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

Abstract

The application 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 following steps: pre-processing a multidimensional observation data set according to a preset data processing strategy to obtain a standard multidimensional observation data set; identifying defects of the standard multidimensional observation data set based on a preset defect identification channel to determine multidimensional defect identification features, wherein the features include a defect proportion; judging the defect proportion according to a preset proportion threshold to generate a monitoring warning signal, including a warning type and a warning level; and visually displaying the warning type and the warning level. The application can solve the technical problem that the existing method for monitoring the on-orbit observation quality of a meteorological satellite remote sensing instrument lacks monitoring of other state parameters and data quality features of the instrument, which leads to the inability to discover potential instrument defects and causes poor on-orbit observation quality of the instrument, and can guarantee the on-orbit observation quality of the instrument and provide protection for data application.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent monitoring, and particularly relates to an intelligent monitoring method for in-orbit observation quality of a meteorological satellite remote sensing instrument. BACKGROUND

[0002] Meteorological satellite remote sensor observation data has played a great role in the application fields of meteorological prediction, climate analysis, disaster prevention and mitigation, etc. With the continuous improvement of application requirements, the requirements for the calibration and positioning quality of Fengyun satellite observation data are also getting higher and higher.

[0003] At present, relying on the working state of the monitoring instrument and the integrity of the observation data, only the presence or absence of the observation data or the preset extreme abnormal problems of the instrument can be reflected, and the data quality after calibration and positioning cannot be monitored. The full play of the application benefits of the observation data mainly depends on the data quality after calibration and positioning. Based on the long-term inspection results of the data quality, the instrument observation data quality (precision, spatial and temporal stability, etc.) can be comprehensively evaluated, but the monitoring of other state parameters and data quality characteristics of the instrument cannot be performed, so that potential problems of the instrument cannot be found, and the changes of the observation system cannot be traced back in time after the calibration and positioning problems are found, which is not conducive to the timely solution of the quality problems.

[0004] In summary, the existing method for monitoring the in-orbit observation quality of a meteorological satellite remote sensing instrument lacks monitoring of other state parameters and data quality characteristics of the instrument, which leads to the inability to find potential instrument quality defects and causes the technical problem of poor in-orbit observation quality of the instrument. SUMMARY

[0005] The purpose of the present application is to provide an intelligent monitoring method for in-orbit observation quality of a meteorological satellite remote sensing instrument, so as to solve the technical problem that the existing method for monitoring the in-orbit observation quality of a meteorological satellite remote sensing instrument lacks monitoring of other state parameters and data quality characteristics of the instrument, which leads to the inability to find potential instrument quality defects and causes poor in-orbit observation quality of the instrument.

[0006] In view of the above problems, the present application provides an intelligent monitoring method for on-orbit observation quality of a meteorological satellite remote sensing instrument, which comprises the following steps: receiving a multi-dimensional observation data set based on an interactive analysis platform, wherein the multi-dimensional observation data set comprises a satellite platform parameter set, an instrument state parameter set and a calibration data set; pre-processing the multi-dimensional observation data set according to a preset data processing strategy to obtain a standard multi-dimensional observation data set; identifying defects of the standard multi-dimensional observation data set based on a preset defect identification channel to determine multi-dimensional defect identification features, wherein the defect identification features comprise a defect proportion; judging the defect proportion according to a preset proportion threshold, generating a monitoring warning signal based on the judgment result, wherein the monitoring warning signal comprises a warning type and a warning level; and sending the warning type and the warning level to an information publishing window for visual display.

[0007] The 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, wherein the multi-dimensional observation data set comprises a satellite platform parameter set, an instrument state parameter set and a calibration data set; pre-processing the multi-dimensional observation data set according to a preset data processing strategy to obtain a standard multi-dimensional observation data set; identifying defects of the standard multi-dimensional observation data set based on a preset defect identification channel to determine multi-dimensional defect identification features, wherein the defect identification features comprise a defect proportion; judging the defect proportion according to a preset proportion threshold, generating a monitoring warning signal based on the judgment result, wherein the monitoring warning signal comprises a warning type and a warning level; and sending the warning type and the warning level to an information publishing window for visual display. The technical problems that the existing method for monitoring on-orbit observation quality of a meteorological satellite remote sensing instrument lacks monitoring of other state parameters and data quality features of the instrument, which leads to failure to discover potential instrument quality defects and causes poor on-orbit observation quality of the instrument can be solved. Comprehensive monitoring of the instrument state and data quality can be achieved, and thus sudden abnormal events, long-term decay and other observation quality problems encountered by the instrument can be discovered and solved in a timely manner, the on-orbit observation quality of the instrument is ensured, and the data application is guaranteed.

