A satellite simulated on-orbit payload data acquisition system and acquisition method

Abstract

The present invention discloses a satellite simulation on-orbit payload data acquisition system and acquisition method. The on-orbit payload data is loaded through a data loading unit, and all the payload data is transmitted to a related collection unit. The related collection unit receives the payload data transmitted by the data loading unit and performs related collection. According to the related processing method of the related collection, cold data, important data and neutral data are obtained; then, with the help of several storage libraries on the storage end, combined with a processor and a load distribution library, all the cold data, important data and neutral data are classified and stored in the storage end. The specific storage is achieved by encrypting or hiding the data according to its importance. Through the several storage libraries of the present invention, different forms of payload data can be stored in different ways, the storage method can be optimized to facilitate subsequent extraction, and basic hidden distinction can also be made for key data.

Description

Technical Field

[0001] The present invention belongs to the field of subsequent processing of payload data and relates to a technology, in particular to a satellite simulated on-orbit payload data acquisition system and acquisition method. Background Art

[0002] Patent publication number CN106842157B discloses a SAR satellite simulated on-orbit payload data acquisition system and method. SAR echo data is generated based on SAR satellite imaging parameters and stored in a mission planning computer. A navigation signal simulator and attitude and orbit control ground equipment synchronously output simulated on-orbit flight-related excitations to the SAR satellite, executing simulated on-orbit flight. Imaging planning instructions are sent to the SAR satellite, conveying the imaging mode, radar operating parameter information, imaging time, imaging duration, imaging process attitude information, and the time to download payload data. When the scheduled SAR satellite imaging time arrives, an echo signal is sent to the SAR satellite. The SAR satellite packages the data into payload data. When the scheduled SAR satellite downlink time arrives, a payload data receiving device receives the payload data signal from the SAR satellite, performs demodulation, and restores it, completing payload data reception.

[0003] However, in the above system, after the on-orbit payload data is acquired, how to store the acquired data and make targeted separation and differentiation according to the different characteristics of the data while ensuring confidentiality is provided. Based on this, a solution is provided. Summary of the Invention

[0004] The purpose of the present invention is to provide a satellite simulated on-orbit payload data acquisition system and acquisition method.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A satellite simulated on-orbit payload data acquisition system includes a data loading unit, a related collection unit, a processor, a payload distribution library, a management unit and a storage terminal;

[0007] The data loading unit is used to load on-orbit payload data and transmit all payload data to the relevant collection unit. The relevant collection unit receives the payload data transmitted by the data loading unit and performs relevant collection on it to obtain cold data, important data and neutral data.

[0008] The storage end includes several storage repositories, namely Storage Repository 1, Storage Repository 2, ... Storage Repository n. The relevant collection unit is used to transmit cold data, important data, and neutral data to the processor. The processor is used to combine the load distribution library to classify and store all cold data, important data, and neutral data in the storage end. The specific storage method is as follows:

[0009] S1: Get all cold data, important data and neutral data;

[0010] S2: Divide the essential data into two parts according to their size, obtaining essential data 1 and essential data 2;

[0011] S3: According to the number of load data in the important data, mark the number as sub-items; take the load data of sub-items as a group, divide the neutral data into several groups of component data, and mark the groups as sub-item groups;

[0012] S4: add two to the item group and then add one to get the stored number;

[0013] S5: Then, the number n of storage banks is set to the corresponding number of storages;

[0014] S6: After that, the essential data is obtained, the total data size of the essential data is obtained, the value of the data size is marked as a mark value, and the value of the unit digit of the mark value is obtained and marked as a characteristic value;

[0015] S7: When the characteristic value is greater than n, take the remainder after dividing it by n and recalibrate the remainder as the characteristic value; otherwise, do nothing;

[0016] S8: Mark the storage bins 1 to n as Ci, i=1...n;

[0017] S9: Let i = characteristic value, select the corresponding storage, and store the important data 1 in the storage; then assign the value of i to the characteristic value plus 1, and store the important data 2 in the corresponding storage;

[0018] S10: then remove the storage bins storing the first and second essential data, mark the storage bin with the largest i value in the remaining storage bins as a cold storage bin, and store the cold data in the cold storage bin;

[0019] S11: Afterwards, the number of all neutral data is obtained and stored evenly in each repository according to the specific value of the number;

[0020] S12: Complete the storage of all data.

