LED solar simulator control system and control method

Abstract

The invention relates to the technical field of LED driving, and particularly discloses an LED solar simulator control system and control method.A detection unit detects the external environment of a machine body in real time, meanwhile, a communication unit and a GPRS module are combined, the current weather climate of a solar simulator can be read in real time, a lamp bead unit is adjusted and controlled in combination with a deep learning module, and the LED solar simulator control system and control method are provided. According to the solar simulator control system, difference compensation is carried out on required illumination intensity in a network reading and deep learning mode, real-time automatic adjustment is realized, the intelligence of the control system is improved, the use flexibility of the solar simulator is effectively improved, and an energy-saving effect is realized.

Description

Technical Field

[0001] This invention relates to the field of LED driving technology, and in particular to an LED solar simulator control system and control method. Background Technology

[0002] Currently, constant current drive circuits generally use switching mode PWM controllers. The adjustment of the drive current is mainly divided into analog adjustment method and PWM adjustment method. To adjust the color temperature of the white LED module light source, it is necessary to control the drive current of the two types of LED devices in the white LED module separately, which requires two independent constant current drive circuits.

[0003] The prior art CN204231708U provides an energy-saving LED system that simulates sunlight, including a constant voltage switching power supply. The constant voltage switching power supply is connected to a first dimmable DC / DC constant current drive power supply and a second dimmable DC / DC constant current drive power supply. The first dimmable DC / DC constant current drive power supply is connected to a first white LED light source module and a microcontroller. The second dimmable DC / DC constant current drive power supply is connected to a second white LED light source module and the microcontroller. The microcontroller is connected to a temperature sensor and a color sensor.

[0004] However, in existing technologies, solar simulators cannot automatically adjust according to real-time environment and weather, resulting in waste of excess power and low flexibility of use. Summary of the Invention

[0005] The purpose of this invention is to provide an LED solar simulator control system and control method, which aims to solve the technical problem that existing solar simulators cannot automatically adjust according to real-time environment and weather, resulting in waste of excess power and low flexibility of use.

[0006] To achieve the above objectives, the present invention employs an LED solar simulator control system, comprising an LED bead unit, a detection unit, a heat dissipation module, a GPRS module, a communication unit, a deep learning module, and a circuit control mechanism; The LED unit is used to provide light of different wavelengths to simulate various colors and effects in sunlight; The GPRS module is used to provide the geographic location and time of the LED solar simulator; The communication unit is used to connect to external devices or networks and read the real-time environmental status of the current coordinates through the devices or networks; The detection unit is used to detect the temperature and light intensity of the LED unit, the external temperature, light intensity and humidity of the simulator, and to collect data and transmit the data to the deep learning module. The heat dissipation module is used to provide air cooling for the LED unit, ensuring the internal temperature of the simulator and preventing the LED unit from being damaged due to excessive temperature. The deep learning module reads the data from the detection unit and the communication unit, and calculates the current optimal solution based on the data. The circuit control mechanism is used to disconnect the circuit of the simulator when an abnormality occurs, thereby preventing damage to the entire simulator.

[0007] The deep learning module uses a convolutional neural network to extract and analyze the data features of the LED unit, establishes a mapping relationship between spectral distribution and application scenario requirements, and continuously trains and optimizes the model so that the system can automatically output spectral combinations that meet the requirements. This can be used for spectral optimization, light intensity control, illumination uniformity improvement, and system fault diagnosis to improve the accuracy and stability of the simulation.

[0008] The lamp unit includes an RGB lamp module, an amber LED, a full-spectrum LED, and a solar-inspired LED. The RGB light module is used to provide multiple sets of colors, and adjusts the sunlight according to the brightness of different colors; The amber LED can provide light with a wavelength of 590nm to supplement the yellow light in the sunlight, making the simulated sunlight spectrum more complete and closer to the real solar spectrum. The full-spectrum LED is used to provide visible light with wavelengths between 380nm and 780nm, and can also simultaneously emit infrared light with wavelengths greater than 760nm and ultraviolet light with wavelengths between 290nm and 400nm. The simulated solar light source LED provides light wavelengths covering 360nm-1300nm, which can better simulate the real solar spectrum.

