Intelligent monitoring method for on-orbit observation quality of meteorological satellite remote sensing instrument

Abstract

The invention provides an intelligent monitoring method for on-orbit observation quality of a meteorological satellite remote sensing instrument, and relates to the technical field of intelligent monitoring, and the method comprises the steps: carrying out the preprocessing of a multi-dimensional observation data set according to a preset data processing strategy, and obtaining a standard multi-dimensional observation data set; performing defect identification on the standard multi-dimensional observation data set based on a preset defect identification channel, and determining a multi-dimensional defect identification feature which comprises a defect proportion; the defect proportion is judged according to a preset proportion threshold value, and a monitoring early warning signal is generated and comprises an early warning type and an early warning level; and carrying out visual display on the early warning type and the early warning level. The technical problems that potential instrument defects cannot be found and the on-orbit observation quality of the instrument is poor due to the fact that an existing on-orbit observation quality monitoring method for the meteorological satellite remote sensing instrument lacks monitoring on other state parameters and data quality characteristics of the instrument can be solved, the on-orbit observation quality of the instrument can be guaranteed, and the on-orbit observation quality of the instrument is improved. And guarantee is provided for data application.

Description

Technical Field

[0001] This application relates to the field of intelligent monitoring technology, and in particular, to an intelligent monitoring method for the on-orbit observation quality of meteorological satellite remote sensing instruments. Background Art

[0002] The observation data of meteorological satellite remote sensors has played a great role in application fields such as meteorological forecasting, climate analysis, disaster prevention and reduction. With the continuous improvement of application requirements, the requirements for the calibration and positioning quality of FY satellite observation data are also getting higher and higher.

[0003] Currently, relying on the working state of the monitoring instrument and the integrity of the observation data can only reflect the presence or absence of the observation data or preset extreme abnormal problems of the instrument, and cannot monitor the data quality after calibration and positioning. The full play of the application benefits of the observation data mainly depends on the data quality after calibration and positioning. Based on the results of long-term data quality inspection, the quality of the instrument observation data (accuracy, spatio-temporal stability, etc.) can be comprehensively evaluated, but the monitoring of other state parameters of the instrument and data quality characteristics is lacking, so potential problems that have not yet emerged in the instrument cannot be found; even if calibration and positioning problems are found, it is impossible to trace the changes in the observation system in a timely manner, which is not conducive to the timely solution of quality problems.

[0004] In summary, the existing methods for monitoring the on-orbit observation quality of meteorological satellite remote sensing instruments lack the monitoring of other state parameters of the instrument and data quality characteristics, resulting in the technical problem that potential instrument quality defects cannot be found, causing poor on-orbit observation quality of the instrument. Summary of the Invention

[0005] The purpose of this application is to provide an intelligent monitoring method for the on-orbit observation quality of meteorological satellite remote sensing instruments, so as to solve the technical problem that the existing methods for monitoring the on-orbit observation quality of meteorological satellite remote sensing instruments lack the monitoring of other state parameters of the instrument and data quality characteristics, resulting in the inability to discover potential instrument quality defects and causing poor on-orbit observation quality of the instrument.

[0006] In view of the above problems, the present application provides an intelligent monitoring method for the on-orbit observation quality of meteorological satellite remote sensing instruments. The method includes: receiving a multi-dimensional observation data set based on an interactive analysis platform, where the multi-dimensional observation data set includes a satellite platform parameter set, an instrument status parameter set, and a calibration data set; preprocessing the multi-dimensional observation data set according to a preset data processing strategy to obtain a standard multi-dimensional observation data set; identifying defects in the standard multi-dimensional observation data set based on a preset defect identification channel to determine multi-dimensional defect identification features, where the defect identification features include a defect ratio; judging the defect ratio according to a preset ratio threshold, and generating a monitoring warning signal based on the judgment result, where the monitoring warning signal includes a warning type and a warning level; and sending the warning type and the warning level to an information release window for visual display.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] By receiving a multi-dimensional observation data set based on an interactive analysis platform, where the multi-dimensional observation data set includes a satellite platform parameter set, an instrument status parameter set, and a calibration data set; preprocessing the multi-dimensional observation data set according to a preset data processing strategy to obtain a standard multi-dimensional observation data set; identifying defects in the standard multi-dimensional observation data set based on a preset defect identification channel to determine multi-dimensional defect identification features, where the defect identification features include a defect ratio; judging the defect ratio according to a preset ratio threshold, and generating a monitoring warning signal based on the judgment result, where the monitoring warning signal includes a warning type and a warning level; and sending the warning type and the warning level to an information release window for visual display. It is possible to solve the technical problem that the existing methods for monitoring the on-orbit observation quality of meteorological satellite remote sensing instruments lack the monitoring of other status parameters and data quality characteristics of the instruments, resulting in the inability to discover potential instrument quality defects and poor on-orbit observation quality of the instruments; it is possible to achieve comprehensive monitoring of the instrument status and data quality, and then timely discover and solve observation quality problems such as sudden abnormal events and long-term attenuation encountered by the instrument, ensure the on-orbit observation quality of the instrument, and provide guarantee for data application.