[0009] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following detailed description can be implemented in accordance with 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 easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are only exemplary and, for those skilled in the art, other drawings can be obtained without creative effort on the basis of the provided drawings.

[0011] Figure 1 A flowchart of an intelligent monitoring method for on-orbit observation quality of a meteorological satellite remote sensing instrument according to the present application;

[0012] Figure 2 A flowchart of pre-processing of the multidimensional observation data set according to a preset data processing strategy in the intelligent monitoring method for on-orbit observation quality of a meteorological satellite remote sensing instrument according to the present application. DETAILED DESCRIPTION

[0013] The present application provides an intelligent monitoring method for on-orbit observation quality of a meteorological satellite remote sensing instrument, which solves the technical problem that the prior art method for monitoring on-orbit observation quality of a meteorological satellite remote sensing instrument lacks monitoring of other state parameters of the instrument and data quality characteristics, resulting in failure to discover potential instrument defects and poor on-orbit observation quality of the instrument. The present application can achieve comprehensive monitoring of the state of the instrument and the quality of the data, thereby discovering and solving sudden abnormal events, long-term decay and other observation quality problems encountered by the instrument in a timely manner, ensuring on-orbit observation quality of the instrument and providing protection for data application.

[0014] The technical solutions in the present application will be described in detail below with reference to the accompanying 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 exemplary embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of the present application. In addition, it should be noted that, for convenience of description, only parts related to the present application are shown in the accompanying drawings, rather than all parts.

[0015] EMBODIMENT

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

[0017] Step 1: Based on an interactive analysis platform, a multidimensional observation data set is received, wherein the multidimensional observation data set comprises a satellite platform parameter set, an instrument state parameter set and a calibration data set.

[0018] Step 2: The multidimensional observation data set is pre-processed according to a preset data processing strategy to obtain a standard multidimensional observation data set.

[0019] Step three: defect identification is performed on the standard multi-dimensional observation data set based on a preset defect identification channel, and multi-dimensional defect identification features are determined, wherein the defect identification features include a defect proportion.

[0020] Step four: the defect proportion is judged according to a preset proportion threshold, and a monitoring warning signal is generated based on the judgment result, wherein the monitoring warning signal includes a warning type and a warning level.

[0021] Step five: the warning type and the warning level are sent to an information publishing window for visual display.

[0022] Specifically, first, based on an interactive analysis platform, a multi-dimensional observation data set is received, wherein the multi-dimensional observation data set includes a satellite platform parameter set, an instrument state parameter set, and a calibration data set; the satellite platform parameter set includes various time parameters, orbit parameters, and attitude parameters; the instrument state parameter set includes various temperature parameters, basic parameters related to calibration, angle parameters reflecting the scanning state, and the like. Then, a preset data processing strategy is obtained, which includes a redundancy elimination strategy, a missing supplement strategy, and an error correction strategy; further, the multi-dimensional observation data set is preprocessed according to the preset data processing strategy to obtain a standard multi-dimensional observation data set.

[0023] Based on a BP neural network, a preset defect identification channel is constructed, which includes a satellite parameter defect identification unit, an instrument parameter defect identification unit, and a calibration quality defect identification unit; then, defect identification is performed on the standard multi-dimensional observation data set based on the preset defect identification channel, and multi-dimensional defect identification features are determined, wherein the defect identification features include a defect proportion. Further, the defect proportion is judged according to a preset proportion threshold, and a monitoring warning signal is generated according to the defect features that do not meet the preset proportion threshold, wherein 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 publishing window for visual display. Comprehensive monitoring of instrument state and data quality can be achieved, and thus sudden abnormal events encountered by the instrument, long-term decay, and other observation quality problems can be discovered and solved in a timely manner, ensuring the in-orbit observation quality of the instrument and providing support for data application.