[0009] The RGB light module includes red LEDs, green LEDs, and blue LEDs. The red LED is used to provide light in the red light band, with a wavelength between 610nm and 720nm, which can simulate the red light in sunlight; The green LED is used to provide light in the green light band, with a wavelength between 500nm and 525nm, which can simulate the green light in sunlight; The blue LED is used to provide light in the blue light band, with a wavelength between 450nm and 475nm, which can simulate the blue light in sunlight.

[0010] The communication unit includes a cellular network module, a Wi-Fi module, a Bluetooth module, and an RJ45 connector; The cellular network module is used to enable 2G, 3G, 4G and 5G connectivity and to transmit data through the mobile communication network; The Wi-Fi module is used to enable wireless local area network communication and to transmit data through the wireless local area network. The Bluetooth module is used to enable short-range wireless communication; The RJ45 connector is used to connect to the solar simulator via a wired connection, enabling wired data transmission.

[0011] The detection module includes an LED temperature sensor, an LED light intensity sensor, an external temperature sensor, an external light intensity sensor, a current sensor, and a voltage sensor. The LED temperature sensor is used to detect the temperature of the LED bead unit; The LED light intensity sensor is used to detect the light intensity emitted by the LED bead unit; The external temperature sensor is used to detect the real-time ambient temperature outside the machine body; The external light intensity sensor is used to detect the real-time ambient brightness outside the machine body; The current sensor is used to detect the current value of the solar simulator; The voltage sensor is used to detect the voltage value of the solar simulator.

[0012] The present invention also provides an LED solar simulator control method, applied to an LED solar simulator control system as described above, comprising the following steps: The GPRS module provides real-time positioning for the solar simulator; The communication unit connects to an external network and reads the current weather conditions of the area through the network. The detection unit detects the real-time temperature and light intensity of the lamp bead unit, and the detection unit also detects the real-time temperature and light intensity outside the machine body in real time. The deep learning module performs joint calculations on the collected data and the current weather conditions of the area, and analyzes the optimal lighting conditions based on usage requirements, and then automatically adjusts them.

[0013] This invention discloses an LED solar simulator control system and method. The detection unit monitors the external environment in real time, while the communication unit and GPRS module combine to read the current weather and climate conditions of the solar simulator. A deep learning module is used to adjust and control the LED units. During adjustment, when the light intensity is insufficient, the difference between the required light intensity and the ambient light intensity is calculated, and the LED units are controlled to compensate for the difference. When the ambient light intensity meets the required light intensity, the LED units are turned off. This structure, combined with network reading and deep learning, compensates for the difference in light intensity and achieves real-time automated adjustment, improving the intelligence of the control system, effectively increasing the flexibility of the solar simulator, and achieving energy savings. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a system diagram of an LED solar simulator control system according to the present invention.

[0016] Figure 2 This is a system diagram of the LED unit of the present invention.

[0017] Figure 3 This is a system diagram of the communication unit of the present invention.

[0018] Figure 4 This is a system diagram of the detection unit of the present invention.

[0019] Figure 5 This is a system diagram of the circuit control mechanism of the present invention.

[0020] Figure 6 This is a flowchart of the steps of an LED solar simulator control method according to the present invention.

[0021] 101-LED unit, 102-Detection unit, 103-Heat dissipation module, 104-GPRS module, 105-Communication unit, 106-Deep learning module, 107-Circuit control mechanism, 108-RGB lamp group module, 109-Amber LED, 110-Full spectrum LED, 111-Imitation sunlight LED, 112-Red LED, 113-Green LED, 114-Blue LED, 115-Cellular network module, 116-Wi-Fi module, 117-Bluetooth module, 118-RJ45 connector, 119-LED temperature sensor, 120-LED light intensity sensor, 121-External temperature sensor, 122-External light intensity sensor, 123-Current sensor, 124-Voltage sensor, 125-Rectifier, 126-Circuit protection unit, 127-Overcurrent protection module, 128-Overvoltage protection module, 129-Undervoltage protection module. Detailed Implementation

