Semiconductor light source control system and method and computer equipment

Abstract

The invention is suitable for the technical field of light source temperature control, and provides a semiconductor light source control system and method and computer equipment, and the system comprises a semiconductor light source which comprises at least one light-emitting diode chip; the temperature sensor is used for monitoring the junction temperature of each light emitting diode chip in real time; and the control unit is used for predicting the junction temperature change of the light-emitting diode chip in a future period of time according to the signal of the temperature sensor, and adjusting the working current of the light-emitting diode chip according to the junction temperature change in the future period of time and the quantum efficiency of the light-emitting diode chip. And keeping the junction temperature of the light-emitting diode chip within a preset temperature range. According to the semiconductor light source, the working current of the semiconductor chip can be dynamically controlled, the light efficiency of the light emitting diode is improved to the greatest extent while overheating is prevented, and the overall performance of the semiconductor light source is improved.

Description

Technical Field

[0001] The present application belongs to the field of light source temperature control technology, and in particular, relates to a semiconductor light source control system, method and computer equipment. Background Art

[0002] At present, semiconductor light sources (especially LED light sources) have been widely used in various lamps. Semiconductor light source lamps usually generate a lot of heat during operation, which leads to an increase in junction temperature. Excessive junction temperature not only affects the light efficiency of the semiconductor light source, but also shortens its service life. In order to deal with this problem, semiconductor light sources are usually temperature controlled.

[0003] Existing temperature control solutions for semiconductor light sources mostly rely on simple heat dissipation design, such as reducing the temperature by adding heat sinks or using fans, but fail to effectively achieve dynamic temperature control. Summary of the invention

[0004] The embodiments of the present application provide a semiconductor light source control system, method and computer device, which can realize dynamic regulation of the temperature and current of the semiconductor light source.

[0005] In a first aspect, an embodiment of the present application provides a semiconductor light source control system, including:

[0006] A semiconductor light source comprising at least one light emitting diode chip;

[0007] A temperature sensor for real-time monitoring of the junction temperature of each LED chip;

[0008] The control unit is used to predict the junction temperature change of the above-mentioned light-emitting diode chip in a future period of time according to the signal of the above-mentioned temperature sensor, and adjust the working current of the above-mentioned light-emitting diode chip according to the junction temperature change in the future period of time and the quantum efficiency of the above-mentioned light-emitting diode chip, so that the junction temperature of the above-mentioned light-emitting diode chip is maintained within a preset temperature range.

[0009] In a possible implementation manner of the first aspect, the control unit is specifically configured to:

[0010] Based on the signal of the temperature sensor, a machine learning algorithm is used to predict the junction temperature change of the light emitting diode chip within a period of time in the future.

[0011] Exemplarily, the above-mentioned machine learning algorithm is a linear regression algorithm, a neural network algorithm or a time series analysis algorithm.

[0012] Exemplarily, the temperature sensor is a thermocouple, a resistance temperature sensor or a semiconductor temperature sensor.

[0013] In a possible implementation manner of the first aspect, the control unit is specifically configured to:

[0014] If the junction temperature in the future period exceeds the first temperature threshold, the operating current of the light emitting diode chip is adjusted to be less than 55% of the rated current.

[0015] In a possible implementation manner of the first aspect, the control unit is specifically configured to:

[0016] According to the signal of the temperature sensor and at least one of the power of the light emitting diode chip, the ambient temperature, the ambient humidity and the wind speed, the junction temperature change of the light emitting diode chip in the future is predicted.

[0017] In a second aspect, an embodiment of the present application provides a semiconductor light source control method, including:

[0018] Real-time monitoring of the junction temperature of each light-emitting diode chip in a semiconductor light source; the semiconductor light source comprises at least one light-emitting diode chip;

[0019] The junction temperature change of the above-mentioned LED chip in a future period is predicted based on the junction temperature of the LED chip. According to the junction temperature change in the future period and the quantum efficiency of the above-mentioned LED chip, the working current of the above-mentioned LED chip is adjusted to keep the junction temperature of the above-mentioned LED chip within a preset temperature range.

