CN112713926A - Intelligent planning and cloud service scheduling method and system for satellite measurement, operation and control resources - Google Patents
Intelligent planning and cloud service scheduling method and system for satellite measurement, operation and control resources Download PDFInfo
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Abstract
The embodiment of the invention provides an intelligent planning and scheduling method for satellite measurement, operation and control resources, which comprises the following steps: integrating all problem instances according to problem instances generated by satellite measurement, operation and control resource planning and scheduling problems to construct an instance library; establishing a scheduling algorithm according to the problem example and integrating the scheduling algorithm into an algorithm library; forming a model according to the problem example and an algorithm for solving the problem example, integrating the model, and constructing a model library; testing the problem example and an algorithm for solving the problem example to generate a test case, integrating the test case and constructing a test library; and evaluating the operation state of the algorithm according to the operation conditions of the example library, the algorithm library, the model library and the test library, acquiring the current operation force resource requirement of the system, and dynamically distributing the operation force. And efficient and stable satellite measurement, operation and control resource planning and scheduling are realized.
Description
Technical Field
The invention relates to the technical field of satellite autonomous scheduling, in particular to a method and a system for intelligently planning, operating and controlling resources of a satellite and scheduling cloud services.
Background
Scheduling problems arise in many applications, such as satellite scheduling, airline personnel scheduling, vehicle scheduling problems, and the like. Applications such as these involve the allocation of resources to behaviors over time. Often resources are scarce and constrained in a number of ways, such as the capacity of the resource and/or the order of activity. Solving a typical scheduling problem may involve creating a schedule of activities that satisfy constraints and are optimal according to some criteria. Implementing the schedule may include finding a desired solution to a scheduling problem that accounts for a variety of constraints, communicating the desired solution to the appropriate person or system, and executing.
The applicant has found that the following problems need to be solved in the prior art: the planning and scheduling of the satellite measurement, operation and control resources cannot be efficiently and stably carried out.
Disclosure of Invention
The embodiment of the invention needs to solve the problem that the planning and scheduling of the satellite measurement, operation and control resources cannot be efficiently and stably carried out.
In order to achieve the above object, in one aspect, an embodiment of the present invention provides an intelligent planning and scheduling method for satellite measurement, operation and control resources, including the following steps:
integrating all problem instances according to problem instances generated by satellite measurement, operation and control resource planning and scheduling problems to construct an instance library;
establishing a scheduling algorithm according to the problem example and integrating the scheduling algorithm into an algorithm library;
forming a model according to the problem example and an algorithm for solving the problem example, integrating the model, and constructing a model library;
testing the problem example and an algorithm for solving the problem example to generate a test case, integrating the test case and constructing a test library;
and evaluating the operation state of the algorithm according to the operation conditions of the example library, the algorithm library, the model library and the test library, acquiring the current operation force resource requirement of the system, and dynamically distributing the operation force.
In another aspect, an embodiment of the present invention provides an intelligent planning and scheduling system for satellite measurement, operation and control resources, including:
the instance unit is used for integrating all the problem instances according to the problem instances generated by the satellite measurement, operation and control resource planning and scheduling problems and constructing an instance library;
the algorithm unit is used for establishing a scheduling algorithm according to the problem example and integrating the scheduling algorithm into an algorithm library;
the model unit is used for forming a model according to the problem example and an algorithm for solving the problem example, integrating the model and constructing a model library;
the test unit is used for testing the problem examples and the algorithm for solving the problem examples, generating test cases, integrating the test cases and constructing a test library;
and the distribution unit is used for evaluating the operation state of the algorithm according to the operation conditions of the instance library, the algorithm library, the model library and the test library, acquiring the current calculation force resource requirement of the system and dynamically distributing the calculation force.
