LED spectrum optimization wax melting efficiency improving method, device and equipment

Abstract

The invention relates to the technical field of LEDs, and discloses an LED spectrum optimization wax melting efficiency improving method, device and equipment. The method comprises the following steps: detecting spectral absorption data on the surface of a candle; controlling a red light LED array, a first near-infrared LED array and a second near-infrared LED array according to the spectral absorption data to obtain temperature distribution data and melting speed data; according to the temperature distribution data and the melting speed data, the LED power adjusting amount and the light beam angle adjusting amount are calculated; the output power and the light beam direction of the red light LED array, the first near-infrared LED array and the second near-infrared LED array are adjusted according to the LED power adjusting amount and the light beam angle adjusting amount, and uniform heating of the surface of the candle is achieved. The problems of local overheating and uneven heating caused by traditional fixed light beam distribution are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of LED technology, and in particular to a method, apparatus and equipment for improving LED spectrum optimization and wax melting efficiency. Background Technology

[0002] The existing temperature control system for LED wax melting lamps uses a simple single-point temperature detection and linear PID control method. This results in serious control failure and response lag near the wax phase transition point, making it impossible to accurately predict and adjust temperature changes during the melting process. Consequently, the temperature control system lacks precision, and local overheating or uneven heating is likely to occur during the melting process, which seriously affects the wax melting efficiency and the user experience of the candle. Summary of the Invention

[0003] This invention provides a method, apparatus, and equipment for improving the efficiency of LED spectral optimization and wax melting. This invention achieves precise adjustment of the beam direction of each LED module and spatial redistribution of power density, effectively solving the problems of local overheating and uneven heating caused by traditional fixed beam distribution.

[0004] In a first aspect, the present invention provides a method for improving the wax melting efficiency of LEDs through spectral optimization, the method comprising: Detect the spectral absorption data of the candle surface; Based on the spectral absorption data, control the red LED array, the first near-infrared LED array, and the second near-infrared LED array to obtain temperature distribution data and melting rate data; Based on the temperature distribution data and the melting rate data, calculate the LED power adjustment amount and the beam angle adjustment amount; The output power and beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array are adjusted according to the LED power adjustment amount and the beam angle adjustment amount to achieve uniform heating of the candle surface.

[0005] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, the detection of spectral absorption data on the surface of the candle includes: The first reflectivity is measured by illuminating the candle surface with a red LED array and measuring the first reflectivity by the corresponding photodiode; the second reflectivity is measured by illuminating the candle surface with a first near-infrared LED array and measuring the third reflectivity by the corresponding photodiode; the third reflectivity is measured by illuminating the candle surface with a second near-infrared LED array and measuring the third reflectivity by the corresponding photodiode. A first spectral absorption coefficient is calculated by combining the first reflectance and transmittance measurement data; a second spectral absorption coefficient is calculated by combining the second reflectance and transmittance measurement data; and a third spectral absorption coefficient is calculated by combining the third reflectance and transmittance measurement data. The first spectral absorption coefficient, the second spectral absorption coefficient, and the third spectral absorption coefficient are respectively matched and compared with the standard absorption curve of paraffin CH bond vibration in the wax spectral database to obtain the corresponding first correction coefficient, second correction coefficient, and third correction coefficient. The first spectral absorption coefficient is corrected based on the first correction coefficient to obtain the fourth spectral absorption coefficient; the second spectral absorption coefficient is corrected based on the second correction coefficient to obtain the fifth spectral absorption coefficient; and the third spectral absorption coefficient is corrected based on the third correction coefficient to obtain the sixth spectral absorption coefficient. Spectral absorption data are generated based on the fourth spectral absorption coefficient, the fifth spectral absorption coefficient, and the sixth spectral absorption coefficient.

[0006] In conjunction with the first aspect, in a second implementation of the first aspect of the present invention, the step of controlling the red LED array, the first near-infrared LED array, and the second near-infrared LED array based on the spectral absorption data to obtain temperature distribution data and melting rate data includes: The fourth spectral absorption coefficient in the spectral absorption data is extracted as the reference parameter for the red LED array, the fifth spectral absorption coefficient is extracted as the reference parameter for the first near-infrared LED array, and the sixth spectral absorption coefficient is extracted as the reference parameter for the second near-infrared LED array. The sum of the first spectral absorption coefficient, the second spectral absorption coefficient, and the third spectral absorption coefficient is calculated as the total absorption coefficient value. The first spectral absorption coefficient, the second spectral absorption coefficient, and the third spectral absorption coefficient are divided by the total absorption coefficient value and normalized to obtain the first weight, the second weight, and the third weight. Multiply the first weight by the rated power of the red LED array to obtain the target power value of the red LED array; multiply the second weight by the rated power of the first near-infrared LED array to obtain the target power value of the first near-infrared LED array; multiply the third weight by the rated power of the second near-infrared LED array to obtain the target power value of the second near-infrared LED array. The target power value of the red LED array is used as the output power weight of the red LED array, the target power value of the first near-infrared LED array is used as the output power weight of the first near-infrared LED array, and the target power value of the second near-infrared LED array is used as the output power weight of the second near-infrared LED array. The red LED array, the first near-infrared LED array, and the second near-infrared LED array are controlled according to the output power weight, and temperature distribution data and melting rate data are acquired.

[0007] In conjunction with the first aspect, in a third implementation of the first aspect of the present invention, the step of controlling the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the output power weight, and acquiring temperature distribution data and melting rate data, includes: The output power weights of the red LED array, the first near-infrared LED array, and the second near-infrared LED array are respectively converted into the duty cycle parameters of the corresponding PWM controllers. The first beam of red LED array, the second beam of near-infrared LED array, and the third beam of near-infrared LED array are driven to output a set power according to the duty cycle parameters, and simultaneously illuminate the surface of the candle. The candle temperature is monitored by arranging a grid in the radial, angular, and height directions, and the real-time temperature values ​​of each measurement point are collected to form temperature distribution data. The melting rate data for each region was calculated based on temperature distribution data.

[0008] In conjunction with the first aspect, in the fourth implementation of the first aspect of the present invention, the step of monitoring the candle temperature according to a grid arrangement in the radial direction, angular direction, and height direction, and collecting real-time temperature values ​​at each measurement point to form temperature distribution data, includes: Calculate the coordinate positions of the candle surface in the radial, angular, and height directions within the grid coordinate system. Thermocouples are installed according to the coordinate positions to correspond to different spatial positions on the surface and inside of the candle, and real-time temperature measurement values ​​are collected at each measurement point. The real-time temperature measurements are used to construct temperature distribution data according to the corresponding radius coordinates, angle coordinates, and height coordinates.

[0009] In conjunction with the first aspect, in a fifth implementation of the first aspect of the present invention, the calculation of melting rate data for each region based on temperature distribution data includes: Extract the real-time temperature values ​​of each measurement point from the temperature distribution data and compare the real-time temperature values ​​with the melting temperature of the wax to obtain the temperature deviation value of each region; The phase transition activation factor of each region is obtained by performing a nonlinear transformation on the temperature deviation values ​​of each region. The power of the first beam is extracted and calculated with the corresponding fourth spectral absorption coefficient to obtain the first effective heating power; the power of the second beam is extracted and calculated with the corresponding fifth spectral absorption coefficient to obtain the second effective heating power; the power of the third beam is extracted and calculated with the corresponding sixth spectral absorption coefficient to obtain the third effective heating power. Based on the first effective heating power, the second effective heating power, and the third effective heating power, the instantaneous melting rate of each region is calculated in conjunction with the phase change activation factor; The instantaneous melting rate of each region is integrated to construct melting speed data according to radius coordinates, angle coordinates, and height coordinates.

