Test method, device and equipment of satellite task scheduling algorithm, medium and product

Abstract

The embodiment of the invention provides a satellite task scheduling algorithm testing method and device, equipment, a medium and a product. The method comprises the following steps: obtaining a plurality of indexes corresponding to a target satellite task, carrying out subjective weight calculation on the plurality of indexes based on a preset subjective weight algorithm to obtain the subjective weight of each index, and carrying out objective weight calculation on the plurality of indexes based on a preset objective weight algorithm to obtain the objective weight of each index, and correcting the subjective weight of any index according to the objective weight of each index to obtain a target weight after index correction. A plurality of to-be-tested satellite task scheduling algorithms are obtained, each satellite task scheduling algorithm comprises a plurality of indexes, the plurality of satellite task scheduling algorithms are tested based on the corrected target weight of each index, and thus a test result corresponding to each satellite task scheduling algorithm is obtained. According to the method, the accuracy of the test result of the satellite task scheduling algorithm is improved.

Description

Technical Field

[0001] This application relates to the field of satellite technology, and in particular to a testing method, apparatus, equipment, medium, and product for a satellite mission scheduling algorithm. Background Technology

[0002] In satellite constellation systems, the constellation system needs to operate under dynamic orbital environments, limited resource constraints, and complex task priorities. The performance of its task scheduling algorithm will directly affect the completion of tasks.

[0003] In related technologies, when testing multiple satellite task scheduling algorithms, a single metric is usually used for subjective evaluation, and the test results are obtained based on the subjective evaluation.

[0004] However, the test results obtained by the above method are not accurate enough. Summary of the Invention

[0005] This application provides a testing method, apparatus, equipment, medium, and product for satellite mission scheduling algorithms, in order to improve the accuracy of the results.

[0006] In a first aspect, embodiments of this application provide a method for testing a satellite mission scheduling algorithm, comprising:

[0007] Acquire multiple indicators corresponding to the target satellite mission;

[0008] Subjective weights are calculated for multiple indicators based on a preset subjective weighting algorithm to obtain the subjective weights of each indicator.

[0009] The objective weights of multiple indicators are calculated based on a preset objective weighting algorithm to obtain the objective weights of each indicator.

[0010] The subjective weight of any indicator is adjusted based on the objective weight of each indicator to obtain the target weight of the indicator after adjustment.

[0011] Obtain multiple satellite mission scheduling algorithms to be tested, each of which includes multiple metrics;

[0012] Based on the target weights after the correction of each indicator, multiple satellite mission scheduling algorithms were tested, and the test results for each satellite mission scheduling algorithm were obtained.

[0013] In one possible implementation, subjective weights are calculated for multiple indicators based on a preset subjective weighting algorithm to obtain the subjective weights of each indicator, including:

[0014] Obtain the judgment matrix preset by the target satellite mission, which includes the importance data between any two indicators;

[0015] Based on the importance data between any two indicators included in the judgment matrix and the total number of multiple indicators, determine the first weight of each indicator;

[0016] Acquire the trend data of each indicator, as well as the preset maximum and minimum trend data of each indicator;

[0017] Based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator, the first weight of each indicator is corrected to obtain the subjective weight of each indicator.

[0018] In one possible implementation, the first weight of each indicator is determined based on the importance data between any two indicators included in the judgment matrix and the total number of indicators, including:

[0019] The fuzziness of each indicator is determined based on the importance data between any two indicators included in the judgment matrix.

[0020] Based on the ambiguity of each indicator, the winning probability of each indicator is determined according to a preset winning probability algorithm;

[0021] Based on the winning probability of each indicator, a probability matrix is ​​constructed;

[0022] Based on the probability matrix and the total number of indicators, determine the first weight of each indicator.

[0023] In one possible implementation, the first weight of each indicator is corrected based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator, to obtain the subjective weight of each indicator, including:

[0024] The impact value of each indicator is determined based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator.

[0025] Based on the influence value of each indicator, the first weight of each indicator is adjusted to obtain the adjusted weight of each indicator.

[0026] The weights of each indicator after correction are normalized, and the weights obtained after normalization are determined as subjective weights.

[0027] In one possible implementation, objective weights are calculated for multiple indicators based on a preset objective weighting algorithm to obtain the objective weights of each indicator, including:

[0028] Multiple indicators are normalized to obtain normalized indicators.

[0029] Based on the normalized multiple indicators, determine the correlation coefficients between each pair of indicators;

[0030] Based on the correlation coefficients between pairs of indicators, determine the conflict level data for each indicator;

[0031] Based on the conflict level data of each indicator, determine the information content data of each indicator;

[0032] Based on the amount of information in each indicator, determine the objective weight of each indicator.

[0033] In one possible implementation, the objective weight of each indicator is determined based on the information content data of each indicator, including:

[0034] The information content data of each indicator is summed to determine the total information content data;

[0035] For any indicator, the information content data of the indicator is divided by the total information content data, and the result of the division is determined as the objective weight of the indicator.

[0036] In one possible implementation, the subjective weight of any indicator is corrected based on the objective weight of each indicator to obtain the corrected target weight of the indicator, including:

[0037] The objective weights of each indicator are averaged to obtain the averaged objective weights.

[0038] Obtain the preset correction intensity parameters;

[0039] For any indicator, the subjective weight of the indicator is corrected based on the preset correction strength parameter, the objective weight of the indicator, and the objective weight after mean processing, so as to obtain the target weight of the indicator after correction.

[0040] In one possible implementation, for any indicator, based on a preset correction strength parameter, the objective weight of the indicator, and the objective weight after mean processing, the subjective weight of the indicator is corrected to obtain the corrected target weight of the indicator, including:

[0041] The weight ratio is determined based on the objective weight of the indicator and the objective weight after mean processing.

[0042] The result of multiplying the weight ratio by the preset correction intensity parameter is added to the preset coefficient to obtain the summed data;

[0043] The summed data is multiplied by the subjective weight of the indicator, and the result of the multiplication is determined as the target weight of the indicator after correction.

[0044] In one possible implementation, based on the target weights adjusted for each index, multiple satellite mission scheduling algorithms are tested to obtain test results for each algorithm, including:

[0045] Construct a decision matrix based on the corresponding index values ​​of each index in each satellite mission scheduling algorithm;

[0046] The decision matrix is ​​normalized to obtain the normalized decision matrix;

[0047] Based on the target weights of each indicator after correction, the normalized decision matrix is ​​weighted to obtain the weighted decision matrix.

