Satellite mission planning method and system for combined observation mission

Abstract

The application provides a satellite task planning method and system for combined observation tasks, a storage medium and an electronic device, and relates to the technical field of satellite task planning. The application generates a time interval and an observation time window set according to a preset time threshold, calculates an observation time window imaging quality factor, timeliness factor and competitiveness factor to obtain the heuristic factor values of each observation time window in the time interval, classifies and weights the observation time window priorities in the time interval according to optical satellites and SAR satellites, calculates the priority of the time interval and sorts the priority, so as to realize effective selection of the constructed time interval, traverses the observation time window according to the time interval priority, executes an insertion algorithm, performs constraint judgment and updates the planning scheme. The heuristic factor design and the task planning algorithm construction for combined observation tasks guide the generation of the planning scheme and improve the acquisition efficiency of the task target difference characteristics.

Description

Technical Field

[0001] This invention relates to the field of satellite mission planning technology, and specifically to a satellite mission planning method, system, storage medium, and electronic device for combined observation missions. Background Technology

[0002] Optical satellites and SAR satellites are the two main categories of remote sensing satellites. With the increasing ability of imaging satellites to acquire image information, users' demands for Earth observation are also growing, and their requirements are becoming more diverse, with a greater emphasis on the quality of remote sensing data.

[0003] In existing research, when using remote sensing satellite resources to complete observation tasks, the same type of satellite is often used for information acquisition. However, this results in incomplete information. While optical satellites contain grayscale information across multiple bands, enabling effective target identification and classification, they are susceptible to distortion due to factors such as weather, lighting, and day / night cycles, leading to image obscuration by clouds. SAR satellites, on the other hand, record echo information in only one band, but their unique phase information is unavailable from other sensors.

[0004] Therefore, in practical applications, due to the high timeliness, dynamism, and accuracy requirements of missions, there is a strong need for fusion observations of mission targets using both optical and SAR satellites. This fusion observation can provide differentiated information about the target based on existing single-type resource observations. For example, when a moving target at sea experiences an anomaly in its anchorage, observation can be conducted using a combination of optical and SAR satellite imagery. Optical satellites can effectively identify the target, while SAR satellites can image the ship multiple times, accurately acquiring the target's differentiated characteristics and providing crucial technical means for ship rescue and search operations. This need for fusion observations, which acquires information about the same target within a certain time interval, presents new challenges for satellite mission planning. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a satellite mission planning method, system, storage medium, and electronic device for combined observation missions, solving the technical problem that existing remote sensing observations do not take into account the fusion requirements of the mission's observation time window.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A satellite mission planning method for combined observation missions includes:

[0010] S1. Sort the observation time window set of each observation task according to the start time, and traverse each observation task;

[0011] S2. Traverse the set of observation time windows for the current observation task in chronological order. If the traversal is complete, proceed to S6.

[0012] S3. Based on the start time of the observation time window, generate a time interval and a corresponding set of observation time windows using a preset time threshold;

[0013] S4. Determine whether the time window set contains both SAR satellite time windows and optical satellite time windows. If it does, record the time interval and the corresponding time window set. If it does not, go to S2.

[0014] S5. Construct the starting time window for the next time interval based on the latest time window in the set of recorded time windows, and go back to S2 to perform the next round of traversal.

[0015] S6. Count the number of SAR time windows and optical time windows within the time interval respectively;

[0016] S7. Calculate the heuristic factors of the observation time window in each time interval, including the observation time window imaging quality factor, the observation time window timeliness factor, and the observation time window competition factor.

[0017] S8. Based on the values ​​of each heuristic factor, classify and weight the observation time windows within the time interval according to optical satellites and SAR satellites, calculate the priority of the time intervals and sort them.

[0018] S9. Traverse the observation time window according to the priority of the time interval, execute the insertion algorithm, and make constraint judgments. If the insertion is successful, update the planning scheme of the current task and proceed to the planning of the next task, and jump to S1.

[0019] Preferably, S9 specifically includes:

[0020] S91. Sort the time intervals according to their priority and iterate through them in turn. If the iteration is complete, proceed directly to the planning of the next task and jump to S1; otherwise, execute S92.

[0021] S92. Read the observation time windows with a smaller number of observations within the current time interval, and read their corresponding observation time window priorities;

[0022] S93. Sort the observation time windows with fewer items in the current time interval according to priority, call the insertion algorithm, and perform constraint judgment. If the insertion is successful, update the first sub-planning scheme of the current task.

[0023] S94. Read the observation time window with the most occurrences in the current time interval, and read its corresponding observation time window priority;

[0024] S95. Sort the observation time windows with the most observations in the current time interval according to priority, call the insertion algorithm, and perform constraint judgment. If the insertion is successful, update the second sub-planning scheme of the current task.

[0025] S96. Merge the first and second sub-planning schemes of the current task, obtain the planning scheme of the current task, and proceed to the next task planning, then jump to S1.

[0026] Preferably, the specific process of the insertion algorithm in S93 includes:

[0027] 5) Obtain task data and sorted observation time window data;

[0028] 6) Traverse the sorted observation time window data and insert time windows. If there is a conflict with the planned task, continue traversing the observation time window; if there is no conflict, proceed to the next step.