[0020] In a possible implementation of the second aspect, predicting a change in junction temperature of the light emitting diode chip within a future period of time according to the junction temperature of the light emitting diode chip includes:

[0021] According to the junction temperature of the LED chip and at least one of the power of the LED chip, the ambient temperature, the ambient humidity and the wind speed, the change of the junction temperature of the LED chip within a future period of time is predicted.

[0022] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the second aspects is implemented.

[0023] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement any method described in the second aspect above.

[0024] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a computer device, enables the computer device to execute any of the methods described in the second aspect.

[0025] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0026] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0027] The present application monitors the junction temperature of each LED chip in real time, and based on this, predicts the change of the junction temperature of the LED chip in the future, and then adjusts the working current of the LED chip according to the change of the junction temperature in the future and the quantum efficiency of the LED chip, so as to achieve dynamic regulation of the working current of the LED chip. Through this dynamic regulation, the junction temperature of the LED chip can be kept within a preset temperature range while maintaining the quantum efficiency as high as possible, thereby improving the overall performance of the semiconductor light source. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 It is a structural schematic diagram of a semiconductor light source control system provided by an embodiment of the present application;

[0030] Figure 2 This is a schematic diagram of an application scenario of a semiconductor light source control system provided by an embodiment of the present application;

[0031] Figure 3 is a schematic flow chart of a semiconductor light source control method provided in one embodiment of the present application;

[0032] Figure 4 It is a structural diagram of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0034] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0035] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0036] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0037] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0038] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0039] The technical solutions in the embodiments of the present application are described in detail below.

[0040] Figure 1 1 is a schematic diagram of the structure of a semiconductor light source control system provided by an embodiment of the present application. In some embodiments, the semiconductor light source control system can be applied to semiconductor lamps and other devices involving semiconductor light sources. Figure 1 As shown, the semiconductor light source control system includes: a semiconductor light source, a temperature sensor and a control unit. Among them:

[0041] A semiconductor light source comprises at least one semiconductor light source chip.

[0042] Exemplarily, the semiconductor light source may be an LED (light-emitting diode), an OLED (Organic Electroluminescence Display), etc. The semiconductor light source chip is a light-emitting unit made of semiconductor materials, which converts electrical energy into light energy through the principle of electroluminescence. Corresponding to the semiconductor light source, the semiconductor light source chip may be a light-emitting diode (LED) chip, an OLED chip (the organic light-emitting layer of the OLED may be regarded as an OLED chip), etc. The following scheme description mainly takes the LED chip as an example for explanation.

[0043] The temperature sensor is used to monitor the junction temperature of each semiconductor light source chip in real time.

[0044] For example, the temperature sensor may be a thermocouple, a resistance temperature detector (RTD) sensor, or a semiconductor temperature sensor. In a semiconductor light source, the junction temperature is the actual temperature of the PN junction inside the semiconductor light source, which is a key parameter for measuring the internal operating temperature of the device and directly affects the performance, efficiency, and life of the device.

[0045] The control unit is used to predict the junction temperature change of the semiconductor light source chip in the future period according to the signal of the temperature sensor, and adjust the working current of the semiconductor light source chip according to the junction temperature change in the future period and the quantum efficiency of the semiconductor light source chip, so that the junction temperature of the semiconductor light source chip is maintained within a preset temperature range.

[0046] In one embodiment, the control unit may use a machine learning algorithm to predict the junction temperature change of the semiconductor light source chip within a period of time in the future according to the signal of the temperature sensor. For example, the machine learning algorithm may be a machine learning algorithm capable of realizing temperature prediction, such as a linear regression algorithm, a neural network algorithm, or a time series analysis algorithm.

[0047] In one embodiment, when predicting the change in junction temperature of the semiconductor light source chip in the future, the control unit may predict the change in junction temperature of the semiconductor light source chip in the future based on the signal of the temperature sensor and at least one of the power, ambient temperature, ambient humidity, and wind speed of the semiconductor light source chip. The comprehensive analysis of these data enables the system to dynamically perceive changes in the external environment and achieve more accurate temperature prediction.