[0021] Furthermore, the specific methods of relevant collection are:

[0022] Step 1: Get the load data;

[0023] Step 2: Then obtain the relevant data of the load data, including the number of citations, analysis times, occupancy value and acquisition time;

[0024] Step 3: Mark the citation times, analysis times, occupancy values ​​and acquisition times as Yc, Fc, Zy and Ht respectively;

[0025] Step 4: Use the formula to calculate the essential value Py corresponding to the load data. The specific calculation formula is:

[0026] Py=0.35*Yc+0.25*Fc+0.23 / Zy+0.17 / Ht;

[0027] In the formula, 0.35, 0.25, 0.23 and 0.17 are preset weights used to highlight the importance of different factors;

[0028] Step 5: Calculate the importance value Py of all load data according to the formula, and sort the load data in descending order according to the importance value Py;

[0029] Step 6: Mark the top 30% of the load data as important data, the bottom 10% as cold data, and the rest as neutral data;

[0030] Step 7: Get cold data, important data and neutral data.

[0031] Furthermore, the number of citations in step 2 refers to the number of all users who have cited or viewed the corresponding load data, and this number is marked as the citation times; the analysis times refers to the total number of times the load data has been cited and viewed by people, and a single view refers to the user not viewing the load data again within T1 time after citing the load data, which is marked as one time; the occupancy value refers to the data size of the load data; the acquisition time refers to the time from the initial acquisition of the corresponding load data to the present time, which is the acquisition time.

[0032] Furthermore, the management unit is in communication with the processor for inputting all preset values.

[0033] Furthermore, in step S2, the integrity of the individual payload data must be ensured during the process of dividing the essential data into two parts according to size, and the data must not be split. If equal division is not possible, the size difference between essential data 1 and essential data 2 is minimized.

[0034] A method for acquiring satellite simulated on-orbit payload data comprises the following steps:

[0035] SS1: Obtain payload data;

[0036] SS2: Then, relevant data of the payload data is obtained, including the number of citations, analysis times, occupancy value, and acquisition time;

[0037] SS3: The citation times, analysis times, occupancy values ​​and acquisition times are marked as Yc, Fc, Zy and Ht respectively;

[0038] SS4: Use the formula to calculate the essential value Py corresponding to the load data. The specific calculation formula is:

[0039] Py=0.35*Yc+0.25*Fc+0.23 / Zy+0.17 / Ht;

[0040] In the formula, 0.35, 0.25, 0.23 and 0.17 are preset weights used to highlight the importance of different factors;

[0041] SS5: Calculate the importance value Py of all load data according to the formula, and sort the load data in descending order according to the importance value Py;

[0042] SS6: Mark the top 30 percent of load data as important data, the bottom 10 percent as cold data, and the rest as neutral data;

[0043] SS7: obtains cold data, essential data, and neutral data;

[0044] SS8: Divide the essential data into two parts according to their size, obtaining essential data 1 and essential data 2;

[0045] SS9: According to the number of load data in the important data, mark the number as sub-item; take the load data of sub-item as a group, divide the neutral data into several groups of component data, and mark the group as sub-item group;

[0046] SS10: Add two to the item group and then add one to get the stored number;

[0047] SS11: Then set the number of storage banks n to the corresponding number of storage banks;

[0048] SS12: After obtaining the required data, obtain the total size of the required data, mark the corresponding data size as the mark value, obtain the value in the unit digit of the mark value, and mark it as the characteristic value;

[0049] SS13: When the characteristic value is greater than n, take the remainder after dividing it by n and recalibrate the remainder as the characteristic value; otherwise, no processing is performed;

[0050] SS14: Mark storage bins 1 to n as Ci, where i = 1...n;

[0051] SS15: Let i = the characteristic value, select the corresponding storage, and store the important data 1 in the storage; then assign the value of i to the characteristic value plus one, and store the important data 2 in the corresponding storage;

[0052] SS16: After that, the storage repositories storing the first and second essential data are removed, and the storage repositories with the largest i value among the remaining storage repositories are marked as cold storage repositories, and the cold data are stored in the cold storage repositories;

[0053] SS17: After that, the number of all neutral data is obtained and stored evenly in each repository according to the specific value of the number;

[0054] SS18: Complete storage of all data.

[0055] Beneficial effects of the present invention:

[0056] The present invention loads on-orbit payload data through a data loading unit and transmits all payload data to a related collection unit. The related collection unit receives the payload data transmitted by the data loading unit and performs related collection on the payload data. According to the related processing method of the related collection, cold data, important data and neutral data are obtained.

