RGB light source small integrating sphere jointed board calibration method and system

Abstract

The invention relates to the field of optical engineering, and provides an RGB light source small integrating sphere jointed board calibration method, which comprises the following steps: firstly, acquiring an integrating sphere jointed board under an RGB light source, analyzing optical structural elements of the integrating sphere jointed board to determine a spectral response range, evaluating initial calibration precision requirements, then detecting actual spectral data, calculating a calibration error coefficient, and calculating a calibration error coefficient; the method comprises the following steps: formulating a calibration strategy and implementing calibration operation, analyzing calibrated spectrum stable parameters, then calculating illumination adaptive values in different illumination environments to determine an optimized calibration mode, inquiring calibration interference factors to construct a calibration process, finally generating a calibration tracking system based on the calibration process, and monitoring calibration change conditions to obtain an analysis result. Therefore, a calibration management scheme is formulated. The integral calibration effect of the integrating sphere jointed board can be improved.

Description

Technical Field

[0001] The present invention relates to the field of optical engineering, and in particular to a method and system for calibrating a small integrating sphere panel of an RGB light source. Background Art

[0002] In the field of optical engineering, good sleep quality is crucial to people's physical and mental health. It is closely related to people's daily work efficiency, emotional state, physical immunity and many other aspects. In terms of sleep quality monitoring and analysis, the smart bed sleep quality analysis method is key to accurately assessing sleep conditions and providing personalized sleep recommendations.

[0003] At present, traditional sleep quality assessment mainly relies on subjective feelings and some simple wearable devices, such as bracelets. This method has exposed many shortcomings in the face of the growing demand for accurate sleep monitoring and complex and diverse individual sleep differences. On the one hand, subjective feelings are highly subjective and uncertain, and it is difficult to fully and objectively reflect the real sleep quality. On the other hand, although traditional wearable devices can collect some sleep data, the data dimensions are often limited. For example, bracelets mainly focus on monitoring heart rate, movement and other data, and lack multi-dimensional information such as changes in sleeping posture, mattress pressure distribution, and breathing patterns during sleep. It is impossible to conduct in-depth and accurate analysis of sleep quality, and it is difficult to meet the needs of personalized sleep improvement. Therefore, a calibration method for small integrating sphere panels with RGB light sources is needed to improve the overall effect of integrating sphere panel calibration. Summary of the invention

[0004] The present invention provides a method and system for calibrating a small integrating sphere panel of an RGB light source, the main purpose of which is to improve the overall effect of the calibration of the integrating sphere panel.

[0005] To achieve the above purpose, the present invention provides a RGB light source small integrating sphere mosaic calibration method, comprising:

[0006] Obtain an integrating sphere mosaic under RGB light source, analyze the optical structural elements of the integrating sphere mosaic, determine the spectral response range corresponding to the integrating sphere mosaic according to the optical structural elements, and evaluate the initial calibration accuracy requirement corresponding to the integrating sphere mosaic based on the spectral response range;

[0007] Based on the initial calibration accuracy requirement, actual spectral data corresponding to the integrating sphere mosaic are detected, spectral deviation points corresponding to the integrating sphere mosaic are analyzed according to the actual spectral data, and calibration error coefficients corresponding to the integrating sphere mosaic are calculated according to the spectral deviation points;

[0008] Based on the calibration error coefficient, a calibration strategy corresponding to the integrating sphere mosaic is formulated; according to the calibration strategy, a calibration operation corresponding to the integrating sphere mosaic is performed; data changes in the calibration operation are recorded; and based on the data changes, a spectral stability parameter corresponding to the integrating sphere mosaic after calibration is analyzed;

[0009] Based on the spectral stability parameter, calculate the illumination adaptation value corresponding to the integrating sphere puzzle under different illumination environments, determine the optimized calibration mode corresponding to the integrating sphere puzzle based on the illumination adaptation value, query the calibration interference factor in the optimized calibration mode, and construct the calibration process corresponding to the integrating sphere puzzle based on the calibration interference factor;

[0010] Based on the calibration process, a calibration tracking system corresponding to the integrating sphere puzzle is generated; based on the calibration tracking system, calibration changes corresponding to the integrating sphere puzzle are monitored, and the calibration changes are analyzed in real time to obtain calibration analysis results; based on the calibration analysis results, a calibration management plan corresponding to the integrating sphere puzzle is formulated.

[0011] Optionally, the evaluating the initial calibration accuracy requirement corresponding to the integrating sphere panel based on the spectral response range includes:

[0012] Determining a sensitive spectral band in the spectral response range;

[0013] Analyzing the light intensity variation characteristics corresponding to the sensitive spectral band;

[0014] Based on the light intensity variation characteristics, the measurement error range of the integrating sphere panel under different light intensities is preliminarily analyzed;

[0015] Based on the measurement error range, an initial accuracy reference interval corresponding to the integrating sphere mosaic is formulated;

[0016] Based on the initial accuracy reference interval, the initial calibration accuracy requirement corresponding to the integrating sphere mosaic is evaluated.

[0017] Optionally, based on the initial calibration accuracy requirement, detecting actual spectral data corresponding to the integrating sphere mosaic panel includes:

[0018] Determining key accuracy indicators in the initial calibration accuracy requirements;

[0019] Analyze the spectral accuracy range corresponding to the key accuracy index;

[0020] Based on the spectral accuracy range, determining the detection direction of the integrating sphere panel for the actual spectrum;

[0021] Based on the detection direction, collecting actual spectrum samples corresponding to the integrating sphere mosaic;

[0022] Based on the actual spectrum sample, actual spectrum data corresponding to the integrating sphere panel is detected.

[0023] Optionally, calculating the calibration error coefficient corresponding to the integrating sphere panel according to the spectral deviation point includes:

[0024] The calibration error coefficient corresponding to the integrating sphere panel is calculated using the following formula:

[0025]

[0026] Wherein, BW represents the calibration error coefficient corresponding to the integrating sphere mosaic, n represents the total number of spectral deviation points, i represents the number index corresponding to the spectral deviation points, γ i represents the wavelength of the ith spectral deviation point, I actual (γ i Indicates the wavelength γ i The actual light intensity, I ideal (γ i ) is at wavelength γ i The ideal light intensity at t0 and t1 represent the starting point and end point of the measurement time range, m represents the total number of calibration time points, j represents the number index corresponding to the calibration time point, P j (t) represents the light source measurement parameter corresponding to the jth calibration time point at time t.

[0027] Optionally, formulating a calibration strategy corresponding to the integrating sphere panel based on the calibration error coefficient includes:

[0028] Dividing the calibration error coefficient into coefficient intervals to obtain coefficient division intervals;

[0029] identifying coefficient error sources in the coefficient partition interval;

[0030] Classifying the coefficient error sources to obtain a classified error source set;

[0031] Analyzing the error source mechanism corresponding to the classified error source set;

[0032] Calibrate the error source mechanism to obtain a calibration result;

[0033] Based on the verification result, a calibration strategy corresponding to the integrating sphere panel is formulated.

[0034] Optionally, analyzing the spectral stability parameters corresponding to the integrating sphere panel calibration based on the data change includes:

[0035] Extracting a data change sequence from the data change situation;

[0036] Performing trend analysis on the data change sequence to obtain trend analysis results;

[0037] Querying key trend parameters in the trend analysis results;

[0038] Performing parameter fitting on the key trend parameters to obtain a trend fitting curve;

[0039] Extracting a fitting stability index from the trend fitting curve;

[0040] Based on the fitting stability index, the spectral stability parameters corresponding to the integrating sphere panel calibration are analyzed.

[0041] Optionally, the calculating, based on the spectral stability parameter, the illumination adaptation value corresponding to the integrating sphere panel under different illumination environments includes:

[0042] The following formula is used to calculate the illumination adaptation value of the integrating sphere panel under different illumination environments:

[0043]

[0044] Wherein, LA represents the illumination adaptation value of the integrating sphere puzzle corresponding to different illumination environments, k represents the number of environments corresponding to different illumination environments, p represents the number index corresponding to different illumination environments, SSP P represents the spectral stability parameter corresponding to the Pth illumination environment, I p represents the light intensity in the Pth lighting environment, α p represents the weight coefficient related to the pth lighting environment, U represents the lighting uniformity, β represents the weight coefficient corresponding to the lighting uniformity, c represents the total number of parameters of the lighting environment change parameter, v represents the parameter index corresponding to the lighting environment change parameter, t'1 and t'2 represent the interval start and end of the lighting duration interval respectively, L v (t) The function of the vth lighting environment change parameter changing with time t.

[0045] Optionally, constructing a calibration process corresponding to the integrating sphere panel based on the calibration interference factor includes:

[0046] Classifying the calibration interference factors to obtain a factor classification set;

[0047] Query the number of classifications in the factor classification set;

[0048] Based on the number of classifications, generating a calibration compensation strategy corresponding to the calibration interference factor;

[0049] Querying the calibration operation steps in the calibration compensation strategy;

[0050] Constructing a calibration step framework corresponding to the calibration operation steps;

[0051] Based on the calibration step framework, a calibration process corresponding to the integrating sphere panel is determined.

