An asynchronous calibration observation optimization method and system for a high-resolution fiber-optic spectrometer
By constructing a multi-factor wavelength drift prediction model in a high-resolution fiber optic spectrometer, and combining it with temperature, humidity, and air pressure sensors, the observation process was optimized, solving the problems of high difficulty, high cost, and low efficiency in environmental stability control, and achieving efficient and stable spectral observation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FUQING BRANCH OF FUJIAN NORMAL UNIV
- Filing Date
- 2026-04-14
- Publication Date
- 2026-07-10
AI Technical Summary
Existing high-resolution fiber optic spectrometers suffer from challenges such as difficulty in controlling environmental stability, high instrument construction and maintenance costs, low observation efficiency, and rapid equipment wear and tear, especially during frequent wavelength calibration processes.
The ThaAr lamp single-fiber asynchronous calibration mode is adopted, combined with high-precision temperature, humidity and barometric pressure sensors, to construct a multi-factor wavelength drift prediction model, realize real-time monitoring of the spectrometer's internal environmental parameters and real-time model calibration, and optimize the observation process to 'single initial calibration - continuous target observation - real-time model calibration correction'.
It improved observation efficiency and data output, reduced instrument construction and maintenance costs, ensured the stability and accuracy of wavelength calibration, and reduced equipment wear and tear.
Smart Images
Figure CN122360686A_ABST
Abstract
Description
Technical Field
[0001] This invention pertains to spectroscopic observation technology in the field of astronomical observation, specifically a method and system for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer. Background Technology
[0002] High-resolution fiber optic echelle spectrometers are core precision instruments in the field of astronomical observation. They guide the beam of light being measured into the optical system for dispersion via optical fiber, and then image it onto a CCD detector to obtain a high-resolution two-dimensional spectral image. They are crucial equipment for acquiring astrophysical parameters and conducting astronomical research. Wavelength calibration is a core step in spectroscopic observation, directly determining the wavelength accuracy and reliability of spectral data. Currently, most mainstream high-resolution fiber optic spectrometers use ThAr lamps as wavelength calibration light sources and employ a single-fiber asynchronous calibration observation mode.
[0003] The existing single-fiber asynchronous calibration observation mode involves a repetitive cycle of "wavelength calibration observation - background observation - target observation - wavelength calibration observation." By frequently interspersing calibration lamp spectrum observations, the wavelength calibration data is made to temporally approximate the target spectrum, thus compensating for spectrometer wavelength drift errors caused by changes in environmental temperature, humidity, and air pressure. Practical application verification has revealed the following clear shortcomings in this approach: 1. High difficulty in controlling environmental stability and poor long-term stability: The optical system of high-resolution fiber optic spectrometers is large in size, and even small changes in ambient temperature, humidity and air pressure can cause wavelength drift. Existing solutions require high-precision temperature and pressure control and dehumidification devices, and some scenarios use liquid nitrogen immersion for temperature control, which not only makes temperature control difficult, but also makes it difficult to achieve long-term stable operation of the instrument.
[0004] 2. High instrument construction and maintenance costs: High-precision temperature and pressure control systems significantly increase the hardware construction costs of spectrometers; at the same time, the dynamic stability of the temperature control system decays over time, requiring continuous maintenance and calibration, which further increases the maintenance costs throughout the instrument's life cycle.
[0005] 3. Low observation efficiency and rapid equipment wear and tear: Frequent interspersed calibration lamp spectrum observations require additional instrument mode switching, calibration lamp switching, and calibration lamp observation time. Background observations are also required to eliminate residual CCD signals, which consumes a large amount of astronomical observation time and reduces observation efficiency and data output. Frequent starting and stopping of calibration lamps will shorten their lifespan and increase equipment replacement costs. Summary of the Invention
[0006] To address the practical technical shortcomings of existing high-resolution fiber optic spectrometers in asynchronous calibration mode, such as difficulty in controlling environmental stability, high instrument construction and maintenance costs, low observation efficiency, and rapid equipment wear and tear, this invention provides a low-cost, highly stable, and highly efficient asynchronous calibration observation optimization method and system. While ensuring wavelength calibration accuracy, it improves the output of astronomical spectroscopic observation data and reduces instrument costs and equipment wear and tear.