[0009] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are given below. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0011] Figure 1 It is a schematic flowchart of the intelligent monitoring method for the on-orbit observation quality of the meteorological satellite remote sensing instrument of the present application;

[0012] Figure 2 It is a schematic flowchart of preprocessing the multi-dimensional observation data set according to a preset data processing strategy in the intelligent monitoring method for the on-orbit observation quality of the meteorological satellite remote sensing instrument of the present application. Detailed implementation manners

[0013] By providing an intelligent monitoring method for the on-orbit observation quality of a meteorological satellite remote sensing instrument, the present application solves the technical problem that the existing methods for monitoring the on-orbit observation quality of meteorological satellite remote sensing instruments lack the monitoring of other state parameters and data quality characteristics of the instruments, resulting in the inability to detect potential instrument defects and poor on-orbit observation quality of the instruments. It can realize the comprehensive monitoring of the instrument state and data quality, and then can timely discover and solve the observation quality problems such as sudden abnormal events and long-term attenuation encountered by the instrument, ensure the on-orbit observation quality of the instrument, and provide guarantee for data application.

[0014] Next, the technical solutions in the present application will be clearly and completely described with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application. In addition, it should be noted that for the convenience of description, only the parts related to the present application are shown in the drawings rather than all.

[0015] Embodiment

[0016] Please refer to the attached Figure 1 , the present application provides an intelligent monitoring method for the on-orbit observation quality of a meteorological satellite remote sensing instrument, and the method specifically includes the following steps:

[0017] Step 1: Based on the interactive analysis platform, receive a multi-dimensional observation data set, where the multi-dimensional observation data set includes a satellite platform parameter set, an instrument state parameter set, and a calibration data set.

[0018] Step 2: Preprocess the multi-dimensional observation data set according to a preset data processing strategy to obtain a standard multi-dimensional observation data set.

[0019] Step 3: Based on the preset defect recognition channels, perform defect recognition on the standard multi-dimensional observation dataset to determine multi-dimensional defect recognition features, where the defect recognition features include defect ratios.

[0020] Step 4: Judge the defect ratio according to the preset ratio threshold, and generate a monitoring warning signal based on the judgment result. The monitoring warning signal includes a warning type and a warning level.

[0021] Step 5: Send the warning type and the warning level to the information release window for visual display.

[0022] Specifically, first, based on the interactive analysis platform, receive a multi-dimensional observation dataset, where the multi-dimensional observation dataset includes a satellite platform parameter set, an instrument status parameter set, and a calibration dataset; the satellite platform parameter set includes various time parameters, orbit parameters, and attitude parameters; the instrument status parameter set includes various temperature parameters, basic parameters related to calibration, angle parameters reflecting the scanning status, etc. Then obtain the preset data processing strategies, which include redundancy elimination strategies, missing data supplementation strategies, and error correction strategies; further preprocess the multi-dimensional observation dataset according to the preset data processing strategies to obtain a standard multi-dimensional observation dataset.

