A method for evaluating the effectiveness of remote sensing satellite earth observation

Abstract

The invention relates to a remote sensing satellite earth observation effectiveness evaluation method, belongs to the field of engineering technology, solves the problem of how to accurately evaluate the remote sensing satellite earth observation effectiveness, and includes: constructing a remote sensing satellite earth observation effectiveness evaluation index system, obtaining target data composed of original data, meteorological condition correction coefficients and space environment correction coefficients; obtaining the target data secondary evaluation index score according to the secondary evaluation index scoring requirements of the remote sensing satellite earth observation effectiveness evaluation index system; inputting the target data secondary evaluation index score and the secondary evaluation index weighting coefficient into the secondary evaluation index aggregation model, and aggregating to form the target data primary evaluation index score; inputting the target data primary evaluation index score and the primary evaluation index weighting coefficient into the primary evaluation index aggregation model, and aggregating to form the remote sensing satellite earth observation effectiveness evaluation result. The invention improves the scientificity, accuracy and practicality of the remote sensing satellite earth observation effectiveness evaluation.

Description

Technical Field

[0001] The invention belongs to the field of engineering technology, and in particular relates to a method for evaluating the effectiveness of remote sensing satellite earth observation. Background Art

[0002] Remote sensing satellite earth observation is a technology that uses the photoelectric sensors or radars and other payload equipment on the satellite platform to obtain information about ground targets. It has the characteristics of high orbit, wide coverage, strong continuity, high timeliness, and is not restricted by national boundaries and geographical conditions. Common remote sensing satellites include visible light imaging satellites, infrared imaging satellites, radar imaging satellites, etc. At present, countries around the world are competing to explore remote sensing satellite earth observation technology, and it is still worth studying to effectively evaluate the earth observation efficiency of remote sensing satellites.

[0003] Satellite remote sensing has the advantages of high viewpoint, wide viewing distance, and dynamic and continuous monitoring. It has become one of the important means of earth observation and provides important space-based observation data for earth observation activities under complex meteorological conditions and space environment. With the advancement of remote sensing technology, more and more facilities and equipment are involved in various subsystems. In view of their relatively high cost, organizing and implementing comprehensive performance evaluation of remote sensing satellite projects is of great significance to standardize and guide the improvement and development of subsequent satellite projects. The important goal of this evaluation is to propose the main aspects that need to be improved and upgraded through performance evaluation, and to provide guiding decision-making references and research support for the development of a new generation of earth observation remote sensing satellite systems.

[0004] In the 1930s, effectiveness evaluation began to develop, from the initial theoretical foundations of probability theory, operations research and planning theory for evaluating single equipment to systematic, automated and intelligent equipment system effectiveness evaluation methods such as ADC method, hierarchical analysis method, genetic algorithm, fuzzy comprehensive evaluation method and neural network. The two methods that are more widely used in equipment system effectiveness evaluation are analytical method and multi-index comprehensive evaluation method.

[0005] The evaluation index system established at this stage is still not perfect. For example, some of them only consider a few indicators at the application level of remote sensing satellite systems, which makes the existing system incompatible with the cognitive process and difficult to integrate with specific satellite engineering data. In addition, the existing remote sensing satellite system performance evaluation methods rarely consider the uncertainty of the evaluation method. Some studies on performance evaluation methods only study the uncertainty of performance evaluation indicators in the scoring process, while the hierarchical analysis method or ADC method itself does not consider the impact of uncertainty. In the existing technology, such as:

[0006] Lin Renhong, Wang Qi, Gao Lichun. Thinking on promoting high-quality development of satellite remote sensing industry through engineering performance evaluation. China Aerospace, 2022, (04): 57-63. In this paper, based on the top-level design and reasonable planning of the earth observation system, a hierarchical indicator system for performance analysis is constructed, and the weight design of indicators is optimized by using the hierarchical analysis method. Based on the quantitative and qualitative evaluation index values, a system performance calculation model based on the fuzzy comprehensive method is constructed. However, since the indicators are difficult to quantify, it is difficult to measure the performance evaluation, and it is difficult to exert the synergistic effect. The evaluation is not innovative enough, and the quality of relevant data is not good enough.

[0007] Qin Pengcheng, Hao Shengyong, Qin Guozheng. Research on contribution evaluation method based on remote sensing satellite application system. Electronic Design Engineering, 2019, 27(02): 70-73+79. In this paper, a remote sensing satellite application system contribution evaluation method was proposed, and an example analysis was conducted based on ship detection missions. However, its earth observation needs are limited. The proposed indicators are only applicable to ocean detection missions. A universal satellite earth observation performance evaluation method should also be explored.

[0008] Yi Shan. Effectiveness evaluation of remote sensing satellite systems based on uncertainty. Harbin Institute of Technology, 2021. The proposed hierarchical analysis method itself has the defect of seriously relying on the subjective judgment of experts when determining the indicator weights, which may lead to unreasonable distribution of performance indicator weights and inaccurate performance evaluation results.