[0048] Based on the weighted decision matrix, determine the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm;

[0049] Based on the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm, the proximity coefficient of each satellite mission scheduling algorithm is determined.

[0050] The proximity coefficients of each satellite mission scheduling algorithm are sorted in descending order to obtain the sorting results, which are the test results corresponding to each satellite mission scheduling algorithm.

[0051] In one possible implementation, the proximity coefficient of each satellite mission scheduling algorithm is determined based on the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm, including:

[0052] Based on the weighted decision matrix and the positive ideal solution corresponding to each satellite mission scheduling algorithm, determine the first Euclidean distance from each satellite mission scheduling algorithm to the corresponding positive ideal solution;

[0053] Based on the weighted decision matrix and the negative ideal solution corresponding to each satellite mission scheduling algorithm, determine the second Euclidean distance from each satellite mission scheduling algorithm to the corresponding negative ideal solution;

[0054] Based on the first Euclidean distance and the corresponding second Euclidean distance for each satellite mission scheduling algorithm, the proximity coefficient of each satellite mission scheduling algorithm is determined.

[0055] Secondly, embodiments of this application provide a testing apparatus for a satellite mission scheduling algorithm, comprising:

[0056] The acquisition module is used to acquire multiple indicators corresponding to the target satellite mission;

[0057] The processing module is used to calculate the subjective weights of multiple indicators based on a preset subjective weighting algorithm, and obtain the subjective weights of each indicator.

[0058] The processing module is also used to calculate the objective weights of multiple indicators based on a preset objective weighting algorithm, so as to obtain the objective weights of each indicator.

[0059] The correction module is used to correct the subjective weight of any indicator based on the objective weight of each indicator, so as to obtain the target weight of the indicator after correction.

[0060] The acquisition module is also used to acquire multiple satellite mission scheduling algorithms to be tested, each of which includes multiple metrics;

[0061] The testing module is used to test multiple satellite mission scheduling algorithms based on the target weights after the adjustment of each indicator, and to obtain the test results corresponding to each satellite mission scheduling algorithm.

[0062] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0063] The memory stores the instructions that the computer executes;

[0064] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0065] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0066] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0067] The satellite mission scheduling algorithm testing method, apparatus, equipment, medium, and product provided in this application construct a multi-dimensional evaluation system by introducing multiple evaluation indicators of the target satellite mission, rather than being limited to a single indicator. It integrates subjective and objective weighting algorithms to generate subjective and objective weights respectively, and uses the objective weights to correct the subjective weights, thereby obtaining a target weight that combines expert experience and data objectivity. Finally, multiple scheduling algorithms are tested based on this target weight. This method not only compensates for the one-sidedness of single-indicator evaluation but also reduces the human bias of purely subjective evaluation, thus significantly improving the accuracy of satellite mission scheduling algorithm test results. Attached Figure Description

[0068] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0069] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application;

[0070] Figure 2 A flowchart illustrating a testing method for a satellite mission scheduling algorithm provided in an embodiment of this application;

[0071] Figure 3 A flowchart illustrating a method for obtaining the subjective weights of various indicators, provided in an embodiment of this application;

[0072] Figure 4 A flowchart illustrating a method for obtaining the objective weights of various indicators, provided in an embodiment of this application;

[0073] Figure 5 A flowchart illustrating a method for correcting the subjective weight of an indicator, provided in an embodiment of this application;

[0074] Figure 6 A flowchart illustrating a method for obtaining test results corresponding to various satellite mission scheduling algorithms, provided in an embodiment of this application;

[0075] Figure 7 A schematic diagram of the structure of a test device for a satellite mission scheduling algorithm provided in an embodiment of this application;

[0076] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0077] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0078] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0079] With the rapid development of aerospace technology, satellite applications are becoming increasingly diversified, ranging from communication relay and meteorological observation to deep space exploration. The number and complexity of tasks that satellite systems need to undertake are constantly rising. As the core support for the efficient operation of satellite systems, the performance of satellite mission scheduling algorithms directly determines the utilization rate of satellite resources, the mission completion rate, and the response efficiency to emergency missions. Therefore, accurate testing of satellite mission scheduling algorithms is a crucial step in ensuring the stable operation of satellite systems.

[0080] In related technologies, the testing of satellite mission scheduling algorithms often employs relatively simple evaluation methods. Typically, testers use a single-dimensional indicator as the evaluation criterion, and subjectively assess the performance of that indicator across different satellite mission scheduling algorithms to obtain the algorithm test results.

[0081] However, the subjective testing method based on a single indicator has the technical problem of poor accuracy in test results.

[0082] Therefore, addressing the aforementioned issues in related technologies, this application improves the accuracy of testing multiple satellite mission scheduling algorithms by constructing a multi-dimensional indicator system and a weight correction mechanism that integrates subjective and objective factors. Specifically, by overcoming the limitations of single indicators, multiple evaluation indicators corresponding to the target satellite mission are obtained, forming a multi-dimensional evaluation basis covering mission execution. Pre-set subjective and objective weight algorithms are used to calculate the subjective and objective weights of each indicator, preserving expert judgments on the importance of indicators while providing objective quantitative basis based on data characteristics. The subjective weights are then corrected using objective weights to obtain target weights that balance expert experience and data objectivity, reducing human bias in subjective evaluations. Finally, multiple scheduling algorithms are tested based on the corrected target weights, ultimately outputting accurate test results that reflect the performance of the satellite mission scheduling algorithms.

[0083] To facilitate understanding of the method in this application, an exemplary application scenario is provided below. Please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application. In this application scenario, a satellite 01 that performs a space mission and a terminal 02 that tests and manages the satellite mission scheduling algorithm are deployed. The satellite 01 and the terminal 02 can establish a stable connection through a satellite communication network or a dedicated aerospace communication link.

[0084] Terminal 02 can acquire various operational data of Satellite 01 in real time during the execution of the target satellite mission. When it is necessary to perform performance tests on multiple satellite mission scheduling algorithms adapted to the target satellite mission, Terminal 02 first collects multiple preset indicators corresponding to the target satellite mission from Satellite 01 and the mission planning system. It then calculates the subjective and objective weights of each indicator using preset subjective and objective weight algorithms, and uses the objective weights to correct the subjective weights to obtain more accurate target weights. Terminal 02 then acquires the multiple satellite mission scheduling algorithms to be tested and their specific performance data under the aforementioned indicators. Finally, it tests each satellite mission scheduling algorithm based on the target weights to obtain output test results, thus providing a decision-making basis for Satellite 01 to select a suitable mission scheduling algorithm.