[0023] Construct a preset defect recognition channel based on the BP neural network. The preset defect recognition channels include a satellite parameter defect recognition unit, an instrument parameter defect recognition unit, and a calibration quality defect recognition unit; then perform defect recognition on the standard multi-dimensional observation dataset based on the preset defect recognition channels to determine multi-dimensional defect recognition features, where the defect recognition features include defect ratios. Further judge the defect ratio according to the preset ratio threshold, and generate a monitoring warning signal according to the defect features that do not meet the preset ratio threshold. The monitoring warning signal includes a warning type and a warning level. Finally, send the warning type and the warning level to the information release window for visual display. It can realize the comprehensive monitoring of the instrument status and data quality, and then can timely discover and solve the observation quality problems such as sudden abnormal events and long-term attenuation encountered by the instrument, ensure the in-orbit observation quality of the instrument, and provide guarantee for data application.

[0024] The above method can solve the technical problem that the existing methods for monitoring the in-orbit observation quality of meteorological satellite remote sensing instruments lack the monitoring of other state parameters and data quality characteristics of the instruments, resulting in the inability to detect potential instrument defects and poor in-orbit observation quality of the instruments. First, based on an interactive analysis platform, a multi-dimensional observation data set is received, where the multi-dimensional observation data set includes a satellite platform parameter set, an instrument state parameter set, and a calibration data set; then, the multi-dimensional observation data set is pre-processed according to a preset data processing strategy to obtain a standard multi-dimensional observation data set; next, defect identification is performed on the standard multi-dimensional observation data set based on a preset defect identification channel to determine multi-dimensional defect identification features, where the defect identification features include a defect ratio; then, the defect ratio is judged according to a preset ratio threshold, and a monitoring warning signal is generated based on the judgment result, and the monitoring warning signal includes a warning type and a warning level; finally, the warning type and the warning level are sent to an information release window for visual display. It can realize the comprehensive monitoring of the instrument state and data quality, and then can timely discover and solve observation quality problems such as sudden abnormal events and long-term attenuation encountered by the instrument, ensure the in-orbit observation quality of the instrument, and provide guarantee for data application.

[0025] Furthermore, the present application further includes the following steps:

[0026] Obtain a preset data quality evaluation strategy; perform multi-dimensional calibration quality evaluation of the electromagnetic spectrum band and observation characteristics on the calibration data set based on the preset data evaluation strategy to obtain a calibration quality parameter set; update the calibration data set based on the calibration quality parameter set to obtain an updated multi-dimensional observation data set.

[0027] Specifically, first, obtain a preset data quality evaluation strategy, which can be set according to actual situations. For example: according to the electromagnetic spectrum band of the data, provide a variety of internationally commonly used comparison reference sources and comparison methods, such as comparison with a simulation reference source obtained by using a radiation transfer model and numerical forecast field data, comparison with an observation reference source of cross-observation of the same type of instruments at home and abroad, comparison with stable targets such as the ocean, rainforest, desert, clouds, etc.; according to the observation characteristics, provide a variety of dimensional quality parameters, such as radiation and radiation difference statistical values of different granularities (time, space, scan angle, channel, detector element, etc.). Then perform multi-dimensional calibration quality evaluation of the electromagnetic spectrum band and observation characteristics on the calibration data set based on the preset data quality evaluation strategy to obtain a calibration quality parameter set. Further update the calibration data set based on the calibration quality parameter set to obtain an updated multi-dimensional observation data set.

[0028] Furthermore, as Figure 2 shown, the present application further includes the following steps:

[0029] The preset data processing strategy includes a redundancy elimination strategy, a missing data supplementation strategy, and an error correction strategy; based on the preset data processing strategy, preprocessing is performed on the satellite platform parameter set, the instrument status parameter set, and the calibration quality parameter set to obtain the standard multi-dimensional observation data set.

[0030] Specifically, obtain a preset data processing strategy, where the preset data processing strategy includes a redundancy elimination strategy, a missing data supplementation strategy, and an error correction strategy, which are used to improve data quality and make it more suitable for subsequent data analysis; redundant data refers to information that appears repeatedly in the data set or information that is highly correlated with other data. The goal of the redundancy elimination strategy is to identify and delete this redundant data to reduce the scale and complexity of the data set while maintaining the integrity and accuracy of the data; missing data is the part of the data set without values or missing information. The goal of the missing data supplementation strategy is to fill these missing values through various methods to make the data set more complete and accurate; error data refers to errors or inconsistent information in the data set. The goal of the error correction strategy is to identify and correct this error data to improve the accuracy and reliability of the data set. Then, data processing is performed on the satellite platform parameter set, the instrument status parameter set, and the calibration quality parameter set according to the preset data processing strategy to obtain the standard multi-dimensional observation data set.

