CN112433806A - Computing model structure and scheduling execution method for cloud simulation service - Google Patents
Computing model structure and scheduling execution method for cloud simulation service Download PDFInfo
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Abstract
The invention relates to a computing model structure and a scheduling execution method for cloud simulation service, wherein the computing model structure comprises a cloud server, and a model initialization module, a model input module, a working parameter and state adjustment module, a model output module and a model resolving module which are respectively connected with the cloud server; the model initialization module acquires data in the interface parameters and initializes the data; the working parameter and state adjusting module sets the working parameters and states of the model according to the contents of the interface parameters; the model input module sets input parameters of a model; the model resolving module resolves the value of the corresponding moment according to the given simulation time; the model output module reads the model output data from the output buffer area and assigns values to corresponding output structural elements. The invention provides a cloud simulation service-oriented computing model structure and a scheduling execution method, which can provide high-performance cloud simulation service and automatically generate simulation application.
Description
Technical Field
The invention belongs to the field of computers, relates to a computing model, and particularly relates to a computing model structure and a scheduling execution method for cloud simulation service.
Background
The research and test operation of various simulation technologies cannot be separated from a simulation platform. A stable, convenient and efficient simulation platform is one of the key points for promoting the development of simulation technology. The traditional simulation platform is centered on networks and computers, but with the development of computer technology, especially the rise of cloud computing and virtualization technology, huge changes are brought to the simulation platform. The service mode of the simulation platform is gradually transferred from taking a computer and a network as the center to taking a user as the center, and a cloud simulation platform based on a cloud computing concept and fusing a virtualization technology appears. The virtualization technology is one of the key points for realizing resource supply on demand of cloud computing, and is also an indispensable technology of a cloud simulation platform. Virtualization is a logical representation of computer resources, abstracts and converts resources such as servers, CPUs, network resources, memories, hard disks and the like, solves the problem that original physical resources cannot be divided according to user requirements, and enables users to apply the resources in a more reasonable and efficient manner than the original organization structure of the resources. The maximum advantage of virtualization is that the virtualization is not limited by physical limitations, and even virtual resources more than physical resources can be allocated to users, so that the demand of the users for allocating resources as required is met, and the isolation among the resources is ensured. Early cloud simulation platforms used virtual machines to run simulation resources. In a cloud simulation environment based on a virtual machine, the requirement of a user for using simulation resources as required is met by a method for scheduling the virtual machine. Because the virtual machine has complete virtual hardware and virtualizes the whole operating system, the virtualization degree is higher, the requirements of various application programs can be met, but the problems of long time consumption for starting and great waste of physical resources exist at the same time. The same problem exists with a virtual machine based cloud emulation platform. In addition, the problems of complex simulation resource management, low federal execution efficiency and the like exist. These problems have become the bottleneck for providing users with high-quality simulation services by the cloud simulation platform.
In recent years, the Docker container technology is widely concerned by the advantages of simplicity, easy use, second-level starting and the like. The container technology is virtualization at an operating system level, and a kernel of a host operating system is shared among containers. Compared with the virtual machine technology, the method is a more lightweight virtualization technology. The container technology is an important technology in cloud computing quickly by virtue of the advantages of small mirror image volume, simple configuration, quick deployment and the like, and all large service merchants gradually release cloud products based on the container technology and even have the concept of container as a service. In the simulation platform, most simulation resources only need a model program and a dependent environment thereof during running, and are packaged into a Docker mirror image, which is undoubtedly more suitable than a heavy virtual machine. While the Docker container technology is rapidly developed, a plurality of used tools appear in the ecosphere, and the problems of container group deployment, container cross-host communication, container arrangement and the like are solved. The container simulation platform also enables a clustered container platform, and provides technical support for the container technology-based cloud simulation platform. In summary, the virtual machine has the problems of large volume, low starting speed, large waste of physical resources and the like, so that the efficiency of the existing cloud simulation platform is low, and the application of the lightweight virtualization Docker container technology to the cloud simulation platform is considered, so that the simulation resources can be managed and distributed more conveniently and uniformly, the running efficiency of the simulation platform is improved, and the existing physical node resources can be utilized efficiently. The cloud simulation platform based on Docker can provide better simulation service for users.