[0057] Then, with the help of several storage repositories on the storage side, namely storage repositories 1, storage repositories 2... storage repositories n; combined with processors and load sharding libraries, all cold data, important data, and neutral data are classified and stored in the storage side. The specific storage is achieved by encrypting or hiding the data according to its importance.

[0058] Through the multiple storage libraries of the present invention, different forms of payload data can be stored in different ways, the storage method can be optimized to facilitate subsequent retrieval, and basic hidden distinction can also be made for key data. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0060] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0061] This application provides a method for subsequent processing of acquired data;

[0062] like Figure 1 As shown, a satellite simulated on-orbit payload data acquisition system includes a data loading unit, a related collection unit, a processor, a payload distribution library, a management unit and a storage terminal;

[0063] The data loading unit is used to load on-orbit payload data and transmit all payload data to the relevant collection unit. The relevant collection unit receives the payload data transmitted by the data loading unit and performs relevant collection on it. The specific method of relevant collection is as follows:

[0064] Step 1: Get the load data;

[0065] Step 2: Relevant data of the payload data is then obtained, including the number of citations, analysis times, occupancy value, and acquisition time. The number of citations here refers to the number of users who have cited or viewed the corresponding payload data, which is marked as the citation times; the analysis times refers to the total number of times the payload data has been cited and viewed. A single view refers to the user who has not viewed the payload data again within T1 time after citing the payload data, which is marked as one time; the occupancy value refers to the data size of the payload data; the acquisition time refers to the time from the initial acquisition of the corresponding payload data to the current time, which is the acquisition time.

[0066] Step 3: Mark the citation times, analysis times, occupancy values ​​and acquisition times as Yc, Fc, Zy and Ht respectively;

[0067] Step 4: Use the formula to calculate the essential value Py corresponding to the load data. The specific calculation formula is:

[0068] Py=0.35*Yc+0.25*Fc+0.23 / Zy+0.17 / Ht;

[0069] In the formula, 0.35, 0.25, 0.23 and 0.17 are preset weights used to highlight the importance of different factors;

[0070] Step 5: Calculate the importance value Py of all load data according to the formula, and sort the load data in descending order according to the importance value Py;

[0071] Step 6: Mark the top 30% of the load data as important data, the bottom 10% as cold data, and the rest as neutral data;

[0072] Step 7: Get cold data, important data and neutral data;

[0073] The storage end includes several storage repositories, namely Storage Repository 1, Storage Repository 2, ... Storage Repository n. The relevant collection unit is used to transmit cold data, important data, and neutral data to the processor. The processor is used to combine the load distribution library to classify and store all cold data, important data, and neutral data in the storage end. The specific storage method is as follows:

[0074] S1: Get all cold data, important data and neutral data;

[0075] S2: Divide the essential data into two parts according to their size, obtaining essential data 1 and essential data 2. In this step, the integrity of the individual load data must be ensured and they must not be split. If equal division is not possible, the data is divided into two parts with the smallest size difference between essential data 1 and essential data 2.

[0076] S3: According to the number of load data in the important data, mark the number as sub-items; take the load data of sub-items as a group, divide the neutral data into several groups of component data, and mark the groups as sub-item groups;

[0077] S4: add two to the item group and then add one to get the stored number;

[0078] S5: Then, the number n of storage banks is set to the corresponding number of storages;

[0079] S6: After that, the essential data is obtained, the total data size of the essential data is obtained, the value of the data size is marked as a mark value, and the value of the unit digit of the mark value is obtained and marked as a characteristic value;

[0080] S7: When the characteristic value is greater than n, take the remainder after dividing it by n and recalibrate the remainder as the characteristic value; otherwise, do nothing;

[0081] S8: Mark the storage bins 1 to n as Ci, i=1...n;

[0082] S9: Let i = characteristic value, select the corresponding storage, and store the important data 1 in the storage; then assign the value of i to the characteristic value plus 1, and store the important data 2 in the corresponding storage;

[0083] S10: then remove the storage bins storing the first and second essential data, mark the storage bin with the largest i value in the remaining storage bins as a cold storage bin, and store the cold data in the cold storage bin;

[0084] S11: Afterwards, the number of all neutral data is obtained and stored evenly in each repository according to the specific value of the number;

[0085] S12: Complete the storage of all data.