[0010] In conjunction with the first aspect, in a sixth implementation of the first aspect of the present invention, the step of calculating the LED power adjustment amount and the beam angle adjustment amount based on the temperature distribution data and the melting rate data includes: The square root of the sum of squares of the differences between the temperature at each measurement point and the average temperature is taken from the temperature distribution data to obtain the temperature non-uniformity coefficient. The melting progress of each region is compared with the preset target value from the melting rate data to obtain the melting progress deviation value. The temperature non-uniformity coefficient is substituted into the temperature prediction layer to calculate the temperature prediction value at the next moment, and the melting progress deviation value is input into the power correction layer to calculate the initial power correction amount for each band. The initial power correction amount is extracted and multiplied by the corresponding decoupling weight coefficient in the decoupling optimizer to output the LED power adjustment amount; The temperature deviation sign is calculated for areas where the temperature non-uniformity coefficient exceeds the threshold, and the beam angle adjustment is calculated based on the temperature deviation sign.

[0011] In conjunction with the first aspect, in the seventh implementation of the first aspect of the present invention, adjusting the output power and beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the LED power adjustment amount and the beam angle adjustment amount to achieve uniform heating of the candle surface includes: The system receives the LED power adjustment amount and converts it into a new duty cycle value for the corresponding PWM controller, and immediately updates the actual output power of the red LED array, the first near-infrared LED array, and the second near-infrared LED array based on the new duty cycle value. The corresponding stepper motor is driven to rotate by a corresponding angle according to the beam angle adjustment amount, so that the beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array is deflected. Calculate the actual power density distribution of each LED module illuminating different areas of the candle after adjustment, identify the locations of areas with excessive or insufficient power, and formulate a power redistribution strategy. The power redistribution strategy is executed synchronously to achieve uniform heating of the candle surface.

[0012] Secondly, the present invention provides an LED spectrum optimization wax melting efficiency improvement device, the LED spectrum optimization wax melting efficiency improvement device comprising: The detection module is used to detect the spectral absorption data of the candle surface; The control module is used to control the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the spectral absorption data, and to acquire temperature distribution data and melting rate data; The calculation module is used to calculate the LED power adjustment amount and beam angle adjustment amount based on the temperature distribution data and the melting rate data; The adjustment module is used to adjust the output power and beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the LED power adjustment amount and the beam angle adjustment amount, so as to achieve uniform heating of the candle surface.

[0013] A third aspect of the present invention provides an LED spectrum optimization wax melting efficiency improvement device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the LED spectrum optimization wax melting efficiency improvement device to execute the above-described LED spectrum optimization wax melting efficiency improvement method.

[0014] The technical solution provided by this invention establishes a wax spectral absorption database and achieves precise three-band spectral matching based on the absorption peak of the CH bond vibration in paraffin wax. Compared with the traditional fixed spectral output method, it can automatically adjust the spectral ratio according to the characteristics of different wax materials, significantly improving the spectral energy utilization rate. An independent PWM controller design with a 120-degree phase difference effectively eliminates power coupling interference between the red LED array, the first near-infrared LED array, and the second near-infrared LED array, achieving complete electrical isolation and independent precise control between each band. A complete three-dimensional temperature monitoring network is constructed by arranging a 24-point thermocouple array in a grid according to radius, angle, and height directions. Compared with the traditional single-point temperature detection method, it can comprehensively grasp the real-time temperature distribution of the candle surface and interior. A melting dynamics prediction model based on the sigmoid activation function accurately describes the nonlinear melting behavior characteristics of wax near the phase transition point, overcoming the control failure problem of traditional linear control models in the phase transition region. A cascaded control structure integrating a temperature prediction layer, a power correction layer, and a decoupling optimization layer achieves deep coupling and coordination between temperature control and spectral control, significantly shortening the system response time and improving control accuracy. An adjustable beam angle mechanism driven by a stepper motor enables precise adjustment of the beam direction of each LED module and spatial redistribution of power density, effectively solving the problems of localized overheating and uneven heating caused by traditional fixed beam distribution. Based on real-time temperature distribution data, high-temperature and low-temperature regions are identified, and targeted power adjustment strategies are automatically formulated, achieving a technological upgrade from point light source heating to uniform surface light source heating, ensuring uniform temperature distribution on the candle surface. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic flowchart illustrating the method for improving LED spectrum melting efficiency through wax melting, as provided in this application embodiment; Figure 2 A schematic block diagram of the structure of the LED spectrum optimization and wax melting efficiency improvement device provided in the embodiments of this application; Figure 3 A schematic block diagram of the structure of the LED spectrum optimization and wax melting efficiency improvement device provided in the embodiments of this application. Detailed Implementation

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

[0018] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the described order. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change based on the actual situation.

[0019] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0022] Please see Figure 1 , Figure 1This is a flowchart illustrating the method for improving LED spectral optimization and wax melting efficiency provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the method for improving LED spectrum optimization and wax melting efficiency includes: Step S100: Detect the spectral absorption data of the candle surface; Specifically, the wax identification sensor works in conjunction with a red LED array, a first near-infrared LED array, and a second near-infrared LED array. Different wavelengths of LED arrays are sequentially activated using a time-division multiplexing method to illuminate the candle surface. This causes the illuminated area to generate specific reflection signals under the corresponding wavelength light source, which are then collected in real-time by a photodiode array of the corresponding wavelength to obtain the first, second, and third reflectivities. Simultaneously, the system works with a transmitted light detection channel to acquire corresponding transmittance data, ensuring that reflection and transmission information for each wavelength is collected within the same time window to reduce ambient light interference. Using the spectral absorption coefficient formula, the first spectral absorption coefficient is calculated by combining the first reflectivity with the corresponding transmittance; the second spectral absorption coefficient is calculated by combining the second reflectivity with the corresponding transmittance; and the third spectral absorption coefficient is calculated by combining the third reflectivity with the corresponding transmittance. The initial absorption coefficient is matched and compared with the standard absorption curve of the CH bond vibration of paraffin in a pre-established wax spectral database. Correction coefficients for corresponding bands are obtained by calculating waveform differences and peak shifts. The 660nm band corresponds to the first correction coefficient, the 850nm band to the second correction coefficient, and the 1200nm band to the third correction coefficient. The first spectral absorption coefficient is corrected based on the first correction coefficient to obtain the fourth spectral absorption coefficient; the second spectral absorption coefficient is corrected based on the second correction coefficient to obtain the fifth spectral absorption coefficient; and the third spectral absorption coefficient is corrected based on the third correction coefficient to obtain the sixth spectral absorption coefficient. The control unit combines the fourth, fifth, and sixth spectral absorption coefficients to generate the spectral absorption data of the current candle surface.

[0023] Step S200: Control the red LED array, the first near-infrared LED array, and the second near-infrared LED array based on the spectral absorption data to obtain temperature distribution data and melting rate data; Specifically, a fourth spectral absorption coefficient is extracted from the spectral absorption data as the reference control parameter for the red LED array. Simultaneously, a fifth spectral absorption coefficient is extracted as the reference control parameter for the first near-infrared LED array, and a sixth spectral absorption coefficient is extracted as the reference control parameter for the second near-infrared LED array. The values ​​of the first, second, and third spectral absorption coefficients are summed to obtain a total absorption coefficient value. Each original absorption coefficient is then divided by the total absorption coefficient value to achieve normalization, generating a first weight, a second weight, and a third weight, reflecting the relative absorption contribution ratio of each spectral band to the wax material. The control unit multiplies the first weight by the rated power of the red LED array to obtain the target power value of the red LED array; multiplies the second weight by the rated power of the first near-infrared LED array to obtain the target power value of the first near-infrared LED array; and multiplies the third weight by the rated power of the second near-infrared LED array to obtain the target power value of the second near-infrared LED array. The target power values ​​are input as output power weight parameters for the corresponding LED arrays into the multi-band decoupled drive control module. An independent PWM drive channel adjusts the drive current duty cycle to achieve power distribution control of the three-band LED array. After power adjustment, the three-band LED array is driven in real-time to irradiate according to the set power. Distributed multi-point temperature sensors collect three-dimensional temperature distribution data of the candle's surface and interior. Simultaneously, a melting kinetic model is used to calculate the mass change rate, yielding melting speed data.