[0029] 7) At least perform constraint checks on imaging duration, energy, and storage. If the constraint checks are not met, return to the previous step and continue the traversal; if the constraint checks are met, execute S94.

[0030] 8) If insertion is still not possible after traversal is complete, then jump to S91;

[0031] Preferably, the specific process of the insertion algorithm in S95 includes:

[0032] 5) Obtain task data and sorted observation time window data;

[0033] 6) Traverse the sorted observation time window data and insert time windows. If there are conflicts, continue traversing the observation time window; if there are no conflicts, proceed to the next step.

[0034] 7) Perform constraint checks on imaging duration, energy, and storage at least. If the constraint checks are not met, return to the previous step and continue the traversal; if the constraint checks are met, execute S96.

[0035] 8) If insertion is still not possible after traversal is complete, then jump to S91.

[0036] Preferably, obtaining the imaging quality factor of the observation time window in S7 specifically includes:

[0037] (1) SAR satellites:

[0038]

[0039] Among them, img_qua SAR This represents the imaging resolution utility value of the SAR satellite and serves as the imaging quality factor for the SAR satellite's observation time window; ob_res SAR The imaging resolution of the SAR satellite; This is the maximum value of the SAR satellite imaging resolution, which is a constant.

[0040] (2) Optical satellites:

[0041]

[0042] in, ob_res represents the imaging resolution utility value for optical satellites. Opt For the imaging resolution of optical satellites; This represents the maximum resolution of optical satellite imaging, which is a constant.

[0043]

[0044] in, Include These are the utility values ​​for solar altitude angle ob_sun, lateral swing angle ob_swi, and cloud cover ob_clo, respectively, all ranging from [0,1]. It is the attribute value of the i-th task on the j-th resource in the k-th available observation time window on attribute m. Let m be the maximum value of attribute m in the observation time window set corresponding to the i-th task. Let m be the minimum value of attribute m in the observation time window set corresponding to the i-th task;

[0045]

[0046] Among them, img_qua Opt is the imaging quality factor for the optical satellite observation time window; w1, w2, w3, and w4 are the weights of the utility values ​​of the optical satellite observation time window attributes: imaging resolution, solar elevation angle, side sway angle, and cloud cover, respectively, representing the importance of each attribute, and w1+w2+w3+w4=1.

[0047] Preferably, obtaining the observation time window timeliness factor in S7 specifically includes:

[0048] 1) Obtain the observation time window of the task and its immediately following data transmission time window;

[0049] 2) Sort the data transmission time windows according to their start time, starting with the number 0;

[0050]

[0051] Among them, sort() is a sorting function that sorts the data transmission time windows according to their start time; The start time of the data transmission time window corresponding to the k-th available observation time window of the i-th task on the j-th resource; The sequence number of the data transmission time window corresponding to the k-th available observation time window of the i-th task on the j-th resource;

[0052] 3) Assign the sorted data transmission time window index to the observation time window number;

[0053]

[0054] in, Let be the index of the k-th available observation time window for the i-th task on the j-th resource;

[0055] 4) Calculate the timeliness factor value of the observation time window according to the assigned observation time window subscript using the following formula:

[0056]

[0057] in, C is the timeliness factor for this observation time window; C is the cutoff value set to consider the timeliness of the observation time window.

[0058] Preferably, obtaining the observation time window competition factor in S7 specifically includes:

[0059] 1) Obtain the observation time window of the task and the set of all observation time windows of other tasks to be planned, np_obwin;

[0060] 2) Determine if there is a conflict by checking whether the start and end times of the task's observation time window overlap with the start and end times of the task's observation time window. If it exists, the value is 1. The calculation formula is as follows:

[0061]

[0062] in, The conflict calculation result is the conflict between the observation time window a, which is currently used for conflict judgment, and the observation time window b, which is to be planned. The value is 1 when the two observation time windows overlap, and 0 otherwise.

[0063] and Let a be the start and end time of the observation time window a for the task to be planned; and Determine the start and end times of observation time window b for the current conflict;

[0064] 3) Traverse the set of time windows for tasks to be planned, and calculate the time window conflict results (comp) for the current task. count And calculate the conflict factor of the observation time window for the current task. The calculation formula is as follows:

[0065]

[0066]

[0067] Where, comp is the set of competition results for all time windows of the current task; max(comp) is the maximum value in the set of competition results for all time windows of the current task; This is the competition factor for the i-th task on the j-th resource during the k-th observation time window.

[0068] Preferably, S8 specifically includes:

[0069] S81. Based on the values ​​of each heuristic factor, the priority of the observation time window within the time interval is classified and weighted according to optical satellites and SAR satellites;

[0070]

[0071]

[0072] in, and α1, α2, and α3 represent the priority of observation time windows for SAR satellites and optical satellites within the time interval, respectively; α1, α2, and α3 represent the weights of each heuristic factor in the observation time window of SAR satellites, indicating the importance of each heuristic factor, and α1+α2+α3=1; β1, β2, and β3 represent the weights of each heuristic factor in the observation time window of optical satellites, indicating the importance of each heuristic factor, and β1+β2+β3=1;

[0073] S82. Retain the first preset number of time windows with high priority, sum the priority of the observation time windows of optical satellites and SAR satellites respectively, and take the smaller value between the two as the priority of the time interval. Finally, sort the priorities of all time intervals.

