CN113792787B

CN113792787B - Remote sensing big data processing method and system

Info

Publication number: CN113792787B
Application number: CN202111073611.5A
Authority: CN
Inventors: 吉玮; 缪维; 赵耀; 刘圣雅
Original assignee: Jiangsu Speed Remote Sensing Big Data Research Institute Co ltd
Current assignee: Jiangsu Speed Remote Sensing Big Data Research Institute Co ltd
Priority date: 2021-09-14
Filing date: 2021-09-14
Publication date: 2022-05-06
Anticipated expiration: 2041-09-14
Also published as: CN113792787A

Abstract

The application relates to a remote sensing big data processing method and a system, a collaborative task execution requirement is created based on collaborative task diversity description of a first remote sensing big data processing end carrying the same requirement configuration key words, the created collaborative task execution requirement can effectively assist the remote sensing big data processing end with the corresponding requirement configuration statistical result in a collaborative task mode, high matching performance and compatibility of the created collaborative task execution requirement are improved, in addition, the collaborative task execution requirement not only can be used for assisting the first remote sensing big data processing end in a cluster of the corresponding remote sensing big data processing end, but also can be used for assisting other remote sensing big data processing ends carrying the same requirement configuration key words, and the abundance degree of application scenes of the created collaborative task execution requirement is guaranteed.

Description

Remote sensing big data processing method and system

Technical Field

The application relates to the technical field of remote sensing big data processing, in particular to a method and a system for processing remote sensing big data.

Background

Remote sensing (remote sensing) refers to a non-contact, remote sensing technique. Generally, this is understood to mean the detection of the radiation, reflection properties of electromagnetic waves of an object using a sensor/remote sensor. Remote sensing is the detection of a target ground object by means of a remote sensor or other electromagnetic wave sensitive instrument under the conditions of being far away from the target and not contacting the target object. The remote sensing data can be generally understood as remote sensing images, which refer to films or photos recording electromagnetic waves of various ground objects. With the development of big data, the remote sensing technology and the big data technology are gradually fused to gradually form the remote sensing big data technology. At present, the remote sensing technology is more and more widely applied, the challenge is more and more increased, in order to realize the processing of the related remote sensing task, the data processing end is generally required to be indicated, however, the related technology is difficult to ensure the matching and compatibility when the remote sensing task processing of the data processing end is indicated, and is also difficult to deal with some application scenarios.

Disclosure of Invention

In a first aspect, an embodiment of the present application provides a method for processing remote sensing big data, which is applied to a system for processing remote sensing big data, and the method at least includes: determining the description of the diversity of the collaborative tasks of a plurality of first remote sensing big data processing ends; clustering the plurality of first remote sensing big data processing ends according to the requirement configuration statistical result of the first remote sensing big data processing ends to obtain at least one cluster of the remote sensing big data processing ends, wherein the different cluster of the remote sensing big data processing ends have different requirement configuration keywords; and for a first remote sensing big data processing end cluster in the at least one remote sensing big data processing end cluster, establishing a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the diversity of the cooperative tasks of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster.

By the design, the cooperative task execution requirement is created based on the cooperative task diversity description of the first remote sensing big data processing end carrying the same requirement configuration keyword, the created cooperative task execution requirement can effectively assist the remote sensing big data processing end with the corresponding requirement configuration statistical result in a cooperative task, the high matching performance and compatibility of the created cooperative task execution requirement are improved, in addition, the cooperative task execution requirement not only can be used for assisting the first remote sensing big data processing end in the corresponding remote sensing big data processing end cluster, but also can be used for assisting other remote sensing big data processing ends carrying the same requirement configuration keyword, the abundance degree of the application scene of the created cooperative task execution requirement is ensured, and the adaptability of the cooperative task execution requirement to different application scenes is improved.

For some design schemes that can be independently implemented, the creating, through description of diversity of collaborative tasks of a localized first remote sensing big data processing end in the first remote sensing big data processing end cluster, a collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster includes: creating regional diversity description through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster; and creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the regional diversity description.

By the design, the cooperative task execution requirement with a better auxiliary effect can be created according to regional diversity description, the execution requirement can effectively assist the cooperative task for the remote sensing big data processing end with the requirement configuration keyword corresponding to the first remote sensing big data processing end cluster, and the change adaptability of the determined cooperative task execution requirement is improved.

For some design schemes that can be independently implemented, for a first remote sensing big data processing end cluster in the at least one remote sensing big data processing end cluster, creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through description of diversity of cooperative tasks of a localized first remote sensing big data processing end in the first remote sensing big data processing end cluster, including: determining a first execution requirement corresponding to each first remote sensing big data processing end in the localized first remote sensing big data processing ends respectively through the description of the collaborative task diversity of the localized first remote sensing big data processing ends in the first remote sensing big data processing end cluster; and creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster according to the created first execution requirement.

By the design, the first execution requirement created by the collaborative task diversity description of the first remote sensing big data processing end can be used for assisting the corresponding first remote sensing big data processing end and the remote sensing big data processing end carrying the same requirement configuration statistical result with the first remote sensing big data processing end, in addition, effective collaborative task assistance can be provided for the remote sensing big data processing ends with the corresponding requirement configuration keywords through the collaborative task execution requirement created by the first execution requirements corresponding to the plurality of first remote sensing big data processing ends, and the diversification of application scenes of the created collaborative task execution requirement is ensured.

For some independently implementable designs, the method further comprises: and binding and recording the cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster and the requirement configuration key word corresponding to the first remote sensing big data processing end cluster.

