A remote sensing data demand closed-loop drive processing method and device

Abstract

The present invention provides a remote sensing data demand closed-loop driving processing method and device thereof, which relates to the technical field of remote sensing data processing, including parsing user data demand, obtaining historical data reprocessing demand R i1 and at least one observation requirement f ik ; In the remote sensing data processing system, there is a time and space range of historical data that meets the reprocessing requirements. i1 In the case of O historical data reprocessing sub-tasks, O historical data reprocessing sub-tasks are generated, and the historical data corresponding to the O historical data reprocessing sub-tasks are sent to the user according to the status of the O historical data; according to the historical data reprocessing demand R i1 and observation requirements f ik , generating the unobserved space range SG i1 and the unobserved space range SW ik , according to the unobserved space range SW ik , determine whether to accept the observation request f ik ; Generate distribution tasks corresponding to each observation requirement based on the received observation requirements; Perform closed-loop processing on each generated distribution task to generate task results corresponding to each distribution task.

Description

Technical Field

[0001] The present disclosure relates to the technical field of remote sensing data processing, and in particular to a remote sensing data demand closed-loop driven processing method and a device thereof. Background Art

[0002] Remote sensing science is an emerging and complex interdisciplinary subject, involving space, electronics, computers, surveying and mapping, and earth sciences, which leads to a high threshold for the application of remote sensing systems. Therefore, in the existing relevant technical solutions, researchers believe that the demand for remote sensing data is submitted to the remote sensing system only after the expert user has made a reasonable judgment. In the demand, key parameters such as observation platform, payload type, working mode, observation time / area, etc. can be clearly defined, so as to focus the main research direction on the task planning problem of multi-satellite and multi-task, and solve the optimal utilization of observation resources under the constraints of limited resources. However, with the development of technology, the application fields of remote sensing systems include not only traditional fields such as agriculture, forestry, geology, oceanography, meteorology, hydrology, military, and environmental protection, but also emerging fields such as unmanned driving and futures trading. It is required that the new generation of remote sensing systems can be oriented to users at different levels, intelligently understand user data needs, organize storage resources and observation resources in the remote sensing system, and quickly extract, observe, and produce remote sensing data that meets user needs.

[0003] In the relevant technologies, the main problems in processing user demand data include: 1) Traditional remote sensing data requires users to have a very high professional background, and the submitted data requirements require the ability to clarify key parameters such as observation platform, payload type, working mode, observation time / area, etc., which has a high technical entry threshold. Therefore, how to lower the threshold for reporting user data requirements of remote sensing systems is a technical problem faced by personnel in the relevant technical fields of this field. 2) The remote sensing system has accumulated PB-level historical archived data. In response to the user's fuzzy data requirements, how to reuse historical archived data and coordinate the selection of observation platforms such as aviation remote sensing (balloons, aircraft), aerospace remote sensing (artificial satellites, spacecraft, space stations, rockets) and optical, radar, gravity and magnetic sensors to collect optical, radar, and electromagnetic physical characteristics of ground objects is also a technical problem faced by personnel in the relevant technical fields of this field. 3) The remote sensing system has entered the era of clustered observation platforms such as constellations, star clusters, and drone groups from a single satellite or a single aircraft single observation platform. There are multiple similar observation platforms and payloads. In response to the user's fuzzy data requirements, how to reasonably select observation platforms and payload types that meet the user's data requirements for observation is a technical problem faced by personnel in the relevant technical fields of this field. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the embodiments of the present invention provide a closed-loop driven processing method, device, electronic device and storage medium for remote sensing data demand. The method analyzes user data requirements to generate historical data reprocessing requirements or observation requirements, aiming to solve the technical problem of how to quickly extract, observe and produce remote sensing data that meets user needs.

[0005] The first aspect of the present disclosure provides a closed-loop driven processing method for remote sensing data demand, comprising: S1, parsing user data demand to obtain historical data reprocessing demand R i1 and at least one observation requirement f ik ; Where 1≤i≤N, k≥1; N represents the number of virtual data requirements into which the user data demand is decomposed; S2, the time and space range of the historical data in the remote sensing data processing system that meets the historical data reprocessing demand R i1 In the case of generating and reprocessing requirements for historical data R i1 The corresponding O historical data sub-reprocessing tasks, and according to the status of the O scene historical data, send the historical data corresponding to the O historical data sub-reprocessing tasks to the user; where O≥1; S3, according to the historical data reprocessing demand R i1 and at least one observation requirement f ik , respectively generate and historical data reprocessing requirements R i1 The corresponding unobserved space range SG i1 and at least one observation requirement f ik The corresponding unobserved space range SW ik , and according to the unobserved spatial range SW ik , determine whether to receive at least one observation request f ik ; S4, based on the observation requirements received in S3, generate distribution tasks corresponding to each observation requirement; S5, perform closed-loop processing on each distribution task generated in S4, and generate task results corresponding to each distribution task.

[0006] Further, S1 includes: S11, constructing multiple virtual observation platforms to be established; S12, parsing user data requirements to obtain data requirement information; wherein the data requirement information includes user information, data requirement information, data type, data timeliness range [T s , T e], data demand frequency F, data parameters, data quality, data object space range SA and data priority; S13, create a data demand number corresponding to the user data demand, and generate a data demand closed-loop driven processing task corresponding to the user data demand according to the data demand number; S14, perform data conversion on the data type, data parameter and data quality to obtain identifiable sensor working parameters, product type, product production parameters and product quality; S15, when the data demand information does not include the data object space range SA, generate the data object space range SA according to the data demand information; when the generation of the data object space range SA fails, the task corresponding to the user data demand fails to close the loop; S16, when the data type includes multiple sensors and each type of sensor is required to fully cover the data object space range SA, according to multiple virtual observation platforms to be established, the user data demand is decomposed into N virtual data demands; S17, the demand frequency F of the i-th virtual data demand among the N virtual data demands i is greater than 1, and the processing period Δt of the i-th virtual data demand is greater than the return period T of the i-th virtual data demand i In the case of , the i-th virtual data requirement is decomposed into F i sub-virtual data requirements; where 1≤i≤N; S18, in the existing space range SH i1 The historical data meets F i The fth of the sub-virtual data requirements i1 If the child virtual data is required, the f i1 The spatial range in the sub-virtual data requirement is SH i1 The region is decomposed into R i1 Historical data reprocessing tasks, as well as The spatial range SG i1 The region is decomposed into the fth i1 Observe the demand and ik The sub-virtual data requirements are decomposed into the fth ik Observation requirements, 1≤k≤F i .

[0007] Furthermore, S1 also includes: S19, when the i-th sub-virtual data demand does not have a decomposed historical data reprocessing demand or an observation demand, the task corresponding to the user data demand fails to close the loop.

[0008] Further, S2 includes: S21, when there are O scenes of historical data in time and space that satisfy the f i1 In the case of sub-virtual data demand, generate O historical data sub-reprocessing tasks; where O ≥ 1; S22, there are J sub-reprocessing tasks successfully closed in the O historical data sub-reprocessing tasks, and the spatial range SR generated by the J historical data sub-reprocessing tasks isi1 With spatial range SH i1 In the same case, the historical data reprocessing requirement is R i1 The loop is closed successfully; where 1≤J≤O.

[0009] Furthermore, S2 further includes: S23, in the space range SR i1 With spatial range SH i1 In different situations, and Then the spatial range SH i1r The region is re-decomposed and merged into the fth i1 Observation needs.

[0010] Further, S21 includes: S211, when the processing parameters and data quality of the j-th scene historical data meet the f-th scene historical data, i1 In the case of a sub-virtual data demand, according to the status of the j-th scene historical data, a distribution task of the j-th scene historical data, a migration task of the j-th scene historical data, or a low-level historical data migration and re-production task corresponding to the j-th scene historical data is generated; wherein, 1≤j≤O; S212, when the processing parameters and data quality of the j-th scene historical data do not meet the requirements of the f-th scene historical data, i1 In case of child virtual data requirements, according to the f i1 Whether there are optional processing parameters or scientific algorithms for the sub-virtual data requirements to end the task processing of the j-th scene historical data, generate the re-production task of the j-th scene historical data, or generate the low-level historical data migration task corresponding to the j-th scene historical data.

[0011] Further, S3 includes: S31, according to the f i1 Sub-virtual data requirements, generation and f i1 Unobserved spatial range SW corresponding to observation requirements i1 ; Among them, SW i1 =SH i1r ∪SG i1 ; S32, according to the parameters of the i-th virtual observation platform and the f-th ik Observe the operating parameters of the demand platform and determine the f ik The timeliness range of observation requirements [T iks , T ike ] within the observable range ST ik , and ST ik ∩SA ik =SP ik ; In SP ik >Y×SA ik In the case of ik Observation requirements; otherwise, refuse to receive the f ikObservation requirements; where Y represents the long-term deduction error correction coefficient, 0.9≤Y≤1; S33, when there is the fth ik When the observation request is rejected, the task corresponding to the user data request fails.

[0012] Furthermore, S3 also includes: S34, when the mission planning cycle of the i-th virtual observation platform of the remote sensing data processing system is ΔT, and Then obtain the initial parameters of the i-th virtual observation platform and the f-th ik Platform operating parameters and unobserved space range SW required for observation jk , generate M strip observation metatasks observable within ΔT and lock the observation area, and send the M strip observation metatasks to the task planning subsystem; among them, the spatial range of the mth strip observation metatask is SA ikm , 1≤m≤M; S35, when the task planning subsystem refuses to accept the m-th stripe observation meta-task, the observation area SA locked corresponding to the m-th stripe observation meta-task is released ikm ; Among them, when the task planning subsystem receives the mth band observation meta-task, and the actual planned observation area is SA m , then lock the unobserved space range SW ik SA m area and release SA ikm -SA m Region; The observation meta-task of the mth strip is successfully closed, if there is JI scene data that satisfies the fth ik The spatial range after observation and splicing is S JI , then release SA m region, initiate distribution tasks for scene data, and update observed regions and unobserved regions; S36, determine the fth region according to the observed regions and unobserved regions updated in step S35 ik Observe whether the demand loop is closed successfully.

[0013] Further, S4 includes: S41, in the fth ik When the observation demand is successfully closed, each distribution task is matched with the historical data information stored in the remote sensing data processing system; wherein the historical data information includes the product number, data demand number, virtual data demand number, virtual sub-data demand number, observation demand number and product data; S42, when the j-th scene data distribution task in the JI scene data is completed and no user feedback is received within the preset time, the j-th scene data distribution task is successfully closed.

[0014] Further, S4 also includes: S43, when the j-th scene distribution task is completed and user feedback is received within a preset time, generating an alarm message to prompt the user; and matching to the f-th scene distribution task according to the matching result of S41. ik Observe the requirements and return them to S2 for reprocessing. If the user data requirements are met, the task is redistributed.