[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides an optimization method for asynchronous calibration observation of a high-resolution fiber optic spectrometer, applicable to a high-resolution fiber optic echelle grating spectrometer employing a ThAr lamp single-fiber asynchronous calibration mode in the field of astronomical observation. The optical system of the spectrometer is housed inside a sealed, insulated housing, and includes the following steps: S1. Hardware Deployment and Environmental Monitoring Configuration: High-precision temperature, humidity and air pressure sensors, which are free from radiation interference, are installed inside the spectrometer's insulation housing and in the same operating environment as the optical system. A slave device is set up outside the spectrometer to configure the sampling frequency and data transmission rules of the temperature, humidity and air pressure sensors, so as to realize the real-time acquisition, transmission and storage of the internal environmental parameters of the spectrometer around the clock. S2. Initial calibration and synchronous data acquisition: Before the start of the observation period, complete one ThAr lamp wavelength calibration observation to obtain initial zero-point wavelength calibration data; during the observation period, collect ThAr lamp spectral data at preset low-frequency intervals, and synchronously collect the corresponding temperature, humidity, and air pressure environmental parameters. After preprocessing the ThAr lamp spectral data, match it with the environmental parameters by timestamp to form a model training dataset. S3. Construction of a multi-factor wavelength drift prediction model: Using the temperature, humidity, and air pressure parameters in the model training dataset as independent variables and the wavelength drift at the corresponding time as the dependent variable, a linear correlation model between temperature, humidity, air pressure, and wavelength drift is established by least squares linear regression fitting. S4. Real-time wavelength calibration data calculation: During the target spectrum observation process, the temperature, humidity and air pressure parameters inside the spectrometer are collected in real time by temperature, humidity and air pressure sensors, and substituted into the linear correlation model to calculate the real-time wavelength drift; combined with the initial zero-point wavelength calibration data obtained in step S2, the real-time ThAr lamp wavelength calibration data at the current moment is calculated. S5. Optimization of asynchronous calibration observation process: The observation process of "single initial calibration - continuous target observation - real-time model calibration correction" is adopted to achieve uninterrupted execution of target observation.
[0008] Preferably, in step S1, the sampling frequency of the temperature, humidity and air pressure sensors is configured to 0.2Hz. The lower-level computer establishes a communication connection with the temperature, humidity and air pressure sensors through the IIC bus, and transmits the collected environmental parameters to the data acquisition computer of the spectrometer at a frequency of 0.2Hz. The environmental parameters are stored in files in units of natural days.
[0009] Preferably, in step S2, the low-frequency acquisition interval of the ThAr lamp spectral data is 15 minutes / time; the time difference between the spectral data and the environmental parameter timestamp is ≤3 seconds; the spectral data preprocessing includes two-dimensional spectral data preprocessing, one-dimensional spectral extraction, reference spectral line selection, and accurate calculation of spectral line positions.
[0010] Preferably, in step S3, the expression for the linear correlation model is: ,in, denoted as wavelength drift, a as temperature linearity coefficient, b as humidity linearity coefficient, c as air pressure linearity coefficient, and D as a constant term; T is the real-time temperature value at the calibration time, H is the real-time humidity value at the calibration time, and P is the real-time air pressure value at the calibration time.
[0011] Preferably, in step S3, the model training samples are obtained by using observation data from a spectrometer for 12 consecutive months and corresponding environmental parameters. The model parameters obtained by fitting are as follows: temperature coefficient a = -0.0182, humidity coefficient b = -0.1120, air pressure coefficient c = -0.0609, and constant term D = 0.0005.
[0012] Preferably, in step S4, the calculation accuracy of the real-time wavelength calibration data satisfies the following: the average residual RMS between the model prediction value and the actual observation value is 0.000611 pixels, corresponding to a wavelength accuracy of 0.0144 mÅ.
[0013] Preferably, in step S5, the optimized observation process performs ThAr lamp calibration observation only once at the beginning and once at the end of the observation period, and the target observation link within the observation period completes the spectral wavelength correction through the wavelength calibration data calculated in real time by the model.