[0031] Furthermore, the present application further includes the following steps:

[0032] Construct a preset defect recognition channel based on a BP neural network. The preset defect recognition channel includes a satellite parameter defect recognition unit, an instrument parameter defect recognition unit, and a calibration quality parameter defect recognition unit; use the satellite parameter defect recognition unit to perform defect recognition on the standard satellite platform parameter set to determine satellite defect recognition features; use the instrument parameter defect recognition unit to perform defect recognition on the standard instrument status parameter set to determine instrument defect recognition features; use the calibration quality parameter defect recognition unit to perform recognition on the standard calibration quality parameter set to determine calibration quality defect recognition features; obtain the multi-dimensional defect recognition features according to the satellite defect recognition features, the instrument defect recognition features, and the calibration quality defect recognition features.

[0033] Specifically, a BP neural network, that is, a backpropagation neural network, is a multi-layer feedforward neural network trained according to the error backpropagation algorithm and is also one of the most widely used neural network models. It has an input layer, a hidden layer, and an output layer, where each neuron is connected to all neurons in the previous layer, transmits signals through weighted sums, and undergoes a non-linear transformation through an activation function. The core of the BP neural network is the BP algorithm, and its basic idea is to use the gradient descent method to minimize the mean square error between the actual output value and the expected output value of the network.

[0034] Build a preset defect recognition channel based on a BP neural network, where the preset defect recognition channel includes a satellite parameter defect recognition unit, an instrument parameter defect recognition unit, and a calibration quality parameter defect recognition unit; the input data of the satellite parameter defect recognition unit is a satellite platform parameter set, and the output data is a satellite parameter defect feature; the input data of the instrument parameter defect recognition unit is an instrument status parameter set, and the output data is an instrument parameter defect feature; the input data of the calibration quality defect recognition unit is a calibration quality parameter set, and the output data is a calibration quality defect feature.

[0035] Then, use the satellite parameter defect recognition unit to perform defect recognition on the standard satellite platform parameter set to determine the satellite defect recognition feature; use the instrument parameter defect recognition unit to perform defect recognition on the standard instrument status parameter set to determine the instrument defect recognition feature; use the calibration quality parameter defect recognition unit to perform recognition on the standard calibration quality parameter set to determine the calibration quality defect recognition feature. Finally, obtain the multi-dimensional defect recognition feature based on the satellite defect recognition feature, the instrument defect recognition feature, and the calibration quality defect recognition feature.

[0036] Furthermore, the present application further includes the following steps:

[0037] Calculate the defect proportion based on the satellite defect recognition feature, the instrument defect recognition feature, and the calibration quality defect recognition feature respectively to obtain the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion; obtain the multi-dimensional defect recognition feature based on the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion.

[0038] Specifically, then calculate the defect proportion based on the satellite defect recognition feature, the instrument defect recognition feature, and the calibration quality defect recognition feature respectively, where the defect proportion is the ratio of the number of defect data in the defect recognition feature to the total number of data, to obtain the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion. Then obtain the multi-dimensional defect recognition feature based on the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion.

[0039] Furthermore, the present application further includes the following steps:

[0040] Obtain preset ratio thresholds, where the preset ratio thresholds include a satellite defect ratio threshold, an instrument defect ratio threshold, and a calibration quality defect ratio threshold; judge the satellite defect ratio, the instrument defect ratio, and the calibration quality defect ratio according to the satellite defect ratio threshold, the instrument defect ratio threshold, and the calibration quality defect ratio threshold respectively; and perform a warning mark on the features that do not meet the ratio threshold to determine the warning type; calculate the defect ratio deviation based on the warning type to generate a warning level; map the warning type and the warning level to generate the monitoring warning signal.