[0015] Further, S5 includes: S51, generating a regional vector SS according to the distribution task successfully closed in S4; wherein the regional vector SS is used by the user to adjust the data demand strategy; S52, at the i-th virtual observation platform f ik The observation requirements and their corresponding distribution tasks are successfully closed and T0≤T ie +ΔT e In the case of , the i-th virtual data demand successfully closes the loop; S53, when T0≤T e +ΔT e And when all N virtual data demands are successfully closed, the user data demand is successfully closed.

[0016] Furthermore, S52 further includes: at the i-th virtual observation platform f ik The observation requirements and their corresponding distribution tasks are successfully closed and T o >T ie +ΔT e In the case of a warning message, an alarm message is generated and [T is , T ie +ΔT e ]The regional vector SS of the observation data obtained within the range i .

[0017] A second aspect of the present disclosure provides a remote sensing data demand closed-loop drive processing device, including: a data demand parsing module, configured to parse user data demand to obtain a historical data reprocessing demand R i1 and at least one observation requirement f ik ; Wherein, 1≤i≤N, k≥1; N represents the number of virtual data requirements into which the user data demand is decomposed; the historical demand processing module is configured to satisfy the historical data reprocessing demand R when the time and space range of the scene historical data in the remote sensing data processing system exists i1 In the case of generating and reprocessing requirements for historical data R i1 The corresponding O historical data sub-reprocessing tasks, and according to the status of O historical data, send the historical data corresponding to the O historical data sub-reprocessing tasks to the user; where O≥1; the observation demand processing module is configured to reprocess the demand R according to the historical data i1 and at least one observation requirement f ik , respectively generate and historical data reprocessing requirements Ri1 The corresponding unobserved space range SG i1 and at least one observation requirement f ik The corresponding unobserved space range SW ik , and according to the unobserved spatial range SW ik , determine whether to receive at least one observation request f ik ; A distribution task generation module is configured to generate distribution tasks corresponding to each observation requirement based on the observation requirements received in the observation requirement processing module; a distribution task processing module is configured to perform closed-loop processing on each distribution task generated in the distribution task generation module to generate task results corresponding to each distribution task.

[0018] The third aspect of the present disclosure provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the remote sensing data demand closed-loop driven processing method provided by the first aspect of the present disclosure is implemented.

[0019] The fourth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the remote sensing data demand closed-loop driven processing method provided by the first aspect of the present disclosure is implemented.

[0020] Compared with the prior art, the present invention has at least the following beneficial effects:

[0021] (1) The method provided by the present invention adopts natural language processing technology to extract the main feature information that can be identified by the remote sensing system from the user data demand description, and generates the user's virtual data demand based on the feature information, thereby solving the problem of high threshold for user remote sensing data demand submission.

[0022] (2) The method provided in the present invention introduces a virtual observation platform. According to the virtual data requirements, physical observation platforms and payloads with the same observation capabilities are unified into the same virtual observation platform, and the virtual data requirements are decomposed into historical data reprocessing tasks and observation tasks according to the virtual observation platform, so as to solve the problem of unified planning and work of multiple platforms and multiple payloads.

[0023] (3) The method provided by the present invention adopts a historical data reprocessing mechanism to reprocess the historical data that meets the user's data needs, and automatically converts the tasks that fail in reprocessing into observation needs, thereby solving the problem of historical data reuse and re-observation.

[0024] (4) The method provided by the present invention adopts a closed-loop driving mode to perform closed-loop management of historical data reprocessing, observation requirements and distribution tasks, and automatically drives each sub-task of data requirements, supports users to monitor and track remote sensing data acquisition and closed-loop processes throughout the entire link, and can adjust remote sensing data requirements according to data acquisition conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] For a more complete understanding of the present disclosure and its advantages, reference will now be made to the following description taken in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A flowchart of a closed-loop driving processing method for remote sensing data demand according to an embodiment of the present disclosure is schematically shown;

[0027] Figure 2 A flowchart schematically shows a closed-loop processing of a historical data reprocessing task according to an embodiment of the present disclosure;

[0028] Figure 3 A flowchart schematically shows a closed-loop processing of a historical data reprocessing task according to an embodiment of the present disclosure;

[0029] Figure 4 A flowchart of observation demand acceptance and closed-loop processing according to an embodiment of the present disclosure is schematically shown;

[0030] Figure 5 A flowchart of a closed-loop processing of a distribution task according to an embodiment of the present disclosure is schematically shown;

[0031] Figure 6 A flowchart of closed-loop processing of data demand tasks according to an embodiment of the present disclosure is schematically shown;

[0032] Figure 7 A block diagram schematically shows a remote sensing data demand closed-loop driving processing device according to an embodiment of the present disclosure; and

[0033] Figure 8 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0034] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0035] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0036] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0037] In the case of using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0038] Some block diagrams and / or flow charts are shown in the accompanying drawings. It should be understood that some boxes or combinations thereof in the block diagrams and / or flow charts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that these instructions can create a device for implementing the functions / operations described in these block diagrams and / or flow charts when executed by the processor. The technology of the present disclosure can be implemented in the form of hardware and / or software (including firmware, microcode, etc.). In addition, the technology of the present disclosure can take the form of a computer program product on a computer-readable storage medium storing instructions, which can be used by an instruction execution system or used in conjunction with an instruction execution system.

[0039] The technical solution of the present disclosure will be described in detail below in conjunction with the specific process of the closed-loop driven processing method for remote sensing data demand in the specific embodiment of the present disclosure. It should be understood that the process and calculation structure of the closed-loop driven processing method for remote sensing data demand shown in the accompanying drawings are only exemplary to help those skilled in the art understand the technical solution of the present disclosure, and are not intended to limit the scope of protection of the present disclosure.

[0040] Figure 1The flowchart of the closed-loop driving processing method according to the remote sensing data requirements is schematically shown.

[0041] like Figure 1 As shown, the method includes: steps S1 to S5.

[0042] In operation S1, the user data requirements are parsed to obtain the historical data reprocessing requirements R i1 and at least one observation requirement f ik . Where 1≤i≤N, k≥1. N represents the number of virtual data demands into which the user data demand is decomposed.

[0043] In operation S2, the time and space range of the scene historical data in the remote sensing data processing system meets the historical data reprocessing requirements. ik In the case of generating and reprocessing requirements for historical data R i1 The corresponding O historical data sub-reprocessing tasks, and according to the status of the O historical data, send the historical data corresponding to the O historical data sub-reprocessing tasks to the user. Where O≥1.

[0044] In operation S3, the demand R is reprocessed according to the historical data. i1 and at least one observation requirement f ik , respectively generate and historical data reprocessing requirements R i1 The corresponding unobserved space range SG i1 and at least one observation requirement f ik The corresponding unobserved space range SW ik , and according to the unobserved spatial range SW ik , determine whether to receive at least one observation request f ik .

[0045] In operation S4, distribution tasks corresponding to the observation requirements are generated according to the observation requirements received in S3.

[0046] In operation S5, closed-loop processing is performed on each distribution task generated in S4 to generate a task result corresponding to each distribution task.

[0047] According to the embodiments of the present disclosure, the method analyzes the user data requirements, extracts the main feature information that can be identified by the remote sensing system from the user data requirement description, and generates the user virtual data requirements based on the feature information, thereby solving the problem of high threshold for reporting user remote sensing data requirements. In addition, the method adopts a historical data reprocessing mechanism to reprocess the historical data that meets the user data requirements, and automatically converts the tasks that failed to be reprocessed into observation requirements, thereby solving the problem of historical data reuse and re-observation.

[0048] In the disclosed embodiment, the remote sensing data demand is a remote sensing data extraction demand submitted by the end user of the remote sensing data product to the remote sensing system by means of instructions, files, fax, telephone or visual interaction according to the purpose. After receiving the user's data demand, the remote sensing data processing system accepts or rejects the user's data demand according to the system's observation capability and the status of the archived historical data.

[0049] Specifically, Figure 2 As shown, step S1 specifically includes: steps S11 to S19.

[0050] In operation S11, a plurality of virtual observation platforms to be assembled are constructed.

[0051] In the disclosed embodiment, a plurality of virtual observation platforms to be assembled that can work independently are constructed according to the actual observation platform and the types of sensors carried thereon.

[0052] In the disclosed embodiments, the aerospace observation platform exists in the form of a single star, a constellation, a star group, a single machine, a drone group, etc. Each platform can carry multiple sensors, and there is a "one-to-many" relationship between the observation platform and the sensors. Most of the sensors carried by the platform can work independently and do not affect each other. However, sensors such as the three-line array front, front, and rear stereo cameras are strongly correlated and need to work simultaneously.

[0053] For example, the same type of sensors are mounted on different observation platforms and require the sensors to work together, such as a distributed interferometric radar satellite consisting of two satellites, where the radar antenna of satellite 01 transmits echoes and the radar antennas of satellites 01 / 02 simultaneously receive echo data. In view of the relationship between the actual observation platform and the sensors mounted thereon, step S11 constructs a virtual observation platform based on the principle that the actual observation platform and sensors in the remote sensing system can work independently, and the virtual platform can independently obtain observation data.

[0054] For example, the three-line array front, front and rear stereo cameras are virtualized into a virtual stereo data observation platform, which has the ability to obtain stereo images of the observed target. The dual-star distributed interferometric radar satellite system is virtualized into a distributed interferometric radar observation platform, which has the ability to obtain interferometric images of the observed target.

[0055] In operation S12, the user data demand is parsed to obtain data demand information. The data demand information includes user information, data demand information and optional demand information. The optional demand information includes: data type, data validity range [T s , T e ], data demand frequency F, data parameters, data quality, data object space range SA and data priority.

[0056] In the embodiments of the present disclosure, remote sensing data refers to data, images, videos, etc. that record various physical characteristics of the observed object. Limited by the user's professional knowledge, the submitted remote sensing data requirements may be a vague description such as "remote sensing data of a certain object" or a detailed description such as "remote sensing data of a certain sensor observing and processing a certain object according to specified parameters and meeting specific quality requirements". The embodiments of the present disclosure use natural language processing technology to extract the main feature information that can be identified by the remote sensing system from the user's data requirement description, where:

[0057] 1) Data objects include: place names, building names, administrative regions, geographical scope, etc.

[0058] 2) Data types include: visible light, multispectral, hyperspectral, infrared, microwave radar, interferometric radar, gravity, magnetism, electronics, etc.

[0059] 3) Data timeliness range [T s , T e ]: The observation time range of remote sensing data. s is the start time of remote sensing data observation, T e is the start time of remote sensing data observation, and T0 is the current time of the remote sensing data processing system. e <T0, indicating that only historical data is needed; when T s <T0, and T e >T0, if the data demand frequency F is 1, re-observation is required only when the archived historical data of the remote sensing data processing system does not meet other characteristics of the data demand; when T s >T0, the remote sensing data processing system is required to obtain the data required by the user through observation.