[0024] Step S300: Calculate the LED power adjustment amount and beam angle adjustment amount based on the temperature distribution data and melting rate data; Specifically, temperature distribution data is read from a multi-point temperature monitoring network. The square root of the sum of the squares of the deviations between the measured temperatures and the average temperatures is taken to obtain the temperature non-uniformity coefficient, which characterizes the overall thermal field dispersion. Simultaneously, the comparison between the melting rate of each region and the preset target progress is read from the melting kinetics module, and the difference is input into the control link as the melting progress deviation value. At the temperature prediction layer, the controller performs look-ahead calculations of the temperature at the next moment based on a nonlinear melting kinetics model. The current temperature non-uniformity coefficient is used to adaptively weight the model output for sensitivity amplification and suppression to obtain the predicted temperature value. At the power correction layer, the melting progress deviation is mapped to the initial power correction amount for each band. If the deviation is positive, positive power compensation is allocated to the corresponding band; if the deviation is negative, reduction is implemented. The compensation magnitude is constrained by both the magnitude and rate of change of the progress deviation to avoid overshoot. The controller invokes the spectrum-temperature decoupling optimizer, multiplying the initial power correction for each band by a preset decoupling weight coefficient, and outputting the resulting LED power adjustment. The decoupling weight is set based on the calibration results of the inter-band coupling coefficient matrix. Typical mutual coupling values ​​are, for example, 0.05 for bands one and two, 0.03 for bands one and three, and 0.04 for bands two and three. This minimizes crosstalk in the three independent PWM drive channels and works in conjunction with the phase-shifting control of the decoupling drive circuit. Simultaneously, a threshold judgment is performed on the temperature non-uniformity coefficient. When the temperature non-uniformity coefficient exceeds 2.5℃, a spatial redistribution routine is triggered. The sign of the temperature deviation is calculated for the regions exceeding the threshold, and the correction direction of the beam direction is determined accordingly: negative deflection is used to reduce local power density in regions with temperatures above the average temperature, and positive deflection is used to increase illumination coverage in regions with temperatures below the average temperature. Angle adjustment is performed by a collimation / lens mechanism driven by a stepper motor. The single adjustment range is controlled within ±15° with a resolution of 0.5°. A rate limiting and jitter suppression strategy is superimposed to ensure that the dual-channel linkage of angle and power does not cause thermal field oscillation. The LED power adjustment and beam angle adjustment are jointly sent to the red light (660nm), first near-infrared (850nm), and second near-infrared (1200nm) arrays to achieve differentiated heat flux compensation for the target area.

[0025] Step S400: Adjust the output power and beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the LED power adjustment amount and the beam angle adjustment amount to achieve uniform heating of the candle surface.

[0026] Specifically, the LED power adjustment output from the control algorithm is received and converted into a new duty cycle value for the corresponding PWM controller using a power-to-duty cycle conversion formula. This new duty cycle value is then sent in real-time to the independent PWM channels controlling the red LED array, the first near-infrared LED array, and the second near-infrared LED array, ensuring that the actual output power of the three-band light sources is updated within milliseconds, minimizing the response delay between spectral power adjustment and changes in the thermal field. Simultaneously, the beam angle adjustment is read and the corresponding band's stepper motor is precisely rotated to the specified angle, causing the adjustable optical mechanisms (including collimating lenses or reflector assemblies) of each LED module to deflect the beam direction, thereby changing the irradiation range and energy coverage of the target area. After power and angle adjustments are completed, based on joint measurements using multi-point temperature sensors and an optical power meter, the actual power density distribution formed by each LED module in different areas of the candle surface after adjustment is calculated. The measured data is compared with the ideal uniform heating distribution to pinpoint areas where excessive power causes localized overheating and areas where insufficient power causes slow melting. The control unit formulates a power redistribution strategy based on the identification results. While maintaining the overall spectral ratio, it fine-tunes the power of units in each LED array corresponding to the illumination direction, appropriately reducing the output in areas with excessive power and moderately increasing the output in areas with insufficient power. This is combined with slight adjustments to the beam angle to expand or shrink the illumination coverage. The power redistribution strategy and beam angle adjustment are executed synchronously. The three-band LED array achieves dynamic coordination between power and spatial illumination distribution at the hardware level. The temperature non-uniformity coefficient on the candle surface is reduced to close to 1.2℃ in the continuous closed loop, improving heating uniformity.

[0027] In one specific embodiment, the process of performing step S100 may specifically include the following steps: The first reflectivity is measured by illuminating the candle surface with a red LED array and measuring the first reflectivity by the corresponding photodiode; the second reflectivity is measured by illuminating the candle surface with a first near-infrared LED array and measuring the third reflectivity by the corresponding photodiode; the third reflectivity is measured by illuminating the candle surface with a second near-infrared LED array and measuring the third reflectivity by the corresponding photodiode. The first spectral absorption coefficient is calculated by combining the first reflectance and transmittance measurement data, the second spectral absorption coefficient is calculated by combining the second reflectance and transmittance measurement data, and the third spectral absorption coefficient is calculated by combining the third reflectance and transmittance measurement data. The first, second, and third spectral absorption coefficients were matched and compared with the standard absorption curves of paraffin CH bond vibration in the wax spectral database to obtain the corresponding first, second, and third correction coefficients. The first spectral absorption coefficient is corrected based on the first correction coefficient to obtain the fourth spectral absorption coefficient; the second spectral absorption coefficient is corrected based on the second correction coefficient to obtain the fifth spectral absorption coefficient; and the third spectral absorption coefficient is corrected based on the third correction coefficient to obtain the sixth spectral absorption coefficient. Spectral absorption data are generated based on the fourth, fifth, and sixth spectral absorption coefficients.

[0028] Specifically, a time-division illumination and synchronous acquisition mechanism is established, controlling the red LED array to light up sequentially at 660nm, the first near-infrared LED array at 850nm, and the second near-infrared LED array at 1200nm, with only one band emitting light each time. Dark current sampling and ambient light baseline subtraction are performed before lighting to suppress interference from ambient light and device background on reflectivity measurement. The front ends of the red, first near-infrared, and second near-infrared photodiodes are configured with bandpass filters and transimpedance amplifiers matching the wavelengths. The sampling end uses an analog-to-digital converter of no less than 16 bits and superimposes multiple frames of time averaging to improve the signal-to-noise ratio of weak reflection signals. After each band completes one illumination, the steady-state output of the corresponding photodiode is read. Combined with the incident reference channel reading within the same time window and the reflection calibration constant of the standard Lambertian white board, the first, second, and third reflectivities are calculated. Simultaneously, the transmitted light intensity of each band is recorded in the transmission detection channel at the bottom or side wall of the wax body, and the transmittance is obtained through the same calibration process. Three sets of reflectance and transmittance values ​​were used in pairs under the same operating condition. The absorption coefficients of the first, second, and third spectra were calculated with the constraint of incident energy conservation, ensuring the consistency of the closed-loop analysis of reflection, transmission, and absorption. A short-time window was used to compensate for refractive index changes caused by instantaneous temperature rise, avoiding measurement drift caused by the phase transition front. After initial absorption coefficient estimation, the standard absorption curves of paraffin C–H bond vibrations from the wax spectral database were used for matching. The comparison covered the three main peaks: 660 nm stretching vibration, 850 nm bending vibration, and 1200 nm rocking vibration. The corresponding reference absorption intensity distribution in the database could be obtained by calling α... 660 ≈0.82, α 850 ≈0.76, α 1200Calibration data of approximately 0.68 was used as the fitting benchmark. During the matching process, a dual-constraint strategy of peak offset correction and bandwidth consistency correction was employed to align the peak center positions and half-peak widths of the measured absorption curve and the standard curve. Simultaneously, the peak amplitude and the relative intensity ratio between peaks were compared to obtain the first, second, and third correction coefficients. The physical meanings of these three sets of coefficients correspond to the systematic deviations of the three bands under the current material batch, surface roughness, and temperature field conditions, respectively, and are used to correct amplitude errors introduced by differences in the sensing link, surface scattering, and microstructure. The correction coefficients were applied to the initial absorption coefficients in a one-to-one correspondence manner to form the fourth, fifth, and sixth spectral absorption coefficients. Before application, outlier interception and confidence interval constraints were performed to ensure that the correction process did not amplify occasional noise or transient flicker. To improve stability, the controller performed time-series consistency smoothing and temperature-dimensional interpolation extrapolation on the three sets of corrected absorption coefficients to align them with real-time temperature lattice data, thereby ensuring that the power allocation and temperature prediction models operate on the same time reference. The absorption coefficients of the fourth, fifth, and sixth spectra are assembled into a structured spectral absorption data frame, with metadata such as timestamp, corresponding band identifier, sampling integration time, reference channel reading, and matching residual, and then written into the spectral matching calculation module.