[0009] The publication number is CN107563594A, the publication date is 2020-08-25, and the name of the invention is: A method for evaluating the effective data acquisition capability of a remote sensing satellite. It mainly focuses on dividing the global geography according to climate regional characteristics, and then obtains the data acquisition capability of the remote sensing satellite through weighted summation. It only evaluates a single capability, lacks a comprehensive evaluation of the overall situation, and is one-sided. Summary of the invention

[0010] In order to solve the technical problem of how to accurately evaluate the effectiveness of remote sensing satellites in earth observation, the present invention proposes a remote sensing satellite earth observation effectiveness evaluation method, which provides an effectiveness evaluation method for remote sensing satellites in earth observation activities under complex meteorological conditions and complex space environments. On the basis of collecting raw data, the raw data is processed according to two-level evaluation indicators, and aggregated from bottom to top to form a quantitative evaluation of the effectiveness of remote sensing satellites in earth observation. The two-level division of evaluation indicators and the determination of scores are based on the basic performance of current remote sensing satellites, oriented to the actual needs of earth observation activities, and are derived by integrating historical data, equipment parameters, and objective effects, thereby reducing the uncertainty of indicator weights. The proposed indicators are more universal and convenient for quantification of indicators.

[0011] In order to achieve the above object, the technical solution proposed by the present invention is as follows:

[0012] A remote sensing satellite earth observation effectiveness evaluation method comprises the following steps:

[0013] Step 1: constructing a remote sensing satellite earth observation effectiveness evaluation index system through the first-level evaluation index composed of the second-level evaluation index; the remote sensing satellite earth observation effectiveness evaluation index system simultaneously stores the preset scoring requirements and weighting coefficients of the second-level evaluation indicators and the weighting coefficients of the first-level evaluation indicators;

[0014] Step 2: According to the remote sensing satellite earth observation activity simulation task, the original data of the secondary evaluation indicators corresponding to the remote sensing satellite earth observation activity simulation task requirements stored in the server are obtained;

[0015] Step 3: Determine the meteorological condition correction coefficient according to the meteorological conditions corresponding to the first-level evaluation indicators; determine the space environment correction coefficient according to the space environment complexity corresponding to the first-level evaluation indicators; the target data is composed of the original data, the meteorological condition correction coefficient and the space environment correction coefficient;

[0016] Step 4: Score the target data according to the scoring requirements of the secondary evaluation indicators to obtain the secondary evaluation indicator scores of the target data;

[0017] Step 5: Input the secondary evaluation indicator score of the target data and the weighted coefficient of the secondary evaluation indicator corresponding to the target data into the secondary evaluation indicator aggregation model to aggregate and form the primary evaluation indicator score of the target data;

[0018] Step 6: Input the first-level evaluation index score of the target data and the weighted coefficient of the first-level evaluation index corresponding to the target data into the first-level evaluation index aggregation model to aggregate and form the remote sensing satellite earth observation effectiveness evaluation result;

[0019] Among them, in step 1, the remote sensing satellite earth observation effectiveness evaluation index system includes three first-level evaluation indicators, namely: target discovery effectiveness index, target recognition effectiveness index and target position calibration effectiveness index; among them,

[0020] The target discovery effectiveness index includes six secondary evaluation indicators, and the original data are: target discovery times, maximum discovery time interval, target discovery probability, discovery response time, meteorological condition correction factor, and space environment correction factor;

[0021] The target recognition effectiveness index includes six secondary evaluation indicators, and the original data are: target recognition times, maximum recognition time interval, target recognition probability, recognition response time, meteorological condition correction factor and space environment correction factor;

[0022] The target position calibration efficiency index includes three secondary evaluation indicators, and the original data are: the number of target position calibration updates, the maximum time interval of target position calibration, and the number of position calibrations that meet the requirements.

[0023] In step 1, the scoring requirements for the preset secondary evaluation indicators are:

[0024] Target Discovery Score

[0025] Maximum discovery interval score

[0026] Target detection probability score

[0027] Target Discovery Response Time Score

[0028] Target detection efficiency index weather condition correction coefficient score

[0029]

[0030] Target detection effectiveness index space environment correction factor score Target recognition score Maximum recognition time interval score Target recognition probability score Target recognition response time score Target recognition effectiveness index meteorological condition correction coefficient score:

[0031]

[0032] Target recognition effectiveness index space environment correction coefficient score Target position calibration update score Target position calibration maximum time interval score

[0033] The number of target position calibrations that meet the requirements is scored

[0034] In step 1, the weighted coefficients of the preset secondary evaluation indicators are:

[0035] The weighted coefficient of the secondary indicator of target discovery efficiency is: the weighted coefficient of target discovery times p a1 =0.2, weighting coefficient p of the maximum discovery time interval a2 =0.15, weighted coefficient p of target detection probability a3 = 0.2, find the weighting coefficient p of the response time a4 =0.25, weighted coefficient of meteorological condition correction factor p a5= 0.1 and the weighting factor p of the space environment correction factor a6 =0.1;

[0036] The weighted coefficient of the secondary index of target recognition efficiency is: the weighted coefficient of target recognition times p b1 =0.2, weighting coefficient p of the maximum recognition time interval b2 =0.15, weighted coefficient p of target recognition probability b3 =0.2, weighting coefficient p of identification response time b4 =0.25, weighted coefficient of meteorological condition correction factor p b5 = 0.1 and the weighting factor p of the space environment correction factor b6 =0.1;

[0037] The weighted coefficient of the secondary indicator of target position calibration efficiency is: the weighted coefficient of the target position calibration update times p c1 =0.3, weighting coefficient p of the maximum target position calibration time interval c2 = 0.3 and the weighting coefficient p of the number of times the target position calibration requirement is met c3 =0.4.

[0038] In step 1, the weighted coefficient of the preset first-level evaluation index is:

[0039] The weighted coefficient p of the target discovery efficiency index in the first-level evaluation index a =0.2; weighted coefficient p of target recognition efficiency index in the first-level evaluation index b =0.3; weighted coefficient p of target position calibration efficiency index in the first-level evaluation index c =0.5.