By the design, the corresponding collaborative task execution requirement can be flexibly determined according to the requirement configuration key words for binding type recording, the efficiency of determining the created collaborative task execution requirement is improved, on the premise that the requirement configuration key words comprise various different requirement configuration statistical results, the requirement configuration key words can be directly recorded without recording the covered requirement configuration statistical results during binding type recording, the data information required to be recorded can be reduced as much as possible, and the memory resource overhead of the system is reduced.

For some independently implementable designs, after the creating the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster, the method further comprises: determining a demand configuration statistical result of a second remote sensing big data processing end; and responding to a first target remote sensing big data processing end cluster corresponding to a demand configuration keyword which has a corresponding relation with a demand configuration statistical result of the second remote sensing big data processing end in the first remote sensing big data processing end cluster, determining a cooperative task execution requirement corresponding to the first target remote sensing big data processing end cluster as the cooperative task execution requirement of the second remote sensing big data processing end, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the second remote sensing big data processing end.

By the design, the statistical result is configured based on the demand of the second remote sensing big data processing end, the cooperative task execution requirement suitable for the second remote sensing big data processing end can be accurately positioned in the first remote sensing big data processing end cluster, and in addition, the cooperative task execution requirement is uploaded to the remote sensing task server corresponding to the second remote sensing big data processing end and can be used for assisting the second remote sensing big data processing end in cooperative task processing.

For some independently implementable designs, after the creating the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster, the method further comprises: obtaining a determination application of a cooperative task execution requirement transmitted by a remote sensing task server corresponding to a third remote sensing big data processing end; responding to a requirement configuration statistical result of a third remote sensing big data processing end covered in the determination application, determining a second target remote sensing big data processing end cluster corresponding to a requirement configuration keyword corresponding to the requirement configuration statistical result of the third remote sensing big data processing end in the first remote sensing big data processing end cluster, determining a cooperative task execution requirement corresponding to the second target remote sensing big data processing end cluster as the cooperative task execution requirement of the third remote sensing big data processing end, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the third remote sensing big data processing end.

By the design, the statistical result is configured based on the requirement of the third remote sensing big data processing end, the targeted cooperative task execution requirement can be accurately positioned for the third remote sensing big data processing end sending the confirmation application, and high-quality cooperative task assistance of the third remote sensing big data processing end is realized.

For some independently implementable designs, the method further comprises: determining a description of the diversity of the cooperative tasks of a fourth remote sensing big data processing end, and creating a first execution requirement corresponding to the fourth remote sensing big data processing end according to the description of the diversity of the cooperative tasks of the fourth remote sensing big data processing end; and in response to the inconsistency between the first execution requirement and a target cooperative task execution requirement, determining a second execution requirement corresponding to the fourth remote sensing big data processing end according to the first execution requirement and/or the target cooperative task execution requirement, where the target cooperative task execution requirement is a cooperative task execution requirement in the cooperative task execution requirements of the first remote sensing big data processing end cluster, where the cooperative task execution requirement has a corresponding relationship with the fourth remote sensing big data processing end.

By means of the design, the first execution requirement is determined based on the cooperative task diversity description of the fourth remote sensing big data processing end, the determined first execution requirement can be guaranteed to assist the cooperative task of the fourth remote sensing big data processing end in a high quality, according to the difference analysis situation of the first execution requirement and the target cooperative task execution requirement, the quality of the target cooperative task execution requirement can be checked, and the second execution requirement capable of performing high-quality cooperative task assistance on the cooperative task of the fourth remote sensing big data processing end can be created based on the first execution requirement and the target cooperative task execution requirement on the premise that the first execution requirement and the target cooperative task execution requirement are determined to be inconsistent.

For some independently implementable designs, the determining of the description of the diversity of the collaborative tasks of the first remote sensing big data processing ends comprises: for the first remote sensing big data processing end, determining a remote sensing big data log comprising the first remote sensing big data processing end; and determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log.

By the design, the collaborative task diversity description is determined based on the determined remote sensing big data log, and the service adaptability, timeliness and richness of the determined collaborative task diversity description are improved.

For some independently implementable designs, the collaborative task diversity description includes processing process characterization information; determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log, wherein the description comprises the following steps: carrying out remote sensing big data processing end identification on the determined remote sensing big data log, and creating a target identification result contained in the remote sensing big data log; and determining the processing process characteristic information of the first remote sensing big data processing terminal according to the created target identification result.

By the design, firstly, a target identification result can be created based on the first remote sensing big data processing end, then, the processing process characteristic information of the first remote sensing big data processing end is determined according to the created target identification result, the equipment state description corresponding to the first remote sensing big data processing end can be determined based on the target identification result, then, the first remote sensing big data processing end based on the equipment state description carries out processing process characteristic identification, and the intelligent degree of the processing process characteristic identification is improved.

For some independently implementable designs, the target identification result comprises a data port identification result; the determining the processing process characteristic information of the first remote sensing big data processing terminal according to the created target identification result comprises the following steps: determining feature mapping results of a plurality of port identifications covered by the data port identification result in a feature mapping space corresponding to the remote sensing big data log respectively; determining data port updating information of a first remote sensing big data processing end positioned by the data port identification result according to the characteristic mapping result; the data port updating information comprises quantitative difference of data interaction evaluation of the first remote sensing big data processing end compared with interaction benchmark evaluation under a data port standard process; and determining the data port updating information as the processing process characteristic information of the first remote sensing big data processing terminal.

By the design, the processing process characteristic information of the first remote sensing big data processing end can be accurately and reliably determined by using the data port updating information.