The cloud simulation platform is a networked modeling and simulation platform appearing in the cloud computing era and is a further improvement of the simulation grid. The idea of cloud computing is that users can obtain computing service resources on demand. In the cloud simulation platform, a user can obtain simulation resources as required, and compared with a simulation grid, the cloud simulation platform further has the capability of supporting multi-user access, the simulation resource sharing capability with finer granularity, the better fault tolerance capability and the more complete safety mechanism.
The cloud simulation platform is mainly characterized in that a user can acquire simulation resources from the cloud simulation platform as required, and can execute own simulation object service by using the acquired simulation resources to obtain a required simulation result. In order to enable the cloud simulation platform to be capable of continuously expanding and updating, users meeting conditions need to be endowed with the capability of releasing self-developed simulation model resources, and meanwhile, in order to standardize the simulation model developed by the users, the users should use a simulation development tool provided by the platform to develop the simulation model.
The disadvantages of the prior art are mainly three:
(1) cloud simulation platform lacking light-weight container
The existing cloud simulation service platforms are mostly based on virtual machines, and the cloud simulation platforms based on the lightweight containers are fewer, some of the cloud simulation platforms are not mature, the problems of low operation efficiency, complex use and the like exist, and the dynamic combination requirement of a simulation model of an equipment system is difficult to meet. Particularly, how to support functions such as virtualization and automatic assembly of isomorphic/heterogeneous model resources, dynamic combination of simulation models and the like based on a cloud platform service technology of a lightweight container is an important problem to be solved by a cloud simulation service platform.
(2) Complex simulation model reusing and combining method
At present, although a large amount of research provides good foundation for the simulation modeling of a complex system, the methods focus more on good mathematical structure and formalization analysis, and more on the effectiveness and completeness of the method and the specification given in a theoretical level. In actual cloud simulation application, the characteristics of dynamic combination of models of equipment system simulation and the like are difficult to meet, and modeling complexity is uncontrollable due to excessive levels.
(3) The development time of the simulation application is long
The development of simulation application needs to integrate a simulation model, and the existing simulation application has the problems of difficult dynamic combination and long integration time of the simulation model when integrating the simulation model service.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a computing model structure and a scheduling execution method for cloud simulation service, which can provide high-performance cloud simulation service and automatically generate simulation application.
In order to achieve the purpose, the invention adopts the following technical scheme:
a computing model structure facing cloud simulation service comprises a cloud server, and a model initialization module, a model input module, a working parameter and state adjustment module, a model output module and a model resolving module which are respectively connected with the cloud server; the model initialization module is used for acquiring data in the interface parameters and initializing the model; the working parameter and state adjusting module sets the working parameters and states of the model according to the contents of the interface parameters; the model input module is used for setting input parameters of a model; the model resolving module resolves the value of the corresponding moment according to the given simulation time, and the model resolving module can call a user-defined function to realize the internal processing logic of model resolving; the model output module reads the model output data from the output buffer area and assigns values to corresponding output structural elements.
Preferably, the model initialization module, the model input module, the working parameter and state adjustment module, the model output module and the model calculation module provided by the invention are all composed of one or more heavy-load interface functions.
Preferably, the computing model structure for the cloud simulation service provided by the invention further comprises a configuration file module connected with the cloud server.
A scheduling execution method based on the computing model structure facing the cloud simulation service, the method comprising the following steps:
1) initializing a model, acquiring values of all elements in a structural body corresponding to a structural pointer transmitted by a scheduling party, and assigning initial values to attribute variables corresponding to the model;
2) judging whether the working parameters and the states are adjusted, if so, resetting the working parameters and the states of the model by a working parameter and state adjusting module according to an adjusting command, and then performing the step 3); if not, directly performing the step 3);
3) during the operation of the simulation system, assigning values to input variables in the model through the model input module;
4) resolving results of all actions, logic judgment and action control at a specified moment through a model resolving module; the specified time is the current time T + L;
5) obtaining model output data, and storing various output data in an output buffer area as a result after the execution of the model processing operation is finished; the get output data operation takes out different output data from the output buffer according to different data types.
