Spectrum data calibration method and device, electronic equipment and storage medium

Abstract

The present application relates to the technical field of spectral data calibration, and provides a spectral data calibration method, device, electronic equipment and storage medium, the spectral data calibration method comprises: obtaining a conversion coefficient of a measurement jig according to tristimulus values obtained by a spectrometer and reading values obtained by the measurement jig; obtaining a model conversion coefficient according to the reading values obtained by the measurement jig and peak value response normalization curves corresponding to each channel of the measurement jig; calibrating at least one of first spectral data and second spectral data measured by the measurement jig, the first calibrated spectral data is obtained according to the conversion coefficient of the measurement jig; and the second calibrated spectral data is obtained according to the conversion coefficient and the model conversion coefficient of the measurement jig. The problems of inaccurate measurement of spectral data by using a conventional jig and high cost of a spectrometer required for measurement of complex light source indicators can be solved, and the spectral data of the conventional measurement jig is calibrated, and accurate measurement of complex light source indicator parameters is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of spectral data calibration, and in particular to a spectral data calibration method and device, an electronic device and a storage medium. BACKGROUND

[0002] LED lighting has become the mainstream of current lighting. For the emerging fields of intelligent lighting and healthy lighting, intelligent sensing of the lighting environment is very important. Sensing of the lighting environment is achieved through sensors, which make intelligent lighting and healthy lighting more convenient and make people's lives more colorful. Due to differences between each light sensor chip, the spectral data obtained by each sensor may differ, resulting in inaccurate calculation of light source indicators. In addition, some complex light source indicator parameters, such as color rendering index, need to be measured by a spectrometer with a complex measurement principle. However, such a spectrometer is expensive, inconvenient to measure, and large in size, and thus is not suitable for large-scale application in intelligent lighting. SUMMARY

[0003] The present application provides a spectral data calibration method and device, an electronic device and a storage medium to solve the problem of inaccurate spectral data obtained by a traditional color sensor, the problem that a spectrometer capable of measuring complex light source parameters is expensive and not suitable for large-scale application in intelligent lighting.

[0004] To solve the above technical problems, the technical solution of the present application provides a spectral data calibration method, comprising:

[0005] obtaining a conversion coefficient of the measurement jig according to the tristimulus values obtained by the spectrometer and the reading values obtained by the measurement jig;

[0006] obtaining a model conversion coefficient according to the reading values obtained by the measurement jig and the peak value response normalization curve corresponding to each channel of the measurement jig;

[0007] calibrating at least one of the first spectral data and the second spectral data measured by the measurement jig to obtain at least one of the first calibrated spectral data and the second calibrated spectral data; the first calibrated spectral data is obtained according to the conversion coefficient of the measurement jig; and the second calibrated spectral data is obtained according to the conversion coefficient of the measurement jig and the model conversion coefficient.

[0008] Optionally, the method further comprises:

[0009] obtaining the peak value response normalization curve corresponding to each channel of the measurement jig according to the spectral data normalization model;

[0010] training the spectral data normalization model according to the key-value pairs composed of a plurality of spectral measured values and spectral standard values.

[0011] Optionally, the conversion coefficient of the measuring fixture is obtained according to the tristimulus values obtained by the spectrometer and the reading values obtained by the measuring fixture, comprising:

[0012] measuring the spectra of different light sources by the spectrometer and the measuring fixture to obtain a plurality of standard tristimulus values and a plurality of measuring fixture reading values;

[0013] calculating the conversion coefficient between the tristimulus values and the measuring fixture reading values according to the plurality of standard tristimulus values and the plurality of measuring fixture reading values, and taking the conversion coefficient as the conversion coefficient of the measuring fixture.

[0014] Optionally, the plurality of standard tristimulus values form a matrix N, the plurality of measuring fixture reading values form a matrix M, and the conversion coefficient matrix [K sensor ] between the tristimulus values and the measuring fixture reading values is: sensor [K -1 ] = (M'M) (M'N).

