Remote sensing satellite system application performance evaluation method for disaster monitoring task

Abstract

The invention relates to a disaster monitoring task-oriented remote sensing satellite system application performance evaluation method. The method comprises the following steps of S1, constructing an evaluation index system of a remote sensing satellite system application task layer, a capability demand layer and a system index layer; s2, in combination with the constructed evaluation index system, establishing an index measurement model for different index characteristics, and carrying out standardization processing on an index result; and S3, obtaining all index measurement results of the remote sensing satellite system, determining an index weight based on an analytic hierarchy process, and performing comprehensive effectiveness evaluation of the system. According to the method, the reasonable and feasible method for performing multi-dimensional comprehensive evaluation on the disaster monitoring task execution application efficiency of the remote sensing satellite system is realized, and a guiding effect is provided for design and evaluation work of the remote sensing satellite system.

Description

Technical Field

[0001] This invention relates to the field of remote sensing satellite system performance evaluation technology, and in particular to a method for evaluating the application performance of remote sensing satellite systems for disaster monitoring missions. Background Technology

[0002] Remote sensing satellite systems, as an important means of Earth observation, play an irreplaceable role in disaster monitoring, resource surveys, and environmental monitoring. With the increasing number of remote sensing satellites and the improvement of their technological level, how to scientifically evaluate their application effectiveness has become a crucial issue in satellite system design, mission planning, and performance optimization.

[0003] Several patent documents in the prior art relate to methods for evaluating the effectiveness of remote sensing satellite systems. For example, Chinese invention patent CN111582669A discloses a method for constructing an evaluation index system for the effectiveness of a space-air-ground collaborative remote sensing system. This method divides system effectiveness into four dimensions: range monitoring capability, information sharing capability, multi-platform data fusion capability, and response capability, and measures them through multiple quantitative indicators. However, this evaluation index system is designed for general remote sensing tasks and fails to fully consider the specific and phased needs of disaster monitoring tasks, particularly lacking targeted design for the capability differences at different stages such as pre-disaster early warning, disaster emergency response, and post-disaster assessment.

[0004] Chinese invention patent CN104732079A proposes a method for determining the effectiveness of remote sensing satellite systems for marine applications. It constructs an evaluation model comprising five layers of indicators (overall capability, mission layer, observation element layer, capability layer, and performance indicator layer) and uses the analytic hierarchy process (AHP) to determine the indicator weights. While this method establishes a mapping relationship from mission to capability, its indicator system is specifically designed for marine applications and lacks applicability to disaster monitoring tasks. Furthermore, it fails to consider the impact of different indicator characteristics on the measurement model and still employs a uniform linear evaluation model.

[0005] Alternatively, Chinese invention patent CN116257784A provides a comprehensive performance evaluation method for satellite internet. This method combines static capability indicators and dynamic task indicators, employing the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation. While this method introduces dynamic task indicators, its evaluation perspective primarily focuses on general indicators such as communication capabilities and coverage, failing to establish specific evaluation dimensions for the unique needs of disaster monitoring tasks, such as rapid response and adaptation to harsh environments.

[0006] In summary, existing remote sensing satellite performance evaluation methods have the following shortcomings: First, the evaluation index system lacks consideration for the specific characteristics of disaster monitoring tasks and fails to reflect the differentiated needs at different stages before, during, and after a disaster; second, the evaluation models mostly use uniform linear measurement methods, failing to fully consider the differences in the characteristics of different indicators, especially the measurement accuracy of nonlinear indicators such as resolution and radiometric accuracy is insufficient; third, there is a lack of specific evaluation of environmental adaptability, which cannot accurately reflect the satellite system's working ability under adverse weather conditions such as clouds and rain.