[0060] 4) Data demand frequency F: that is, the number of times data needs to be obtained to meet the data demand within the data time limit. Generally, only special areas or targets such as geometric calibration fields and radiation calibration fields need to conduct multiple normalized observations.

[0061] 5) Data parameters: including platform operating parameters and remote sensing system processing parameters, mainly including operating mode, swing angle, width, spatial resolution, spectral resolution, wavelength, wave position, polarization mode, product level, etc.

[0062] 6) Data quality: relative radiation correction accuracy, absolute radiation correction accuracy, cloud and snow cover, uncontrolled positioning accuracy, absolute positioning accuracy, etc.

[0063] 7) Data object spatial scope SA: data that describes the spatial scope of the data object, including the center longitude and latitude, four-angle point longitude and latitude, polygon longitude and latitude, spatial vector description file, etc.

[0064] 8) Data priority: indicates the priority of user data demand in the remote sensing data processing system for reprocessing tasks, observation tasks, and distribution tasks. In the remote sensing data processing system, different users can submit different data demand priorities and priority weights. If it is empty, the default priority is used.

[0065] In operation S13, a data requirement number corresponding to the user data requirement is created, and a data requirement closed-loop driven processing task corresponding to the user data requirement is generated according to the data requirement number.

[0066] In the embodiment of the present disclosure, the data requirement number is unique.

[0067] For example, a data requirement number is generated according to the data type, user number, and current time. The optional formats are: data type_user number_time, and based on the unique data requirement number, a data requirement closed-loop driven processing task is generated.

[0068] In operation S14, data conversion is performed on the data type, data parameter and data quality to obtain identifiable sensor working parameters, product type, product production parameters and product quality.

[0069] In the disclosed embodiment, sensors carried by different observation platforms can obtain remote sensing data with different working parameters, processing parameters, and data quality. In this step, the data type, data parameter, and data quality requirements in the user data requirements are converted into sensor working parameters, product types, product production parameters, and product quality that can be recognized by the remote sensing data processing system.

[0070] For example, taking the Gaofen series as an example: if the user needs a relative radiometric correction image product with a spatial resolution of 1 meter, the Gaofen-2 high-resolution 1-meter panchromatic, Gaofen-3 1-meter C-band multi-polarization synthetic aperture radar, and Gaofen-7 0.8-meter resolution panchromatic stereoscopic image level 1 products can all meet the user's needs. If the user further requires a swath width of 40 kilometers, only the Gaofen-2 satellite high-resolution 1-meter panchromatic level 1 product can meet the user's needs.

[0071] In operation S15, if the data requirement information does not include the data object spatial range SA, the data object spatial range SA is generated according to the data requirement information. If the data object spatial range SA fails to be generated, the task corresponding to the user data requirement fails to close the loop, and the spatial range and format requirements that the remote sensing data processing system can service are provided to the user.

[0072] In the embodiment of the present disclosure, when the spatial scope of the data object in the user data demand data is unclear and only contains typical building names, place names, administrative names, etc., the spatial scope of the data object corresponding to the data demand is automatically extracted from the global basic geographic spatial vector library according to the name.

[0073] For example, when the data object is a point, when the image size of the minimum data processing unit of the remote sensing data processing system corresponds to the geographic coverage width, the data requirement spatial range size (Lat±s / 2, Lon±s / 2) is generated with the point (Lat, Lon) as the center. If the spatial range of the data object fails to be obtained, the remote sensing data processing system will reject the user's data requirement according to the unclear spatial range in the data requirement, and the closed-loop task will fail to close the loop due to the unclear data requirement. At the same time, according to the existing observation platforms in the remote sensing system and the observation services that each observation platform can provide, the spatial range and format requirements that the remote sensing system can provide are provided to the user, and the user resubmits the data requirements that meet the requirements of the spatial range that the remote sensing system can identify.

[0074] In operation S16, when the data type includes multiple sensors and each type of sensor is required to completely cover the data object space range SA, the user data requirements are decomposed into N virtual data requirements according to multiple virtual observation platforms to be established. If only the sensor data that meets the data requirements is required to cover the data object space range after splicing, then N is 1, that is, regardless of the sensor type, all virtual observation platforms to be established that meet the requirements are combined into one virtual observation platform. i Indicates the i-th virtual data demand space range requirement and satisfies SA i =SA, 1≤i≤N.

[0075] In the disclosed embodiment, the user data demand is decomposed into N virtual data demands according to the sensor type and the corresponding virtual observation platform to be built. For example, one sensor corresponds to N virtual observation platforms to be built, that is, N virtual data demands are generated.

[0076] For example, global basic geographic observations require that optical, radar and other sensors complete global land observations. For such requirements, virtual observation platforms are mapped according to sensor types. If there are N virtual observation platforms to be established that meet the data requirements, the user data requirements are decomposed into N virtual data requirements. The virtual data requirements correspond to the virtual observation platforms one by one, and the spatial range of each virtual data requirement is consistent.

[0077] For example, for the remote sensing data processing system, there are virtual observation platforms of the same type, which can observe the same observation object and obtain the same type of data. At this time, these virtual observation platforms to be established are combined into a virtual observation platform constellation. For example, the GF-1B / C / D satellite constellation consists of three 2-meter / 8-meter optical satellites with the same performance and consistent status (i.e., GF-1B, C, and D satellites).

[0078] For example, in order to quickly obtain data from a large area, a multi-source image stitching method is adopted. It is only required that the stitched data can cover the spatial range of the data requirements, and it is not required that all sensors completely cover the spatial range of the data requirements. In this case, N is 1, that is, the sensor type and virtual observation platform are not distinguished. At this time, all virtual observation platforms to be established that meet the requirements are combined into a virtual observation platform constellation.

[0079] In the disclosed embodiment, the virtual observation platform and the virtual observation platform constellation are collectively referred to as virtual observation platforms, and correspond one-to-one with the virtual data requirements. i Indicates the i-th virtual data demand space range requirement and satisfies SA i =SA, 1≤i≤N.

[0080] In operation S17, the demand frequency F of the i-th virtual data demand among the N virtual data demands is i is greater than 1, and the processing period Δt of the i-th virtual data demand is greater than the return period T of the i-th virtual data demand i In the case of , the i-th virtual data requirement is decomposed into F i There are sub-virtual data requirements, where 1≤i≤N.

[0081] In the embodiment of the present disclosure, when the demand frequency F of the i-th virtual data demand i is greater than 1, if the return period of the i-th virtual observation platform is T i , T0 is the current time, T1 is the actual observation time, if T s >T0, then take T1=T s , otherwise T1=T0. When Δt=(T e -T1) / F i , if Δt<T i , then the i-th virtual data demand is closed according to the insufficient return capability of the virtual observation platform, and the task ends. i , decompose the i-th virtual data requirement into F i sub-virtual data requirements. Among them, each sub-virtual data requirement has the same requirements except for the time limit. And if the fth ik The time limit of the sub-virtual data requirement is [T iks , T ike ], then, T iks =T1+Δt×(k-1), T ike =T1+Δt×k, 1≤k≤F i , 1≤f ik ≤F i In particular, when k = 1, T iks =Ts.

[0082] For example, when T sIf it is less than the current time T0 of the remote sensing data processing system, it means that the user data demand can include historical data, and the start time T1 that needs to be re-observed can be the current time T0. s If it is greater than the current time T0 of the remote sensing data processing system, it means that the data that meets the data requirements needs to be re-observed and acquired. The observation start time is the start time of the data submitted by the user, that is, T1 = T s .

[0083] For example, key observation objects such as calibration sites need to be regularly re-observed for sensor normalization calibration. i times, then the average interval between each observation is Δt=(T e -T1) / F i .

[0084] For example, when Δt<T i , that is, the average interval between each observation is less than the observation platform re-entry period T i , then the i-th virtual observation platform does not have the ability to observe F i The i-th virtual data demand can only be closed according to the insufficient return capacity of the virtual observation platform. Among them, the observation platform return cycle T i It is the shortest time interval for repeated observation of the same observation object by the observation platform using methods such as side swing.

[0085] For example, when Δt ≥ T i , it means that the i-th virtual observation platform is within the data timeliness range [T s , T e ] has observation F i Secondary capacity, according to the observation frequency F i and the average interval time between each observation Δt, decomposing the i-th virtual data demand into F i Each sub-virtual data requirement has a sub-virtual data requirement. iks , T ike ] are inconsistent, but other data types, data parameters, quality, and spatial range requirements are the same. ik The time limit of the sub-virtual data requirement can be calculated by the formula: T iks =T1+Δt×(k-1), T ike =T1+Δt×k, 1≤k≤F i , 1≤f ik ≤F i . In particular, when k = 1, due to T s Can be less than the current time, so take T i1s =T s , used to contain historical archive data. ikIndicates the f ik The sub-virtual data requires a certain space range and meets the SA ik =SA i , 1≤i≤N. At this time, the optional virtual data requirement number formats are: data type_user number_time_virtual data requirement sequence number i.

[0086] In operation S18, in the presence space range SH i1 The historical data meets F i The fth of the sub-virtual data requirements i1 If the child virtual data is required, the f i1 The space range in the sub-virtual data requirement is SH i1 The region is decomposed into R i1 Historical data reprocessing tasks, as well as The spatial range SG i1 The region is decomposed into the fth i1 Observe the demand and ik The sub-virtual data requirements are decomposed into the fth ik Observation requirements, 2≤k≤F i .

[0087] In the embodiment of the present disclosure, when there is a space range of SH in the remote sensing data processing system i1 The historical archived data complies with Article f i1 Sub-virtual data requirements, if SH i1 ∩SA i1 =SH i1 , and SH i1 =SA i1 , it means that the historical data completely covers the fth i1 Sub-virtual data demand space range requirements, at this time, only the sub-virtual data demand needs to be decomposed into historical data reprocessing requirements. i1 ≠SA i1 , it means that the historical data does not completely cover the f i1 Sub-virtual data requires spatial range requirements, at this time the spatial range belongs to SH i1 The sub-virtual data demand is decomposed into historical data reprocessing demand, which belongs to SA i1 -SH i1 =SG i1 The sub-virtual data requirements of the ith virtual observation platform are decomposed into the fth i1 Observation needs.

[0088] At the same time, for the f ik Sub-virtual data requirements, when k ≥ 2, due to T iksIf the fth time is greater than the current time, then there is obviously no historical data that meets the data requirements, and it can only be obtained by re-observation. ik The sub-virtual data requirements are decomposed into the fth ik Observation requirements. At this time, the optional virtual sub-data requirement number format is: data type_user number_time_virtual data requirement sequence number i_virtual sub-data requirement sequence number f ik ; The reprocessing task number and observation requirement number are: task type_user number_time_virtual data requirement number i_virtual sub-data requirement number f ik .

[0089] In operation S19, if there is no decomposed historical data reprocessing demand or observation demand for the i-th sub-virtual data demand, the task corresponding to the user data demand fails to close the loop, and the remote sensing data processing system provides the serviceable data type, timeliness, data parameters and quality to the user.

