Ground object spectrometer calibration scheme based on two-dimensional code

Abstract

The invention discloses a ground object spectrometer calibration scheme based on a two-dimensional code, and relates to the technical field of spectral measurement. The problem that the two-dimensional code area is difficult to automatically identify and analyze according to the standard diffuse reflection whiteboard image is solved; secondly, through cooperation of a shutter, a white board and a two-dimensional code, automatic calibration is realized; then, the sensitivity after automatic adjustment is difficult to analyze according to the calibration factor; and finally, whether the calibration is successful or not is difficult to verify according to a result after the calibration is completed. The parameter information of the whiteboard is provided for the ground feature spectrum system through the two-dimensional code on the surface of the whiteboard, after the ground feature spectrum system obtains the two-dimensional code, a user is prompted to start calibration or not, and related processes are fully automatically completed after confirmation.

Description

Technical Field

[0001] The invention belongs to the technical field of spectral measurement, and in particular is a calibration scheme for a ground object spectrometer based on a two-dimensional code. Background Art

[0002] The ground object spectrometer is an instrument based on the principle of spectroscopy, used to measure the spectral characteristics of ground objects such as reflection, absorption, and scattering. The standard diffuse reflectance whiteboard is a calibrated whiteboard with spectral reflectance data provided by the National Institute of Metrology. This whiteboard is an important tool in optical measurement and calibration tasks. Its stability and high precision ensure the accuracy and reliability of the measurement results. Users often need to use multiple whiteboards for frequent calibration to offset measurement errors caused by changes in the lighting environment, equipment drift, etc. The traditional method requires users to enter the whiteboard parameters and perform relatively cumbersome calibration operations. The traditional whiteboard calibration method is not only prone to errors, but also inefficient.

[0003] The prior art has the following problems: first, it is difficult to automatically identify and parse the QR code area based on the standard diffuse reflection whiteboard image; second, automatic calibration is achieved through the coordination of the shutter, whiteboard, and QR code; then, it is difficult to analyze the automatically adjusted sensitivity based on the calibration factor; finally, it is difficult to verify whether the calibration is successful based on the results after the calibration is completed. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a ground object spectrometer calibration solution based on a two-dimensional code, which is used to solve the following technical problems:

[0005] First, it is difficult to automatically identify and parse the QR code area based on the standard diffuse reflection whiteboard image; second, automatic calibration is achieved through the coordination of the shutter, whiteboard, and QR code; then, it is difficult to analyze the automatically adjusted sensitivity based on the calibration factor; finally, it is difficult to verify whether the calibration is successful based on the results after the calibration is completed.

[0006] To solve the above problems, the first aspect of the present invention provides a ground object spectrometer calibration solution based on a two-dimensional code, comprising the following steps:

[0007] S1: Design a QR code on the reflective surface of a whiteboard; place a standard diffuse reflective whiteboard containing the QR code at the detection position of the ground object spectrometer;

[0008] S2: Start the ground object spectrometer and open the shutter to collect the standard diffuse reflection whiteboard image and the actual reflectivity data in real time;

[0009] S3: preprocessing the collected standard diffuse reflectance whiteboard image, including: image correction, denoising, grayscale and image enhancement; preprocessing the collected actual reflectance data, including: denoising and correction;

[0010] S4: automatically identifying and extracting the QR code area according to the preprocessed standard diffuse reflection whiteboard image; parsing the identified and extracted QR code area to extract whiteboard parameters;

[0011] S5: After extracting the whiteboard parameters of the QR code, prompt the user whether to start calibration; when the user chooses to start calibration, according to the extracted whiteboard parameters, analyze and calculate the difference value and calibration factor, and then automatically adjust the calibration parameters of the ground object spectrometer; analyze and calculate the sensitivity after automatic adjustment according to the calibration factor; when the user does not choose to start calibration, return to step S2 and re-collect the standard diffuse reflection whiteboard image and actual reflectivity data;

[0012] S6: Verify whether the calibration is successful based on the calibration parameters that are automatically adjusted according to the whiteboard parameters.