[0041] Specifically, first, obtain preset ratio thresholds, where the preset ratio thresholds include a satellite defect ratio threshold, an instrument defect ratio threshold, and a calibration quality defect ratio threshold, and the preset ratio thresholds can be set by oneself according to the actual situation. Then judge the satellite defect ratio, the instrument defect ratio, and the calibration quality defect ratio according to the satellite defect ratio threshold, the instrument defect ratio threshold, and the calibration quality defect ratio threshold respectively; and perform a warning mark on the features that do not meet the ratio threshold to determine the warning type. Then calculate the defect ratio deviation based on the warning type, and generate a warning level according to the deviation calculation result. Finally, map the warning type and the warning level to generate the monitoring warning signal.

[0042] Furthermore, the present application further includes the following steps:

[0043] Calculate the defect ratio deviation based on the warning type to determine the defect ratio deviation value; input the defect ratio deviation value into a level matching database for matching to output the warning level, where the level matching database is constructed based on a classification decision tree.

[0044] Specifically, calculate the defect ratio deviation according to the warning type, that is, calculate the difference between the defect ratio and the corresponding defect ratio threshold to obtain the determined defect ratio deviation value; then input the defect ratio deviation value into the level matching database for matching to output the warning level, where the level matching database is constructed based on a classification decision tree. A classification decision tree is a supervised learning model based on a tree structure, mainly used for classifying data. It recursively partitions the input data through a series of rules, and finally achieves the purpose of predicting the output category; in a classification decision tree, each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category.

[0045] Furthermore, the present application further includes the following steps:

[0046] Transfer the warning type and the warning level to the strategy analysis platform; based on the strategy analysis platform, analyze the correction plan for the warning type and the warning level to determine an optimized correction strategy, wherein an expert system is embedded in the strategy analysis platform; perform defect correction according to the optimized correction strategy.

[0047] Specifically, transfer the warning type and the warning level to the strategy analysis platform, and within the strategy analysis platform, analyze the correction plan for the warning type and the warning level through the expert system embedded in the strategy analysis platform to determine an optimized correction strategy. An expert system is a computer program that simulates human experts to solve complex problems in a specific field. It combines the knowledge and experience of domain experts and analyzes and makes decisions on specific problems through methods such as rule-based reasoning, case-based reasoning, and model-based reasoning. In the strategy analysis platform, the expert system can be used as a core component to assist in analyzing warning data and providing correction and optimization suggestions. Finally, perform defect correction according to the optimized correction strategy.

[0048] In summary, the intelligent monitoring method for the on-orbit observation quality of a meteorological satellite remote sensing instrument provided by the present application has the following technical effects:

[0049] Through the above method, it is possible to solve the technical problem that the existing methods for monitoring the on-orbit observation quality of meteorological satellite remote sensing instruments lack the monitoring of other state parameters and data quality characteristics of the instruments, resulting in the inability to discover potential instrument defects and poor on-orbit observation quality of the instruments. First, based on the interactive analysis platform, receive a multi-dimensional observation data set, wherein the multi-dimensional observation data set includes a satellite platform parameter set, an instrument state parameter set, and a calibration data set; then, preprocess the multi-dimensional observation data set according to a preset data processing strategy to obtain a standard multi-dimensional observation data set; next, based on a preset defect identification channel, identify defects in the standard multi-dimensional observation data set to determine multi-dimensional defect identification features, wherein the defect identification features include a defect ratio; then, judge the defect ratio according to a preset ratio threshold, and generate a monitoring warning signal based on the judgment result, and the monitoring warning signal includes a warning type and a warning level; finally, send the warning type and the warning level to an information release window for visual display. It is possible to achieve comprehensive monitoring of the instrument state and data quality, and thus timely discover and solve observation quality problems such as sudden abnormal events and long-term attenuation encountered by the instrument, ensure the on-orbit observation quality of the instrument, and provide guarantee for data application.