For some independently implementable designs, the target recognition result comprises a business intent recognition result; the determining the processing process characteristic information of the first remote sensing big data processing terminal according to the created target identification result comprises the following steps: performing service intention theme recognition on the service intention record of the first remote sensing big data processing end covered in the service intention recognition result to obtain a service intention theme recognition result; determining business intention skip quantization difference of the first remote sensing big data processing end in the business intention record of the first remote sensing big data processing end according to the business intention theme identification result; the business intention skip quantization difference comprises a quantization difference of business intention skip description of the first remote sensing big data processing end compared with a business intention reference description under a data port standard process; and determining the business intention jump quantitative difference as the processing process characteristic information of the first remote sensing big data processing end.

By the design, the processing process characteristic information of the first remote sensing big data processing end can be accurately and reliably determined by the aid of business intention skip quantitative difference.

For some independently implementable designs, the collaborative task diversity description includes a local focus feature; determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log, wherein the description comprises the following steps: identifying a target collaborative task aiming at the determined remote sensing big data log to obtain a target collaborative task identification result; the target cooperative task comprises not less than a non-standardized cooperative task expressing the abnormal local focusing; and determining the local focusing characteristic of the first remote sensing big data processing end according to the target cooperative task identification result.

Due to the design, the data processing quality of the first remote sensing big data processing end is possibly reduced due to the fact that the local focusing is abnormal, the local focusing characteristic of the first remote sensing big data processing end is identified to determine the local focusing characteristic of the first remote sensing big data processing end, the richness of the determined collaborative task diversity description can be improved, and more credible execution requirements can be determined.

For some design schemes which can be independently implemented, the determining, according to the target collaborative task recognition result, a collaborative task diversity description of the first remote sensing big data processing end includes: detecting whether the first remote sensing big data processing end has abnormal risk of local focusing or not according to the cooperative task time sequence characteristics of the non-standardized cooperative task corresponding to the target cooperative task identification result; and determining the local focusing characteristic of the first remote sensing big data processing end based on the detection result of whether the local focusing of the first remote sensing big data processing end has abnormal risk.

By the design, in order to further improve the accuracy of detecting the local focusing abnormal risk, whether the local focusing abnormal risk exists at the first remote sensing big data processing end can be determined by combining the target cooperative task identification result and the cooperative task time sequence characteristics corresponding to the target cooperative task.

For some design schemes that can be independently implemented, the detecting whether the local focusing of the first remote sensing big data processing end has an abnormal risk or not through the cooperative task time sequence characteristics of the non-standardized cooperative task corresponding to the target cooperative task identification result includes: and on the premise that the characteristic value of the cooperative task time sequence characteristic of the non-standardized cooperative task corresponding to the target cooperative task identification result is larger than the set characteristic value, determining that the local focusing of the first remote sensing big data processing end has abnormal risk.

Therefore, even if the non-standardized learning cooperative task exists at the first remote sensing big data processing end, the characteristic value of the time sequence characteristic of the cooperative task is difficult to meet the requirement of the set characteristic value, and the first remote sensing big data processing end cannot be determined to have the abnormal risk of local focusing, so that the deviation problem of the cooperative task with short duration on the local focusing identification is effectively weakened, and the accuracy of the local focusing identification is improved.

In a second aspect, an embodiment of the present application provides a remote sensing big data processing system, where the remote sensing big data processing system includes: the clustering module is used for determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing ends; clustering the plurality of first remote sensing big data processing ends according to the requirement configuration statistical result of the first remote sensing big data processing ends to obtain at least one cluster of the remote sensing big data processing ends, wherein the different cluster of the remote sensing big data processing ends have different requirement configuration keywords; and the creating module is used for creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the diversity of the cooperative tasks of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster for the first remote sensing big data processing end cluster in at least one remote sensing big data processing end cluster.

For some design schemes which can be independently implemented, the system further comprises a recording module, wherein the recording module is used for binding and recording the cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster and the requirement configuration key word corresponding to the first remote sensing big data processing end cluster.

For some design schemes which can be independently implemented, after the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster is created, the system further comprises a first uploading module used for determining a requirement configuration statistical result of a second remote sensing big data processing end; and determining a cooperative task execution requirement corresponding to the first target remote sensing big data processing end cluster as the cooperative task execution requirement of the second remote sensing big data processing end on the premise of determining the first target remote sensing big data processing end cluster corresponding to the demand configuration keyword corresponding to the demand configuration statistical result of the second remote sensing big data processing end in the first remote sensing big data processing end cluster, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the second remote sensing big data processing end.

For some design schemes that can be independently implemented, the creating module creates a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the diversity of the cooperative tasks of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster, and the method includes: creating regional diversity description through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster; establishing a collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster through the regional diversity description;

the creating module creates a collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster, and the creating module comprises the following steps: determining a first execution requirement corresponding to each first remote sensing big data processing end in the localized first remote sensing big data processing ends respectively through the description of the collaborative task diversity of the localized first remote sensing big data processing ends in the first remote sensing big data processing end cluster; creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster according to the created first execution requirement;

after the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster is created, the system further comprises a second uploading module used for obtaining a determination application of the collaborative task execution requirement transmitted by a remote sensing task server corresponding to a third remote sensing big data processing end; determining a collaborative task execution requirement corresponding to a second target remote sensing big data processing end cluster as the collaborative task execution requirement of a third remote sensing big data processing end under the premise that the second target remote sensing big data processing end cluster corresponding to a requirement configuration keyword which has a corresponding relation with the requirement configuration statistical result of the third remote sensing big data processing end is determined in the first remote sensing big data processing end cluster according to the requirement configuration statistical result of the third remote sensing big data processing end covered in the determination application, and uploading the collaborative task execution requirement to a remote sensing task server corresponding to the third remote sensing big data processing end;

the system further comprises a determining module, configured to determine a description of the diversity of the cooperative task of the fourth remote sensing big data processing end, and create a first execution requirement corresponding to the fourth remote sensing big data processing end according to the description of the diversity of the cooperative task of the fourth remote sensing big data processing end; and under the condition that the first execution requirement is inconsistent with a target cooperative task execution requirement, determining a second execution requirement corresponding to the fourth remote sensing big data processing end according to the first execution requirement and/or the target cooperative task execution requirement, wherein the target cooperative task execution requirement is a cooperative task execution requirement which has a corresponding relation with the fourth remote sensing big data processing end in the cooperative task execution requirements of the first remote sensing big data processing end cluster.