[0074]

[0075] Among them, inter_pro c The priority of the time interval is q = 1, ..., N, which are the sequence numbers of the first preset number of time windows to be reserved.

[0076] A satellite mission planning system for combined observation missions includes:

[0077] The sorting module is used to execute S1, sort the observation time window set of each observation task according to the start time, and traverse each observation task.

[0078] The traversal module is used to execute S2 and traverse the set of observation time windows of the current observation task in chronological order. If the traversal is complete, it will switch to the statistics module to execute S6.

[0079] The generation module is used to execute S3 and generate a set of time intervals and corresponding observation time windows based on the start time of the observation time window and a preset time threshold.

[0080] The judgment module is used to execute S4 and determine whether the time window set contains both SAR satellite time windows and optical satellite time windows. If it does, the time interval and the corresponding time window set are recorded. If it does not, the process is transferred to the traversal module to execute S2.

[0081] The specified module is used to execute S5, construct the starting time window for the next time interval based on the latest time window in the set of recorded time windows, and then transfer to the traversal module S2 to perform the next round of traversal.

[0082] The statistics module is used to execute S6 and count the number of SAR time windows and optical time windows within the time interval, respectively.

[0083] The first calculation module is used to execute S7 and calculate the heuristic factors of the observation time window in each time interval, including the observation time window imaging quality factor, the observation time window timeliness factor, and the observation time window competition factor.

[0084] The second calculation module is used to execute S8, classify and weight the observation time windows within the time interval according to optical satellites and SAR satellites based on the values ​​of each heuristic factor, calculate the priority of the time interval and sort them.

[0085] The insertion module is used to execute S9, traverse the observation time window according to the priority of the time interval, execute the insertion algorithm, perform constraint judgment, and if the insertion is successful, update the planning scheme of the current task and proceed to the planning of the next task, and jump to the sorting module to execute S1.

[0086] A storage medium storing a computer program for satellite mission planning for a combined observation mission, wherein the computer program causes a computer to execute the satellite mission planning method described above.

[0087] An electronic device, comprising:

[0088] One or more processors;

[0089] Memory; and

[0090] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing satellite mission planning as described above.

[0091] (III) Beneficial Effects

[0092] This invention provides a satellite mission planning method, system, storage medium, and electronic device for combined observation missions. Compared with existing technologies, it has the following advantages:

[0093] In this invention, firstly, a time interval and a corresponding set of observation time windows are generated based on a preset time threshold. Imaging quality factors, timeliness factors, and competition factors for each observation time window are selected and calculated to obtain the heuristic factor values ​​for each observation time window within the time interval. Secondly, based on the heuristic factor values ​​for each observation time window, the priority of the observation time windows within the time interval is classified and weighted according to optical satellites and SAR satellites. The priority of the time interval is calculated and sorted, thereby achieving effective selection of the constructed time interval. Then, the observation time windows are traversed according to the time interval priority, an insertion algorithm is executed, constraint judgments are performed, and the planning scheme is updated. By analyzing the planning characteristics in the combined collaborative observation process in detail, a task planning algorithm for combined observation tasks is constructed. Multiple heuristic factors are designed to guide the generation of planning schemes, improving the efficiency of obtaining the differences in task objectives. Attached Figure Description

[0094] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0095] Figure 1 A flowchart illustrating a satellite mission planning method for combined observation missions provided in an embodiment of the present invention;

[0096] Figure 2 This is a flowchart illustrating another satellite mission planning method for combined observation missions provided in an embodiment of the present invention. Detailed Implementation

[0097] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0098] This application provides a satellite mission planning method, system, storage medium, and electronic device for combined observation missions, which solves the technical problem that existing remote sensing observations do not take into account the fusion requirements of the mission's observation time window.

[0099] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0100] This invention proposes a satellite mission planning method for combined observation tasks. This method utilizes multiple imaging sessions conducted by SAR and optical satellites within a specific timeframe to acquire differentiated target characteristics from different satellite payloads, thereby fulfilling the requirements for fusion-based collaborative observation by imaging satellites. The satellite mission planning method for combined observation tasks includes heuristic factor calculation for observation time windows, priority calculation for time intervals, and execution of the insertion algorithm. The main steps are as follows:

[0101] (1) First, the heuristic factor calculation of the observation time window is performed.

[0102] Based on the time threshold, time intervals are constructed. For the observation time windows in the time interval, the imaging quality factor, timeliness factor, and competitiveness factor of the observation time window are selected and calculated to obtain the heuristic factor values ​​of each observation time window in the time interval.

[0103] (2) Then comes the time interval priority calculation.

[0104] Based on the heuristic factor values ​​of each observation time window, the priority of the observation time windows within the time interval is classified and weighted according to optical satellites and SAR satellites. The priority of the time interval is calculated and sorted, thereby achieving effective selection of the constructed time interval.

[0105] (3) Finally, the insertion algorithm is executed. The observation time window is traversed according to the priority of the time interval, the insertion algorithm is executed, constraint judgment is performed, and the planning scheme is updated.