[0048] It should be noted that the above-mentioned "future period of time" and "preset temperature range" can be set in a variety of ways according to actual needs. In one embodiment, at least one of the two can be set manually based on experience or multiple experimental results. In another embodiment, at least one of the two can be generated by training a machine learning algorithm based on the historical operating data of the semiconductor light source chip. Exemplarily, the "future period of time" can be set to the next 5 minutes, 1 hour, etc., and the "preset temperature range" can be set to 40°C to 60°C.

[0049] In some embodiments, at least one temperature threshold may be further set, and when the junction temperature predicted to exceed the temperature threshold in the future, a specific control means may be used for temperature control. For example, if the junction temperature in the future exceeds the first temperature threshold (such as 85°C), the operating current of the semiconductor light source chip is adjusted to less than 55% of the rated current, so that in the case of large temperature changes, it can be quickly cooled to a safe operating temperature, which can effectively improve the overall performance of the light-emitting diode, including improving the luminous efficiency of the light-emitting diode. For another example, if the junction temperature in the future exceeds the second temperature threshold (such as 60°C) but does not exceed the first temperature threshold (such as 85°C), the operating current of the semiconductor light source chip is adjusted to 80% of the rated current, so as to reduce the negative impact of temperature fluctuations while keeping the semiconductor light source running smoothly as much as possible.

[0050] The quantum efficiency of a semiconductor light source, or luminous efficiency, is an important parameter that measures the efficiency of a light source in converting electrical energy into light energy, and usually decreases with increasing junction temperature. The present application monitors the junction temperature of each semiconductor light source chip in real time, and based on this, predicts the junction temperature change of the semiconductor light source chip in the future, and then adjusts the operating current of the semiconductor light source chip according to the junction temperature change in the future and the quantum efficiency of the semiconductor light source chip, thereby achieving dynamic regulation of the operating current of the semiconductor light source chip. Through this dynamic regulation, the junction temperature of the semiconductor light source chip can be kept within a preset temperature range while maintaining a quantum efficiency as high as possible, thereby improving the overall performance of the semiconductor light source.

[0051] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0052] To facilitate understanding of the semiconductor light source control system provided by the present application, a description is provided below in conjunction with specific embodiments.

[0053] Figure 2 Schematic diagram of an application scenario of a semiconductor light source control system provided by an embodiment of the present application. Figure 2 As shown, the semiconductor light source control system can be applied to an LED lamp, which includes a housing, a heat dissipation system, a driving power supply, an LED chip, a circuit board, a temperature sensor, and a control unit. Among them, the combination of the LED chip, the temperature sensor, and the control unit can be regarded as the semiconductor light source control system provided in the embodiment of the present application.

[0054] The algorithm involved in the present application is exemplarily described below, and LED chips are mainly used for exemplification in the description, but this example should not be regarded as limiting the scope of protection of the present application.

[0055] Example 1:

[0056] In this example, the time series analysis algorithm is used to predict the junction temperature change of the LED chip, and the working current of the LED chip is dynamically adjusted according to the junction temperature change. The time series analysis algorithm has a powerful ability to process time-sequential data, and is particularly suitable for systems such as LED lamps that have time-varying environments and load changes.

[0057] The following are the detailed steps and principles of the time series analysis algorithm - based on the seasonal autoregressive integrated moving average model (SARIMA). Compared with the traditional time series analysis model, the SARIMA model can effectively capture the seasonal fluctuations in the data and is suitable for environments with obvious periodic changes (such as temperature fluctuations when LED lamps are working). The following is a detailed introduction to the application of the SARIMA model in LED temperature prediction and the current control mechanism.

[0058] 1. Overview of SARIMA Model

[0059] The SARIMA model is an extension of the ARIMA model (Autoregressive Integrated Moving Average), which can handle time series data with seasonal patterns. Compared with the ARIMA model, the SARIMA model not only considers non-seasonal components (i.e., trend and white noise), but also adds seasonal autoregression (SAR), seasonal difference (SI) and seasonal moving average (SMA) components, and can handle data with obvious seasonal changes over time. The basic form of the SARIMA model is:

[0060] SARIMA(p,d,q)(P,D,Q)s

[0061] in:

[0062] p, d, q: represent the order of autoregressive (AR), differencing (I) and moving average (MA) parts respectively.

[0063] P, D, Q: represent the orders of seasonal autoregression (SAR), seasonal difference (SI) and seasonal moving average (SMA) parts respectively.

