Efficiency evaluation method for remote sensing satellite observation task

Abstract

The invention discloses an efficiency evaluation method for remote sensing satellite observation tasks. The method mainly comprises the following steps: establishing a satellite observation task index pool; for a received satellite observation task, obtaining indexes related to the task; constructing a multi-dimensional optimal sequence diagram, and carrying out subjective weighting on the weight of the task index based on expert scoring; objective weighting is carried out on the weights of the task indexes by adopting a standard deviation method; comprehensively integrating the subjective weight and the objective weight of the index to obtain the final weight of the index; and obtaining an overall efficiency evaluation value of the task based on grey correlation analysis. According to the method, the universality and adaptability of the remote sensing satellite observation task efficiency evaluation method in various application scenes and the matching degree and the cooperative effect between the remote sensing satellite observation task efficiency evaluation method and the application tasks can be effectively improved, and more accurate and comprehensive guidance is provided for efficiency evaluation of the remote sensing satellite.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing satellites, in particular to a performance evaluation method for remote sensing satellite observation tasks. BACKGROUND

[0002] Under the background of rapid development and wide application of remote sensing satellites, the performance evaluation of remote sensing satellites has become a research hotspot. At present, a variety of performance evaluation methods have emerged for different application scenarios and task requirements of remote sensing satellites, which have played a positive role in promoting the development of the field of remote sensing satellite performance evaluation:

[0003] (1) Focus on the data acquisition capability of the satellite, and provide a basis for evaluating the performance of the satellite by quantifying indicators such as the quantity, quality and acquisition efficiency of the data. However, this method is single in evaluation dimension and does not comprehensively cover key performances such as data processing and image interpretation of remote sensing satellites.

[0004] (2) The overall performance of the space-based management and control system is comprehensively considered by constructing a multi-dimensional evaluation system such as response speed and control accuracy, and using fuzzy comprehensive evaluation technology. This method does not fully consider the specific performance of remote sensing satellites when facing different task requirements and environmental conditions, and how these factors affect the overall performance of satellite applications.

[0005] (3) Based on task planning, resource scheduling, fault diagnosis and other links, the performance of the satellite management and control system is comprehensively evaluated. This method does not fully consider the matching degree, collaborative effect and performance in different application scenarios when evaluating the performance of a single remote sensing satellite.

[0006] In summary, although the existing technology has achieved certain results in the performance evaluation of remote sensing satellites, there are still problems such as single evaluation dimension, failure to fully consider the diversity of application scenarios and the matching degree between satellites and application tasks. SUMMARY

[0007] To make up for the shortcomings of the prior art, the present disclosure provides a more comprehensive and objective performance evaluation method for remote sensing satellite observation tasks, which not only focuses on the performance of remote sensing satellites in specific application tasks, but also considers the universality, adaptability and matching degree and collaborative effect between the satellites and the application tasks in various application scenarios, providing more accurate and comprehensive guidance for the performance evaluation of remote sensing satellites.

[0008] The performance evaluation method for remote sensing satellite observation tasks provided by the present disclosure mainly includes the following steps:

[0009] S1, a satellite observation task index pool is established; for a received satellite observation task, the indexes involved in the task are obtained;

[0010] S2, constructing a multi-dimensional priority diagram, and subjectively weighting the task index weight based on expert scoring;

[0011] S3, using a standard deviation method to objectively weight the task index weight;

[0012] S4, integrating the subjective weight and the objective weight of the index to obtain the final weight of the index;

[0013] S5, obtaining the overall performance evaluation value of the task based on gray correlation analysis.

[0014] Further, the step S2 specifically comprises:

[0015] S21, dividing the satellite observation task flow into three independent dimensions: simulation deduction dimension, data reception dimension and data processing dimension, and there is no direct correlation between the index factors of the three dimensions;

[0016] S22, for each dimension, a priority diagram is constructed separately, including:

[0017] (1) extracting the index elements of the dimension, and constructing an index importance matrix in the form of two-by-two comparison between each index and all other indexes in the dimension:

[0018] Suppose there are n indexes in a dimension, then the index importance matrix is an n×n matrix A, A ij represents the importance comparison result between the i-th index and the j-th index;

[0019] (2) according to the expert experience, scoring the two-by-two comparison value of the indexes in the dimension, then the dimension weight of the i-th index P i is defined as:

[0020]

[0021] Wherein, the value range of A ij is [0,1], wherein A ij =1 indicates that the i-th index is more important than the j-th index, A ij =0 indicates that the i-th index is less important than the j-th index, and A ij =0.5 indicates that the importance of the two is equivalent or close;