[0106] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0107] Example:

[0108] like Figure 1 As shown, this embodiment of the invention provides a satellite mission planning method for combined observation missions, including:

[0109] S1. Sort the observation time window set of each observation task according to the start time, and traverse each observation task;

[0110] S2. Traverse the set of observation time windows for the current observation task in chronological order. If the traversal is complete, proceed to S6.

[0111] S3. Based on the start time of the observation time window, generate a time interval and a corresponding set of observation time windows using a preset time threshold;

[0112] S4. Determine whether the time window set contains both SAR satellite time windows and optical satellite time windows. If it does, record the time interval and the corresponding time window set. If it does not, go to S2.

[0113] S5. Construct the starting time window for the next time interval based on the latest time window in the set of recorded time windows, and go back to S2 to perform the next round of traversal.

[0114] S6. Count the number of SAR time windows and optical time windows within the time interval respectively;

[0115] S7. Calculate the heuristic factors of the observation time window in each time interval, including the observation time window imaging quality factor, the observation time window timeliness factor, and the observation time window competition factor.

[0116] S8. Based on the values ​​of each heuristic factor, classify and weight the observation time windows within the time interval according to optical satellites and SAR satellites, calculate the priority of the time intervals and sort them.

[0117] S9. Traverse the observation time window according to the priority of the time interval, execute the insertion algorithm, and make constraint judgments. If the insertion is successful, update the planning scheme of the current task and proceed to the planning of the next task, and jump to S1.

[0118] In existing technologies, there is a lack of research on the planning of combined observation missions, especially missions involving combined observations by different types of imaging satellites within a certain time interval; more importantly, existing research has not considered the coordinated application of different types of satellites.

[0119] However, with the increasing diversification of user needs and the refinement of data requirements, new technologies are needed to solve this problem of combined collaborative observation. This invention, through detailed analysis of the planning characteristics in the process of combined collaborative observation, constructs a task planning algorithm for combined observation tasks and designs multiple heuristic factors to guide the generation of planning schemes, thereby improving the efficiency of obtaining the differences in task objectives.

[0120] The following section will detail each step of the above technical solution:

[0121] First, it should be noted that the basic idea of ​​the satellite mission planning for combined observation tasks proposed in this embodiment of the invention is to find a relatively satisfactory combination of SAR and optical satellites with multiple imaging time windows for acquiring target information under limited resources. Furthermore, it is set that when each batch of observation tasks arrives, each observation task has multiple observation time windows and corresponding associated data transmission time windows.

[0122] In step S1, the observation time window set of each observation task is sorted according to the start time, and each observation task is traversed.

[0123] In step S2, the observation time window set of the current observation task is traversed in sequence. If the traversal is complete, proceed to step S6.

[0124] In step S3, a time interval and a corresponding set of observation time windows are generated based on the start time of the observation time window and a preset time threshold.

[0125] It is understood that the time threshold in this step can be determined by those skilled in the art based on the actual situation. Specifically, in this embodiment of the invention, the time threshold is set to 1 hour.

[0126] In step S4, it is determined whether the time window set contains both SAR satellite time windows and optical satellite time windows. If it does, the time interval and the corresponding time window set are recorded. If it does not, proceed to S2.

[0127] In step S5, the next time window is constructed based on the latest time window in the set of recorded time windows as the starting time window for the next time interval, and then the process returns to S2 to perform the next round of traversal.

[0128] In step S6, the number of SAR time windows and optical time windows within the time interval are counted respectively.

[0129] In step S7, heuristic factors for observation time windows within each time interval are calculated, including observation time window imaging quality factor, observation time window timeliness factor, and observation time window competition factor. The process of obtaining each heuristic factor will be described in detail below.

[0130] First, regarding the imaging quality factor of the observation time window.

[0131] When using satellites for observation, the observation mission has high requirements for image quality. The higher the image quality, the stronger the satisfaction of the mission. However, different satellites have different resolutions. The higher the resolution, the higher the image quality obtained. In addition, different observation methods also lead to different calculation methods for image quality. For example, the image quality of SAR satellites is only related to resolution. The image quality of optical satellites is not only related to resolution, but also to solar elevation angle, side swing angle and cloud cover. Therefore, different observation time windows and image quality calculation factors are designed for different satellite types.

[0132] Therefore, obtaining the imaging quality factor of the observation time window in S7 specifically includes:

[0133] (1) SAR satellites:

[0134]

[0135] Among them, img_qua SAR This represents the imaging resolution utility value of the SAR satellite and serves as the imaging quality factor for the SAR satellite's observation time window; ob_res SAR The imaging resolution of the SAR satellite; This is the maximum value of the SAR satellite imaging resolution, which is a constant.

[0136] (2) Optical satellites:

[0137]

[0138] in, ob_res represents the imaging resolution utility value for optical satellites. Opt For the imaging resolution of optical satellites; This represents the maximum resolution of optical satellite imaging, which is a constant.

[0139] Since the imaging quality of optical satellites is also affected by the solar elevation angle, side swi angle, and cloud cover, the attributes of solar elevation angle ob_sun, side swi angle ob_swi, and cloud cover ob_clo for each observation time window in the set of observation time windows corresponding to each mission are normalized.