[0086] The management unit is in communication with the processor and is used to input all preset values.

[0087] A method for acquiring satellite simulated on-orbit payload data, the method specifically comprising the following steps:

[0088] SS1: Obtain payload data;

[0089] SS2: Then, relevant data of the payload data is obtained, including the number of citations, analysis times, occupancy value, and acquisition time;

[0090] SS3: The citation times, analysis times, occupancy values ​​and acquisition times are marked as Yc, Fc, Zy and Ht respectively;

[0091] SS4: Use the formula to calculate the essential value Py corresponding to the load data. The specific calculation formula is:

[0092] Py=0.35*Yc+0.25*Fc+0.23 / Zy+0.17 / Ht;

[0093] In the formula, 0.35, 0.25, 0.23 and 0.17 are preset weights used to highlight the importance of different factors;

[0094] SS5: Calculate the importance value Py of all load data according to the formula, and sort the load data in descending order according to the importance value Py;

[0095] SS6: Mark the top 30 percent of load data as important data, the bottom 10 percent as cold data, and the rest as neutral data;

[0096] SS7: obtains cold data, essential data, and neutral data;

[0097] SS8: Divide the essential data into two parts according to their size, obtaining essential data 1 and essential data 2;

[0098] SS9: According to the number of load data in the important data, mark the number as sub-item; take the load data of sub-item as a group, divide the neutral data into several groups of component data, and mark the group as sub-item group;

[0099] SS10: Add two to the item group and then add one to get the stored number;

[0100] SS11: Then set the number of storage banks n to the corresponding number of storage banks;

[0101] SS12: After obtaining the required data, obtain the total size of the required data, mark the corresponding data size as the mark value, obtain the value in the unit digit of the mark value, and mark it as the characteristic value;

[0102] SS13: When the characteristic value is greater than n, take the remainder after dividing it by n and recalibrate the remainder as the characteristic value; otherwise, no processing is performed;

[0103] SS14: Mark storage bins 1 to n as Ci, where i = 1...n;

[0104] SS15: Let i = the characteristic value, select the corresponding storage, and store the important data 1 in the storage; then assign the value of i to the characteristic value plus one, and store the important data 2 in the corresponding storage;

[0105] SS16: After that, the storage repositories storing the first and second essential data are removed, and the storage repositories with the largest i value among the remaining storage repositories are marked as cold storage repositories, and the cold data are stored in the cold storage repositories;

[0106] SS17: After that, the number of all neutral data is obtained and stored evenly in each repository according to the specific value of the number;

[0107] SS18: Complete storage of all data.

[0108] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A satellite simulated on-orbit payload data acquisition system, characterized in that: It includes data loading unit, related collection unit, processor, load distribution library, management unit and storage end; The data loading unit is used to load on-orbit payload data and transmit all payload data to the relevant collection unit. The relevant collection unit receives the payload data transmitted by the data loading unit and performs relevant collection on it to obtain cold data, important data and neutral data. The storage end includes several storage repositories, namely Storage Repository 1, Storage Repository 2, ... Storage Repository n. The relevant collection unit is used to transmit cold data, important data, and neutral data to the processor. The processor is used to combine the load distribution library to classify and store all cold data, important data, and neutral data in the storage end. The specific storage method is as follows: S1: Get all cold data, important data and neutral data; S2: Divide the essential data into two parts according to their size, obtaining essential data 1 and essential data 2; S3: According to the number of load data in the important data, mark the number as sub-items; take the load data of sub-items as a group, divide the neutral data into several groups of component data, and mark the groups as sub-item groups; S4: add two to the item group and then add one to get the stored number; S5: Then, the number n of storage banks is set to the corresponding number of storages; S6: Afterwards, the essential data is obtained, the total data size of the essential data is obtained, the corresponding value of the data size is marked as a mark value, and the value of the unit digit of the mark value is obtained and marked as a characteristic value; S7: When the characteristic value is greater than n, take the remainder after dividing it by n and recalibrate the remainder as the characteristic value; otherwise, do nothing; S8: Mark the storage bins 1 to n as Ci, i=1...n; S9: Let i = characteristic value, select the corresponding storage, and store the important data 1 in the storage; then assign the value of i to the characteristic value plus 1, and store the important data 2 in the corresponding storage; S10: then remove the storage bins storing the first and second essential data, mark the storage bin with the largest i value in the remaining storage bins as a cold storage bin, and store the cold data in the cold storage bin; S11: Afterwards, the number of all neutral data is obtained and stored evenly in each repository according to the specific value of the number; S12: Complete the storage of all data.