[0090] In the disclosed embodiment, when there is no archived historical data for remote sensing data processing or a new observation platform or sensor that meets the data type, timeliness, data parameters and data quality in the user data requirements, the remote sensing data processing system will not be able to obtain remote sensing data that meets the user data requirements, such as obtaining global images in a short time, etc., then the remote sensing data processing system refuses to accept the user data requirements, and the closed-loop task fails to close according to the platform capability deficiency. At the same time, the remote sensing data processing system provides the data type, timeliness, data parameters and quality that can serve the user based on the historical archived data and the observation platform situation. Otherwise, the closed-loop task status is changed to receiving user data requirements.

[0091] The historical archived data of the remote sensing data processing system generally adopts a hierarchical storage solution, with the latest observed data and hot spot data stored in online storage, and historical data stored in near-line or offline storage. In order to save storage resources, only certain types of products are stored in near-line and offline storage systems. Therefore, when the archived data required by the user is stored in the online storage system, it can be distributed directly; when it is stored in the near-line or offline storage system, it needs to be migrated back before it can be distributed; if it has been deleted, it is necessary to migrate the corresponding low-level product data of the data back and then re-produce it, and then distribute the product data after the re-production is successful.

[0092] Specifically, Figure 3 As shown, step S2 specifically includes: steps S21 to S23.

[0093] In operation S21, if there are O scenes of historical data, the time and space satisfy the first i1 In the case of sub-virtual data requirements, O historical data sub-reprocessing tasks are generated, where O≥1.

[0094] In the embodiment of the present disclosure, the process of generating O historical data sub-reprocessing tasks specifically includes: S211 to S214.

[0095] In operation S211, the processing parameters and data quality of the j-th scene historical data both meet the requirements of the f-th scene historical data. i1 In the case of sub-virtual data demand, according to the status of the j-th scene historical data, a distribution task of the j-th scene historical data, a migration task of the j-th scene historical data, or a low-level historical data migration and re-production task corresponding to the j-th scene historical data is generated. Among them, 1≤j≤O.

[0096] For example, the status of the j-th scene historical data includes online, near-line, offline, or deleted.

[0097] For example, when the processing parameters and data quality of the jth scene historical data meet the requirements of the fth scene historical data, i1 In the case of sub-virtual data demand, if the j-th scene historical data is online, the corresponding j-th sub-reprocessing task is successfully closed and the j-th scene data distribution task is initiated. If the j-th scene historical data is near-line or offline, the j-th scene historical data migration task is initiated. After the migration is successful, the j-th sub-reprocessing task is successfully closed and the j-th scene data distribution task is initiated; if the j-th scene historical data is deleted, the low-level historical data migration and re-production task corresponding to the j-th scene historical data is initiated.

[0098] For example, in a remote sensing data processing system, the data levels can be divided into 0 to 3. If the j-th scene historical data is level 3 data, the lower-level historical data corresponding to the j-th scene historical data is level 2, level 1, and level 0 data. According to the status of the corresponding low-level historical data, the low-level historical data migration and re-production tasks corresponding to the j-th scene historical data are initiated in sequence until the low-level historical data migration and re-production tasks succeed or fail.

[0099] In the disclosed embodiment, remote sensing data is managed and distributed in units of scenes and frames. The same scene / frame remote sensing data can be processed with multiple sets of processing parameters and scientific algorithms to generate remote sensing data products of different levels and qualities. i1 If there is a need for sub-virtual data, O historical data sub-reprocessing tasks are initiated in units of scenes.

[0100] For example, if the jth scene historical data processing parameters and product quality just meet the fth scene i1 The sub-virtual data demand determines the storage status of the j-th scene data. If it is online, the j-th scene data does not need to be re-produced. The sub-reprocessing task is successfully closed with the data online as the status, and the distribution task in step S4 is initiated.

[0101] For example, if the j-th scene historical data is near-line or offline, the historical data migration task is initiated first. After the migration is successful, the sub-reprocessing task is successfully closed with the data migration success status, and the distribution task in step S4 is initiated.

[0102] For example, if the historical data of the jth scene has been deleted, the low-level product data migration task of the scene is initiated first, and after the migration is successful, the historical data re-production task is initiated.

[0103] In operation S212, the processing parameters and data quality of the j-th scene historical data do not meet the requirements of the f-th scene historical data. i1 In case of child virtual data requirements, according to the f i1 Whether there are optional processing parameters or scientific algorithms for the sub-virtual data requirements to end the task processing of the j-th scene historical data, generate the re-production task of the j-th scene historical data, or generate the low-level historical data migration task corresponding to the j-th scene historical data.

[0104] In the disclosed embodiment, when the j-th scene historical data processing parameters and quality do not meet the data requirements, if there are no optional processing parameters or scientific algorithms, the j-th sub-reprocessing task does not meet the closed loop according to the data processing parameters and quality (that is, the j-th sub-reprocessing task fails the closed loop). If there are optional processing parameters or scientific algorithms, the storage status of the low-level data corresponding to the j-th scene is determined. If the low-level data corresponding to the j-th scene is online, the historical data re-production task is initiated; if the low-level data corresponding to the j-th scene is near-line or offline, the j-th scene low-level historical data migration task is initiated first, and after the migration is successful, the historical data re-production task is initiated again.

[0105] In the disclosed embodiment, the processing parameters or scientific algorithms of the remote sensing data processing system are registered in the system in the form of configuration files, plug-ins, etc. In the normal mode, the remote sensing data processing system processes with default parameters and scientific algorithms, and provides differentiated processing parameters and scientific algorithms to process specific scene data. Therefore, when the processing parameters or quality of the j-th scene historical data in the remote sensing data processing system do not meet the requirements, it is first determined whether there are optional processing parameters, scientific algorithms, processing procedures, etc. for this type of data in addition to the default processing parameters and scientific algorithm versions in the remote sensing data processing system.

[0106] For example, if there are no optional processing parameters, scientific algorithms, processing flows, etc., it means that the data quality can no longer be improved using the default processing parameters and scientific algorithm versions, and the j-th sub-processing task can only be performed according to the data processing parameters, and the quality does not meet the closed loop.

[0107] For example, if there are optional processing parameters, scientific algorithms, processing procedures, etc., the remote sensing data processing system selects appropriate processing parameters and scientific algorithms to re-produce the j-th scene historical data product according to the user data requirements. At this time, the remote sensing system only needs to determine whether the low-level data required for the re-production of the j-th scene historical data is online. If the j-th scene historical data is online, the j-th scene data re-production task can be directly initiated. If the j-th scene historical data is near-line or offline, the task of re-migration of the low-level data required for the j-th scene re-production is initiated first, and then the task of re-production of the j-th scene data is initiated after the re-migration task is successful.

[0108] In operation S213, when the j-th scene historical data migration task fails, a failure alarm is generated. If the failure alarm times out, the j-th scene historical data reprocessing task is closed with the migration task failure.

[0109] In the disclosed embodiment, since the historical archived data is stored in an offline storage system such as a tape library, there is a situation where data loss or storage medium damage results in the inability to restore the archived data normally, causing the historical data migration task to fail; at the same time, network and software bugs and other reasons will also cause the migration task to fail. Therefore, when the migration task fails, a task failure alarm is first issued to prompt the remote sensing data processing system to use automatic or manual methods to troubleshoot the cause of the failure. If the fault is eliminated within the specified time and the data is successfully migrated back, the subsequent distribution or re-production task is initiated according to step S21. If the fault is not eliminated or the data is not successfully migrated back within the specified time, the j-th scene historical data reprocessing task will be closed with the migration task failure. Among them, the failure alarm timeout period is generally 48 hours.

[0110] In operation S214, after the j-th scene historical data re-production task is successful, if the processing parameters and data quality meet the data requirements, the j-th sub-re-processing task is successfully closed and the j-th scene data distribution task is initiated. If the re-production task fails or the processing parameters and data quality do not meet the data requirements, the j-th sub-re-processing task is closed if the re-production task fails or the processing parameters and data quality do not meet the data requirements.

[0111] In the disclosed embodiment, after the low-level data migration task required for the re-production of the j-th scene historical data is successfully completed, the remote sensing data processing system automatically generates a remote sensing data processing production order according to the specified processing parameters, and calls the specified scientific algorithm plug-in to re-produce the remote sensing data. When there are abnormalities in the data, processing algorithms, scientific algorithms, computing equipment, networks, etc., the production task will fail.

[0112] For example, if the remote sensing system still cannot produce qualified products after automatic and manual processing by the remote sensing system, the jth sub-processing task will be closed with the failure of the re-production task.

[0113] For example, if the product production task is successful, but the processing parameters or product quality still do not meet the data requirements, such as the cloud / snow coverage is less than 20%, the uncontrolled positioning accuracy is better than 20 meters, etc., then the j-th sub-processing task will fail to close the loop due to the processing parameters or product quality not meeting the data requirements.

[0114] For example, if the processing parameters and data quality meet the data requirements, the j-th sub-processing task is successfully closed and the j-th scene data distribution task is initiated.

[0115] In operation S22, there are J sub-reprocessing tasks that are successfully closed among the O historical data sub-reprocessing tasks, and the spatial range SR generated by merging the J historical data sub-reprocessing tasks is i1 With spatial range SH i1 In the same case, the historical data reprocessing requirement is R i1 The loop is closed successfully. Where 1≤J≤O.

[0116] In the embodiment of the present disclosure, after the processing of steps S211 to S214, if there are J (J≤M) sub-reprocessing tasks in the O sub-reprocessing tasks that are successfully closed, and the merged spatial range is SR i1 , if SR i1 =SH i1 , then R i1 The reprocessing task is successfully closed. i1 ≠SH i1 ,and Then R i1 The reprocessing task is not completely successful to alert the closed loop (i.e., the R i1 Reprocess the task failure loop). Then R i1 Reprocessing task failure loop (i.e., R i1 Reprocessing task failure loop).

[0117] In the embodiment of the present disclosure, the same observation object has multiple archived data observed in different time periods in the remote sensing data processing system. Therefore, although there is a failure in the relocation task in step S213 or a failure in the re-production task in step S214, as long as the spatial range SR generated by merging the successfully reprocessed J scene data is i1 =SH i1 , that is, under the premise of satisfying data timeliness, processing parameters and data quality, although some sub-processing tasks fail, the merged spatial range still fully meets the requirements of the fth i1 Sub-virtual data requirements, then the R i1 The reprocessing task is successfully closed. At the same time, due to the failure of some tasks, the spatial range does not fully meet the requirements of the fth task. i1 Sub-virtual data requirements, i.e. SR i1 ≠SHi1 ,and Then R i1 The reprocessing task is closed with an alarm of incomplete success. In extreme cases, although the relocation task and the reproduction task are successful, there is no remote sensing data that meets the data timeliness, processing parameters and data quality to meet the requirements of the ith child virtual data, that is, Then R i1 Reprocess the task failure loop.