[0085] It is understood that the above examples are for illustrative purposes only and do not limit the scope of this application. The specific details can be determined based on the actual application situation.

[0086] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0087] Please see Figure 2 , Figure 2 This is a flowchart illustrating a testing method for a satellite mission scheduling algorithm provided in this application embodiment. The execution entity of this method can be a testing device for the satellite mission scheduling algorithm. This testing device can be implemented using a computer program, a medium storing the relevant computer program (such as a USB flash drive and / or optical disc), or a physical device integrating or installing the relevant computer program, such as a chip or electronic device. The electronic device can be a server, server cluster, smart terminal, etc. The method may include:

[0088] S201. Obtain multiple indicators corresponding to the target satellite mission.

[0089] Based on the satellite constellation mission planning scenario, the target satellite mission can be either an Earth observation mission or a data transmission mission. In this embodiment, a systematic input indicator system has been established in advance for both the Earth observation mission and the data transmission mission.

[0090] Optionally, Earth observation missions may include the following metrics:

[0091] The task completion benefit metric measures the overall benefit of satellite task scheduling algorithms in completing various observation tasks. The optimization objective is to maximize the sum of the priorities of the scheduled tasks. It can be obtained through the following formula (1):

[0092]

[0093] Where p represents the p-th task; T represents the total task; Indicates task The preset revenue value; Indicates task Whether it is executed; This indicates whether to round up to the nearest integer to indicate whether to execute the command.

[0094] Total task time is a metric used to represent the total time cost from planning to completion, including time spent in the planning phase and execution phase. It can be obtained through the following formula (2):

[0095]

[0096] in, Indicates task The time required for calculations during the planning phase; Indicates task Execution time, The decision variable that indicates whether a task is performed.

[0097] Energy consumption rate is a metric used to reflect the energy resources consumed by a mission. It can be obtained through the following formula (3):

[0098]

[0099] in, ; Indicates satellite The execution of the first Each task; the numerator represents the energy required for the satellite to perform its task, and the denominator represents the satellite's maximum energy capacity; Indicates the total number of tasks; This indicates the total number of satellites.

[0100] Load balancing metrics are used to measure the degree of balance in task distribution across different platforms. It can be obtained through the following formula (4):

[0101]

[0102] Among them, by This represents the average load across all satellites. Represents the variance of the satellite payload distribution; This represents the payload of satellite q.

[0103] The performance degradation rate metric describes the degree of performance degradation of a task under perturbation conditions. It can be obtained through the following formula (5):

[0104]

[0105] in, This represents the value of a certain performance index under disturbance conditions. .

[0106] Optionally, the data backhaul task may include the following metrics:

[0107] The average return latency metric is used to represent the average time required for task data to be transmitted from the start to the end. The average return latency metric A can be obtained by the following formula (6):

[0108]

[0109] in, Indicates task The end time; Indicates task The beginning moment.

[0110] Throughput metrics are used to represent the amount of data transmitted per unit of time. It can be obtained through the following formula (7):

[0111]

[0112] in, Indicates task The amount of data transmitted.

[0113] Packet loss rate is a metric used to measure link reliability. It can be obtained through the following formula (8):

[0114]

[0115] in, This indicates the total number of data packets sent by the satellite. This indicates the number of data packets lost during satellite transmission.

[0116] Communication bandwidth utilization is a metric used to reflect the efficiency of inter-satellite link resource utilization. It can be obtained through the following formula (9):

[0117]

[0118] in, Indicates the number of links. Indicates link Bandwidth occupied during the scheduling period; This indicates the total bandwidth capacity of the link.

[0119] The performance degradation rate metric describes the performance decline under disturbances such as link interruption and node failure. It can be obtained through the following formula (10):

[0120]

[0121] in, This indicates the percentage of degradation of the performance metric in detour scenarios.

[0122] It is understood that the aforementioned target satellite missions are for illustrative purposes only and do not limit this application.

[0123] S202. Calculate the subjective weights of multiple indicators based on a preset subjective weight algorithm to obtain the subjective weights of each indicator.

[0124] Subjective weighting algorithms are a class of algorithms used in multi-indicator evaluation or decision-making scenarios to quantify the importance of each evaluation indicator based on the evaluator's professional knowledge, practical experience, domain knowledge, and other subjective information, thereby determining the indicator weights.

[0125] In this embodiment, the subjective weighting algorithm can be a fuzzy analytic hierarchy process (FAHP) based on scene-driven triangular fuzzy numbers (TFN).

[0126] S203. Based on the preset objective weight algorithm, calculate the objective weight of multiple indicators to obtain the objective weight of each indicator.

[0127] Objective weighting algorithms are a general term for a class of algorithms in the field of multi-indicator evaluation and decision-making. They rely entirely on the objective data characteristics of the evaluation indicators, rather than human subjective judgment, and use mathematical statistics or data mining methods to quantify the importance of the indicators, thereby determining the weight of each indicator.

[0128] In this embodiment, the objective weighting algorithm can be a weighting determination method based on the correlation between indicators (CriteriaImportance Through Inter-criteria Correlation, CRITIC).

[0129] S204. Based on the objective weights of each indicator, the subjective weight of any indicator is corrected to obtain the corrected target weight of the indicator.

[0130] Using the subjective weight of the indicators as a benchmark, the objective weights are used as weighting factors for weight fusion to correct the subjective weights, thereby obtaining the corrected target weights of each indicator.

[0131] S205. Obtain multiple satellite mission scheduling algorithms to be tested. Each satellite mission scheduling algorithm includes multiple indicators.

[0132] Obtain multiple pre-set satellite mission scheduling algorithms to be tested. Each satellite mission scheduling algorithm will include multiple indicators and their specific values ​​for different missions.

[0133] S206. Based on the target weights after the correction of each indicator, test multiple satellite mission scheduling algorithms and obtain the test results corresponding to each satellite mission scheduling algorithm.

[0134] The specific implementation details will be described in detail in the following embodiments, please refer to the following embodiments.