[0029] In this embodiment, the first, second, and third spectral absorption coefficients are matched and compared with the standard absorption curves of paraffin CH bond vibrations in the wax spectral database to obtain the corresponding first, second, and third correction coefficients. This includes: reading the standard absorption curves of paraffin CH bonds in the 660nm red band for stretching vibration, the 850nm first near-infrared band for bending vibration, and the 1200nm second near-infrared band for rocking vibration in the wax spectral database; numerically matching the first spectral absorption coefficient with the 660nm standard absorption curve to calculate the absorption peak deviation; and comparing the second spectral absorption coefficient with the standard absorption curve in the 660nm band to obtain the corresponding correction coefficients. The absorption coefficient is numerically matched with the standard absorption curve in the 850nm band to calculate the absorption peak deviation. The third spectral absorption coefficient is numerically matched with the standard absorption curve in the 1200nm band to calculate the absorption peak deviation. Based on the absorption peak deviation of each band, the specific wax material type of the candle under test is identified, and the accurate absorption parameters of the corresponding material type are extracted from the wax spectral database as the calibration reference value. The first calibration coefficient is calculated based on the numerical difference between the calibration reference value and the measured absorption coefficient to calibrate the 660nm band data, the second calibration coefficient is calculated to calibrate the 850nm band data, and the third calibration coefficient is calculated to calibrate the 1200nm band data, ensuring the accuracy of the spectral absorption data.

[0030] In one specific embodiment, the process of performing step S200 may specifically include the following steps: The fourth spectral absorption coefficient in the spectral absorption data is extracted as the reference parameter for the red LED array, the fifth spectral absorption coefficient is extracted as the reference parameter for the first near-infrared LED array, and the sixth spectral absorption coefficient is extracted as the reference parameter for the second near-infrared LED array. The sum of the first spectral absorption coefficient, the second spectral absorption coefficient, and the third spectral absorption coefficient is calculated as the total absorption coefficient value. The first spectral absorption coefficient, the second spectral absorption coefficient, and the third spectral absorption coefficient are divided by the total absorption coefficient value and normalized to obtain the first weight, the second weight, and the third weight. Multiply the first weight by the rated power of the red LED array to obtain the target power value of the red LED array; multiply the second weight by the rated power of the first near-infrared LED array to obtain the target power value of the first near-infrared LED array; multiply the third weight by the rated power of the second near-infrared LED array to obtain the target power value of the second near-infrared LED array. The target power value of the red LED array is used as the output power weight of the red LED array, the target power value of the first near-infrared LED array is used as the output power weight of the first near-infrared LED array, and the target power value of the second near-infrared LED array is used as the output power weight of the second near-infrared LED array. The red LED array, the first near-infrared LED array, and the second near-infrared LED array are controlled according to the output power weight, and temperature distribution data and melting rate data are acquired.

[0031] Specifically, the spectral detection module performs multi-band spectrum reflection and transmission measurements on the candle surface and performs standard curve matching correction to obtain spectral absorption data containing absorption information of multiple bands. The control unit extracts the fourth spectral absorption coefficient from the spectral absorption dataset and defines it as the reference control parameter for the red LED array. Simultaneously, it extracts the fifth spectral absorption coefficient as the reference control parameter for the first near-infrared LED array and the sixth spectral absorption coefficient as the reference control parameter for the second near-infrared LED array. The first, second, and third spectral absorption coefficients are summed to obtain the total absorption coefficient value. By dividing the first, second, and third spectral absorption coefficients by the total value, the first, second, and third weights are obtained, reflecting the proportion of each band's contribution to light energy absorption under the current wax conditions, and the weight sum is kept to 1. The control unit multiplies the first weight by the rated power of the red LED array to obtain the target power value of the red LED array; multiplies the second weight by the rated power of the first near-infrared LED array to obtain the target power value of the first near-infrared LED array; and multiplies the third weight by the rated power of the second near-infrared LED array to obtain the target power value of the second near-infrared LED array. The multiplication process directly maps the absorption ratio to a power allocation scheme and automatically adapts to the rated power differences of different arrays, thereby ensuring optimal absorption utilization of each band while fully leveraging hardware performance. These three target power values ​​are assigned to their respective LED arrays, serving as the actual output power weights input to the multi-band decoupled PWM drive control module. An independent PWM controller adjusts the duty cycle to control the drive current of each band, achieving independent power adjustment for the red LED array, the first near-infrared LED array, and the second near-infrared LED array. The entire control process uses phase-shifting technology and an LC filter structure to suppress electromagnetic coupling interference between bands, ensuring that the actual output power stably follows the target power weight changes. After the power allocation adjustment is completed, real-time temperature distribution data is acquired using thermocouple arrays placed at different locations on and inside the candle surface. A three-dimensional temperature field reconstruction algorithm is used to calculate the spatial distribution of temperature at each measuring point. Simultaneously, based on the melting kinetics prediction model and the mass change rate monitoring module, the melting rate data of the wax is calculated in real time.

[0032] In one specific embodiment, the process of controlling the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the output power weight, and acquiring temperature distribution data and melting rate data, may specifically include the following steps: The output power weights of the red LED array, the first near-infrared LED array, and the second near-infrared LED array are respectively converted into the duty cycle parameters of the corresponding PWM controllers. The first beam of red LED array, the second beam of near-infrared LED array, and the third beam of near-infrared LED array are driven to output a set power according to the duty cycle parameters, and simultaneously illuminate the surface of the candle. The candle temperature is monitored by arranging a grid in the radial, angular, and height directions, and the real-time temperature values ​​of each measurement point are collected to form temperature distribution data. The melting rate data for each region was calculated based on temperature distribution data.

