CN115827641A - Simulation data storage method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a simulation data storage method, a simulation data storage device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring simulation result data of a model to be simulated in real time in the process of simulating the model to be simulated; converting the simulation result data into frame data in a corresponding format based on a preset frame protocol; storing the frame data in an IoTDB database. According to the method, simulation result data of a model to be simulated are stored in the IoTDB database according to the data model mapping relation between the simulation result data and the IoTDB database through operations such as acquisition and analysis of the simulation result data, so that high-speed storage operation of mass simulation result data in a high-concurrency simulation scene is realized, and the requirement for real-time analysis of mass simulation result data is met.
Description
Technical Field
The invention relates to the technical field of software engineering, in particular to a simulation data storage method and device, electronic equipment and a storage medium.
Background
With the increasing demands of collaborative modeling and simulation of industrial software, collaborative modeling and highly concurrent simulation services are correspondingly provided for each large industrial software. A simulation service based on a Modelica model may generate massive simulation result data, but a method for storing, reading and analyzing the massive simulation result data in real time by multiple terminals is lacked.
Apache IoTDB is a processing engine integrating collection, storage and analysis aiming at time sequence data, and can meet the requirements of high-speed writing, complex analysis and query on massive time sequence data in the industrial Internet of things. Therefore, the key for solving the problems is to research how to realize the storage, multi-terminal real-time reading and analysis operation of massive simulation result data of the Modelica model based on the Apache IoTDB technology.
Disclosure of Invention
The invention provides a simulation data storage method, a simulation data storage device, electronic equipment and a storage medium, and aims to solve the problem of real-time storage of mass simulation result data of a model to be simulated.
According to an aspect of the present invention, there is provided a simulation data storage method, the method including:
acquiring simulation result data of a model to be simulated in real time in the process of simulating the model to be simulated;
converting the simulation result data into frame data in a corresponding format based on a preset frame protocol;
storing the frame data in an IoTDB database.
According to another aspect of the present invention, there is provided an emulation data storage device, including:
the simulation result data acquisition module is used for acquiring simulation result data of the model to be simulated in real time in the process of simulating the model to be simulated;
the simulation result data conversion module is used for converting the simulation result data into frame data in a corresponding format based on a preset frame protocol;
and the frame data storage module is used for storing the frame data in an IoTDB database.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of storing emulation data according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the simulation data storage method according to any one of the embodiments of the present invention when the computer instructions are executed.
The embodiment of the invention provides a simulation data storage method, which simulates a model to be simulated by responding to a simulation starting instruction sent by a simulation service to obtain simulation result data of the model to be simulated, converts the simulation result data into frame data in a corresponding format through a preset frame protocol, and stores the frame data in an IoTDB database according to data model constraint of the IoTDB database, wherein the frame data comprises a set of all physical quantities of the model to be simulated. According to the technical scheme, the simulation result data of the model to be simulated are stored in the IoTDB database according to the data model mapping relation between the simulation result data and the IoTDB database through operations such as collection, management and analysis of the simulation result data, so that high-speed storage and multi-terminal real-time reading operation of mass simulation result data in a high-concurrency simulation scene are realized, and the requirement for real-time analysis of mass simulation result data is met.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for simulating data storage according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for storing emulation data according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a curve simulation result involved in a simulation data storage method according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating animation simulation results involved in a simulation data storage method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of csv format simulation result data derived according to a simulation data storage method provided by an embodiment of the invention;
FIG. 6 is a schematic structural diagram of an emulation data storage device provided in accordance with an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device implementing the simulation data storage method according to the embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a simulation data storage method according to an embodiment of the present invention, where the present embodiment is applicable to a case of storing massive simulation result data generated by simulating a model to be simulated, and the method may be executed by a simulation data storage device, where the simulation data storage device may be implemented in a hardware and/or software manner, and the simulation data storage device may be configured in any electronic device with a network communication function. As shown in fig. 1, the method includes:
s110, acquiring simulation result data of the model to be simulated in real time in the process of simulating the model to be simulated.
