Sda spectral design automation method and system

Abstract

The application provides an SDA spectrum design automation method, comprising: setting a database, the database storing curve data and radiation flux parameters of standard lamps, and importing corresponding target curves; generating a plurality of formula groups according to the target curves, each formula group containing at least one setting parameter of a standard lamp, the setting parameter comprising a lamp name, a current value and an occupancy ratio; performing curve fitting calculation on each formula group to generate a fitting curve, respectively acquiring an absolute amplitude curve of each group of standard lamps, and performing area integral superposition on all absolute amplitude curves participating in fitting to obtain the fitting curve; verifying the fitting curve through a multi-dimensional verification strategy to obtain a score result, selecting a formula group with the highest score as an optimal formula group, and judging whether the optimal formula group meets an expectation; if the optimal formula group meets the expectation, outputting the optimal formula group; if the optimal formula group does not meet the expectation, initializing a formula and regenerating a new formula group.

Description

Technical Field

[0001] This invention relates to spectral design, and more particularly to an automated method and system for SDA spectral design. Background Technology

[0002] In the field of spectral design, especially in applications involving standard lamp combinations to achieve target spectra, traditional spectral design methods generally suffer from low efficiency, insufficient accuracy, and reliance on manual experience. Currently, the industry's operations for generating target spectra from standard lamp combinations mostly require technicians to manually select standard lamp types, adjust the current parameters of the standard lamps, and determine the occupancy ratio of each standard lamp based on their professional knowledge. The entire process lacks systematic database support, making it difficult to comprehensively cover the curve data and radiant flux parameters of various standard lamps. Furthermore, the limitations of manual selection easily lead to a limited selection of standard lamp combinations, failing to meet the design requirements of complex target spectra.

[0003] In the curve fitting stage, traditional methods typically employ simple linear superposition, failing to fully consider the precise calculation of the absolute amplitude curves of the standard lamps and the scientific rigor of area integral superposition. This results in significant deviations between the generated fitted curves and the target curves, making it difficult to meet the requirements of high-precision spectral design. Furthermore, the verification stage of the fitted curves lacks multi-dimensional verification strategies, relying solely on a single indicator to judge the fitting effect. This fails to comprehensively assess the adaptability of the fitted curves across different wavebands and radiation intensity ranges, potentially leading to poor stability and substandard spectral performance in practical applications of the final standard lamp formulation. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an automated method and system for SDA spectral design, so as to overcome the above-mentioned defects in the existing technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] SDA spectral design automation methods include:

[0007] The curve selection step involves setting up a database containing curve data and radiant flux parameters of standard lamps, and importing the corresponding target curve. Based on the target curve, multiple formula groups are generated, and each formula group contains setting parameters for at least one standard lamp, including lamp name, current value, and occupancy ratio.

[0008] The curve fitting step involves performing curve fitting calculations for each formulation group to generate a fitted curve. The curve fitting calculation includes obtaining the absolute amplitude curve of each standard lamp group through the radiant flux parameter and the relative spectral curve, and superimposing the area integrals of all the absolute amplitude curves involved in the fitting to obtain the fitted curve.

[0009] The curve output step verifies the fitted curve using a multi-dimensional verification strategy, obtains the scoring results, selects the formula group with the highest score as the optimal formula group, and determines whether the optimal formula group meets the expectations. If it does, the optimal formula group is output; otherwise, the formula is initialized and a new formula group is generated.

[0010] Preferably, the multi-dimensional verification strategy is used to obtain basic information about the standard lamp, obtain production cost parameters through the basic information about the standard lamp, and use the performance indicators of the formula group as performance parameters by analyzing spectral features, and calculate the similarity with the target curve to obtain matching parameters. Based on the production cost parameters, performance parameters and matching parameters, the scoring result is obtained through dynamic weight calculation.

[0011] Preferably, the multi-dimensional verification strategy includes a cost calculation sub-step, which is used to obtain the material cost and production parameters of the standard lamp, and to calculate the production cost parameters of the formula group based on the material cost and production parameters of the standard lamp.