[0033] Specifically, the system receives the output power weights of the red LED array, the first near-infrared LED array, and the second near-infrared LED array generated by the spectral absorption calculation and power allocation module. These power weights are then combined with the rated power parameters of each array to convert them into the actual target output power value. To map the power value to the hardware driver layer, the control unit calls the power-current-duty cycle conversion model of each LED driver. This model integrates the photoelectric conversion efficiency of the LED array, the linear range of the drive current, the PWM modulation resolution, and the impact of temperature rise on luminous efficiency, minimizing power fluctuation errors while maintaining stable light output. The converted duty cycle parameters are written in high-precision numerical form into the independent PWM controller registers of the red LED array, the first near-infrared LED array, and the second near-infrared LED array. Upon receiving the updated duty cycle, the driver circuit adjusts the drive current waveform to achieve real-time adjustment of the output power. A red LED array is driven to generate a first beam with a wavelength of approximately 660 nm and a power matching the set value; a first near-infrared LED array generates a second beam with a wavelength of approximately 850 nm and a power matching the set value; and a second near-infrared LED array generates a third beam with a wavelength of approximately 1200 nm and a power matching the set value. These three beams, after optical collimation and lens shaping in a spatial arrangement, simultaneously illuminate the candle surface. Through multi-band spectral superposition, the system achieves synergistic excitation of different vibrational modes of wax molecules, improving thermal coupling efficiency. While the LED arrays continuously illuminate the candle, the system's temperature monitoring network begins operation. This network consists of multiple thermocouples or infrared temperature measurement units arranged on and inside the candle surface. The measurement points are arranged in a three-dimensional grid layout with radius directions r=0, R / 2, R, angular directions θ=0°, 90°, 180°, 270°, and height directions z=0, H / 2, H. This grid covers both the core heating area and the surrounding temperature-changing areas of the candle, thus reflecting the changing state of the spatial thermal field. The collected temperature data is aggregated within millisecond intervals by a multi-channel data acquisition system and stored as temperature distribution data in a three-dimensional matrix within the controller. Based on the temperature distribution data, the melting rate data for each region is calculated. A mass change prediction model based on melting kinetics is invoked, comparing the temperature-time curve of each monitoring region with the melting temperature threshold and phase transition bandwidth of the wax material. Volumetric units within the phase transition range are identified, and the mass ratio of these units transforming from solid to liquid per unit time is calculated based on the temperature rise rate and heat conduction conditions. Simultaneously, by combining the spatial location and material density of each monitoring point, the local phase transition rate is converted into regionally average melting rate data.

[0034] Specifically, the output power weights of the red LED array, the first near-infrared LED array, and the second near-infrared LED array are converted into the duty cycle parameters of their respective PWM controllers. This includes: calculating the phase difference parameters between the red LED array PWM controller, the first near-infrared LED array PWM controller, and the second near-infrared LED array PWM controller; setting the first phase difference to 0 degrees, the second phase difference to 120 degrees, and the third phase difference to 240 degrees to form a three-phase symmetrical drive current system; configuring independent drive circuits for each PWM controller based on the phase difference parameters; converting the output power weights of the red LED array into the first PWM duty cycle parameters according to the drive current formula; and converting the output power weights of the first near-infrared LED array... The output power weight is converted into the second PWM duty cycle parameter according to the drive current formula, and the output power weight of the second near-infrared LED array is converted into the third PWM duty cycle parameter according to the drive current formula. Three independent PWM controllers are started to generate drive signals with a phase difference of 120 degrees according to the corresponding duty cycle parameters. The red LED array, the first near-infrared LED array, and the second near-infrared LED array are precisely current isolated and controlled through the current detection circuit and the feedback adjustment loop. The power coupling coefficient between LED arrays of each band is monitored. When the power coupling coefficient between bands exceeds the preset threshold, the phase difference parameter and the duty cycle parameter are automatically adjusted to control the power coupling interference between bands within the allowable range and output a stable decoupling drive signal.

[0035] In one specific embodiment, the process of monitoring candle temperature according to a grid arrangement in the radial, angular, and vertical directions, and collecting real-time temperature values ​​at each measurement point to form temperature distribution data, can specifically include the following steps: Calculate the coordinate positions of the candle surface in the radial, angular, and height directions within the grid coordinate system. Thermocouples were installed according to the coordinate positions to correspond to different spatial positions on the surface and inside the candle, and real-time temperature measurement values ​​were collected at each measurement point. The real-time temperature measurements are used to construct temperature distribution data according to the corresponding radius coordinates, angle coordinates, and height coordinates.

[0036] Specifically, a three-dimensional spatial positioning system applicable to the surface and interior of the candle is established. The candle's shape is mathematically abstracted into a cylindrical geometric model, and a cylindrical coordinate system with the central axis of the candle as the Z-axis is constructed on this model. The radial direction *r* describes the radial distance from the center to the outer edge, the angular direction *θ* describes the orientation around the central axis, and the height direction *z* describes the vertical position from the bottom to the top. To obtain sufficient spatial resolution and cover temperature changes in all areas of the candle's surface and interior, the radial direction is divided into several equally spaced sampling levels based on the candle's geometry, such as the three key positions *r=0*, *R / 2*, and *R*, where *R* represents the candle's radius. The angular direction is selected according to the principle of equal division of the circumference, selecting four reference orientations such as 0°, 90°, 180°, and 270° to capture temperature differences in different orientations. The height direction is segmented based on the total height *H* of the candle, such as the three levels of *z=0* (bottom), *H / 2* (middle), and *H* (top). The coordinates (r, θ, z) of each intersection point of the 3D mesh represent the target location of a temperature measurement point, covering the core heat transfer path and edge heat dissipation areas of the surface and interior. Thermocouples, acting as temperature acquisition elements, are precisely positioned according to the spatial location of the coordinate points. For surface measurement points, the thermocouple probes are fixed to the candle surface or close to the wax surface using miniature supports to ensure accurate response to rapid changes in surface temperature. For internal measurement points, microholes with a diameter adapted to the probe are created in the solid phase of the candle, and the thermocouples are embedded at predetermined radial and height positions. The holes are then backfilled with a thermally conductive sealing wax material to restore the continuity of the thermal field, thus avoiding localized thermal distortion caused by installation. To reduce measurement delay and external interference, each thermocouple is connected to the acquisition module using a low thermal resistance, high shielding signal line, and a small cold junction compensation unit is configured near the probe end to correct for the impact of ambient temperature fluctuations on measurement accuracy. After all thermocouples are installed according to the coordinate layout and their channel numbers and positions are mapped, the temperature of all measurement points is collected in real time through a multi-channel synchronous data acquisition device. The sampling frequency and resolution are set according to the thermal dynamic characteristics of the wax melting process, for example, maintaining an accuracy of ±0.1℃ at a millisecond-level time resolution. The acquired raw temperature signals are then compensated for by cold junctions, filtered for noise reduction, and quantized for calibration. They are then matched with a pre-calculated grid coordinate system, associating each measurement value with its (r, θ, z) position. The control system stores the temperature values ​​in a structured manner according to the order of radius coordinates, angle coordinates, and height coordinates, forming a three-dimensional matrix of temperature distribution data. The three index dimensions of the matrix correspond to the radius, angle, and height, respectively, and the element values ​​of the matrix are the real-time temperatures of the corresponding measurement points.

[0037] In one specific embodiment, the process of calculating the melting rate data of each region based on temperature distribution data may specifically include the following steps: Extract the real-time temperature values ​​of each measurement point from the temperature distribution data and compare the real-time temperature values ​​with the melting temperature of the wax to obtain the temperature deviation value of each region; The phase transition activation factor of each region is obtained by performing a nonlinear transformation on the temperature deviation values ​​of each region. The power of the first beam is extracted and calculated with the corresponding fourth spectral absorption coefficient to obtain the first effective heating power; the power of the second beam is extracted and calculated with the corresponding fifth spectral absorption coefficient to obtain the second effective heating power; the power of the third beam is extracted and calculated with the corresponding sixth spectral absorption coefficient to obtain the third effective heating power. The instantaneous melting rate of each region is calculated based on the first effective heating power, the second effective heating power, and the third effective heating power, combined with the phase change activation factor. The instantaneous melting rate of each region is integrated to construct melting speed data according to radius coordinates, angle coordinates, and height coordinates.