[0007] Therefore, there is an urgent need for a remote sensing satellite system application performance evaluation method that can be oriented towards disaster monitoring tasks, consider the needs of different stages, adopt differentiated measurement models, and accurately assess environmental adaptability, in order to solve the problems of insufficient evaluation targeting, low accuracy, and weak practicality in existing technologies. Summary of the Invention

[0008] To address the technical problems existing in the prior art, the present invention aims to provide a method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks, thereby solving problems such as the single evaluation dimension, poor adaptability of indicators, and the disconnect between evaluation results and actual task requirements in the prior art.

[0009] To achieve the above-mentioned objectives, this invention provides a method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks, comprising the following steps:

[0010] Step S1: Construct a three-layer evaluation index system for remote sensing satellite systems, consisting of the application mission layer, capability requirement layer, and system index layer.

[0011] Step S2: Based on the constructed evaluation index system, establish index measurement models for different index characteristics and standardize the index results.

[0012] Step S3: Obtain the measurement results of all indicators of the remote sensing satellite system, determine the indicator weights based on the analytic hierarchy process, and conduct a comprehensive system performance evaluation.

[0013] According to a technical solution of the present invention, step S1 specifically includes:

[0014] Step S11: Analyze the capability requirements of the remote sensing satellite system based on the phases of the disaster monitoring mission;

[0015] The disaster monitoring task includes the following phases: pre-disaster early warning, disaster-affected monitoring, post-disaster relief monitoring, and damage assessment.

[0016] Step S12: Map the capability requirements to at least one of the following: spatiotemporal coverage capability, information identification capability, system response capability, data processing capability, data transmission capability, and environmental adaptability.

[0017] The spatiotemporal coverage capability is measured by at least one of the following indicators: coverage area, regional repeated coverage cycle, regional complete coverage cycle, and number of areas that can be guaranteed.

[0018] According to a technical solution of the present invention, in step S2, the results of various indicators of the remote sensing satellite system indicator layer are obtained, and the indicator results are measured, and the different indicator dimensions are standardized to [0,1] without dimension.

[0019] For indicators that change linearly, a linear measurement model is used; for indicators that change non-linearly, a power function measurement model is used.

[0020] According to a technical solution of the present invention, in step S2, for linearly changing indicators, a linear model is used for measurement, and the linearly changing indicators include timeliness, coverage frequency, and target quantity;

[0021] For indicators where a larger value equates to better performance, the linear model is expressed as follows:

[0022] For indicators where smaller values ​​are better, the linear model is expressed as:

[0023] in, For the design or test results of the indicators, This represents the optimal value required for the indicator. This is the worst acceptable value for this indicator.

[0024] According to one technical solution of the present invention, in step S2, the nonlinear change index is measured using a power function model, and the nonlinear change index includes resolution, radiometric accuracy, and signal-to-noise ratio.

[0025] For indicators where a larger value is better, the model is as follows:

[0026] For indicators where smaller values ​​are better, the model is as follows:

[0027] in, For the design or test results of the indicators, This represents the optimal value required for the indicator. This is the worst acceptable value for this indicator.

[0028] According to one technical solution of the present invention, step S3 specifically includes:

[0029] Step S31: Based on the importance of each indicator, quantify the mutual importance of each indicator when facing a specific task, and determine the indicator weights;

[0030] Step S32: Based on the evaluation results of the underlying indicators and the weight matrix between each level, the overall performance of the satellite system is calculated.

[0031] According to a technical solution of the present invention, step S31 specifically includes:

[0032] Step S311: Construct an importance judgment matrix for each system indicator. ;

[0033] Step S312: Based on the judgment matrix Calculate the eigenvalues ​​and eigenvectors, and find the largest eigenvalue. The corresponding eigenvectors are normalized to obtain the weight matrix. , Each component in That is, the weight of the corresponding element, and , For the number of weights;

[0034] Step S313: Perform a consistency check on the indicator weights. If the check passes, determine the weights; otherwise, adjust the judgment matrix.

