zone.

[0081] Furthermore, dynamically adjusting weight parameters can reflect differences in application sensitivity to temperature or current. For example, in high-temperature environments, increasing the temperature deviation weight to 0.7 and reducing the current deviation weight to 0.3 will rapidly increase the score to 1 under the same 15°C temperature rise, facilitating early intervention for thermal runaway risks. If the system is more sensitive to current overload, reversing the weighting can trigger a more precise response.

[0082] In summary, the scoring formula can not only make continuous and adjustable scoring responses to deviations of different intensities, but also quantitatively grade and adapt the response mechanism to specific application scenarios. It has a good engineering implementation foundation and practical application value.

[0083] This embodiment introduces a comprehensive scoring mechanism based on current deviation and temperature deviation to establish a quantifiable and adjustable fault severity assessment model, achieving refined judgment of fault states in LED drive control systems. It has the following significant technical advantages: the dual-parameter coupled analysis method of current and temperature can comprehensively reflect the actual operating state of LED lamp beads, avoid misjudgments or omissions caused by single-parameter judgment, and improve the accuracy of fault type identification and severity classification; by setting weight coefficients and positive adjustment factors, the scoring system has adaptive adjustment capabilities and can flexibly adjust sensitivity according to the application environment or system characteristics, thereby enhancing the universality and stability of the system under different load, electrical interference or thermal environment conditions; the fault severity is converted into a specific scoring value, which can be directly used as the trigger for executing protection strategies (such as current limiting, power outages or alarms), avoiding control failures caused by empirical or subjective judgments, and improving the scientific nature and precision of system responses.

[0084] Current deviation weight factor The specific calculation formula is: ; in, represents the current deviation weight factor, Indicates the resistance used when obtaining current, represents the standard deviation of current fluctuation, Indicates the deviation between the measured current value and the target setting value. Represents the heat capacity constant, that is, the amount of heat that can be absorbed or released per unit temperature change. Indicates the deviation between the measured temperature and the reference temperature; =1- ; in, Represents the temperature deviation weight factor.

[0085] This embodiment proposes a method for determining a current deviation weight factor based on physical property calculations, aiming to achieve a dynamic and accurate quantitative allocation between current weights and temperature weights in the fault scoring model, thereby improving the accuracy and adaptability of fault identification and response strategies.

[0086] In this method, the system first calculates a current deviation weighting factor based on the physical parameters of the current path and the current state data. This weighting factor is calculated based on two factors: the current current deviation and its fluctuation, and the current temperature deviation and its relationship with the system's thermal characteristics.

[0087] Specifically, the system first calculates a weighted expression of the current. This expression is composed of two factors: the current deviation value sampled by the system, which is the difference between the actual measured current and the target current; and the degree of dynamic current fluctuation, typically measured by the current standard deviation. These two quantities form a combined value, whose physical significance lies in reflecting the absolute deviation and stability of the current state.

[0088] This combined value is then multiplied by the resistance of the resistor in the current sampling path to provide a current deviation contribution.

[0089] The system also assesses the impact of temperature deviation on fault severity assessment. This is calculated by multiplying the deviation between the current sample temperature and the reference temperature by a heat capacity factor. The heat capacity factor represents the amount of heat absorbed or released by the LED device or system material per unit temperature change, representing the system's sensitivity to temperature changes. This factor is used to indicate the risk of thermal stress or heat dissipation anomalies caused by temperature fluctuations.

[0090] The system constructs each of these two terms into a ratio structure, with the numerator being the current deviation contribution term and the denominator being the sum of the current deviation term and the temperature deviation term. This final ratio is the current deviation weighting factor. This factor ranges from 0 to 1. When current changes dramatically but temperature changes less, the factor approaches 1, indicating that the system should prioritize electrical anomalies. Conversely, when temperature changes dominate the deviation source, the factor approaches 0, indicating that thermal failure risks should be prioritized.

[0091] After the calculation is complete, the system uses this current weighting factor as an amplification factor for the current deviation term in the scoring function. It also derives the weight of the temperature deviation term by subtracting this value from 1, thus completing the adaptive allocation of the two scoring factors. This structure ensures that the sum of the weights is 1, preventing control bias imbalance, and provides stable numerical properties and good physical interpretation.