[0024] The above method can solve the technical problem that the existing method for monitoring the on-orbit observation quality of a meteorological satellite remote sensing instrument lacks monitoring of other state parameters and data quality characteristics of the instrument, which cannot discover potential instrument defects and causes poor on-orbit observation quality of the instrument. First, based on an interactive analysis platform, a multi-dimensional observation data set is received, wherein 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 preprocessed according to a preset data processing strategy to obtain a standard multi-dimensional observation data set. Next, the standard multi-dimensional observation data set is subjected to defect identification based on a preset defect identification channel to determine multi-dimensional defect identification features, wherein the defect identification features include a defect proportion. Next, the defect proportion is judged according to a preset proportion threshold, and a monitoring warning signal is generated based on the judgment result, wherein 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 publishing window for visual display. The instrument state and data quality can be comprehensively monitored, and the observation quality problems such as sudden abnormal events and long-term decay encountered by the instrument can be discovered and solved in a timely manner, thereby ensuring the on-orbit observation quality of the instrument and providing support for data application.

[0025] Further, the present application further comprises the following steps:

[0026] A preset data quality evaluation strategy is obtained, and multi-dimensional calibration quality evaluation of the electromagnetic wave spectrum and observation characteristics of the calibration data set is performed based on the preset data evaluation strategy to obtain a calibration quality parameter set. The calibration data set is updated based on the calibration quality parameter set to obtain an updated multi-dimensional observation data set.

[0027] Specifically, first, a preset data quality evaluation strategy is obtained, which can be set according to actual conditions, such as: according to the electromagnetic wave spectrum of the data, a plurality of commonly used international reference sources and comparison methods are provided, such as simulated reference sources compared by using radiation transfer mode and numerical prediction field data, observation reference sources compared by cross-observation of domestic and foreign instruments of the same type, and comparison of stable targets such as oceans, rainforests, deserts, and clouds; according to the observation characteristics, a plurality of dimensional quality parameters are provided, such as radiation and radiation difference statistics of different granularities (time, space, scanning angle, channel, and probe element, etc.). Then, multi-dimensional calibration quality evaluation of the electromagnetic wave spectrum and observation characteristics of the calibration data set is performed based on the preset data quality evaluation strategy to obtain a calibration quality parameter set. Further, the calibration data set is updated according to the calibration quality parameter set to obtain an updated multi-dimensional observation data set.

[0028] Further, as shown in Figure 2 the present application further comprises the following steps:

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

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

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

[0032] A preset defect recognition channel is constructed 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; the standard satellite platform parameter set is subjected to defect recognition by using the satellite parameter defect recognition unit to determine satellite defect recognition features; the standard instrument state parameter set is subjected to defect recognition by using the instrument parameter defect recognition unit to determine instrument defect recognition features; the standard calibration quality parameter set is subjected to recognition by using the calibration quality parameter defect recognition unit to determine calibration quality defect recognition features; and the multi-dimensional defect recognition features are obtained according to the satellite defect recognition features, the instrument defect recognition features, and the calibration quality defect recognition features.

[0033] Specifically, the BP neural network, i.e., the back propagation neural network, is a multi-layer feedforward neural network trained according to the error back propagation algorithm, and is one of the most widely used neural network models, which has an input layer, a hidden layer, and an output layer, wherein each neuron is connected to all neurons of the previous layer, transmits signals through weighted sum, and performs nonlinear transformation through an activation function, the core of the BP neural network is the BP algorithm, and the basic idea is to use the gradient descent method to minimize the mean square error of the actual output value and the expected output value of the network.

[0034] The preset defect recognition channel is constructed based on the BP neural network, wherein the preset defect recognition channel comprises 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 state 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, the standard satellite platform parameter set is subjected to defect recognition by using the satellite parameter defect recognition unit to determine a satellite defect recognition feature; the standard instrument state parameter set is subjected to defect recognition by using the instrument parameter defect recognition unit to determine an instrument defect recognition feature; and the standard calibration quality parameter set is subjected to recognition by using the calibration quality parameter defect recognition unit to determine a calibration quality defect recognition feature. Finally, the multi-dimensional defect recognition feature is obtained according to the satellite defect recognition feature, the instrument defect recognition feature, and the calibration quality defect recognition feature.

[0036] Further, the application further comprises the following steps:

[0037] The satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion are obtained by respectively performing defect proportion calculation based on the satellite defect recognition feature, the instrument defect recognition feature, and the calibration quality defect recognition feature; and the multi-dimensional defect recognition feature is obtained according to the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion.

[0038] Specifically, the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion are obtained by respectively performing defect proportion calculation based on the satellite defect recognition feature, the instrument defect recognition feature, and the calibration quality defect recognition feature, wherein the defect proportion is the ratio of the number of defect data in the defect recognition feature to the number of overall data. Then, the multi-dimensional defect recognition feature is obtained according to the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion.