The technical scheme has the following beneficial effects: the information exchange means is simplified, the instantaneity is strong, and the requirements of communication and network processing functions can be met; the reasonable allocation of the overall resources of the satellite is facilitated, and the overall social benefit is improved. And efficient and stable satellite measurement, operation and control resource planning and scheduling are realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an intelligent planning and scheduling method for satellite measurement, operation and control resources according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an intelligent planning and scheduling system for satellite measurement, operation and control resources according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the embodiment of the present invention, as shown in fig. 1, an intelligent planning and scheduling method for satellite measurement, operation and control resources is provided, which includes the following steps:
s10: integrating all problem instances according to problem instances generated by satellite measurement, operation and control resource planning and scheduling problems to construct an instance library;
the instance library is responsible for storage and management optimization of instance data, and a user-defined instance can be expanded. And collecting or randomly generating data based on each module of the satellite measurement, operation and control resource planning and scheduling module library, and combining the data to obtain a test example set of various planning and scheduling problems. Diversified test examples under various application scenes are generated for planning and scheduling problems of each type of satellite measurement, operation and control resources, the example sets of all the problems are integrated, and finally a system example library is constructed to lay a foundation for designing a scheduling optimization algorithm with high performance and strong stability.
S20: establishing a scheduling algorithm according to the problem example and integrating the scheduling algorithm into an algorithm library;
the current advanced algorithm is integrated, algorithm calling optimization is realized, and a user-defined algorithm is added into a system or a platform.
S30: forming a model according to the problem example and an algorithm for solving the problem example, integrating the model, and constructing a model library;
the high-efficiency modeling of complex problems is realized by using a new technical means, and the method is independent of a specific algorithm.
S40: testing the problem example and an algorithm for solving the problem example to generate a test case, integrating the test case and constructing a test library;
the test library comprises automatic test case generation, accurate defect positioning of the cloud platform system and compiling optimization of the program.
S50: and evaluating the operation state of the algorithm according to the operation conditions of the example library, the algorithm library, the model library and the test library, acquiring the current operation force resource requirement of the system, and dynamically distributing the operation force.
The intelligent planning and autonomous scheduling of the satellite measurement, operation and control resources under the complex dynamic scene are realized. Meanwhile, a visual model for better solving the problem of planning and scheduling of satellite measurement, operation and control resources is obtained according to reasonable abstract representation of the instance library, the model library and the algorithm library, and the optimization problem and the optimization target appearing in the space-ground integrated satellite measurement, operation and control resource planning and scheduling are clearly represented.
The instance base further includes: the problem instance is updated. And decomposing the function of the problem example into three scales of typical function expressions according to the problem function set scale, the typical function scale and the typical kernel function scale. And then, aiming at the knowledge bases with different scales, respectively adopting an adaptive knowledge base completion method to judge and realize completion and updating of the knowledge bases.
The algorithm library supports adding custom algorithms. The method library provides calculation, analysis and processing algorithms for solving the problems in the satellite scheduling process, and improves the operation efficiency of the model.
The model library comprises model integration, model calling and model building. The method realizes efficient modeling of complex problems and is independent of a specific method.
The dynamic allocation of the operational force specifically includes: and determining an algorithm and a model according to the dynamic problem example data, and dynamically combining the algorithm and the model. The dynamic problem is solved, and meanwhile, the calculation force is reasonably utilized.
In an embodiment of the present invention, as shown in fig. 2, there is also provided an intelligent planning and scheduling system for satellite measurement, operation and control resources, including:
the instance unit 21 is configured to integrate all problem instances according to the problem instances generated by the satellite measurement, operation and control resource planning and scheduling problems, and construct an instance library;
the instance library is responsible for storage and management optimization of instance data, and a user-defined instance can be expanded. And collecting or randomly generating data based on each module of the satellite measurement, operation and control resource planning and scheduling module library, and combining the data to obtain a test example set of various planning and scheduling problems. Diversified test examples under various application scenes are generated for planning and scheduling problems of each type of satellite measurement, operation and control resources, the example sets of all the problems are integrated, and finally a system example library is constructed to lay a foundation for designing a scheduling optimization algorithm with high performance and strong stability.