[0092] During the implementation process, the specific meaning and acquisition method of each parameter are as follows: The resistance value is determined by the design of the current sampling path and is usually a known fixed value; The current deviation is obtained by directly subtracting the real-time sampling value from the target setting value; The standard deviation of current fluctuation is obtained by taking the square root of the variance of the current at several nearby sampling points; The temperature deviation is directly obtained from the difference between the actual temperature and the reference stable temperature; The heat capacity factor is determined based on the physical thermal properties of the LED device or driver module material through experiments or reference data sheets.

[0093] For example, when the LED system is working, the current detection resistor used is 0.1 ohms, the target value of the system operating current is 300mA, the actual monitored current is 340mA, the current fluctuation standard deviation is 10mA, and the current deviation is 40mA; at the same time, the system detects a temperature of 38°C, which is 13°C less than the set reference temperature of 25°C. On this basis, the thermal capacitance constant is selected as 1.5J / °C, which is the amount of heat absorbed or released by the device per unit temperature change. Substituting it into the formula, it can be calculated that the effective value of the current deviation part is about 41.2mA, which is 4.12mV after multiplying by the resistance; the thermal impact generated by the temperature part is 19.5 J. The final result is ≈0.0002, which corresponds to ≈0.9998, indicating that when high temperature anomalies dominate, the system will automatically reduce the proportion of current deviation in the total score, reflecting a good risk perception tendency.

[0094] Consider another operating state, in which the current deviation increases to 100mA and the temperature deviation decreases to 2°C. According to the formula, the current deviation component is about 100.5mA, which is multiplied by the resistance to get 10.05mV, while the temperature component is only 3.0J. Under this condition, the formula outputs The value rises significantly to approximately 0.0033. While still relatively small, it represents an order of magnitude improvement compared to the previous condition, indicating that the weight of current deviation in the scoring mechanism dynamically adjusts as its absolute value changes. If the thermal capacitance constant is further reduced (for example, by using a packaging material with lower thermal capacitance), the impact of current on the score will be even more significant under the same deviation conditions, helping to improve the system's overcurrent response capability in low thermal inertia environments.

[0095] Continuous analysis of the various operating conditions described above reveals that this formula structure exhibits excellent dimensional consistency and dynamic adaptability. It automatically adjusts weights when temperature or current changes dominate, avoiding the risk of misjudgment caused by manually setting fixed ratios. This calculation method is particularly effective when LED driver systems are exposed to high-frequency disturbances or thermal transients, accurately classifying the cause of the fault and facilitating the activation of appropriate protection mechanisms. Furthermore, the adjustable parameters in the formula (such as the thermal capacitance constant and the current sampling resistor) provide optimization opportunities for system design, allowing for flexible selection based on the application scenario, thereby improving the overall control system's response efficiency and judgment accuracy.

[0096] In summary, the current deviation weight factor calculation formula is not only continuous and differentiable in mathematics, but also reflects a clear implementation path and adjustment basis in actual engineering, and has strong practical value and promotion significance.

[0097] Through the above mechanism, the system can dynamically judge the relative impact of current deviation and temperature deviation on the overall scoring model according to the current working status, making the fault judgment mechanism both accurate and adaptive and physically reasonable.

[0098] This implementation introduces a current deviation weight calculation method based on the principle of thermal-electric coupling. This method dynamically adjusts the influence of current and temperature on fault scoring based on current fluctuations, temperature changes, and the system's thermal capacity. This allows for a more rational allocation of scoring factors, improving the accuracy and adaptability of fault diagnosis. Furthermore, this method boasts a simple computational structure, strong real-time performance, and ease of integration into embedded systems, ensuring excellent engineering feasibility.

[0099] The data acquisition and analysis module includes a sampling resistor, a temperature sensor and an analog-to-digital converter, and the resolution of the analog-to-digital converter is not less than 16 bits.