The simulation result data may be data generated in a process of simulating a model to be simulated. Simulation models may be tools for collaborative modeling and simulation of complex physical systems or software, etc.
Optionally, the model to be simulated includes a modeica model.
The Modelica model can be an open, object-oriented and equation-based computer language, can span different fields, conveniently realizes the modeling of a complex physical system, and can be a mechanical, electronic, electric, hydraulic, thermal, control and process-oriented system model.
As an optional but non-limiting implementation manner, simulating a model to be simulated may include steps A1-A3:
a1, loading the model to be simulated and a target model on which the model to be simulated depends in response to a simulation starting instruction sent by a simulation service;
specifically, the simulation service sends a simulation start instruction to the solver management program, and the solver management program schedules and loads the model to be simulated and the target model on which the model to be simulated depends. For example, the solver manager may load the model to be simulated and the target model on which the model to be simulated depends through the homonym simulation kernel.
Step A2, compiling and solving the model to be simulated and the target model to generate an isomorphic simulation solver;
specifically, the solver management program compiles and solves the model to be simulated and the target model on which the model to be simulated depends, and generates the homonymous simulation solver.
And A3, scheduling the homomorphic simulation solver to simulate the model to be simulated.
Specifically, the solver manager schedules the homonymous simulation solver to simulate the model to be simulated.
Optionally, the simulation state of the homonymous simulation solver is monitored in real time, and the simulation state is fed back to the simulation service.
Specifically, the solver management program detects the simulation state of the homonymy simulation solver in real time in the process of simulating the model to be simulated, and feeds the simulation state back to the simulation service, so that the simulation service has the advantages that the current simulation state can be informed to the user in time, and anxiety of the user caused by waiting is reduced.
Illustratively, fig. 2 is a schematic diagram of a simulation data storage method according to an embodiment of the present invention. As shown in fig. 2, when a model to be simulated needs to be subjected to simulation operation, a task scheduler sends a simulation start instruction to a solver management program through a simulation service, the solver management program loads the model to be simulated and a target model on which the model to be simulated depends, and the model to be simulated and the target model are compiled and solved to generate a same-element simulation solver, and the same-element simulation solver responds to a scheduling instruction to complete the simulation operation on the model to be simulated.
As an optional but non-limiting implementation manner, after the simulation state is fed back to the simulation service, the method further includes the step A4:
and A4, when the simulation service determines that the simulation state is in simulation, reading the simulation result data from the IoTDB database in real time, and processing the simulation result data.
Specifically, when the simulation service receives that the homonymous simulation solver is in a simulation state, simulation result data can be read from the IoTDB database in real time, and then the simulation result data can be processed.
Optionally, the heartbeat data is sent to the simulation service at regular time.
Specifically, the same-element simulation solver is scheduled to simulate a model to be simulated, the simulation state of the same-element simulation solver is detected in real time, heartbeat data is sent to the simulation service, the heartbeat data is sent, the simulation service can conveniently manage the running state of a solver management program and the running state of the same-element simulation solver, and running is prevented from being in a rigid state.
Optionally, the simulation result data includes curve result data and animation result data;
specifically, when the simulation service detection homonym simulation solver is in a simulation state, simulation result data can be read from the IoTDB database in real time, and the simulation result data can include curve data and animation result data.
Exemplarily, when the homonymous simulation solver is in a "simulation" state, simulation result data is read from the IoTDB database in real time, as shown in fig. 3, the simulation result data is processed through a series of data to generate two-dimensional curve result data, or the simulation result data is processed through a series of data to generate three-dimensional animation result data as shown in fig. 4, and as shown in fig. 5, the simulation result data can be exported to csv-format data, and an Excel tool is used for performing various statistical analyses.
And S120, converting the simulation result data into frame data in a corresponding format based on a preset frame protocol.