[0015] Optionally, the model conversion coefficient includes a first model coefficient, and the first model coefficient of each channel of the measuring fixture is calculated according to the measured spectrum obtained by the measuring fixture, the peak response curve obtained by each channel, and the reading value of each channel.

[0016] Optionally, the first model coefficient of each channel of the measuring fixture is calculated according to the measured spectrum obtained by the measuring fixture, the normalized peak response curve obtained by each channel, and the reading value of each channel, comprising:

[0017] the first model coefficient Coefficient1 i = (spd*Response Curve1 i ) / Channel i ,

[0018] wherein, spd is the measured spectrum, Response Curve1 i is the normalized peak response curve obtained by each channel, Channel is the reading value obtained by the measuring fixture, i is the channel label, and * represents inner product calculation.

[0019] Optionally, the model conversion coefficient includes a second model coefficient, and the second model coefficient of each channel of the measuring fixture is calculated according to a preset virtual spectrum, a normalized peak response curve of each channel, and a y component of the tristimulus value.

[0020] Optionally, the second model coefficient of each channel of the measuring tool is calculated according to a preset virtual spectrum, a normalized peak response curve of each channel and a y component of a tristimulus value, and includes: a second model coefficient Coefficient2 i =(spd_line*Response Curve1 i ) / (spd_line*ytri i )

[0021] wherein, spd_line is the preset virtual spectrum, Response Curve1 i is the normalized peak response curve obtained by each channel, ytri i is the y component of the tristimulus value, i is the label of the channel, and * represents inner product calculation.

[0022] Optionally, the second spectrum data measured by the measuring tool is sp2, and the second calibration spectrum sp2 cal is obtained according to the conversion coefficient of the measuring tool and the model conversion coefficient, and includes: sp2 cal = sp2*K sensor *Coefficient2 i / Coefficient1 i .

[0023] Optionally, the second spectrum data includes a color rendering index and / or a ratio of a circadian stimulus value to an equivalent melatonin illuminance.

[0024] Optionally, the first spectrum data includes at least one of illuminance, color coordinates and color temperature.

[0025] The technical scheme of the present application also provides a spectrum data calibration device, which includes:

[0026] A first obtaining module is configured to obtain a conversion coefficient of a measuring tool according to a tristimulus value obtained by a spectrometer and a reading value obtained by the measuring tool.

[0027] A second obtaining module is configured to obtain a model conversion coefficient according to a reading value obtained by the measuring tool and a peak response normalization curve corresponding to each channel of the measuring tool.

[0028] A calibration module is configured to calibrate at least one of first spectrum data and second spectrum data measured by the measuring tool, and correspondingly obtain at least one of first calibration spectrum data and second calibration spectrum data; the first calibration spectrum data is obtained according to the conversion coefficient of the measuring tool; and the second calibration spectrum data is obtained according to the conversion coefficient of the measuring tool and the model conversion coefficient.

[0029] The technical scheme of the present application also provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the spectrum data calibration method according to any one of the above.

[0030] The technical scheme of the present application also provides a non-transitory computer readable storage medium, which stores a computer program, wherein the computer program is executable by a processor to implement the steps of the spectrum data calibration method according to any one of the above.

[0031] Compared with the prior art, the technical scheme of the present application has the following beneficial effects:

[0032] The spectrum data calibration method, device, electronic device and storage medium provided by the technical scheme of the present application obtain the conversion coefficient of the measurement jig according to the tristimulus value obtained by the spectrometer and the reading value obtained by the measurement jig; obtain the model conversion coefficient according to the reading value obtained by the measurement jig and the peak response normalization curve corresponding to each channel of the measurement jig; calibrate at least one of the first spectrum data and the second spectrum data measured by the measurement jig, and correspondingly obtain at least one of the first calibrated spectrum data and the second calibrated spectrum data, wherein the first calibrated spectrum data is obtained according to the conversion coefficient of the measurement jig; the second calibrated spectrum data is obtained according to the conversion coefficient of the measurement jig and the model conversion coefficient. The spectrum data obtained by the ordinary measurement jig can be calibrated, and the accurate measurement of the complex light source index parameter can be realized, the measurement cost of the measurement jig is reduced, and the measurement jig of the present application is suitable for large-scale application in intelligent lighting. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0034] Figure 1 is one of the flowcharts of the spectrum data calibration method provided by the embodiments of the present application;