[0040] In step 2, the original data of the secondary evaluation indicators corresponding to the requirements of the remote sensing satellite earth observation activity simulation task stored in the server are obtained, including:

[0041] Target discovery times, maximum discovery time interval, target discovery probability, discovery response time, target identification times, maximum identification time interval, target identification probability, identification response time, target position calibration update times, target position calibration maximum time interval and / or target position calibration times that meet requirements.

[0042] The original data is recorded as follows:

[0043] The number of target discoveries is recorded as the average number of discoveries per day;

[0044] The maximum discovery time interval is recorded as the maximum time interval between two consecutive discovery of the target. The default value is 10000.

[0045] The target detection probability is recorded as: the number of target detections ÷ the number of detections × 100;

[0046] The discovery response time is recorded as: first discovery time - observation start time, the default value is 10000;

[0047] The number of target identifications was recorded as the average number of sightings per day;

[0048] The maximum recognition time interval is recorded as the maximum time interval between two consecutive target detections. The default value is 10000.

[0049] The target recognition probability is recorded as: the number of target recognitions ÷ the number of detections × 100;

[0050] The recognition response time is recorded as: first recognition time - first discovery time, the default value is 10000;

[0051] The target position calibration update times are recorded as: the average number of target discoveries per day + the number of target identifications;

[0052] The maximum time interval for target position calibration is the maximum value of the maximum discovery time interval and the maximum recognition time interval;

[0053] The number of target position calibrations that meet the requirements is recorded as the average number of times per day that the requirements are met.

[0054] In step 3, the meteorological condition correction coefficient is determined according to the meteorological conditions corresponding to the first-level evaluation index; the method for determining the space environment correction coefficient according to the space environment complexity corresponding to the first-level evaluation index includes:

[0055] The meteorological condition correction coefficient is the value range of the meteorological condition correction coefficient determined according to the meteorological conditions corresponding to the target discovery efficiency index and the target identification efficiency index in the first-level evaluation index;

[0056] In the target discovery efficiency index and target identification efficiency index of the first-level evaluation index, the value range of the meteorological condition correction coefficient is {1, 2, 3, 4, 5};

[0057] Meteorological conditions are five kinds of meteorological conditions that affect the effectiveness of remote sensing satellites in observing the earth. Among the values ​​of meteorological condition correction coefficients, 1 indicates that the meteorological conditions for satellite observation are the most ideal; 2 indicates that the meteorological conditions for satellite observation are relatively ideal; 3 indicates that the meteorological conditions for satellite observation are average; 4 indicates that the meteorological conditions for satellite observation are relatively bad; 5 indicates that the meteorological conditions for satellite observation are the worst;

[0058] The space environment correction coefficient is the value range of the space environment correction coefficient determined according to the space environment complexity corresponding to the target discovery effectiveness index and the target recognition effectiveness index in the first-level evaluation index;

[0059] In the target detection efficiency index and target identification efficiency index of the first-level evaluation index, the value range of the space environment correction coefficient is {1, 2, 3, 4, 5}; among them,

[0060] The space environment complexity refers to the complexity of the space environment during satellite observation. Among the values ​​of the space environment correction coefficient, 1 indicates that the space environment complexity is low; 2 indicates that the space environment complexity is slight; 3 indicates that the space environment is generally complex; 4 indicates that the space environment is relatively complex; and 5 indicates that the space environment is the most complex.

[0061] In step 5, the secondary evaluation index aggregation model includes:

[0062]

[0063]

[0064]

[0065] Among them, A is the score of the first-level evaluation index to which the target discovery efficiency index belongs; a i is the score of the secondary evaluation index to which the target discovery efficiency index belongs; p ai is the weighting coefficient of the secondary indicator of the target discovery efficiency index; B is the score of the primary evaluation indicator of the target recognition efficiency index; b j is the score of the secondary evaluation index to which the target recognition effectiveness index belongs; p bj is the weighting coefficient of the secondary indicator of the target recognition efficiency index; C is the score of the primary evaluation indicator of the target position calibration efficiency index; c k is the score of the secondary evaluation index of the target position calibration efficiency index; p ck It is the weighting coefficient of the secondary indicator of the target position calibration efficiency index.

[0066] In step 6, the aggregation model of the first-level evaluation indicators is:

[0067] P=A×p a +B×p b +C×p c ;

[0068] Among them, P is the evaluation result of remote sensing satellite earth observation efficiency; A is the score of the first-level evaluation index of the target discovery efficiency index; p a is the weighting coefficient of the first-level evaluation index to which the target discovery efficiency index belongs; B is the score of the first-level evaluation index to which the target recognition efficiency index belongs; p b is the weighting coefficient of the first-level evaluation index of the target recognition efficiency index; C is the score of the first-level evaluation index of the target position calibration efficiency index; p c It is the weighting coefficient of the first-level evaluation index to which the target position calibration efficiency index belongs.

[0069] Beneficial effects:

[0070] 1. The present invention focuses on the actual needs of remote sensing satellite earth observation activities. According to the regular characteristics of remote sensing satellite in-orbit motion, a remote sensing satellite earth observation performance evaluation index system is constructed, which focuses on the needs and is highly targeted.

[0071] 2. The present invention introduces two indicators, namely, meteorological condition correction coefficient and space environment correction coefficient, into the remote sensing satellite earth observation effectiveness evaluation index system. This not only reflects the impact of meteorological factors on the observation effect, but also takes into account the complex space environment in the earth observation process, making the evaluation result more accurate and in line with actual needs.