[0118] In operation S23, in the spatial range SR i1 With spatial range SH i1 In different situations, and Then the spatial range SH i1r The region is re-decomposed and merged into the fth i1 Observation needs.

[0119] In the embodiment of the present disclosure, as shown in step S22, there is a sub-reprocessing task failure, resulting in the final reprocessing to meet the f-th i1 The spatial range of the data scene produced by the data demand cannot fully cover the R i1 Reprocessing task space range SH i1 Situation, namely SH i1 -SR i1 =SH i1r ,and At this time, for SH i1r The spatial range area can only be obtained by re-observation, that is, i1 The sub-virtual data space required is SH i1r The area is re-decomposed and merged into the i-th virtual observation platform f-th i1 Observation needs.

[0120] After receiving the observation requirements proposed by various users, the remote sensing system coordinates the observation requirement priorities, virtual observation platform resources, and ground receiving resources, resolves conflicts and merges the observation requirements, generates observation plans and instructions, and sends them to the virtual observation platform corresponding to the physical observation platform to perform observation tasks. Therefore, one observation can meet the observation needs of multiple users; at the same time, due to the maximum single working time of the virtual observation platform (generally less than 15 minutes), sensor width and observation data quality, when the data space range that meets user needs obtained in one observation cannot fully cover user needs, the same observation requirement needs to perform multiple observation tasks.

[0121] Specifically, Figure 4 As shown, step S3 specifically includes: steps S31 to S36.

[0122] In operation S31, according to the f i1Sub-virtual data requirements, generation and f i1 Unobserved spatial range SW corresponding to observation requirements i1 Among them, SW i1 =SH i1r ∪SG i1 .

[0123] In the embodiment of the present disclosure, when the spatial range of the historical archive data after splicing cannot completely cover the first i1 When sub-virtual data is required, that is At this time, the user data demand belongs to SG i1 The data of the region can only be obtained through observation. i1 After the reprocessing task fails, the spatial range of all archived data after reprocessing successfully cannot completely cover the Rth i1 Reprocessing task space range SH i1 ,Right now Also belongs to SH i1r The data of the area can only be obtained through observation. For these two areas that need to be re-observed, the spatial range is unified into the fth i1 Observation needs.

[0124] S32, according to the parameters of the i-th virtual observation platform and the f-th ik Observe the operating parameters of the demand platform and determine the f ik The timeliness range of observation requirements [T iks， T ike ] within the observable range ST ik , and ST ik ∩SA ik =SP ik ; In SP ik >Y×SA ik In the case of ik Observation requirements; otherwise, refuse to receive the f ik Observation needs.

[0125] In the embodiment of the present disclosure, the parameters of the i-th virtual observation platform and the f-th virtual observation platform are used. ik Observation demand platform working parameters, deduction [T iks , T ike ] Observable range within time range ST ik , and ST ik ∩SA ik =SP ik , if SP ik >Y×SA ik Then receive the f ik observation requirements, otherwise the observation capability is insufficient and the reception of the f ikObservation requirements. Where Y represents the long-term deduction error correction coefficient, which is generally taken as 0.9≤Y≤1.

[0126] In the disclosed embodiment, the precise orbital elements or two-line messages at time T0 can be used to predict the trajectory of satellites and other observation platforms at any time. Based on the predicted platform trajectory and platform sensor operating parameters, the ith virtual observation platform can be deduced and calculated at [T iks , T ike ] within the time range according to the f ik Observation requirements Sensor working parameters The spatial range of observation data that can be obtained ST ik If ST ik ∩SA ik =SP ik , and SP ik =SA ik , it means that the i-th virtual observation platform has the ability to obtain the f-th ik Observation demand data capabilities.

[0127] In the disclosed embodiment, due to the factors such as the accuracy of the initial parameters of the platform trajectory, solar pressure, gravity, etc., the long-term trajectory prediction error can reach several hundred kilometers, and the farther from the current time T0, the lower the deduction accuracy. ik Is it equal to SA? ik Simulate and judge whether the i-th virtual observation platform has the ability to obtain the f-th ik There is a certain error in the observation requirement capability; at the same time, when the remote sensing system detects that the observation platform trajectory drift is greater than the threshold, the observation platform trajectory maintenance strategy will be adopted to ensure that the observation platform trajectory remains within a certain range. Therefore, the deduction error correction coefficient Y is introduced in the embodiment of the present disclosure. If SP ik >Y×SA ik , 0.9≤Y≤1, then it is considered that the i-th virtual observation platform has the ability to obtain the f-th ik Otherwise, the observation capability is insufficient and the reception of the fth ik Observe the needs and based on SP ik Generate the i-th virtual observation platform for the f-th ik Observation requirement: Maximum observable space range vector.

[0128] S33, when there is f ik In the case where the observation demand is rejected, the task corresponding to the user data demand fails, and at the same time, the maximum data service capacity of the remote sensing data processing system is provided to the user.

[0129] In the embodiment of the present disclosure, if the simulation deduction method is used in step S32 to find [T iks , T ike] The maximum observation capability of the i-th virtual observation platform cannot meet the f-th virtual observation platform within the time range. ik Observation requirements: At this time, the closed-loop task is closed due to insufficient platform capabilities, and for each observation requirement, the maximum observable spatial range vector that the platform can serve is provided to the user. Support users to adjust the observation spatial range, data type and frequency to ensure that the resubmitted data requirements are executable.

[0130] S34, when the mission planning period of the i-th virtual observation platform of the remote sensing data processing system is ΔT, and Then obtain the initial parameters of the i-th virtual observation platform and the f-th ik Platform operating parameters and unobserved space range SW required for observation ik , generate M stripe observation metatasks observable within ΔT and lock the observation area, and send the M stripe observation metatasks to the task planning subsystem. Among them, the spatial range of the mth stripe observation metatask is SA ikm , 1≤m≤M.

[0131] In the embodiment of the present disclosure, if the task planning period of the i-th virtual observation platform of the remote sensing system is ΔT, then Then obtain the initial parameters of the i-th virtual observation platform and the f-th ik Observation demand platform operating parameters and unobserved spatial range SW ik , generate M strip observation meta-tasks observable within ΔT and lock the observation area, and submit them to the task planning subsystem at the same time, where the mth strip space range is recorded as SA ikm At this time, the locked observation area is The area where the observation is not completed is The optional observation meta-task priorities are: largest coverage area, fastest timeliness, and gap filling between strips.

[0132] In the embodiment of the present disclosure, when the observation width of the virtual observation platform is S and the maximum length that can be obtained in a single observation is L, the maximum length of the strip data that can be obtained in each observation is approximately S×L. ik The rectangular envelope of the observation required space range is W×H, where W and H are width and height respectively. ik The observation requirement is at least INT(W / S+0.5)×INT(H / L+0.5)=S M The observations are needed to fully cover the fth ik The observation space range is at least S M The strip data can be spliced ​​to fully cover SA ik , and each strip corresponds to at least one observation task.

[0133] In the remote sensing data processing system, the ground task planning subsystem or the observation platform autonomous planning system resolves conflicts and merges the observation meta-tasks according to the observation meta-tasks, priorities, observation areas, virtual observation platform resources, ground receiving resources and meteorological observation conditions submitted by the remote sensing system to generate executable observation plans and instructions.

[0134] In remote sensing data processing systems, when That is, based on the current time T0, the f ik The observation time range of the observation demand is within the task planning cycle ΔT of the i-th virtual observation platform. ik Observation requirements exist in the unobserved spatial range SW ik , then obtain the initial parameters of the i-th virtual observation platform and the f-th ik Observe the operating parameters of the demand platform, perform high-precision observation deduction, and determine whether there are M observable strips covering SW within [T0, T0+ΔT]. ik If the area exists, each strip generates an observation meta-task and submits it to the task planning subsystem, and locks the observation area SA of each strip ikm Avoid repeated observations. If the locked observation area is SL ik ,but At this time, the areas that have not been observed are: The optional observation meta-task priorities are: largest coverage area, fastest timeliness, and gap filling between strips.

[0135] S35, when the task planning subsystem refuses to accept the m-th band observation meta-task, it releases the observation area SA locked corresponding to the m-th band observation meta-task ikm Among them, when the task planning subsystem receives the mth band observation meta-task, and the actual planned observation area is SA m , then lock the unobserved space range SW ik SA m area and release SA ikm -SA m Region; The observation meta-task of the mth strip is successfully closed, if there is JI scene data that satisfies the fth ik The spatial range after observation and splicing is S JI , then release SA m region, initiate distribution tasks for JI scene data, and update the observed areas and unobserved areas.

[0136] In the embodiment of the present disclosure, when the task planning subsystem refuses to accept the mth observation meta-task, the corresponding locked observation area SA is released. ikm , and update the locked area and unobserved area. Among them, SK ik =SL ik -SAikm , S.W. ik =SW ik ∪SA ikm , ensure to resubmit according to step S34 in the next observation cycle.

[0137] In the embodiment of the present disclosure, when the task planning subsystem receives the mth observation meta-task and the actual planned observation area is SA m , then lock SW ik ZhongSA m area and release SA ikm -SA m Area, after the update, the locked area and the unobserved area are: SK ik =SL ik -SA ikm +SA m , S.W. ik =SW ik +SA ikm -SA m Waiting for the observation task to be closed, if there is JI scene data that satisfies the fth ik The spatial range after observation and splicing is S JI , then release SA m region, initiate the distribution task of JI scene data, and update the SS of the completed observation area ik =SS ik ∪SA J1 and unobserved area SW ik =SA ik -SS ik -SL ik .

[0138] In the disclosed embodiment, the remote sensing system task planning subsystem receives the decomposed observation meta-task information, coordinates the observation meta-task priority, platform resources, and ground receiving resources, and when the observation time and observation area of ​​the m1th and m2th observation meta-tasks conflict, the observation meta-task with lower priority is rejected according to the task priority; when the observation time and observation area of ​​the m1th and m2th observation meta-tasks overlap, and the observation area after merging meets the platform resource constraints, the m1th and m2th observation meta-tasks are merged; when the remaining platform resources and ground receiving resources of the observation platform are insufficient to complete the m1th observation meta-task, the following are generally adopted: (1) only observe part of the observation area of ​​the m1th observation meta-task according to the maximum observation capability and make the actual planned observation area SA m Feedback to the user; (2) Refuse to accept the m1th observation meta-task, and obtain the m2th observation meta-task that matches the remaining observation capacity of the observation platform, and generate the final observation plan and instructions. Based on this, the following situations are included:

[0139] 1) When the task planning subsystem refuses to accept the mth observation meta-task, it can only release the area SA that the mth observation meta-task plans to observe. ikm , ensuring that this area can participate in the next observation cycle observation meta-task decomposition according to the unfinished observation status in the next observation cycle. At this time, the locked area and the unobserved area are SK ik =SL ik -SA ikm , S.W. ik =SW ik +SA ikm .