[0013] As a further solution of the present invention: the automatic identification and extraction of the QR code area according to the pre-processed standard diffuse reflection whiteboard image in step S4 includes the following steps:

[0014] According to the collected and processed standard diffuse reflection whiteboard image, the QR code area in the standard diffuse reflection whiteboard image is automatically identified by using a deep learning model;

[0015] By manually marking the QR code area, the labeled standard diffuse reflection whiteboard image and the QR code area include: the location and size of the QR code area to generate a data set, and the data set is input into the deep learning model for training;

[0016] According to the trained deep learning model, the standard diffuse reflection whiteboard image is collected and preprocessed in real time, and the standard diffuse reflection whiteboard image collected and preprocessed in real time is input into the trained deep learning model to automatically identify the QR code area; according to the output result of the deep learning model, the QR code area in the image is extracted.

[0017] As a further solution of the present invention: the step S4 is to identify and extract the two-dimensional code area for parsing and extracting the whiteboard parameters, including the following steps:

[0018] According to the identification and extraction of the QR code area, the three positioning patterns of the QR code are detected using Hough transform or template matching algorithm, and the boundary and direction of the QR code are extracted according to the relative position and size of the positioning patterns;

[0019] According to the encoding rules of the QR code, the QR code matrix is ​​scanned, and each unit in the QR code matrix is ​​scanned to extract binary data; the scanned binary data is converted into text information using the QR code decoding algorithm; the text information in the QR code is parsed through natural language processing technology to extract whiteboard parameters, which include: whiteboard model, reflectivity and calibration parameters.

[0020] As a further solution of the present invention: in step S5, when the user chooses to start calibration, the calibration parameters of the ground object spectrometer are automatically adjusted after analyzing and calculating the difference value and the calibration factor according to the extracted whiteboard parameters, including the following steps:

[0021] When the user chooses to start calibration, the calibration parameters in the whiteboard parameters are input into the calibration algorithm for automatic adjustment; according to the calibration parameters, setting instructions executable by the ground object spectrometer are generated, and the generated setting instructions are sent to the ground object spectrometer to automatically adjust the parameters of the ground object spectrometer;

[0022] Compare the actual reflectivity data collected with the standard reflectivity of the whiteboard parameters, and use the analytical formula: C = R 标准 (λ)-R 测量 (λ)

[0023] Get the difference value C; where R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, R 测量 (λ) represents the reflectivity actually measured by the ground object spectrometer;

[0024] According to the comparison results, through the analysis formula:

[0025] Get the calibration factor α; where R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, R 测量 (λ) represents the reflectivity actually measured by the ground object spectrometer; λ represents the wavelength;

[0026] The calibration factor is input into the calibration algorithm of the ground object spectrometer, the sensitivity at each wavelength is adjusted, and the calibration parameters of the ground object spectrometer are automatically adjusted, and the calibration parameters include: gain and offset.

[0027] As a further solution of the present invention: the step S5 of calculating the automatically adjusted sensitivity according to the calibration factor analysis includes the following steps:

[0028] By analyzing the formula: S 更新 (λ) = S 原始 (λ)*α

[0029] Get the adjusted sensitivity S更新 (λ); where S 原始 (λ) represents the original sensitivity of the ground object spectrometer at each wavelength, and α represents the calibration factor.

[0030] As a further solution of the present invention: the step S6 includes the following steps:

[0031] By using the calibrated ground object spectrometer, measuring the known standard sample again or performing repeated measurements under the same measurement environment conditions, verifying whether the calibration is successful; the measurement environment conditions include: light source intensity and ambient temperature;

[0032] According to the spectral reflectance measured again, through the analysis formula:

[0033] Get the spectral reflectance deviation Y; where R 再次测量 (λ) represents the spectral reflectance measured again, R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, and λ represents the wavelength;

[0034] Set a threshold value based on the calculated spectral reflectance deviation to verify whether the calibration is successful;

[0035] When Y≤±2%, the calibration is successful; when Y>±2%, the calibration is unsuccessful; when the calibration is unsuccessful, return to step S2, re-collect the standard diffuse reflection whiteboard image and actual reflectivity data until the calibration is successfully verified and the calibration is ended.