[0035] Figure 2 is the second flowchart of the spectrum data calibration method provided by the embodiments of the present application;

[0036] Figure 3 is the third flowchart of the spectrum data calibration method provided by the embodiments of the present application;

[0037] Figure 4 is a comparison chart of the peak response curve before and after normalization provided by the embodiments of the present application;

[0038] Figure 5 is a structural schematic diagram of a spectrum data calibration device provided by an embodiment of the present application;

[0039] Figure 6 is an application scene diagram of a measurement jig provided by an embodiment of the present application;

[0040] Figure 7 is a measurement spectrum data principle diagram of a measurement jig provided by an embodiment of the present application;

[0041] Figure 8 is a structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0042] Reference signs:

[0043] 501: first calculation module; 502: second calculation module; 503: calibration module. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0045] Figure 1 A flowchart of a spectrum data calibration method provided by an embodiment of the present application is shown in FIG. 1, and the spectrum data calibration method comprises the following steps. Figure 1

[0046] Step 101: obtaining a conversion coefficient of a measurement jig according to tristimulus values obtained by a spectrometer and reading value obtained by the measurement jig.

[0047] In the embodiments of the present application, the measurement jig is, for example, a color sensor, and the response curve wavelength range of the color sensor is uniformly distributed in the visible light range of 380-780 nm. The number of channels of the color sensor is, for example, 11. There are 8 channels in the wavelength range of 380-780 nm of visible light.

[0048] Step 102: obtaining a model conversion coefficient according to the reading value obtained by the measurement jig and a peak value response normalization curve corresponding to each channel of the measurement jig.

[0049] ​Step 103, calibrating at least one of the first spectral data and the second spectral data measured by the measuring fixture, corresponding to acquiring at least one of the first calibration spectral data and the second calibration spectral data; the first calibration spectral data is acquired according to the conversion coefficient of the measuring fixture; the second calibration spectral data is acquired according to the conversion coefficient of the measuring fixture and the model conversion coefficient.

[0050] In the embodiment of the present application, the first spectral data includes but is not limited to illumination, color coordinates and color temperature, etc.; the second spectral data includes but is not limited to color rendering index, circadian stimulus value and ratio of equivalent melatonin illuminance, etc.

[0051] When the conventional light sensor chip acquires spectral data, there is a difference between each light sensor chip, so the spectral data acquired by each sensor may be different, resulting in inaccurate calculation of the light source index, in addition, some complex light source index parameters such as color rendering index need to be measured by a complex spectral instrument, but such a spectral instrument is expensive, the measurement process is not convenient, and the spectral instrument is large in size, so it is not suitable for large-scale application in intelligent lighting.

[0052] The spectral data calibration method provided by the embodiment of the present application can acquire the conversion coefficient of the measuring fixture according to the tristimulus value acquired by the spectrometer and the reading value acquired by the measuring fixture; acquire the model conversion coefficient according to the reading value acquired by the measuring fixture and the peak value response normalization curve corresponding to each channel of the measuring fixture; calibrate at least one of the first spectral data and the second spectral data measured by the measuring fixture, corresponding to acquire at least one of the first calibration spectral data and the second calibration spectral data, the first calibration spectral data is acquired according to the conversion coefficient of the measuring fixture; the second calibration spectral data is acquired according to the conversion coefficient of the measuring fixture and the model conversion coefficient, which can calibrate the spectral data acquired by the ordinary measuring fixture, and can realize accurate measurement of complex light source index parameters, reduce the measurement cost of the measuring fixture, and the measuring fixture of the present application is suitable for large-scale application in intelligent lighting.