[0072] 3. The multi-level indicator framework and aggregation model proposed in the present invention fill the gap in the performance evaluation of remote sensing satellite systems under complex meteorological conditions and space environments. It is of great significance to the development of my country's new aerospace equipment, the comprehensive performance of remote sensing satellite network systems, and the improvement of remote sensing satellite application capabilities. It also provides theoretical and tool support for the identification and assessment of aerospace equipment.

[0073] 4. Carrying out an evaluation of the effectiveness of remote sensing satellite earth observation will help to more accurately and effectively reflect the capabilities of satellite systems, expand the application areas of the satellite remote sensing industry, promote closed-loop management of satellite engineering construction, and further optimize the construction of remote sensing satellite systems.

[0074] 5. There is currently no unified standard for the effectiveness of satellite remote sensing earth observation. The present invention clearly defines the indicators, namely, the degree of satisfaction of specific remote sensing mission objectives for earth observation, reflecting the space-ground integrated service capability of satellite remote sensing, and selectively selecting performance indicators as the main factors affecting effectiveness evaluation.

[0075] 6. Uncertainty is ubiquitous in the real world. The present invention reduces the impact of uncertainty in remote sensing earth observation tasks by representing and quantifying uncertainty. The constructed three-level performance evaluation index system provides a basic idea and context for scientific and reasonable evaluation.

[0076] 7. The aggregation model proposed in the present invention integrates multiple levels of indicators, overcomes the fuzzy influence of traditional indicators, makes the weight of the indicator have the influence of both subjective decision-making and objective correction, and can make the indicator weight more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0078] Figure 1The present invention is a flow chart of a method for evaluating the effectiveness of remote sensing satellite earth observation.

[0079] Figure 2 It is a schematic diagram of the evaluation index system for remote sensing satellite earth observation effectiveness. DETAILED DESCRIPTION

[0080] First of all, it should be pointed out that the embodiments of the present invention only disclose preferred implementation modes and should not be understood as limiting the implementation of the present invention. The protection scope of the present invention shall still be based on the contents disclosed in the claims.

[0081] like Figure 1 and Figure 2 As shown, the present invention proposes a remote sensing satellite earth observation effectiveness evaluation method, and the evaluation process includes the following steps:

[0082] Step 1: constructing a remote sensing satellite earth observation effectiveness evaluation index system through the first-level evaluation index composed of the second-level evaluation index; the remote sensing satellite earth observation effectiveness evaluation index system simultaneously stores the preset scoring requirements and weighting coefficients of the second-level evaluation indicators and the weighting coefficients of the first-level evaluation indicators;

[0083] Step 2: According to the remote sensing satellite earth observation activity simulation task, the original data of the secondary evaluation indicators corresponding to the remote sensing satellite earth observation activity simulation task requirements stored in the server are obtained;

[0084] Step 3: Determine the meteorological condition correction coefficient according to the meteorological conditions corresponding to the first-level evaluation indicators; determine the space environment correction coefficient according to the space environment complexity corresponding to the first-level evaluation indicators; the target data is composed of the original data, the meteorological condition correction coefficient and the space environment correction coefficient;

[0085] Step 4: Score the target data according to the scoring requirements of the secondary evaluation indicators to obtain the secondary evaluation indicator scores of the target data;

[0086] Step 5: Input the secondary evaluation indicator score of the target data and the weighted coefficient of the secondary evaluation indicator corresponding to the target data into the secondary evaluation indicator aggregation model to aggregate and form the primary evaluation indicator score of the target data;

[0087] Step 6: Input the first-level evaluation index score of the target data and the weighted coefficient of the first-level evaluation index corresponding to the target data into the first-level evaluation index aggregation model to aggregate and form the remote sensing satellite earth observation effectiveness evaluation result;

[0088] Among them, in step 1, the remote sensing satellite earth observation effectiveness evaluation index system includes three first-level evaluation indicators, namely: target discovery effectiveness index, target recognition effectiveness index and target position calibration effectiveness index; among them,

[0089] The target discovery effectiveness index includes six secondary evaluation indicators, and the original data are: target discovery times, maximum discovery time interval, target discovery probability, discovery response time, meteorological condition correction factor, and space environment correction factor;

[0090] The target recognition effectiveness index includes six secondary evaluation indicators, and the original data are: target recognition times, maximum recognition time interval, target recognition probability, recognition response time, meteorological condition correction factor and space environment correction factor;

[0091] The target position calibration efficiency index includes three secondary evaluation indicators, and the original data are: the number of target position calibration updates, the maximum time interval of target position calibration, and the number of position calibrations that meet the requirements.

[0092] The remote sensing satellite earth observation efficiency evaluation index system constructed by the present invention can quickly evaluate the earth observation efficiency according to the task requirements when the emergency task is issued, determine the best ground observation longitude and latitude, and make the target discovery efficiency, identification efficiency, and calibration efficiency the best, so as to play an emergency auxiliary decision-making role. The remote sensing satellite earth observation efficiency evaluation index system is constructed by the first-level evaluation index composed of the second-level evaluation index. According to different task requirements, the two-level evaluation index, the scoring requirements and weighting coefficients of the preset second-level evaluation index, and the weighting coefficients of the first-level evaluation index can be stored in the server in advance, so that the index system is refined; according to the planned emergency tasks, it is convenient to call for different tasks, use a large language model, based on natural language processing, automatically extract keywords related to the indicators, and search for performance indicator data related to the keywords in the server, through the two-level aggregation model, according to the given weight, that is, the weighting coefficient, the efficiency evaluation is performed, the reaction time of the evaluation system is reduced, and the time for inputting data for evaluation is saved.