[0036] As a further solution of the present invention: a two-dimensional code is designed on the reflective surface of the whiteboard, wherein the two-dimensional code can also be replaced by a barcode, RFID radio frequency identification technology and a contact chip card.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention greatly improves the efficiency and reliability of calibration work by designing a whiteboard with a QR code. The improvement effect is particularly obvious for scenes with multiple whiteboards and variable ambient light. By setting a QR code on the whiteboard, the user's operation difficulty is greatly reduced.

[0039] The present invention realizes an efficient, accurate and user-friendly ground object spectrometer calibration method through the cooperation of shutter, whiteboard and QR code, improves the efficiency and accuracy of calibration, and reduces the error caused by human operation; at the same time, by parsing the spectral data in the QR code and comparing it with the actual measurement value, the calibration parameters can be further accurately calculated to ensure the measurement accuracy of the spectrometer;

[0040] The present invention realizes intelligent and automatic calibration of the ground object spectrometer by combining two-dimensional code technology and automatic calibration algorithm; this can not only reduce manual intervention, but also improve the automation level of calibration, make the calibration process more reliable and stable, and shorten the calibration time. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0043] Figure 2 It is a schematic diagram of the whiteboard reflective surface of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1-Figure 2 As shown, the first embodiment of the present invention provides a ground object spectrometer calibration solution based on a two-dimensional code, comprising the following steps:

[0046] S1: Design a QR code on the reflective surface of a whiteboard; place a standard diffuse reflective whiteboard containing the QR code at the detection position of the ground object spectrometer;

[0047] S2: Start the ground object spectrometer and open the shutter to collect the standard diffuse reflection whiteboard image and the actual reflectivity data in real time;

[0048] S3: preprocessing the collected standard diffuse reflectance whiteboard image, including: image correction, denoising, grayscale and image enhancement; preprocessing the collected actual reflectance data, including: denoising and correction;

[0049] S4: automatically identifying and extracting the QR code area according to the preprocessed standard diffuse reflection whiteboard image; parsing the identified and extracted QR code area to extract whiteboard parameters;

[0050] S5: After extracting the whiteboard parameters of the QR code, prompt the user whether to start calibration; when the user chooses to start calibration, according to the extracted whiteboard parameters, analyze and calculate the difference value and calibration factor, and then automatically adjust the calibration parameters of the ground object spectrometer; analyze and calculate the sensitivity after automatic adjustment according to the calibration factor; when the user does not choose to start calibration, return to step S2 and re-collect the standard diffuse reflection whiteboard image and actual reflectivity data;

[0051] S6: Verify whether the calibration is successful based on the calibration parameters that are automatically adjusted according to the whiteboard parameters.

[0052] Specifically, a two-dimensional code is designed on the reflective surface of the whiteboard, and a standard diffuse reflection whiteboard containing the two-dimensional code is placed at the detection position of the ground object spectrometer. The ground object spectrometer is started and the shutter is opened to collect the image of the standard diffuse reflection whiteboard, and the actual reflectivity data is collected in real time. The collected standard diffuse reflection whiteboard image is preprocessed, including steps such as image correction, denoising, graying and enhancing image comparison. The collected actual reflectivity data is also preprocessed accordingly, such as denoising and correction. According to the preprocessed standard diffuse reflection whiteboard image, the two-dimensional code area is automatically identified and extracted, and then the area is parsed to extract the parameter information of the whiteboard. After the whiteboard parameters of the two-dimensional code are extracted, the ground object spectrometer system will prompt the user whether to start calibration. If the user chooses to start calibration, then according to the extracted whiteboard parameters, the difference value and calibration factor are analyzed and calculated, and then the calibration parameters of the ground object spectrometer are automatically adjusted. At the same time, the sensitivity after automatic adjustment is analyzed and calculated according to the calibration factor. If the user does not choose to start calibration, it returns to step S2 and re-collects the image and actual reflectivity data of the standard diffuse reflection whiteboard. Verify whether the calibration is successful based on the calibration parameters automatically adjusted by the whiteboard parameters. If the calibration is successful, the calibration process ends; if the calibration fails, return to step S2 and perform the calibration process again.