[0035] According to a technical solution of the present invention, step S313 specifically includes:

[0036] Step S313a: Based on the matrix order The average random consistency index corresponding to the table lookup ;

[0037] Step S313b: Based on the eigenvalues And matrix order Calculate the consistency index ;

[0038] Step S313c: Calculate the consistency ratio ,like If the condition is met, the judgment passes; otherwise, the judgment matrix needs to be corrected, where: .

[0039] According to a technical solution of the present invention, in step S32, the performance is comprehensively analyzed based on the evaluation results of the underlying indicators and the weight matrix between each level, as expressed as:

[0040] *

[0041] in, The weights of each capability from the system capability layer to the task layer. The weights of each indicator from the bottom indicator layer to the system capability layer. For the task-level evaluation results, The results of the system capability layer assessment This is the evaluation result of the underlying indicators.

[0042] According to one aspect of the present invention, an evaluation system is provided for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks as described in any of the above-described technical solutions, characterized in that it comprises:

[0043] The indicator system construction module is used to construct a three-level evaluation indicator system for remote sensing satellite systems, consisting of the application task layer, capability requirement layer, and system indicator layer.

[0044] The indicator measurement module is used to establish indicator measurement models for different indicator characteristics and to standardize indicator results.

[0045] The performance evaluation module is used to determine the weights of indicators based on the analytic hierarchy process and to conduct a comprehensive system performance evaluation.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] This invention proposes a method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks. Addressing the shortcomings of current methods and the limited evaluation dimensions in remote sensing satellite system application effectiveness evaluation, this invention analyzes the characteristics of different stages of disaster monitoring tasks and establishes an evaluation index system for remote sensing satellite systems from six dimensions: spatiotemporal coverage capability, information identification capability, task response capability, data processing energy, data transmission capability, and environmental adaptability. Linear and power function measurement models are designed to measure the capability indicators based on the different relationships between satellite system capability indicators and application effectiveness benefits. Finally, a comprehensive system effectiveness evaluation model is established based on the analytic hierarchy process (AHP). This provides a reasonable and feasible method for multi-dimensional comprehensive evaluation of the application effectiveness of remote sensing satellite systems in performing disaster monitoring tasks, offering guidance for the design and evaluation of remote sensing satellite systems.

[0048] This invention establishes a precise mapping between each stage of disaster monitoring and the capabilities of the satellite system through a three-tiered design of "task-capability-indicator". During the disaster monitoring stage, the weights of "system response capability" and "environmental adaptability" are automatically increased to ensure that the assessment results are consistent with the actual mission requirements. This solves the problem of the "one-size-fits-all" approach in existing indicator systems, and the indicator system is highly targeted and adaptable to the phased needs of disaster monitoring.

[0049] This invention designs dedicated models for linear and nonlinear indices respectively, realizes the dimensionlessness of all indices, enables direct horizontal comparison, accurately standardizes index measurement, eliminates dimensional differences, and avoids the defect of "incomparable indices" in existing technologies.

[0050] This invention uses the analytic hierarchy process (AHP) and consistency checks to ensure the logic and rationality of indicator weights. The consistency check will prompt adjustments, ensuring that the weights are scientifically and reliably determined, avoiding subjective contradictions, and preventing weight discrepancies from distorting the evaluation results.

[0051] This invention outputs a comprehensive performance value that can accurately measure the degree to which a satellite system meets its mission requirements, enabling quantitative assessment and providing a quantitative basis for design optimization. It can also pinpoint capability shortcomings, and the assessment results and actual disaster support effects are far superior to existing qualitative assessment methods. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0053] Figure 1 This diagram illustrates the composition of the indicator system for evaluating the application effectiveness of a remote sensing satellite system for disaster monitoring tasks, as shown in an embodiment of the present invention.

[0054] Figure 2 This illustration illustrates the linear metric model in an embodiment of the present invention.

[0055] Figure 3 This illustration illustrates the power function measurement model in an embodiment of the present invention.