The algorithm unit 22 is used for establishing a scheduling algorithm according to the problem example and integrating the scheduling algorithm into an algorithm library;
the current advanced algorithm is integrated, algorithm calling optimization is realized, and a user-defined algorithm is added into a system or a platform.
The model unit 23 is used for forming a model according to the problem example and an algorithm for solving the problem example, integrating the model and constructing a model library;
the high-efficiency modeling of complex problems is realized by using a new technical means, and the method is independent of a specific algorithm.
The test unit 24 is used for testing the problem examples and the algorithm for solving the problem examples, generating test cases, integrating the test cases and constructing a test library;
the test library comprises automatic test case generation, accurate defect positioning of the cloud platform system and compiling optimization of the program.
And the distribution unit 25 is used for evaluating the operation state of the algorithm according to the operation conditions of the example library, the algorithm library, the model library and the test library, obtaining the current calculation force resource requirement of the system and dynamically distributing the calculation force.
The intelligent planning and autonomous scheduling of the satellite measurement, operation and control resources under the complex dynamic scene are realized. Meanwhile, a visual model for better solving the problem of planning and scheduling of satellite measurement, operation and control resources is obtained according to reasonable abstract representation of the instance library, the model library and the algorithm library, and the optimization problem and the optimization target appearing in the space-ground integrated satellite measurement, operation and control resource planning and scheduling are clearly represented.
The instance unit further includes: the problem instance is updated.
And decomposing the function of the problem example into three scales of typical function expressions according to the problem function set scale, the typical function scale and the typical kernel function scale. And then, aiming at the knowledge bases with different scales, respectively adopting an adaptive knowledge base completion method to judge and realize completion and updating of the knowledge bases.
The algorithm unit supports adding a custom algorithm.
The method library provides calculation, analysis and processing algorithms for solving the problems in the satellite scheduling process, and improves the operation efficiency of the model.
The model unit comprises a model integration module, a model calling module and a model building module.
The method realizes efficient modeling of complex problems and is independent of a specific method.
The dynamic allocation of the operational force specifically includes: the dynamic combination die determines an algorithm and a model according to dynamic problem example data, is favorable for solving the dynamic problem, and simultaneously reasonably utilizes the calculation force.
The technical scheme of the invention simplifies the information exchange means, has strong instantaneity and can meet the requirements of communication and network processing functions; the reasonable allocation of the overall resources of the satellite is facilitated, and the overall social benefit is improved. And efficient and stable satellite measurement, operation and control resource planning and scheduling are realized.
The above technical solutions of the embodiments of the present invention are described in detail below with reference to specific application examples, and reference may be made to the foregoing related descriptions for technical details that are not described in the implementation process.
Example 1:
an intelligent planning and autonomous scheduling cloud service system for space-ground integrated satellite measurement, operation and control resources. The method is established around an intelligent optimization application cloud platform, and efficient and stable satellite measurement, operation and control resource optimization and scheduling are realized.
(1) And (5) designing a cloud platform architecture. In order to ensure the reliability and robustness of military resource scheduling and optimization of a cloud platform system and realize huge dynamic allocation of computational resources, the cloud platform mainly comprises a model library, a method library, an example library, a platform test module and a resource control module. The model library comprises three parts of model integration, model calling and rapid modeling, and efficient modeling of complex problems is realized by using a new technical means, and the method is independent of a specific method. The instance library is responsible for storage and management optimization of instance data in the platform, and a user-defined instance can be expanded. The method library integrates the current advanced algorithm, realizes algorithm calling optimization and supports adding a custom algorithm into a platform. The platform test comprises automatic test case generation, accurate defect positioning of the cloud platform system and compiling optimization of a program. The resource control starts from time complexity analysis and space complexity analysis through the matching of a model, a method, an example and a test, the running state of the algorithm is evaluated, and the current calculation power resource requirement of the system is obtained, so that the dynamic distribution of the calculation power is realized.