[0038] Specifically, real-time temperature values ​​at each measurement location are extracted point-by-point from the temperature distribution data matrix, and these values ​​are compared with the melting temperature threshold of the wax material to calculate the temperature deviation value for each spatial region. The temperature deviation value undergoes a nonlinear transformation to form a phase transition activation factor. This nonlinear transformation employs an S-shaped function similar to the sigmoid function, causing the activation factor to rise rapidly when the temperature approaches the melting temperature, while the change in activation factor slows down in regions far below or above the melting temperature. This aligns with the actual physical process—the melting rate accelerates just before the melting point, while heat energy is primarily used for heating or maintaining the liquid state rather than phase transition when the temperature is far from the melting point. After obtaining the phase transition activation factors for each region, the actual output power of the three LED beams at the current moment is extracted. The first beam comes from a red LED array, the second from a first near-infrared LED array, and the third from a second near-infrared LED array. To account for the selective absorption characteristics of wax by different wavelengths of light, the first effective heating power is calculated by multiplying the first beam power by the fourth spectral absorption coefficient; similarly, the second effective heating power is obtained by multiplying the second beam power by the fifth spectral absorption coefficient; and the third effective heating power is obtained by multiplying the third beam power by the sixth spectral absorption coefficient. The first, second, and third effective heating powers are spatially correlated to each measurement region and combined with the corresponding phase transition activation factor to calculate the instantaneous melting rate of each region. In the calculation, the phase transition activation factor acts as a regulating coefficient, ensuring that even with a high effective heating power, the melting rate is significantly suppressed if the temperature of the measurement region is far below the melting point; conversely, if the temperature is within the phase transition bandwidth, the same effective heating power will produce a higher instantaneous melting rate. This coupled calculation based on both thermal power and phase transition activity improves the accuracy and dynamic response of the melting rate prediction. After calculating the instantaneous melting rate of each region, the results are reorganized according to the original three-dimensional coordinate structure of the temperature distribution, that is, corresponding to the radius coordinate r, the angle coordinate θ, and the height coordinate z in sequence. The instantaneous melting rate values ​​are stored in the corresponding positions of the three-dimensional matrix to form melting speed data.

[0039] The process involves nonlinearly transforming the temperature deviation values ​​of each region to obtain the phase transition activation factor for each region. This includes: extracting the specific numerical difference between the temperature deviation value and the melting temperature of the wax in each region, identifying the measurement point location of the phase transition region near the melting point; inputting the temperature deviation value in the phase transition region into the sigmoid activation function for nonlinear mathematical transformation, and processing the phase transition behavior characteristics of the wax near the melting point through the sigmoid function; calculating the range of activation factor values ​​output by the sigmoid activation function, standardizing the activation factor values ​​between 0 and 1 to obtain the normalized phase transition activation factor; judging the degree of phase transition in each region based on the magnitude of the normalized phase transition activation factor, marking regions with phase transition activation factors close to 1 as active melting regions, marking regions with phase transition activation factors close to 0 as stable solid regions, and outputting the final phase transition activation factor data corresponding to each region for melting rate calculation.

[0040] In one specific embodiment, the process of performing step S300 may specifically include the following steps: The square root of the sum of squares of the differences between the temperature at each measurement point and the average temperature is taken from the temperature distribution data to obtain the temperature non-uniformity coefficient. The melting progress of each region is compared with the preset target value from the melting rate data to obtain the melting progress deviation value. The temperature non-uniformity coefficient is substituted into the temperature prediction layer to calculate the temperature prediction value at the next moment, and the melting progress deviation value is input into the power correction layer to calculate the initial power correction amount for each band. The initial power correction amount is extracted and multiplied by the corresponding decoupling weight coefficient in the decoupling optimizer to output the LED power adjustment amount; The region where the temperature non-uniformity coefficient exceeds the threshold is checked, the corresponding temperature deviation sign is calculated, and the beam angle adjustment is calculated based on the temperature deviation sign.

[0041] Specifically, the real-time temperature values ​​at each measurement location are read point by point from the three-dimensional temperature distribution data output by the temperature monitoring network, and the global average temperature is calculated based on the temperature data of all measurement points. To quantify the dispersion of the thermal field, the difference between the temperature at each measurement point and the average temperature is calculated. The squared deviations are then summed to obtain the temperature variance sum. The square root of the temperature variance sum is then taken to obtain the temperature non-uniformity coefficient at the current moment. The magnitude of the temperature non-uniformity coefficient directly reflects the uniformity of the temperature field on the surface and inside the candle; a larger coefficient indicates a more significant difference in the thermal field. Simultaneously, the actual melting progress of each region is extracted from the melting rate data matrix, and the actual melting progress is compared with the preset target melting progress one by one to obtain the corresponding melting progress deviation value. A positive deviation indicates that the actual progress lags behind the target value, requiring increased heat input; a negative deviation indicates that the progress is ahead, requiring appropriate power reduction. The temperature non-uniformity coefficient is then substituted into the temperature prediction layer, which, based on the previously calibrated nonlinear melting kinetics prediction model, performs a forward-looking calculation of the temperature distribution at the next moment. The prediction model comprehensively considers factors such as the current spectral power distribution, wax absorption characteristics, thermal diffusivity, and latent heat of phase change to obtain a more realistic temperature prediction value. The output of the temperature prediction layer is used to evaluate the natural variation trend of thermal field uniformity without additional intervention. Simultaneously, the melting progress deviation value is input into the power correction layer. The power correction layer uses a proportional-derivative control strategy to map the deviation to the initial power correction amount corresponding to the three bands. The proportional term allocates the correction magnitude according to the deviation, while the derivative term responds quickly to the rate of change of the deviation to prevent control lag or overshoot, thereby achieving a fundamental adjustment of the heating power of different bands. The initial power correction amounts for the three bands are input into the decoupling optimizer and multiplied one by one with the preset decoupling weight coefficients. The decoupling optimizer compensates for the mutual coupling effect of the multi-band LED array during power adjustment. Because different bands have interactive influences in optical reflection, heat conduction paths, and material absorption spectra, direct adjustment will lead to undesirable power changes in non-target bands. By introducing decoupling weighting coefficients (derived from the experimentally measured inter-band coupling coefficient matrix, e.g., 0.05 for bands 1 and 2, 0.03 for bands 1 and 3, and 0.04 for bands 2 and 3), interference to other bands is minimized while maintaining the correction amplitude of the target band. After decoupling calculation, the LED power adjustment amount for each band is output. Simultaneously, a threshold judgment is performed on the temperature non-uniformity coefficient. When its value exceeds the set uniformity threshold (e.g., 2.5℃), the spatial beam direction adjustment judgment process is initiated. The control algorithm scans the temperature distribution in areas where the non-uniformity coefficient exceeds the standard and calculates the deviation sign of the temperature from the average temperature in each area. Areas with a positive deviation sign indicate temperatures higher than the average, belonging to potential overheating areas, and local power density is reduced by beam deflection; areas with a negative deviation sign indicate temperatures lower than the average, belonging to underheating areas, and local energy input is increased by beam deflection.Based on these deviation signs, the corresponding beam angle adjustment is calculated. Combined with the mechanical limitations of the optical module (such as an adjustment range of ±15° and an accuracy of 0.5°), an angle adjustment command is generated and sent directly to the stepper motor driver. The power adjustment and beam angle adjustment are simultaneously sent to the LED arrays and beam control mechanisms in each band, forming a dual-optimization control closed loop for power and spatial distribution. During continuous iteration, the temperature non-uniformity coefficient gradually decreases to the target range, and the melting progress deviation approaches zero.

[0042] The process involves substituting the temperature non-uniformity coefficient into the temperature prediction layer to calculate the predicted temperature value for the next moment, and inputting the melting progress deviation value into the power correction layer to calculate the initial power correction amount for each band. This includes: activating the first layer of the three-layer cascaded control algorithm, the temperature prediction layer, and substituting the temperature non-uniformity coefficient and current temperature distribution data into the temperature prediction calculation formula to predict the temperature change trend of each measurement point in the next control cycle; activating the second layer of the power correction layer based on the temperature prediction result, inputting the melting progress deviation value into the proportional control module and the derivative control module, calculating the proportional correction component by multiplying the deviation value by the proportional coefficient, and calculating the derivative correction component by multiplying the deviation change rate by the derivative coefficient; integrating the proportional correction component and the derivative correction component to obtain the initial power correction amount for the red LED array, the first near-infrared LED array, and the second near-infrared LED array; and passing the initial power correction amount for each band to the third layer of the decoupling optimization layer, eliminating the mutual coupling influence between bands through the decoupling weight coefficient matrix, and outputting the final power correction amount after decoupling processing for subsequent LED power adjustment.