[0022] (3) calculating the total priority number of the indexes in the dimension:

[0023] Suppose the total priority number of the k-th dimension is S k , then the total priority number of the indexes in the dimension is

[0024]

[0025] wherein n is the number of indicators of the dimension;

[0026] S23, the total optimal order numbers of all dimensions are accumulated to obtain a total optimal order number sum S total that is:

[0027]

[0028] wherein m is the number of dimensions in the entire system or the system;

[0029] S24, for each dimension, the proportion of the total optimal order number of the dimension in the total optimal order numbers of all dimensions, i.e. the weight of the dimension, is calculated:

[0030]

[0031] wherein D k reflects the importance of the subsystem or the sub-process represented by the dimension in the system or the system;

[0032] S25, the dimension weight and the weight of the indicator in the dimension are multiplied to obtain the subjective weight W ki of the indicator in the observation task system:

[0033] W ki = D k × P i

[0034] Further, the step S2 further includes the following steps:

[0035] The expert scoring results are saved in the indicator pool and are directly extracted and used before subjective weighting.

[0036] Further, the step S3 specifically includes:

[0037] S31, data collection and arrangement: for a satellite observation task, the original data of each indicator of the task is obtained and normalized and arranged;

[0038] S32, calculation of each indicator standard deviation: for each indicator, the standard deviation B i

[0039]

[0040] wherein B i is the standard deviation of the i-th indicator, x i is the observation value of the i-th indicator, and x i is the basic value of the indicator;

[0041] S33, calculation of each indicator weight: according to the standard deviation of each indicator, the weight V​i

[0042]

[0043] wherein, V i is the weight of the i-th index, B i is the standard deviation of the i-th index, and n is the number of indexes.

[0044] Further, the step S4 comprises:

[0045] The calculation method of the maximum weight of each index is:

[0046] C q = h1×W q +h2×V q

[0047] wherein, C q is the maximum weight of the q-th index, W q is the subjective weight of the q-th index in the system or the system, V q is the objective weight of the q-th index.

[0048] In the formula, h1 is the subjective weight influence factor, h2 is the objective weight influence factor, and h1+h2=1; the determination principle is: according to the degree of authority of experts, the reliability of index information and historical evaluation results, combined with the specific situation of the task to determine.

[0049] Further, the step S5 comprises:

[0050] S51, constructing a weight vector according to the maximum weight of each index in a satellite observation task:

[0051] C = [c1, c2, c3, …, c n ]

[0052] S52, calculating the grey correlation degree: for each index R, calculating the grey correlation coefficient ξ i between the observation value and the reference value:

[0053] ① calculating the absolute difference between the observation value and the reference value of each index: Δr ij = |r ij -r i0 |, wherein r ij is the observation value of the i-th index, and r i0 is the reference value of the index;

[0054] ② determining the minimum value minΔr ij and the maximum value maxΔr ij of the absolute difference;

[0055] ③ Use the grey correlation degree formula to calculate the correlation degree of each index: Wherein, p is the resolution coefficient;

[0056] S53, weighted sum to obtain the performance evaluation value: multiply the grey correlation degree of each index by the corresponding weight to obtain the weighted grey correlation degree; then sum the weighted grey correlation degree to obtain the overall performance evaluation value of the task, and the specific formula is as follows:

[0057]

[0058] Wherein, Y is the overall performance evaluation value of the task.

[0059] Compared with the prior art, the beneficial effects of the present disclosure are: ① all index factors involved in the whole process of remote sensing satellite observation tasks are comprehensively and systematically considered, and all index factors involved in the whole process of simulation deduction, data reception and data processing of each satellite observation task are fully mined; ② the diversity of remote sensing satellite observation task application scenarios and the matching degree of satellites and application tasks are fully considered, and for each satellite observation task, the corresponding index elements are extracted from different angles such as simulation deduction, data reception and data processing according to the actual application scenario, and are independently weighted; ③ the subjective weight generated by the multi-dimensional optimal sequence diagram and the objective weight generated by the standard deviation method are integrated, so that the final weight reflects both subjective judgment and decision-making experience and objective evaluation information; ④ the expert score results are saved in the index pool, which can be directly extracted and used before subjective weighting. BRIEF DESCRIPTION OF DRAWINGS

[0060] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description of exemplary embodiments of the present disclosure taken in conjunction with the accompanying drawings, in which like reference characters refer to the like parts throughout the figures, and in which:

[0061] Figure 1 is a flowchart according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present disclosure will be described below in greater detail with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure is more thorough and complete, and the scope of the present disclosure is fully conveyed to those skilled in the art.