[0056] Figure 4 This diagram illustrates a flowchart of a method for evaluating the application effectiveness of a remote sensing satellite system for disaster monitoring tasks, as shown in an embodiment of the present invention. Detailed Implementation

[0057] The description of the embodiments in this specification should be taken in conjunction with the accompanying drawings, which should form part of the complete specification. In the drawings, the shape or thickness of the embodiments may be exaggerated and may be indicated in a simplified or convenient manner. Furthermore, parts of the various structures in the drawings will be described separately; it is worth noting that elements not shown in the figures or not described in words are in a form known to those skilled in the art.

[0058] The descriptions of the embodiments herein, including any references to directions and orientations, are for ease of description only and should not be construed as limiting the scope of the invention. The following description of preferred embodiments involves combinations of features, which may exist independently or in combination; the invention is not particularly limited to the preferred embodiments. The scope of the invention is defined by the claims.

[0059] like Figures 1 to 4 As shown, the present invention provides a method for evaluating the application effectiveness of a remote sensing satellite system for disaster monitoring tasks, comprising the following steps:

[0060] Step S1: Based on the needs of disaster monitoring tasks, analyze the remote sensing satellite system indicators that affect the effectiveness of task execution at different task stages, and construct a three-layer evaluation indicator system for the remote sensing satellite system: application task layer, capability requirement layer, and system indicator layer. Specifically, this includes:

[0061] Step S11: Analyze the capability requirements of the remote sensing satellite system based on the phases of the disaster monitoring mission;

[0062] The disaster monitoring task includes the following phases: pre-disaster early warning, disaster-affected monitoring, post-disaster relief monitoring, and damage assessment.

[0063] The pre-disaster early warning mission requires the remote sensing satellite system to have the ability to provide regular and routine coverage of a large area and multiple key regions across the country, and to periodically provide meter-level high-resolution images of key areas in order to accurately identify and monitor potential hazards.

[0064] Disaster monitoring missions require remote sensing satellite systems to be able to respond quickly to disaster area monitoring tasks under adverse weather conditions, acquire sub-meter resolution and high positioning accuracy image data of the disaster area, and transmit the data back in a short time for decision-makers to respond quickly.

[0065] The post-disaster recovery monitoring mission requires remote sensing satellite systems to regularly acquire meter-level image data of multiple disaster-stricken areas and their surrounding areas to support the observation of the recovery situation.

[0066] The damage assessment task requires remote sensing satellite missions to acquire meter-resolution remote sensing images of the entire disaster area within a certain time frame to support the assessment of damage in the disaster area.

[0067] Based on the above analysis of mission requirements, the requirements of disaster monitoring missions on satellite systems can be reflected in six aspects: satellite spatiotemporal coverage capability, information identification capability, system response capability, data processing capability, data transmission capability, and environmental adaptability.

[0068] Step S12: Map the capability requirements to at least one of the following: spatiotemporal coverage capability, information identification capability, system response capability, data processing capability, data transmission capability, and environmental adaptability.

[0069] The spatiotemporal coverage capability is measured by at least one of the following indicators: coverage area, regional repeated coverage cycle, regional complete coverage cycle, and number of areas that can be guaranteed.

[0070] Information discrimination capability describes the ability of a satellite system product to extract data of interest for a specific mission. This capability is mainly determined by indicators such as product resolution, radiometric accuracy, signal-to-noise ratio, geometric positioning accuracy, and system sensitivity.

[0071] System response capability describes the speed at which a satellite system responds to specific tasks and emergency missions. Its capability is mainly determined by indicators such as system telemetry, tracking, command and control capabilities, mission response time, mission response capacity, orbital maneuvering capability, and attitude adjustment speed.

[0072] Data processing capability describes the ability of a satellite system to process acquired detection data. Its capability is mainly determined by indicators such as data processing timeliness, target processing capacity, data processing accuracy, image product quality, and target recognition rate.