Preferably, step 1) provided by the present invention is to perform the model initialization operation with the same parameters in one simulation and only once when the attribute variables corresponding to the model are initialized.
Preferably, the input variables in step 3) provided by the present invention include, but are not limited to, target information and a power on/off command.
Preferably, the method provided by the invention further comprises, after step 5):
6) judging whether the simulation is finished or not, if so, directly exiting the method; if not, directly jumping to the step 2) until the simulation is finished.
The invention has the advantages that:
the invention provides a cloud simulation service-oriented computing model structure, which comprises a cloud server, and a model initialization module, a model input module, a working parameter and state adjustment module, a model output module and a model resolving module which are respectively connected with the cloud server; the model initialization module is used for acquiring data in the interface parameters and initializing the model; the working parameter and state adjusting module is used for setting the working parameters and states of the model according to the contents of the interface parameters; the model input module is used for setting input parameters of the model; the model resolving module resolves the value of the corresponding moment according to the given simulation time, and the model resolving module can call a user-defined function to realize the internal processing logic of model resolving; the model output module reads the model output data from the output buffer area and assigns values to corresponding output structural elements. According to the input-output relation between simulation models, the simulation models can be automatically integrated into simulation application to generate simulation application codes; and automatically analyzing whether the interfaces between the simulation models are matched or not according to the types of input and output data between the simulation models.
Drawings
FIG. 1 is a schematic diagram of a cloud emulation architecture;
FIG. 2 is a schematic diagram of a computing model structure for a cloud simulation service provided by the present invention;
FIG. 3 is a flowchart illustrating a method for scheduling execution according to the present invention;
FIG. 4 is a schematic diagram of simulation application code integrated by simulation model combinations;
FIG. 5 is a schematic diagram of a simulation application development process based on simulation model assembly;
FIG. 6 is a simulation model visualization assembly diagram between simulation objects;
FIG. 7 is a schematic diagram of data interaction between simulation objects in an application embodiment.
Detailed Description
Cloud simulation platform structure
The method is based on the traditional cloud computing infrastructure as a service (IaaS)/platform as a service (PaaS)/software as a service (SaaS) framework, combines the virtualization of simulation model resources, increases the model as a service (MaaS), and takes the simulation model resources as a form of service. Based on Kubernets + Docker, a cloud simulation architecture environment with a four-layer structure is constructed, as shown in FIG. 1.
(1) And the infrastructure as a service (IaaS) layer is a set of virtualized hardware resources and related management functions, including virtual machines, virtual networks, virtual storage and resource management platforms.
(2) The platform layer (PaaS) is a set of software resources with universality and reusability, and provides an environment for development, running, management and monitoring of cloud applications.
(3) And the application layer (SaaS) is used for completely storing, managing and updating the simulation application on the cloud, and providing the simulation application service for the terminal user through a network browser or a thin client.
(4) The model layer (MaaS) is a way to interact simulation model resources as services, and is a collection of simulation model resources on the cloud. The simulation model resources are built on the resources provided by the infrastructure layer, delivered to the user through the network for use, support the user to register/view model information, provide simulation model resource management services, and form a simulation application together with the simulation platform (YH-SUPE or HLA).
The invention provides a computing model structure facing cloud simulation service on the basis of the cloud simulation platform structure, wherein the computing model structure facing the cloud simulation service comprises a cloud server, and a model initialization module, a model input module, a working parameter and state adjustment module, a model output module and a model resolving module which are respectively connected with the cloud server; the model initialization module is used for acquiring data in the interface parameters and initializing the model; the working parameter and state adjusting module is used for setting the working parameters and states of the model according to the contents of the interface parameters; the model input module is used for setting input parameters of the model; the model resolving module resolves the value of the corresponding moment according to the given simulation time, and the model resolving module can call a user-defined function to realize the internal processing logic of model resolving; the model output module reads the model output data from the output buffer area and assigns values to corresponding output structural elements.