[0063] The application provides a method for evaluating the observation task efficiency of a remote sensing satellite, which covers satellite simulation, receiving and processing of the whole chain, comprehensively considers all index factors involved in the whole observation task process, and independently weights respectively, thereby improving the universality, adaptability and matching degree of the efficiency matching method to various application scenarios and application tasks.

[0064] The flow chart of the exemplary embodiment according to the present disclosure is shown in FIG. 1, which mainly includes the following steps: Figure 1

[0065] I. Preparation

[0066] For a satellite observation task, an index pool needs to be formed to save the indexes possibly involved in the observation task in the form of a database, including indexes such as resolution, positioning accuracy, width, shooting time length, processing timeliness, available receiving stations and the like.

[0067] For a received satellite observation task, the indexes involved in the task can be obtained through demand decomposition, and the related information of the corresponding indexes can be extracted from the index pool to provide support for subsequent weighting.

[0068] II. Subjective Weighting

[0069] (I) Method Overview

[0070] Since the satellite observation task involves many index factors, and the importance and index priority of some indexes are different in different links of the observation task system, in the embodiment, a multi-dimensional priority graph method is innovatively proposed based on the principle of the traditional priority graph method. This method aims to refine the complex process of satellite application into three core dimensions: simulation deduction dimension, data receiving dimension and data processing dimension. For each dimension, the index elements included therein are analyzed, and then an index element matrix is constructed through pairwise comparison, and then the priority number of each dimension is calculated according to the expert scoring, and the dimension weight is allocated according to the number of index elements in different dimensions. This method can accurately calculate the subjective weight of the system and provide support for decision-making.

[0071] (II) Introduction of Priority Graph Method

[0072] The priority graph method ingeniously uses a matrix diagram to intuitively compare multiple indexes or targets. This method is not only suitable for qualitative problems, but also can effectively handle quantitative problems.

[0073] In the construction process of the priority graph, n indexes are set, and an n x n matrix A is constructed, where A ij represents the importance comparison result between the i th index and the j th index. The value range of A ij is [0, 1], where A ij ​=1 means the ith indicator is more important than the jth indicator, A ij =0 means the ith indicator is less important than the jth indicator, A ij =0.5 means both importance is equivalent or close.

[0074] At this time, the dimension weight P i of the ith indicator can be defined as:

[0075]

[0076] (Three) Multi-dimensional priority diagram

[0077] Because the overall indicators of the system or the system are too many, and the importance and priority of some indicators are different in different links of the system or the system, the whole can be divided into m independent subsystems or sub-processes, and there is no direct correlation between the indicator factors. For each subsystem or sub-process, a priority diagram is established separately, and is divided into an independent dimension. According to the score filled in by the expert, the total priority number of each dimension indicator is calculated. The total priority number represents the importance sum of the indicators in this dimension in all comparisons. Let the total priority number of the kth dimension be S k ,

[0078]

[0079] Where n is the number of indicators in this dimension.

[0080] The total priority numbers of all dimensions are added to obtain the total priority number sum S total . Then, the total priority number of each dimension is divided by the total priority number sum to scientifically calculate the weight D k of this dimension,

[0081]

[0082] Where m is the number of dimensions in the entire system or system. D k The dimension weight reflects the importance of the subsystem or sub-process represented by this dimension in the system or system.

[0083] Finally, based on the dimension weight and the weight of the indicator in the dimension, the subjective weight W ki of a certain indicator in the system or system can be calculated:

[0084] W ki = D k × P i

[0085] (Three) Implementation steps of subjective weighting of satellite observation tasks

[0086] Since the overall process of satellite observation task can be clearly divided into three sub-processes of simulation deduction, data receiving and data processing, and the three sub-processes are independent of each other, there is no direct correlation between the index factors. Therefore, when weighting the satellite observation task, the three sub-processes can be placed in three different dimensions respectively and calculated by the priority graph method. The specific steps are as follows:

[0087] (1) Clearly compare the dimensions: First, accurately determine the three dimensions and related indexes that need to be analyzed to ensure the accuracy and pertinence of subsequent analysis.

[0088] In this embodiment, the process of satellite observation task is divided into three independent dimensions: simulation deduction dimension, data receiving dimension and data processing dimension, and there is no direct correlation between the index factors of the three dimensions.