[0012] Preferably, the multi-dimensional verification strategy also includes a performance calculation sub-step, which is used to obtain the core performance parameters of the standard lamp, including radiant flux and spectral distribution, and generates performance parameters through a weighted algorithm.

[0013] Preferably, the multi-dimensional verification strategy further includes a similarity calculation sub-step, which is used to fit a curve and obtain key detection regions and non-key detection regions based on the target curve. The key detection regions of the target curve and the fitted curve are compared to generate key similarity values, and a similarity value threshold is set. When the key similarity value is greater than the similarity value threshold, the non-key similarity values ​​of the non-key detection regions are obtained. The non-key similarity values ​​and key similarity values ​​are used to obtain matching parameters through a weighting algorithm.

[0014] Preferably, the curve selection step includes a formula group generation step. In this step, the operator sets multiple different current values ​​and occupancy ratios for the standard lamps and test data stored in the database, thereby generating multiple formula groups.

[0015] Preferably, the database generation step is also included, which includes:

[0016] The standard lamp testing sub-step involves testing the standard lamps using a spectrometer and naming them based on the lamp chip performance and test data.

[0017] The database creation sub-step involves searching the database to see if a standard light with the current name exists. If it exists, the database creation for the current standard light is completed; otherwise, an independent database is created.

[0018] In the database creation sub-step, the operator determines whether the database creation is complete based on the requirements. If it is complete, the operator exits the database generation step.

[0019] The SDA spectral design automation system includes:

[0020] The curve selection module is equipped with a database that stores curve data and radiant flux parameters of standard lamps and imports corresponding target curves. Based on the target curves, multiple formula groups are generated. Each formula group contains setting parameters for at least one standard lamp, including lamp name, current value, and occupancy ratio.

[0021] The curve fitting module performs curve fitting calculations for each formulation group to generate a fitted curve. The curve fitting calculations include obtaining the absolute amplitude curve of each standard lamp group through radiation flux parameters and relative spectral curves, and superimposing the area integrals of all the absolute amplitude curves involved in the fitting to obtain the fitted curve.

[0022] The curve output module verifies the fitted curve using a multi-dimensional verification strategy, obtains the scoring results, selects the formula group with the highest score as the optimal formula group, and determines whether the optimal formula group meets the expectations. If it does, the optimal formula group is output; otherwise, the formula is initialized and a new formula group is generated.

[0023] The beneficial effects of this invention are as follows: By establishing a dedicated database storing standard lamp curve data and radiant flux parameters, a systematic data support is provided for formula group generation, avoiding the errors and inefficiencies of manual data collection. Simultaneously, multiple formula groups containing parameters such as lamp name, current value, and occupancy ratio are automatically generated based on the target curve, overcoming the limitations of manual design and covering more potential optimal solutions, significantly improving the efficiency and diversity of formula group generation. In the curve fitting step, the absolute amplitude curve of each standard lamp is accurately obtained through coupled calculation of radiant flux parameters and relative spectral curves. Then, the scientific synthesis of the spectrum is achieved through area integral superposition, reducing the deviation between the fitted curve and the target curve in terms of band distribution and amplitude, thus improving fitting accuracy. Attached Figure Description

[0024] Figure 1 This is an overall flowchart of the present invention;

[0025] Figure 2 This is a flowchart of the database creation process of this invention;

[0026] Figure 3This is a graph of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be noted that when a component is said to be fixed to another component, it can be directly on the other component or it may have a component in between. When a component is said to be connected to another component, it can be directly connected to the other component or it may have a component in between. When a component is said to be set to another component, it can be directly set to the other component or it may have a component in between. The terms vertical, horizontal, left, right, and similar expressions used in this document are for illustrative purposes only.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The terminology used herein includes, and / or encompasses, any and all combinations of one or more of the associated listed items.