[0140]

[0141] in, Include These are the utility values ​​for solar altitude angle ob_sun, lateral swing angle ob_swi, and cloud cover ob_clo, respectively, all ranging from [0,1]. It is the attribute value of the i-th task on the j-th resource in the k-th available observation time window on attribute m. Let m be the maximum value of attribute m in the observation time window set corresponding to the i-th task. Let m be the minimum value of attribute m in the observation time window set corresponding to the i-th task.

[0142] In summary, the observation time imaging quality factor of optical satellites can be calculated as follows:

[0143]

[0144] Among them, img_qua Opt is the imaging quality factor for the optical satellite observation time window; w1, w2, w3, and w4 are the weights of the utility values ​​of the optical satellite observation time window attributes: imaging resolution, solar elevation angle, side sway angle, and cloud cover, respectively, representing the importance of each attribute, and w1+w2+w3+w4=1.

[0145] Second, regarding the timeliness factor of the observation time window.

[0146] The timeliness of observation tasks affects the task fulfillment rate. Generally, users require observation tasks to be completed as quickly as possible, thus placing high demands on observation time. Earlier observation times mean stronger task timeliness; therefore, an observation time window timeliness factor was designed. However, multiple observation time windows and data transmission time windows can slow down computational efficiency.

[0147] Therefore, obtaining the observation time window timeliness factor in S7 specifically includes:

[0148] 1) Obtain the observation time window of the task and its immediately following data transmission time window;

[0149] 2) Sort the data transmission time windows according to their start time, starting with the number 0;

[0150]

[0151] Among them, sort() is a sorting function that sorts the data transmission time windows according to their start time; The start time of the data transmission time window corresponding to the k-th available observation time window of the i-th task on the j-th resource; The sequence number of the data transmission time window corresponding to the k-th available observation time window of the i-th task on the j-th resource;

[0152] 3) Assign the sorted data transmission time window index to the observation time window number;

[0153]

[0154] in, Let be the index of the k-th available observation time window for the i-th task on the j-th resource;

[0155] 4) Calculate the timeliness factor value of the observation time window according to the assigned observation time window subscript using the following formula:

[0156]

[0157] in, C is the timeliness factor for this observation time window; C is the cutoff value set to consider the timeliness of the observation time window, currently set to 10.

[0158] It should be noted that the current cutoff value C is related to the observation time window number. The difference value indicates the distance between the current observation time window number and the truncation time window. A larger value indicates a greater distance from the truncation value and higher timeliness, while a smaller value indicates a closer distance from the truncation value and lower timeliness. Take the larger value between the cutoff value and the observation time window number to address... When the sequence number is equal to or greater than the cutoff value sequence number, the difference between the cutoff value and the observation time window sequence number is uniformly assigned to 1, and then compared with the set cutoff value C to generate the observation time window timeliness factor.

[0159] Third, regarding the competition factor of the observation time window.

[0160] Existing satellite resources are limited under heavy mission demands, leading to multiple missions competing for a single observation window. This overlap and conflict means that executing one mission within the same window can prevent other missions from being effectively executed, thus impacting the efficiency of the planning scheme. To address this, we designed an observation window competition factor to measure the conflict when multiple observation missions simultaneously occupy the same window. Since the selection of the observation window for the mission to be planned is uncertain, the competition factor calculation only considers conflicts with the time window of the mission to be planned.

[0161] Therefore, obtaining the observation time window competition factor in S7 specifically includes:

[0162] 1) Obtain the observation time window of the task and the set of all observation time windows of other tasks to be planned, np_obwin;

[0163] 2) Determine if there is a conflict by checking whether the start and end times of the task's observation time window overlap with the start and end times of the task's observation time window. If it exists, the value is 1. The calculation formula is as follows:

[0164]

[0165] in, The conflict calculation result is the conflict between the observation time window a, which is currently used for conflict judgment, and the observation time window b, which is to be planned. The value is 1 when the two observation time windows overlap, and 0 otherwise.

[0166] and Let a be the start and end time of the observation time window a for the task to be planned; and Determine the start and end times of observation time window b for the current conflict;

[0167] 3) Traverse the set of time windows for tasks to be planned, and calculate the time window conflict results (comp) for the current task. count And calculate the conflict factor of the observation time window for the current task. The calculation formula is as follows:

[0168]

[0169]

[0170] Where, comp is the set of competition results for all time windows of the current task; max(comp) is the maximum value in the set of competition results for all time windows of the current task; This is the competition factor for the i-th task on the j-th resource during the k-th observation time window.

[0171] It should be noted that the difference between the competition degree calculated for the current observation time window and the competition degree calculated for all observation time windows of the task is used to calculate the distance between the maximum competition degree and the competition degree of the current observation time window. A larger value indicates a greater distance from the maximum competition degree result, and a lower competition degree; a smaller value indicates a closer distance from the maximum competition degree result, and a higher competition degree. The difference is compared with the maximum competition degree result to generate the corresponding observation time window competition degree factor. Furthermore, when the maximum competition degree result is 0, it indicates that there is no conflict in the current time window, and the competition degree is 1.

[0172] The three heuristic factors proposed in this step will be further used to calculate the priority of observation time windows and the priority of time intervals in subsequent steps, thus providing important technical support for the realization of combined observation requirements. See the following for details.