For some design schemes which can be independently implemented, the clustering module determines the description of the diversity of the collaborative tasks of a plurality of first remote sensing big data processing ends, and the description comprises the following steps: for the first remote sensing big data processing end, determining a remote sensing big data log comprising the first remote sensing big data processing end; determining the diversity description of the collaborative task of the first remote sensing big data processing end through the remote sensing big data log;

the diversity description of the cooperative task comprises processing process characteristic information; the clustering module determines the description of the diversity of the collaborative tasks of the first remote sensing big data processing end through the remote sensing big data log, and the description comprises the following steps: carrying out remote sensing big data processing end identification on the determined remote sensing big data log, and creating a target identification result contained in the remote sensing big data log; determining processing process characteristic information of the first remote sensing big data processing end according to the created target identification result;

wherein the target recognition result comprises a data port recognition result; the clustering module determines processing process characteristic information of the first remote sensing big data processing end according to the created target identification result, and the method comprises the following steps: determining feature mapping results of a plurality of port identifications covered by the data port identification result in a feature mapping space corresponding to the remote sensing big data log respectively; determining data port updating information of a first remote sensing big data processing end positioned by the data port identification result according to the characteristic mapping result; the data port updating information comprises quantitative difference of data interaction evaluation of the first remote sensing big data processing end compared with interaction benchmark evaluation under a data port standard process; determining the data port updating information as processing process characteristic information of the first remote sensing big data processing end;

wherein the target recognition result comprises a business intention recognition result; the clustering module determines processing process characteristic information of the first remote sensing big data processing end according to the created target identification result, and the method comprises the following steps: performing service intention theme recognition on the service intention record of the first remote sensing big data processing end covered in the service intention recognition result to obtain a service intention theme recognition result; determining business intention skip quantization difference of the first remote sensing big data processing end in the business intention record of the first remote sensing big data processing end according to the business intention theme identification result; the business intention skip quantization difference comprises a quantization difference of business intention skip description of the first remote sensing big data processing end compared with a business intention reference description under a data port standard process; the business intention skip quantitative difference is determined as the processing process characteristic information of the first remote sensing big data processing end;

wherein the collaborative task diversity description comprises a local focus feature; the clustering module determines the description of the diversity of the collaborative tasks of the first remote sensing big data processing end through the remote sensing big data log, and the description comprises the following steps: identifying a target collaborative task aiming at the determined remote sensing big data log to obtain a target collaborative task identification result; the target cooperative task comprises no less than one non-standardized cooperative task expressing the local focusing abnormality; determining the local focusing characteristic of the first remote sensing big data processing end according to the target cooperative task identification result;

the clustering module determines the collaborative task diversity description of the first remote sensing big data processing end according to the target collaborative task recognition result, and the method comprises the following steps: detecting whether the first remote sensing big data processing end has abnormal risk of local focusing or not according to the cooperative task time sequence characteristics of the non-standardized cooperative task corresponding to the target cooperative task identification result; determining local focusing characteristics of the first remote sensing big data processing end based on a detection result of whether local focusing exists at the first remote sensing big data processing end or not and abnormal risk exists;

the clustering module detects whether local focusing exists at the first remote sensing big data processing end and abnormal risks exist through collaborative task time sequence characteristics of non-standardized collaborative tasks corresponding to the target collaborative task identification result, and the method comprises the following steps: and on the premise that the characteristic value of the cooperative task time sequence characteristic of the non-standardized cooperative task corresponding to the target cooperative task identification result is larger than the set characteristic value, determining that the local focusing of the first remote sensing big data processing end has abnormal risk.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those of ordinary skill in the art upon examination of the following and the accompanying drawings or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which reference numerals represent similar mechanisms throughout the various views of the drawings.

FIG. 1 is a block diagram illustrating an application environment of an exemplary method of remote sensing big data processing according to some embodiments of the present application.

FIG. 2 is a schematic diagram of the hardware and software components of an exemplary remote sensing big data processing system according to some embodiments of the present application.

FIG. 3 is a flow diagram illustrating an exemplary remote sensing big data processing method and/or process according to some embodiments of the present application.

Detailed Description

In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present application may be practiced without these specific details. In other instances, well-known methods, procedures, systems, compositions, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.

These and other features, functions, methods of execution, and combination of functions and elements of related elements in the structure and economies of manufacture disclosed in the present application may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this application. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale.

Flowcharts are used herein to illustrate the implementations performed by systems according to embodiments of the present application. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.

FIG. 1 is a block diagram illustrating an exemplary remote sensing big data processing system 300, according to some embodiments of the present application, and remote sensing big data processing system 300 may include remote sensing big data processing system 100 and remote sensing task server 200.

In some embodiments, as shown in FIG. 2, remote sensing big data processing system 100 may include a processing engine 110, a network module 120, and a memory 130, processing engine 110 and memory 130 communicating through network module 120.

Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 110 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 110 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type or combination of wired or wireless network. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network module 120 may include wired or wireless network access points, such as base stations and/or network access points.

The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.