[0135] In the above embodiments of this application, multiple indicators corresponding to the target satellite mission are obtained. Subjective weights are calculated for each indicator based on a preset subjective weighting algorithm, and objective weights are calculated for each indicator based on a preset objective weighting algorithm. Then, the subjective weight of any indicator is corrected according to its objective weight to obtain the corrected target weight. Multiple satellite mission scheduling algorithms to be tested are obtained, each including multiple indicators. Based on the corrected target weights for each indicator, the multiple satellite mission scheduling algorithms are tested to obtain the test results for each algorithm. The method of this application constructs a multi-dimensional evaluation system by introducing multiple evaluation indicators of the target satellite mission, rather than being limited to a single indicator. It integrates subjective and objective weighting algorithms to generate subjective and objective weights respectively, and corrects the subjective weights with objective weights to obtain target weights that combine expert experience and data objectivity. Finally, the tests of multiple scheduling algorithms are completed based on these target weights. This not only compensates for the one-sidedness of single-indicator evaluation but also reduces the human bias of purely subjective evaluation, thereby significantly improving the accuracy of satellite mission scheduling algorithm test results.

[0136] Furthermore, based on the above embodiments, the following embodiments illustrate the process of calculating the subjective weights of multiple indicators based on a preset subjective weight algorithm to obtain the subjective weights of each indicator.

[0137] Please see Figure 3 , Figure 3 A flowchart illustrating a method for obtaining the subjective weights of various indicators, provided in this application embodiment, includes the following steps:

[0138] S301. Obtain the target satellite mission's preset judgment matrix, which includes the importance data between any two indicators.

[0139] Based on the indicators corresponding to the target satellite mission, determine the number n of indicators included in the target satellite mission. By comparing each indicator in pairs, construct an n×n fuzzy judgment matrix, which can be simply referred to as the judgment matrix. The judgment matrix can be pre-set based on expert experience.

[0140] Assume the judgment matrix M is as shown in the following formula (11):

[0141]

[0142] in, Each element in Indicators With indicators The importance of the data is compared to the importance of the data. The elements These are the minimum possible value for the judgment, the middle value (i.e., the most likely value) for the judgment, and the maximum possible value for the judgment, respectively.

[0143] S302. Determine the first weight of each indicator based on the importance data between any two indicators included in the judgment matrix and the total number of multiple indicators.

[0144] The fuzziness of each indicator is determined based on the importance data between any two indicators included in the judgment matrix.

[0145] Alternatively, the ambiguity of each index can be determined using the following formula (12):

[0146]

[0147] in, This represents the ambiguity of the i-th indicator.

[0148] The above process essentially involves summing the fuzzy weights of each indicator in the judgment matrix and normalizing them with the fuzzy values ​​of the entire matrix to obtain the importance of each indicator relative to the whole. The calculations involve addition and division of fuzzy numbers and must follow the operational rules of triangular fuzzy numbers.

[0149] Based on the ambiguity of each indicator, the winning probability of each indicator is determined using a preset winning probability algorithm.

[0150] Suppose there are 5 indicators, and their corresponding fuzzy numbers are respectively Then the winning probability vector of the i-th indicator As shown in the following formula (13):

[0151]

[0152] in, This indicates the probability of winning.

[0153] Specifically, The calculation method can be shown in the following formula (14):

[0154]

[0155] in, Represents the fuzzy coefficient. The larger the size, the more conservative; the smaller the size, the more radical. This indicates a conservative estimate, which takes into account the left-side overlap of fuzzy numbers; This indicates a radical estimate that takes into account the right-hand overlap of fuzzy numbers.

[0156] Based on the winning probability of each indicator, a probability matrix is ​​constructed.

[0157] Optionally, based on the winning probability vectors corresponding to each indicator, assuming there are 5 indicators, the constructed probability matrix N is as shown in the following formula (15):

[0158]

[0159] Based on the probability matrix and the total number of indicators, determine the first weight of each indicator.

[0160] Optionally, the first weight of each indicator can be determined by the following formula (16):

[0161]

[0162] in, represents the first weight of the i-th indicator; n represents the total number of indicators.

[0163] If an indicator outperforms all other indicators in most pairwise comparisons, then it has a higher weight.

[0164] Based on the first weight of each indicator, the first weight is normalized to satisfy... .

[0165] S303. Obtain the trend data of each indicator, as well as the preset maximum and minimum trend data of each indicator.

[0166] Obtain the indicators corresponding to the target satellite mission, and derive the feature vector S corresponding to the target satellite mission based on each indicator. Then, normalize it to obtain the normalized S.

[0167] Alternatively, the trend data of each indicator can be determined using the following formula (17):

[0168]

[0169] in, This represents the trend data of the i-th indicator under the current task k; This represents the data of the j-th index in the feature vector S; This represents the sensitivity of the i-th metric under the current task k. .

[0170] Obtain the preset maximum value data of the changing trends of each indicator. and the minimum value data of the trend .

[0171] S304. Based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator, the first weight of each indicator is corrected to obtain the subjective weight of each indicator.

[0172] The influence value of each indicator is determined based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator.

[0173] Alternatively, the influence value of each indicator can be determined using the following formula (18):

[0174]

[0175] in, This represents the influence value of the i-th indicator; This indicates the preset maximum impact value; This indicates the preset minimum impact value.

[0176] By setting the maximum value data of the changing trend and the minimum value data of the trend This allows the impact value to change dynamically within an acceptable range.

[0177] Based on the influence value of each indicator, the first weight of each indicator is adjusted to obtain the adjusted weight of each indicator.

[0178] Optionally, the adjusted weights of each indicator can be determined using the following formula (19):

[0179]

[0180] in, This represents the adjusted weight of the i-th indicator.

[0181] The weights of each indicator after correction are normalized, and the weights obtained after normalization are determined as subjective weights.

[0182] To ensure that the sum of the dynamic weights is 1, the weights of each indicator after correction need to be normalized again. The weights of each indicator after normalization are the subjective weights.

[0183] In the above embodiments of this application, a judgment matrix containing importance data among indicators is obtained from the target satellite mission. The first weight of each indicator is determined based on the total number of indicators. Then, the first weight is corrected according to the indicator trend data and preset maximum and minimum trend data, ultimately yielding the subjective weight. This method, by incorporating expert experience into the judgment matrix to ensure scenario adaptability of weight assignment, and by correcting for indicator trends and extreme values, effectively reduces potential cognitive biases arising from purely subjective judgment, thereby improving the accuracy and rationality of the subjective weight.

[0184] Furthermore, based on the above embodiments, the following embodiments illustrate the process of calculating the objective weights of multiple indicators based on a preset objective weighting algorithm to obtain the objective weights of each indicator.

[0185] Please see Figure 4 , Figure 4 A flowchart illustrating a method for obtaining the objective weights of various indicators, provided in this application embodiment, includes the following steps:

[0186] S401. Normalize multiple indicators to obtain normalized indicators.

[0187] Multiple indicators are normalized to eliminate the influence of dimensions and bring them to the same order of magnitude.