[0064] s: Indicates the length of the seasonal cycle (e.g., 24 hours, 7 days, or 1 month).

[0065] For the temperature data of LED lamps, seasonality may be manifested as the temperature difference between day and night, periodic fluctuations in ambient temperature, etc. Therefore, the SARIMA model can be used to capture these periodic changes.

[0066] 2. Application of SARIMA model in LED junction temperature prediction

[0067] In this example, the control unit uses the SARIMA model to predict the junction temperature variation of the LED chip. The SARIMA model can identify seasonal patterns from historical temperature data and predict future temperature fluctuations, thereby optimizing the operating current of the LED chip. The specific steps are as follows:

[0068] (1) Data collection and preprocessing

[0069] First, in the data acquisition stage, the temperature sensor monitors the junction temperature of the LED chip in real time and transmits the data to the control unit.

[0070] During the data preprocessing phase, the data goes through the following steps:

[0071] De-noising: Use methods such as Kalman filtering, sliding average or median filtering to remove measurement errors and noise.

[0072] Standardization: Temperature data may need to be standardized or normalized in order to fit the model.

[0073] Seasonal feature identification: Identify seasonal fluctuations in temperature data through graphical analysis or statistical methods, for example, identifying patterns in diurnal temperature differences or seasonal changes.

[0074] (2) Building a SARIMA model

[0075] Based on the preprocessed temperature data, the control unit uses the SARIMA model for model training. SARIMA model training includes the following steps:

[0076] Seasonal Difference (D): Seasonal difference is performed on the temperature data to make the temperature data more stable under the influence of seasonal fluctuations.

[0077] Model order selection: Through autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis, select the appropriate autoregressive order p, difference order d, moving average order q, as well as the orders P, D, Q of the seasonal components and the seasonal period s.

[0078] Model fitting and optimization: Use historical temperature data to fit the SARIMA model, solve the model parameters, and optimize the parameters to ensure the fitting accuracy and predictive ability of the model.

[0079] (3) Predicting future junction temperature changes

[0080] Using the trained SARIMA model, the control unit can predict the junction temperature changes of the LED chip in the future. For example, if the SARIMA model predicts that the future temperature will reach 75°C in 1 hour, the system will react in advance to prevent the junction temperature from exceeding the safe operating temperature range of the LED chip.

[0081] 3. Current regulation mechanism and quantum efficiency optimization

[0082] After obtaining the predicted value of the LED chip junction temperature change, the current regulation mechanism will dynamically adjust the working current of the LED chip according to the temperature change to ensure that the LED chip works within the optimal temperature range, thereby improving the light efficiency and extending its service life. The specific current regulation strategy is as follows:

[0083] (1) Current control strategy

[0084] Different current control measures can be adopted according to different temperature ranges corresponding to the predicted junction temperature changes in the future, for example:

[0085] Low temperature range (40℃–60℃): When the junction temperature of the LED chip is within the normal range (e.g. 40℃ to 60℃), the system allows the LED chip to operate at the rated current (e.g. 1000mA), that is, the operating current is not adjusted or controlled to remain at the rated current to ensure maximum light efficiency.

[0086] Medium temperature range (60℃–75℃): When the temperature rises to between 60℃ and 75℃, the control unit can appropriately reduce the current (for example, to 80% of the rated current) based on the temperature prediction results to reduce the negative impact of temperature fluctuations.

[0087] High temperature range (75℃–85℃): When the temperature approaches or exceeds the set safety threshold (such as 85℃), the control unit can reduce the current of the LED chip to less than 50% of the rated current. At this time, although the light efficiency may decrease, the temperature control can keep the LED chip stable and extend its service life.

[0088] (2) Current regulation based on quantum efficiency

[0089] The quantum efficiency (i.e., light efficiency) of LED chips usually decreases as the junction temperature increases. In order to optimize the light efficiency, this application adopts a relationship model between quantum efficiency and current, and dynamically adjusts the current according to the temperature change predicted by the SARIMA model, for example:

[0090] In the low and medium temperature range, the luminous efficiency of LED chips is higher and the current can be maintained at a high level to achieve the best luminous efficiency.