[0073] Data transmission capability describes the ability of a satellite system to transmit data back after acquiring it. This capability is mainly determined by indicators such as data storage capacity, satellite-to-ground transmission delay, and relay transmission delay.

[0074] Environmental adaptability describes the ability of a satellite system to operate under adverse weather conditions such as clouds, rain, and day / night cycles. This capability is determined by the satellite payload's operating mode. Radar imaging payloads have all-day, all-weather operating capabilities, while the performance of optical imaging payloads is severely affected by adverse weather conditions such as clouds, rain, and day / night cycles.

[0075] Based on the hierarchical analysis of satellite system capability indicators, a complete mapping relationship is established from the mission layer to the system capability requirements layer and then to the system indicator layer. On this basis, a targeted and feasible capability indicator system is established, specifically as follows: Figure 1 As shown.

[0076] Step S2: Based on the constructed evaluation index system, establish index measurement models for different index characteristics, and standardize the index results, specifically including:

[0077] Obtain the results of various indicators in the remote sensing satellite system indicator layer, measure the indicator results, and standardize the different indicator dimensions to [0,1] without dimension.

[0078] For indicators that change linearly, a linear measurement model is used; for indicators that change non-linearly, a power function measurement model is used.

[0079] Step S3: Obtain the measurement results of all indicators of the remote sensing satellite system, determine the indicator weights based on the analytic hierarchy process (AHP), and conduct a comprehensive system performance evaluation, specifically including:

[0080] Step S31: Based on the importance of each indicator, quantify the mutual importance of each indicator when facing a specific task, and determine the indicator weights, specifically including:

[0081] Step S311: Construct an importance judgment matrix for each system indicator. ;

[0082] For a specific ability assessment target, the importance of each element at the next lower level is compared, and the difference in importance between two elements is measured using a 1-9 scale to obtain an importance judgment matrix. The specific meanings of the scales are shown in Table 1 below:

[0083]

[0084] Table 1

[0085] Step S312: Based on the judgment matrix Calculate the eigenvalues ​​and eigenvectors, and find the largest eigenvalue. The corresponding eigenvectors are normalized to obtain the weight matrix. , Each component in That is, the weight of the corresponding element, and , For the number of weights;

[0086] Step S313: Perform a consistency check on indicator weights. If the check passes, the weights are determined; otherwise, the judgment matrix is ​​adjusted to prevent contradictory information regarding indicator importance. This includes:

[0087] Step S313a: The weight matrix constructed for the specific task is determined according to the matrix order. The average random consistency index corresponding to the table lookup ;

[0088] in, The parameter table is shown in Table 2 below:

[0089]

[0090] Table 2

[0091] Step S313b: Based on the eigenvalues and matrix order Calculate the consistency index ;

[0092] Step S313c: Calculate the consistency ratio ,like If the condition is met, the judgment passes; otherwise, the judgment matrix needs to be corrected, where:

[0093] .

[0094] According to a technical solution of the present invention, in step S32, the performance is comprehensively analyzed based on the evaluation results of the underlying indicators and the weight matrix between each level, as expressed as:

[0095] *

[0096] in, The weights of each capability from the system capability layer to the task layer. The weights of each indicator from the bottom indicator layer to the system capability layer. For the task-level evaluation results, The results of the system capability layer assessment The results are based on the evaluation of underlying indicators;

[0097] Step S32: Based on the evaluation results of the underlying indicators and the weight matrix between each level, the overall performance of the satellite system is calculated.

[0098] In some embodiments of the present invention, in step S2, for linearly changing indicators, a linear model is used for measurement, and the linearly changing indicators include timeliness, coverage frequency, and target quantity.

[0099] For indicators where a larger value equates to better performance, the linear model is expressed as follows:

[0100]

[0101] For indicators where smaller values ​​are better, the linear model is expressed as:

[0102]

[0103] in, For the design or test results of the indicators, This represents the optimal value required for the indicator. This is the worst acceptable value for this indicator.