[0188] S402. Based on the normalized multiple indicators, determine the correlation coefficients between each pair of indicators.

[0189] Alternatively, the correlation coefficient between any two indicators can be determined using the following formula (20):

[0190]

[0191] in, This represents the covariance between the i-th and j-th indicators; Let represent the autovariance of the i-th index; Let represent the autovariance of the j-th index.

[0192] The correlation coefficient measures the degree of redundancy between two indicators. The closer the correlation coefficient is to ±1, the more similar the two indicators are or the linear relationship they have, and the higher the degree of information overlap.

[0193] S403. Determine the conflict level data for each indicator based on the correlation coefficient between each pair of indicators.

[0194] Optionally, the conflict level data for each indicator can be determined using the following formula (21):

[0195]

[0196] in, This represents the conflict level data for the i-th indicator.

[0197] Conflict level data measures the degree of independence of a particular indicator from other indicators. If an indicator is highly uncorrelated with other indicators, that is... Small, then The larger the value, the more independent information the indicator contains.

[0198] S404. Based on the conflict level data of each indicator, determine the information content data of each indicator.

[0199] By combining the conflict level data of the indicator with its dispersion, the information content data of the indicator is determined.

[0200] Optionally, the information content data of each indicator can be determined by the following formula (22):

[0201]

[0202] in, This represents the information content data of the i-th indicator; The standard deviation of the i-th indicator represents the dispersion of its values, reflecting the indicator's ability to distinguish different evaluation objects.

[0203] S405. Determine the objective weight of each indicator based on the information content data of each indicator.

[0204] The information content data of each indicator is summed to determine the total information content data.

[0205] For any indicator, the information content data of the indicator is divided by the total information content data, and the result of the division is determined as the objective weight of the indicator.

[0206] Optionally, the objective weights of each indicator can be determined using the following formula (23):

[0207]

[0208] in, This represents the total amount of information. This represents the objective weight of the i-th indicator.

[0209] The information content is normalized using the above formula to obtain the final weight of each indicator, and in this process, the sum of all weights is 1.

[0210] In the above embodiments of this application, objective weights are obtained by normalizing multiple indicators, calculating the correlation coefficients between pairs of indicators, determining conflict degree data and information content data, thereby achieving objectivity and rationality in indicator weight allocation. Normalization eliminates the interference of differences in the dimensions of different indicators on weight calculation; the correlation coefficients between pairs of indicators accurately reflect the degree of association between indicators; the conflict degree data determined based on the correlation coefficients can quantify the degree of redundancy or contradiction between indicators; the information content data further transformed from the conflict degree data can reflect the unique contribution of each indicator to the overall evaluation; and finally, objective weights are allocated based on the information content data, reducing the bias of subjective weight allocation and ensuring that the weights match the actual value of the indicators, thereby improving the accuracy of subsequent test results.

[0211] Furthermore, based on any of the above embodiments, the following examples illustrate the process of correcting the subjective weight of any indicator according to the objective weight of each indicator to obtain the corrected target weight of the indicator.

[0212] Please see Figure 5 , Figure 5 A flowchart illustrating a method for correcting the subjective weight of an indicator, provided in an embodiment of this application, is shown below. The method may include the following steps:

[0213] S501. The objective weights of each indicator are averaged to obtain the averaged objective weights.

[0214] Assuming there are 5 indicators corresponding to the target satellite mission, then the subjective weights for each indicator are... As shown in the following formulas (24) to (25):

[0215]

[0216]

[0217] Objective weights corresponding to each indicator As shown in the following formulas (26) to (27):

[0218]

[0219]

[0220] Optionally, the mean can be calculated using the following formula (28) to obtain the mean-processed objective weights. :

[0221]

[0222] The average value of the objective weights is 1 / n due to weight normalization.

[0223] S502. Obtain the preset correction intensity parameters.

[0224] Obtain the preset correction strength parameters , This is used to control the influence of objective weights on subjective weights. If the influence of objective factors is not needed, it can be set to 0.

[0225] S503. For any indicator, based on the preset correction strength parameter, the objective weight of the indicator, and the objective weight after mean processing, the subjective weight of the indicator is corrected to obtain the target weight of the indicator after correction.

[0226] One possible approach is to determine a weight ratio based on the objective weights of the indicator and the objective weights after mean processing. The result of multiplying this weight ratio by a preset correction intensity parameter is then added to a preset coefficient to obtain the summed data. This summed data is then multiplied by the subjective weights of the indicator, and the result is determined as the target weight of the indicator after correction.

[0227] Optionally, the target weight after index correction can be determined by the following formula (29):

[0228]

[0229] in, This represents the target weight of the i-th indicator; This represents the subjective weight of the i-th indicator; This represents the objective weight of the i-th indicator; Indicates the weight ratio; 1 indicates a preset coefficient; This represents the data after addition.

[0230] After obtaining the target weights corresponding to each indicator, they are normalized to obtain the normalized target weights corresponding to each indicator.

[0231] In the above embodiments of this application, the subjective weighting method can fully reflect the subjective judgment of experts, but relying solely on subjective weights can easily overlook the objective characteristics of the data itself. Therefore, in this embodiment, subjective weights are used as prior weights, and objective weights obtained by the objective weighting method are used as the basis for correction. This retains the dominant role of expert experience while using objective weights to dynamically correct prior weights, thereby taking into account both subjective cognition and objective facts, and thus improving the scientificity and applicability of weight allocation.

[0232] Furthermore, based on any of the above embodiments, the following embodiment describes the process of testing multiple satellite mission scheduling algorithms based on the target weights after correction of each index, and obtaining the test results corresponding to each satellite mission scheduling algorithm.

[0233] Please see Figure 6 , Figure 6 This application provides a flowchart illustrating a method for obtaining test results corresponding to various satellite mission scheduling algorithms. The method may include the following steps:

[0234] S601. Construct a decision matrix based on the corresponding index values ​​of each index in each satellite mission scheduling algorithm.

[0235] This embodiment can be based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), as shown in Table 6. The specific contents of the table will be explained in detail in this embodiment. Please refer to this embodiment.

[0236] Table 6

[0237]

[0238] Suppose there are m satellite mission scheduling algorithms, denoted as... Each algorithm has a corresponding performance value, or index value, under n evaluation metrics. Let the index set be denoted as . Construct the decision matrix as shown in the following formula (30). :

[0239]

[0240] in, This represents the original index value of the c-th algorithm on the j-th index. .