[0173] In step S8, based on the heuristic factor values, the priority of observation time windows within the time interval is classified and weighted according to optical satellites and SAR satellites, and the priority of the time intervals is calculated and sorted; specifically including:

[0174] S81. Based on the values ​​of each heuristic factor, the priority of the observation time window within the time interval is classified and weighted according to optical satellites and SAR satellites;

[0175]

[0176]

[0177] in, and α1, α2, and α3 represent the priority of observation time windows for SAR satellites and optical satellites within the time interval, respectively; α1, α2, and α3 represent the weights of each heuristic factor in the observation time window of SAR satellites, indicating the importance of each heuristic factor, and α1+α2+α3=1; β1, β2, and β3 represent the weights of each heuristic factor in the observation time window of optical satellites, indicating the importance of each heuristic factor, and β1+β2+β3=1;

[0178] S82. Retain the first preset number of time windows with high priority, sum the priority of the observation time windows of optical satellites and SAR satellites respectively, and take the smaller value between the two as the priority of the time interval. Finally, sort the priorities of all time intervals.

[0179]

[0180] Among them, inter_pro c The priority of the time interval is q = 1, ..., N, which are the sequence numbers of the first preset number of time windows to be retained. Specifically, in this embodiment of the invention, N = 5.

[0181] It should be noted that the reason for choosing the lower priority between the two is to increase the probability of the combined observation task being planned and completed when traversing time intervals during the planning process, and to reduce the chance of not being selected due to the lower priority of the time interval, which would have given more opportunities to arrange observation time windows but were not arranged.

[0182] In step S9, the observation time window is traversed according to the priority of the time interval, the insertion algorithm is executed, and constraint judgment is performed. If the insertion is successful, the planning scheme of the current task is updated, and the next task planning is initiated, jumping to S1; Figure 2 As shown, it specifically includes:

[0183] S91. Sort the time intervals according to their priority and iterate through them in turn. If the iteration is complete, proceed directly to the planning of the next task and jump to S1; otherwise, execute S92.

[0184] S92. Read the observation time windows (optical or SAR) with a small number of observations within the current time interval, and read their corresponding observation time window priorities;

[0185] S93. Sort the observation time windows with fewer items in the current time interval according to priority, call the insertion algorithm, and perform constraint judgment. If the insertion is successful, update the first sub-planning scheme of the current task.

[0186] The specific process of the insertion algorithm in S93 includes:

[0187] 1) Obtain task data and sorted observation time window data;

[0188] 2) Traverse the sorted observation time window data and insert time windows. If there is a conflict with the planned task, continue traversing the observation time window; if there is no conflict, proceed to the next step.

[0189] 3) At least perform constraint checks on imaging duration, energy, and storage. If the constraint checks are not met, return to the previous step and continue the traversal; if the constraint checks are met, execute S94.

[0190] 4) If insertion is still not possible after traversal is complete, then jump to S91;

[0191] S94. Read the observation time window with the most occurrences in the current time interval, and read its corresponding observation time window priority;

[0192] S95. Sort the observation time windows (SAR or optical) with the most quantity in the current time interval according to priority, call the insertion algorithm, perform constraint judgment, and if the insertion is successful, update the second sub-planning scheme of the current task.

[0193] The specific process of the insertion algorithm in S95 includes:

[0194] 1) Obtain task data and sorted observation time window data;

[0195] 2) Traverse the sorted observation time window data and insert time windows. If there are conflicts, continue traversing the observation time window; if there are no conflicts, proceed to the next step.

[0196] 3) At least perform constraint checks on imaging duration, energy, and storage. If the constraint checks are not met, return to the previous step and continue the traversal; if the constraint checks are met, execute S96.

[0197] 4) If insertion is still not possible after traversal is complete, then jump to S91;

[0198] S96. Merge the first and second sub-planning schemes of the current task, obtain the planning scheme of the current task, and proceed to the next task planning, then jump to S1.

[0199] In summary, this invention provides a task planning algorithm for combined observation tasks. This algorithm is used to address the collaborative planning problem of different observation resources under combined observation tasks, thereby optimizing the satellite resource selection process.

[0200] This invention provides a satellite mission planning system for combined observation missions, comprising:

[0201] The sorting module is used to execute S1, sort the observation time window set of each observation task according to the start time, and traverse each observation task.

[0202] The traversal module is used to execute S2 and traverse the set of observation time windows of the current observation task in chronological order. If the traversal is complete, it will switch to the statistics module to execute S6.

[0203] The generation module is used to execute S3 and generate a set of time intervals and corresponding observation time windows based on the start time of the observation time window and a preset time threshold.

[0204] The judgment module is used to execute S4 and determine whether the time window set contains both SAR satellite time windows and optical satellite time windows. If it does, the time interval and the corresponding time window set are recorded. If it does not, the process is transferred to the traversal module to execute S2.

[0205] The specified module is used to execute S5, construct the starting time window for the next time interval based on the latest time window in the set of recorded time windows, and then transfer to the traversal module S2 to perform the next round of traversal.

[0206] The statistics module is used to execute S6 and count the number of SAR time windows and optical time windows within the time interval, respectively.

[0207] The first calculation module is used to execute S7 and calculate the heuristic factors of the observation time window in each time interval, including the observation time window imaging quality factor, the observation time window timeliness factor, and the observation time window competition factor.