[0089] (2) Build a multi-dimensional priority graph: According to the number of comparison dimensions determined, build a three-dimensional priority graph. This graph is presented in the form of a three-dimensional matrix, ensuring that each dimension or index can be compared with all other dimensions or all indexes in this dimension.

[0090] In this embodiment, for each dimension, the index elements of the dimension are extracted, and an index importance matrix is constructed in the form of two-by-two comparison of each index with all other indexes in the dimension:

[0091] Suppose a dimension has n indexes, then the index importance matrix is an n x n matrix A, A ij represents the importance comparison result between the i-th index and the j-th index.

[0092] (3) Experts two-by-two comparison: By experts or decision makers, the indexes in each dimension are rigorously compared two-by-two, and the importance scores are accurately filled in the corresponding positions of the multi-dimensional priority graph. The score results will be saved in the index pool and can be directly extracted and used before subjective weighting.

[0093] In this embodiment, according to the experience of experts, the two-by-two comparison values of indexes in the dimension are scored, and at this time, the weight of the i-th index in the dimension P i is defined as:

[0094]

[0095] Where, the value range of A ij is [0, 1], where A ij = 1 means that the i-th index is more important than the j-th index, A ij = 0 means that the i-th index is not as important as the j-th index, and A ij = 0.5 means that the importance of the two is equivalent or close.

[0096] (4) Calculate the total priority number: for each dimension extracted from the index pool, calculate the sum of the importance scores of all indexes in the multi-dimensional priority diagram, thereby obtaining the total priority number of each dimension.

[0097] In this embodiment, let the total priority number of the kth dimension be S k , then the total priority number of the indexes in this dimension is

[0098]

[0099] where n is the number of indexes in this dimension.

[0100] (5) Determine the dimension weight: add up the total priority numbers of all dimensions to obtain the total sum of the total priority numbers. Then, divide the total priority number of each dimension by the total sum of the total priority numbers to calculate the weight of this dimension.

[0101] In this embodiment, the total priority numbers of all dimensions are added up to obtain the total sum of the total priority numbers S total , that is,

[0102]

[0103] where m is the number of dimensions in the entire system or system;

[0104] For each dimension, calculate the proportion of the total priority number of this dimension in the total priority numbers of all dimensions, that is, the weight of this dimension:

[0105]

[0106] where D k reflects the importance of the subsystem or sub-process represented by this dimension in the system or system.

[0107] (6) Determine the subjective weight: multiply the intra-dimension weight of each index by the dimension weight of the index corresponding dimension to obtain the subjective weight of each index.

[0108] Multiply the dimension weight and the weight of the intra-dimension index to obtain the subjective weight W ki of a certain index in the observation task system.

[0109] W ki = D k × P i

[0110] (7) Result analysis and application: based on the calculated weight results, the dimensions or indexes can be ordered or analyzed in depth.

[0111] ​Therefore, in the embodiment, based on expert knowledge, the importance order of each dimension and index of satellite application can be accurately established, thereby providing a reliable subjective basis for weighting of satellite observation tasks.

[0112] II. Objective Weighting

[0113] (I) Method Overview

[0114] In addition to subjective weighting, the embodiment adds objective weighting to balance the subjectivity of satellite observation task effectiveness evaluation.

[0115] The objective weighting adopts the standard deviation method. The standard deviation method is a commonly used objective weighting method. The standard deviation of each index is calculated to measure the variation degree, and then the weight of each index is determined. The principle of standard deviation weighting is that the greater the variation degree of an index, the greater the amount of information it contains, and the greater the impact on comprehensive evaluation, so it should be given a greater weight. The standard deviation is an important indicator to measure the variation degree of an index. By calculating the standard deviation of each index, the weight of each index can be obtained.

[0116] (II) Implementation Steps of Objective Weighting of Satellite Observation Tasks

[0117] Based on satellite observation tasks, the specific steps of objective weighting of the standard deviation method are as follows:

[0118] 1. Data collection and arrangement: For a satellite observation task, collect the original data of each index of this task and normalize and summarize to ensure the accuracy and completeness of the data;

[0119] 2. Calculate the standard deviation of each index: For each index, calculate the standard deviation B i

[0120]

[0121] where B i is the standard deviation of the ith index, x i is the observation value of the ith index, and x is the base value of the index.

[0122] 3. Calculate the weight of each index: According to the calculated standard deviation of each index, calculate the weight V i

[0123]

[0124] where V i is the weight of the ith index, B i is the standard deviation of the ith index, and n is the number of indexes.