[0241] For example, indicators This can represent the number of tasks resulting from the scheduling. Indicates the average imaging delay. This indicates the task priority score.

[0242] S602. Normalize the decision matrix to obtain the normalized decision matrix.

[0243] To ensure the comparability of indicators with different dimensions, the indicators in the original decision matrix need to be normalized. Common normalization methods include vector normalization, range normalization, and standard deviation normalization. While vector normalization can preserve the relative proportions of different algorithms under the same indicator, its normalization results are usually not limited to a fixed numerical range. This may affect the consistency of the numerical distribution and the intuitive interpretation of the results when constructing positive and negative ideal solutions and calculating Euclidean distance.

[0244] The vector normalization algorithm is shown in the following formula (31):

[0245]

[0246] in, This represents the vector-normalized index value of the c-th algorithm on the j-th index.

[0247] In this embodiment, the range normalization method is selected to determine all indicators in the [0,1] interval, so as to simplify the subsequent calculation process and improve the interpretability of the numerical values.

[0248] In this embodiment, the indicators are pre-classified into benefit-type indicators and cost-type indicators based on their attributes. Benefit-type indicators indicate that the higher the indicator value, the better, while cost-type indicators indicate that the lower the indicator value, the better. This classification facilitates the normalization process and the construction of the ideal solution, ensuring the scientific validity and comparability of the ranking results.

[0249] For benefit-type indicators, the normalization formula is shown in the following formula (32):

[0250]

[0251] This represents the range normalized value of the c-th algorithm on the j-th metric.

[0252] Formula (32) normalizes each benefit-type indicator to [0,1], where 1 is the optimal value. Since TOPSIS uniformly uses the judgment logic of "the larger the better", cost-type indicators need to be converted into benefit-type indicators for normalization.

[0253] For cost-related indicators, the normalization formula is shown in the following formula (33):

[0254]

[0255] Using the above formula (33), each cost index is normalized to [0,1], where 1 is the optimal value.

[0256] S603. Based on the target weights of each indicator after correction, the normalized decision matrix is ​​weighted to obtain the weighted decision matrix.

[0257] Optionally, the weighted decision matrix of each indicator can be determined using the following formula (34):

[0258]

[0259] in, Let represent the decision matrix for the j-th index of the c-th algorithm; This represents the target weight of the j-th indicator.

[0260] S604. Based on the weighted decision matrix, determine the positive ideal solution and negative ideal solution corresponding to each satellite mission scheduling algorithm.

[0261] The TOPSIS method is based on the principle of approximating the positive ideal solution and avoiding the negative ideal solution. The positive ideal solution (A^+) and the negative ideal solution (A^-) are defined as follows:

[0262] For benefit-type indicators, the positive ideal solution and the negative ideal solution are shown in the following formula (35):

[0263]

[0264] For cost-type indicators, the positive and negative ideal solutions are shown in the following formula (36):

[0265]

[0266] S605. Based on the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm, determine the proximity coefficient of each satellite mission scheduling algorithm.

[0267] Based on the weighted decision matrix and the positive ideal solution corresponding to each satellite mission scheduling algorithm, the first Euclidean distance from each satellite mission scheduling algorithm to the corresponding positive ideal solution is determined.

[0268] Calculate the Euclidean distance between each satellite mission scheduling algorithm and the positive and negative ideal solutions. Euclidean distance is simple and intuitive, and possesses good discriminative power when the dimensionality is low, therefore it is widely used in the TOPSIS method.

[0269] Alternatively, the first Euclidean distance can be determined using the following formula (37):

[0270]

[0271] in, It represents the first Euclidean distance from the scheduling algorithm of the c-th satellite mission to the corresponding positive ideal solution.

[0272] Based on the weighted decision matrix and the negative ideal solution corresponding to each satellite mission scheduling algorithm, the second Euclidean distance from each satellite mission scheduling algorithm to the corresponding negative ideal solution is determined.

[0273] Alternatively, the first Euclidean distance can be determined by the following formula (38):

[0274]

[0275] in, It represents the second Euclidean distance from the c-th satellite mission scheduling algorithm to the corresponding positive ideal solution.

[0276] Based on the first Euclidean distance and the corresponding second Euclidean distance for each satellite mission scheduling algorithm, the proximity coefficient of each satellite mission scheduling algorithm is determined.

[0277] Optionally, the proximity coefficient of each satellite mission scheduling algorithm can be determined using the following formula (39):

[0278]

[0279] in, This represents the proximity coefficient of the scheduling algorithm for the c-th satellite mission.

[0280] The larger the proximity coefficient, the closer the corresponding satellite mission scheduling algorithm is to the ideal solution, and the better its overall performance.

[0281] S606. Sort the proximity coefficients of each satellite mission scheduling algorithm in descending order to obtain the sorting results. The sorting results are the test results corresponding to each satellite mission scheduling algorithm.

[0282] Based on the proximity coefficients of each satellite mission scheduling algorithm, they are sorted in descending order to obtain the final sorting result.

[0283] The ranking results can be used to handle emergency task scheduling. Based on the overall performance of each algorithm in the ranking results, when a certain type of algorithm is identified as having a significant performance advantage in emergency task scenarios, the task scheduling and routing module can directly list that algorithm as the preferred option for emergency tasks, thereby ensuring the processing efficiency and execution effect of emergency tasks and reducing task delays caused by insufficient algorithm adaptability.

[0284] The ranking results can also be used to trigger dynamic algorithm adjustments. If the ranking results indicate that an algorithm exhibits significant performance degradation under high energy consumption or link congestion conditions, the algorithm switching process can be automatically triggered based on the ranking results to replace it with a more adaptable algorithm, or the parameter adjustment program can be started to optimize the algorithm's operating parameters, thereby mitigating the performance degradation of the algorithm under complex operating conditions and maintaining the overall stability of the system.

[0285] The ranking results can also be used to build an algorithm performance optimization database. Integrating the ranking results with historical algorithm execution data can form a comprehensive algorithm performance database, which can provide experience support for algorithm selection in subsequent tasks. It also supports the analysis and prediction of algorithm performance trends, enabling continuous upgrading of algorithm selection and task allocation by accumulating historical experience.

[0286] It is understood that the above applications are for illustrative purposes only and do not limit this application. The specific applications can be determined based on actual application circumstances.