[0052] Optionally, generating a calibration tracking system corresponding to the integrating sphere panel based on the calibration process includes:

[0053] Perform node identification on key nodes in the calibration process to obtain an identification node set;

[0054] Extracting node features from the identified node set;

[0055] Constructing a feature vector set corresponding to the node feature;

[0056] Setting monitoring indicators corresponding to the feature vector set;

[0057] Analyze the indicator tracking index corresponding to the monitoring indicator;

[0058] Based on the indicator tracking index, a calibration tracking system corresponding to the integrating sphere puzzle is generated.

[0059] Optionally, in order to solve the above problem, the present invention provides an RGB light source small integrating sphere mosaic calibration system, the system comprising:

[0060] An evaluation module is required to obtain an integrating sphere mosaic under an RGB light source, analyze the optical structural elements of the integrating sphere mosaic, determine the spectral response range corresponding to the integrating sphere mosaic according to the optical structural elements, and evaluate the initial calibration accuracy requirement corresponding to the integrating sphere mosaic based on the spectral response range;

[0061] An error coefficient calculation module is used to detect actual spectral data corresponding to the integrating sphere puzzle based on the initial calibration accuracy requirement, analyze spectral deviation points corresponding to the integrating sphere puzzle according to the actual spectral data, and calculate the calibration error coefficient corresponding to the integrating sphere puzzle according to the spectral deviation points;

[0062] A parameter analysis module, for formulating a calibration strategy corresponding to the integrating sphere mosaic based on the calibration error coefficient, performing a calibration operation corresponding to the integrating sphere mosaic according to the calibration strategy, recording data changes during the calibration operation, and analyzing spectral stability parameters corresponding to the integrating sphere mosaic after calibration based on the data changes;

[0063] A process construction module, for calculating the illumination adaptation value corresponding to the integrating sphere panel under different illumination environments based on the spectral stability parameter, determining the optimized calibration mode corresponding to the integrating sphere panel based on the illumination adaptation value, querying the calibration interference factor in the optimized calibration mode, and constructing the calibration process corresponding to the integrating sphere panel based on the calibration interference factor;

[0064] A plan formulation module is used to generate a calibration tracking system corresponding to the integrating sphere puzzle based on the calibration process, monitor the calibration changes corresponding to the integrating sphere puzzle based on the calibration tracking system, perform real-time analysis on the calibration changes to obtain calibration analysis results, and formulate a calibration management plan corresponding to the integrating sphere puzzle based on the calibration analysis results.

[0065] Firstly, the present invention obtains the integrating sphere puzzle under the RGB light source, analyzes the optical structural elements of the integrating sphere puzzle, and can accurately determine the spectral response range according to the structure, thereby providing a key basis for evaluating the initial calibration accuracy requirement, helping to build a scientific and reasonable calibration system, improving the accuracy and efficiency of RGB light source calibration, and ensuring the accurate and stable color presentation of RGB light sources in multi-scene applications. At the same time, the present invention detects the actual spectral data corresponding to the integrating sphere puzzle based on the initial calibration accuracy requirement, can measure the true spectral performance of the integrating sphere puzzle according to the established accuracy standard, accurately know its fit with the expected accuracy, can timely discover deviations and anomalies in the actual spectral data, and provide a reliable basis for subsequent analysis of spectral deviation points and optimization of calibration work. The present invention formulates a calibration strategy corresponding to the integrating sphere puzzle based on the calibration error coefficient, can adjust the integrating sphere puzzle in a targeted manner according to the error coefficient, makes the calibration work more targeted, and can effectively reduce Low calibration error coefficient, helps to establish a long-term and effective calibration mechanism, ensures that the integrating sphere panel always maintains good performance during use, and reduces problems caused by measurement deviations. The present invention is based on the spectral stability parameter, calculates the corresponding illumination adaptation value of the integrating sphere panel under different illumination environments, can accurately measure the adaptability of the integrating sphere panel to various illumination environments, and provides a performance evaluation basis for its application in complex optical scenes, can quickly screen out the integrating sphere panel that is most suitable for a specific illumination environment, optimize the optical system configuration, and improve the accuracy and reliability of overall optical detection and experiments. Further, the present invention is based on the calibration process, generates a calibration tracking system corresponding to the integrating sphere panel, can monitor the progress of each link of the calibration in real time, and promptly discover the anomalies and deviations in the process, which is convenient for rapid adjustment and optimization, and helps to accumulate the data and situations of each calibration, and provides a strong basis for subsequent analysis of the calibration effect and improvement of the calibration strategy, and ensures the accuracy and reliability of the calibration of the integrating sphere panel. Therefore, a RGB light source small integrating sphere panel calibration method and system proposed by the present invention can improve the overall effect of the calibration of the integrating sphere panel. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of a flow chart of a method for calibrating a small integrating sphere panel of an RGB light source provided by an embodiment of the present invention;

[0067] Figure 2 A schematic diagram of a module for implementing a small integrating sphere panel calibration system for an RGB light source provided in one embodiment of the present invention.

[0068] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0069] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0070] The embodiment of the present application provides a method for calibrating a small integrating sphere mosaic panel of an RGB light source. The execution subject of the method for calibrating a small integrating sphere mosaic panel of an RGB light source includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for calibrating a small integrating sphere mosaic panel of an RGB light source can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0071] Embodiment 1:

[0072] Reference Figure 1 FIG. 1 is a flow chart of a method for calibrating a small integrating sphere panel of an RGB light source provided by an embodiment of the present invention. In this embodiment, the method for calibrating a small integrating sphere panel of an RGB light source includes:

[0073] S1. Obtain an integrating sphere panel under an RGB light source, analyze the optical structural elements of the integrating sphere panel, determine the spectral response range corresponding to the integrating sphere panel according to the optical structural elements, and evaluate the initial calibration accuracy requirement corresponding to the integrating sphere panel based on the spectral response range.

[0074] The present invention obtains an integrating sphere puzzle under an RGB light source and analyzes the optical structural elements of the integrating sphere puzzle, so as to accurately determine the spectral response range according to the structure, thereby providing a key basis for evaluating the initial calibration accuracy requirement, and helping to build a scientific and reasonable calibration system, improve the accuracy and efficiency of RGB light source calibration, and ensure the accurate and stable color presentation of RGB light sources in multi-scenario applications.

[0075] Among them, the RGB light source refers to a light source that can emit red, green, and blue light. Various colors can be produced by mixing these three primary colors in different proportions. It is widely used in many fields such as display backlight and stage lighting; the integrating sphere puzzle refers to a device composed of a plurality of integrating spheres, which is used to perform specific operations such as collecting, mixing, and measuring light to realize the analysis of light source characteristics; the optical structural elements refer to the structural components in the integrating sphere puzzle that are related to the propagation, reflection, and scattering of light, such as the inner wall material of the integrating sphere, shape, opening size and position, as well as internal light-blocking plates, detector installation positions, etc. Optionally, the obtaining of the integrating sphere puzzle under the RGB light source can be achieved by a mechanical assembly method, such as: using an automated robotic arm, under precise programmed control, placing individual integrating spheres in predetermined positions in turn and connecting and fixing them; the analysis of the optical structural elements of the integrating sphere puzzle can be achieved by a ray tracing method, such as: using a ray tracing algorithm to simulate the reflection, refraction, scattering and other processes of light after it enters the integrating sphere puzzle from the light source, thereby determining optical structural elements such as the reflectivity of the inner wall and the influence of the opening on the light path.

[0076] Furthermore, the present invention can accurately control the sensitivity and receiving capacity of the integrating sphere panel to light of different wavelengths by determining the spectral response range corresponding to the integrating sphere panel according to the optical structural elements, thereby laying a foundation for subsequent calibration work, helping to optimize the matching degree between the light source and the integrating sphere panel, and enabling the light emitted by the RGB light source to be processed more efficiently and accurately in the integrating sphere panel.

[0077] Among them, the optical structural elements refer to various physical structure-related factors in the integrating sphere puzzle that affect light propagation and optical performance, including the geometric shape of the integrating sphere puzzle (such as the radius of the sphere, the overall size and shape of the puzzle), the internal reflective coating (reflectivity, diffuse reflection characteristics), the position, size and number of openings (because the openings will affect the entry and exit of light and the internal light field distribution), the internal blocking structure (such as the position, shape and angle of the light baffle), and the installation position of the detector in the integrating sphere puzzle; the spectral response range refers to the wavelength range of light that the integrating sphere puzzle can effectively respond to. For example, the spectral response range of a certain integrating sphere puzzle is 400-700nm, which means that the integrating sphere puzzle can better process light within this wavelength range, and light outside this range cannot be effectively utilized or will cause inaccurate measurements. Optionally, the determination of the spectral response range corresponding to the integrating sphere puzzle can be achieved through optical simulation tools, such as: TracePro, LightTools, etc.

[0078] The present invention evaluates the initial calibration accuracy requirements corresponding to the integrating sphere puzzle based on the spectral response range, and can reasonably set the accuracy standard according to the sensitivity and response characteristics of the integrating sphere puzzle to a specific spectrum, thereby avoiding waste of resources and increased errors caused by excessively high or low requirements. At the same time, it can ensure that the calibration accuracy is compatible with the optical performance of the integrating sphere puzzle in actual application scenarios, thereby improving the reliability and practicality of the calibration results.

[0079] Among them, the initial calibration accuracy requirement refers to the precise calibration accuracy index determined for the integrating sphere panel in a specific application scenario and spectral response characteristics to ensure the accuracy and reliability of its spectral measurement. For example, for high-precision spectral analysis applications, the initial calibration accuracy requirement can be set to ±1%. This requires that during the calibration process, the various parameters of the integrating sphere panel be accurately adjusted and calibrated so that its measurement error is controlled within this smaller range.