[0125] According to the calculated weight results, each index is sorted or analyzed in depth. The index with a larger weight indicates that it has a larger deviation from the standard index in the satellite observation task, has a greater influence and importance on the satellite task, and should be paid more attention to.

[0126] III. Combination weighting

[0127] The subjective weight generated by the multi-dimensional precedence graph and the objective weight generated by the standard deviation method are integrated to make the final weight reflect both subjective experience and decision preference and objective evaluation information. The final weight of the index is C q

[0128] C q = h1 x W q + h2 x V q

[0129] Where C q is the final weight of the qth index, W q is the subjective weight of the qth index in the system or system, and V q is the objective weight of the qth index.

[0130] In the formula, h1 is the subjective weight influence factor, h2 is the objective weight influence factor, and h1+h2=1. The determination principle is: according to the degree of authority of experts, the reliability of index information and historical evaluation results, combined with the specific situation of the task to determine.

[0131] IV. Performance quantitative evaluation

[0132] (I) Method overview

[0133] Grey correlation analysis is a statistical method for analyzing the correlation between multiple factors. In task evaluation, the reference value of each index can be regarded as an ideal reference sequence, and the observation value can be regarded as a comparison sequence to be evaluated. By calculating the grey correlation degree between the comparison sequence and the reference sequence, the performance of the task on each index can be evaluated, and the overall performance evaluation value of the task can be obtained.

[0134] (II) Implementation method of satellite observation task performance quantitative evaluation

[0135] Based on the satellite observation task, the specific steps of performance quantitative evaluation based on grey correlation analysis are as follows:

[0136] 1. Data collection and preprocessing: For a satellite observation task, the original data of each index of this task needs to be collected in detail and normalized to ensure the accuracy and integrity of the data and ensure that the data are in the same dimension.

[0137] 2. Determine the weight: according to the calculated maximum weight, construct the weight vector C = [c1, c2, c3, …, c n ]

[0138] 3. Calculate the grey correlation degree: for each index R, calculate the grey correlation coefficient ξ between the observation value and the reference value i

[0139] ① Calculate the absolute difference between the observation value and the reference value of each index: Δr ij = |r ij -r i0 |, where r ij is the observation value of the i-th index, r i0 is the reference value of the index;

[0140] ② Determine the minimum value minΔr ij and the maximum value maxΔr ij ;

[0141] ③ Calculate the correlation degree of each index using the grey correlation degree formula: (where ρ is the resolution coefficient, generally taken as 0.5);

[0142] 4. Weighted sum to get the performance evaluation value: multiply the grey correlation degree of each index by the corresponding weight to get the weighted grey correlation degree; then sum the weighted grey correlation degrees to get the overall performance evaluation value of the task. The specific formula is as follows:

[0143]

[0144] where Y is the overall performance evaluation value of the task.

[0145] In this embodiment, a subjective weighting method based on the multi-dimensional priority graph method is used, which refines the complex process of satellite observation tasks into three core dimensions. For each dimension, the corresponding index elements are analyzed and refined, and an index importance matrix is constructed by pairwise comparison. Then, according to the expert scoring, the total priority number of each index in each dimension is calculated. For each dimension, the proportion of the total priority number in all dimensions is calculated, i.e. the weight of the dimension. Finally, the dimension weight and the weight of the index within the dimension are multiplied to obtain the subjective weight of the index in the observation task system. This method provides a reliable subjective basis for the weighting of satellite observation tasks.

[0146] The overall performance evaluation value of the task is obtained by using grey relational analysis: the original data of each indicator are normalized, and then for each indicator R, the grey relational degree between its observed value and the reference value is calculated. The grey relational degree of each indicator is multiplied by the indicator weight obtained by combining and weighting to obtain the weighted grey relational degree. Finally, the weighted grey relational degrees are summed to obtain the overall performance evaluation value of the task.

[0147] The above technical solutions are merely exemplary embodiments of the present invention. For those skilled in the art, based on the application methods and principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the methods described in the specific embodiments of the present invention. Therefore, the methods described above are only preferred and not restrictive.

Claims

1. A performance evaluation method for remote sensing satellite observation missions, characterized in that, The method comprises the following steps: S1, establishing a satellite observation task index pool; for a received satellite observation task, obtaining the indexes involved in the task; S2, constructing a multi-dimensional priority diagram, and subjectively weighting the task index weight based on expert scoring; S3, objectively weighting the task index weight by using a standard deviation method; S4, comprehensively integrating the subjective weight and the objective weight of the indexes to obtain the final weight of the indexes; S5, obtaining the overall performance evaluation value of the task based on gray correlation analysis.