[0093] In step 1, the scoring requirements for the preset secondary evaluation indicators are:

[0094] Target Discovery Score

[0095] Maximum discovery interval score

[0096] Target detection probability score Target Discovery Response Time Score Target detection efficiency index weather condition correction coefficient score

[0097]

[0098] Target detection effectiveness index space environment correction factor score Target recognition score Maximum recognition time interval score Target recognition probability score Target recognition response time score Target recognition effectiveness index meteorological condition correction coefficient score:

[0099]

[0100] Target recognition effectiveness index space environment correction factor score Target position calibration update score

[0101] Target position calibration maximum time interval score

[0102] The number of target position calibrations that meet the requirements is scored

[0103] In step 1, the weighted coefficients of the preset secondary evaluation indicators are:

[0104] The weighted coefficient of the secondary indicator of target discovery efficiency is: the weighted coefficient of target discovery times p a1 =0.2, weighting coefficient p of the maximum discovery time interval a2 =0.15, weighted coefficient p of target detection probability a3 =0.2, find the weighting coefficient p of the response time a4 =0.25, weighted coefficient of meteorological condition correction factor p a5 = 0.1 and the weighting factor p of the space environment correction factor a6 =0.1;

[0105] The weighted coefficient of the secondary index of target recognition efficiency is: the weighted coefficient of target recognition times p b1 =0.2, weighting coefficient p of the maximum recognition time interval b2 =0.15, weighted coefficient p of target recognition probability b3 =0.2, weighting coefficient p of identification response time b4 =0.25, weighted coefficient of meteorological condition correction factor p b5 = 0.1 and the weighting factor p of the space environment correction factor b6 =0.1;

[0106] The weighted coefficient of the secondary indicator of target position calibration efficiency is: the weighted coefficient of the target position calibration update times p c1 =0.3, weighting coefficient p of the maximum target position calibration time interval c2 = 0.3 and the weighting coefficient p of the number of times the target position calibration requirement is met c3 =0.4.

[0107] In step 1, the weighted coefficient of the preset first-level evaluation index is:

[0108] The weighted coefficient p of the target discovery efficiency index in the first-level evaluation index a =0.2; weighted coefficient p of target recognition efficiency index in the first-level evaluation index b =0.3; weighted coefficient p of target position calibration efficiency index in the first-level evaluation index c =0.5.

[0109] In step 2, the original data of the secondary evaluation indicators corresponding to the requirements of the remote sensing satellite earth observation activity simulation task stored in the server are obtained, including:

[0110] Number of target discoveries (times / day), maximum discovery time interval (hours), target discovery probability (%), discovery response time (hours), number of target identifications (times / day), maximum identification time interval (hours), target identification probability (%), identification response time (hours), number of target position calibration updates (times / day), maximum target position calibration time interval (hours), number of target position calibration updates that meet the requirements (times / day).

[0111] The original data is recorded as follows:

[0112] The number of target discoveries is recorded as the average number of discoveries per day;

[0113] The maximum discovery time interval is recorded as the maximum time interval between two consecutive discovery of the target. The default value is 10000.

[0114] The target detection probability is recorded as: the number of target detections ÷ the number of detections × 100;

[0115] The discovery response time is recorded as: first discovery time - observation start time, the default value is 10000;

[0116] The number of target identifications was recorded as the average number of sightings per day;

[0117] The maximum recognition time interval is recorded as the maximum time interval between two consecutive target detections. The default value is 10000.

[0118] The target recognition probability is recorded as: the number of target recognitions ÷ the number of detections × 100;

[0119] The recognition response time is recorded as: first recognition time - first discovery time, the default value is 10000;

[0120] The target position calibration update times are recorded as: the average number of target discoveries per day + the number of target identifications;

[0121] The maximum time interval for target position calibration is the maximum value of the maximum discovery time interval and the maximum recognition time interval;

[0122] The number of target position calibrations that meet the requirements is recorded as the average number of times per day that the requirements are met.

[0123] For example: if a target is found 7 times within 7 days, the average number of discoveries per day is 7÷7=1 time; if no target is found, the default maximum time interval is recorded as 10,000 hours and the maximum response time is recorded as 10,000 hours; if the maximum discovery time interval is 3 times and the maximum recognition time interval is 2 times, the maximum target position calibration time is 3 times.

[0124] In step 3, the meteorological condition correction coefficient is determined according to the meteorological conditions corresponding to the first-level evaluation index; the method for determining the space environment correction coefficient according to the space environment complexity corresponding to the first-level evaluation index includes:

[0125] The meteorological condition correction coefficient is the value range of the meteorological condition correction coefficient determined according to the meteorological conditions corresponding to the target discovery efficiency index and the target identification efficiency index in the first-level evaluation index;

[0126] In the target discovery efficiency index and target identification efficiency index of the first-level evaluation index, the value range of the meteorological condition correction coefficient is {1, 2, 3, 4, 5};

[0127] Meteorological conditions are five kinds of meteorological conditions that affect the effectiveness of remote sensing satellite earth observation. Among the values ​​of meteorological condition correction coefficients, 1 means that the meteorological conditions for satellite observation are the most ideal, and the meteorological factors have the least impact on the observation effect; 2 means that the meteorological conditions for satellite observation are relatively ideal, and the meteorological factors have a small impact on the observation effect; 3 means that the meteorological conditions for satellite observation are average, and the meteorological factors have a certain impact on the observation effect; 4 means that the meteorological conditions for satellite observation are relatively bad, and the meteorological factors have a great impact on the observation effect, which is not conducive to the implementation of observation activities; 5 means that the meteorological conditions for satellite observation are the worst, and the meteorological factors have the greatest obstruction to the observation activities;