[0014] Preferably, the method is applicable to all high-resolution fiber optic spectrometers in the field of astronomical observation that employ asynchronous calibration with cathode lamps.
[0015] The present invention also provides an asynchronous calibration observation optimization system for a high-resolution fiber optic spectrometer, used to implement the asynchronous calibration observation optimization method for a high-resolution fiber optic spectrometer as described in any of the first aspects, including an environmental monitoring module, a lower-level machine, a data acquisition and storage server, a data processing and model calculation module, and an observation process control module; The environmental monitoring module is a high-precision temperature, humidity and air pressure sensor, which is installed inside the thermal insulation shell of the spectrometer and operates in the same environment as the spectrometer's optical system. It is used to collect the temperature, humidity and air pressure environmental parameters inside the spectrometer in real time, and there are no radiation interference signals in the working band of the spectrometer. The lower-level machine is connected to the environmental monitoring module and is used to receive environmental parameters collected by the environmental monitoring module and transmit them to the data acquisition and storage server. The data acquisition and storage server is connected to the lower-level machine, the spectrometer CCD controller, and the observation process control module, respectively, and is used to store environmental parameters, Thaar lamp spectral data, target spectral data, and synchronize the timestamp information of all data. The data processing and model calculation module is connected to the data acquisition and storage server to complete the preprocessing of ThAr lamp spectral data, time matching of environmental parameters and spectral data, construction and training of wavelength drift prediction model, and calculation of real-time wavelength drift and real-time calibration data. The observation process control module is connected to the data processing and model calculation module, the shutter of the spectrometer, the calibration lamp, and the telescope optical path switching mechanism, respectively. It is used to execute the optimized observation process, control the start and stop of the calibration lamp, the switching of the observation mode, and the continuous execution of target observation.
[0016] Furthermore, the lower-level machine is a microcontroller or Raspberry Pi, which communicates with the environmental monitoring module via IIC / SPI bus and with the data acquisition and storage server via serial port / RS485 / USB interface; it also includes a data processing and storage server, which is synchronously connected to the data acquisition and storage server via network for backing up environmental parameters and spectral data, as well as batch training of wavelength drift prediction models.
[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. Significantly improved observation efficiency and data output: Actual measurements show that the optimized spectrometer's data throughput increased by 28.57%. Based on a 10-hour nighttime observation cycle, if each spectrum is exposed for 30 minutes, switching observations (calibration observation, observation mode switching, calibration lamp on / off operation, CCD readout) takes up approximately 140 minutes of invalid observation time. This adds four additional 30-minute scientific spectroscopic observations, improving the utilization rate of astronomical observation resources and scientific output.
[0018] 2. Stable and controllable wavelength calibration accuracy: The average residual RMS between the predicted value and the actual observation value of the multi-factor linear model is 0.000611 pixels, corresponding to a wavelength accuracy of 0.0144mÅ, which meets the calibration requirements of high-resolution spectral observation of this aperture telescope and can achieve stable calibration without the need for extreme environmental control.
[0019] 3. Reduced instrument construction and maintenance costs: There is no need to configure ultra-high precision temperature and pressure control systems. Drift correction is achieved only through low-cost temperature, humidity and air pressure sensors and algorithm models, reducing hardware construction costs; reducing the frequent start and stop of calibration lamps, extending their service life, and reducing maintenance and parts replacement costs.
[0020] 4. The observation process is simplified and the instrument stability is improved by reducing repetitive operations such as optical path switching, calibration lamp switching, and background observation, thereby reducing the frequency of mechanical and electronic component operations, reducing the probability of failure, and improving long-term operational stability; at the same time, the operation process is simplified and the complexity of manual observation is reduced. Attached Figure Description
[0021] Figure 1 This is a simplified structural diagram of a spectrometer system in the prior art; Figure 2 This is a schematic diagram of the overall structure of the wavelength drift correction system of the present invention; Figure 3 This is a time series diagram of ThAr spectral line pixel drift and environmental parameter changes tested on July 23, 2023, according to the present invention. Figure 4 This is a time series diagram of ThAr spectral line pixel drift and environmental parameter changes tested on September 12, 2023, according to the present invention. Figure 5 This is a flowchart of the real-time ThAr lamp wavelength calibration data calculation process of the present invention; Figure 6 This is a Gantt chart comparing the observation process before and after optimization in this invention; Figure 7 This is a comparison chart of the residuals between the wavelength drift model predictions and actual observations of this invention; Figure 8 This is a residual histogram of the wavelength drift model prediction and the actual observation values of this invention. Detailed Implementation
[0022] The technical solution of the present invention will be clearly and completely described below with reference to specific measured embodiments and accompanying drawings. All embodiments are based on actual astronomical observation scenarios to ensure that the content is true and verifiable.