2. The method of claim 1, wherein, In the step S1, the indexes in the index pool include one or more of resolution, positioning accuracy, width, shooting duration, processing timeliness, and available receiving stations.

3. The method of claim 1, wherein, The step S2 specifically comprises: S21, dividing the process of the satellite observation task into three independent dimensions: simulation deduction dimension, data receiving dimension, and data processing dimension, and there is no direct correlation between the index factors in the three dimensions; S22, for each dimension, separately constructing a priority diagram, including: (1) extracting the index elements of the dimension, and constructing an index importance matrix in the form of comparing each index with all other indexes in the dimension two by two; If there are n indicators in one dimension, the indicator importance matrix is an n x n matrix A, A ij represents the importance comparison result between the i-th indicator and the j-th indicator. (2) According to the experience of experts, the pairwise comparison values of the indicators in the dimension are scored, and at this time, the intra-dimension weight P of the i-th indicator is i defined as: wherein A ij is a value in the range [0, 1], wherein A ij = 1 indicates that the ith indicator is more important than the jth indicator, A ij = 0 indicates that the ith indicator is less important than the jth indicator, and A ij = 0.5 indicates that both indicators are equally or nearly equally important. (3) calculating the total priority number of the indexes in the dimension: Let S be the total dominance number of the kth dimension k then the total dominance number of the indicators in this dimension is Wherein, n is the number of indexes in the dimension; S23, accumulate the total preference numbers of all dimensions to obtain a total preference number sum S total That is: Wherein, m is the number of dimensions in the entire system or system; S24, for each dimension, calculating the proportion of the total priority number of the dimension in the total priority number of all dimensions, that is, the weight of the dimension: wherein D k reflects the importance of the subsystem or sub-process represented by the dimension in the architecture or system; S25, multiplying the dimension weight and the weight of the intra-dimension index to obtain the subjective weight W of the index in the observation task system ki : W ki = D k x P i .

4. The method of claim 3, wherein, The step S2 further comprises the following steps: Saving the expert scoring results in the index pool and directly extracting and using them before subjective weighting.

5. The method of claim 1, wherein, The step S3 specifically comprises: S31, data collection and arrangement: for a satellite observation task, obtaining the original data of each index of the task and performing normalization arrangement; S32, calculating the standard deviation of each index: for each index, the standard deviation B of each index is calculated i where B i is the standard deviation of the ith indicator, x i is the observed value of the ith indicator, is the base value for the indicator; S33, calculating the weight of each index: according to the standard deviation of each index, calculating the weight V of each index i where V i is the weight of the i-th indicator, B i is the standard deviation of the i-th indicator, and n is the number of indicators.

6. The method of claim 1, wherein, In the step S4: The calculation method of the final weight of each index is: C q = h1 x W q + h2 x V q wherein C q is the maximum weight of the qth indicator, W q is the subjective weight of the qth indicator within the system or hierarchy, V q is the objective weight of the qth indicator; In the formula, h1 is a subjective weight influence factor, h2 is an objective weight influence factor, and h1+h2=1; the determination principle is: according to the expert authority degree, the credibility of index information, and the historical evaluation results, the specific situation of the task is combined to determine.

7. The method according to any one of claims 1 to 6, characterized in that, The step S5 specifically comprises: S51, constructing a weight vector according to the final weight of each index in a satellite observation task: C = [c1, c2, c3,..., c n ] S52, calculate the grey correlation degree: for each indicator R, calculate the grey correlation coefficient ξ between its observation value and the reference value i : ① Calculate the absolute difference between the observed value and the reference value for each indicator: Δr ij =|r ij -r i0 |, where r ij Let r be the observed value of the i-th indicator. i0 This serves as a reference value for the indicator. ii) determining the minimum value minDr of the absolute differences ij with the maximum value maxDr ij ; ③The correlation degree of each index is calculated by using the grey correlation degree formula: where p is the resolution coefficient. S53, weighted summation to obtain the performance evaluation value: multiplying the gray correlation degree of each index by the corresponding weight to obtain the weighted gray correlation degree; then summing the weighted gray correlation degrees to obtain the overall performance evaluation value of the task, and the specific formula is as follows: Wherein, Y is the overall performance evaluation value of the task.

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