[0128] The space environment correction coefficient is the value range of the space environment correction coefficient determined according to the space environment complexity corresponding to the target discovery effectiveness index and the target recognition effectiveness index in the first-level evaluation index;

[0129] In the target detection efficiency index and target identification efficiency index of the first-level evaluation index, the value range of the space environment correction coefficient is {1, 2, 3, 4, 5}; among them,

[0130] The space environment complexity refers to the complexity of the space environment during satellite observation. It represents the five levels of influence of factors such as the space traffic environment, space electromagnetic environment, and high-energy particle environment during remote sensing satellite earth observation on the observation activities. Among the values ​​of the space environment correction coefficient, 1 means that the space environment complexity is low and the impact on the observation activities is minimal; 2 means that the space environment complexity is slight and has a small impact on satellite observation activities; 3 means that the space environment is generally complex and has a certain impact on satellite observation activities; 4 means that the space environment is relatively complex and satellite observation activities may be greatly hindered; 5 means that the space environment is the most complex and the satellite may be disabled or damaged, which will seriously hinder the observation activities.

[0131] For example: if the environmental conditions are very ideal, and the lighting and meteorological conditions are particularly conducive to Earth observation activities, the meteorological condition correction coefficient can be taken as 1; if during Earth observation activities, the space traffic environment or the space electromagnetic environment is relatively complex, and in the event of approaching space debris or eruption of a solar storm, there is a risk of satellite disability or degradation, and Earth observation activities are greatly hindered, then the space environment correction coefficient can be taken as 5.

[0132] In step 5, the secondary evaluation index aggregation model includes:

[0133]

[0134]

[0135]

[0136] Among them, A is the score of the first-level evaluation index to which the target discovery efficiency index belongs; a i is the score of the secondary evaluation index to which the target discovery effectiveness index belongs; p ai is the weighting coefficient of the secondary indicator of the target discovery efficiency index; B is the score of the primary evaluation indicator of the target recognition efficiency index; b j is the score of the secondary evaluation index to which the target recognition effectiveness index belongs; p bj is the weighting coefficient of the secondary indicator of the target recognition efficiency index; C is the score of the primary evaluation indicator of the target position calibration efficiency index; c k is the score of the secondary evaluation index of the target position calibration efficiency index; p ck It is the weighting coefficient of the secondary indicator of the target position calibration efficiency index.

[0137] According to the scoring requirements of each secondary evaluation indicator, the original data, meteorological condition correction coefficient and space environment correction coefficient are scored to obtain the scores of each secondary evaluation indicator of the target data, that is, the original data corresponding to the obtained secondary indicators are converted into scores in percentage;

[0138] For example, if the target discovery frequency is 1 time / day, the target discovery frequency score is a1=60 points; if the target discovery frequency is 32 times / day, it is calculated according to the linear equation: The target discovery times score is a1 = 92.4 points, and the methods for determining the values ​​of indicators a2, a4, b1, b2, b4, c1, c2, and c3 are similar; if the meteorological condition correction coefficient is 2, the meteorological condition correction coefficient score is 40 points; if the space environment correction coefficient is 4, the space environment correction coefficient score is 80 points.

[0139] Input the scores of each secondary evaluation indicator of the target data and the weighted coefficients of each secondary evaluation indicator into the secondary evaluation indicator aggregation model to aggregate and form the scores of each primary evaluation indicator of the target data;

[0140] For example, the aggregation method of target discovery performance (A) is:

[0141] A=a1×0.2+a2×0.15+a3×0.2+a4×0.25+a5×0.1+a6×0.1

[0142] In step 6, the aggregation model of the first-level evaluation indicators is:

[0143] P=A×p a +B×p b +C×p c ;

[0144] Among them, P is the evaluation result of remote sensing satellite earth observation efficiency; A is the score of the first-level evaluation index of the target discovery efficiency index; p a is the weighting coefficient of the first-level evaluation index to which the target discovery efficiency index belongs; B is the score of the first-level evaluation index to which the target recognition efficiency index belongs; p b is the weighting coefficient of the first-level evaluation index of the target recognition efficiency index; C is the score of the first-level evaluation index of the target position calibration efficiency index; p c It is the weighting coefficient of the first-level evaluation index to which the target position calibration efficiency index belongs.

[0145] For example:

[0146] P=A×0.2+B×0.3+C×0.5.

[0147] The present invention has the following characteristics:

[0148] 1. The present invention focuses on the actual needs of remote sensing satellite earth observation activities. According to the regular characteristics of remote sensing satellite in-orbit motion, a remote sensing satellite earth observation performance evaluation index system is constructed, which focuses on the needs and is highly targeted.

[0149] 2. The present invention introduces two indicators, namely, meteorological condition correction coefficient and space environment correction coefficient, into the remote sensing satellite earth observation effectiveness evaluation index system. This not only reflects the impact of meteorological factors on the observation effect, but also takes into account the complex space environment in the earth observation process, making the evaluation result more accurate and in line with actual needs.

[0150] 3. The multi-level indicator framework and aggregation model proposed in the present invention fill the gap in the performance evaluation of remote sensing satellite systems under complex meteorological conditions and space environments. It is of great significance to the development of my country's new aerospace equipment, the comprehensive performance of remote sensing satellite network systems, and the improvement of remote sensing satellite application capabilities. It also provides theoretical and tool support for the identification and assessment of aerospace equipment.