[0039] Further, the application further comprises the following steps:

[0040] obtaining preset proportion thresholds, the preset proportion thresholds comprising a satellite defect proportion threshold, an instrument defect proportion threshold, and a calibration quality defect proportion threshold; judging the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion according to the satellite defect proportion threshold, the instrument defect proportion threshold, and the calibration quality defect proportion threshold respectively; performing early warning identification on features that do not satisfy the proportion thresholds, and determining an early warning type; performing defect proportion deviation calculation based on the early warning type, and generating an early warning level; and mapping the early warning type and the early warning level to generate the monitoring early warning signal.

[0041] Specifically, first, preset proportion thresholds are obtained, the preset proportion thresholds comprising a satellite defect proportion threshold, an instrument defect proportion threshold, and a calibration quality defect proportion threshold, which can be set according to actual conditions. Then, the satellite defect proportion, the instrument defect proportion, and the calibration quality defect proportion are judged according to the satellite defect proportion threshold, the instrument defect proportion threshold, and the calibration quality defect proportion threshold respectively; early warning identification is performed on features that do not satisfy the proportion thresholds, and an early warning type is determined. Then, defect proportion deviation calculation is performed based on the early warning type, and an early warning level is generated according to the deviation calculation result. Finally, the monitoring early warning signal is generated by mapping the early warning type and the early warning level.

[0042] Further, the application further comprises the following steps:

[0043] Defect proportion deviation calculation is performed based on the early warning type, and a defect proportion deviation value is determined; the defect proportion deviation value is input into a level matching database for matching, and the early warning level is output, wherein the level matching database is constructed based on a classification decision tree.

[0044] Specifically, defect proportion deviation calculation is performed according to the early warning type, that is, the difference between the defect proportion and the corresponding defect proportion threshold is calculated to determine a defect proportion deviation value; then the defect proportion deviation value is input into a level matching database for matching, and the early warning level is output, wherein the level matching database is constructed based on a classification decision tree. The classification decision tree is a supervised learning model based on a tree structure, mainly used for classification tasks of data, which recursively divides the input data through a series of rules to finally achieve the purpose of predicting the output category. In the 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] Further, the application further comprises the following steps:

[0046] The early warning type and the early warning level are transferred to a strategy analysis platform; a correction scheme analysis is performed on the early warning type and the early warning level based on the strategy analysis platform, and an optimized correction strategy is determined, wherein the strategy analysis platform is embedded with an expert system; and defect correction is performed according to the optimized correction strategy.

[0047] Specifically, the early warning type and the early warning level are transferred to a strategy analysis platform, and a correction scheme analysis is performed on the early warning type and the early warning level in the strategy analysis platform by an expert system embedded in the strategy analysis platform, and an optimized correction strategy is determined, wherein the expert system is a computer program simulating human experts solving complex problems in a specific field, which combines the knowledge and experience of field experts, and analyzes and decides specific problems through rule-based reasoning, case-based reasoning, model reasoning, etc. In the strategy analysis platform, the expert system can be used as a core component to assist in analyzing early warning data and providing correction and optimization suggestions. Finally, defect correction is performed according to the optimized correction strategy.

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

[0049] The above method can solve the technical problem that the existing method for monitoring the in-orbit observation quality of a meteorological satellite remote sensing instrument lacks monitoring of other state parameters and data quality characteristics of the instrument, which leads to the inability to discover potential instrument defects and causes poor in-orbit observation quality of the instrument. First, based on an interactive analysis platform, a multi-dimensional observation data set is received, wherein 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 preprocessed according to a preset data processing strategy to obtain a standard multi-dimensional observation data set. Next, the standard multi-dimensional observation data set is subjected to defect recognition based on a preset defect recognition channel to determine multi-dimensional defect recognition features, wherein the defect recognition features include a defect proportion. Then, the defect proportion is judged according to a preset proportion threshold, and a monitoring early warning signal is generated based on the judgment result, wherein the monitoring early warning signal includes an early warning type and an early warning level. Finally, the early warning type and the early warning level are sent to an information publishing window for visual display. This can achieve comprehensive monitoring of the instrument state and data quality, and thus can timely discover and solve sudden abnormal events, long-term decay, and other observation quality problems encountered by the instrument, thereby ensuring the in-orbit observation quality of the instrument and providing support for data application.