[0023] This embodiment is applied to a fiber optic grating high-resolution spectrometer for astronomical observation. The optical system of the spectrometer is placed inside a sealed multi-layer thermal insulation shell and is equipped with an air pressure and temperature control system. A ThaAr lamp is used as the calibration light source. The light beam is guided into the optical system through an optical fiber for spectral dispersion and imaging on a CCD detector. The CCD detector is equipped with a CCD controller. The original observation mode was single-fiber asynchronous calibration.
[0024] Example 1: Optimizing the Hardware Deployment and Debugging of the System The optimized system in this embodiment is deployed as follows, and all hardware has undergone actual compatibility testing: 1. The environmental monitoring module uses high-precision temperature, humidity and air pressure sensors, which are installed inside the insulated housing and in the same environment as the spectrometer's optical system. After testing, the temperature, humidity and air pressure sensors are free from radiation interference in the spectrometer's astronomical observation band, and the acquisition accuracy meets the requirements for environmental parameter monitoring.
[0025] 2. Lower-level machine: A microcontroller is selected as the lower-level machine and installed outside the insulation shell. It communicates with the sensor through the IIC bus and is connected to the data acquisition computer 9 through a serial-to-USB cable. After debugging, the communication is stable and can realize real-time transmission of environmental parameters.
[0026] 3. Data Acquisition and Storage Server: The spectrometer is equipped with a data acquisition computer that communicates with the lower-level computer, CCD controller, and observation process control module. After testing, it can realize real-time storage of environmental parameters and spectral data, with a timestamp synchronization error of less than or equal to 3 seconds, and stores the data in .bat format files according to natural days.
[0027] 4. Data Processing and Model Calculation Module: Deployed on the data acquisition computer and data processing and storage server, the server synchronizes data with the data acquisition computer via a local area network. Testing has shown that it can complete spectral data preprocessing, data time matching, model building, and real-time calculations, with calculation latency meeting observation requirements. This section describes existing technology and will not be elaborated further.
[0028] 5. Observation process control module: Electrically connected to all relevant components, after debugging, it can accurately control the start and stop of the calibration lamp, the switching of the optical path and the continuous execution of target observation without any lag or malfunction.
[0029] The observation process control module is connected to the data processing and model calculation module, the spectrometer shutter, the calibration lamp, and the telescope optical path switching mechanism. It executes the optimized observation process, controlling the starting and stopping of the calibration lamp, the switching of observation modes, and the continuous execution of target observations. This is existing technology and will not be elaborated upon further.
[0030] Example 2 Practical application testing of optimization methods This embodiment is based on the system of Embodiment 1 and was tested in a real astronomical observation scenario. The testing period was from July 2023 to June 2024. The specific steps and results are as follows: 1. Hardware Deployment and Environmental Monitoring Configuration: Complete the installation and debugging of temperature, humidity and air pressure sensors and slave devices. Configure the sampling frequency of temperature, humidity and air pressure sensors to 0.2Hz and the transmission frequency of slave devices to 0.2Hz, so as to realize uninterrupted collection and storage of environmental parameters throughout the year, and ensure that the data continuity meets the model training requirements.
[0031] 2. Initial Calibration and Synchronous Data Acquisition: Before the start of each observation period, a ThAr lamp calibration observation was completed to obtain initial zero-point data. During the observation period, ThAr lamp spectral data was collected every 15 minutes, and environmental parameters were collected synchronously. After spectral data preprocessing, timestamp matching was performed to form the training dataset. During the testing period, observation data and environmental parameters for 12 months were collected, covering different seasons and environmental conditions to ensure the model's generalization ability.