[0091] In the high temperature range, quantum efficiency decreases and the system reduces current to reduce the heat load, thereby reducing the impact of droop on performance.

[0092] This application automatically adjusts the current output not only based on the predicted temperature, but also based on the quantum efficiency at different temperatures to ensure that the light efficiency of the LED chip is in the optimal state.

[0093] (3) Real-time feedback and optimization

[0094] During system operation, the junction temperature and current of the LED chip will be affected by environmental changes, load fluctuations and other factors. The control unit will continuously update the temperature prediction model and adjust the current in real time based on the latest temperature data to keep the LED chip in the best working state. This process is highly adaptive and can be optimized according to environmental changes.

[0095] 4. Example of application of SARIMA model in current regulation

[0096] Assume that the temperature data of the LED lamp is as follows:

[0097] T1=58℃, T2=60℃, T3=62℃, T4=65℃, T5=67℃

[0098] The control unit is trained and predicted by the SARIMA model. Assume that the control unit predicts that the junction temperature of the LED chip will reach 75°C in the next hour. Based on this prediction, the control unit automatically adjusts the current of the LED chip from the rated current of 1000mA to 800mA (i.e. 80% of the rated current) to avoid further temperature increase and ensure the stability of the LED light effect.

[0099] 5. Technical advantages and effects of Example 1

[0100] (1) Improved light efficiency and energy efficiency: Intelligent current regulation avoids the decrease in light efficiency caused by overheating of the LED chip, thereby improving the overall energy efficiency.

[0101] (2) Extending LED life: By reducing the impact of high temperature on LED chips, the service life of LEDs is extended and thermal attenuation caused by excessive temperature is reduced.

[0102] (3) Precise control: The SARIMA model can capture seasonal fluctuations in the LED working environment, making temperature prediction more accurate and thus achieving more precise current regulation.

[0103] Example 2:

[0104] To further improve the accuracy and robustness of the system, the control unit of this application can introduce multi-dimensional sensor data, including key factors such as LED junction temperature, power, ambient temperature, humidity and wind speed. The comprehensive analysis of these data enables the semiconductor light source control system to dynamically perceive changes in the external environment, and achieve more accurate temperature prediction and current control through the seasonal autoregressive integrated moving average model (SARIMA) and multivariate time series prediction model.

[0105] Compared with the single temperature data input in Example 1, the multi-data input mechanism effectively enhances the system's ability to perceive the external environment and further optimizes the current control strategy, ensuring efficient energy saving and stable operation under various environmental conditions.

[0106] 1. Multi-dimensional input of collected data

[0107] The data collection process mainly obtains the following key data through sensors:

[0108] LED junction temperature (T LED ): Directly affects the luminous efficiency and life of the LED chip.

[0109] Working power (P LED ): Represents the input power of the LED lamp. Power fluctuations will affect the junction temperature.

[0110] Ambient temperature (T env ): Changes in external ambient temperature will directly affect the heat dissipation effect of the LED chip.

[0111] Humidity (H): Humidity affects the heat dissipation conditions and thus indirectly affects the junction temperature.

[0112] Wind speed (W): Changes in wind speed will accelerate or slow down the heat dissipation process and directly affect the junction temperature change of the LED.

[0113] The data collection frequency can be set to once per second or once per minute, depending on the complexity of the environment and the control requirements.

[0114] In a specific example, the collected multi-dimensional data can be seen in Table 1:

[0115] Table 1

[0116] time <![CDATA[Junction temperature (T LED )]]> <![CDATA[Power (P LED )]]> <![CDATA[Ambient temperature (T env )]]> Humidity(H) Wind speed(W) 10:00 60℃ 50W 28℃ 60％ 1.2m / s 10:01 61℃ 50W 28.5℃ 62％ 1.5m / s 10:02 63℃ 50W 29℃ 65％ 1.8m / s

[0117] 2. Modeling and optimization of control units

[0118] Based on the original SARIMA model, the system was upgraded to a multivariate SARIMA model (MultivariateSARIMA, MSARIMA), which enables it to simultaneously consider the combined impact of multiple input variables. This improved multivariate forecasting method not only considers the seasonal trend of the time series, but also incorporates the interaction between multiple sensor data, significantly improving the accuracy of temperature forecasting and its adaptability to the external environment.