It will be appreciated that the configuration shown in FIG. 2 is merely illustrative, and that remote sensing big data processing system 100 may also include more or fewer components than shown in FIG. 2, or have a different configuration than shown in FIG. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

FIG. 3 is a flow chart of an exemplary remote sensing big data processing method and/or process, which is applied to the remote sensing big data processing system 100 in FIG. 1, and further includes the following steps.

S101, determining the description of the diversity of the collaborative tasks of a plurality of first remote sensing big data processing ends; and clustering the plurality of first remote sensing big data processing ends according to the demand configuration statistical result of the first remote sensing big data processing ends to obtain at least one cluster of the remote sensing big data processing ends, wherein the demand configuration keywords corresponding to the cluster of the remote sensing big data processing ends with differences are inconsistent.

In this embodiment of the application, the remote sensing big data processing end may be a processing device that is in communication connection with the remote sensing big data processing system through the remote sensing task server, for example, a handheld terminal, a notebook computer, or a mainframe computer, and the embodiment of the application is not limited.

Further, the description of the diversity of the collaborative task may be understood as a task state or a task feature, such as an aerial survey task state, a forest coverage monitoring processing task state, a city planning processing task state, and the like. In the embodiment of the application, the remote sensing big data can relate to various remote sensing image data, and the embodiment of the application is not listed. In addition, the statistical result of the demand configuration can be understood as attribute information or configuration information of different remote sensing big data processing terminals, and the clustering processing can be understood as grouping processing.

In some possible embodiments, the determining of the description of the collaborative task diversity of the first remote sensing big data processing terminals described in S101 may include the following technical solutions described in S1011 and S1012.

S1011, for the first remote sensing big data processing end, determining a remote sensing big data log comprising the first remote sensing big data processing end.

And S1012, determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log.

For some alternative embodiments, the collaborative task diversity description includes processing process characteristic information, and based on this, determining the collaborative task diversity description of the first remote sensing big data processing end through the remote sensing big data log described in S1012 includes: carrying out remote sensing big data processing end identification on the determined remote sensing big data log, and creating a target identification result contained in the remote sensing big data log; and determining the processing process characteristic information of the first remote sensing big data processing terminal according to the created target identification result.

For some alternative embodiments, the target recognition result includes a data port recognition result, and based on this, determining the processing progress characteristic information of the first remote sensing big data processing end according to the created target recognition result may include: determining feature mapping results of a plurality of port identifications covered by the data port identification result in a feature mapping space corresponding to the remote sensing big data log respectively; determining data port updating information of a first remote sensing big data processing end positioned by the data port identification result according to the characteristic mapping result; the data port updating information comprises quantitative difference of data interaction evaluation of the first remote sensing big data processing end compared with interaction benchmark evaluation under a data port standard process; and determining the data port updating information as the processing process characteristic information of the first remote sensing big data processing terminal.

For some alternative embodiments, the target recognition result includes a service intention recognition result, and based on this, determining the processing progress characteristic information of the first remote sensing big data processing end according to the created target recognition result may include: performing service intention theme recognition on the service intention record of the first remote sensing big data processing end covered in the service intention recognition result to obtain a service intention theme recognition result; determining the business intention jump quantitative difference of the first remote sensing big data processing end in the business intention record of the first remote sensing big data processing end according to the business intention theme identification result; the business intention skip quantization difference comprises a quantization difference of business intention skip description of the first remote sensing big data processing end compared with a business intention reference description under a data port standard process; and determining the business intention skip quantitative difference as the processing process characteristic information of the first remote sensing big data processing terminal.

For some alternative embodiments, the collaborative task diversity description includes a local focus feature (such as an attention feature), and based on this, determining the collaborative task diversity description of the first telemetric big data processing end through the telemetric big data log described in S1012 includes: identifying a target collaborative task aiming at the determined remote sensing big data log to obtain a target collaborative task identification result; the target cooperative task comprises no less than one non-standardized cooperative task expressing the local focusing abnormality; and determining the local focusing characteristic of the first remote sensing big data processing end according to the target cooperative task identification result.

For some alternative embodiments, the determining the description of the collaborative task diversity of the first remote sensing big data processing end according to the target collaborative task recognition result may include the following: detecting whether the first remote sensing big data processing end has abnormal risk of local focusing or not according to the cooperative task time sequence characteristics of the non-standardized cooperative task corresponding to the target cooperative task identification result; and determining the local focusing characteristic of the first remote sensing big data processing end based on the detection result of whether the local focusing of the first remote sensing big data processing end has abnormal risk.

Further, detecting whether the first remote sensing big data processing end has local focusing and has abnormal risk or not according to the cooperative task time sequence characteristics of the non-standardized cooperative task corresponding to the target cooperative task identification result, including: and on the premise that the characteristic value of the cooperative task time sequence characteristic of the non-standardized cooperative task corresponding to the target cooperative task identification result is larger than the set characteristic value, determining that the local focusing of the first remote sensing big data processing end has abnormal risk.

S102, for a first remote sensing big data processing end cluster in the at least one remote sensing big data processing end cluster, establishing a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the diversity of the cooperative tasks of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster.

In the embodiment of the application, the localized first remote sensing big data processing end can be understood as a part of the first remote sensing big data processing end, generally more than two first remote sensing big data processing ends, and based on this, the cooperative task execution requirement can be understood as a cooperative task guide or a cooperative task auxiliary prompt. In this embodiment of the present application, the cooperative task may be understood as a task that requires multiple first remote sensing big data processing terminals to perform cooperative processing, for example, an aerial survey processing task for each layer of a certain area, or a task that combines an unmanned aerial vehicle to perform cooperative aerial survey and remote sensing image processing, and the like, which is not limited in this embodiment of the present application.