[0287] In the embodiments described above, a decision matrix is ​​constructed to systematically integrate performance data of various satellite mission scheduling algorithms under different indicators, ensuring comprehensive and complete evaluation criteria. Normalization eliminates interference from differences in the dimensions of indicators, ensuring that different types of indicator data can directly participate in calculations. Combining the modified target weights with matrix weighting highlights the actual impact of each indicator in the satellite mission scheduling scenario, making the evaluation more aligned with business needs. By determining positive and negative ideal solutions and calculating the proximity coefficient, the fit between each algorithm and its performance can be accurately measured. The final ranking result intuitively presents the overall performance ranking of each algorithm, reducing the bias of single-indicator evaluation and providing an objective and reliable basis for the selection and optimization of satellite mission scheduling algorithms.

[0288] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a test device for a satellite mission scheduling algorithm provided in an embodiment of this application, as shown below. Figure 7 As shown, the test apparatus for the satellite mission scheduling algorithm provided in this embodiment includes:

[0289] The acquisition module 701 is used to acquire multiple indicators corresponding to the target satellite mission.

[0290] The processing module 702 is used to calculate the subjective weights of multiple indicators based on a preset subjective weight algorithm, so as to obtain the subjective weights of each indicator.

[0291] The processing module 702 is also used to calculate the objective weights of multiple indicators based on a preset objective weighting algorithm, so as to obtain the objective weights of each indicator.

[0292] The correction module 703 is used to correct the subjective weight of any indicator based on the objective weight of each indicator, so as to obtain the target weight of the indicator after correction.

[0293] The acquisition module 701 is also used to acquire multiple satellite mission scheduling algorithms to be tested, each of which includes multiple indicators.

[0294] Test module 704 is used to test multiple satellite mission scheduling algorithms based on the target weights after the correction of each index, and obtain the test results corresponding to each satellite mission scheduling algorithm.

[0295] In one possible implementation, processing module 702 is specifically used for:

[0296] Obtain the judgment matrix preset by the target satellite mission. The judgment matrix includes the importance data between any two indicators.

[0297] The first weight of each indicator is determined based on the importance data between any two indicators included in the judgment matrix and the total number of indicators.

[0298] Obtain trend data for each indicator, as well as preset maximum and minimum values ​​for each indicator.

[0299] Based on the trend data of each indicator and the preset maximum and minimum values ​​of each indicator, the first weight of each indicator is adjusted to obtain the subjective weight of each indicator.

[0300] In one possible implementation, processing module 702 is specifically used for:

[0301] The fuzziness of each indicator is determined based on the importance data between any two indicators included in the judgment matrix.

[0302] Based on the ambiguity of each indicator, the winning probability of each indicator is determined using a preset winning probability algorithm.

[0303] Based on the winning probability of each indicator, a probability matrix is ​​constructed.

[0304] Based on the probability matrix and the total number of indicators, determine the first weight of each indicator.

[0305] In one possible implementation, processing module 702 is specifically used for:

[0306] The influence value of each indicator is determined based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator.

[0307] Based on the influence value of each indicator, the first weight of each indicator is adjusted to obtain the adjusted weight of each indicator.

[0308] The weights of each indicator after correction are normalized, and the weights obtained after normalization are determined as subjective weights.

[0309] In one possible implementation, processing module 702 is specifically used for:

[0310] Multiple indicators are normalized to obtain normalized indicators.

[0311] Based on the normalized multiple indicators, the correlation coefficients between each pair of indicators are determined.

[0312] The degree of conflict for each indicator is determined based on the correlation coefficient between each pair of indicators.

[0313] Based on the conflict level data of each indicator, determine the information content data of each indicator.

[0314] Based on the amount of information in each indicator, determine the objective weight of each indicator.

[0315] In one possible implementation, processing module 702 is specifically used for:

[0316] The information content data of each indicator is summed to determine the total information content data.

[0317] For any indicator, the information content data of the indicator is divided by the total information content data, and the result of the division is determined as the objective weight of the indicator.

[0318] In one possible implementation, the correction module 703 is specifically used for:

[0319] The objective weights of each indicator are averaged to obtain the averaged objective weights.

[0320] Obtain the preset correction intensity parameters.

[0321] For any indicator, the subjective weight of the indicator is corrected based on the preset correction strength parameter, the objective weight of the indicator, and the objective weight after mean processing, so as to obtain the target weight of the indicator after correction.

[0322] In one possible implementation, the correction module 703 is specifically used for:

[0323] The weight ratio is determined based on the objective weight of the indicator and the objective weight after mean processing.

[0324] The result of multiplying the weight ratio by the preset correction intensity parameter is added to the preset coefficient to obtain the summed data.

[0325] The summed data is multiplied by the subjective weight of the indicator, and the result of the multiplication is determined as the target weight of the indicator after correction.

[0326] In one possible implementation, test module 704 is specifically used for:

[0327] A decision matrix is ​​constructed based on the corresponding index values ​​of each index in each satellite mission scheduling algorithm.

[0328] The decision matrix is ​​normalized to obtain the normalized decision matrix.

[0329] Based on the corrected target weights of each indicator, the normalized decision matrix is ​​weighted to obtain the weighted decision matrix.

[0330] Based on the weighted decision matrix, the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm are determined.

[0331] Based on the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm, the proximity coefficient of each satellite mission scheduling algorithm is determined.

[0332] The proximity coefficients of each satellite mission scheduling algorithm are sorted in descending order to obtain the sorting results, which are the test results corresponding to each satellite mission scheduling algorithm.

[0333] In one possible implementation, test module 704 is specifically used for:

[0334] Based on the weighted decision matrix and the positive ideal solution corresponding to each satellite mission scheduling algorithm, the first Euclidean distance from each satellite mission scheduling algorithm to the corresponding positive ideal solution is determined.

[0335] Based on the weighted decision matrix and the negative ideal solution corresponding to each satellite mission scheduling algorithm, the second Euclidean distance from each satellite mission scheduling algorithm to the corresponding negative ideal solution is determined.

[0336] Based on the first Euclidean distance and the corresponding second Euclidean distance for each satellite mission scheduling algorithm, the proximity coefficient of each satellite mission scheduling algorithm is determined.

[0337] The satellite mission scheduling algorithm testing device provided in this embodiment can execute the satellite mission scheduling algorithm testing method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0338] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 8As shown, the electronic device provided in this embodiment includes at least one processor 801 and a memory 802. Optionally, the device further includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus 804.

[0339] In a specific implementation, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to perform the above-described method.