[0119] (1) Multivariate SARIMA model structure

[0120] The basic form of the multivariate SARIMA model is:

[0121] MSARIMA(p,d,q)×(P,D,Q)s

[0122] Compared with the univariate SARIMA model, the autoregressive (AR), differencing (I), and moving average (MA) components of MSARIMA are extended to a multivariate form that can incorporate multiple input variables.

[0123] in:

[0124] X t =[T LED ,P LED ,T env ,H,W] represents the multivariate input data at time t.

[0125] p,d,q and P,D,Q represent the orders of the nonseasonal and seasonal components.

[0126] s represents the length of a season, for example, a day-night cycle is 24 hours, a week is 7 days, etc.

[0127] (2) Construction process of MSARIMA

[0128] 1) Data preprocessing

[0129] Data cleaning: Remove outliers (such as sudden power anomalies or data interruptions caused by sensor failures).

[0130] Missing value filling: Linear interpolation, KNN filling and other methods are used to fill in the missing sensor data.

[0131] Standardization and normalization: Standardize characteristic data such as temperature, humidity, and power to eliminate the impact of different units.

[0132] Seasonal differencing: Differentiate seasonal data (such as day-night temperature difference and daily ambient temperature fluctuations) to ensure data stability.

[0133] 2) Model training and parameter selection

[0134] The specific steps include:

[0135] Order selection: Use ACF (autocorrelation function) and PACF (partial autocorrelation function) to analyze the autocorrelation of multiple variables and determine the best combination of p, d, q and P, D, Q.

[0136] Seasonality determination: Based on the periodic changes in temperature and environmental data, the seasonal cycle is set to 24 hours (diurnal cycle) or 7 days (weekly fluctuation).

[0137] Model fitting: Use historical data to train the MSARIMA model and optimize the model parameters (such as AIC and BIC criteria).

[0138] (3) Model prediction

[0139] Combined with the past X t =[T LED ,P LED ,T env ,H,W] data to predict the future trend of LED chip junction temperature change T LED .

[0140] Rolling forecast: During the operation of the system, the model is updated regularly and the forecast results are adjusted.

[0141] 3. Optimization of current regulation mechanism

[0142] Based on the prediction results of the MSARIMA model, the current regulation mechanism will adopt a more sophisticated control strategy to ensure that the LED chip always operates within the optimal light efficiency range and the optimal junction temperature range.

[0143] (1) Dynamic current regulation strategy

[0144] Low temperature range (40℃–60℃): The LED chip is in the best condition and the operating current remains at the rated current (100%).

[0145] Medium temperature range (60℃–75℃): A piecewise linear current control strategy is adopted. As the temperature increases, the current decreases from 100% to 70% in a certain linear attenuation manner.

[0146] High temperature range (75℃–85℃): Based on the prediction results of MSARIMA, the system adjusts the LED current to less than 50% of the rated current in advance to prevent the temperature from continuing to rise.

[0147] (2) Multivariable Feedback Control System

[0148] Through real-time temperature, ambient temperature, humidity, power and wind speed data, a feedback control mechanism is adopted to continuously optimize the current regulation strategy.

[0149] Feedforward control: When MSARIMA predicts that the temperature may rise to a dangerous range (such as exceeding 80°C) in the future, the current is reduced in advance.

[0150] Feedback control: If the actual junction temperature fed back by the sensor exceeds expectations, the current is further reduced to ensure that the junction temperature does not exceed a safe range.

[0151] 4. Example 2 Advantages and Technical Effects

[0152] High-efficiency energy efficiency optimization: Through real-time monitoring and prediction, the operating current of the LED chip is optimized in different environments, and the light efficiency is improved by more than 10%.

[0153] Temperature control accuracy: Combining multiple environmental parameters (humidity, wind speed, etc.) improves the accuracy of temperature prediction and avoids large temperature fluctuations.

[0154] High robustness: The multivariable model combines feedback and feedforward control, and the system has adaptive capabilities to adapt to a variety of environmental conditions.

[0155] Extended life: Through dynamic current regulation, the junction temperature is always within the optimal operating range, reducing the possibility of thermal failure and extending the service life of the LED chip.