Regarding some exemplary angles, creating a collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the collaborative task diversity of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster described in S102 may include the following: creating regional diversity description (which can be understood as a characteristic set) through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster; and creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the regional diversity description.

Viewed from other exemplary angles, the creating of the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the collaborative task diversity of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster described in S102 may include the following: determining a first execution requirement corresponding to each first remote sensing big data processing end in the localized first remote sensing big data processing ends respectively through the description of the diversity of the cooperative tasks of the localized first remote sensing big data processing ends in the cluster of the first remote sensing big data processing ends; and creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster according to the created first execution requirement. For example, the first execution requirement may be a task instruction corresponding to different first remote sensing big data processing terminals.

By implementing the above S101 and S102, the cooperative task execution requirement is created based on the description of the diversity of the cooperative task of the first remote sensing big data processing end carrying the same requirement configuration keyword, and the created cooperative task execution requirement can effectively assist the cooperative task of the remote sensing big data processing end having the corresponding requirement configuration statistical result, so that the high matching and compatibility of the created cooperative task execution requirement are improved.

On the basis of the above, the method may further include: and binding and recording the cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster and the requirement configuration key word corresponding to the first remote sensing big data processing end cluster. For example, a binding record may be understood as an associative storage.

For some further embodiments, after the creating of the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster, the method may further include: determining a demand configuration statistical result of a second remote sensing big data processing end; and determining a cooperative task execution requirement corresponding to the first target remote sensing big data processing end cluster as the cooperative task execution requirement of the second remote sensing big data processing end on the premise of determining the first target remote sensing big data processing end cluster corresponding to the demand configuration keyword corresponding to the demand configuration statistical result of the second remote sensing big data processing end in the first remote sensing big data processing end cluster, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the second remote sensing big data processing end. Further, the remote sensing task server can monitor the task processing of the second remote sensing big data processing terminal based on the cooperative task execution requirement.

For some further embodiments, after the creating of the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster, the method may further include: obtaining a determination application of a cooperative task execution requirement transmitted by a remote sensing task server corresponding to a third remote sensing big data processing end; and determining the cooperative task execution requirement corresponding to the second target remote sensing big data processing end cluster as the cooperative task execution requirement of the third remote sensing big data processing end and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the third remote sensing big data processing end on the premise of determining a second target remote sensing big data processing end cluster corresponding to a demand configuration keyword corresponding to a demand configuration statistical result of the third remote sensing big data processing end in the first remote sensing big data processing end cluster.

On the basis of the above, the method may further include: determining a description of the diversity of the cooperative tasks of a fourth remote sensing big data processing end, and creating a first execution requirement corresponding to the fourth remote sensing big data processing end according to the description of the diversity of the cooperative tasks of the fourth remote sensing big data processing end; and under the condition that the first execution requirement is inconsistent with a target cooperative task execution requirement, determining a second execution requirement corresponding to the fourth remote sensing big data processing end according to the first execution requirement and/or the target cooperative task execution requirement, wherein the target cooperative task execution requirement is a cooperative task execution requirement which has a corresponding relation with the fourth remote sensing big data processing end in the cooperative task execution requirements of the first remote sensing big data processing end cluster.

Based on the same inventive concept, the embodiment of the present application further provides a remote sensing big data processing system, which includes: the clustering module is used for determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing ends; clustering the plurality of first remote sensing big data processing ends according to the requirement configuration statistical result of the first remote sensing big data processing ends to obtain at least one cluster of the remote sensing big data processing ends, wherein the different cluster of the remote sensing big data processing ends have different requirement configuration keywords; and the creating module is used for creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the diversity of the cooperative tasks of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster for the first remote sensing big data processing end cluster in at least one remote sensing big data processing end cluster.

Under a possible design idea, the system further comprises a recording module, configured to perform binding type recording on the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster and the requirement configuration keyword corresponding to the first remote sensing big data processing end cluster.

Under a possible design idea, after the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster is created, the system further comprises a first uploading module used for determining a requirement configuration statistical result of a second remote sensing big data processing end; and determining a cooperative task execution requirement corresponding to the first target remote sensing big data processing end cluster as the cooperative task execution requirement of the second remote sensing big data processing end on the premise of determining the first target remote sensing big data processing end cluster corresponding to the demand configuration keyword corresponding to the demand configuration statistical result of the second remote sensing big data processing end in the first remote sensing big data processing end cluster, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the second remote sensing big data processing end.

Under a possible design idea, the creating module creates a collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster, and the creating module includes: creating regional diversity description through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster; establishing a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the regional diversity description;

after the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster is created, the system further comprises a second uploading module used for obtaining a determination application of the collaborative task execution requirement transmitted by a remote sensing task server corresponding to a third remote sensing big data processing end; determining a cooperative task execution requirement corresponding to a second target remote sensing big data processing end cluster as a cooperative task execution requirement of a third remote sensing big data processing end under the premise that the second target remote sensing big data processing end cluster corresponding to a demand configuration keyword which has a corresponding relation with the demand configuration statistical result of the third remote sensing big data processing end is determined in the first remote sensing big data processing end cluster according to the demand configuration statistical result of the third remote sensing big data processing end covered in the determination application, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the third remote sensing big data processing end;