(2) Platform interface constraint specification. When algorithm expansion is carried out, rapidness, convenience and accuracy can be ensured, and a reasonable platform interface constraint specification is a foundation stone for ensuring the reliability, the robustness and the safety of a platform. The method specifically comprises the following steps: parameter validity check, avoiding complex and overlong parameters, method single functionalization, method exception problem, method authority control, returning to uniform format, method naming avoiding ambiguity and the like.
(3) The algorithm encapsulates the deployment constraint specification. In order to ensure the stable operation, strict management and standard implementation of the system, an algorithm encapsulation deployment standard is formulated.
Testing before algorithm deployment: before the algorithm is formally deployed, complete test and evaluation are carried out on the algorithm program to be deployed, and the test environment is the same as the online platform environment and comprises an operating system, a database, a server version and the like. The project responsible person should organize the test data according to the test requirement before the test, and the tester uses this data test, the business function, performance and the index of the abundant test algorithm. And (4) making a test plan by a project principal, and determining the arrangement of testers of each functional module. The tester of each functional module should be assumed by the developers who are not the module.
And (3) algorithm deployment: the algorithm use description needs to contain a deployment list, including each function module and a version list. The project responsible person needs to select whether to back up the in-use system, back up the content and back up the executor according to the project content deployed at this time.
And (3) algorithm verification: the project principal needs to update the content according to the version, determine a project verification scheme, and the project verification should include: service starting verification is carried out to ensure that related services are all started; updating content test, and verifying the newly added function and the corrected BUG for 1 time; the project responsible person needs to select a processing scheme according to the verification test result, and can select modes such as rollback, field repair, later update and the like.
BUG management: and finding that BUGs in pre-deployment tests and project verification are all uniformly incorporated into a BUG management system, and determining a processing scheme by a project principal according to BUG conditions and use influences on users and merchants. And a system rollback mode is adopted for severe BUGs, and the BUGs are uniformly incorporated into a development plan of a subsequent version. For finding the BUG in the deployment process, a conservative solution is adopted.
(4) Algorithm run constraint specification
An algorithm fault tolerance mechanism comprises the following steps: when the calling algorithm fails in an unpredictable way, the platform should be able to report the error quickly and call other alternative algorithms to complete the task.
Algorithm management: aiming at the deployed algorithm, the functions of version updating, rollback, unloading and the like are provided, and the flexibility of the cloud platform is improved.
Calling an algorithm: the user may select the appropriate algorithm to deploy in the platform for different scenarios.
(5) And designing standardization of a cloud platform interface and operating carrier specification. Method definition specification of encapsulation calling algorithm: considering that the method for packaging the calling algorithm needs to have easy operation, stability and reliability, unified standardization should be satisfied when defining the algorithm so as to have simple operation when calling the algorithm and timely find the algorithm even if errors occur.
The method of the packaging calling algorithm sets the specification by function: considering that the method for packaging and calling the algorithm has the characteristics of low risk, adjustability and the like, the method can be combined for use when the function is set to be small and special and when the large-scale problem is processed.
And (3) setting the specification of the method authority of the packaging calling algorithm: in view of the security and applicability of the method of encapsulating the invocation algorithm, it is necessary to accurately define the disclosure and privacy of the method of encapsulating the invocation algorithm, and whether it is static or not.
Method parameter inspection specification of encapsulation call algorithm: considering that the validity of the parameter check of the method of the encapsulation calling algorithm is important, when the validity does not pass, a corresponding processing strategy is adopted: the disclosed method adopts display to check the throwing exception, and indicates the reason of the throwing exception; the private method adopts an assertion mode to check the validity of the construction method and ensures the uniform state of the object.