[0030] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings:

[0031] like Figures 1-3 As shown, this invention provides an automated method for SDA spectral design, including:

[0032] The curve selection process involves setting up a database that stores curve data and radiant flux parameters of standard lamps. The database then imports the corresponding target curve. Based on the target curve, multiple formula groups are generated. Each formula group contains setting parameters for at least one standard lamp, including lamp name, current value, and occupancy ratio. Operators need to determine the target spectrum requirements based on the actual application scenario and import the target curve into the SDA system in a standardized format. After receiving the target curve, the system automatically preprocesses the data, including removing outlier data points and filling in missing wavelength range data, ultimately generating a clear and complete target curve data file.

[0033] The curve selection step includes a formula group generation step. This step uses standard lamps and test data stored in the database. The operator sets multiple different current values ​​and occupancy ratios to generate multiple formula groups. Based on the wavelength range and radiation intensity requirements of the target curve, the operator filters the database for potentially suitable standard lamps. Then, for each selected standard lamp, multiple different current values ​​and occupancy ratios are set. After receiving the parameters set by the operator, the system automatically combines them according to the dimensions of standard lamp selection, current value, and occupancy ratio to generate multiple formula groups.

[0034] It also includes a database generation step, which includes:

[0035] The standard lamp testing sub-step involves testing the standard lamps using a spectrometer and naming them based on the lamp chip performance and test data. A high-precision spectrometer is then used to test the lamp chips, covering key spectral parameters, including but not limited to relative spectral curves and radiant flux parameters. The basic performance indicators of the lamp chips are also recorded, including color temperature, color rendering index, and lifespan. Based on the test data and lamp chip performance, standardized naming rules are established, including key information that distinguishes the lamp chip characteristics, such as model number, color temperature, rated power, and calibration date. This ensures rapid identification of the lamp chip type during subsequent database searches and avoids confusion.

[0036] The database creation sub-step involves searching the database for the existence of a standard lamp with the current name. If it exists, the database creation for the current standard lamp is completed; otherwise, a separate database is created. In the existing database system, the name of the standard lamp to be included in the database is entered, and the automatic search function is activated. The system will traverse the stored standard lamp names in the database to determine if there is a completely identical entry. If the search result shows an existence, it means that the curve data and radiant flux parameters of this type of standard lamp have already been stored, and there is no need to re-enter them; the database creation process for the current standard lamp is completed directly. If the search result shows no existence, a separate database creation process is initiated. The relative spectral curves, radiant flux parameters, and basic performance indicators collected in the standard lamp testing sub-step are entered according to the database's preset format, such as a table, including the lamp name and relative spectral curve data. Simultaneously, a data index is created for easy and quick retrieval later.

[0037] In the database creation sub-step, the operator determines whether to complete the database creation based on requirements. If completed, the operator exits the database generation step. After completing the database creation sub-step for one or more standard lamps, the operator needs to determine whether to continue adding standard lamp data based on actual design requirements. If the types and quantities of standard lamps currently stored in the database can cover the design requirements of the subsequent target spectrum, the operator selects to complete the database creation, the system automatically saves the current database data, exits the database generation step, and proceeds to the subsequent curve selection stage. If the existing data still cannot meet the requirements, the operator returns to the standard lamp testing sub-step, reselects new calibrated lamp beads for testing and naming, and repeats the test-database creation process until the database data meets the design requirements.

[0038] The curve fitting step involves performing curve fitting calculations for each formulation group to generate a fitted curve. The curve fitting calculation includes obtaining the absolute amplitude curve of each standard lamp group using the radiant flux parameter and the relative spectral curve, and then performing area integration and superposition on all the absolute amplitude curves involved in the fitting to obtain the fitted curve.

[0039] For the current formula group to be fitted, the rated radiant flux parameters and relative spectral curve data of the corresponding standard lamps are retrieved from the database; at the same time, combined with the actual current value set in the formula group, the corrected radiant flux is calculated through the built-in current-radiant flux correction model to ensure that the parameters are consistent with the actual working state of the standard lamps; based on the corrected radiant flux as the total energy, combined with the wavelength distribution ratio of the relative spectral curve, the absolute amplitude curve of a single set of standard lamps is obtained through integral normalization calculation.