[0050] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Numerous modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without the use of the inventive faculty. Therefore, the present application is not intended to 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] It will be readily apparent to one skilled in the art that varying substitutions and modifications can be made to the application disclosed herein without departing from the scope and spirit of the application. Accordingly, it is intended that all such alterations and modifications be considered as within the scope of the application.

Claims

1. An intelligent monitoring method for on-orbit observation quality of meteorological satellite remote sensing instruments, characterized in that: The method comprises: Receiving a multidimensional observation data set based on an interactive analysis platform, wherein the multidimensional observation data set includes a satellite platform parameter set, an instrument state parameter set, and a calibration data set; Preprocessing the multidimensional observation data set according to a preset data processing strategy to obtain a standard multidimensional observation data set; Performing defect identification on the standard multidimensional observation data set based on a preset defect identification channel to determine a multidimensional defect identification feature, wherein the defect identification feature includes a defect ratio; Determine the defect ratio according to a preset ratio threshold, and generate a monitoring warning signal based on the determination result, wherein the monitoring warning signal includes a warning type and a warning level; Sending the warning type and the warning level to the information release window for visual display; Defect identification is performed on the standard multidimensional observation data set based on a preset defect identification channel, including: Constructing a preset defect recognition channel based on a BP neural network, wherein 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; Using the satellite parameter defect identification unit to perform defect identification on a standard satellite platform parameter set to determine satellite defect identification features; Using the instrument parameter defect identification unit to perform defect identification on a standard instrument status parameter set to determine an instrument defect identification feature; Using the calibration quality parameter defect identification unit to identify the standard calibration quality parameter set, and determine the calibration quality defect identification feature; Obtaining the multidimensional defect identification feature according to the satellite defect identification feature, the instrument defect identification feature, and the calibration quality defect identification feature; Obtaining the multidimensional defect identification feature according to the satellite defect identification feature, the instrument defect identification feature, and the calibration quality defect identification feature includes: Calculating defect ratios based on the satellite defect identification features, the instrument defect identification features, and the calibration quality defect identification features to obtain a satellite defect ratio, an instrument defect ratio, and a calibration quality defect ratio; The multi-dimensional defect recognition feature is obtained according to the satellite defect ratio, the instrument defect ratio and the calibration quality defect ratio.

2. The method according to claim 1, characterized in that The method further comprises: Obtain preset data comparison and evaluation strategies; Performing a multi-dimensional calibration quality assessment of the electromagnetic spectrum segments and observation characteristics on the calibration data set based on the preset data comparison and evaluation strategy to obtain a calibration quality parameter set; The calibration data set is updated based on the calibration quality parameter set to obtain an updated multidimensional observation data set.

3. The method according to claim 2, characterized in that Preprocessing the multidimensional observation data set according to a preset data processing strategy includes: The preset data processing strategies include a redundancy elimination strategy, a missing complement strategy, and an error correction strategy; The satellite platform parameter set, the instrument state parameter set and the calibration quality parameter set are preprocessed based on the preset data processing strategy to obtain the standard multidimensional observation data set.

4. The method according to claim 1, wherein Generate monitoring and early warning signals based on the judgment results, including: Obtaining 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; Determining the satellite defect ratio, instrument defect ratio, and calibration quality defect ratio respectively according to the satellite defect ratio threshold, instrument defect ratio threshold, and calibration quality defect ratio threshold; And the features that do not meet the ratio threshold are marked as early warnings and the warning type is determined; Calculate the defect ratio deviation based on the warning type and generate a warning level; The monitoring warning signal is generated according to the warning type and the warning level mapping.

5. The method according to claim 4, characterized in that Based on the warning type, the defect ratio deviation is calculated to generate the warning level, including: Calculate the defect ratio deviation based on the warning type and determine the defect ratio deviation value; The defect ratio deviation value is input into a grade matching database for matching, and the warning grade is output, wherein the grade matching database is constructed based on a classification decision tree.

6. The method according to claim 1, characterized in that The warning type and the warning level are sent to the information release window for visual display, and then the following steps are further included: Transferring the warning type and the warning level to the strategy analysis platform; Performing correction scheme analysis on the warning type and the warning level based on the strategy analysis platform to determine an optimized correction strategy, wherein the strategy analysis platform is embedded with an expert system; Defect correction is performed according to the optimized correction strategy.

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