[0050] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0051] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. An intelligent monitoring method for the on-orbit observation quality of a meteorological satellite remote sensing instrument, characterized in that, The method includes: Based on an interaction analysis platform, receiving a multi-dimensional observation data set, where the multi-dimensional observation data set includes a satellite platform parameter set, an instrument status parameter set, and a calibration data set; Preprocessing the multi-dimensional observation data set according to a preset data processing strategy to obtain a standard multi-dimensional observation data set; Based on a preset defect identification channel, performing defect identification on the standard multi-dimensional observation data set to determine multi-dimensional defect identification features, where the defect identification features include defect ratios; Judging the defect ratio according to a preset ratio threshold, and generating a monitoring warning signal based on the judgment result, where the monitoring warning signal includes a warning type and a warning level; Sending the warning type and the warning level to an information release window for visual display.

2. The method according to claim 1, characterized in that The method further includes: Obtaining a preset data comparison and evaluation strategy; Based on the preset data comparison and evaluation strategy, performing multi-dimensional calibration quality evaluation on the calibration data set for the electromagnetic spectrum band and observation features, and obtaining a calibration quality parameter set; Updating the calibration data set based on the calibration quality parameter set to obtain an updated multi-dimensional observation data set.

3. The method according to claim 2, wherein Preprocessing the multi-dimensional observation data set according to a preset data processing strategy includes: The preset data processing strategy includes a redundancy elimination strategy, a missing data supplementation strategy, and an error correction strategy; Based on the preset data processing strategy, preprocessing the satellite platform parameter set, the instrument status parameter set, and the calibration quality parameter set to obtain the standard multi-dimensional observation data set.

4. The method according to claim 1, wherein Performing defect identification on the standard multi-dimensional observation data set based on a preset defect identification channel includes: Constructing a preset defect identification channel based on a BP neural network, where the preset defect identification channel includes a satellite parameter defect identification unit, an instrument parameter defect identification unit, and a calibration quality parameter defect identification unit; Using the satellite parameter defect identification unit to perform defect identification on the standard satellite platform parameter set to determine satellite defect identification features; Using the instrument parameter defect identification unit to perform defect identification on the standard instrument status parameter set to determine instrument defect identification features; Using the calibration quality parameter defect identification unit to identify the standard calibration quality parameter set to determine calibration quality defect identification features; Obtaining the multi-dimensional defect identification features according to the satellite defect identification features, the instrument defect identification features, and the calibration quality defect identification features.

5. The method according to claim 4, wherein Obtaining the multi-dimensional defect identification features according to the satellite defect identification features, the instrument defect identification features, and the calibration quality defect identification features includes: Based on the satellite defect identification features, the instrument defect identification features, and the calibration quality defect identification features, respectively calculating the defect occupancy ratios to obtain a satellite defect ratio, an instrument defect ratio, and a calibration quality defect ratio; Obtaining the multi-dimensional defect identification features according to the satellite defect ratio, the instrument defect ratio, and the calibration quality defect ratio.

6. The method according to claim 5, characterized in that, Generating a monitoring warning signal based on the judgment result includes: Obtaining a preset ratio threshold, where the preset ratio threshold includes a satellite defect ratio threshold, an instrument defect ratio threshold, and a calibration quality defect ratio threshold; Judge the satellite defect ratio, instrument defect ratio, and calibration quality defect ratio according to the satellite defect ratio threshold, instrument defect ratio threshold, and calibration quality defect ratio threshold respectively; And perform early warning identification on the features that do not meet the ratio threshold to determine the early warning type; Calculate the defect ratio deviation based on the early warning type to generate an early warning level; Map and generate the monitoring early warning signal according to the early warning type and the early warning level.

7. The method according to claim 6, wherein Calculating the defect ratio deviation based on the early warning type to generate an early warning level, including: Calculating the defect ratio deviation based on the early warning type to determine the defect ratio deviation value; Input the defect ratio deviation value into the level matching database for matching and output the early warning level, where the level matching database is constructed based on a classification decision tree.

8. The method according to claim 1, wherein Send the early warning type and the early warning level to the information release window for visual display, and then it also includes: Transfer the early warning type and the early warning level to the strategy analysis platform; Analyze the correction plan for the early warning type and the early warning level based on the strategy analysis platform to determine the optimized correction strategy, where the strategy analysis platform is embedded with an expert system; Execute defect correction according to the optimized correction strategy.

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