[0100] In this embodiment, the data acquisition and analysis module serves as the front-end sensing unit of the drive control system, and is mainly composed of three parts: a sampling resistor, a temperature sensor, and an analog-to-digital converter. It is used to obtain the analog signals of the LED drive current and the working environment temperature in real time, and convert them into digital signals for subsequent control logic analysis.

[0101] A sampling resistor is connected in series with the LED's current loop to sense current changes. When the LED is operating, current flows through the sampling resistor, generating a voltage proportional to the current across it. This voltage signal is highly instantaneous and has a low amplitude, requiring a high-precision, low-noise analog-to-digital converter circuit for signal capture and digitization.

[0102] Temperature sensors are installed in key locations near the LED chip or driver circuitry to measure the ambient or housing temperature during device operation. These sensors can be thermistors, digital temperature chips, or MEMS sensors. Their output signal is a continuous analog voltage or current, representing the current temperature.

[0103] The analog-to-digital converter (ADC), a core component for converting analog signals to digital, is responsible for converting the voltage signal from the sampling resistor and the output signal from the temperature sensor into high-precision digital data. To ensure accurate reproduction of minute voltage changes (e.g., millivolts) and temperature differences (e.g., 0.1°C), this implementation utilizes an ADC with a resolution of at least 16 bits. This 16-bit resolution allows the input analog signal to be divided into 65,536 discrete levels, enhancing the system's ability to detect subtle anomalies.

[0104] High-resolution ADCs also allow the system to operate with lower sampling errors and smaller signal variation ranges, avoiding quantization errors and system hysteresis associated with low resolution. This ensures the accuracy and real-time nature of subsequent fault identification, classification, severity scoring, and control strategy adjustments. Furthermore, the sampling frequency and sampling window can be flexibly configured to suit different application scenarios. For example, in high-speed drive applications, thousands of samples per second can be selected to ensure timely and complete system response.

[0105] This implementation significantly improves the sensitivity and accuracy of current and temperature sampling by integrating a sampling resistor, temperature sensor, and an analog-to-digital converter with a resolution of at least 16 bits into the data acquisition module. This enhances the system's ability to detect subtle fault signs, enabling more accurate condition monitoring and fault warning. Furthermore, the high-resolution analog-to-digital converter effectively reduces sampling error and quantization bias, improving the response quality and stability of subsequent control steps, ensuring greater system reliability and adaptability. This makes it particularly suitable for high-performance, low-tolerance LED driver control applications.

[0106] The original current and temperature data sequence includes current values ​​and temperature values ​​collected at least once every 1 millisecond, and is accompanied by corresponding timestamp information.

[0107] In this implementation, to ensure timely and precise monitoring of the LED driver system's operating status, the collection of raw current and temperature data sequences is rigorously designed to combine high frequency and time correlation. The system's sampling strategy requires current and temperature signals to be collected every 1 millisecond, with a timestamp appended to indicate the acquisition time for each set of data.

[0108] The current sampling component acquires the tiny voltage signal corresponding to the current through a sampling resistor and converts it into a digital signal using a high-precision analog-to-digital converter. The temperature sampling component uses a temperature sensor to collect the current ambient or device temperature signal and also digitizes it using an analog-to-digital converter. The system's clock module synchronously records the system time or relative time code at each sampling moment, forming a triplet structure with the current and temperature values ​​collected at that moment, namely the "current value-temperature value-timestamp" data point.

[0109] These data points, sampled in order, form a time series known as the raw current and temperature data sequence. This sequence is cached in high-speed storage during operation, allowing subsequent modules to perform anomaly trend detection, feature extraction, fault classification, and severity scoring. By maintaining a sampling period of 1 millisecond, up to 1,000 complete data sets can be recorded per second, effectively capturing transient anomalies such as current spikes, temperature jumps, and other highly dynamic fault characteristics.

[0110] To ensure timestamp accuracy, the system uses an on-chip, highly stable clock source or an external RTC module to generate the time signal, typically with microsecond accuracy. If the system requires synchronized sampling across modules, a unified clock strategy can be used across the bus to maintain data consistency.