[0080] As an embodiment of the present invention, the evaluating the initial calibration accuracy requirement corresponding to the integrating sphere panel based on the spectral response range includes: determining the sensitive spectral band in the spectral response range; analyzing the light intensity variation characteristics corresponding to the sensitive spectral band; based on the light intensity variation characteristics, preliminarily analyzing the measurement error range of the integrating sphere panel under different light intensities; based on the measurement error range, formulating the initial accuracy reference interval corresponding to the integrating sphere panel; based on the initial accuracy reference interval, evaluating the initial calibration accuracy requirement corresponding to the integrating sphere panel.

[0081] Among them, the sensitive spectral band refers to the specific wavelength range within the spectral response range of the integrating sphere puzzle, where the optical properties such as light absorption, reflection, and transmission change more significantly, and have a greater impact on the overall performance of the integrating sphere puzzle (such as light collection efficiency, spectral measurement accuracy, etc.). For example, in some integrating sphere puzzles, the 400-500nm band is a sensitive spectral band, because in this band, the reflectivity of the inner wall coating of the integrating sphere puzzle changes rapidly with the wavelength, which will significantly affect the propagation and mixing effects of light in the sphere; the light intensity change characteristics refer to the law that the light intensity shows as the wavelength changes within the sensitive spectral band, including the trend of increase and decrease of light intensity, the rate of change, and the position of peak and valley values. and size, etc.; the measurement error range refers to the deviation range between the measurement result and the true value when the integrating sphere puzzle is used for spectral measurement under different light intensities. For example, when the light intensity is low, the signal-to-noise ratio of the detector decreases, which will cause the measurement error to increase; and when the light intensity is too high, light saturation will occur, which will also cause the measurement error to exceed the normal range. The interval defined by the upper and lower limits of the error is the measurement error range; the initial accuracy reference interval refers to an approximate range preliminarily determined based on the measurement error range for evaluating the calibration accuracy of the integrating sphere puzzle. For example, if the measurement error range fluctuates between ±2% and ±5%, then the initial accuracy reference interval can be set to ±3% to ±6%.

[0082] Furthermore, the determination of the sensitive spectral band in the spectral response range can be achieved by a spectral scanning method, such as: using a spectrometer to carefully scan the integrating sphere panel in the entire spectral response range, and recording parameters such as luminous flux, reflectivity or absorptivity at different wavelengths; the analysis of the light intensity change characteristics corresponding to the sensitive spectral band can be achieved by an optical power meter, such as: Thorlabs' PM100D optical power meter; the preliminary analysis of the measurement error range of the integrating sphere panel under different light intensities can be achieved by a standard light source comparison method, such as: using a standard light source with known spectrum and light intensity (such as a standard halogen tungsten lamp), introducing it into the integrating sphere panel at different light intensities, and using a high-precision spectrometer to measure the output spectrum of the integrating sphere panel, and comparing it with the known light intensity of the standard light source. The spectra are compared, and the measurement error range is determined by calculating the spectral differences (such as spectral matching, light intensity deviation at a specific wavelength, etc.); the proposed initial accuracy reference interval corresponding to the integrating sphere puzzle can be achieved by data statistical tools, such as Excel, SPSS and other tools; the evaluation of the initial calibration accuracy requirements corresponding to the integrating sphere puzzle can be achieved by application scenario demand analysis method, such as: according to the specific application scenarios of the integrating sphere puzzle, such as display backlight detection, stage lighting calibration or spectral analysis in scientific research, the requirements for spectral measurement accuracy vary greatly. Combined with the key indicators that are sensitive to spectral accuracy in the application scenarios (such as color coordinate accuracy, spectral resolution, etc.), refer to the initial accuracy reference interval to determine the final initial calibration accuracy requirements.

[0083] S2. Based on the initial calibration accuracy requirement, detect the actual spectral data corresponding to the integrating sphere puzzle, analyze the spectral deviation points corresponding to the integrating sphere puzzle according to the actual spectral data, and calculate the calibration error coefficient corresponding to the integrating sphere puzzle according to the spectral deviation points.

[0084] The present invention detects the actual spectral data corresponding to the integrating sphere puzzle based on the initial calibration accuracy requirement, can measure the actual spectral performance of the integrating sphere puzzle according to the established accuracy standard, accurately know its fit with the expected accuracy, and can promptly discover deviations and anomalies in the actual spectral data, providing a reliable basis for subsequent analysis of spectral deviation points and optimization of calibration work.

[0085] The actual spectral data refers to the detailed data obtained after comprehensive and systematic collection and measurement of the actual spectrum of the integrating sphere panel, including information such as light intensity values ​​at different wavelengths, spectral curve shapes, and spectral bandwidth.

[0086] As an embodiment of the present invention, the actual spectral data corresponding to the integrating sphere puzzle is detected based on the initial calibration accuracy requirement, including: determining the key accuracy indicators in the initial calibration accuracy requirement; analyzing the spectral accuracy range corresponding to the key accuracy indicators; based on the spectral accuracy range, determining the detection direction of the integrating sphere puzzle for the actual spectrum; based on the detection direction, collecting actual spectral samples corresponding to the integrating sphere puzzle; based on the actual spectral samples, detecting the actual spectral data corresponding to the integrating sphere puzzle.

[0087] Among them, the key accuracy index refers to a specific accuracy parameter that plays a decisive role in the initial calibration accuracy requirements and has a significant impact on the overall calibration accuracy of the integrating sphere puzzle. For example, it may be the allowable error range of light intensity measurement at a specific wavelength, the minimum standard value of spectral resolution, the accuracy requirement of wavelength measurement, etc.; the spectral accuracy range refers to the allowable accuracy fluctuation range in spectral measurement determined based on the key accuracy index. For example, if the key accuracy index stipulates that the light intensity measurement accuracy at a certain wavelength is ±2%, then the spectral accuracy range corresponding to the wavelength is centered on the theoretical accurate value and fluctuates by 2%; the actual spectrum refers to the spectral distribution of light received, processed and output by the RGB light source during the actual operation or test of the integrating sphere puzzle, which reflects the integrating sphere puzzle under real working conditions. Comprehensive optical properties such as response, transmission and mixing of light of different wavelengths; The detection direction refers to the key focus direction and strategy determined according to the spectral accuracy range when collecting and analyzing the actual spectrum. For example, if the spectral accuracy range shows that the accuracy requirement in the blue light band is more stringent, then the detection direction will focus on the detailed collection and analysis of the blue light band spectral data, including the detection of the light intensity change, spectral shape, and proportional relationship with other bands in the blue light band; The actual spectrum sample refers to a part of a representative spectral data set selected from the actual spectrum output by the integrating sphere puzzle according to specific sampling rules and detection directions. These samples can be selected at different time points, under different light intensity input conditions or in different spectral regions, and are used to preliminarily analyze and understand the general characteristics and changing trends of the actual spectrum of the integrating sphere puzzle.

[0088] Furthermore, the determination of the key accuracy indicators in the initial calibration accuracy requirements can be achieved through a requirements hierarchy analysis method, such as: according to the application scenario of the integrating sphere puzzle, the accuracy requirements of spectral measurement are decomposed into different levels, such as basic accuracy requirements (including wavelength measurement accuracy, light intensity measurement accuracy, etc.), advanced accuracy requirements (such as spectral resolution, color coordinate accuracy, etc.), and by comparing the importance of each level of requirements in pairs, constructing a judgment matrix, and calculating the relative weight of each accuracy indicator, thereby determining the key accuracy indicators; the analysis of the spectral accuracy range corresponding to the key accuracy indicators can be achieved through a quality function deployment tool, such as: using tools such as QFD, customer needs (such as requirements for spectral accuracy) can be converted into product design requirements (spectral accuracy range), and by establishing a quality house, the key accuracy indicators and spectral accuracy can be analyzed. The relationship between the degree range is used to determine the appropriate accuracy range; the determination of the detection direction of the integrating sphere puzzle for the actual spectrum can be achieved by a partial derivative calculation method, such as: calculating the partial derivatives of key accuracy indicators with respect to different spectral parameters (such as wavelength, light intensity, etc.), and the spectral region corresponding to the parameter with a larger absolute value of the partial derivative is the key detection direction; the collection of actual spectrum samples corresponding to the integrating sphere puzzle can be achieved by a stratified sampling method, such as: selecting sample points according to equally spaced wavelengths in the visible light region, and selecting sample points of some representative wavelengths in the ultraviolet region according to their importance in affecting the overall spectrum; the detection of the actual spectrum data corresponding to the integrating sphere puzzle can be achieved by a comparative detection method, such as: comparing the collected actual spectrum data with the standard spectrum template generated based on the initial calibration accuracy requirements to obtain the actual spectrum data.

[0089] The present invention analyzes the spectral deviation points corresponding to the integrating sphere puzzle based on the actual spectral data, and can accurately locate the places where the spectral performance of the integrating sphere puzzle is inconsistent with expectations, providing clear clues for subsequent finding of the root cause of the problem, and helping to specifically evaluate the performance of each link of the integrating sphere puzzle, thereby guiding optimization and calibration work and improving the accuracy and stability of its spectral measurement.