[0208] The second calculation module is used to execute S8, classify and weight the observation time windows within the time interval according to optical satellites and SAR satellites based on the values ​​of each heuristic factor, calculate the priority of the time interval and sort them.

[0209] The insertion module is used to execute S9, traverse the observation time window according to the priority of the time interval, execute the insertion algorithm, perform constraint judgment, and if the insertion is successful, update the planning scheme of the current task and proceed to the planning of the next task, and jump to the sorting module to execute S1.

[0210] This invention provides a storage medium storing a computer program for satellite mission planning for combined observation missions, wherein the computer program causes a computer to execute the satellite mission planning method described above.

[0211] This invention provides an electronic device, comprising:

[0212] One or more processors;

[0213] Memory; and

[0214] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing satellite mission planning as described above.

[0215] It is understood that the satellite mission planning system, storage medium and electronic device for combined observation missions provided in the embodiments of the present invention correspond to the satellite mission planning method for combined observation missions provided in the embodiments of the present invention. The explanation, examples and beneficial effects of the relevant contents can be referred to the corresponding parts of the blockchain-based satellite mission planning method, and will not be repeated here.

[0216] In summary, compared with existing technologies, it has the following beneficial effects:

[0217] This invention, through detailed analysis of the planning characteristics in the combined collaborative observation process, constructs a task planning algorithm for combined observation tasks and designs multiple heuristic factors to guide the generation of planning schemes, thereby improving the efficiency of obtaining the differences in task objectives.

[0218] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0219] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A satellite mission planning method for combined observation missions, characterized in that, include: S1. Sort the observation time window set of each observation task according to the start time, and traverse each observation task; S2. Traverse the set of observation time windows for the current observation task in chronological order. If the traversal is complete, proceed to S6. S3. Based on the start time of the observation time window, generate a time interval and a corresponding set of observation time windows using a preset time threshold; S4. Determine whether the time window set contains both SAR satellite time windows and optical satellite time windows. If it does, record the time interval and the corresponding time window set. If it does not, go to S2. S5. Construct the starting time window for the next time interval based on the latest time window in the set of recorded time windows, and go back to S2 to perform the next round of traversal. S6. Count the number of SAR time windows and optical time windows within the time interval respectively; S7. Calculate the heuristic factors of the observation time window in each time interval, including the observation time window imaging quality factor, the observation time window timeliness factor, and the observation time window competition factor. S8. Based on the values ​​of each heuristic factor, classify and weight the observation time windows within the time interval according to optical satellites and SAR satellites, calculate the priority of the time intervals and sort them. S9. Traverse the observation time window according to the priority of the time interval, execute the insertion algorithm, and make constraint judgments. If the insertion is successful, update the planning scheme of the current task and proceed to the planning of the next task, and jump to S1. Obtaining the imaging quality factor of the observation time window in S7 specifically includes: (1) SAR satellites: in, This represents the imaging resolution utility value of the SAR satellite and serves as the imaging quality factor for the observation time window of the SAR satellite. The imaging resolution of the SAR satellite; This is the maximum value of the SAR satellite imaging resolution, which is a constant. (2) Optical satellites: in, This represents the imaging resolution utility value of the optical satellite; For the imaging resolution of optical satellites; This represents the maximum resolution of optical satellite imaging, which is a constant. in, Include , , , respectively, solar altitude angle Side swing angle and cloud cover The utility value of is in the range of [0,1]. It is the first i The task in the first j The first resource k One available observation time window in the attribute The attribute values ​​on For the first i The attributes in the observation time window set corresponding to each task The maximum value, For the first i The attributes in the observation time window set corresponding to each task The minimum value; in, For the imaging quality factor of the optical satellite observation time window; The weights of the utility values ​​for the optical satellite observation time window attributes—imaging resolution, solar elevation angle, side sway angle, and cloud cover—represent the importance of each attribute. .

2. The satellite mission planning method as described in claim 1, characterized in that, S9 specifically includes: S91. Sort the time intervals according to their priority and iterate through them in turn. If the iteration is complete, proceed directly to the planning of the next task and jump to S1; otherwise, execute S92. S92. Read the observation time windows with a smaller number of observations within the current time interval, and read their corresponding observation time window priorities; S93. Sort the observation time windows with fewer items in the current time interval according to priority, call the insertion algorithm, and perform constraint judgment. If the insertion is successful, update the first sub-planning scheme of the current task. S94. Read the observation time window with the most occurrences in the current time interval, and read its corresponding observation time window priority; S95. Sort the observation time windows with the most observations in the current time interval according to priority, call the insertion algorithm, and perform constraint judgment. If the insertion is successful, update the second sub-planning scheme of the current task. S96. Merge the first and second sub-planning schemes of the current task, obtain the planning scheme of the current task, and proceed to the next task planning, then jump to S1.