[0022] Please see Figures 1 to 5 The present invention provides an LED solar simulator control system, including an LED bead unit 101, a detection unit 102, a heat dissipation module 103, a GPRS module 104, a communication unit 105, a deep learning module 106, and a circuit control mechanism 107. The lamp bead unit 101 is used to provide light of different wavelengths to simulate various colors and effects in sunlight; The GPRS module 104 is used to provide the geographic location and time of the LED solar simulator; The communication unit 105 is used to connect to external devices or networks and read the real-time environmental status of the current coordinates through the devices or networks; The detection unit 102 is used to detect the temperature and light intensity of the lamp bead unit 101, the external temperature, light intensity and humidity of the simulator, and to collect data and transmit the data to the deep learning module 106. The heat dissipation module 103 is used to perform air cooling on the LED unit 101 to ensure the internal temperature of the simulator and prevent the LED unit 101 from being damaged due to excessive temperature. The deep learning module 106 reads the data from the detection unit 102 and the communication unit 105, and calculates the current best solution based on the data. The circuit control mechanism 107 is used to disconnect the circuit of the simulator when an abnormality occurs, thereby avoiding damage to the entire simulator.

[0023] In this embodiment, the detection unit 102 performs real-time detection of the external environment of the machine. At the same time, the combination of the communication unit 105 and the GPRS module 104 can read the current weather and climate of the solar simulator in real time. Combined with the deep learning module 106, the LED unit 101 is adjusted and controlled. During the adjustment and control, when the light intensity is insufficient, the difference between the required light intensity and the ambient light intensity is calculated, and the LED unit 101 is controlled to supplement the difference. When the ambient light intensity meets the required light intensity, the illumination of the LED unit 101 is turned off.

[0024] Furthermore, the deep learning module 106 uses a convolutional neural network to extract and analyze the data features of the LED unit 101, establishes a mapping relationship between spectral distribution and application scenario requirements, and through continuous training and optimization of the model, enables the system to automatically output spectral combinations that meet the requirements. This can be used for spectral optimization, light intensity control, illumination uniformity improvement, and system fault diagnosis to improve the accuracy and stability of the simulation.

[0025] In this implementation, deep learning, with its powerful computing capabilities and parallel processing advantages, can quickly analyze and mine these massive amounts of data, extract valuable information, and improve real-time performance and flexibility.

[0026] Furthermore, the LED unit 101 includes an RGB LED module 108, an amber LED 109, a full-spectrum LED 110, and a simulated sunlight LED 111; The RGB light module 108 is used to provide multiple sets of colors and adjust the sunlight according to the brightness of different colors; The amber LED109 can provide light with a wavelength of 590nm to supplement the yellow light in the sunlight, making the simulated sunlight spectrum more complete and closer to the real solar spectrum. The full-spectrum LED110 is used to provide visible light with a wavelength between 380nm and 780nm, and can also simultaneously emit infrared light with a wavelength greater than 760nm and ultraviolet light with a wavelength between 290nm and 400nm. The simulated solar light source LED111 provides light wavelengths covering 360nm-1300nm, which can better simulate the real solar spectrum.

[0027] In this embodiment, a combination of red, green, and blue LED beads can provide light of various colors. The brightness of different colors can be adjusted to simulate and regulate sunlight, meeting the spectral requirements of different scenarios. The color and intensity of the light can be flexibly adjusted according to the needs of different application scenarios, making it suitable for various needs such as plant growth lighting, indoor lighting, and stage lighting.

[0028] Furthermore, the RGB light group module 108 includes a red LED 112, a green LED 113, and a blue LED 114; The red LED 112 is used to provide light in the red light band, with a wavelength between 610nm and 720nm, which can simulate the red light in sunlight; The green LED 113 is used to provide light in the green light band, with a wavelength between 500nm and 525nm, which can simulate the green light in sunlight; The blue LED114 is used to provide light in the blue light band, with a wavelength between 450nm and 475nm, which can simulate the blue light in sunlight.

[0029] In this embodiment, the RGB light module 108 has a relatively simple structure and is easy to integrate with other electronic components, sensors, controllers, etc. It is programmable and automated, enhancing the adaptability to application scenarios. The RGB light module 108 can provide multi-band light similar to sunlight, so it can provide a more natural lighting environment in applications that require near-natural lighting conditions.

[0030] Furthermore, the communication unit 105 includes a cellular network module 115, a Wi-Fi module 116, a Bluetooth module 117, and an RJ45 connector 118; The cellular network module 115 is used to enable 2G, 3G, 4G and 5G connections and to transmit data through the mobile communication network; The Wi-Fi module 116 is used to realize wireless local area network communication and to realize data transmission through wireless local area network; The Bluetooth module 117 is used to achieve short-range wireless communication; The RJ45 connector 118 is connected to the solar simulator via a wired connection to enable wired data transmission.