The model initialization module, the model input module, the working parameter and state adjustment module, the model output module and the model calculation module are all composed of one or more heavy-load interface functions. The computing model structure facing the cloud simulation service further comprises a configuration file module connected with the cloud server.
Specifically, the cloud simulation service-oriented computing model structure provided by the invention provides a model structure based on a service interface so as to realize distributed development, rapid assembly and cross-platform reuse of a simulation model. Five types of standard operation interfaces of the calculation model are defined, and an interface calling execution flow, a model internal constraint rule and a local time iteration algorithm are described in detail.
The service interface of the model is embodied in the realization of the service interface of the model, and the framework defines five types of standard service interfaces provided by the model, including a model initialization module, a model input module, a working parameter and state adjustment module, a model output module and a model calculation module, as shown in fig. 2. The model initialization module mainly has the functions of acquiring data in interface parameters and initializing a model; the main function of the working parameter and state adjusting module is to set the working parameters and state of the model according to the content of the interface parameters; the main function of the model input module is to set the input parameters of the model; the model calculation module has the main function that the model calculates the value at the corresponding moment according to the given simulation time, and the operation interface can call the user-defined function to realize the internal processing logic of model calculation; the main function of the model output module is to read the model output data from the output buffer and assign values to the corresponding output structure elements.
The parameters may be simple data types or custom data types. Parameters can be classified into three categories according to interface types: the initialization variable, the input variable and the output variable correspond to the interface respectively. The initialization variables and the input variables belong to the model from the outside in the information transmission direction, but have certain differences in the physical meaning and the information use time of the model: the initialization variables generally correspond to some attribute variables, performance indexes, structural parameters and the like of the entity model, and only need to be assigned once in one simulation operation; the input variables are data information (such as output of other models) which needs to be provided from the outside during model calculation, and are often not only once in a simulation process. In addition, for initialization variables and input variables, the model only needs to run read operations when in use, and cannot perform modify operations.
The computational model is used as an independent service entity, and the only way for information interaction with the outside world is to implement the computational model through a service interface. Direct access to the outside world is prohibited inside the model, and direct access to information inside the model is also prohibited outside the model. Each class of service interface defined by the simulation model is composed of one or more overloaded interface functions. On the premise of not influencing reusability, a model developer can customize corresponding interfaces and is respectively used for directly obtaining model configuration data and environment data such as geography, electromagnetism, climate and the like from files.
In addition, the model may have its own profile module, and the reading of its profile module is solved entirely internally by the model. Meanwhile, the model can also directly call corresponding interface functions so as to efficiently and directly acquire environment data such as geography, electromagnetism, climate and the like from files.
The calculation model scheduling execution flow is shown in fig. 3.
(1) Initializing the model, acquiring values of all elements in the structural body corresponding to the structural pointer transmitted by the scheduling party, and assigning initial values to attribute variables corresponding to the model. The model initialization operations with the same parameters are performed in one simulation and only once.
(2) And adjusting the working parameters and the states, namely resetting the working parameters and the states of the model according to an adjusting command before the model resolving module is dispatched to meet the requirement of dynamically adjusting the working parameters and the states of the model. When the interface is called, the dispatcher is required to provide corresponding parameter data.
(3) And the model input data setting is mainly responsible for assigning values to input variables inside the model during the operation of the simulation system. For example, for a radar simulation model, the input data includes target information, power on and power off instructions, and the like.
(4) The model calculation operation is mainly responsible for realizing all functions of action, logic judgment, behavior control and the like of the model at a specified time (usually, the current time T + L). Before calling the model calculation operation interface, the scheduling side program needs to call the model input parameter setting operation interface to assign values to the internal variables of the model. And the model resolving operation carries out corresponding logic behavior processing according to the current values of the variables.