[0090] Among them, the spectral deviation point refers to the specific position or interval where the actual spectrum deviates from the expected value in terms of wavelength, light intensity, spectral shape, bandwidth, etc. in the process of comparing the actual spectral data of the integrating sphere puzzle with the expected spectral data (obtained based on the initial calibration accuracy requirements). Specifically, in the wavelength dimension, if the light intensity corresponding to a certain wavelength in the actual spectrum is significantly different from the expected light intensity, this wavelength position is the spectral deviation point. Optionally, the analysis of the spectral deviation point corresponding to the integrating sphere puzzle can be achieved through visualization tools, such as: OriginPro, Excel and other tools.

[0091] The present invention calculates the calibration error coefficient corresponding to the integrating sphere puzzle according to the spectral deviation point, and can quantify the spectral deviation as the calibration error coefficient, so that the deviation degree of the integrating sphere puzzle is accurately reflected in numerical value, which is convenient for intuitively evaluating the deviation from the standard. The calibration accuracy of the integrating sphere puzzle can be accurately judged according to the coefficient, which provides a key basis for the subsequent formulation of calibration strategy and improves the pertinence of subsequent calibration work.

[0092] The calibration error coefficient refers to a quantitative indicator used to measure the degree of deviation between the integrating sphere panel and the ideal state in terms of spectral measurement.

[0093] As an embodiment of the present invention, the step of calculating the calibration error coefficient corresponding to the integrating sphere panel according to the spectral deviation point includes:

[0094] The calibration error coefficient corresponding to the integrating sphere panel is calculated using the following formula:

[0095]

[0096] Wherein, BW represents the calibration error coefficient corresponding to the integrating sphere mosaic, n represents the total number of spectral deviation points, i represents the number index corresponding to the spectral deviation points, γ i represents the wavelength of the ith spectral deviation point, I actual (γ i Indicates the wavelength γ i The actual light intensity, I ideal (γ i ) is at wavelength γ i The ideal light intensity at t0 and t1 represent the starting point and end point of the measurement time range, m represents the total number of calibration time points, j represents the number index corresponding to the calibration time point, P j (t) represents the light source measurement parameter corresponding to the jth calibration time point at time t.

[0097] Specifically, the actual light intensity refers to the wavelength γ at a specific spectral deviation point during the actual measurement of the integrating sphere mosaic panel. i The light intensity value measured at this location. This value reflects the light intensity output of the integrating sphere panel under actual working conditions; the ideal light intensity refers to the wavelength γ at a specific spectral deviation point. iThe light intensity value expected according to the initial calibration accuracy requirements of the integrating sphere puzzle or the theoretical design; the measurement time range refers to the time interval for measuring the light intensity of the integrating sphere puzzle, from the start time t0 to the end time t1. This time range covers the entire time period of the measurement process, which is used to comprehensively consider the changes in light intensity during the measurement process; the calibration time point refers to a specific time point selected within the measurement time range, which is used to measure and analyze the light intensity of the integrating sphere puzzle; the light source measurement parameters refer to the measurement parameters related to the light source at different calibration time points. These parameters may include the power, luminous efficiency, spectral distribution, etc. of the light source.

[0098] S3. Based on the calibration error coefficient, formulate a calibration strategy corresponding to the integrating sphere puzzle, implement the calibration operation corresponding to the integrating sphere puzzle according to the calibration strategy, record the data changes in the calibration operation, and based on the data changes, analyze the spectral stability parameters corresponding to the integrating sphere puzzle after calibration.

[0099] The present invention formulates a calibration strategy corresponding to the integrating sphere puzzle based on the calibration error coefficient, and can adjust the integrating sphere puzzle in a targeted manner according to the error coefficient, making the calibration work more targeted, and can effectively reduce the calibration error coefficient, which is helpful to establish a long-term and effective calibration mechanism, ensuring that the integrating sphere puzzle always maintains good performance during use and reducing problems caused by measurement deviations.

[0100] Among them, the calibration strategy refers to a set of methods and solutions for adjusting and optimizing the performance of the integrating sphere panel formulated based on the verification results. If the verification results show that the calibration measures effectively reduce the calibration error coefficient, then the calibration strategy can include standardizing this calibration method, determining the calibration cycle, etc.

[0101] As an embodiment of the present invention, the calibration strategy corresponding to the integrating sphere puzzle is formulated based on the calibration error coefficient, including: dividing the calibration error coefficient into coefficient intervals to obtain coefficient division intervals; identifying coefficient error sources in the coefficient division intervals; classifying the coefficient error sources to obtain a classified error source set; analyzing the error source mechanism corresponding to the classified error source set; calibrating the error source mechanism to obtain a verification result; and formulating the calibration strategy corresponding to the integrating sphere puzzle based on the verification result.

[0102] Among them, the coefficient division interval refers to dividing the calibration error coefficient into several different numerical ranges according to certain rules and standards. For example, the calibration error coefficient can be divided into a low error interval (0-0.05), a medium error interval (0.05-0.15) and a high error interval (above 0.15) according to historical data, industry standards or empirical values; the coefficient error source refers to the specific reason that causes the calibration error coefficient to deviate from the ideal value. For example, in the high error interval, it may be a manufacturing process defect of the integrating sphere puzzle, such as uneven inner wall coating resulting in inconsistent light reflection; or external environmental factors, such as the influence of temperature and humidity on optical components; the classified error source set refers to classifying and summarizing the coefficient error sources identified in different coefficient division intervals according to their properties, sources and other characteristics. to a set; the error source mechanism refers to an in-depth analysis of the principle and process of error generation for each type of error source in the classified error source set. Taking manufacturing process defects as an example, the error source mechanism may be that during the processing of the integrating sphere puzzle, improper operation in a certain production link leads to geometric shape deviation of the optical element, thereby affecting the light propagation path and light intensity distribution, which is ultimately reflected in the calibration error coefficient; the verification result refers to the result obtained by re-measurement and evaluation after targeted calibration of the error source mechanism. For example, after taking measures to re-process or repair the optical element for the error source mechanism caused by manufacturing process defects, the calibration error coefficient of the integrating sphere puzzle is measured again and compared with the coefficient before calibration. This comparison result and the evaluation of the effectiveness of the calibration measures are the verification results.

[0103] Furthermore, the coefficient interval division of the calibration error coefficient can be achieved through a statistical binning method, such as: collecting a large amount of historical data of the calibration error coefficient, and dividing the interval according to the distribution characteristics of the data, such as the mean, standard deviation and other statistical quantities; the identification of the coefficient error source in the coefficient division interval can be achieved through a cause-and-effect diagram method, such as: in a high error interval, taking the calibration error coefficient as the result, and taking the factors therein such as the material properties of the integrating sphere puzzle, the stability of the light source, the measurement environment, etc. as the causes, and identifying the error source by analyzing the cause-and-effect relationship therebetween; the classification processing of the coefficient error source can be achieved through a clustering algorithm, such as: K-Means clustering and other algorithms; the analysis of the error source mechanism corresponding to the classified error source set can be achieved through The fault tree analysis method is implemented, such as: for the classified error source set of "optical material aging", the analysis of its underlying events can include changes in the chemical composition of the material, too long illumination time, etc., and whether there is an "and" relationship or an "or" relationship between them, and a fault tree is constructed to deeply analyze the error source mechanism; the calibration processing of the error source mechanism can be implemented through a model predictive control method, such as: for the error source mechanism caused by temperature change, a temperature-error relationship model is established, and the error generated is predicted according to the current temperature, and then calibrated by adjusting the temperature compensation device of the integrating sphere puzzle; the formulation of the calibration strategy corresponding to the integrating sphere puzzle can be implemented through a decision tree method, such as: based on the verification results and error source analysis, a decision tree is constructed to formulate a calibration strategy.

[0104] The present invention implements the calibration operation corresponding to the integrating sphere puzzle according to the calibration strategy, and records the data changes in the calibration operation, so as to ensure that the calibration operation is carried out in an orderly manner according to the established strategy, ensure the accuracy and standardization of the calibration, and can intuitively present the calibration effect, so as to facilitate timely discovery of abnormalities and targeted adjustments, accumulate experience data for subsequent calibration work, and be conducive to further optimizing the calibration strategy, so as to ensure that the integrating sphere puzzle is in a good performance state for a long time.

[0105] The calibration operation refers to a series of adjustments and corrections performed on the integrating sphere panels according to the established calibration strategy. For example, if the calibration strategy indicates that the calibration error coefficient is high due to the aging of optical materials, the calibration operation may include replacing the aged optical material components, adjusting the emission angle or intensity of the light source, optimizing the sensitivity setting of the detector, etc. The data change refers to the dynamic changes in the data related to the performance of the integrating sphere panels during the calibration operation, specifically including the real-time changes in the calibration error coefficient, which reflects the direct impact of the calibration operation on the overall performance; and the changes in the light intensity measurement values ​​in different spectral bands, which reflects the adjustment effect on the spectral characteristics; and Fluctuations in data such as light source stability indicators (such as power fluctuation range) and detector response time. Optionally, the calibration operation corresponding to the integrating sphere puzzle can be implemented through a step-by-step calibration method, such as: first make a preliminary adjustment to the key parameters of the integrating sphere puzzle, then measure the calibration error coefficient, and further fine-tune the parameters according to the measurement results, and iterate repeatedly to gradually approach the ideal calibration accuracy requirements; the recording of data changes in the calibration operation can be implemented through a database management system, such as: creating a special data recording table before the calibration operation begins, containing fields such as timestamp, calibration steps, calibration error coefficient, and parameters of key components (such as light source power, detector gain, etc.).