[0043] In one specific embodiment, the process of performing step S400 may specifically include the following steps: The system receives the LED power adjustment and converts it into a new duty cycle value for the corresponding PWM controller. Based on the new duty cycle value, it immediately updates the actual output power of the red LED array, the first near-infrared LED array, and the second near-infrared LED array. The corresponding stepper motor is driven to rotate by the corresponding angle according to the beam angle adjustment, so that the beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array is deflected. Calculate the actual power density distribution of each LED module illuminating different areas of the candle after adjustment, identify the locations of areas with excessive or insufficient power, and formulate a power redistribution strategy. A power redistribution strategy is executed synchronously to achieve uniform heating of the candle surface.

[0044] Specifically, the system receives three LED power adjustment values ​​from the control algorithm output, corresponding to the final correction values ​​for the red LED array, the first near-infrared LED array, and the second near-infrared LED array, respectively. The control unit superimposes the power adjustment value of each channel with the rated power of the corresponding LED array to calculate the target output power value. Then, using a power-current-PWM duty cycle calibration function, the target power value is converted into a new duty cycle value for the corresponding PWM controller. The conversion process considers the photoelectric conversion efficiency of the LED array, the nonlinear relationship between the drive current and light output, and the impact of temperature rise on luminous efficiency, ensuring a highly consistent correspondence between the duty cycle change and the actual light output. The new duty cycle is written to the registers of the three independent PWM controllers via a high-speed communication interface and takes effect at the start of the next PWM cycle, thus updating the actual output power of the red, first near-infrared, and second near-infrared LED arrays in milliseconds. Simultaneously, the beam angle adjustment value is read, derived from temperature non-uniformity analysis and heat flow redistribution algorithms. The controller distributes the angle adjustment amount to the three stepper motor drive modules according to the band correspondence, and converts the instructions into stepping pulses and direction signals to drive the adjustable optical components (such as reflectors or collimating lenses) to rotate the corresponding angle. The deflection process can adjust the beam direction with a resolution of 0.5° within a range of ±15°, thereby changing the projection position and coverage of each LED array beam on the candle surface, achieving targeted compensation for underheated areas or reducing the irradiation density of overheated areas. After the power and beam direction are adjusted synchronously, the optical power density calculation module is called to calculate the irradiation effect of each LED module on different areas of the candle surface. The calculation integrates the light source power, beam divergence angle, lens focusing parameters, and the relative geometric relationship between the irradiated area and the light source, and calculates the actual power density distribution of each spatial grid unit through a light intensity attenuation model. The distribution map is compared with the expected uniform heating power density curve to automatically identify the location range of areas with excessive power and insufficient power. Areas with excessive power will cause local overheating and wax surface depression, while areas with insufficient power will cause melting delay and uneven thermal field. Based on the identification results, the power redistribution strategy generation stage begins. While maintaining the overall spectral ratio, the control unit fine-tunes the output power of specific irradiated areas within the three-LED array. This reduces the light intensity in excessively high-power areas to a safe range, while increasing the light intensity in insufficient-power areas to the target level. The power redistribution strategy also considers the cumulative effect of beam direction adjustment, avoiding overcompensation or undercompensation caused by solely relying on power adjustment. Furthermore, it introduces regional coupling coefficient correction to ensure that adjustments do not disrupt the thermal balance of adjacent areas. The power redistribution strategy is executed synchronously with the beam angle adjustment command. The three-LED array achieves dual optimization of power output and beam spatial distribution at the hardware level, causing the temperature non-uniformity coefficient on the candle surface to converge to within the target threshold within a short time.

[0045] Please see Figure 2 , Figure 2 A schematic block diagram of the structure of the LED spectrum optimization and wax melting efficiency improvement device provided in the embodiments of this application is shown below. Figure 2 As shown, the LED spectrum optimization and wax melting efficiency improvement device includes: The detection module 210 is used to detect the spectral absorption data of the candle surface; Control module 220 is used to control the red LED array, the first near-infrared LED array, and the second near-infrared LED array based on spectral absorption data, and to acquire temperature distribution data and melting rate data; Calculation module 230 is used to calculate the LED power adjustment amount and beam angle adjustment amount based on temperature distribution data and melting rate data; The adjustment module 240 is used to adjust the output power and beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the LED power adjustment amount and the beam angle adjustment amount, so as to achieve uniform heating of the candle surface.

[0046] Through the collaborative efforts of the aforementioned components, a wax spectral absorption database is established, and precise three-band spectral matching based on the absorption peaks of the CH bond vibration in paraffin wax is achieved. Compared to traditional fixed spectral output methods, this approach automatically adjusts the spectral ratio according to the characteristics of different wax materials, significantly improving spectral energy utilization. An independent PWM controller design with a 120-degree phase difference effectively eliminates power coupling interference between the red LED array, the first near-infrared LED array, and the second near-infrared LED array, achieving complete electrical isolation and independent precise control across each band. A complete three-dimensional temperature monitoring network is constructed using a 24-point thermocouple array arranged in a grid along the radius, angle, and height directions. Compared to traditional single-point temperature detection methods, this approach provides a comprehensive understanding of the real-time temperature distribution on the candle's surface and inside. A melting kinetic prediction model based on the sigmoid activation function accurately describes the nonlinear melting behavior of wax near the phase transition point, overcoming the control failure problem of traditional linear control models in the phase transition region. A cascaded control structure integrating a temperature prediction layer, a power correction layer, and a decoupling optimization layer achieves deep coupling and coordination between temperature control and spectral control, significantly shortening system response time and improving control accuracy. An adjustable beam angle mechanism driven by a stepper motor enables precise adjustment of the beam direction of each LED module and spatial redistribution of power density, effectively solving the problems of localized overheating and uneven heating caused by traditional fixed beam distribution. Based on real-time temperature distribution data, high-temperature and low-temperature regions are identified, and targeted power adjustment strategies are automatically formulated, achieving a technological upgrade from point light source heating to uniform surface light source heating, ensuring uniform temperature distribution on the candle surface.

[0047] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of the LED spectrum optimization wax melting efficiency improvement device 300 provided in the embodiments of this application. The LED spectrum optimization wax melting efficiency improvement device 300 includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected through a device bus 303. The memory 302 may include a non-volatile storage medium and internal memory.

[0048] The non-volatile storage medium can store a computer program. The computer program includes program instructions that, when executed by the processor 301, cause the processor 301 to perform any of the aforementioned methods for improving the efficiency of LED spectrum optimization and wax melting.

[0049] The processor 301 provides computing and control capabilities to support the operation of the entire LED spectrum optimization and wax melting efficiency improvement device 300.

[0050] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor 301, the processor 301 can execute any of the above-mentioned methods for improving the efficiency of LED spectrum optimization and wax melting.

[0051] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the LED spectrum optimization wax melting efficiency improvement device 300 involved in the present application. The specific LED spectrum optimization wax melting efficiency improvement device 300 may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0052] It should be understood that processor 301 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0053] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0054] If the integrated unit is implemented as 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 technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for improving the wax melting efficiency of LEDs through spectral optimization, characterized in that, include: Detect the spectral absorption data of the candle surface; Based on the spectral absorption data, control the red LED array, the first near-infrared LED array, and the second near-infrared LED array to obtain temperature distribution data and melting rate data; Based on the temperature distribution data and the melting rate data, calculate the LED power adjustment amount and the beam angle adjustment amount; The output power and beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array are adjusted according to the LED power adjustment amount and the beam angle adjustment amount to achieve uniform heating of the candle surface.