(5) Model output data acquisition, with various output data being stored in an output buffer as a result after the model processing operation has been performed. The get output data operation takes out different output data from the output buffer according to different data types.
The framework defines interfaces of the computational model, and in order to support the reuse of the computational model among different simulation applications, the internal logic of the computational model needs to be restricted. In order to solve the problem that the computational model realized based on the single-step iterative algorithm is used in the event-driven simulation, a local iterative algorithm for defining the computational model is also needed.
Example (b):
the simulation application is quickly constructed: the interactive relationship between a simulation object model and an object is used as a basic construction element, a simulation model combination integration method based on an object type interactive graph establishes the mapping relationship between the object interactive graph and the real world through the visual representation of the simulation object and the interaction, so that modeling personnel can intuitively and efficiently construct simulation application on an object-oriented level. The method comprises the steps of adopting XML description specifications to store simulation application composition elements and user configuration information developed by a visualization method, constructing a mapping relation between the XML elements and code templates in a cloud simulation application general modeling paradigm, and generating codes capable of running on a cloud computing platform. As shown in fig. 4.
In the cloud architecture, a simulation application development process based on simulation model assembly is different from object-oriented programming, and models physically distributed in a cloud end need to be assembled into a logic whole according to a certain logic rule. The simplified process of building the equipment architecture simulation application based on the simulation model combination integration method is shown in the following figure. Firstly, selecting a proper simulation calculation model from a cloud calculation model resource library according to the description of the simulation object to construct the simulation object, and establishing a correct input-output relationship among the simulation calculation models; secondly, an interaction relation between the simulation objects is constructed by adopting a proper message updating mechanism according to the simulation protocol, and the message updating service is provided by a simulation platform supporting the operation of the simulation objects. In FIG. 5, the dotted directed lines between simulation models indicate that the simulation objects schedule the execution of the simulation models in turn according to the input/output dependency relationship, and the solid directed lines between the simulation objects indicate that the simulation objects communicate through the message update service provided by the simulation support platform.
The assembly between simulation objects, i.e. two simulation models belonging to different simulation objects, is shown in fig. 6. It is assumed that the simulation object SimObjA contains a simulation event processing Function EventA _ Function, which is constituted by model a. Meanwhile, the simulation object SimObjB includes a simulation event processing Function EventB _ Function, which is formed of a model b. Firstly, a simulation event processing Function EventA _ Function in a simulation object SimObjA starts to be executed, input data are provided for ModlA through an input interface, a process interface of a model is scheduled for calculation, and then a calculation result of ModlA is obtained through an output interface; secondly, the simulation event processing Function EventA _ Function transmits the calculation result of ModelA to the EventB _ Function processing Function of the simulation object SimObjB by using a data updating mechanism; the event processing Function EventB _ Function analyzes the calculation result of the ModelA as input data of the ModelB, schedules a process interface of the simulation model to execute, and then acquires the calculation result of the ModelB through an output interface.
From a grammatical point of view, the main issues considered in simulation model assembly are external service interfaces, variable and parameter matching, data access, time scheduling and management, etc. of the simulation model. Because the simulation model only provides domain calculation service, the time of the simulation system is not promoted (the promotion of the simulation time is responsible for by the bottom layer simulation operation support platform), and the simulation model has a uniform external service interface and a uniform scheduling mode, so that the grammar assembly between the Model A and the Model B can be realized as long as the input and output parameters between the Model A and the Model B are matched, namely the quantity of the input and output interface parameters corresponding to the model is the same and the types of the parameter data are consistent.
From the perspective of semantic assembly, the simulation model assembly considers whether the large model generated by assembly can reflect the essential rule of the simulation system, namely whether the modeling constraint and the function description of the Model A meet the requirements of Model B execution. Taking a radar detection process in military simulation application as an example, assuming that a radar model Modela calculates target position information and transmits the target position information to a target recognition model Modela of an early warning center as input data, if the Modela adopts a rectangular coordinate system and the Modela adopts a spherical coordinate system, the Modela and Modela are invalid from a semantic perspective. Because the multi-level simulation model description method provides normalized description information for each simulation model, whether semantic assembly of the two simulation models is effective or not can be judged by matching the description information of the simulation models.