[0032] Through time series analysis (such as Figure 3 , Figure 4 As shown in the figure, the pixel drift of the ThAr spectral line is positively correlated with air pressure and humidity, and negatively correlated with temperature, which verifies the feasibility of the multi-factor linear model.
[0033] 3. Construction of a multi-factor wavelength drift prediction model: Based on 12 months of training samples, the model parameters were obtained through least squares linear regression fitting: a=-0.0182, b=-0.1120, c=-0.0609, D=0.0005. The model expression is as follows: =-0.0182T -0.1120H -0.0609P + 0.0005.
[0034] 4. Real-time wavelength calibration data calculation: During target observation, temperature, humidity, and barometric pressure sensors collect environmental parameters in real time. These parameters are substituted into the model to calculate the real-time drift, and combined with the initial zero-point data to obtain real-time calibration data. (See test results). Figure 7 , Figure 8 The average residual RMS between the model prediction and the actual observation is 0.000611 pixels, corresponding to a wavelength accuracy of 0.0144 mÅ, which meets the requirements for high-resolution astronomical spectroscopic observation.
[0035] 5. Observation process optimization test: Compare the observation process before and after optimization (see...) Figure 6 The original process required cumbersome steps such as background observation, optical path switching, and calibration observation after each target observation. The optimized process only requires one calibration observation at the beginning and end of the observation cycle, ensuring uninterrupted target observation. Actual measurements show that within a 10-hour observation cycle, the optimized process saves 140 minutes of ineffective time, increases data throughput by 28.57%, and significantly improves observation efficiency. It replaces the existing cyclical process of "calibration observation - background observation - target observation - calibration observation," eliminating the frequent switching of ThAr lamps, corresponding background observations, and mode switching during the observation process.
[0036] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this invention.
Claims
1. A method for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer, applied to a high-resolution fiber optic echelle grating spectrometer employing a ThAr lamp single-fiber asynchronous calibration mode in the field of astronomical observation, wherein the optical system of the spectrometer is housed within a sealed, insulated casing, characterized in that... Includes the following steps: S1. Hardware Deployment and Environmental Monitoring Configuration: High-precision temperature, humidity and air pressure sensors, which are free from radiation interference, are installed inside the spectrometer's insulation shell and in the same operating environment as the optical system. A slave device is set up outside the spectrometer to configure the sampling frequency and data transmission rules of the temperature, humidity and air pressure sensors, so as to realize the real-time acquisition, transmission and storage of the internal environmental parameters of the spectrometer around the clock. S2. Initial calibration and synchronous data acquisition: Before the start of the observation period, complete one ThAr lamp wavelength calibration observation to obtain initial zero-point wavelength calibration data; during the observation period, collect ThAr lamp spectral data at preset low-frequency intervals, and synchronously collect the corresponding temperature, humidity, and air pressure environmental parameters. After preprocessing the ThAr lamp spectral data, match it with the environmental parameters by timestamp to form a model training dataset. S3. Construction of a multi-factor wavelength drift prediction model: Using the temperature, humidity, and air pressure parameters in the model training dataset as independent variables and the wavelength drift at the corresponding time as the dependent variable, a linear correlation model between temperature, humidity, air pressure, and wavelength drift is established by least squares linear regression fitting. S4. Real-time wavelength calibration data calculation: During the target spectrum observation process, the temperature, humidity and air pressure parameters inside the spectrometer are collected in real time by temperature, humidity and air pressure sensors. These parameters are substituted into the linear correlation model to calculate the real-time wavelength drift. Combined with the initial zero-point wavelength calibration data obtained in step S2, the real-time ThAr lamp wavelength calibration data at the current moment is calculated. S5. Optimization of asynchronous calibration observation process: The observation process of "single initial calibration - continuous target observation - real-time model calibration correction" is adopted to achieve uninterrupted execution of target observation.
2. The method for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer according to claim 1, characterized in that, In step S1, the sampling frequency of the temperature, humidity and air pressure sensors is configured to 0.2Hz. The lower-level computer establishes a communication connection with the temperature, humidity and air pressure sensors through the IIC bus and transmits the collected environmental parameters to the data acquisition computer of the spectrometer at a frequency of 0.2Hz. The environmental parameters are stored in files in units of natural days.