2. The method for improving LED spectral optimization and wax melting efficiency according to claim 1, characterized in that, The spectral absorption data of the detected candle surface includes: The first reflectivity is measured by illuminating the candle surface with a red LED array and then measuring the first reflectivity by the corresponding photodiode; the second reflectivity is measured by illuminating the candle surface with a first near-infrared LED array and then measuring the third reflectivity by illuminating the candle surface with a second near-infrared LED array and then measuring the third reflectivity by the corresponding photodiode. A first spectral absorption coefficient is calculated by combining the first reflectance and transmittance measurement data; a second spectral absorption coefficient is calculated by combining the second reflectance and transmittance measurement data; and a third spectral absorption coefficient is calculated by combining the third reflectance and transmittance measurement data. The first spectral absorption coefficient, the second spectral absorption coefficient, and the third spectral absorption coefficient are respectively matched and compared with the standard absorption curve of paraffin CH bond vibration in the wax spectral database to obtain the corresponding first correction coefficient, second correction coefficient, and third correction coefficient. The first spectral absorption coefficient is corrected based on the first correction coefficient to obtain the fourth spectral absorption coefficient; the second spectral absorption coefficient is corrected based on the second correction coefficient to obtain the fifth spectral absorption coefficient; and the third spectral absorption coefficient is corrected based on the third correction coefficient to obtain the sixth spectral absorption coefficient. Spectral absorption data are generated based on the fourth spectral absorption coefficient, the fifth spectral absorption coefficient, and the sixth spectral absorption coefficient.

3. The method for improving LED spectral optimization and wax melting efficiency according to claim 1, characterized in that, The step of controlling the red LED array, the first near-infrared LED array, and the second near-infrared LED array based on the spectral absorption data to obtain temperature distribution data and melting rate data includes: The fourth spectral absorption coefficient in the spectral absorption data is extracted as the reference parameter for the red LED array, the fifth spectral absorption coefficient is extracted as the reference parameter for the first near-infrared LED array, and the sixth spectral absorption coefficient is extracted as the reference parameter for the second near-infrared LED array. The sum of the first spectral absorption coefficient, the second spectral absorption coefficient, and the third spectral absorption coefficient is calculated as the total absorption coefficient value. The first spectral absorption coefficient, the second spectral absorption coefficient, and the third spectral absorption coefficient are divided by the total absorption coefficient value and normalized to obtain the first weight, the second weight, and the third weight. Multiply the first weight by the rated power of the red LED array to obtain the target power value of the red LED array; multiply the second weight by the rated power of the first near-infrared LED array to obtain the target power value of the first near-infrared LED array; multiply the third weight by the rated power of the second near-infrared LED array to obtain the target power value of the second near-infrared LED array. The target power value of the red LED array is used as the output power weight of the red LED array, the target power value of the first near-infrared LED array is used as the output power weight of the first near-infrared LED array, and the target power value of the second near-infrared LED array is used as the output power weight of the second near-infrared LED array. The red LED array, the first near-infrared LED array, and the second near-infrared LED array are controlled according to the output power weight, and temperature distribution data and melting rate data are acquired.

4. The method for improving LED spectral optimization and wax melting efficiency according to claim 3, characterized in that, The step of controlling the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the output power weight, and acquiring temperature distribution data and melting rate data, includes: The output power weights of the red LED array, the first near-infrared LED array, and the second near-infrared LED array are respectively converted into the duty cycle parameters of the corresponding PWM controllers. The first beam of red LED array, the second beam of near-infrared LED array, and the third beam of near-infrared LED array are driven to output a set power according to the duty cycle parameters, and simultaneously illuminate the surface of the candle. The candle temperature is monitored by arranging a grid in the radial, angular, and height directions, and the real-time temperature values ​​of each measurement point are collected to form temperature distribution data. The melting rate data for each region was calculated based on temperature distribution data.

5. The method for improving LED spectral optimization and wax melting efficiency according to claim 4, characterized in that, The method of monitoring candle temperature by arranging a grid according to the radial, angular, and height directions, and collecting real-time temperature values ​​at each measurement point to form temperature distribution data, includes: Calculate the coordinate positions of the candle surface in the radial, angular, and height directions within the grid coordinate system. Thermocouples are installed according to the coordinate positions to correspond to different spatial positions on the surface and inside of the candle, and real-time temperature measurement values ​​are collected at each measurement point. The real-time temperature measurements are used to construct temperature distribution data according to the corresponding radius coordinates, angle coordinates, and height coordinates.

6. The method for improving LED spectral optimization and wax melting efficiency according to claim 5, characterized in that, The calculation of melting rate data for each region based on temperature distribution data includes: Extract the real-time temperature values ​​of each measurement point from the temperature distribution data and compare the real-time temperature values ​​with the melting temperature of the wax to obtain the temperature deviation value of each region; The phase transition activation factor of each region is obtained by performing a nonlinear transformation on the temperature deviation values ​​of each region. The power of the first beam is extracted and calculated with the corresponding fourth spectral absorption coefficient to obtain the first effective heating power; the power of the second beam is extracted and calculated with the corresponding fifth spectral absorption coefficient to obtain the second effective heating power; the power of the third beam is extracted and calculated with the corresponding sixth spectral absorption coefficient to obtain the third effective heating power. Based on the first effective heating power, the second effective heating power, and the third effective heating power, the instantaneous melting rate of each region is calculated in conjunction with the phase change activation factor; The instantaneous melting rate of each region is integrated to construct melting speed data according to radius coordinates, angle coordinates, and height coordinates.

7. The method for improving LED spectral optimization and wax melting efficiency according to claim 1, characterized in that, The step of calculating the LED power adjustment and beam angle adjustment based on the temperature distribution data and the melting rate data includes: The square root of the sum of squares of the differences between the temperature at each measurement point and the average temperature is taken from the temperature distribution data to obtain the temperature non-uniformity coefficient. The melting progress of each region is compared with the preset target value from the melting rate data to obtain the melting progress deviation value. The temperature non-uniformity coefficient is substituted into the temperature prediction layer to calculate the temperature prediction value at the next moment, and the melting progress deviation value is input into the power correction layer to calculate the initial power correction amount for each band. The initial power correction amount is extracted and multiplied by the corresponding decoupling weight coefficient in the decoupling optimizer to output the LED power adjustment amount; The temperature deviation sign is calculated for areas where the temperature non-uniformity coefficient exceeds the threshold, and the beam angle adjustment is calculated based on the temperature deviation sign.

8. The method for improving LED spectral optimization and wax melting efficiency according to claim 1, characterized in that, The step of adjusting the output power and beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the LED power adjustment amount and the beam angle adjustment amount to achieve uniform heating of the candle surface includes: The system receives the LED power adjustment amount and converts it into a new duty cycle value for the corresponding PWM controller, and immediately updates the actual output power of the red LED array, the first near-infrared LED array, and the second near-infrared LED array based on the new duty cycle value. The corresponding stepper motor is driven to rotate by a corresponding angle according to the beam angle adjustment amount, so that the beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array is deflected. Calculate the actual power density distribution of each LED module illuminating different areas of the candle after adjustment, identify the locations of areas with excessive or insufficient power, and formulate a power redistribution strategy. The power redistribution strategy is executed synchronously to achieve uniform heating of the candle surface.

9. A device for optimizing LED spectrum and improving wax melting efficiency, characterized in that, The method for improving the wax melting efficiency of LED spectrum optimization as described in any one of claims 1-8 includes: The detection module is used to detect the spectral absorption data of the candle surface; The control module is used to control the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the spectral absorption data, and to acquire temperature distribution data and melting rate data; The calculation module is used to calculate the LED power adjustment amount and beam angle adjustment amount based on the temperature distribution data and the melting rate data; The adjustment module is used to adjust the output power and beam direction of the red LED array, the first near-infrared LED array, and the second near-infrared LED array according to the LED power adjustment amount and the beam angle adjustment amount, so as to achieve uniform heating of the candle surface.

10. A device for optimizing LED spectrum and improving wax melting efficiency, characterized in that, The LED spectrum optimization and wax melting efficiency improvement device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the LED spectrum optimization wax melting efficiency improvement device to perform the LED spectrum optimization wax melting efficiency improvement method as described in any one of claims 1-8.

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