2. A satellite simulated on-orbit payload data acquisition system according to claim 1, characterized in that: The specific methods of relevant collection are: Step 1: Get the load data; Step 2: Then obtain the relevant data of the load data, including the number of citations, analysis times, occupancy value and acquisition time; Step 3: Mark the citation times, analysis times, occupancy values ​​and acquisition times as Yc, Fc, Zy and Ht respectively; Step 4: Use the formula to calculate the essential value Py corresponding to the load data. The specific calculation formula is: Py=0.35*Yc+0.25*Fc+0.23 / Zy+0.17 / Ht; In the formula, 0.35, 0.25, 0.23, and 0.17 are preset weights used to highlight the importance of different factors; Step 5: Calculate the importance value Py of all load data according to the formula, and sort the load data in descending order according to the importance value Py; Step 6: Mark the top 30% of the load data as important data, the bottom 10% as cold data, and the rest as neutral data; Step 7: Get cold data, important data and neutral data.

3. The satellite simulated on-orbit payload data acquisition system according to claim 2, characterized in that: The citation times in step 2 refers to the number of users who have cited or viewed the corresponding payload data, which is marked as the citation times; the analysis times refers to the total number of times the payload data has been cited and viewed. A single view refers to the period T1 after a user has cited the payload data and has not viewed the payload data again, which is marked as one time; the occupancy value refers to the data size of the payload data; the acquisition time refers to the time from the initial acquisition of the corresponding payload data to the present time, which is the acquisition time.

4. The satellite simulated on-orbit payload data acquisition system according to claim 1, characterized in that: The management unit is in communication with the processor and is used to input all preset values.

5. The satellite simulated on-orbit payload data acquisition system according to claim 1, characterized in that: In step S2, when dividing the essential data into two parts according to size, the integrity of the individual load data must be guaranteed and it will not be split. If equal division is not possible, the size difference between essential data 1 and essential data 2 is minimized.

6. A method for acquiring satellite simulated on-orbit payload data, characterized in that: The method specifically comprises the following steps: SS1: Obtain payload data; SS2: Then, relevant data of the payload data is obtained, including the number of citations, analysis times, occupancy value, and acquisition time; SS3: The citation times, analysis times, occupancy values ​​and acquisition times are marked as Yc, Fc, Zy and Ht respectively; SS4: Use the formula to calculate the essential value Py corresponding to the load data. The specific calculation formula is: Py=0.35*Yc+0.25*Fc+0.23 / Zy+0.17 / Ht; In the formula, 0.35, 0.25, 0.23 and 0.17 are preset weights used to highlight the importance of different factors; SS5: Calculate the importance value Py of all load data according to the formula, and sort the load data in descending order according to the importance value Py; SS6: Mark the top 30 percent of load data as important data, the bottom 10 percent as cold data, and the rest as neutral data; SS7: obtains cold data, essential data, and neutral data; SS8: Divide the essential data into two parts according to their size, obtaining essential data 1 and essential data 2; SS9: According to the number of load data in the important data, mark the number as sub-item; take the load data of sub-item as a group, divide the neutral data into several groups of component data, and mark the group as sub-item group; SS10: Add two to the item group and then add one to get the stored number; SS11: Then set the number of storage banks n to the corresponding number of storage banks; SS12: After obtaining the essential data, the total data size of the essential data is obtained, the corresponding value of the data size is marked as the mark value, and the value in the unit digit of the mark value is obtained and marked as the characteristic value; SS13: When the characteristic value is greater than n, take the remainder after dividing it by n and recalibrate the remainder as the characteristic value; otherwise, no processing is performed; SS14: Mark storage bins 1 to n as Ci, where i = 1...n; SS15: Let i = characteristic value, select the corresponding storage library, and store the characteristic data in the storage library; Then, the value of i is assigned to the characteristic value plus one, and the corresponding important data 2 is stored in the corresponding storage repository; SS16: After that, the storage repositories storing the first and second essential data are removed, and the storage repositories with the largest i value among the remaining storage repositories are marked as cold storage repositories, and the cold data are stored in the cold storage repositories; SS17: After that, the number of all neutral data is obtained and stored evenly in each repository according to the specific value of the number; SS18: Complete storage of all data.

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