[0106] Furthermore, based on the data changes, the present invention analyzes the spectral stability parameters corresponding to the integrating sphere panel after calibration, and can intuitively know the stability of the spectrum after calibration according to the data, accurately judge whether the calibration effect meets expectations, provide strong support for the quality evaluation of the calibration work, and help to timely discover potential unstable factors, so as to optimize the subsequent calibration strategy or perform maintenance in a targeted manner, and ensure the long-term stable operation of the integrating sphere panel.

[0107] Among them, the spectral stability parameters refer to a set of parameters that are obtained after comprehensive consideration of the fitting stability index and other relevant factors, and are used to comprehensively and quantitatively describe the spectral stability after the integrating sphere panel is calibrated. They may include the amplitude range of light intensity fluctuations within a specific wavelength range, the stability coefficient of energy distribution between different spectral bands, the standard deviation of the offset of the spectral peak position, etc.

[0108] As an embodiment of the present invention, the analysis of the spectral stability parameters corresponding to the integrating sphere panel after calibration based on the data change situation includes: extracting a data change sequence in the data change situation; performing trend analysis on the data change sequence to obtain a trend analysis result; querying key trend parameters in the trend analysis result; performing parameter fitting on the key trend parameters to obtain a trend fitting curve; extracting a fitting stability index from the trend fitting curve; and analyzing the spectral stability parameters corresponding to the integrating sphere panel after calibration based on the fitting stability index.

[0109] The data change sequence refers to a set of data sequences arranged in chronological order or in the order of calibration operation steps, reflecting the dynamic changes of various relevant data (such as calibration error coefficient, light intensity values ​​of different wavelengths, light source power fluctuations, etc.) of the integrating sphere panel during the calibration process; the trend analysis result refers to a summary of the trend and law of data changes obtained by applying statistical analysis methods or mathematical models to the data change sequence. For example, after using the linear regression analysis method to process the sequence of changes in light source power over calibration time, the results obtained show that the light source power shows a gradually increasing trend, and the rate of increase is a certain value, or shows the characteristics of periodic fluctuations, etc.; the key trend parameter refers to a key factor that plays a key role in the trend analysis result. Function: Parameters that can significantly characterize the trend characteristics of data changes. Taking the polynomial trend model as an example, if the trend analysis results show that the data conforms to the quadratic polynomial trend, then the quadratic term coefficient, the linear term coefficient and the constant term are the key trend parameters; the trend fitting curve refers to a mathematical curve constructed based on the key trend parameters, which is used to accurately fit the data change sequence so that it can be highly close to the change trajectory of the actual data; the fitting stability index refers to a quantitative index extracted from the trend fitting curve to measure the stability of data changes. Common ones include the slope standard deviation of the curve, the goodness of fit, etc. The slope standard deviation reflects the fluctuation of the slope of the curve at different positions. The smaller the standard deviation, the smoother the curve change, that is, the higher the data stability.

[0110] Furthermore, the extraction of the data change sequence in the data change situation can be achieved through a sliding window method, such as: for data containing calibration error coefficients at different times during the integrating sphere mosaic calibration process, the window length is set to 10 data points, starting from the starting position, one data point is moved each time, and the data in the window is extracted to form a data change sequence subset. This process is repeated continuously to obtain multiple continuous and overlapping data change sequences; the trend analysis of the data change sequence can be achieved through a trend analysis method, such as: when the data change sequence presents seasonal fluctuations and trend changes, this method can effectively decompose and analyze its trend components; the query of key trend parameters in the trend analysis results can be achieved through a principal component analysis method, such as: comprehensive data trend analysis in integrating sphere mosaic calibration In the analysis, PCA can find out the principal components that contribute most to the overall trend change. These principal components are linear combinations of the original multiple parameters, and their coefficients reflect the relative importance of each parameter in the trend formation, so as to determine the key trend parameters, simplify the complex trend structure, and focus on the core influencing factors; the parameter fitting of the key trend parameters can be achieved by the least squares method, such as: Matlab's polyfit function can be used for polynomial fitting; the extraction of the fitting stability index in the trend fitting curve can be achieved by an index extraction tool, such as: Excel, Python's numpy and scikit-learn library and other tools; the analysis of the spectral stability parameters corresponding to the calibration of the integrating sphere puzzle can be achieved by fuzzy logic tools, such as: Matlab and other tools.

[0111] S4. Based on the spectral stability parameters, calculate the illumination adaptation values ​​corresponding to the integrating sphere panel under different illumination environments, determine the optimized calibration mode corresponding to the integrating sphere panel based on the illumination adaptation values, query the calibration interference factors in the optimized calibration mode, and construct the calibration process corresponding to the integrating sphere panel based on the calibration interference factors.

[0112] The present invention calculates the illumination adaptation value corresponding to the integrating sphere panel under different illumination environments based on the spectral stability parameters, can accurately measure the adaptability of the integrating sphere panel to various illumination environments, and provide a performance evaluation basis for its application in complex optical scenes. It can quickly screen out the integrating sphere panel that is most suitable for a specific illumination environment, optimize the optical system configuration, and improve the accuracy and reliability of the overall optical detection and experiment.

[0113] Among them, the lighting adaptation value refers to the quantitative value of the adaptability of the integrating sphere puzzle in a specific lighting environment, which comprehensively considers the matching situation between the characteristics of the integrating sphere puzzle and the lighting environment. The higher the value, the more effectively the integrating sphere puzzle can work under the lighting environment, and can accurately realize its optical functions, such as uniform distribution of light and accuracy of spectral measurement.

[0114] As an embodiment of the present invention, the calculating, based on the spectral stability parameter, the illumination adaptation value corresponding to the integrating sphere panel under different illumination environments includes:

[0115] The following formula is used to calculate the illumination adaptation value of the integrating sphere panel under different illumination environments:

[0116]

[0117] Among them, LA represents the illumination adaptation value of the integrating sphere puzzle corresponding to different illumination environments, k represents the number of environments corresponding to different illumination environments, p represents the number index corresponding to different illumination environments, SSP P represents the spectral stability parameter corresponding to the Pth illumination environment, I p represents the light intensity in the Pth lighting environment, α p represents the weight coefficient related to the pth lighting environment, U represents the lighting uniformity, β represents the weight coefficient corresponding to the lighting uniformity, c represents the total number of parameters of the lighting environment change parameter, v represents the parameter index corresponding to the lighting environment change parameter, t'1 and t'2 represent the interval start and end of the lighting duration interval respectively, L v (t) The function of the vth lighting environment change parameter changing with time t.

[0118] In detail, the different lighting environments refer to lighting conditions under various conditions, including but not limited to natural light sources (such as sunlight in different weather and at different times) and artificial light sources (such as different types of lights, such as incandescent lamps, fluorescent lamps, LED lamps, etc.); the spectral stability parameters refer to parameters related to the integrating sphere puzzle, which are used to describe the stability of its spectral characteristics during operation. These parameters may include the amplitude range of light intensity fluctuations within a specific wavelength range, the stability coefficient of energy distribution between different spectral bands, the standard deviation of the offset of the spectral peak position, etc.; the light intensity refers to the luminous flux of visible light received per unit area, which is used to measure the intensity of light; the light Illumination uniformity refers to the uniformity of light intensity distribution in a certain area. High illumination uniformity means that the difference in illumination intensity at various locations in the area is small and the light distribution is relatively uniform. Conversely, low illumination uniformity indicates that there is a large difference in illumination intensity in the area. The illumination duration interval refers to the time period during which illumination lasts when performing optical-related operations or measurements. For example, in a photochemical reaction experiment, the period from the start of illumination to the end of the reaction is the illumination duration interval. The variation function refers to a mathematical function that describes the time-varying changes of certain parameters in the illumination environment. For example, the time-varying function of illumination intensity indicates how the illumination intensity changes over time within the illumination duration interval.

[0119] Furthermore, the present invention determines the optimized calibration mode corresponding to the integrating sphere puzzle based on the illumination adaptation value, and queries the calibration interference factors in the optimized calibration mode, so as to accurately find the calibration mode that is most suitable for the integrating sphere puzzle, improve the accuracy of calibration, and clarify the calibration interference factors in the optimized calibration mode, which is helpful to eliminate adverse factors in advance, reduce calibration errors, and ensure the optical performance and measurement accuracy of the integrating sphere puzzle in actual use.

[0120] The optimized calibration mode refers to an operation mode in which the integrating sphere panels are calibrated to achieve the best performance through specific methods and parameter settings. For example, it may include selecting the appropriate light source type, intensity and wavelength, determining the precise measurement time and angle, setting the accurate data acquisition frequency, etc. The calibration interference factors refer to various factors that will have a negative impact on the calibration results during the calibration of the integrating sphere panels. These factors can come from multiple aspects, such as environmental factors, including changes in ambient temperature and humidity, which will affect the optical properties and measurement results of the integrating sphere panels; fluctuations in the lighting environment, such as the instability of the light source (including changes in light intensity, spectral drift, etc.), will The accuracy of the interference calibration; there are also factors of the instrument itself, such as the noise of the detector, the reflectivity change of the inner surface of the integrating sphere, etc. Optionally, the determination of the optimized calibration mode corresponding to the integrating sphere puzzle can be achieved through a response surface method tool, such as: it can help design experimental plans, such as central composite design, Box-Behnken design, etc., conveniently input experimental data and perform model fitting, intuitively display response surface graphs, and determine the optimal operating conditions by analyzing the graphs and model parameters, that is, the optimized calibration mode of the integrating sphere puzzle; the query of the calibration interference factors in the optimized calibration mode can be achieved through variance analysis methods, such as: one-way analysis of variance, multi-factor analysis of variance and other methods.