[0104] When the measured value is less than When this occurs, it means that the indicator cannot function effectively in the system. Typical standardized results of the model are as follows: Figure 2 ( Figure 2 a and Figure 2As shown in b). For the assessment of environmental adaptability, since its performance is primarily limited by the satellite's operational system, the performance of environmental adaptability can be measured by the proportion of radar payload operating time in the total operating time of the satellite system. That is, the larger the proportion of radar payload operating time in the satellite system, the stronger the satellite system's environmental adaptability. This indicator can be evaluated using a linear measurement method.

[0105] In some embodiments of the present invention, in step S2, a power function model is used to measure the nonlinear change index, which includes resolution, radiometric accuracy, and signal-to-noise ratio.

[0106] For indicators where a larger value is better, the model is as follows:

[0107]

[0108] For indicators where smaller values ​​are better, the model is as follows:

[0109]

[0110] in, For the design or test results of the indicators, This represents the optimal value required for the indicator. This is the worst acceptable value for this indicator.

[0111] Typical standardized results of the power function model are as follows: Figure 3 ( Figure 3 a and Figure 3 As shown in b).

[0112] According to one aspect of the present invention, an evaluation system is provided for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks as described in any of the above-described technical solutions, characterized in that it comprises:

[0113] The indicator system construction module is used to construct a three-level evaluation indicator system for remote sensing satellite systems, consisting of the application task layer, capability requirement layer, and system indicator layer.

[0114] The indicator measurement module is used to establish indicator measurement models for different indicator characteristics and to standardize indicator results.

[0115] The performance evaluation module is used to determine the weights of indicators based on the analytic hierarchy process and to conduct a comprehensive system performance evaluation.

[0116] In summary, this invention provides a method for probabilistic estimation of window-type targets using a multi-satellite constellation based on a sliding window. Addressing the shortcomings of current remote sensing satellite system application effectiveness evaluation methods and the limited evaluation dimensions, this invention analyzes the characteristics of different mission stages in disaster monitoring and establishes an evaluation index system for remote sensing satellite systems from five dimensions: spatiotemporal coverage capability, information identification capability, mission response capability, data transmission capability, and environmental adaptability. Linear and power function measurement models are designed to measure the capability indicators based on the different relationships between satellite system capability indicators and application effectiveness benefits. Finally, a comprehensive system effectiveness evaluation model is established based on the analytic hierarchy process (AHP). This provides a reasonable and feasible method for multi-dimensional comprehensive evaluation of the application effectiveness of remote sensing satellite systems in performing disaster monitoring tasks, offering guidance for the design and evaluation of remote sensing satellite systems.

[0117] This invention analyzes the characteristics of different mission stages in disaster monitoring and establishes an evaluation index system for remote sensing satellite systems from six dimensions: spatiotemporal coverage capability, information identification capability, mission response capability, data processing capability, data transmission capability, and environmental adaptability.

[0118] To measure the different relationships between satellite system capability indicators and application effectiveness benefits, linear and power function measurement models were designed respectively.

[0119] A system comprehensive performance evaluation model was established based on the analytic hierarchy process (AHP), realizing a reasonable and feasible method for multi-dimensional comprehensive evaluation of the application performance of remote sensing satellite systems in performing disaster monitoring tasks.

[0120] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0121] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks, characterized in that, Includes the following steps: Step S1: Construct an evaluation index system with three layers: application mission layer, capability requirement layer, and system index layer for remote sensing satellite systems; Step S2: Based on the constructed evaluation index system, establish index measurement models for different index characteristics and standardize the index results. Step S3: Obtain the measurement results of all indicators of the remote sensing satellite system, determine the indicator weights based on the analytic hierarchy process, and conduct a comprehensive system performance evaluation.