The frame data may be a set of all physical quantities in one model in one frame.
Specifically, the simulation result data is converted into frame data in a corresponding format according to a preset frame protocol, the homomorphic simulation solver converts the simulation result into a frame in the corresponding format of the model according to the simulation result generated by the model to be simulated and the target model on which the model to be simulated depends, and performs set processing on the frames into which all the simulation result data of the model to be simulated is converted, and executes the following program to generate the frame data of the model to be simulated.
{root.lic.d666.time:1
root.lic.d666.”time(s)”:1.0
root.lic.d666.”PI.x”:2.1
root.lic.d666.”PI.y”:5.0
…}
And S130, storing the frame data in an IoTDB database.
Specifically, according to the above steps, the simulation result generated by the simulation of the model to be simulated is converted into frame data through a preset frame protocol, and the frame data is mapped according to the data model constraint of the IoTDB database, so as to complete the storage of the frame data in the IoTDB database.
Illustratively, the mapping of the frame data to the IoTDB database is: the simulation task ID corresponds to the name of a model to be simulated, a warehouse ID for storing simulation result data and equipment required for executing the simulation task, the simulation result data variable of the model to be simulated is consistent with the physical quantity, the variation of some variables of the model to be simulated along with the extension of a time axis is called as the time sequence of the variables and is equivalent to the time sequence, the frame data generates the unique concept of the IoTDB data model according to the mapping relation of the IoTDB database, the concept comprises the equipment, the physical quantity and the time sequence, and the storage of the frame data in the IoTDB database is completed.
As an optional but non-limiting implementation manner, after storing the frame data in the IoTDB database, the method further includes:
and respectively storing the non-time sequence data in the curve result data and the animation result data in a file according to a preset file data format.
Specifically, the simulation result data read from the IoTDB database comprises curve result data and animation result data, and non-time sequence data in the curve result data and the animation result data are stored in a json format file according to a preset file data format.
Illustratively, recording non-time-series data of the animation result data by using an animation.json file, wherein the recording of relevant content of the animation result data by the animation.json file comprises: the following program is executed to write animation result data related content into animation.json file, and the format of externally-imported file can be dxf, stl and the like, and all internally-generated file and externally-imported file are written into an im folder under-d directory.
{"modelFullName":"RobotR3.fullRobot",
"component":["component1","component2",...
],
"import":[
{"component1":location1},
{"component2":location2},
...
]
"form":[{"component3":"box"},
{"component4":"cir"},
...
]
}
Illustratively, applying the non-time-series data of the result curve of the result file of the result.json file, the related contents of the result data of the result curve of the result file of the result include: the method comprises the steps Of describing the full model name Of a model to be simulated, the variable name and unit Of a simulation result, describing the data type, judging whether the model is an adjustable parameter (isoditable), judging whether a subscript (component ar Index Of var) in a component array in an animation.
{
"modelFullName":"RobotR3.fullRobot",
"variables":[{"name":"variable1","unit":"u1","des":"des1","type":"INT","isEditable":1,"componentArrIndexOfvarParent":5,"is Animation":1},
{"name":"variable2","unit":"u2","des":"des2","type":"REAL","isEditable":0,"componentArrIndexOfvarParent":3,"isAnimation":1}…
]
}
The embodiment of the invention provides a simulation data storage method, which simulates a model to be simulated by responding to a simulation starting instruction sent by a simulation service to obtain simulation result data of the model to be simulated, converts the simulation result data into frame data in a corresponding format by a preset frame protocol, and stores the frame data in an IoTDB database according to data model constraint of the IoTDB database, wherein the frame data comprises a set of all physical quantities of the model to be simulated. According to the technical scheme, the simulation result data of the model to be simulated are stored in the IoTDB database according to the mapping relation between the simulation result data and the data model of the IoTDB database through the operations of collection, management, analysis and the like of the simulation result data, so that the high-speed storage and multi-terminal real-time reading operation of the mass simulation result data in a high-concurrency simulation scene is realized, and the requirement of analyzing the mass simulation result data in real time is met.