Under a possible design idea, the clustering module determines the description of the diversity of the collaborative tasks of a plurality of first remote sensing big data processing terminals, and the description comprises the following steps: for the first remote sensing big data processing end, determining a remote sensing big data log comprising the first remote sensing big data processing end; determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log;

wherein the collaborative task diversity description comprises processing process characteristic information; the clustering module determines the description of the diversity of the collaborative tasks of the first remote sensing big data processing end through the remote sensing big data log, and the description comprises the following steps: carrying out remote sensing big data processing end identification on the determined remote sensing big data log, and creating a target identification result contained in the remote sensing big data log; determining processing process characteristic information of the first remote sensing big data processing end according to the created target identification result;

wherein the collaborative task diversity description comprises a local focus feature; the clustering module determines the description of the diversity of the collaborative tasks of the first remote sensing big data processing end through the remote sensing big data log, and the description comprises the following steps: identifying a target collaborative task aiming at the determined remote sensing big data log to obtain a target collaborative task identification result; the target cooperative task comprises not less than a non-standardized cooperative task expressing the abnormal local focusing; determining the local focusing characteristic of the first remote sensing big data processing end according to the target cooperative task identification result;

The skilled person can unambiguously determine some preset, reference, predetermined, set and target technical features/terms, such as threshold values, threshold intervals, threshold ranges, etc., from the above disclosure. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. Prefixes of technical-feature terms not to be explained, such as "first", "second", "previous", "next", "current", "history", "latest", "best", "target", "specified", and "real-time", etc., can be unambiguously derived and determined from the context. Suffixes of technical feature terms not to be explained, such as "list", "feature", "sequence", "set", "matrix", "unit", "element", "track", and "list", etc., can also be derived and determined unambiguously from the foregoing and the following.

The above disclosure of the embodiments of the present application will be apparent to those skilled in the art from the above description. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered as illustrative and not restrictive of the application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific terminology to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of at least one embodiment of the present application may be combined as appropriate.

In addition, those skilled in the art will recognize that the various aspects of the application may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of procedures, machines, articles, or materials, or any new and useful modifications thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.

A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the execution of aspects of the present application may be written in any combination of one or more programming languages, including object oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, or similar conventional programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages, such as Python, Ruby, and Groovy, or other programming languages. The programming code may execute entirely on the user's computer, as a stand-alone software package, partly on the user's computer, partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order of the process elements and sequences described herein, the use of numerical letters, or other designations are not intended to limit the order of the processes and methods unless otherwise indicated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware means, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

It should also be appreciated that in the foregoing description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. However, this method of disclosure is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Claims

1. A remote sensing big data processing method is characterized by being applied to a remote sensing big data processing system, and at least comprising the following steps:

determining the description of the diversity of the collaborative tasks of a plurality of first remote sensing big data processing ends; clustering the plurality of first remote sensing big data processing ends according to the requirement configuration statistical result of the first remote sensing big data processing ends to obtain at least one cluster of the remote sensing big data processing ends, wherein the different cluster of the remote sensing big data processing ends have different requirement configuration keywords;

for a first remote sensing big data processing end cluster in the at least one remote sensing big data processing end cluster, establishing a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the diversity of the cooperative tasks of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster;

the creating of the cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the diversity of the cooperative task of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster comprises the following steps: creating regional diversity description through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster; establishing a collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster through the regional diversity description;

the creating of the cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster through the description of the diversity of the cooperative task of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster comprises the following steps: determining a first execution requirement corresponding to each first remote sensing big data processing end in the localized first remote sensing big data processing ends respectively through the description of the collaborative task diversity of the localized first remote sensing big data processing ends in the first remote sensing big data processing end cluster; creating a cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster according to the created first execution requirement;

after the creating of the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster, the method further comprises the following steps: obtaining a determination application of a cooperative task execution requirement transmitted by a remote sensing task server corresponding to a third remote sensing big data processing end; determining a cooperative task execution requirement corresponding to a second target remote sensing big data processing end cluster as a cooperative task execution requirement of a third remote sensing big data processing end under the premise that the second target remote sensing big data processing end cluster corresponding to a demand configuration keyword which has a corresponding relation with the demand configuration statistical result of the third remote sensing big data processing end is determined in the first remote sensing big data processing end cluster according to the demand configuration statistical result of the third remote sensing big data processing end covered in the determination application, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the third remote sensing big data processing end;

wherein the method further comprises: determining a description of the diversity of the cooperative tasks of a fourth remote sensing big data processing end, and creating a first execution requirement corresponding to the fourth remote sensing big data processing end according to the description of the diversity of the cooperative tasks of the fourth remote sensing big data processing end; determining a second execution requirement corresponding to the fourth remote sensing big data processing end according to the first execution requirement and/or the target cooperative task execution requirement under the condition that the first execution requirement is inconsistent with the target cooperative task execution requirement, wherein the target cooperative task execution requirement is a cooperative task execution requirement which has a corresponding relationship with the fourth remote sensing big data processing end in the cooperative task execution requirements of the first remote sensing big data processing end cluster;

the determining of the description of the diversity of the collaborative tasks of the plurality of first remote sensing big data processing ends comprises the following steps: for the first remote sensing big data processing end, determining a remote sensing big data log comprising the first remote sensing big data processing end; determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log;

wherein the collaborative task diversity description comprises processing process characteristic information; determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log, wherein the description comprises the following steps: carrying out remote sensing big data processing end identification on the determined remote sensing big data log, and creating a target identification result contained in the remote sensing big data log; determining processing process characteristic information of the first remote sensing big data processing end according to the created target identification result;

wherein the target recognition result comprises a data port recognition result; the determining the processing process characteristic information of the first remote sensing big data processing terminal according to the created target identification result comprises the following steps: determining feature mapping results of a plurality of port identifications covered by the data port identification result in a feature mapping space corresponding to the remote sensing big data log respectively; determining data port updating information of a first remote sensing big data processing end positioned by the data port identification result according to the characteristic mapping result; the data port updating information comprises quantitative difference of data interaction evaluation of the first remote sensing big data processing end compared with interaction benchmark evaluation under a data port standard process; determining the data port updating information as processing process characteristic information of the first remote sensing big data processing end;