[0053] Based on any of the above embodiments, as Figure 2 shown, the conversion coefficient of the measuring fixture is acquired according to the tristimulus value acquired by the spectrometer and the reading value acquired by the measuring fixture, including:

[0054] Step 201, measuring the spectrum of the light source with different spectrum by using the spectrometer and the color sensor, obtaining a plurality of standard tristimulus values and a plurality of color sensor reading values;

[0055] In the embodiment of the present application, the number of light sources with different spectrum is greater than or equal to the number of channels of the color sensor.

[0056] For example, different light sources of different spectra are different color temperature LED lamps. The lamps and the device containing the color sensor are placed in an integrating sphere at the same time, the LED panel is placed in the center of the integrating sphere, the device is placed on the inner surface of the integrating sphere, and the main light sensing direction of the sensor is opposite to the center of the integrating sphere. After the LED panel of different color temperatures is stably lit, the spectrum of the same LED panel is measured by a spectrometer and a sensor to obtain three stimulus values {Xn, Yn, Zn} and sensor readings {Tn1, Tn2, Tn3...Tni}, n represents the nth lamp, and i represents the number of sensor channels.

[0057] In step 202, the conversion coefficient between the three stimulus values and the color sensor reading values is calculated according to the plurality of standard three stimulus values and the plurality of color sensor reading values, and the conversion coefficient is taken as the conversion coefficient of the measuring tool.

[0058] In the embodiment of the present application, the conversion coefficient between XYZ and the sensor reading values measured by each channel is calculated as follows: sensor ]=(M′M) -1 (M′N), wherein

[0059]

[0060] The light source to be measured is tested, and the three stimulus values thereof are calculated.

[0061]

[0062] The first spectrum data after calibration, such as illuminance, color coordinates, color temperature and the like, can be calculated by calculating the three stimulus values. For example:

[0063] The color coordinates (x, y) are calculated as follows: x=X / (X+Y+Z), y=Y / (X+Y+Z);

[0064] The color temperature (CCT) is calculated as follows:

[0065] CCT=437*(x-0.3320) / (0.1858-y)^3+3601*(x-0.3320) / (0.1858-y)^2+6861*(x-0.3320) / (0.1858-y)+5517;

[0066] Illuminance=K 照度 ×Y, K 照度 is a constant obtained after calibration by testing the standard light source at the same position.

[0067] Based on any of the above embodiments, as shown in the following formula, the calculation method of the color rendering index includes: Figure 3

[0068] ​Step 301, a spectrum data normalization model is established, and a peak value response curve of a measurement fixture is normalized according to the spectrum data normalization model to obtain a peak value response normalized curve;

[0069] In the embodiment of the present application, the spectrum data normalization model is trained according to the key-value pairs composed of a plurality of spectrum measured values and spectrum standard values; the spectrum data normalization model is based on big data technology, about 3000+ spectrums are measured for training and construction, and is used to normalize the peak value response curve measured by each measurement fixture to a range. The comparison before and after the normalization of the peak value response curve is shown in FIG. 2. Figure 4

[0070] Step 302, a model conversion coefficient is calculated according to the reading value of the measurement fixture and the peak value response normalized curve;

[0071] In the embodiment of the present application, the model conversion coefficient includes a first model coefficient, and the first model coefficient of each channel of the measurement fixture is calculated according to the measured spectrum obtained by the measurement fixture, the peak value response curve obtained by each channel, and the reading value of each channel.

[0072] The first model coefficient Coefficient1 i = (spd * Response Curve1 i ) / Channel i ,

[0073] Wherein, spd is the measured spectrum, Response Curve1 is the peak value response normalized curve, Channel is the channel value read by the chip, i represents the label of the channel, and * represents inner product calculation.

[0074] In the embodiment of the present application, the model conversion coefficient includes a second model coefficient, and the second model coefficient of each channel of the measurement fixture is calculated according to a preset virtual spectrum, a peak value response normalized curve of each channel, and a y component of a tristimulus value. The second model coefficient Coefficient2 i = (spd_line * Response Curve1 i ) / (spd_line * ytri i );

[0075] Wherein, spd_line is a virtual spectrum of 380nm-780nm full 1, Response Curve1 is the peak value response normalized curve, ytri i is the y component of the tristimulus value, i represents the label of the channel, and * represents inner product calculation.