Setting specification of the length of the method parameter of the packaging calling algorithm: considering that the memory difficulty is caused by overlong method parameters of the packaging and calling algorithm, under the condition that the overlong parameters cannot be avoided, the following method is adopted for processing: using a Builder model; use of auxiliary classes; adopting a static internal type mode; packaging a plurality of parameters into a class object; the parameters are broken down into a plurality of method parameters, and the like.
The method for packaging the calling algorithm comprises the following steps of (1) specification of a cloud platform operation carrier: in consideration of the portability of the algorithm on a cloud platform and the flexibility of deployment, migration and load scheduling, a Docker cluster architecture is adopted for distributed algorithm processing: the Docker is suitable for multi-version control and maintenance based on a mirror image deployment mode; the Docker container virtualization technology can reduce a large amount of configuration environment work and can rapidly deploy each algorithm module.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. To those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described above in connection with the embodiments of the invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store program code in the form of instructions or data structures and which can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An intelligent planning and scheduling method for satellite measurement, operation and control resources is characterized by comprising the following steps:
integrating all problem instances according to problem instances generated by satellite measurement, operation and control resource planning and scheduling problems to construct an instance library;
establishing a scheduling algorithm according to the problem example and integrating the scheduling algorithm into an algorithm library;
forming a model according to the problem example and an algorithm for solving the problem example, integrating the model, and constructing a model library;
testing the problem example and an algorithm for solving the problem example to generate a test case, integrating the test case and constructing a test library;
and evaluating the operation state of the algorithm according to the operation conditions of the example library, the algorithm library, the model library and the test library, acquiring the current operation force resource requirement of the system, and dynamically distributing the operation force.
2. The intelligent planning and scheduling method for satellite measurement, operation and control resources according to claim 1, wherein the instance base further comprises: the problem instance is updated.
3. The intelligent planning, operation and control resource scheduling method for satellite measurement, operation and control according to claim 1, wherein the algorithm library supports adding a custom algorithm.
4. The intelligent planning and scheduling method for satellite measurement, operation and control resources according to claim 1, wherein the model library comprises model integration, model calling and model building.
5. The intelligent planning and scheduling method for satellite measurement, operation and control resources according to claim 1, wherein the dynamic allocation of the operational force specifically comprises:
and determining an algorithm and a model according to the dynamic problem example data, and dynamically combining the algorithm and the model.
6. The utility model provides a satellite measurement, operation and control resource intelligence planning and scheduling system which characterized in that includes:
the instance unit is used for integrating all the problem instances according to the problem instances generated by the satellite measurement, operation and control resource planning and scheduling problems and constructing an instance library;
the algorithm unit is used for establishing a scheduling algorithm according to the problem example and integrating the scheduling algorithm into an algorithm library;
the model unit is used for forming a model according to the problem example and an algorithm for solving the problem example, integrating the model and constructing a model library;
the test unit is used for testing the problem examples and the algorithm for solving the problem examples, generating test cases, integrating the test cases and constructing a test library;
and the distribution unit is used for evaluating the operation state of the algorithm according to the operation conditions of the instance library, the algorithm library, the model library and the test library, acquiring the current calculation force resource requirement of the system and dynamically distributing the calculation force.
7. The intelligent planning and scheduling system for satellite measurement, operation and control resources of claim 6, wherein the instance unit further comprises: the problem instance is updated.
8. The intelligent planning and scheduling system for satellite measurement, operation and control resources of claim 6, wherein the algorithm unit supports adding a custom algorithm.
9. The intelligent planning and scheduling system for satellite measurement, operation and control resources of claim 6, wherein the model unit comprises a model integration module, a model calling module and a model building module.
10. The intelligent planning and scheduling system for satellite measurement, operation and control resources according to claim 6, wherein the dynamic allocation of operational power specifically comprises:
and the dynamic combination module determines an algorithm and a model according to the dynamic problem instance data and dynamically combines the algorithm and the model.
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