[0111] This implementation achieves high-frequency, high-resolution raw data acquisition by synchronously sampling current and temperature signals at 1-millisecond intervals and attaching precise timestamp information. This significantly improves the system's responsiveness to rapidly changing phenomena, such as transient faults, electrical disturbances, and thermal shock. Furthermore, the timestamp mechanism ensures accurate data reconstruction and dynamic evolution tracking during subsequent analysis, facilitating time-series-based intelligent fault diagnosis, trend prediction, and adaptive control strategy generation, significantly enhancing the intelligence and stable operation of LED drive control systems.

[0112] The protection decision-making and execution module adopts a hierarchical response strategy, dividing the protection level into three levels: early warning state, current limiting protection state and circuit breaker protection state. The early warning state sends a risk warning signal through the communication interface, the current limiting protection state controls the output power by reducing the pulse width modulation duty cycle, and the circuit breaker protection state immediately disconnects the current path.

[0113] In this embodiment, the protection decision and execution module adopts a hierarchical response strategy to fine-tune the fault risk and divides the protection behavior into three levels: warning state, current limiting protection state and circuit breaker protection state, corresponding to mild, moderate and severe abnormal situations respectively.

[0114] When the system detects that operating parameters (such as current deviation, temperature deviation, and score) exceed preset safety thresholds but have not yet reached a critical level, the module enters a warning state. In this state, the system does not directly intervene in the current flow. Instead, it sends a risk warning signal to the main controller, display unit, or higher-level monitoring system via a communication interface (such as UART, I²C, or CAN bus), alerting external devices or users of potential system risks and recommending manual intervention or adjustment of the operating environment.

[0115] If the parameter deviation worsens and the score reaches a moderate risk level, the system enters current-limiting protection. In this state, the module proactively adjusts the driver circuit output, reducing the LED input power by lowering the PWM duty cycle. This reduces the operating current, suppresses heat generation, and prevents further abnormality. For example, the original duty cycle can be reduced from 85% to within 60% to achieve temperature and overcurrent control. This process provides partial protection without interrupting lighting output, ensuring system availability.

[0116] If the system determines the score reaches the critical abnormality level, indicating a significant safety risk such as a short circuit or thermal runaway, the system immediately enters a circuit breaker state. Using an MCU control signal or hardware interrupt logic, the system immediately shuts down the MOS transistor or relay output port, forcibly severing the current path and reducing the LED current to zero. This terminates the fault state and prevents the LED chip from burning out or damaging the main control circuit.

[0117] In each state, the protection decision is triggered by a pre-set scoring threshold. The system can dynamically switch between different protection levels according to the real-time operating status, realizing a multi-level protection logic with timely response, controllable risks and gradual intervention.

[0118] This implementation establishes a progressive, intelligent, and hierarchical protection mechanism by categorizing protection actions into three levels: early warning, current limiting, and circuit disconnection. This not only improves the system's ability to identify and respond to anomalies of varying severity, but also effectively balances system security with functional continuity. The early warning mechanism enables early risk notification and intervention, while current limiting reduces the risk of LED driver overload without interrupting operation. The circuit disconnection mechanism ensures absolute system safety under extreme conditions, significantly enhancing the control system's stability, reliability, and engineering flexibility.

[0119] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A control system for driving integrated SMD LED lamp beads, characterized in that: The system comprises: A data acquisition and analysis module is used to obtain real-time current signals and temperature sensor data from the LED drive circuit, digitally sample and process the current signals and temperature signals through a high-precision analog-to-digital converter, generate a raw current and temperature data sequence containing a timestamp, obtain initialized data acquisition results, and determine the fault type classification and severity assessment results based on the initialized data acquisition results; The protection decision-making and execution module is used to generate a protection level classification scheme based on the fault type classification and severity assessment results using output decision reasoning technology. If the protection level exceeds the preset threshold, a circuit breaker or current limiting protection operation is executed within milliseconds, cutting off the abnormal current path through the hardware interrupt mechanism, and obtaining a system status feedback signal after the protection action is executed; The control recovery and adaptive adjustment module is used to reinitialize the control parameters of the drive circuit based on the system status feedback signal and the specific requirements of the protection level classification. It determines whether the fault recovery conditions are met through real-time feedback response. If the recovery conditions are met, it reactivates the current monitoring and control loop to determine the stable operating state after recovery.