Example two
Fig. 6 is a schematic structural diagram of an emulation data storage device according to a second embodiment of the present invention.
As shown in fig. 6, the apparatus includes:
a simulation result data obtaining module 310, configured to obtain simulation result data of a model to be simulated in real time in a process of simulating the model to be simulated;
a simulation result data conversion module 320, configured to convert the simulation result data into frame data in a corresponding format based on a preset frame protocol;
a frame data storage module 330, configured to store the frame data in an IoTDB database.
The model to be simulated comprises a Modelica model.
Further, the simulation result data obtaining module 310 includes:
the target model loading unit is used for responding to a simulation starting instruction sent by a simulation service and loading the model to be simulated and a target model depended by the model to be simulated;
the simulation solver generating unit is used for compiling and solving the model to be simulated and the target model to generate an identical simulation solver;
and the simulation unit of the simulation model is used for scheduling the homonymous simulation solver to simulate the model to be simulated.
Further, the simulation unit of the simulation model is specifically configured to:
and monitoring the simulation state of the homonymous simulation solver in real time, and feeding back the simulation state to the simulation service.
Further, the simulation result data obtaining module 310 includes:
and the simulation result processing unit is used for reading the simulation result data from the IoTDB database in real time and processing the simulation result data when the simulation service determines that the simulation state is in simulation.
Further, the simulation unit of the simulation model is further configured to:
and sending heartbeat data to the simulation service at regular time.
Further, the simulation result data comprises curve result data and animation result data;
the simulation data storage device provided by the embodiment of the invention can execute the simulation data storage method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
FIG. 7 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 7, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the emulated data storage method.
In some embodiments, the simulation data storage method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above-described emulated data storage method may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the emulation data storage method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for storing emulation data, comprising:
acquiring simulation result data of a model to be simulated in real time in the process of simulating the model to be simulated;
converting the simulation result data into frame data in a corresponding format based on a preset frame protocol;
storing the frame data in an IoTDB database.
2. The method of claim 1, wherein the model to be simulated comprises a Modelica model.
3. The method of claim 1, wherein simulating a model to be simulated comprises:
loading the model to be simulated and a target model on which the model to be simulated depends in response to a simulation starting instruction sent by a simulation service;
compiling and solving the model to be simulated and the target model to generate an isomorphic simulation solver;
and scheduling the homonymous simulation solver to simulate the model to be simulated.
4. The method according to claim 3, wherein in the process of scheduling the isomorphic simulation solver to simulate the model to be simulated, the method further comprises:
and monitoring the simulation state of the homonymous simulation solver in real time, and feeding back the simulation state to the simulation service.
5. The method of claim 4, after feeding back the simulation state to the simulation service, further comprising:
and when the simulation service determines that the simulation state is under simulation, reading the simulation result data from the IoTDB database in real time, and processing the simulation result data.
6. The method according to claim 3, wherein in the process of scheduling the isomorphic simulation solver to simulate the model to be simulated, the method further comprises:
and sending heartbeat data to the simulation service at regular time.
7. The method of claim 1, wherein the simulation result data comprises curve result data and animation result data;
after storing the frame data in an IoTDB database, further comprising:
and respectively storing the non-time sequence data in the curve result data and the animation result data in a file according to a preset file data format.
8. An emulated data storage device, comprising:
the simulation result data acquisition module is used for acquiring simulation result data of the model to be simulated in real time in the process of simulating the model to be simulated;
the simulation result data conversion module is used for converting the simulation result data into frame data in a corresponding format based on a preset frame protocol;
and the frame data storage module is used for storing the frame data in an IoTDB database.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of storing emulation data according to any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon computer instructions for causing a processor, when executed, to implement the method of storing simulation data according to any one of claims 1-7.
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