[0151] 4. Carrying out an evaluation of the effectiveness of remote sensing satellite earth observation will help to more accurately and effectively reflect the capabilities of satellite systems, expand the application areas of the satellite remote sensing industry, promote closed-loop management of satellite engineering construction, and further optimize the construction of remote sensing satellite systems.

[0152] 5. There is currently no unified standard for the effectiveness of satellite remote sensing earth observation. The present invention clearly defines the indicators, namely, the degree of satisfaction of specific remote sensing mission objectives for earth observation, reflecting the space-ground integrated service capability of satellite remote sensing, and selectively selecting performance indicators as the main factors affecting effectiveness evaluation.

[0153] 6. Uncertainty is ubiquitous in the real world. The present invention reduces the impact of uncertainty in remote sensing earth observation tasks by representing and quantifying uncertainty. The constructed three-level performance evaluation index system provides a basic idea and context for scientific and reasonable evaluation.

[0154] 7. The aggregation model proposed in the present invention integrates multiple levels of indicators, overcomes the fuzzy influence of traditional indicators, makes the weight of the indicator have the influence of both subjective decision-making and objective correction, and can make the indicator weight more accurate.

[0155] Finally, it should be pointed out that any unit or individual using or implementing the technical solution of the present invention is an infringement of the present invention, and any unit or individual cannot independently implement the present patent without the permission of the applicant. Any unit or individual inspired by the present invention or implementing it after simple adjustments should also be considered within the scope of protection of this patent.

Claims

1. A method for evaluating the effectiveness of remote sensing satellite earth observation, characterized in that: The following steps are involved: Step 1: constructing a remote sensing satellite earth observation effectiveness evaluation index system through the first-level evaluation index composed of the second-level evaluation index; the remote sensing satellite earth observation effectiveness evaluation index system simultaneously stores the preset scoring requirements and weighting coefficients of the second-level evaluation indicators and the weighting coefficients of the first-level evaluation indicators; Step 2: According to the remote sensing satellite earth observation activity simulation task, the original data of the secondary evaluation indicators corresponding to the remote sensing satellite earth observation activity simulation task requirements stored in the server are obtained; Step 3: Determine the meteorological condition correction coefficient according to the meteorological conditions corresponding to the first-level evaluation indicators; determine the space environment correction coefficient according to the space environment complexity corresponding to the first-level evaluation indicators; the target data is composed of the original data, the meteorological condition correction coefficient and the space environment correction coefficient; Step 4: Score the target data according to the scoring requirements of the secondary evaluation indicators to obtain the secondary evaluation indicator scores of the target data; Step 5: Input the secondary evaluation indicator score of the target data and the weighted coefficient of the secondary evaluation indicator corresponding to the target data into the secondary evaluation indicator aggregation model to aggregate and form the primary evaluation indicator score of the target data; Step 6: Input the first-level evaluation index score of the target data and the weighted coefficient of the first-level evaluation index corresponding to the target data into the first-level evaluation index aggregation model to aggregate and form the remote sensing satellite earth observation effectiveness evaluation result; Among them, in step 1, the remote sensing satellite earth observation effectiveness evaluation index system includes three first-level evaluation indicators, namely: target discovery effectiveness index, target recognition effectiveness index and target position calibration effectiveness index; among them, The target discovery effectiveness index includes six secondary evaluation indicators, and the original data are: target discovery times, maximum discovery time interval, target discovery probability, discovery response time, meteorological condition correction factor, and space environment correction factor; The target recognition effectiveness index includes six secondary evaluation indicators, and the original data are: target recognition times, maximum recognition time interval, target recognition probability, recognition response time, meteorological condition correction factor and space environment correction factor; The target position calibration efficiency index includes three secondary evaluation indicators, and the original data are: the number of target position calibration updates, the maximum time interval of target position calibration, and the number of position calibrations that meet the requirements; In step 3, the meteorological condition correction coefficient is determined according to the meteorological conditions corresponding to the first-level evaluation index; the method for determining the space environment correction coefficient according to the space environment complexity corresponding to the first-level evaluation index includes: The meteorological condition correction coefficient is the value range of the meteorological condition correction coefficient determined according to the meteorological conditions corresponding to the target discovery efficiency index and the target identification efficiency index in the first-level evaluation index; In the target discovery efficiency index and target identification efficiency index of the first-level evaluation index, the value range of the meteorological condition correction coefficient is {1, 2, 3, 4, 5}; Meteorological conditions are five kinds of meteorological conditions that affect the effectiveness of remote sensing satellites in observing the earth. Among the values ​​of meteorological condition correction coefficients, 1 indicates that the meteorological conditions for satellite observation are the most ideal; 2 indicates that the meteorological conditions for satellite observation are relatively ideal; 3 indicates that the meteorological conditions for satellite observation are average; 4 indicates that the meteorological conditions for satellite observation are relatively bad; 5 indicates that the meteorological conditions for satellite observation are the worst; The space environment correction coefficient is the value range of the space environment correction coefficient determined according to the space environment complexity corresponding to the target discovery effectiveness index and the target recognition effectiveness index in the first-level evaluation index; In the target detection efficiency index and target identification efficiency index of the first-level evaluation index, the value range of the space environment correction coefficient is {1, 2, 3, 4, 5}; among them, The space environment complexity refers to the complexity of the space environment during satellite observation. Among the values ​​of the space environment correction coefficient, 1 indicates that the space environment complexity is low; 2 indicates that the space environment complexity is slight; 3 indicates that the space environment is generally complex; 4 indicates that the space environment is relatively complex; and 5 indicates that the space environment is the most complex.