wherein the target recognition result comprises a business intention recognition result; the determining the processing process characteristic information of the first remote sensing big data processing terminal according to the created target identification result comprises the following steps: performing service intention theme recognition on the service intention record of the first remote sensing big data processing end covered in the service intention recognition result to obtain a service intention theme recognition result; determining business intention skip quantization difference of the first remote sensing big data processing end in the business intention record of the first remote sensing big data processing end according to the business intention theme identification result; the business intention skip quantization difference comprises a quantization difference of business intention skip description of the first remote sensing big data processing end compared with a business intention reference description under a data port standard process; the business intention jump quantitative difference is determined as the processing process characteristic information of the first remote sensing big data processing end;

wherein the collaborative task diversity description comprises a local focus feature; determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log, wherein the description comprises the following steps: identifying a target collaborative task aiming at the determined remote sensing big data log to obtain a target collaborative task identification result; the target cooperative task comprises no less than one non-standardized cooperative task expressing the local focusing abnormality; determining the local focusing characteristic of the first remote sensing big data processing end according to the target cooperative task identification result;

determining the collaborative task diversity description of the first remote sensing big data processing end according to the target collaborative task identification result, wherein the determining comprises the following steps: detecting whether the first remote sensing big data processing end has abnormal risk of local focusing or not according to the cooperative task time sequence characteristics of the non-standardized cooperative task corresponding to the target cooperative task identification result; determining local focusing characteristics of the first remote sensing big data processing end based on a detection result of whether local focusing exists at the first remote sensing big data processing end or not and abnormal risk exists;

the method for detecting whether the first remote sensing big data processing end has local focusing and has abnormal risk or not through the cooperative task time sequence characteristics of the non-standardized cooperative task corresponding to the target cooperative task identification result comprises the following steps: and on the premise that the characteristic value of the cooperative task time sequence characteristic of the non-standardized cooperative task corresponding to the target cooperative task identification result is larger than the set characteristic value, determining that the local focusing of the first remote sensing big data processing end has abnormal risk.

2. The remote sensing big data processing method according to claim 1, further comprising: and binding and recording the cooperative task execution requirement corresponding to the first remote sensing big data processing end cluster and the requirement configuration key word corresponding to the first remote sensing big data processing end cluster.

3. The remote sensing big data processing method according to claim 1, wherein after the creating of the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster, the method further comprises:

determining a demand configuration statistical result of a second remote sensing big data processing end;

and determining a cooperative task execution requirement corresponding to the first target remote sensing big data processing end cluster as the cooperative task execution requirement of the second remote sensing big data processing end on the premise of determining the first target remote sensing big data processing end cluster corresponding to the demand configuration keyword corresponding to the demand configuration statistical result of the second remote sensing big data processing end in the first remote sensing big data processing end cluster, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the second remote sensing big data processing end.

4. A remote sensing big data processing system, characterized in that, the remote sensing big data processing system includes:

the clustering module is used for determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing ends; clustering the plurality of first remote sensing big data processing ends according to the requirement configuration statistical result of the first remote sensing big data processing ends to obtain at least one cluster of the remote sensing big data processing ends, wherein the different cluster of the remote sensing big data processing ends have different requirement configuration keywords;

the creating module is used for creating a cooperative task execution requirement corresponding to a first remote sensing big data processing end cluster in the at least one remote sensing big data processing end cluster through the description of the diversity of the cooperative tasks of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster;

the creating module creates a collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster, and the creating module comprises the following steps: creating regional diversity description through the collaborative task diversity description of the localized first remote sensing big data processing end in the first remote sensing big data processing end cluster; establishing a collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster through the regional diversity description;

the system further comprises a determining module, configured to determine a description of the diversity of the cooperative task of the fourth remote sensing big data processing end, and create a first execution requirement corresponding to the fourth remote sensing big data processing end according to the description of the diversity of the cooperative task of the fourth remote sensing big data processing end; determining a second execution requirement corresponding to the fourth remote sensing big data processing end according to the first execution requirement and/or the target cooperative task execution requirement under the condition that the first execution requirement is not consistent with the target cooperative task execution requirement, wherein the target cooperative task execution requirement is a cooperative task execution requirement which has a corresponding relation with the fourth remote sensing big data processing end in the cooperative task execution requirements of the first remote sensing big data processing end cluster;

the clustering module determines the description of the diversity of the collaborative tasks of the first remote sensing big data processing ends, and the description comprises the following steps: for the first remote sensing big data processing end, determining a remote sensing big data log comprising the first remote sensing big data processing end; determining the description of the diversity of the collaborative tasks of the first remote sensing big data processing terminal through the remote sensing big data log;

5. The remote sensing big data processing system according to claim 4, further comprising a recording module configured to perform binding type recording on the collaborative task execution requirement corresponding to the first remote sensing big data processing side cluster and the requirement configuration keyword corresponding to the first remote sensing big data processing side cluster.

6. The remote sensing big data processing system according to claim 4, wherein after the creation of the collaborative task execution requirement corresponding to the first remote sensing big data processing end cluster, the system further comprises a first upload module for determining a requirement configuration statistical result of a second remote sensing big data processing end; and determining a cooperative task execution requirement corresponding to the first target remote sensing big data processing end cluster as the cooperative task execution requirement of the second remote sensing big data processing end under the premise of determining the first target remote sensing big data processing end cluster corresponding to the demand configuration keyword which has a corresponding relation with the demand configuration statistical result of the second remote sensing big data processing end in the first remote sensing big data processing end cluster, and uploading the cooperative task execution requirement to a remote sensing task server corresponding to the second remote sensing big data processing end.