[0076] ​Step 303, calibrating the second spectral data measured by the measuring fixture according to the conversion coefficient of the measuring fixture and the model conversion coefficient, to obtain the calibrated second spectral data.

[0077] In the embodiment of the present application, the second spectral data measured by the measuring fixture is sp2, and the second calibration spectral data sp2 cal According to the conversion coefficient of the measuring fixture and the model conversion coefficient, the obtaining includes: sp2 cal =sp2*K sensor *Coefficient2 i / Coefficient1 i .

[0078] In some embodiments, for the production personnel of the production line, the software on the matched PC, the production personnel uses the software on the PC to send commands to the fixture in turn to turn on the light bar of 2700K, 4000K and 5700K color temperature, and then uses the light detection device to read the spectral data and uploads to the PC software. After a set of coefficients are calculated, the PC returns the coefficients to the device and stores them in the ROM flash. When the App connects the device for the first time, the app will send a command to the device to request the coefficients stored in the ROM flash, so as to realize the calibration of the spectral data read by the device.

[0079] The spectral data calibration method provided by the embodiment of the present application not only can improve the accuracy of the spectral data measured by the measuring fixture, but also can measure complex light source index parameters and reduce the measurement cost.

[0080] The spectral data calibration device will be described below. The spectral data calibration device and the spectral data calibration method in the embodiment of the present application can be correspondingly referred to each other, and therefore, the related term explanation will not be repeated.

[0081] As shown in Figure 5 The spectral data calibration device includes:

[0082] The first obtaining module 501 is configured to obtain the conversion coefficient of the measuring fixture according to the tristimulus value obtained by the spectrometer and the reading value obtained by the measuring fixture.

[0083] The second obtaining module 502 is configured to obtain the model conversion coefficient according to the reading value obtained by the measuring fixture and the peak value response normalization curve corresponding to each channel of the measuring fixture.

[0084] The calibration module 503 is configured to calibrate at least one of the first spectral data and the second spectral data measured by the measuring fixture, to obtain at least one of the first calibration spectral data and the second calibration spectral data; the first calibration spectral data is obtained according to the conversion coefficient of the measuring fixture; and the second calibration spectral data is obtained according to the conversion coefficient of the measuring fixture and the model conversion coefficient.

[0085] The spectral data calibration device provided by the embodiment of the present application can obtain the conversion coefficient of the measuring fixture according to the tristimulus value obtained by the spectrometer and the reading value obtained by the measuring fixture; obtain the model conversion coefficient according to the reading value obtained by the measuring fixture and the peak value response normalization curve corresponding to each channel of the measuring fixture; calibrate at least one of the first spectral data and the second spectral data measured by the measuring fixture, to obtain at least one of the first calibration spectral data and the second calibration spectral data; the first calibration spectral data is obtained according to the conversion coefficient of the measuring fixture; and the second calibration spectral data is obtained according to the conversion coefficient of the measuring fixture and the model conversion coefficient. The spectral data obtained by the ordinary measuring fixture can be calibrated, the accurate measurement of the complex light source index parameter can be realized, the measurement cost of the measuring fixture is reduced, and the measuring fixture of the present application is suitable for large-scale application in intelligent lighting.

[0086] In some embodiments, as shown in Figure 6 The spectral data is measured by the measuring fixture, and the principle of measuring the spectral data by the measuring fixture is as shown in Figure 7 The detector detects the light source target, the detector detects the spectral data of the light source target, the spectral data is filtered by the diode filter, and the filtered spectral data is calibrated by using the calibration method.