[0121] Based on the calibration interference factors, the present invention constructs a calibration process corresponding to the integrating sphere puzzle, which can eliminate interference in a targeted manner and improve the accuracy and reliability of calibration. Secondly, it helps to optimize the calibration process, reduce repeated operations caused by interference factors, improve work efficiency, and ensure that the integrating sphere puzzle can provide more accurate optical measurement data in subsequent use.

[0122] The calibration process refers to a complete operation process formed by further refining and improving the specific details, parameter settings, time requirements, data recording methods, etc. of each operation step on the basis of the calibration step framework.

[0123] As an embodiment of the present invention, the calibration process corresponding to the integrating sphere puzzle is constructed based on the calibration interference factors, including: classifying the calibration interference factors to obtain a factor classification set; querying the number of classifications in the factor classification set; generating a calibration compensation strategy corresponding to the calibration interference factors based on the number of classifications; querying the calibration operation steps in the calibration compensation strategy; constructing a calibration step framework corresponding to the calibration operation steps; and determining the calibration process corresponding to the integrating sphere puzzle based on the calibration step framework.

[0124] Among them, the factor classification set refers to a set formed by classifying and dividing the calibration interference factors according to their nature, source or the way they affect the calibration of the integrating sphere puzzle, for example, it can be divided into environmental interference factors (such as temperature, humidity, lighting environment stability, etc.), equipment hardware interference factors (such as light source stability, detector noise, changes in the reflection characteristics of the inner surface of the integrating sphere, etc.), and operation interference factors (such as differences in the operation techniques of calibration personnel, measurement angle deviation, etc.); the number of classifications refers to the number of different types of interference factors included in the factor classification set, for example, the three types of interference factors mentioned above, namely environment, equipment hardware, and operation, have a classification number of 3; the calibration compensation strategy refers to a series of measures and methods formulated for different types of calibration interference factors, which are used to offset or reduce the negative impact of these interference factors on the calibration results. For environmental interference factors, the calibration compensation strategy may include installing temperature and humidity control equipment in the calibration environment to monitor and adjust the ambient temperature and humidity in real time; for equipment hardware interference factors, If the light source is unstable, the strategy is to use a high-precision regulated current power supply to power the light source and regularly calibrate the spectral characteristics of the light source; the calibration operation steps refer to the specific operation links and sequences involved in implementing the calibration compensation strategy. For example, in the calibration compensation strategy for dealing with interference factors of light source stability, the calibration operation steps include: first, using an optical power meter to measure the initial optical power of the light source; then, adjusting the output current of the regulated current power supply according to the optical power fluctuation; then, waiting for a period of time for the light source to stabilize and then measuring the optical power again to confirm whether it reaches the set stability range; if not, repeat the current adjustment and measurement steps until the optical power of the light source is stable within the required range. These operations performed in sequence are the calibration operation steps; the calibration step framework refers to the structural framework formed by organizing and combing each calibration operation step in a logical order and in accordance with their interrelationships. It clarifies the overall process architecture of the calibration process from start to finish, and shows the sequence, branch conditions and loop structure between different calibration operation steps.

[0125] Furthermore, the factor classification of the calibration interference factors can be achieved through the principal component analysis method, such as: treating multiple calibration interference factors as different dimensions of high-dimensional data, reducing the dimensions of these data through PCA, extracting the main components, and using a clustering algorithm (such as hierarchical clustering) to classify the reduced-dimensional data, thereby achieving factor classification; the query of the number of classifications in the factor classification set can be achieved through Python's built-in functions, such as: in the Python programming environment, when a suitable data structure (such as a list or a set) is used to store the factor classification set, the len() function can be directly used to query the number of classifications; the generation of the calibration compensation strategy corresponding to the calibration interference factor can be achieved through the expert system development tool The calibration operation steps in the calibration compensation strategy can be implemented by a text parsing method, such as storing and representing the calibration compensation strategy in text form (such as a strategy document or a string), identifying the key operation descriptions and sequence information therein by text parsing technology, and then converting them into an ordered list of operation steps by a structured extraction algorithm; the calibration step framework corresponding to the calibration operation steps can be constructed by a flowchart drawing tool, such as Microsoft Visio, draw.io and other tools; the calibration process corresponding to the integrating sphere puzzle can be determined by a workflow management system, such as Activiti, Camunda and other systems.

[0126] S5. Based on the calibration process, generate a calibration tracking system corresponding to the integrating sphere puzzle; based on the calibration tracking system, monitor the calibration changes corresponding to the integrating sphere puzzle; perform real-time analysis on the calibration changes to obtain calibration analysis results; based on the calibration analysis results, formulate a calibration management plan corresponding to the integrating sphere puzzle.

[0127] The present invention generates a calibration tracking system corresponding to the integrating sphere puzzle based on the calibration process, which can monitor the progress of each link of the calibration in real time, promptly discover anomalies and deviations in the process, facilitate rapid adjustment and optimization, and help accumulate data and conditions of each calibration, providing a strong basis for subsequent analysis of calibration effects and improvement of calibration strategies, thereby ensuring the accuracy and reliability of the integrating sphere puzzle calibration.

[0128] Among them, the calibration and tracking system refers to a comprehensive system architecture, including hardware equipment (such as high-precision sensors for collecting real-time data of key nodes), software platforms (for data processing, analysis, storage and visualization), and management processes and specifications. For example, the software platform can draw a trend chart of the indicator tracking index in real time, issue an alarm when the index is abnormal, and provide detailed node data and analysis reports to help operators quickly locate problems and take corresponding measures to adjust and optimize, ensuring that the calibration work of the integrating sphere puzzle is always carried out in a controllable and high-quality state.

[0129] As an embodiment of the present invention, the calibration tracking system corresponding to the integrating sphere puzzle is generated based on the calibration process, including: performing node identification on key nodes in the calibration process to obtain an identified node set; extracting node features in the identified node set; constructing a feature vector set corresponding to the node features; setting monitoring indicators corresponding to the feature vector set; analyzing the indicator tracking index corresponding to the monitoring indicator; and generating the calibration tracking system corresponding to the integrating sphere puzzle based on the indicator tracking index.

[0130] Among them, the identification node set refers to a set formed by selecting key operation steps or links that have a significant impact on the accuracy, stability and reliability of the entire calibration process in the calibration process of the integrating sphere puzzle, and uniquely marking them. For example, in the calibration process, the light source calibration link, the detector response test link, the integrating sphere inner surface reflectance measurement link, etc. are all key nodes. These nodes are identified by numbering, naming or other methods to form an identification node set; the node characteristics refer to the unique attributes and properties of each identification node, which can reflect the state, behavior and performance of the node during the calibration process. Taking the light source calibration node as an example, its node characteristics may include the wavelength range, light intensity stability, spectral distribution characteristics, etc. of the light source; for the detector response test node, the node characteristics may include the noise level, response time, sensitivity, etc. of the detector; the feature vector set refers to a vector set formed by quantifying and organizing the node characteristics of each identification node. For example, the feature vector of the light source calibration node may be [lower limit of wavelength range, wavelength long range upper limit value, light intensity stability standard deviation, spectral distribution uniformity index], the feature vectors of all identified nodes together constitute a feature vector set; the monitoring index refers to a specific quantitative standard formulated based on the feature vector set for measuring and evaluating the operating status and performance of key nodes in the calibration process. For example, for the characteristic of the light intensity stability standard deviation of the light source calibration node, the monitoring index is set to "light intensity stability standard deviation threshold". When the light intensity stability standard deviation of the node exceeds this threshold during the actual calibration process, it is considered that there may be a problem with the node; the indicator tracking index refers to a comprehensive quantitative value obtained by collecting, analyzing and calculating the real-time data of the monitoring indicator during the calibration process, which is used to intuitively reflect the overall operating status and trend of the calibration process. For example, for multiple monitoring indicators, the indicator tracking index can be calculated by weighted averaging, normalization processing and other methods. If the indicator tracking index is close to 1, it means that the calibration process is close to the ideal state; if the index gradually deviates from 1 or exceeds the set normal range, it indicates that there is a problem with the calibration process and further analysis and adjustment are required.

[0131] Furthermore, the node identification of key nodes in the calibration process can be achieved through a process mining method, such as: extracting information such as activity sequence, execution time, resource usage, etc. in the calibration process, and automatically identifying key activity nodes that appear frequently and have an important impact on the overall calibration results based on multiple factors such as frequency, duration, resource dependence, and assigning unique identifiers; the extraction of node features in the identified node set can be achieved through a feature engineering method, such as: combining calibration domain knowledge and data-driven methods, for each identified node, collecting and extracting relevant features from multiple dimensions; the construction of a feature vector set corresponding to the node features can be achieved through a Python tool, such as: in Python, the preprocessing module of the Scikit-learn library can be used to standardize features, and a custom function can be used to construct a feature vector; the setting of the monitoring indicators corresponding to the feature vector set can be achieved through an expert system development tool, such as: Jess and other tools; the analysis of the indicator tracking index corresponding to the monitoring indicator can be achieved through a time series analysis method, such as: moving average, exponential smoothing, ARIMA model and other methods; the generation of the calibration tracking system corresponding to the integrating sphere puzzle can be achieved through an Internet of Things platform, such as: AWS IoT, Azure IoT and other platforms.