[0040] Absolute amplitude calculation formula: Taking general visible light as an example, the wavelength range is between 380nm and 780nm.

[0041] Sum = ∫(lower limit 380, upper limit 780)S(x)dx

[0042] Sum: The sum of relative spectral curves in the range of 380nm-780nm.

[0043] S(x): Value of the relative spectral curve at each nm

[0044] R(x) = S(x) * (Φ / Sum)

[0045] R(x): The value of the absolute amplitude curve per nm

[0046] Φ: Radiation flux value;

[0047] Once the absolute amplitude curves of all standard lamps within the formulation group have been calculated, a fitted curve is synthesized by superimposing area integrals. The specific steps are as follows: Because the relative spectral curves of different standard lamps may have different sampling intervals, the system first uses a linear interpolation algorithm to unify all absolute amplitude curves to the same sampling interval, ensuring that each wavelength point has corresponding absolute radiative intensity data and avoiding superposition errors. Using the aligned wavelength as the horizontal axis, for each wavelength point, the absolute radiative intensities of all standard lamps at that point are added together to obtain the intensity value of the fitted curve at that wavelength point. The intensity value data of all wavelength points are then connected sequentially to form an initial fitted curve. Finally, a moving average filtering algorithm is used to remove minor fluctuations, resulting in a smooth and continuous fitted curve.

[0048] The curve output step involves validating the fitted curve using a multi-dimensional validation strategy, obtaining a score, and selecting the formula group with the highest score as the optimal formula group. It then determines whether the optimal formula group meets expectations. If it does, the optimal formula group is output; otherwise, the formula is initialized and a new formula group is generated. The multi-dimensional validation strategy is used to obtain basic information about the standard lamp. Based on this information, production cost parameters are obtained, and the performance indicators of the formula group are analyzed using spectral characteristics as performance parameters. Similarity calculations are performed between these parameters and the target curve to obtain matching parameters. Finally, based on the production cost parameters, performance parameters, and matching parameters, a score is calculated using dynamic weighting. For all fitted curves generated in the curve fitting step, the system first initiates a multi-dimensional verification strategy, collecting data and calculating parameters from three dimensions: cost, performance, and spectral matching. Then, a dynamic weighting algorithm is used to generate a comprehensive score for each formulation group. Subsequently, the formulation group with the highest score is selected as the optimal formulation group and compared with preset expected standards such as cost ceiling, performance target value, and similarity threshold. If it meets the expectations, the complete parameters of the optimal formulation group are directly output. If it does not meet the expectations, the formulation initialization command is automatically triggered, returning to the curve selection step to regenerate a new formulation group and entering the next round of fitting-verification loop until a formulation group that meets the expectations is obtained. Based on current design requirements, dynamic weights are assigned to production cost parameters, performance parameters, and matching parameters. Weight adjustment rules are implemented for different application scenarios; performance parameters have higher weights in medical spectral design, while cost parameters have higher weights in industrial lighting. Subsequently, the three parameters are normalized, and a weighted summation is used to calculate the comprehensive score for each formulation group. The system sorts all formulation groups from highest to lowest comprehensive score, selecting the highest-scoring group as the optimal formulation group. The production cost parameters, performance parameters, and matching parameters of the optimal formulation group are then compared with preset expected standards. If all standards are met, the system is considered to have met expectations, and the complete parameters of the optimal formulation group are output. If any parameter fails to meet the standard, the system is deemed not to have met expectations, automatically initializes the formulation, returns to the curve selection step, regenerates a new formulation group, and proceeds to the next round of the process.