[0053] In one embodiment of the present invention, the step S4 automatically identifies and extracts the QR code area according to the preprocessed standard diffuse reflection whiteboard image, comprising the following steps:

[0054] According to the collected and processed standard diffuse reflection whiteboard image, the QR code area in the standard diffuse reflection whiteboard image is automatically identified by using a deep learning model;

[0055] By manually marking the QR code area, the labeled standard diffuse reflection whiteboard image and the QR code area include: the location and size of the QR code area to generate a data set, and the data set is input into the deep learning model for training;

[0056] According to the trained deep learning model, the standard diffuse reflection whiteboard image is collected and preprocessed in real time, and the standard diffuse reflection whiteboard image collected and preprocessed in real time is input into the trained deep learning model to automatically identify the QR code area; according to the output result of the deep learning model, the QR code area in the image is extracted.

[0057] Specifically, the QR code area is marked in the standard diffuse whiteboard image by manual marking. The marking information includes the position and size of the QR code area. The marked image and the corresponding label including: the position and size of the QR code area are used to generate a data set. A suitable deep learning model architecture is selected, such as a convolutional neural network (CNN). The generated data set is input into the deep learning model for training. During the training process, the deep learning model learns how to identify the QR code area from the image. The trained deep learning model is evaluated using a validation set to ensure that the deep learning model has good recognition accuracy and generalization ability. The trained deep learning model is deployed into the ground object spectrometer system. In practical applications, the image of the standard diffuse whiteboard is collected in real time and preprocessed. The preprocessed image is input into the deployed deep learning model, and the deep learning model automatically identifies the QR code area in the image. According to the output result of the deep learning model, the QR code area in the image is extracted, and the area is further analyzed to obtain the whiteboard parameters. After the whiteboard parameters of the QR code are extracted, the ground object spectrometer system prompts the user whether to start calibration. If the user chooses to start calibration, the difference value and calibration factor are analyzed and calculated based on the extracted whiteboard parameters, and then the calibration parameters of the ground object spectrometer are automatically adjusted. The sensitivity after automatic adjustment is analyzed and calculated based on the calibration factor. If the user does not choose to start calibration, the process returns to step S2 to re-collect the image and actual reflectivity data of the standard diffuse reflection whiteboard.

[0058] In one embodiment of the present invention, the step S4 is to identify and extract the two-dimensional code area for parsing and extracting the whiteboard parameters, and comprises the following steps:

[0059] According to the identification and extraction of the QR code area, the three positioning patterns of the QR code are detected using Hough transform or template matching algorithm, and the boundary and direction of the QR code are extracted according to the relative position and size of the positioning patterns;

[0060] According to the encoding rules of the QR code, the QR code matrix is ​​scanned, and each unit in the QR code matrix is ​​scanned to extract binary data; the scanned binary data is converted into text information using the QR code decoding algorithm; the text information in the QR code is parsed through natural language processing technology to extract whiteboard parameters, which include: whiteboard model, reflectivity and calibration parameters.

[0061] Specifically, the identified and extracted two-dimensional code area is further processed using a Hough transform or template matching algorithm to detect the three positioning patterns of the two-dimensional code. According to the relative position and size of the three positioning patterns, the boundaries and directions of the two-dimensional code are determined. This step ensures the accuracy of subsequent scanning and decoding. According to the determined boundaries and directions of the two-dimensional code, the two-dimensional code matrix is ​​scanned. During the scanning process, each unit in the two-dimensional code matrix is ​​read, usually a black and white pixel. Each read unit is converted into binary data 0 or 1 to form a complete binary string. Using the decoding algorithm of the two-dimensional code, the scanned binary data is converted into text information. This step involves error correction and data verification to ensure the accuracy of decoding. The decoded text information is analyzed and parsed by natural language processing (NLP) technology. The key information in the text, namely the whiteboard parameters, is extracted. These parameters usually include the model, reflectivity and calibration parameters of the whiteboard. The extracted whiteboard parameters are used in the subsequent calibration process. These parameters provide the necessary reference information for the ground object spectrometer to ensure the accuracy and reliability of the calibration.