[0156] The description of the semiconductor light source control system provided by the present application is now completed. Correspondingly, the embodiment of the present application also provides a semiconductor light source control method.

[0157] Figure 3 FIG. 1 is a schematic flow chart of a semiconductor light source control method provided by the present application. Figure 3 As shown, the process includes the following steps:

[0158] S301, real-time monitoring of the junction temperature of each light-emitting diode chip in the semiconductor light source;

[0159] The semiconductor light source comprises at least one light emitting diode chip.

[0160] S302, predicting the junction temperature change of the LED chip in the future based on the junction temperature of the LED chip, and adjusting the operating current of the LED chip based on the junction temperature change in the future and the quantum efficiency of the LED chip to keep the junction temperature of the LED chip within a preset temperature range.

[0161] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0162] It should be noted that since the above method is based on the same concept as the system embodiment of the present application, its specific functions and technical effects can be found in the system embodiment section and will not be repeated here.

[0163] An embodiment of the present application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0164] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0165] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0166] Figure 4 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present application. Figure 4 As shown, the computer device of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, wherein the processor 40 implements the steps of any of the above-mentioned visual programming method embodiments when executing the computer program 42.

[0167] The computer device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0168] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0169] In some embodiments, the memory 41 may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 41 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory 41 may also include both an internal storage unit of the computer device and an external storage device. The memory 41 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0170] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the device / computer device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0171] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0172] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0173] In the embodiments provided in the present application, it should be understood that the disclosed apparatus / computer equipment and methods can be implemented in other ways. For example, the apparatus / computer equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of the apparatus or unit, which can be electrical, mechanical or other forms.

[0174] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A semiconductor light source control system, characterized in that: include: A semiconductor light source comprising at least one light emitting diode chip; A temperature sensor for real-time monitoring of the junction temperature of each LED chip; A control unit is used to predict the junction temperature change of the light-emitting diode chip in a future period of time according to the signal of the temperature sensor, and adjust the operating current of the light-emitting diode chip according to the junction temperature change in the future period of time and the quantum efficiency of the light-emitting diode chip, so that the junction temperature of the light-emitting diode chip is maintained within a preset temperature range.

2. The system according to claim 1, characterized in that The control unit is specifically used for: Based on the signal of the temperature sensor, a machine learning algorithm is used to predict the junction temperature change of the light emitting diode chip within a future period of time.

3. The system according to claim 2, characterized in that The machine learning algorithm is a linear regression algorithm, a neural network algorithm or a time series analysis algorithm.

4. The system according to claim 1, characterized in that The control unit is specifically used for: The junction temperature change of the light emitting diode chip within a future period of time is predicted according to the signal of the temperature sensor and at least one of the power of the light emitting diode chip, the ambient temperature, the ambient humidity and the wind speed.

5. The system according to claim 1, wherein: The temperature sensor is a thermocouple, a resistance temperature sensor or a semiconductor temperature sensor.

6. The system according to claim 1, wherein: The control unit is specifically used for: If the junction temperature in the future period exceeds a first temperature threshold, the operating current of the light emitting diode chip is adjusted to be less than 55% of its rated current.

7. A semiconductor light source control method, characterized in that: include: Real-time monitoring of the junction temperature of each light-emitting diode in a semiconductor light source; the semiconductor light source comprises at least one light-emitting diode chip; The junction temperature change of the light-emitting diode chip in a future period of time is predicted based on the junction temperature of the light-emitting diode chip, and the operating current of the light-emitting diode chip is adjusted based on the junction temperature change in the future period of time and the quantum efficiency of the light-emitting diode chip to keep the junction temperature of the light-emitting diode chip within a preset temperature range.

8. The method according to claim 7, characterized in that The step of predicting the change of the junction temperature of the light emitting diode chip within a period of time in the future according to the junction temperature of the light emitting diode chip comprises: The junction temperature change of the LED chip within a future period of time is predicted according to the junction temperature of the LED chip and at least one of the power of the LED chip, the ambient temperature, the ambient humidity and the wind speed.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 7 to 8 is implemented.

10. A computer program product, characterized in that When the computer program product is executed on a computer device, the computer device is caused to execute the method according to any one of claims 7 to 8.

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