[0140] 2) If the task planning analysis diagram receives the mth observation meta-task, then the area SA of the actual planned observation m Areas with planned observations SA ikm Not necessarily completely consistent. Explanation: The mission planning subsystem did not formulate a SA for this observation. ikm -SA m The regional observation plan, therefore this part of the area can only be released and wait for the next observation. According to step S34, it is re-decomposed into observation meta-tasks. At this time, the locked area and the unobserved area are: SK ik =SL ik -SA ikm +SA m , S.W. ik =SW ik +SA ikm -SA m . And SA, which has already formulated an observation plan m Region, first lock SA m The area should avoid repeated observations during the observation plan closure (the observation plan closure usually takes 12 hours to 48 hours). Secondly, wait for the observation plan to close and release SA m Area, while obtaining closed-loop information, if there is JI1 scene product data covering SA m Area and satisfy the f ik If the processing parameters and data quality requirements in the observation requirements are met, the JI1 scene product data will initiate the distribution task; if there is JI2 scene product data covering SA m If the processing parameters or data quality do not meet the requirements, the J12 scene data will initiate a reprocessing task according to step S2. If there is JI after regeneration 21 If the scene meets the processing parameters and data quality requirements, then JI 21 The scene product data initiates the distribution task. At this time, JI=JI1+JI 21 Due to factors such as observation platform error, meteorological interference, and processing error in the process of observation data acquisition and processing, the actual area that meets the observation task is not consistent with the planned observation area, that is, SJI ≠SA m At this time, the observation area SS has been completed ik =SS ik ∪SA J1 , the locked observation area is SL ik =SL ik -SA m , the unobserved area is SW ik =SA ik -SS ik -SL ik .

[0141] S36, determining the fth region according to the observed region and the unobserved region updated in step S35 ik Observe whether the demand loop is closed successfully.

[0142] In the embodiment of the present disclosure, when SS ik ∩SA ik =SA ik And T0≤T ike , then the f ik The observation demand is successfully closed. If T0>T ike , then an alarm is issued and [T iks , T ike ] range, the user confirms whether to extend T ike , or by current spatial extent SS ik Closed loop. If the delay ΔT e , then all subsequent unfinished observation requirements are delayed by ΔT e , that is, T iks =T0+Δt×(k-1)+ΔT e , T ike =T0+Δt×k+ΔT e , f ik <k≤F i In particular, when k = f ik , T iks Unchanged, T ike =T0+Δt×k+ΔT e , and repeat the process at step S35. ik If the demand is met, then the f ik The observation requirement is successfully closed. If the user aborts or the confirmation times out, the f ik Observe demand failure loop.

[0143] In the embodiment of the present disclosure, when the observation resources and ground receiving resources are sufficient, the i-th virtual observation platform of the remote sensing data processing system can complete the f-th virtual observation platform in advance. ik Observation Requirements SA ikRegional observation and the parameters and quality of the observation data obtained meet the requirements of the f ik Sub-virtual data requirements, then the f ik The observation requirement is closed successfully. However, when the f ik When the observation demand has a low priority and the observation resources and ground receiving resources are scarce, T0>T ike When the observation area completed by the i-th virtual observation platform does not satisfy SS ik ∩SA ik =SA ik At this time, in order to ensure that users can fully obtain the data required, first generate [T iks , T ike ] within the range of f ik The observation demand has obtained the spatial range vector of the observation data and issued an alarm; the user determines whether to extend the end time of the observation demand T according to the observation data vector that has been obtained ike Or terminate the observation demand. If the user extends the observation demand end time ΔT e , then the observation time range of all unexecuted observation requirements corresponding to the subsequent i-th virtual observation platform is extended by ΔT e , that is, T iks =T0+Δt×(k-1)+ΔT e , T ike =T0+Δt×k+ΔT e , f ik <k≤F i In particular, when k = f ik , T iks Unchanged, T ike =T0+Δt×k+ΔT e Since most of the spatial ranges in the data requirements submitted by users are not accurate, if the user reviews the data, even if the SS ik ∩SA ik ≠SA ik But I think SS ik The actual data requirements have been met, then the f ik The observation requirement is successfully closed; if the user review is not satisfied or the confirmation times out, then the f ik The observation requirement fails to close the loop. Among them, the user review timeout is less than the observation task closing time of the next observation cycle, which is generally 48 hours.

[0144] The remote sensing system distributes product data in a distributed manner, that is, as long as a certain scene / frame of data meets the user's requirements, it will be transmitted to the user through the network, CD, etc., supporting the user to further provide feedback based on usage and optimize the remote sensing system data collection process.

[0145] Specifically, Figure 5As shown, step S4 specifically includes: steps S41 to S43.

[0146] In operation S41, at the f ik When the observation demand is successfully closed, each distribution task is matched with the historical data information stored in the remote sensing data processing system. The historical data information includes product number, data demand number, virtual data demand number, virtual sub-data demand number, observation demand number and product data.

[0147] In the disclosed embodiment, a remote sensing data product can be distributed to multiple users, each of whom has different quality requirements and uses for the same remote sensing data. Therefore, in the product data distribution process, the product distribution task is stored in the database with the product number, data requirement number, virtual data requirement number, virtual sub-data requirement number, observation requirement number and product data matching relationship to ensure that each distribution task can be traced back to the data requirement through the number.

[0148] In operation S42, if the distribution task of the ji scene data in the ji scene data is completed and no user feedback is received within a preset time, the distribution task of the ji scene data is successfully closed.

[0149] In operation S43, when the distribution task of the first scene is completed and user feedback is received within the preset time, an alarm message is generated to prompt the user; and according to the matching result of S41, the matching task of the first scene is matched to the f scene. ik Observe the requirements and return them to S2 for reprocessing. If the user data requirements are met, the task is redistributed.

[0150] In the embodiment of the present disclosure, if the closed-loop task driver initiates the jth scene distribution task and has been completed, if no user quality unsatisfactory feedback is received within the specified time, the distribution task is successfully closed. Otherwise, an alarm is issued and the matching relationship is matched to the fth scene distribution task according to step S41. ik Observe the requirements, automatically reprocess according to step S2 and redistribute if the user data requirements are met. If the reprocessing does not meet the requirements, when T0>T ike +ΔT e , the distribution task is closed according to the failure loop. When T0≤T ike +ΔT e , then modify the jith scene to correspond to the fth scene ik Observation requirements have been completed Observation area SS ik =SS ik -SC ji and unobserved area SW ik =SW ik +SC ji SC ji It is the spatial scope covered by the first scene.

[0151] In the disclosed embodiment, the remote sensing data processing system can actively / passively initiate various distribution tasks. When the user obtains the remote sensing data product distributed by the remote sensing system, further in-depth processing is required to confirm whether the product data meets the requirements. If the distribution task of the jth scene is automatically initiated and completed by the closed-loop task, when the remote sensing system does not receive the quality unsatisfactory information from the user within the specified time, the remote sensing data processing system will successfully close the distribution task. If the quality unsatisfactory information is received from the user, the distribution closed-loop task will first issue an alarm, and according to the association relationship between the distribution task and the product number, data requirement number, virtual data requirement number, virtual sub-data requirement number, observation requirement number and product data in step S41, the corresponding fth scene of the jth scene is obtained. ik Observation demand information. Secondly, according to the method in step S2, determine whether the remote sensing data processing system still has optional processing parameters and scientific algorithms to process the data. If so, reprocess the data. If the reprocessing is successful and meets the user's needs, re-initiate the distribution task. If it fails or does not meet the requirements, determine whether the current time T0 is less than the fth time. ik Observe the end time of demand, when T0>T ike +ΔT e , then it means that the f ik The observation requirement has been closed, and the first scene distribution task can only be closed as a failure. ike +ΔT e , indicating that ik The observation demand has not yet ended, and the remote sensing data processing system still has time to re-observe the ji scene data range area SC ji Therefore, modify the f ik Observation requirements have been completed. The observation area is SS. ik =SS ik -SC ji , the unobserved area is SW ik =SW ik +SC ji The remote sensing data processing system will process SC in the next observation cycle. ji The region re-determines the observation task according to the method in step S3.

[0152] Remote sensing data needs can be divided into short-term data needs and long-term data needs according to time. Among them, short-term data needs can generally meet user needs by reprocessing archived data or observing several times, while long-term data needs require remote sensing data processing systems to observe multiple times over a long period of time. Figure 6 As shown, step S5 specifically includes: steps S51 to S53.

[0153] In operation S51, a regional vector SS is generated according to the distribution task successfully closed in step S4. The regional vector SS is used by the user to adjust the data demand strategy.

[0154] In the embodiment of the present disclosure, according to the distribution task of the successful closed loop in step S4, a regional vector SS is generated in which the user data demand has completed the re-production, observation and distribution closed loop. The user adjusts the data demand strategy according to SS, including: terminating the closed loop task in advance, adjusting the data type and data timeliness range [T s , T e ], data requirement frequency F, data parameters, data quality, data object space range SA and priority.

[0155] In the disclosed embodiment, as the data demand closed-loop task is executed, the remote sensing data processing system pushes more and more remote sensing data products to the user. At the same time, the remote sensing data generates a regional vector SS that meets the user's data demand according to the closed-loop situation of the re-production, observation and distribution of the data corresponding to the data demand. The vector file includes all the feature information of each scene data, and supports the user to adjust the data demand strategy according to the SS and the actual data usage, which may specifically include:

[0156] 1) When the user finds that the data actually received has met the demand, the unfinished closed-loop observation demand closed-loop task is terminated to avoid waste of remote sensing system resources, and the data demand is closed successfully.

[0157] 2) When users find that a certain type of data is not needed or has already met the demand, they can terminate the closed-loop observation demand task of this type of data in advance, which has not yet completed the closed-loop, to avoid wasting resources of the remote sensing data processing system. The virtual data demand of this type of data is closed successfully.

[0158] 3) When the user finds that a certain type of data needs to be added, the virtual data demand is increased according to the method in step S1, and is decomposed into reprocessing tasks and observation requirements. At the same time, the reprocessing loop is completed according to step S2; and the observation requirement loop is completed according to step S3.

[0159] 4) When users find that the data validity range [T s , T e ] or the data demand frequency F does not meet the actual application requirements, then adjust T e and F, and the method in step S1 adjusts the validity range of the unfinished observation demand data [T iks , T ike ].

[0160] 5) When the user finds that data parameters need to be modified or data quality requirements need to be improved / reduced, the observation data that has been acquired is processed using the new data parameters and quality requirements according to the method in step S2, and the data parameters and quality requirements corresponding to the observation requirements that have not been completed are modified at the same time.

[0161] 6) When the user finds that the spatial range is insufficient and needs to be increased, firstly, historical data is regenerated for the increased area according to the method in step S2, and at the same time, the observation area is increased according to the user's needs for the tasks that have not yet completed the closed loop of observation requirements. When the user finds that the spatial range is too large and needs to be reduced, only the observation area of ​​the tasks whose observation requirements have not been closed needs to be modified.