2. The method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks according to claim 1, characterized in that, Step S1 specifically includes: Step S11: Analyze the capability requirements of the remote sensing satellite system based on the phase division of disaster monitoring tasks; The disaster monitoring tasks include the following phases: pre-disaster early warning, disaster-affected monitoring, post-disaster relief monitoring, and damage assessment. Step S12: Map the capability requirements to at least one of the following: spatiotemporal coverage capability, information identification capability, system response capability, data processing capability, data transmission capability, and environmental adaptability. The spatiotemporal coverage capability is measured by at least one of the following indicators: coverage area, regional repeated coverage cycle, regional complete coverage cycle, and number of areas that can be guaranteed.

3. The method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks according to claim 1, characterized in that, In step S2, the results of various indicators of the remote sensing satellite system indicator layer are obtained, and the indicator results are measured. The different indicator dimensions are standardized to [0,1] without dimension. For indicators that change linearly, a linear measurement model is used; for indicators that change non-linearly, a power function measurement model is used.

4. The method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks according to claim 3, characterized in that, In step S2, linearly changing indicators are measured using a linear model. These linearly changing indicators include timeliness, coverage frequency, and number of targets. For indicators where a larger value equates to better performance, the linear model is expressed as follows: For indicators where smaller values ​​are better, the linear model is expressed as: in, For the design or test results of the indicators, This represents the optimal value required for the indicator. This is the worst acceptable value for this indicator.

5. The method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks according to claim 3, characterized in that, In step S2, the power function model is used to measure the nonlinear change indicators, which include resolution, radiometric accuracy, and signal-to-noise ratio. For indicators where a larger value is better, the model is as follows: For indicators where smaller values ​​are better, the model is as follows: in, For the design or test results of the indicators, This represents the optimal value required for the indicator. This is the worst acceptable value for this indicator.

6. The method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks according to claim 1, characterized in that, Step S3 specifically includes: Step S31: Based on the importance of each indicator, quantify the mutual importance of each indicator when facing a specific task, and determine the indicator weights; Step S32: Based on the evaluation results of the underlying indicators and the weight matrix between each level, the overall performance of the satellite system is calculated.

7. The method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks according to claim 6, characterized in that, Step S31 specifically includes: Step S311: Construct an importance judgment matrix for each system indicator. ; Step S312: Based on the judgment matrix Calculate the eigenvalues ​​and eigenvectors, and find the largest eigenvalue. The corresponding eigenvectors are normalized to obtain the weight matrix. , Each component in That is, the weight of the corresponding element, and , For the number of weights; Step S313: Perform a consistency check on the indicator weights. If the check passes, determine the weights; otherwise, adjust the judgment matrix.

8. The method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks according to claim 7, characterized in that, Step S313 specifically includes: Step S313a: Based on the matrix order The average random consistency index corresponding to the table lookup ; Step S313b: Based on the eigenvalues And matrix order Calculate the consistency index ; Step S313c: Calculate the consistency ratio ,like If the condition is met, the judgment passes; otherwise, the judgment matrix needs to be corrected, where: 。 9. The method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks according to claim 6, characterized in that, In step S32, the performance is synthesized based on the evaluation results of the underlying indicators and the weight matrix between each level, as follows: * in, The weights of each capability from the system capability layer to the task layer. The weights of each indicator from the bottom indicator layer to the system capability layer. For the task-level evaluation results, The results of the system capability layer assessment This is the evaluation result of the underlying indicators.

10. An evaluation system for implementing the method for evaluating the application effectiveness of remote sensing satellite systems for disaster monitoring tasks as described in any one of claims 1 to 9, characterized in that, include: The indicator system construction module is used to construct a three-level evaluation indicator system for remote sensing satellite systems, consisting of the application task layer, capability requirement layer, and system indicator layer. The indicator measurement module is used to establish indicator measurement models for different indicator characteristics and to standardize indicator results. The performance evaluation module is used to determine the weights of indicators based on the analytic hierarchy process and to conduct a comprehensive system performance evaluation.

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