[0031] In this embodiment, multiple network connection methods and multiple communication methods are integrated. Users can choose the appropriate communication method according to their actual needs, which improves the flexibility and adaptability of the system. It can connect and communicate with various devices and systems. Users can access and control the solar simulator remotely via the Internet, monitor the operating status of the device in real time, adjust parameters, and acquire data, thereby improving the efficiency of device use and management convenience. The communication unit 105 can be integrated with various smart devices and systems to improve efficiency and accuracy.

[0032] Furthermore, the detection module includes an LED temperature sensor 119, an LED light intensity sensor 120, an external temperature sensor 121, an external light intensity sensor 122, a current sensor 123, and a voltage sensor 124. The LED temperature sensor 119 is used to detect the temperature of the LED bead unit 101; The LED light intensity sensor 120 is used to detect the light intensity emitted by the lamp bead unit 101; The external temperature sensor 121 is used to detect the real-time ambient temperature outside the machine body; The external light intensity sensor 122 is used to detect the real-time ambient brightness outside the machine body; The current sensor 123 is used to detect the current value of the solar simulator; The voltage sensor 124 is used to detect the voltage value of the solar simulator.

[0033] In this embodiment, different data are collected and transmitted to the deep learning module 106. The deep learning module 106 performs calculations and analyses to determine the next step of operation, thereby realizing real-time control of the solar simulator. Furthermore, real-time data collection and analysis can improve the flexibility and accuracy of system operation and reduce analysis errors.

[0034] Furthermore, the circuit control mechanism 107 includes a rectifier 125 and a circuit protection unit 126; The rectifier 125 is used to convert the input AC power into DC power to provide a stable power supply for the solar simulator. The circuit protection unit 126 is used to protect the circuit and prevent fires caused by abnormal charging.

[0035] In this embodiment, the rectifier 125 provides a stable current to the solar simulator and enhances the protection of the solar simulator through the circuit protection unit 126 to prevent equipment damage caused by overvoltage, overcurrent and undervoltage problems.

[0036] Furthermore, the circuit protection unit 126 includes an overcurrent protection module 127, an overvoltage protection module 128, and an undervoltage protection module 129; When the current exceeds a preset threshold, the overcurrent protection module 127 automatically cuts off the power supply circuit. When the voltage exceeds a preset threshold, the overvoltage protection module 128 automatically reduces the voltage or cuts off the power supply circuit. The undervoltage protection circuit will issue a warning or cut off the power supply when the input voltage is below the minimum threshold and cannot provide effective current to the solar simulator.

[0037] In this embodiment, the overcurrent protection module 127 provides overcurrent protection for the circuit, the overvoltage protection module 128 provides overvoltage protection for the circuit, and the undervoltage protection module 129 provides undervoltage protection for the circuit. This improves the safety of the solar simulator's operation, enabling it to operate stably and safely. Even in abnormal situations, the connection can be promptly disconnected to protect other components.

[0038] Please see Figure 6 The present invention also provides an LED solar simulator control method, applied to an LED solar simulator control system as described above, comprising the following steps: S1: The GPRS module 104 performs real-time positioning of the solar simulator; S2: The communication unit 105 connects to an external network and reads the current weather conditions of the area through the network; S3: The detection unit 102 detects the real-time temperature and light intensity of the lamp bead unit 101, and the detection unit 102 also detects the real-time temperature and real-time light intensity outside the machine body in real time; S4: The deep learning module 106 performs joint calculations on the collected data and the current weather conditions of the area, and calculates and analyzes the optimal lighting conditions based on usage requirements, and then automatically adjusts them.

[0039] In this embodiment, the network reads the regional climate and the deep learning module 106 combines the light intensity required for calculation and analysis to improve prediction accuracy. It automatically learns and identifies complex nonlinear relationships and hidden patterns, enhancing data processing capabilities. With its powerful computing capabilities and parallel processing advantages, deep learning can quickly analyze and mine these massive amounts of data, extracting valuable information and improving real-time performance and flexibility.