[0162] 7) When the user finds that the priority is too low or too high, the user can modify the priority of the unclosed-loop task of the observation requirement so that the task planning subsystem can increase or decrease the observation probability.

[0163] In operation S52, at the i-th virtual observation platform f ik The observation requirements and their corresponding distribution tasks are successfully closed and T0≤T ie +ΔT e In this case, the i-th virtual data demand is successfully closed.

[0164] In the embodiment of the present disclosure, when the i-th virtual observation platform F ik The observation requirements and corresponding distribution tasks are successfully closed and T0≤T ie +ΔT e , then the i-th virtual data demand successfully closes the loop. If T0>T ie +ΔT e , then an alarm is issued and [T is , T ie +ΔT e ]The spatial range vector SS of the observation data obtained within the range i If the user confirms SS i If the requirements are met, the i-th virtual data requirement is successfully closed. If the user aborts or the confirmation times out, the i-th virtual data requirement fails to close.

[0165] In the disclosed embodiment, as described in the above steps S16 to S18, the user data demand is decomposed into N virtual data demands according to the virtual observation platform, and each virtual data demand is decomposed into F sub-virtual data demands according to the frequency, each sub-virtual data demand corresponds to an observation demand one by one, and each observation demand is decomposed into multiple observation tasks for observation and distribution. Therefore, when the i-th virtual observation platform F ik Each observation requirement and the corresponding data distribution task are successfully closed, and T0≤T ie +ΔT e, then the i-th virtual data needs to be closed successfully, otherwise it will be closed unsuccessfully; if T0>T ie +ΔT e , but there are still observation requirements that have not been successfully closed. ik and distribute tasks, then an alarm is issued and the i-th virtual observation platform is generated in [T is , T ie +ΔT e ] The spatial range vector has been obtained through re-production and observation within the range. If the user verifies and confirms that the spatial range and batch meet the data requirements, the i-th virtual data requirement is successfully closed. If the user aborts or the confirmation times out, the i-th virtual data requirement fails to close.

[0166] In operation S53, when T0≤T e +ΔT e If all N virtual data requirements are successfully closed, the user data requirement is successfully closed. Otherwise, an alarm is generated and the user reviews the SS. If the SS meets the requirements, the data requirement is successfully closed. If it is not met or the confirmation times out, the user data requirement fails to close.

[0167] In the disclosed embodiment, in order to complete the re-production and observation of the user data demand data, the remote sensing data processing system decomposes the user data demand into N virtual data demands according to the virtual observation platform registered by the remote sensing system. Therefore, the user data demand must be closed successfully within the time limit for all N virtual data demands to be closed successfully. e +ΔT e If the data demand has not been closed, an alarm is first generated to remind the user that the data demand has ended, and the remote sensing data processing system confirms again whether the observed data vector SS obtained meets the actual demand. If it does, the user data demand is successfully closed. If it does not meet or the user confirmation times out, the user data demand can only be closed in failure, and the data demand parameters cannot be adjusted according to the method in step S51.

[0168] It can be understood that in the embodiment of the present disclosure, the task failure closed loop refers to the end of task failure, and the demand failure closed loop refers to the end of demand failure.

[0169] Figure 7 The block diagram of a remote sensing data demand closed-loop driving processing device according to an embodiment of the present disclosure is schematically shown.

[0170] like Figure 7 As shown, the remote sensing data demand closed-loop driving processing device 700 includes: a data demand parsing module 710, a historical demand processing module 720, an observation demand processing module 730, a distribution task generation module 740 and a distribution task processing module 750. The device 700 can be used to implement reference Figure 1The described remote sensing data demand closed-loop driven processing method.

[0171] The data requirement analysis module 710 is configured to analyze the user data requirement and obtain the historical data reprocessing requirement R i1 and at least one observation requirement f ik Wherein, 1≤i≤N, k≥1. N represents the number of virtual data demands into which the user data demand is decomposed. The data demand parsing module 710 can be used to execute the above reference Figure 1 The S1 step described is not repeated here.

[0172] The historical demand processing module 720 is configured to: when the time and space range of the scene historical data in the remote sensing data processing system meets the historical data reprocessing demand R i1 In the case of generating and reprocessing requirements for historical data R i1 The historical data processing module 720 can be used to execute the above referenced Figure 1 The described S2 step will not be repeated here.

[0173] Observation demand processing module 730, configured to reprocess demand R according to historical data i1 and at least one observation requirement f ik , respectively generate and historical data reprocessing requirements R i1 The corresponding unobserved space range SG i1 and at least one observation requirement f ik The corresponding unobserved space range SW ik , and according to the unobserved spatial range SW ik , determine whether to receive at least one observation request f ik The observation requirement processing module 730 can be used to perform the above reference Figure 1 The S3 step described is not repeated here.

[0174] The distribution task generation module 740 is configured to generate distribution tasks corresponding to each observation requirement according to the observation requirements received in the observation requirement processing module. The distribution task generation module 740 can be used to perform the above reference Figure 1 The described S4 step will not be repeated here.

[0175] The distribution task processing module 750 is configured to perform closed-loop processing on each distribution task generated in the distribution task generation module to generate a task result corresponding to each distribution task. The distribution task processing module 750 can be used to perform the above reference Figure 1The described step S5 will not be repeated here.

[0176] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), on-chip devices, devices on substrates, devices on packages, application-specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0177] For example, any multiple of the data demand parsing module 710, the historical demand processing module 720, the observation demand processing module 730, the distribution task generation module 740, and the distribution task processing module 750 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the data demand parsing module 710, the historical demand processing module 720, the observation demand processing module 730, the distribution task generation module 740, and the distribution task processing module 750 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), an on-chip device, a device on a substrate, a device on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware, and firmware, or by a suitable combination of any of them. Alternatively, at least one of the data requirement parsing module 710, the historical requirement processing module 720, the observation requirement processing module 730, the distribution task generation module 740, and the distribution task processing module 750 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0178] Figure 8 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 8 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0179] like Figure 8 As shown, the electronic device 800 described in this embodiment includes: a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 to a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0180] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0181] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the I / O interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed into the storage portion 808 as needed.

[0182] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above-mentioned functions defined in the device of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the apparatus, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0183] The disclosed embodiment also provides a computer-readable storage medium, which may be included in the device / apparatus / device described in the above embodiment; or may exist independently without being assembled into the device / apparatus / device. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the remote sensing data demand closed-loop drive processing method according to the disclosed embodiment is implemented.

[0184] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution device, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 802 and / or RAM 803 described above and / or one or more memories other than ROM 802 and RAM 803.

[0185] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer device, the program code is used to enable the computer device to implement the remote sensing data demand closed-loop driving processing method provided by the embodiment of the present disclosure.

[0186] The above functions defined in the apparatus / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 801. According to the embodiment of the present disclosure, the apparatus, device, module, unit, etc. described above can be implemented by a computer program module.

[0187] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0188] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the device of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the above-described devices, equipment, means, modules, units, etc. can be implemented by computer program modules.

[0189] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0190] It should be noted that the functional modules in the various embodiments of the present disclosure may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present disclosure may essentially or in other words, the part that contributes to the prior art or all or part of the technical solution may be embodied in the form of a software product.

[0191] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the devices, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of boxes in the block diagram or flow chart, can be implemented with a dedicated hardware-based device that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0192] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or protection scopes of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments and / or protection scopes of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0193] Although the present disclosure has been shown and described with reference to specific exemplary embodiments of the present disclosure, it should be understood by those skilled in the art that various changes in form and detail may be made to the present disclosure without departing from the spirit and scope of the present disclosure as defined by the attached protection range and its equivalents. Therefore, the scope of the present disclosure should not be limited to the above-mentioned embodiments, but should be determined not only by the attached protection range, but also by the equivalents of the attached protection range.

Claims

1. A closed-loop driving processing method for remote sensing data demand, characterized in that: include: S1, analyze the user data requirements and obtain the historical data reprocessing requirements R i1 and at least one observation requirement f ik ; Wherein, 1≤i≤N, k≥1; N represents the number of virtual data demands into which the user data demand is decomposed; S2, in the remote sensing data processing system, there is O scene historical data in time and space range that meets the historical data reprocessing requirement R i1 In the case of the above, the historical data reprocessing requirement R is generated. i1 Corresponding O historical data sub-reprocessing tasks, and according to the status of the O historical data, sending the historical data corresponding to the O historical data sub-reprocessing tasks to the user; wherein O≥1; S3, reprocessing the demand R according to the historical data i1 and the at least one observation requirement f ik , respectively generate the historical data reprocessing requirements R i1 The corresponding unobserved space range SG i1 and the at least one observation requirement f ik The corresponding unobserved space range SW ik , and according to the unobserved spatial range SW ik , determine whether to receive the at least one observation requirement f ik ; S4, generating distribution tasks corresponding to each observation requirement according to the observation requirements received in S3; S5, performing closed-loop processing on each distribution task generated in S4 to generate a task result corresponding to each distribution task; Wherein, the S1 includes: S11, construct multiple virtual observation platforms to be established; S12, analyzing the user data demand to obtain data demand information; wherein the data demand information includes user information, data demand information, data type, data time range [T s , T e ], data demand frequency F, data parameters, data quality, data object space range SA and data priority; S13, creating a data requirement number corresponding to the user data requirement, and generating a data requirement closed-loop driven processing task corresponding to the user data requirement according to the data requirement number; S14, performing data conversion on the data type, the data parameter and the data quality to obtain identifiable sensor working parameters, product type, product production parameters and product quality; S15, when the data demand information does not include the data object space range SA, generating the data object space range SA according to the data demand information; when the generation of the data object space range SA fails, the task corresponding to the user data demand fails to close the loop; S16, when the data type includes multiple sensors and each type of sensor is required to completely cover the data object space range SA, decomposing the user data demand into N virtual data demands according to the multiple virtual observation platforms to be established; S17, the demand frequency F of the i-th virtual data demand among the N virtual data demands. i is greater than 1, and the processing period ∆t of the i-th virtual data demand is greater than the return period T of the i-th virtual data demand i In the case of i sub-virtual data requirements; where 1≤i≤N; S18, in the space range of existence SH i1 The historical data meets the F i The fth of the sub-virtual data requirements i1 If the child virtual data is required, the f i1 The space range in the sub-virtual data requirement is SH i1 The region is decomposed into R i1 Historical data reprocessing tasks, and when SA i1 -SH i1 =SG i1 ≠ , the spatial range SG i1 The region is decomposed into the fth i1 Observe the demand and ik The sub-virtual data requirements are decomposed into the fth ik Observation requirements, 2≤k≤F i , S.A. i1 Indicates the f i1 Sub-virtual data demand space range demand; The S2 includes: S21, when there are O scenes of historical data, time and space satisfying the first i1 In the case of sub-virtual data demand, O historical data sub-reprocessing tasks are generated; where O ≥ 1; S22: Among the O historical data sub-reprocessing tasks, there are J historical data sub-reprocessing tasks that are successfully closed, and the spatial range SR generated by merging the J historical data sub-reprocessing tasks is i1 With the spatial range SH i1 In the same case, the historical data reprocessing requirement R i1 Successfully closed loop; where 1≤J≤O; S23, in the spatial range SR i1 With the spatial range SH i1 In different cases, and SH i1 -SR i1 =SH i1r ≠ , then the spatial range SH i1r The region is re-decomposed and merged into the fth i1 Observation requirements; The S3 includes: S31, according to the f i1 Sub-virtual data requirements, generated with the f i1 Unobserved spatial range SW corresponding to observation requirements i1 ; Among them, SW i1 =SH ilr ∪SG i1 ; S32, according to the parameters of the i-th virtual observation platform and the f-th ik Observe the operating parameters of the demand platform and determine the f ik The timeliness range of observation requirements [T iks , T ike ] within the observable range ST ik , and ST ik ∩SA ik =SP ik ; In SP ik >Y×SA ik In the case of ik Observation requirements; otherwise, refuse to receive the f ik Observation requirements; where Y represents the long-term derivation error correction coefficient, 0.9≤Y≤1, SA ik Indicates the f ik Sub-virtual data demand space range demand; S33, when there is the f ik In the case where the observation request is rejected, the task corresponding to the user data request fails.