[0340] The specific implementation process of processor 801 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0341] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0342] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0343] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0344] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0345] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0346] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0347] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0348] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0349] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0350] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0351] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0352] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0353] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A testing method for a satellite mission scheduling algorithm, characterized in that, include: Acquire multiple indicators corresponding to the target satellite mission; Subjective weights are calculated for the multiple indicators based on a preset subjective weighting algorithm to obtain the subjective weights of each indicator. The objective weights of the multiple indicators are calculated based on a preset objective weighting algorithm to obtain the objective weights of each indicator. The subjective weight of any one indicator is corrected based on the objective weight of each indicator to obtain the corrected target weight of the indicator. Obtain multiple satellite mission scheduling algorithms to be tested, each of which includes the aforementioned multiple metrics; Based on the target weights after the correction of each indicator, the multiple satellite mission scheduling algorithms are tested to obtain the test results corresponding to each satellite mission scheduling algorithm.

2. The method according to claim 1, characterized in that, The subjective weight calculation of the multiple indicators based on the preset subjective weight algorithm, to obtain the subjective weight of each indicator, includes: Obtain the preset judgment matrix of the target satellite mission, wherein the judgment matrix includes the importance data between any two indicators; Based on the importance data between any two indicators included in the judgment matrix and the total number of the multiple indicators, determine the first weight of each indicator; Acquire the trend data of each indicator, as well as the preset maximum and minimum trend data of each indicator; Based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator, the first weight of each indicator is corrected to obtain the subjective weight of each indicator.

3. The method according to claim 2, characterized in that, The step of determining the first weight of each indicator based on the importance data between any two indicators included in the judgment matrix and the total number of the multiple indicators includes: Based on the importance data between any two indicators included in the judgment matrix, determine the fuzziness of each indicator; Based on the ambiguity of each indicator, the winning probability of each indicator is determined according to a preset winning probability algorithm; Based on the winning probability of each indicator, a probability matrix is ​​constructed; Based on the probability matrix and the total number of the multiple indicators, the first weight of each indicator is determined.

4. The method according to claim 2, characterized in that, The subjective weight of each indicator is obtained by correcting its first weight based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator, including: Based on the trend data of each indicator and the preset maximum and minimum trend data of each indicator, the influence value of each indicator is determined. Based on the influence value of each indicator, the first weight of each indicator is corrected to obtain the corrected weight of each indicator. The corrected weights of each indicator are normalized, and the weights obtained after normalization are determined as subjective weights.

5. The method according to claim 1, characterized in that, The objective weight calculation for the multiple indicators based on the preset objective weight algorithm yields the objective weight of each indicator, including: The multiple indicators are normalized to obtain normalized indicators. Based on the normalized indicators, determine the correlation coefficients between each pair of indicators; Based on the correlation coefficients between the pairs of indicators, the conflict degree data of each indicator is determined; Based on the conflict level data of each indicator, determine the information content data of each indicator; Based on the information content of each indicator, the objective weight of each indicator is determined.

6. The method according to claim 5, characterized in that, The step of determining the objective weight of each indicator based on the information content data of each indicator includes: The information content data of each indicator is summed to determine the total information content data; For any indicator, the information content data of the indicator is divided by the total information content data, and the division result is determined as the objective weight of the indicator.

7. The method according to claim 1, characterized in that, The step of correcting the subjective weight of any indicator based on the objective weight of each indicator to obtain the corrected target weight of the indicator includes: The objective weights of each indicator are averaged to obtain the averaged objective weights. Obtain the preset correction intensity parameters; For any indicator, the subjective weight of the indicator is corrected based on the preset correction intensity parameter, the objective weight of the indicator, and the objective weight after mean processing, so as to obtain the corrected target weight of the indicator.

8. The method according to claim 7, characterized in that, For any indicator, based on the preset correction intensity parameter, the objective weight of the indicator, and the objective weight after mean processing, the subjective weight of the indicator is corrected to obtain the corrected target weight of the indicator, including: The weight ratio is determined based on the objective weight of the indicator and the objective weight after mean processing. The result of multiplying the weight ratio by the preset correction intensity parameter is added to the preset coefficient to obtain the summed data; The summed data is multiplied by the subjective weight of the indicator, and the result of the multiplication is determined as the target weight of the indicator after correction.

9. The method according to claim 1, characterized in that, The multiple satellite mission scheduling algorithms are tested based on the target weights after adjustments to each indicator, and the test results for each satellite mission scheduling algorithm are obtained, including: Construct a decision matrix based on the corresponding index values ​​of each index in each satellite mission scheduling algorithm; The decision matrix is ​​normalized to obtain the normalized decision matrix; Based on the target weights corrected for each indicator, the normalized decision matrix is ​​weighted to obtain the weighted decision matrix. Based on the weighted decision matrix, determine the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm; Based on the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm, the proximity coefficient of each satellite mission scheduling algorithm is determined. The proximity coefficients of each satellite mission scheduling algorithm are sorted in descending order to obtain the sorting result, which is the test result corresponding to each satellite mission scheduling algorithm.

10. The method according to claim 9, characterized in that, The step of determining the proximity coefficient of each satellite mission scheduling algorithm based on the positive and negative ideal solutions corresponding to each satellite mission scheduling algorithm includes: Based on the weighted decision matrix and the positive ideal solution corresponding to each satellite mission scheduling algorithm, determine the first Euclidean distance from each satellite mission scheduling algorithm to the corresponding positive ideal solution. Based on the weighted decision matrix and the negative ideal solution corresponding to each satellite mission scheduling algorithm, determine the second Euclidean distance from each satellite mission scheduling algorithm to the corresponding negative ideal solution. Based on the first Euclidean distance and the corresponding second Euclidean distance for each satellite mission scheduling algorithm, the proximity coefficient of each satellite mission scheduling algorithm is determined.

11. A test apparatus for a satellite mission scheduling algorithm, characterized in that, include: The acquisition module is used to acquire multiple indicators corresponding to the target satellite mission; The processing module is used to calculate the subjective weights of the multiple indicators based on a preset subjective weighting algorithm, so as to obtain the subjective weights of each indicator. The processing module is also used to calculate the objective weights of the multiple indicators based on a preset objective weighting algorithm to obtain the objective weights of each indicator. The correction module is used to correct the subjective weight of any indicator based on the objective weight of each indicator, so as to obtain the corrected target weight of the indicator. The acquisition module is also used to acquire multiple satellite mission scheduling algorithms to be tested, and each satellite mission scheduling algorithm includes the multiple indicators; The testing module is used to test the multiple satellite mission scheduling algorithms based on the target weights after the correction of each indicator, and to obtain the test results corresponding to each satellite mission scheduling algorithm.

12. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-10.

14. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-10.

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