[0049] The multi-dimensional verification strategy includes a cost calculation sub-step. This sub-step obtains the material costs and production parameters of the standard lamps, and calculates the production cost parameters of the formulation group based on these parameters. Using the basic cost data of the standard lamps as the core, the system calculates the production cost parameters of a single formulation group, ensuring quantifiable assessment of the cost dimension. The system retrieves the basic cost data of all standard lamps within the current formulation group from the database, including two parts: first, material costs, such as the chip cost of the LED, packaging material cost, pin cost, etc., which are stored in the database by lamp name; second, production parameters, such as the production energy consumption, processing time, and testing costs of the standard lamps, which are stored by lamp name + current value.

[0050] Cost calculation: The total cost of the formula group is calculated based on the proportion of standard lamps used in the formula group.

[0051] The multi-dimensional verification strategy also includes a performance calculation sub-step. This sub-step is used to obtain the core performance parameters of the standard lamps, including radiant flux and spectral distribution. Performance parameters are generated through a weighted algorithm. The weighted algorithm integrates multi-dimensional performance indicators into a single performance parameter, avoiding the one-sidedness of single-indicator evaluation. Specifically, the process extracts the core performance parameters of all standard lamps within the formulation group from the database and curve fitting results, including: radiant flux and spectral distribution. First, weight coefficients are assigned to each core performance parameter; second, each performance parameter is normalized according to its actual value / standard value; finally, the performance parameter is calculated through weighted summation. The standard values ​​are derived from industry standards, internal company standards, and user-defined standards.

[0052] The multi-dimensional verification strategy also includes a similarity calculation sub-step. This sub-step is used to fit the curve and obtain key and non-key detection regions based on the target curve. It compares the key detection regions of the target curve and the fitted curve to generate key similarity values, setting a similarity threshold. When the key similarity value is greater than the threshold, the non-key similarity values ​​of the non-key detection regions are obtained. The non-key and key similarity values ​​are then weighted using a algorithm to obtain matching parameters. Through the logic of prioritizing key region verification, the similarity between the fitted curve and the target curve is accurately calculated, generating matching parameters and avoiding the influence of non-key region deviations on the overall judgment. Specifically, the system automatically or manually divides the key and non-key detection regions according to the application scenario of the target curve and records the wavelength range of each region. A cosine similarity algorithm is used to compare the overlap between the fitted curve and the target curve in the key detection regions. A preset similarity threshold is called. If the key similarity value is greater than the threshold, the non-key similarity values ​​of the non-key detection regions are calculated; if the key similarity value is less than the threshold, the matching degree is directly determined to be substandard, and the non-key similarity value is counted as 0. The key similarity values ​​and non-key similarity values ​​are integrated through a weighted algorithm, and the matching parameters are calculated.

[0053] The SDA spectral design automation system includes:

[0054] The curve selection module has a database that stores curve data and radiant flux parameters of standard lamps. It also imports the corresponding target curves and generates multiple recipe groups based on the target curves. Each recipe group contains setting parameters for at least one standard lamp, including lamp name, current value, and occupancy ratio.

[0055] The curve fitting module performs curve fitting calculations for each formulation group to generate a fitted curve. The curve fitting calculation includes obtaining the absolute amplitude curve of each standard lamp group through the radiation flux parameter and the relative spectral curve, and superimposing the area integrals of all the absolute amplitude curves involved in the fitting to obtain the fitted curve.

[0056] The curve output module verifies the fitted curve using a multi-dimensional verification strategy, obtains the scoring results, selects the formula group with the highest score as the optimal formula group, and determines whether the optimal formula group meets the expectations. If it does, the optimal formula group is output; otherwise, the formula is initialized and a new formula group is generated.