[0062] In one embodiment of the present invention, when the user chooses to start calibration in step S5, the calibration parameters of the ground object spectrometer are automatically adjusted after analyzing and calculating the difference value and the calibration factor according to the extracted whiteboard parameters, including the following steps:

[0063] When the user chooses to start calibration, the calibration parameters in the whiteboard parameters are input into the calibration algorithm for automatic adjustment; according to the calibration parameters, setting instructions executable by the ground object spectrometer are generated, and the generated setting instructions are sent to the ground object spectrometer to automatically adjust the parameters of the ground object spectrometer;

[0064] Compare the actual reflectivity data collected with the standard reflectivity of the whiteboard parameters, and use the analytical formula: C = R 标准 (λ)-R 测量 (λ)

[0065] Get the difference value C; where R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, R 测量 (λ) represents the reflectivity actually measured by the ground object spectrometer;

[0066] According to the comparison results, through the analysis formula:

[0067] Get the calibration factor α; where R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, R 测量 (λ) represents the reflectivity actually measured by the ground object spectrometer; λ represents the wavelength;

[0068] The calibration factor is input into the calibration algorithm of the ground object spectrometer, the sensitivity at each wavelength is adjusted, and the calibration parameters of the ground object spectrometer are automatically adjusted, and the calibration parameters include: gain and offset.

[0069] Specifically, the user confirms to start the calibration process, and after the ground object spectrometer system receives the user's confirmation, it is ready to perform the calibration operation. The calibration parameters in the whiteboard parameters are input into the calibration algorithm, and these parameters include the standard reflectance and other necessary calibration information. According to the calibration parameters, the setting instructions executable by the ground object spectrometer are generated. These instructions are used to adjust the parameters of the ground object spectrometer, such as gain and offset. The generated setting instructions are sent to the ground object spectrometer to automatically adjust the corresponding parameters. The actual reflectance data is collected by the ground object spectrometer. The collected actual reflectance data is compared with the standard reflectance in the whiteboard parameters. The difference value is obtained by using the analysis formula, and the calibration factor is obtained by the analysis formula according to the comparison result. The calibration factor is input into the calibration algorithm of the ground object spectrometer. According to the calibration factor, the sensitivity at each wavelength is adjusted. The calibration parameters of the ground object spectrometer, including gain and offset, are automatically adjusted. These adjustments ensure that the ground object spectrometer can more accurately measure the reflectance at different wavelengths.

[0070] In one embodiment of the present invention, the step S5 of calculating the automatically adjusted sensitivity according to the calibration factor analysis includes the following steps:

[0071] By analyzing the formula: S 更新 (λ) = S 原始 (λ)*α

[0072] Get the adjusted sensitivity S 更新 (λ); where S 原始 (λ) represents the original sensitivity of the ground object spectrometer at each wavelength, and α represents the calibration factor.

[0073] Specifically, the original sensitivity of the ground object spectrometer at each wavelength is obtained. These data are usually stored in the calibration parameters or configuration file of the ground object spectrometer. According to the previous steps, the calibration factor has been calculated by the formula. This calibration factor reflects the proportional relationship between the actual measured reflectivity and the standard reflectivity. Use the analytical formula to calculate the adjusted sensitivity. The core idea of ​​this formula is to adjust the original sensitivity according to the calibration factor to compensate for the measurement deviation caused by instrument errors, environmental changes and other factors. The calculated adjusted sensitivity is input into the calibration algorithm of the ground object spectrometer. The ground object spectrometer will adjust its measurement parameters, including gain and offset, according to this new sensitivity setting.