2. The remote sensing data demand closed-loop driven processing method according to claim 1, characterized in that: The S1 further comprises: S19: When the i-th sub-virtual data demand does not have a decomposed historical data reprocessing demand or an observation demand, the task corresponding to the user data demand fails to close the loop.

3. The remote sensing data demand closed-loop driven processing method according to claim 1, characterized in that: The S21 includes: S211, the processing parameters and data quality of the j-th scene historical data meet the requirements of the f-th scene historical data. i1 In the case of a sub-virtual data demand, according to the state of the j-th scene historical data, a distribution task of the j-th scene historical data is generated, a migration task of the j-th scene historical data is generated, or a low-level historical data migration and re-production task corresponding to the j-th scene historical data is generated; wherein 1≤j≤O; S212, when the processing parameters and data quality of the j-th scene historical data do not meet the requirements of the f-th scene historical data. i1 In case of child virtual data requirements, according to the f i1 Whether there are optional processing parameters or scientific algorithms for the sub-virtual data requirements, end the task processing for the j-th scene historical data, generate a re-production task for the j-th scene historical data, or generate a low-level historical data migration task corresponding to the j-th scene historical data.

4. The remote sensing data demand closed-loop driven processing method according to claim 1, characterized in that: The S3 further includes: S34, when the mission planning period of the i-th virtual observation platform of the remote sensing data processing system is ∆T, and , then obtain the initial parameters of the i-th virtual observation platform and the f-th ik Platform operating parameters and unobserved space range SW required for observation ik , generate M stripe observation metatasks observable within ∆T and lock the observation area, and send the M stripe observation metatasks to the task planning subsystem; where the spatial range of the mth stripe observation metatask is SA ikm , 1≤m≤M; S35, when the task planning subsystem refuses to accept the m-th stripe observation meta-task, the observation area SA locked corresponding to the m-th stripe observation meta-task is released. ikm ; Wherein, when the task planning subsystem receives the mth band observation meta-task, and the actual planned observation area is SA m , then lock the unobserved space range SW ik SA m area and release SA ikm -SA m In the mth band observation meta-task, if there is JI scene data that satisfies the fth ik The spatial range after observation and splicing is S JI , then release the SA m Region, the JI scene data initiates the distribution task, and updates the observed area and the unobserved area; S36, determining the fth ik Observe whether the demand loop is closed successfully.

5. The remote sensing data demand closed-loop driven processing method according to claim 4, characterized in that: The S4 includes: S41, in the f ik When the observation demand is successfully closed, each distribution task is matched with the historical data information stored in the remote sensing data processing system; wherein the historical data information includes a product number, a data demand number, a virtual data demand number, a virtual sub-data demand number, an observation demand number and product data; S42: When the distribution task of the ji scene data in the ji scene data is completed and no user feedback is received within a preset time, the distribution task of the ji scene data is successfully closed.

6. The remote sensing data demand closed-loop driven processing method according to claim 5, characterized in that: The S4 further comprises: S43, when the j-th scene data distribution task is completed and user feedback is received within a preset time, generate an alarm message to prompt the user; and match to the f-th scene data distribution task according to the matching result of S41. ik The requirements are observed and returned to S2 for reprocessing. If the user data requirements are met, the task is redistributed.

7. The remote sensing data demand closed-loop driven processing method according to claim 6, characterized in that: The S5 includes: S51, generating a regional vector SS according to the distribution task successfully closed in S4; wherein the regional vector SS is used by the user to adjust the data demand strategy; S52, at the i-th virtual observation platform f ik The observation requirements and their corresponding distribution tasks are successfully closed and T0≤T ie +∆T e In the case of , the i-th virtual data demand successfully closes the loop; S53, when T0≤T e +∆T e And when all N virtual data demands are successfully closed, the user data demand is successfully closed.

8. The remote sensing data demand closed-loop driven processing method according to claim 7, characterized in that: The S52 further includes: At the i-th virtual observation platform, f ik The observation requirements and their corresponding distribution tasks are successfully closed and T0>T ie +∆T e In the case of a warning message, an alarm message is generated and [T is , T ie +∆T e ]The regional vector SS of the observation data obtained within the range i .

9. A remote sensing data demand closed-loop drive processing device, characterized in that: include: The data demand analysis module is configured to analyze the user data demand and obtain the historical data reprocessing demand R i1 and at least one observation requirement f ik ; Wherein, 1≤i≤N, k≥1; N represents the number of virtual data demands into which the user data demand is decomposed; The historical demand processing module is configured to: in the remote sensing data processing system, the time and space range of the scene historical data that exists meets the historical data reprocessing demand R i1 In the case of the above, the historical data reprocessing requirement R is generated. i1 Corresponding O historical data sub-reprocessing tasks, and according to the status of the O historical data, sending the historical data corresponding to the O historical data sub-reprocessing tasks to the user; wherein O≥1; An observation demand processing module is configured to reprocess the demand R according to the historical data i1 and the at least one observation requirement f ik , respectively generate the historical data reprocessing requirements R i1 The corresponding unobserved space range SG i1 and the at least one observation requirement f ik The corresponding unobserved space range SW ik , and according to the unobserved spatial range SW ik , determine whether to receive the at least one observation requirement f ik ; a distribution task generation module, configured to generate distribution tasks corresponding to each observation requirement according to the observation requirements received in the observation requirement processing module; A distribution task processing module, configured to perform closed-loop processing on each distribution task generated in the distribution task generation module, and generate a task result corresponding to each distribution task; The data demand analysis module analyzes the user data demand and obtains the historical data reprocessing demand R i1 and at least one observation requirement f ik ,include: Construct multiple virtual observation platforms to be established; The user data demand is parsed to obtain data demand information; wherein the data demand information includes user information, data demand information, data type, data timeliness range [T s , T e ], data demand frequency F, data parameters, data quality, data object space range SA and data priority; Creating a data requirement number corresponding to the user data requirement, and generating a data requirement closed-loop driven processing task corresponding to the user data requirement according to the data requirement number; Performing data conversion on the data type, the data parameter and the data quality to obtain identifiable sensor working parameters, product type, product production parameters and product quality; In the case where the data demand information does not include the data object space range SA, generating the data object space range SA according to the data demand information; in the case where the generation of the data object space range SA fails, the task corresponding to the user data demand fails to close the loop; In the case where the data type includes multiple sensors and each type of sensor is required to completely cover the data object space range SA, the user data demand is decomposed into N virtual data demands according to the multiple virtual observation platforms to be established; The demand frequency F of the i-th virtual data demand among the N virtual data demands i is greater than 1, and the processing period ∆t of the i-th virtual data demand is greater than the return period T of the i-th virtual data demand i In the case of i sub-virtual data requirements; where 1≤i≤N; In the space of existence SH i1 The historical data meets the F i The fth of the sub-virtual data requirements i1 If the child virtual data is required, the f i1 The space range in the sub-virtual data requirement is SH i1 The region is decomposed into R i1 Historical data reprocessing tasks, and when SA i1 -SH i1 =SG i1 ≠ , the spatial range SG i1 The region is decomposed into the fth i1 Observe the demand and ik The sub-virtual data requirements are decomposed into the fth ik Observation requirements, 2≤k≤F i , S.A. i1 Indicates the f i1 Sub-virtual data demand space range demand; The historical demand processing module has the time and space range of the historical data in the remote sensing data processing system to meet the historical data reprocessing demand. i1 In the case of the above, the historical data reprocessing requirement R is generated. i1 The method further comprises: performing the O historical data sub-reprocessing tasks corresponding to the O historical data sub-reprocessing tasks, and sending the historical data corresponding to the O historical data sub-reprocessing tasks to the user according to the status of the O historical data. In the presence of O historical data, time and space satisfy the first i1 In the case of sub-virtual data demand, O historical data sub-reprocessing tasks are generated; where O ≥ 1; Among the O historical data sub-reprocessing tasks, there are J historical data sub-reprocessing tasks that are successfully closed, and the spatial range SR generated by merging the J historical data sub-reprocessing tasks is i1 With the spatial range SH i1 In the same case, the historical data reprocessing requirement R i1 Successfully closed loop; where 1≤J≤O; In the spatial range SR i1 With the spatial range SH i1 In different cases, and SH i1 -SR i1 =SH i1r ≠ , then the spatial range SH i1r The region is re-decomposed and merged into the fth i1 Observation requirements; The observed demand processing module is configured to reprocess the demand R according to the historical data i1 and the at least one observation requirement f ik , respectively generate the historical data reprocessing requirements R i1 The corresponding unobserved space range SG i1 and the at least one observation requirement f ik The corresponding unobserved space range SW ik , and according to the unobserved spatial range SW ik , determine whether to receive the at least one observation requirement f ik ,include: According to the f i1 Sub-virtual data requirements, generated with the f i1 Unobserved spatial range SW corresponding to observation requirements i1 ; Among them, SW i1 =SH ilr ∪SG i1 ; According to the parameters of the i-th virtual observation platform and the f-th ik Observe the operating parameters of the demand platform and determine the f ik The timeliness range of observation requirements [T iks , T ike ] within the observable range ST ik , and ST ik ∩SA ik =SP ik ; In SP ik >Y×SA ik In the case of ik Observation requirements; otherwise, refuse to receive the f ik Observation requirements; where Y represents the long-term derivation error correction coefficient, 0.9≤Y≤1, SA ik Indicates the f ik Sub-virtual data demand space range demand; When there is the f ik In the case where the observation request is rejected, the task corresponding to the user data request fails.

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