[0057] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An automated method for SDA spectral design, characterized in that, include: The curve selection step involves setting up a database containing curve data and radiant flux parameters of standard lamps, and importing the corresponding target curve. Based on the target curve, multiple formula groups are generated, and each formula group contains setting parameters for at least one standard lamp, including lamp name, current value, and occupancy ratio. The curve fitting step involves performing curve fitting calculations for each formulation group to generate a fitted curve. The curve fitting calculation includes obtaining the absolute amplitude curve of each standard lamp group through the radiant flux parameter and the relative spectral curve, and superimposing the area integrals of all the absolute amplitude curves involved in the fitting to obtain the fitted curve. The curve output step verifies the fitted curve using a multi-dimensional verification strategy, obtains the scoring results, selects the formula group with the highest score as the optimal formula group, and determines whether the optimal formula group meets the expectations. If it meets the expectations, the optimal formula group is output; otherwise, the formula is initialized and a new formula group is generated. The multi-dimensional verification strategy is used to obtain basic information about the standard lamp, obtain production cost parameters through the basic information about the standard lamp, and use the performance indicators of the formula group as performance parameters by analyzing spectral features. The similarity between the performance indicators and the target curve is calculated to obtain matching parameters. Based on the production cost parameters, performance parameters and matching parameters, the scoring result is obtained through dynamic weight calculation.

2. The automated SDA spectral design method according to claim 1, characterized in that, The multi-dimensional verification strategy includes a cost calculation sub-step, which is used to obtain the material cost and production parameters of the standard lamp, and to calculate the production cost parameters of the formula group based on the material cost and production parameters of the standard lamp.

3. The automated SDA spectral design method according to claim 1, characterized in that, The multi-dimensional verification strategy also includes a performance calculation sub-step, which is used to obtain the core performance parameters of the standard lamp, including radiant flux and spectral distribution, and generates performance parameters through a weighted algorithm.

4. The automated SDA spectral design method according to claim 1, characterized in that, The multi-dimensional verification strategy also includes a similarity calculation sub-step, which is used to fit a curve and obtain key detection regions and non-key detection regions based on the target curve. The key detection regions of the target curve and the fitted curve are compared to generate key similarity values, and a similarity value threshold is set. When the key similarity value is greater than the similarity value threshold, the non-key similarity values ​​of the non-key detection regions are obtained. The non-key similarity values ​​and key similarity values ​​are used to obtain matching parameters through a weighting algorithm.

5. The automated SDA spectral design method according to claim 1, characterized in that, The curve selection step includes a recipe group generation step. In this step, the operator sets multiple different current values ​​and occupancy ratios based on the standard lamps and test data stored in the database, thereby generating multiple recipe groups.

6. The automated SDA spectral design method according to claim 1, characterized in that, It also includes a database generation step, which includes: The standard lamp testing sub-step involves testing the standard lamps using a spectrometer and naming them based on the lamp chip performance and test data. The database creation sub-step involves searching the database to see if a standard light with the current name exists. If it exists, the database creation for the current standard light is completed; otherwise, an independent database is created. In the database creation sub-step, the operator determines whether the database creation is complete based on the requirements. If it is complete, the operator exits the database generation step.

7. An automated SDA spectral design system, characterized in that, include: The curve selection module is equipped with a database that stores curve data and radiant flux parameters of standard lamps and imports corresponding target curves. Based on the target curves, multiple formula groups are generated. Each formula group contains setting parameters for at least one standard lamp, including lamp name, current value, and occupancy ratio. The curve fitting module performs curve fitting calculations for each formulation group to generate a fitted curve. The curve fitting calculations include obtaining the absolute amplitude curve of each standard lamp group through radiation flux parameters and relative spectral curves, and superimposing the area integrals of all the absolute amplitude curves involved in the fitting to obtain the fitted curve. The curve output module verifies the fitted curve using a multi-dimensional verification strategy, obtains the scoring results, selects the formula group with the highest score as the optimal formula group, and determines whether the optimal formula group meets the expectations. If it meets the expectations, the optimal formula group is output; otherwise, the formula is initialized and a new formula group is generated. The multi-dimensional verification strategy is used to obtain basic information about the standard lamp, obtain production cost parameters through the basic information about the standard lamp, and use the performance indicators of the formula group as performance parameters by analyzing spectral features. The similarity between the performance indicators and the target curve is calculated to obtain matching parameters. Based on the production cost parameters, performance parameters and matching parameters, the scoring result is obtained through dynamic weight calculation.

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