[0074] In one embodiment of the present invention, the step S6 includes the following steps:

[0075] By using the calibrated ground object spectrometer, measuring the known standard sample again or performing repeated measurements under the same measurement environment conditions, verifying whether the calibration is successful; the measurement environment conditions include: light source intensity and ambient temperature;

[0076] According to the spectral reflectance measured again, through the analysis formula:

[0077] Get the spectral reflectance deviation Y; where R 再次测量 (λ) represents the spectral reflectance measured again, R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, and λ represents the wavelength;

[0078] Set a threshold value based on the calculated spectral reflectance deviation to verify whether the calibration is successful;

[0079] When Y≤±2%, the calibration is successful; when Y>±2%, the calibration is unsuccessful; when the calibration is unsuccessful, return to step S2, re-collect the standard diffuse reflection whiteboard image and actual reflectivity data until the calibration is successfully verified and the calibration is ended.

[0080] Specifically, use the calibrated ground object spectrometer to measure the known standard sample again or repeat the measurement under the same measurement environment conditions. The same measurement environment conditions include factors that may affect the measurement results, such as light source intensity and ambient temperature. Record the spectral reflectance obtained by the re-measurement. According to the spectral reflectance obtained by the re-measurement, the spectral reflectance deviation is obtained by analyzing the formula. According to the calculated spectral reflectance deviation, a threshold is set to determine whether the calibration is successful, wherein the threshold is dynamically adjusted according to the actual application situation. In this embodiment, when Y≤±2%, the calibration is considered to be successful; when Y>±2%, the calibration is considered to be unsuccessful. If the calibration is successful, the calibration process is terminated and the calibration results are recorded. Otherwise, return to step S2, re-collect the standard diffuse reflection whiteboard image and the actual reflectance data until the calibration is verified to be successful and the calibration is terminated.

[0081] In one embodiment of the present invention, a two-dimensional code is designed on the reflective surface of the whiteboard, wherein the two-dimensional code can also be replaced by a barcode, RFID radio frequency identification technology and a contact chip card.

[0082] Specifically, the above-mentioned two-dimensional code can be replaced by a barcode or other methods that can be recognized by optical, radio, contact, etc., such as barcode, RFID radio frequency identification technology, contact chip card, etc.

[0083] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A calibration scheme for a ground object spectrometer based on a two-dimensional code, characterized in that: The following steps are involved: S1: Design a QR code on the reflective surface of a whiteboard; place a standard diffuse reflective whiteboard containing the QR code at the detection position of the ground object spectrometer; S2: Start the ground object spectrometer and open the shutter to collect the standard diffuse reflection whiteboard image and the actual reflectivity data in real time; S3: preprocessing the collected standard diffuse reflectance whiteboard image, including: image correction, denoising, grayscale and image enhancement; preprocessing the collected actual reflectance data, including: denoising and correction; S4: automatically identifying and extracting the QR code area according to the preprocessed standard diffuse reflection whiteboard image; parsing the identified and extracted QR code area to extract whiteboard parameters; S5: After extracting the whiteboard parameters of the QR code, prompt the user whether to start calibration; when the user chooses to start calibration, according to the extracted whiteboard parameters, analyze and calculate the difference value and calibration factor, and then automatically adjust the calibration parameters of the ground object spectrometer; analyze and calculate the sensitivity after automatic adjustment according to the calibration factor; when the user does not choose to start calibration, return to step S2 and re-collect the standard diffuse reflection whiteboard image and actual reflectivity data; S6: Verify whether the calibration is successful based on the calibration parameters that are automatically adjusted according to the whiteboard parameters.

2. According to the QR code-based ground spectrometer calibration scheme of claim 1, it is characterized in that: The step S4 automatically identifies and extracts the QR code area according to the preprocessed standard diffuse reflection whiteboard image, including the following steps: According to the collected and processed standard diffuse reflection whiteboard image, the QR code area in the standard diffuse reflection whiteboard image is automatically identified by using a deep learning model; By manually marking the QR code area, the labeled standard diffuse reflection whiteboard image and the QR code area include: the location and size of the QR code area to generate a data set, and the data set is input into the deep learning model for training; According to the trained deep learning model, the standard diffuse reflection whiteboard image is collected and preprocessed in real time, and the standard diffuse reflection whiteboard image collected and preprocessed in real time is input into the trained deep learning model to automatically identify the QR code area; according to the output result of the deep learning model, the QR code area in the image is extracted.