The application example is as follows:
the invention is verified in the application integration of the missile attack and defense simulation system, and the method is proved to be feasible, and specifically comprises the following steps: the red-party simulation entity comprises (1) a red-party command post, (2) a ballistic missile and (3) an interference plane; the blue party simulation entity comprises (1) a remote early warning radar, (2) a guidance radar, (3) an interception missile, (4) a blue party command post and (5) an aircraft carrier.
During the exercise of the red party and the blue party, for example, when the missile defense and attack process is to be carried out, the following steps are required to be completed: (1) the missile is launched by the command of the Hongmang to give a trajectory; (2) launching a ballistic missile to attack a blue aircraft carrier in a red direction; (3) the red interference plane remotely interferes the blue early warning radar, the guidance radar and the interception bomb by using interference equipment; (4) detecting a red interference machine and a ballistic missile by a blue remote early warning radar; (5) the method comprises the following steps that a guidance radar is used by a bluetooth party to detect a ballistic missile, and after the information about the incoming missile is detected, the missile is reported to an interception system; (6) the interception system transmits an interception bullet to intercept.
Simulation model data interface:
for the red conductor:
TABLE 1 initialization data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pFlyCommandVec | vector<FlyCommand*>* | Takeoff command |
| 2 | pFireCommandVec | vector<FireCommand*>* | Missile launching command |
No input data.
Table 2 output data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pFlyCommandVec | vector<FlyCommand*>* | Takeoff command |
| 2 | pFireCommandVec | vector<FireCommand*>* | Missile launching command |
Ballistic missiles:
TABLE 3 initialization data
TABLE 4 input data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pShipPOSVec | vector<ShipPOS*>* | Aircraft carrier position |
| 2 | pSM3ExplodeReportVec | vector<SM3ExplodeReport*>* | Interception bomb explosion |
Table 5 output data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pNewTargetInfoVec | vector<NewTargetInfo*>* | Guided missile track |
| 2 | pDF26ExplodeReportVec | vector<DF26ExplodeReport*>* | Hit in aircraft carrier or result of spontaneous explosion |
Interfering with the aircraft:
TABLE 6 initialization data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pAircraftDataInit | AircraftDataInit* | Flying along the track point |
| 2 | pAircraftCycleInitData | AircraftCycleInitData* | Flying in a circle along a certain center |
| 3 | pJamerParameter | TP_RadarJamerParameter* | Jammer parameters |
Table 7 input data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pFlyCommandVec | vector<FlyCommand*>* | Takeoff command of red party command post |
Table 8 output data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pNewTargetInfo | vector<NewTargetInfo*>* | Aircraft track |
| 2 | pJamerParameter | vector<TP_RadarJamerParameter*>* | Jammer parameters |
Remote early warning radar:
TABLE 9 initialization data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pSBIRSInitPara | SBIRSInitPara* | Satellite initialization data |
Table 10 input data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pNewTargetInfoVec | vector<NewTargetInfo*>* | Object data |
| 2 | pJamerParameterVec | vector<TP_RadarJamerParameter*>* | Interference parameter |
Table 11 output data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pOutputRadarIntelligence | vector<RadarIntelligence*> | Radar information |
Guidance radar:
table 12 initialization data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pRadarInitData | RadarInitData* | Radar parameter |
Table 13 input data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pNewTargetInfoVec | vector<NewTargetInfo*>* | Object data |
| 2 | pJamerParameter | vector<TP_RadarJamerParameter*>* | Interference parameter |
Table 14 output data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pOutputRadarIntelligence | vector<RadarIntelligence*>* | Radar information |
Intercepting the missile:
table 15 initialization data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pInitAegisUnit | InitAegisUnit* | Intercept projectile system parameters |
Table 16 input data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pSM3GoNumVec | vector<SM3GoNum*>* | Simultaneously launching the number of interception bullets |
| 2 | pRadarIntelligenceVec | vector<RadarIntelligence*>* | Radar information |
| 3 | pShipPOSVec | vector<ShipPOS*>* | Mother ship position |
Table 17 output data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pSAMMovementReportVec | vector<SAMMovementReport*> | Motion track of interceptor projectile |
| 2 | pSM3ExplodeReportVec | vector<SM3ExplodeReport*>* | Interceptor projectile explosion reporting |