2. A control system for driving integrated SMD LED lamp beads according to claim 1, characterized in that: Determining the fault type classification and severity assessment results based on the initialized data collection results includes: Based on the initialized data acquisition results, feature extraction is performed on the correlation between the current signal and the temperature signal, and a mapping relationship between the influence of temperature on current is constructed. The current offset under the current temperature conditions is determined through deviation trend analysis to obtain a preliminary current deviation assessment value.

3. A control system for driving integrated SMD LED lamp beads according to claim 2, characterized in that: Determining the fault type classification and severity assessment results based on the initialized data collection results also includes: Through the preliminary current deviation evaluation value, combined with dynamic parameter adjustment technology, an adaptive proportional integral differential control algorithm is used to optimize the control parameters of the drive circuit in real time. Specifically, the output signal is adjusted by optimizing the duty cycle correction value and switching frequency. At the same time, the error integral accumulation and differential prediction compensation are used to reduce the control error and determine the adjusted drive control instructions.

4. A control system for driving integrated SMD LED lamp beads according to claim 3, characterized in that: Determining the fault type classification and severity assessment results based on the initialized data collection results also includes: The current feedback signal after the adjusted drive control instruction is executed is obtained, and a real-time feedback response is performed on the feedback signal. The control parameters are further corrected by adjusting the control gain to determine whether the current has reached the preset stable range, and the optimized current state information is obtained.

5. A control system for driving integrated SMD LED lamp beads according to claim 4, characterized in that: Determining the fault type classification and severity assessment results based on the initialized data collection results also includes: Based on the optimized current state information, an abnormal fluctuation detection mechanism is constructed. If a sudden change or irregular fluctuation is detected in the current signal, a rapid response mechanism is triggered. The fuzzy logic control algorithm is used to analyze the signal characteristics through the construction of a fuzzy rule base and the input membership function to determine the fault type classification and severity assessment results.

6. A control system for driving integrated SMD LED lamp beads according to claim 1, characterized in that: Determining the fault type classification and severity assessment results includes scoring the comprehensive fault severity. The specific formula is: ； in, Indicates the fault severity rating value, Indicates the deviation between the measured current value and the target setting value. Indicates the deviation between the measured temperature and the reference temperature. represents the current deviation weight factor, represents the temperature deviation weight factor, Represents a positive constant, ∈[0.05, 0.8].

7. The control system for driving integrated SMD LED lamp beads according to claim 1, characterized in that: The current deviation weight factor The specific calculation formula is: ； in, represents the current deviation weight factor, Indicates the resistance used when obtaining current, represents the standard deviation of current fluctuation, Indicates the deviation between the measured current value and the target setting value. Represents the heat capacity constant, that is, the amount of heat that can be absorbed or released per unit temperature change. Indicates the deviation between the measured temperature and the reference temperature; ＝1－ ； in, Represents the temperature deviation weight factor.

8. The control system for driving integrated SMD LED lamp beads according to claim 1, characterized in that: The data acquisition and analysis module includes a sampling resistor, a temperature sensor and an analog-to-digital converter, and the resolution of the analog-to-digital converter is not less than 16 bits.

9. The control system for driving integrated SMD LED lamp beads according to claim 1, characterized in that: The original current and temperature data sequence includes current values ​​and temperature values ​​collected at least once every 1 millisecond, and is accompanied by corresponding timestamp information.

10. The control system for driving integrated SMD LED lamp beads according to claim 1, characterized in that: The protection decision-making and execution module adopts a hierarchical response strategy, dividing the protection level into three levels, specifically including: early warning state, current limiting protection state and circuit breaker protection state; among them, the early warning state sends a risk warning signal through the communication interface, the current limiting protection state controls the output power by reducing the pulse width modulation duty cycle, and the circuit breaker protection state immediately disconnects the current path.

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