[0040] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. An LED solar simulator control system, characterized in that, It includes an LED chip unit, a detection unit, a heat dissipation module, a GPRS module, a communication unit, a deep learning module, and a circuit control mechanism; The LED unit is used to provide light of different wavelengths to simulate various colors and effects in sunlight; The GPRS module is used to provide the geographic location and time of the LED solar simulator; The communication unit is used to connect to external devices or networks and read the real-time environmental status of the current coordinates through the devices or networks; The detection unit is used to detect the temperature and light intensity of the LED unit, the external temperature, light intensity and humidity of the simulator, and to collect data and transmit the data to the deep learning module. The heat dissipation module is used to provide air cooling for the LED unit, ensuring the internal temperature of the simulator and preventing the LED unit from being damaged due to excessive temperature. The deep learning module reads the data from the detection unit and the communication unit, and calculates the current optimal solution based on the data. The circuit control mechanism is used to disconnect the circuit of the simulator when an abnormality occurs, thereby preventing damage to the entire simulator.

2. The LED solar simulator control system as described in claim 1, characterized in that, The deep learning module uses a convolutional neural network to extract and analyze the data features of the LED unit, establishes a mapping relationship between spectral distribution and application scenario requirements, and through continuous training and optimization of the model, enables the system to automatically output spectral combinations that meet the requirements. This can be used for spectral optimization, light intensity control, illumination uniformity improvement, and system fault diagnosis to improve the accuracy and stability of the simulation.

3. The LED solar simulator control system as described in claim 1, characterized in that, The LED unit includes an RGB LED module, an amber LED, a full-spectrum LED, and a simulated sunlight LED. The RGB light module is used to provide multiple sets of colors, and adjusts the sunlight according to the brightness of different colors; The amber LED can provide light with a wavelength of 590nm to supplement the yellow light in the sunlight, making the simulated sunlight spectrum more complete and closer to the real solar spectrum. The full-spectrum LED is used to provide visible light with wavelengths between 380nm and 780nm, and can also simultaneously emit infrared light with wavelengths greater than 760nm and ultraviolet light with wavelengths between 290nm and 400nm. The simulated solar light source LED provides light wavelengths covering 360nm-1300nm, which can better simulate the real solar spectrum.

4. The LED solar simulator control system as described in claim 3, characterized in that, The RGB light module includes red LEDs, green LEDs, and blue LEDs; The red LED is used to provide light in the red light band, with a wavelength between 610nm and 720nm, which can simulate the red light in sunlight; The green LED is used to provide light in the green light band, with a wavelength between 500nm and 525nm, which can simulate the green light in sunlight; The blue LED is used to provide light in the blue light band, with a wavelength between 450nm and 475nm, which can simulate the blue light in sunlight.

5. The LED solar simulator control system as described in claim 1, characterized in that, The communication unit includes a cellular network module, a Wi-Fi module, a Bluetooth module, and an RJ45 connector; The cellular network module is used to enable 2G, 3G, 4G and 5G connectivity and to transmit data through the mobile communication network; The Wi-Fi module is used to enable wireless local area network communication and to transmit data through the wireless local area network. The Bluetooth module is used to enable short-range wireless communication; The RJ45 connector is used to connect to the solar simulator via a wired connection, enabling wired data transmission.

6. The LED solar simulator control system as described in claim 1, characterized in that, The detection module includes an LED temperature sensor, an LED light intensity sensor, an external temperature sensor, an external light intensity sensor, a current sensor, and a voltage sensor. The LED temperature sensor is used to detect the temperature of the LED bead unit; The LED light intensity sensor is used to detect the light intensity emitted by the LED bead unit; The external temperature sensor is used to detect the real-time ambient temperature outside the machine body; The external light intensity sensor is used to detect the real-time ambient brightness outside the machine body; The current sensor is used to detect the current value of the solar simulator; The voltage sensor is used to detect the voltage value of the solar simulator.

7. A control method for an LED solar simulator, applied to an LED solar simulator control system as described in claim 1, characterized in that, Includes the following steps: The GPRS module provides real-time positioning for the solar simulator; The communication unit connects to an external network and reads the current weather conditions of the area through the network. The detection unit detects the real-time temperature and light intensity of the lamp bead unit, and the detection unit also detects the real-time temperature and light intensity outside the machine body in real time. The deep learning module performs joint calculations on the collected data and the current weather conditions of the area, and analyzes the optimal lighting conditions based on usage requirements, and then automatically adjusts them.

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