For the blue commander:
table 18 initialization data
Table 19 input data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pNewTargetInfoVec | vector<NewTargetInfo*>* | Object data |
Table 20 output data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pSM3GoNumVec | vector<SM3GoNum*>* | Intercept projectile system parameters |
An aircraft carrier:
table 21 initialization data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pShipDataInit | ShipDataInit* | Ship route according to track points |
| 2 | pShipCycleInitData | ShipCycleInitData* | Sailing according to the circumference |
Table 22 input data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pDF26ExplodeReportVec | vector<DF26ExplodeReport*>* | Missile hitting aircraft carrier explosion information |
Table 23 output data
| Serial number | Name of variable | Data type | Description of the invention |
| 1 | pShipPOSVec | vector<ShipPOS*>* | Ship track |
Integration of simulation applications
(1) Data interaction between the objects is simulated as shown in FIG. 7.
(2) List of data interactions between simulation objects:
table 24 table of data interactions between simulation objects
(3) Partially auto-generated code
Claims (7)
1. A computing model structure facing cloud simulation service is characterized in that: the computing model structure facing the cloud simulation service comprises a cloud server, and a model initialization module, a model input module, a working parameter and state adjustment module, a model output module and a model resolving module which are respectively connected with the cloud server; the model initialization module is used for acquiring data in the interface parameters and initializing the model; the working parameter and state adjusting module sets the working parameters and states of the model according to the contents of the interface parameters; the model input module is used for setting input parameters of a model; the model resolving module resolves the value of the corresponding moment according to the given simulation time, and the model resolving module can call a user-defined function to realize the internal processing logic of model resolving; the model output module reads the model output data from the output buffer area and assigns values to corresponding output structural elements.
2. The cloud simulation service-oriented computing model structure of claim 1, wherein: the model initialization module, the model input module, the working parameter and state adjustment module, the model output module and the model calculation module are all composed of one or more heavy-load interface functions.
3. The cloud simulation service-oriented computing model structure of claim 1 or 2, wherein: the computing model structure facing the cloud simulation service further comprises a configuration file module connected with the cloud server.
4. A scheduling execution method based on the computing model structure facing the cloud simulation service according to claim 1, 2 or 3, characterized in that: the method comprises the following steps:
1) initializing a model, acquiring values of all elements in a structural body corresponding to a structural pointer transmitted by a scheduling party, and assigning initial values to attribute variables corresponding to the model;
2) judging whether the working parameters and the states are adjusted, if so, resetting the working parameters and the states of the model by a working parameter and state adjusting module according to an adjusting command, and then performing the step 3); if not, directly performing the step 3);
3) during the operation of the simulation system, assigning values to input variables in the model through the model input module;
4) resolving results of all actions, logic judgment and action control at a specified moment through a model resolving module; the specified time is the current time T + L;
5) obtaining model output data, and storing various output data in an output buffer area as a result after the execution of the model processing operation is finished; the get output data operation takes out different output data from the output buffer according to different data types.
5. The method of claim 4, wherein: in the step 1), when the attribute variables corresponding to the models are assigned initial values, the initialization operation of the models with the same parameters is executed in one simulation and is executed only once.
6. The method of claim 5, wherein: the input variables in step 3) include, but are not limited to, target information and a power on/off command.
7. The method according to claim 4 or 5 or 6, characterized in that: the method further comprises, after step 5):
6) judging whether the simulation is finished or not, if so, directly exiting the method; if not, directly jumping to the step 2) until the simulation is finished.
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