2. A remote sensing satellite earth observation effectiveness evaluation method as claimed in claim 1, characterized in that: In step 1, the scoring requirements for the preset secondary evaluation indicators are: Target Discovery Score Maximum discovery interval score Target detection probability score Target Discovery Response Time Score Target detection efficiency index weather condition correction coefficient score Target detection effectiveness index space environment correction factor score Target recognition score Maximum recognition time interval score Target recognition probability score Target recognition response time score Target recognition effectiveness index meteorological condition correction coefficient score: Target recognition effectiveness index space environment correction factor score Target position calibration update score Target position calibration maximum time interval score The number of target position calibrations that meet the requirements is scored 3. A remote sensing satellite earth observation effectiveness evaluation method as claimed in claim 1, characterized in that: In step 1, the weighted coefficients of the preset secondary evaluation indicators are: The weighted coefficient of the secondary indicator of target discovery efficiency is: the weighted coefficient of target discovery times p a1 =0.2, weighting coefficient p of the maximum discovery time interval a2 =0.15, weighted coefficient p of target detection probability a3 = 0.2, find the weighting coefficient p of the response time a4 =0.25, weighted coefficient of meteorological condition correction factor p a5 = 0.1 and the weighting factor p of the space environment correction factor a6 =0.1; The weighted coefficient of the secondary index of target recognition efficiency is: the weighted coefficient of target recognition times p b1 =0.2, weighting coefficient p of the maximum recognition time interval b2 =0.15, weighted coefficient p of target recognition probability b3 =0.2, weighting coefficient p of identification response time b4 =0.25, weighted coefficient of meteorological condition correction factor p b5 = 0.1 and the weighting factor p of the space environment correction factor b6 =0.1; The weighted coefficient of the secondary indicator of target position calibration efficiency is: the weighted coefficient of the target position calibration update times p c1 =0.3, weighting coefficient p of the maximum target position calibration time interval c2 = 0.3 and the weighting coefficient p of the number of times the target position calibration requirement is met c3 =0.

4.

4. A remote sensing satellite earth observation effectiveness evaluation method as claimed in claim 1, characterized in that: In step 1, the weighted coefficient of the preset first-level evaluation index is: The weighted coefficient p of the target discovery efficiency index in the first-level evaluation index a =0.2; weighted coefficient p of target recognition efficiency index in the first-level evaluation index b =0.3; weighted coefficient p of target position calibration efficiency index in the first-level evaluation index c =0.

5.

5. A remote sensing satellite earth observation effectiveness evaluation method as claimed in claim 1, characterized in that: In step 2, the original data of the secondary evaluation indicators corresponding to the requirements of the remote sensing satellite earth observation activity simulation task stored in the server are obtained, including: Target discovery times, maximum discovery time interval, target discovery probability, discovery response time, target identification times, maximum identification time interval, target identification probability, identification response time, target position calibration update times, target position calibration maximum time interval and / or target position calibration times that meet requirements.

6. A remote sensing satellite earth observation effectiveness evaluation method as claimed in claim 5, characterized in that: The original data is recorded as follows: The number of target discoveries is recorded as the average number of discoveries per day; The maximum discovery time interval is recorded as the maximum time interval between two consecutive discovery of the target. The default value is 10000. The target detection probability is recorded as: the number of target detections ÷ the number of detections × 100; The discovery response time is recorded as: first discovery time - observation start time, the default value is 10000; The number of target identifications was recorded as the average number of sightings per day; The maximum recognition time interval is recorded as the maximum time interval between two consecutive target detections. The default value is 10000. The target recognition probability is recorded as: the number of target recognitions ÷ the number of detections × 100; The recognition response time is recorded as: first recognition time - first discovery time, the default value is 10000; The target position calibration update times are recorded as: the average number of target discoveries per day + the number of target identifications; The maximum time interval for target position calibration is the maximum value of the maximum discovery time interval and the maximum recognition time interval; The number of target position calibrations that meet the requirements is recorded as the average number of times per day that the requirements are met.

7. A remote sensing satellite earth observation effectiveness evaluation method as claimed in claim 1, characterized in that: In step 5, the secondary evaluation index aggregation model includes: Among them, A is the score of the first-level evaluation index to which the target discovery efficiency index belongs; a i is the score of the secondary evaluation index to which the target discovery efficiency index belongs; p ai is the weighting coefficient of the secondary indicator of the target discovery efficiency index; B is the score of the primary evaluation indicator of the target recognition efficiency index; b j is the score of the secondary evaluation index to which the target recognition effectiveness index belongs; p bj is the weighting coefficient of the secondary indicator of the target recognition efficiency index; C is the score of the primary evaluation indicator of the target position calibration efficiency index; c k is the score of the secondary evaluation index of the target position calibration efficiency index; p ck It is the weighting coefficient of the secondary indicator of the target position calibration efficiency index.

8. A remote sensing satellite earth observation effectiveness evaluation method as claimed in claim 1, characterized in that: In step 6, the aggregation model of the first-level evaluation indicators is: P=A×p a +B×p b +C×p c ; Among them, P is the evaluation result of remote sensing satellite earth observation efficiency; A is the score of the first-level evaluation index of the target discovery efficiency index; p a is the weighting coefficient of the first-level evaluation index to which the target discovery efficiency index belongs; B is the score of the first-level evaluation index to which the target recognition efficiency index belongs; p b is the weighting coefficient of the first-level evaluation index of the target recognition efficiency index; C is the score of the first-level evaluation index of the target position calibration efficiency index; p c It is the weighting coefficient of the first-level evaluation index to which the target position calibration efficiency index belongs.

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