[0087] Figure 8 An example of an entity structure diagram of an electronic device is shown in Figure 8As shown, the electronic device can include a processor 810, a communications interface 820, a memory 830, and a communications bus 840, wherein the processor 810, the communications interface 820, and the memory 830 complete mutual communication through the communications bus 840. The processor 810 can invoke a logical instruction in the memory 830 to execute a spectral data calibration method, which includes: acquiring a conversion coefficient of a measurement jig according to a tristimulus value acquired by a spectrometer and a reading value acquired by the measurement jig; acquiring a model conversion coefficient according to the reading value acquired by the measurement jig and a peak value response normalization curve corresponding to each channel of the measurement jig; and calibrating at least one of first spectral data and second spectral data measured by the measurement jig, to correspondingly acquire at least one of first calibrated spectral data and second calibrated spectral data, the first calibrated spectral data being acquired according to the conversion coefficient of the measurement jig, and the second calibrated spectral data being acquired according to the conversion coefficient of the measurement jig and the model conversion coefficient.

[0088] In addition, the logical instruction in the memory 830 described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the embodiments of the method of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0089] On the other hand, the embodiments of the present application also provide a non-transitory computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the spectral data calibration method provided by the above-mentioned methods, the method including: acquiring a conversion coefficient of a measurement jig according to a tristimulus value acquired by a spectrometer and a reading value acquired by the measurement jig; acquiring a model conversion coefficient according to the reading value acquired by the measurement jig and a peak value response normalization curve corresponding to each channel of the measurement jig; and calibrating at least one of first spectral data and second spectral data measured by the measurement jig, to correspondingly acquire at least one of first calibrated spectral data and second calibrated spectral data, the first calibrated spectral data being acquired according to the conversion coefficient of the measurement jig, and the second calibrated spectral data being acquired according to the conversion coefficient of the measurement jig and the model conversion coefficient.

[0090] The device embodiments described above are merely illustrative, wherein the units illustrated as separate components can or can not be physically separated, and the components illustrated as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments of the present application according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0091] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and include a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of the embodiments or some parts of the embodiments.

[0092] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the embodiments of the present application, and not to limit them; although the embodiments of the present application have been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of spectral data calibration, characterized by, The method comprises the following steps: According to the tristimulus values obtained by the spectrometer and the reading values obtained by each channel of the measuring tool, the conversion coefficient of the measuring tool is obtained; the measuring tool is a color sensor, and the reading values are a plurality of values corresponding to the number of channels of the color sensor; the conversion coefficient of the measuring tool is obtained according to the tristimulus values obtained by the spectrometer and the reading values obtained by the measuring tool, which comprises the following steps: measuring the spectra of a plurality of different measured light sources by using the spectrometer and the measuring tool to obtain a plurality of standard tristimulus values and reading values; the conversion coefficient between the tristimulus values and the reading values is calculated according to the plurality of standard tristimulus values and reading values, and the conversion coefficient is taken as the conversion coefficient of the measuring tool; According to the reading values obtained by each channel of the measuring tool and the peak response normalization curve corresponding to each channel of the measuring tool, the model conversion coefficient is obtained; the model conversion coefficient comprises a first model coefficient and a second model coefficient, the first model coefficient is calculated according to the measured spectrum obtained by the measuring tool, the peak response curve obtained by each channel and the reading value of each channel; the second model coefficient is calculated according to the preset virtual spectrum, the normalized peak response curve of each channel and the y component of the tristimulus value; At least one of the first spectrum data and the second spectrum data measured by the measuring tool is calibrated to obtain at least one of the first calibration spectrum data and the second calibration spectrum data; The first calibration spectrum data is obtained according to the conversion coefficient of the measuring tool and the first model coefficient; the first spectrum data comprises at least one of illuminance, color coordinates and color temperature, and the first spectrum data is obtained based on the tristimulus values calculated by the conversion coefficient of the measuring tool and the reading value of the measured spectrum measured by the measuring tool; The second calibration spectrum data is obtained according to the conversion coefficient of the measuring tool and the second model coefficient; the second spectrum data comprises the color rendering index of the preset virtual spectrum and / or the ratio of the circadian rhythm stimulus value to the equivalent melatonin illuminance.

2. The method of claim 1, wherein, Further comprising: According to the spectrum data normalization model, the peak response normalization curve corresponding to each channel of the measuring tool is obtained; The spectrum data normalization model is trained according to the key-value pairs composed of a plurality of spectrum measured values and spectrum standard values.