[0132] The present invention is based on the calibration tracking system, monitors the calibration changes corresponding to the integrating sphere puzzle, can grasp the dynamic changes of each link of the calibration in real time, detect abnormal fluctuations in time, facilitate rapid adjustment measures, ensure calibration accuracy, and help accumulate calibration data at different stages. By analyzing the change trend, it can provide a strong basis for subsequent optimization of the calibration process and improvement of calibration quality.

[0133] Among them, the calibration change refers to the changes in various related parameters, links and overall performance over time or different calibration batches during the calibration of the integrating sphere puzzle, such as parameter changes at key nodes, such as fluctuations in the wavelength and intensity of light in the light source calibration link; changes in indicators such as response time and sensitivity in the detector response test link, and also include adjustments to the execution time and sequence of each step in the calibration process, as well as changes in the final calibration results (such as accuracy, error range, etc.), etc. Optionally, the calibration change corresponding to the monitoring of the integrating sphere puzzle can be achieved through the control chart method, such as: a control chart can be drawn for key calibration parameters (such as light intensity, wavelength, etc.). By calculating the center line (such as mean) and upper and lower control limits of the parameters, it is observed whether the actual measured value exceeds the control limit or presents an abnormal distribution pattern (such as a continuous upward or downward trend, periodic fluctuations, etc.), so as to monitor the calibration change.

[0134] Furthermore, the present invention obtains calibration analysis results by performing real-time analysis on the calibration changes, which can quickly locate the root cause of the problem in the calibration process, and promptly discover whether the change is caused by equipment failure, environmental interference or operational error, so as to quickly correct it. The calibration strategy can be dynamically adjusted to ensure that the integrating sphere puzzle is always calibrated in the best state, thereby improving the accuracy and reliability of the calibration.

[0135] Among them, the calibration analysis result refers to a series of information summaries with clear directionality and reference value obtained after in-depth analysis and processing of the calibration changes of the integrating sphere puzzle, such as whether the fluctuation trend of parameters such as light intensity and wavelength over time or under different calibration conditions tends to be stable, gradually deviates, or shows periodic changes; it also includes the identification and positioning of abnormal factors affecting the accuracy of the calibration, and clearly points out that the calibration changes are caused by specific factors such as aging of equipment components, abnormal ambient temperature and humidity, and deviations in operating procedures; at the same time, it includes a comparative evaluation of the current calibration state with the ideal state or the historical normal state, and explains in a quantitative or qualitative way whether the accuracy and reliability of the calibration are within an acceptable range, as well as suggestions for adjusting the direction of subsequent calibration work, such as whether it is necessary to recalibrate the equipment, optimize the operating steps, improve environmental conditions, etc. Optionally, the real-time analysis of the calibration changes can be achieved through real-time analysis methods, such as wavelet analysis, LSTM and other methods.

[0136] Furthermore, based on the calibration analysis results, the present invention formulates a calibration management plan corresponding to the integrating sphere puzzle, which can specifically solve the problems arising in the calibration process, adjust the operating procedures, equipment parameters or environmental conditions according to the analyzed abnormal factors, improve the calibration accuracy, help optimize resource allocation, and reasonably arrange human and material resources for monitoring and improvement of key links according to the calibration change trend, thereby improving the calibration efficiency.

[0137] Among them, the calibration management plan refers to a set of systematic plans and strategies formulated to ensure that the calibration of the integrating sphere puzzle is carried out efficiently, accurately and stably, such as detailed provisions on the steps of light source calibration, the method and frequency of detector calibration, etc.; it includes a maintenance plan for the calibration equipment, including regular inspection of the equipment, maintenance cycles, and replacement plans for wearing parts, etc., to ensure that the equipment is always in good working condition; it also involves management strategies for environmental factors, such as setting an appropriate temperature and humidity range and formulating corresponding adjustment measures, as well as training plans and performance appraisal mechanisms for personnel operations to ensure that operators have professional skills and strictly follow the calibration process. There are also specifications for data management, including data collection, storage, analysis and traceability processes. Optionally, the formulation of the calibration management plan corresponding to the integrating sphere puzzle can be achieved through a plan generation tool, such as: Project, FunAI and other tools.

[0138] Firstly, the present invention obtains the integrating sphere puzzle under the RGB light source, analyzes the optical structural elements of the integrating sphere puzzle, and can accurately determine the spectral response range according to the structure, thereby providing a key basis for evaluating the initial calibration accuracy requirement, helping to build a scientific and reasonable calibration system, improving the accuracy and efficiency of RGB light source calibration, and ensuring the accurate and stable color presentation of RGB light sources in multi-scene applications. At the same time, the present invention detects the actual spectral data corresponding to the integrating sphere puzzle based on the initial calibration accuracy requirement, can measure the true spectral performance of the integrating sphere puzzle according to the established accuracy standard, accurately know its fit with the expected accuracy, can timely discover deviations and anomalies in the actual spectral data, and provide a reliable basis for subsequent analysis of spectral deviation points and optimization of calibration work. The present invention formulates a calibration strategy corresponding to the integrating sphere puzzle based on the calibration error coefficient, can adjust the integrating sphere puzzle in a targeted manner according to the error coefficient, makes the calibration work more targeted, and can effectively reduce Low calibration error coefficient, helps to establish a long-term and effective calibration mechanism, ensures that the integrating sphere panel always maintains good performance during use, and reduces problems caused by measurement deviations. The present invention is based on the spectral stability parameter, calculates the corresponding illumination adaptation value of the integrating sphere panel under different illumination environments, can accurately measure the adaptability of the integrating sphere panel to various illumination environments, and provides a performance evaluation basis for its application in complex optical scenes, can quickly screen out the integrating sphere panel that is most suitable for a specific illumination environment, optimize the optical system configuration, and improve the accuracy and reliability of overall optical detection and experiments. Further, the present invention is based on the calibration process, generates a calibration tracking system corresponding to the integrating sphere panel, can monitor the progress of each link of the calibration in real time, and promptly discover the anomalies and deviations in the process, which is convenient for rapid adjustment and optimization, and helps to accumulate the data and situations of each calibration, and provides a strong basis for subsequent analysis of the calibration effect and improvement of the calibration strategy, and ensures the accuracy and reliability of the calibration of the integrating sphere panel. Therefore, a RGB light source small integrating sphere panel calibration method and system proposed by the present invention can improve the overall effect of the calibration of the integrating sphere panel.

[0139] Embodiment 2:

[0140] like Figure 2 The figure is a module schematic diagram of a RGB light source small integrating sphere mosaic calibration system provided by an embodiment of the present invention.

[0141] The RGB light source small integrating sphere mosaic calibration system 200 of the present invention can be installed in an electronic device. According to the functions to be implemented, the RGB light source small integrating sphere mosaic calibration system 200 may include a requirement evaluation module 201, an error coefficient calculation module 202, a parameter analysis module 203, a process construction module 204, and a program formulation module 205. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0142] In this embodiment, the functions of each module / unit are as follows:

[0143] The requirement evaluation module 201 is used to obtain an integrating sphere mosaic under an RGB light source, analyze the optical structural elements of the integrating sphere mosaic, determine the spectral response range corresponding to the integrating sphere mosaic according to the optical structural elements, and evaluate the initial calibration accuracy requirement corresponding to the integrating sphere mosaic based on the spectral response range;

[0144] The error coefficient calculation module 202 is used to detect the actual spectral data corresponding to the integrating sphere mosaic based on the initial calibration accuracy requirement, analyze the spectral deviation points corresponding to the integrating sphere mosaic according to the actual spectral data, and calculate the calibration error coefficient corresponding to the integrating sphere mosaic according to the spectral deviation points;

[0145] A parameter analysis module 203 is used to formulate a calibration strategy corresponding to the integrating sphere mosaic based on the calibration error coefficient, implement a calibration operation corresponding to the integrating sphere mosaic according to the calibration strategy, record data changes in the calibration operation, and analyze the spectral stability parameters corresponding to the integrating sphere mosaic after calibration based on the data changes;

[0146] A process construction module 204 is used to calculate the illumination adaptation value corresponding to the integrating sphere mosaic under different illumination environments based on the spectral stability parameter, determine the optimized calibration mode corresponding to the integrating sphere mosaic based on the illumination adaptation value, query the calibration interference factor in the optimized calibration mode, and construct the calibration process corresponding to the integrating sphere mosaic based on the calibration interference factor;

[0147] The plan formulation module 205 is used to generate a calibration tracking system corresponding to the integrating sphere puzzle based on the calibration process, monitor the calibration changes corresponding to the integrating sphere puzzle based on the calibration tracking system, perform real-time analysis on the calibration changes to obtain calibration analysis results, and formulate a calibration management plan corresponding to the integrating sphere puzzle based on the calibration analysis results.