3. The satellite mission planning method as described in claim 2, characterized in that, The specific process of the insertion algorithm in S93 includes: 1) Obtain task data and sorted observation time window data; 2) Traverse the sorted observation time window data and insert time windows. If there is a conflict with the planned task, continue traversing the observation time window; if there is no conflict, proceed to the next step. 3) At least perform constraint checks on imaging duration, energy, and storage. If the constraint checks are not met, return to the previous step and continue the traversal; if the constraint checks are met, execute S94. 4) If insertion is still not possible after traversal is complete, then jump to S91; And / or the specific process of the insertion algorithm in S95 includes: 1) Obtain task data and sorted observation time window data; 2) Traverse the sorted observation time window data and insert time windows. If there are conflicts, continue traversing the observation time window; if there are no conflicts, proceed to the next step. 3) At least perform constraint checks on imaging duration, energy, and storage. If the constraint checks are not met, return to the previous step and continue the traversal; if the constraint checks are met, execute S96. 4) If insertion is still not possible after traversal is complete, then jump to S91.

4. The satellite mission planning method as described in claim 1, characterized in that, Obtaining the observation time window timeliness factor in S7 specifically includes: 1) Obtain the observation time window of the task and its immediately following data transmission time window; 2) Sort the data transmission time windows according to their start time, starting with the number 0; in, A sorting function that sorts data transmission time windows according to their start time; For the first i The task in the first j The first resource k The start time of the data transmission time window corresponding to each available observation time window; For the first i The task in the first j The first resource k The sequence number of the data transmission time window corresponding to each available observation time window; 3) Assign the sorted data transmission time window index to the observation time window number; in, For the first i The task in the first j The first resource k The sequence number of each available observation time window; 4) Calculate the timeliness factor value of the observation time window according to the assigned observation time window subscript using the following formula: in, C is the timeliness factor for this observation time window; C is the cutoff value set to consider the timeliness of the observation time window.

5. The satellite mission planning method as described in claim 4, characterized in that, Obtaining the observation time window competition factor in S7 specifically includes: 1) Obtain the observation time window of the task and the set of all observation time windows of other tasks to be planned. ; 2) Determine if there is a conflict by checking whether the start and end times of the task's observation time window overlap with the start and end times of the task's observation time window. If it exists, the value is 1. The calculation formula is as follows: in, The observation time window for current conflict assessment a Observation time window for tasks to be planned b The conflict calculation result is set to 1 when there is an overlap between the two observation time windows, and 0 otherwise; and Observation time window for planned tasks a The start and end times; and Observation time window for current conflict assessment b The start and end times; 3) Traverse the set of time windows for tasks to be planned and calculate the time window conflict results for the current task. And calculate the conflict factor of the observation time window for the current task. The calculation formula is as follows: in, This is the set of competition calculation results for all time windows of the current task; The maximum value in the set of competition calculation results for all time windows of the current task; For the first i The task in the first j The first resource k Competition factor for each observation time window.

6. The satellite mission planning method as described in claim 5, characterized in that, S8 specifically includes: S81. Based on the values ​​of each heuristic factor, the priority of the observation time window within the time interval is classified and weighted according to optical satellites and SAR satellites; in, and These are the observation time window priorities for SAR satellites and optical satellites within the time interval, respectively. These represent the weights of each heuristic factor within the SAR satellite observation time window, signifying the importance of each heuristic factor. ; These represent the weights of each heuristic factor within the optical star observation time window, signifying the importance of each heuristic factor. ; S82. Retain the first preset number of time windows with high priority, sum the priority of the observation time windows of optical satellites and SAR satellites respectively, and take the smaller value between the two as the priority of the time interval. Finally, sort the priorities of all time intervals. in, Priority based on time intervals, The sequence number of the first preset number of time windows to be retained.

7. A satellite mission planning system for combined observation missions, characterized in that, The method for executing the satellite mission planning method as described in any one of claims 1 to 6 includes: The sorting module is used to execute S1, sort the observation time window set of each observation task according to the start time, and traverse each observation task. The traversal module is used to execute S2 and traverse the set of observation time windows of the current observation task in chronological order. If the traversal is complete, it will switch to the statistics module to execute S6. The generation module is used to execute S3 and generate a set of time intervals and corresponding observation time windows based on the start time of the observation time window and a preset time threshold. The judgment module is used to execute S4 and determine whether the time window set contains both SAR satellite time windows and optical satellite time windows. If it does, the time interval and the corresponding time window set are recorded. If it does not, the process is transferred to the traversal module to execute S2. The specified module is used to execute S5, construct the starting time window for the next time interval based on the latest time window in the set of recorded time windows, and then transfer to the traversal module S2 to perform the next round of traversal. The statistics module is used to execute S6 and count the number of SAR time windows and optical time windows within the time interval, respectively. The first calculation module is used to execute S7 and calculate the heuristic factors of the observation time window in each time interval, including the observation time window imaging quality factor, the observation time window timeliness factor, and the observation time window competition factor. The second calculation module is used to execute S8, classify and weight the observation time windows within the time interval according to optical satellites and SAR satellites based on the values ​​of each heuristic factor, calculate the priority of the time interval and sort them. The insertion module is used to execute S9, traverse the observation time window according to the priority of the time interval, execute the insertion algorithm, perform constraint judgment, and if the insertion is successful, update the planning scheme of the current task and proceed to the planning of the next task, and jump to the sorting module to execute S1.

8. A storage medium, characterized in that, It stores a computer program for satellite mission planning for combined observation missions, wherein the computer program causes the computer to execute the satellite mission planning method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, include: One or more processors; Memory; as well as One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing satellite mission planning as described in any one of claims 1 to 6.

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