3. The method of claim 1, wherein, A plurality of standard tristimulus values form a matrix N, a plurality of measurement gauge reading values form a matrix M, and a conversion coefficient matrix [K sensor ] between the tristimulus values and the measurement gauge reading values is: .

4. The method of claim 1, wherein, The first model coefficient of each channel of the measuring tool is calculated according to the measured spectrum obtained by the measuring tool, the normalized peak response curve obtained by each channel and the reading value of each channel, which comprises: First model coefficient Coefficient1 i = (spd * Response Curve1 i ) / Channel i , wherein spd is the measured spectrum, Response Curve1 i Normalized peak response curves obtained for each channel, Channel is the reading value obtained by the measurement fixture, i is the index of the channel, * denotes the inner product calculation.

5. The method of claim 1, wherein, The second model coefficient of each channel of the measuring tool is calculated according to a preset virtual spectrum, a normalized peak value response curve of each channel and a y component of a tristimulus value, and includes: a second model coefficient Coefficient2 i = (spd_line* Response Curve1 i ) / (spd_line* ytri i ), wherein spd_line is a preset virtual spectrum, Response Curve1 i is a normalized peak value response curve obtained for each channel, ytri i is a y component of a tristimulus value, i is a channel label, and * represents inner product calculation.

6. The method of calibrating spectral data according to claim 5, wherein, The second spectral data measured by the measuring tool is sp2, and the second calibration spectral data is sp2 cal The acquiring of the conversion coefficient of the measuring tool and the model conversion coefficient comprises: sp2 cal =sp2*K sensor *Coefficient2 i / Coefficient1 i .

7. A spectral data calibration apparatus, characterized by, Comprising: The first obtaining module is configured to obtain the conversion coefficient of the measuring tool according to the tristimulus values obtained by the spectrometer and the reading values obtained by the measuring tool; the measuring tool is a color sensor, and the reading values are a plurality of values corresponding to the number of channels of the color sensor; The conversion coefficient of the measuring tool is obtained according to the tristimulus values obtained by the spectrometer and the reading values obtained by the measuring tool, which comprises the following steps: measuring the spectra of a plurality of different measured light sources by using the spectrometer and the measuring tool to obtain a plurality of standard tristimulus values and reading values; the conversion coefficient between the tristimulus values and the reading values is calculated according to the plurality of standard tristimulus values and reading values, and the conversion coefficient is taken as the conversion coefficient of the measuring tool; a second obtaining module, configured to obtain a model conversion coefficient according to a reading value obtained by the measurement fixture and a peak value response normalization curve corresponding to each channel of the measurement fixture, the model conversion coefficient comprising a first model coefficient and a second model coefficient, the first model coefficient being calculated according to a measured spectrum obtained by the measurement fixture, a peak value response curve obtained by each channel, and a reading value of each channel; and the second model coefficient being calculated according to a preset virtual spectrum, a normalized peak value response curve of each channel, and a y component of a tristimulus value; a calibration module, configured to calibrate at least one of first spectrum data and second spectrum data measured by the measurement fixture, and obtain at least one of first calibration spectrum data and second calibration spectrum data; the first calibration spectrum data being obtained according to the conversion coefficient of the measurement fixture and the first model coefficient; the first spectrum data comprising at least one of illuminance, color coordinates, and color temperature, and the first spectrum data being obtained based on a tristimulus value calculated by the conversion coefficient of the measurement fixture and a reading value of a measured spectrum measured by the measurement fixture; the second calibration spectrum data being obtained according to the conversion coefficient of the measurement fixture and the second model coefficient; and the second spectrum data comprising a color rendering index of a preset virtual spectrum and / or a ratio of a circadian stimulus value to an equivalent melatonin illuminance.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, the processor implements the steps of the spectrum data calibration method according to any one of claims 1 to 6 when executing the program. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, the computer program implements the steps of the spectrum data calibration method according to any one of claims 1 to 6 when executed by the processor.

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