3. According to the two-dimensional code-based ground spectrometer calibration scheme of claim 2, it is characterized in that: The step S4 is to analyze and extract the whiteboard parameters according to the identified and extracted two-dimensional code area, and comprises the following steps: According to the identification and extraction of the QR code area, the three positioning patterns of the QR code are detected using Hough transform or template matching algorithm, and the boundary and direction of the QR code are extracted according to the relative position and size of the positioning patterns; According to the encoding rules of the QR code, the QR code matrix is ​​scanned, and each unit in the QR code matrix is ​​scanned to extract binary data; the scanned binary data is converted into text information using the QR code decoding algorithm; the text information in the QR code is parsed through natural language processing technology to extract whiteboard parameters, which include: whiteboard model, reflectivity and calibration parameters.

4. According to the QR code-based ground spectrometer calibration scheme of claim 1, it is characterized in that: When the user chooses to start calibration in step S5, the calibration parameters of the ground object spectrometer are automatically adjusted after analyzing and calculating the difference value and the calibration factor according to the extracted whiteboard parameters, including the following steps: When the user chooses to start calibration, the calibration parameters in the whiteboard parameters are input into the calibration algorithm for automatic adjustment; according to the calibration parameters, setting instructions executable by the ground object spectrometer are generated, and the generated setting instructions are sent to the ground object spectrometer to automatically adjust the parameters of the ground object spectrometer; Compare the actual reflectivity data collected with the standard reflectivity of the whiteboard parameters, and use the analytical formula: C = R 标准 (λ)-R 测量 (λ) Get the difference value C; where R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, R 测量 (λ) represents the reflectivity actually measured by the ground object spectrometer; According to the comparison results, through the analysis formula: Get the calibration factor α; where R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, R 测量 (λ) represents the reflectivity actually measured by the ground object spectrometer; λ represents the wavelength; The calibration factor is input into the calibration algorithm of the ground object spectrometer, the sensitivity at each wavelength is adjusted, and the calibration parameters of the ground object spectrometer are automatically adjusted, and the calibration parameters include: gain and offset.

5. According to the QR code-based ground spectrometer calibration scheme of claim 4, it is characterized in that: The step S5 includes calculating the automatically adjusted sensitivity according to the calibration factor analysis, and includes the following steps: By analyzing the formula: S 更新 (λ) = S 原始 (λ)*α Get the adjusted sensitivity S 更新 (λ); where S 原始 (λ) represents the original sensitivity of the ground object spectrometer at each wavelength, and α represents the calibration factor.

6. According to the QR code-based ground spectrometer calibration scheme of claim 1, it is characterized in that: The step S6 includes the following steps: By using the calibrated ground object spectrometer, measuring the known standard sample again or performing repeated measurements under the same measurement environment conditions, verifying whether the calibration is successful; the measurement environment conditions include: light source intensity and ambient temperature; According to the spectral reflectance measured again, through the analysis formula: Get the spectral reflectance deviation Y; where R 再次测量 (λ) represents the spectral reflectance measured again, R 标准 (λ) represents the standard reflectivity of the whiteboard parameters extracted from the QR code at different wavelengths, and λ represents the wavelength; Set a threshold value based on the calculated spectral reflectance deviation to verify whether the calibration is successful; When Y≤±2%, the calibration is successful; when Y>±2%, the calibration is unsuccessful; when the calibration is unsuccessful, return to step S2, re-collect the standard diffuse reflection whiteboard image and actual reflectivity data until the calibration is successfully verified and the calibration is ended.

7. The calibration scheme of a ground object spectrometer based on a two-dimensional code according to claim 1, characterized in that: A two-dimensional code is designed on the reflective surface of the whiteboard, wherein the two-dimensional code can also be replaced by a barcode, RFID radio frequency identification technology and a contact chip card.

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