3. The method for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer according to claim 1, characterized in that, In step S2, the low-frequency acquisition interval of the ThAr lamp spectral data is 15 minutes / time; the time difference between the spectral data and the environmental parameter timestamp is less than or equal to 3 seconds; the spectral data preprocessing includes two-dimensional spectral data preprocessing, one-dimensional spectral extraction, reference spectral line selection, and accurate calculation of spectral line positions.
4. The method for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer according to claim 1, characterized in that, In step S3, the expression for the linear correlation model is: ,in denoted as wavelength drift, a as temperature linearity coefficient, b as humidity linearity coefficient, c as air pressure linearity coefficient, and D as a constant term; T as the real-time temperature value at the calibration time, H as the real-time humidity value at the calibration time, and P as the real-time air pressure value at the calibration time.
5. The method for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer according to claim 4, characterized in that, In step S3, the model training samples use 12 consecutive months of observation data from a spectrometer and corresponding environmental parameters. The model parameters obtained by fitting are as follows: Temperature coefficient a = -0.0182, humidity coefficient b = -0.1120, air pressure coefficient c = -0.0609, constant term D = 0.0005.
6. The method for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer according to claim 1, characterized in that, In step S4, the calculation accuracy of the real-time wavelength calibration data satisfies the following: the average residual RMS between the model prediction value and the actual observation value is 0.000611 pixels, corresponding to a wavelength accuracy of 0.0144 mÅ.
7. The method for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer according to claim 1, characterized in that, In step S5, the optimized observation process only performs one ThAr lamp calibration observation at the beginning and end of the observation period. The target observation link within the observation period completes the spectral wavelength correction through the wavelength calibration data calculated in real time by the model.
8. The method for optimizing asynchronous calibration observations of a high-resolution fiber optic spectrometer according to claim 1, characterized in that, The method is applicable to all high-resolution fiber optic spectrometers in the field of astronomical observation that use asynchronous calibration with cathode lamps.
9. A high-resolution fiber optic spectrometer asynchronous calibration observation optimization system, used to implement the high-resolution fiber optic spectrometer asynchronous calibration observation optimization method according to any one of claims 1-8, characterized in that, It includes an environmental monitoring module, a lower-level machine, a data acquisition and storage server, a data processing and model calculation module, and an observation process control module. The data processing and model calculation module and the observation process control module are deployed on the data acquisition and storage server. The environmental monitoring module is a high-precision temperature, humidity and air pressure sensor installed inside the thermal insulation shell of the spectrometer. It operates in the same environment as the optical system of the spectrometer and is used to collect the temperature, humidity and air pressure environmental parameters inside the spectrometer in real time. There are no radiation interference signals in the working band of the spectrometer. The lower-level machine is connected to the environmental monitoring module and is used to receive environmental parameters collected by the environmental monitoring module and transmit them to the data acquisition and storage server. The data acquisition and storage server is connected to the lower-level machine, the spectrometer CCD controller, and the observation process control module, respectively, and is used to store environmental parameters, Thaar lamp spectral data, target spectral data, and synchronize the timestamp information of all data. The data processing and model calculation module is connected to the data acquisition and storage server and is used to complete the preprocessing of ThAr lamp spectral data, time matching of environmental parameters and spectral data, construction and training of wavelength drift prediction model, and calculation of real-time wavelength drift and real-time calibration data. The observation process control module is connected to the data processing and model calculation module, the shutter of the spectrometer, the calibration lamp, and the telescope optical path switching mechanism, respectively. It is used to execute the optimized observation process, control the start and stop of the calibration lamp, the switching of the observation mode, and the continuous execution of target observation.
10. The high-resolution fiber optic spectrometer asynchronous calibration and observation optimization system according to claim 9, characterized in that, The lower-level machine is a microcontroller or Raspberry Pi, which communicates with the environmental monitoring module via IIC / SPI bus and with the data acquisition and storage server via serial port / RS485 / USB interface; it also includes a data processing and storage server, which is synchronously connected to the data acquisition and storage server via network for backing up environmental parameters and spectral data, as well as batch training of wavelength drift prediction models.