[0148] In detail, the modules described in the RGB light source small integrating sphere panel calibration system 200 described in the embodiment of the present invention adopt the same technical means as the RGB light source small integrating sphere panel calibration method described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0149] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A calibration method for a small integrating sphere panel of an RGB light source, characterized in that: The method comprises: Obtain an integrating sphere mosaic under RGB light source, analyze the optical structural elements of the integrating sphere mosaic, determine the spectral response range corresponding to the integrating sphere mosaic according to the optical structural elements, and evaluate the initial calibration accuracy requirement corresponding to the integrating sphere mosaic based on the spectral response range; Based on the initial calibration accuracy requirement, actual spectral data corresponding to the integrating sphere mosaic are detected, spectral deviation points corresponding to the integrating sphere mosaic are analyzed according to the actual spectral data, and calibration error coefficients corresponding to the integrating sphere mosaic are calculated according to the spectral deviation points; Based on the calibration error coefficient, a calibration strategy corresponding to the integrating sphere mosaic is formulated; according to the calibration strategy, a calibration operation corresponding to the integrating sphere mosaic is performed; data changes in the calibration operation are recorded; and based on the data changes, a spectral stability parameter corresponding to the integrating sphere mosaic after calibration is analyzed; Based on the spectral stability parameter, calculate the illumination adaptation value corresponding to the integrating sphere puzzle under different illumination environments, determine the optimized calibration mode corresponding to the integrating sphere puzzle based on the illumination adaptation value, query the calibration interference factor in the optimized calibration mode, and construct the calibration process corresponding to the integrating sphere puzzle based on the calibration interference factor; Based on the calibration process, a calibration tracking system corresponding to the integrating sphere puzzle is generated; based on the calibration tracking system, calibration changes corresponding to the integrating sphere puzzle are monitored, and the calibration changes are analyzed in real time to obtain calibration analysis results; based on the calibration analysis results, a calibration management plan corresponding to the integrating sphere puzzle is formulated.

2. A RGB light source small integrating sphere mosaic calibration method as claimed in claim 1, characterized in that: The evaluating the initial calibration accuracy requirement corresponding to the integrating sphere panel based on the spectral response range includes: Determining a sensitive spectral band in the spectral response range; Analyzing the light intensity variation characteristics corresponding to the sensitive spectral band; Based on the light intensity variation characteristics, the measurement error range of the integrating sphere panel under different light intensities is preliminarily analyzed; Based on the measurement error range, an initial accuracy reference interval corresponding to the integrating sphere mosaic is formulated; Based on the initial accuracy reference interval, the initial calibration accuracy requirement corresponding to the integrating sphere mosaic is evaluated.

3. A RGB light source small integrating sphere mosaic calibration method as claimed in claim 1, characterized in that: The detecting, based on the initial calibration accuracy requirement, actual spectral data corresponding to the integrating sphere mosaic panel comprises: Determining key accuracy indicators in the initial calibration accuracy requirements; Analyze the spectral accuracy range corresponding to the key accuracy index; Based on the spectral accuracy range, determining the detection direction of the integrating sphere panel for the actual spectrum; Based on the detection direction, collecting actual spectrum samples corresponding to the integrating sphere mosaic; Based on the actual spectrum sample, actual spectrum data corresponding to the integrating sphere panel is detected.

4. A RGB light source small integrating sphere mosaic calibration method as claimed in claim 1, characterized in that: The step of calculating the calibration error coefficient corresponding to the integrating sphere panel according to the spectral deviation point includes: The calibration error coefficient corresponding to the integrating sphere panel is calculated using the following formula: Wherein, BW represents the calibration error coefficient corresponding to the integrating sphere mosaic, n represents the total number of spectral deviation points, i represents the number index corresponding to the spectral deviation points, γ i represents the wavelength of the ith spectral deviation point, I actual ( γ i Indicates the wavelength γ i The actual light intensity, I ideal( γ i) is at wavelength γ i The ideal light intensity at t0 and t1 represent the starting point and end point of the measurement time range, m represents the total number of calibration time points, j represents the number index corresponding to the calibration time point, P j ( t ) Represents the light source measurement parameters corresponding to the jth calibration time point at time t.

5. A RGB light source small integrating sphere mosaic calibration method as claimed in claim 1, characterized in that: The step of formulating a calibration strategy corresponding to the integrating sphere panel based on the calibration error coefficient includes: Dividing the calibration error coefficient into coefficient intervals to obtain coefficient division intervals; identifying coefficient error sources in the coefficient partition interval; Classifying the coefficient error sources to obtain a classified error source set; Analyzing the error source mechanism corresponding to the classified error source set; Calibrate the error source mechanism to obtain a calibration result; Based on the verification result, a calibration strategy corresponding to the integrating sphere panel is formulated.

6. A RGB light source small integrating sphere mosaic calibration method as claimed in claim 1, characterized in that: The analyzing, based on the data change, the spectral stability parameters corresponding to the calibrated integrating sphere panel, includes: Extracting a data change sequence from the data change situation; Performing trend analysis on the data change sequence to obtain trend analysis results; Querying key trend parameters in the trend analysis results; Performing parameter fitting on the key trend parameters to obtain a trend fitting curve; Extracting a fitting stability index from the trend fitting curve; Based on the fitting stability index, the spectral stability parameters corresponding to the integrating sphere panel calibration are analyzed.

7. A RGB light source small integrating sphere mosaic calibration method as claimed in claim 1, characterized in that: The calculating, based on the spectral stability parameter, the illumination adaptation value corresponding to the integrating sphere panel under different illumination environments comprises: The following formula is used to calculate the illumination adaptation value of the integrating sphere panel under different illumination environments: Among them, LA represents the illumination adaptation value of the integrating sphere puzzle corresponding to different illumination environments, k represents the number of environments corresponding to different illumination environments, p represents the number index corresponding to different illumination environments, SSP P represents the spectral stability parameter corresponding to the Pth illumination environment, I p represents the light intensity in the Pth lighting environment, α p represents the weight coefficient related to the pth lighting environment, U represents the lighting uniformity, β represents the weight coefficient corresponding to the lighting uniformity, c represents the total number of parameters of the lighting environment change parameter, v represents the parameter index corresponding to the lighting environment change parameter, t'1 and t'2 represent the interval start and end of the lighting duration interval respectively, L v( t ) The function of the vth lighting environment change parameter changing with time t.

8. A RGB light source small integrating sphere mosaic calibration method as claimed in claim 1, characterized in that: The calibration process corresponding to the integrating sphere panel is constructed based on the calibration interference factor, including: Classifying the calibration interference factors to obtain a factor classification set; Query the number of classifications in the factor classification set; Based on the number of classifications, generating a calibration compensation strategy corresponding to the calibration interference factor; Querying the calibration operation steps in the calibration compensation strategy; Constructing a calibration step framework corresponding to the calibration operation steps; Based on the calibration step framework, a calibration process corresponding to the integrating sphere panel is determined.

9. A RGB light source small integrating sphere mosaic calibration method as claimed in claim 1, characterized in that: The calibration tracking system corresponding to the integrating sphere mosaic is generated based on the calibration process, including: Perform node identification on key nodes in the calibration process to obtain an identification node set; Extracting node features from the identified node set; Constructing a feature vector set corresponding to the node feature; Setting monitoring indicators corresponding to the feature vector set; Analyze the indicator tracking index corresponding to the monitoring indicator; Based on the indicator tracking index, a calibration tracking system corresponding to the integrating sphere puzzle is generated.

10. A RGB light source small integrating sphere panel calibration system, characterized in that: The system is used to execute a RGB light source small integrating sphere mosaic calibration method as described in any one of claims 1 to 9, and the system comprises: An evaluation module is required to obtain an integrating sphere mosaic under an RGB light source, analyze the optical structural elements of the integrating sphere mosaic, determine the spectral response range corresponding to the integrating sphere mosaic according to the optical structural elements, and evaluate the initial calibration accuracy requirement corresponding to the integrating sphere mosaic based on the spectral response range; An error coefficient calculation module is used to detect actual spectral data corresponding to the integrating sphere puzzle based on the initial calibration accuracy requirement, analyze spectral deviation points corresponding to the integrating sphere puzzle according to the actual spectral data, and calculate the calibration error coefficient corresponding to the integrating sphere puzzle according to the spectral deviation points; A parameter analysis module, for formulating a calibration strategy corresponding to the integrating sphere mosaic based on the calibration error coefficient, performing a calibration operation corresponding to the integrating sphere mosaic according to the calibration strategy, recording data changes during the calibration operation, and analyzing spectral stability parameters corresponding to the integrating sphere mosaic after calibration based on the data changes; A process construction module, for calculating the illumination adaptation value corresponding to the integrating sphere panel under different illumination environments based on the spectral stability parameter, determining the optimized calibration mode corresponding to the integrating sphere panel based on the illumination adaptation value, querying the calibration interference factor in the optimized calibration mode, and constructing the calibration process corresponding to the integrating sphere panel based on the calibration interference factor; A plan formulation module is used to generate a calibration tracking system corresponding to the integrating sphere puzzle based on the calibration process, monitor the calibration changes corresponding to the integrating sphere puzzle based on the calibration tracking system, perform real-time analysis on the calibration changes to obtain calibration analysis results, and formulate a calibration management plan corresponding to the integrating sphere puzzle based on the calibration analysis results.

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