CN112379892A - Ammonia spraying prediction code processing method and device, storage medium and terminal equipment - Google Patents
Ammonia spraying prediction code processing method and device, storage medium and terminal equipment Download PDFInfo
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- G06F8/60—Software deployment
- G06F8/61—Installation
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
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Abstract
The embodiment of the application discloses a method, a device, a storage medium and a terminal device for processing an ammonia injection prediction code, wherein the method comprises the following steps: acquiring an ammonia injection prediction code; constructing a micro application according to the ammonia injection prediction code; packaging the micro application into a target mirror image file; copying the target image file to a corresponding server, and loading the target image file into a Docker container of the server; the target image file in the Docker container is started, the one-stop pluggable image file can be constructed, the codes of the ammonia injection prediction algorithm can be executed in the container, the result is output, and a running environment does not need to be independently constructed for the application, so that the application is lighter and the resources are more optimized.
Description
Technical Field
The application relates to the field of industrial control, in particular to a method and a device for processing an ammonia spraying prediction code, a storage medium and terminal equipment.
Background
In a thermal power plant, nitrogen oxides generated after coal combustion pollute the atmospheric environment, and an appropriate ammonia injection amount can be predicted by using an ammonia injection prediction algorithm so as to fully neutralize the nitrogen oxides. The ammonia injection prediction algorithm is specifically a program code written by utilizing a computer programming language, and if the ammonia injection prediction algorithm needs to be used, an operation package which is adapted to the computer programming language needs to be installed in an operating system of a host. In the actual use process, the types and versions of the operating systems of the hosts are diverse, so that the operation packages of the computer programming languages required to be installed are also different, a large amount of debugging work needs to be performed for different operation environments when the ammonia injection prediction algorithm is used, and corresponding cost is increased in the aspect of operation and maintenance.
Disclosure of Invention
The embodiment of the application provides a method and a device for processing an ammonia spraying prediction code, a storage medium and terminal equipment, and can solve the problems of high difficulty and high operation and maintenance cost in deploying the ammonia spraying prediction code in the related technology. The technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for processing an ammonia injection prediction code, where the method includes:
acquiring an ammonia injection prediction code;
constructing a micro application according to the ammonia injection prediction code;
packaging the micro application into a target mirror image file;
copying the target image file to a corresponding server, and loading the target image file into a Docker container of the server;
and starting the target image file in the Docker container.
In a second aspect, an embodiment of the present application provides an apparatus for processing ammonia injection prediction codes, where the apparatus includes:
an acquisition unit configured to acquire an ammonia injection prediction code;
the construction unit is used for constructing the micro application according to the ammonia injection prediction code;
the packaging unit is used for packaging the micro application into a target mirror image file;
the loading unit is used for copying the target image file to a corresponding server and loading the target image file to a Docker container of the server;
and the starting unit is used for starting the target image file in the Docker container.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal device, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The beneficial effects brought by the technical scheme provided by some embodiments of the application at least comprise:
the method comprises the steps of constructing a Docker container of an operating environment based on ammonia injection prediction codes, constructing micro applications based on the ammonia injection prediction codes, packaging the micro applications into the operating environment through a Docker file to form a mirror image file, starting or stopping the mirror image file through a Docker command, so that the ammonia injection prediction codes can shield the difference between the type and the version of a host operating system, integrating the operating environment with a complex ammonia injection prediction algorithm into the Docker container, fully utilizing the transportability of the Docker container to construct a one-stop pluggable mirror image file, and executing the algorithm model in the Docker container, thereby independently constructing the operating environment according to different operating system types and versions of the host, and enabling the application to be lighter and the resources to be more optimized.
Drawings
In order to more clearly illustrate the embodiments of the present application 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 application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a network structure diagram provided in an embodiment of the present application;
FIG. 2 is a flow chart of a method for processing ammonia injection prediction codes according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of a processing apparatus for ammonia injection prediction codes according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims.
In the description of the present application, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art. Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Referring to fig. 1, a network architecture diagram provided for an embodiment of the present application includes: the number of the servers 11 can be multiple, the multiple servers form a server cluster, the multiple servers can be respectively provided with a Docker container, and the terminal device 12 deploys locally acquired ammonia injection prediction codes in the Docker containers of the servers, so that the ammonia injection prediction codes are deployed remotely.
The Docker container is an open-source application container engine, so that developers can package their applications and dependency packages in a uniform manner into a portable container and then distribute the package to any server (including popular Linux machines and windows machines) provided with the Docker engine, and virtualization can be realized. The containers are fully sandboxed without any interface between each other. There is little performance overhead and it can be easily run on machines and data centers.
The communication between the server 11 and the terminal device 12 may be a wireless communication, for example: the terminal device is provided with a cellular network data module inside, and communicates with the service system 11 based on a cellular network, for example: cellular networks include, but are not limited to, 2G, 3G, 4G, 5G, or next generation networks. Each terminal device may be a mobile phone, a tablet computer, a personal computer, or other devices, and the embodiments of the present application are not limited.
Referring to fig. 2, a flow chart of a method for processing an ammonia injection prediction code according to an embodiment of the present invention is shown based on the network architecture of fig. 1. As shown in fig. 2, the method of the embodiment of the present application may include the steps of:
s201, acquiring an ammonia injection prediction code.
Wherein the ammonia injection prediction code is program code written using a particular type of programming language based on an ammonia injection prediction algorithm, such as: the ammonia injection prediction algorithm is written by adopting Python language and is used for predicting the current ammonia injection amount according to the historical pollutant discharge amount so as to neutralize the pH value of pollutants.
In one possible embodiment, different power generation equipment has different pollutant emission conditions, so that the relevant parameters of the ammonia injection prediction algorithm are different, and the difference between different power generation equipment can be considered in the process of acquiring the ammonia injection prediction code. The specific process for acquiring the ammonia injection prediction code comprises the following steps:
receiving an application deployment request from a user; the application deployment request carries an equipment identifier of the power generation equipment, and the equipment identifier uniquely represents the identity of the power generation equipment;
and acquiring a corresponding ammonia injection prediction code in a code warehouse based on the equipment identification.
The code warehouse is pre-stored with a plurality of ammonia injection prediction codes with different parameters, and the corresponding ammonia injection prediction codes are obtained according to different power generation equipment to control the ammonia injection amount, so that the control precision can be improved, and the pollutant discharge amount can be reduced.
And S202, constructing the micro application according to the ammonia spraying prediction code.
The micro application may be a web application, and the micro application may provide an API interface for remote call, for example: restful api interface.
The parameters of the calling interface are configured for the micro application, the parameters comprise an application identifier and a network address, the application identifier represents the identity of the micro application, the network address represents the address of the micro application deployed in the internet, and the address can be represented by using a URL (uniform resource locator) address.
S203, packaging the micro applications into a target image file.
The process of generating the target image file specifically comprises the following steps: and compiling and packaging the docfile to generate a target image file. The dockfile writing process comprises the following steps: selecting a basic image file corresponding to a programming language of the micro application through an FROM command, for example: and if the programming language of the micro application is Python, selecting a basic image file corresponding to the Python, uploading a corresponding micro application code to the basic image file through an ADD command, setting an uploaded micro application code directory as a working directory through a WORKDIR command, setting a dependency package required by the installation and operation of the micro application through a RUN command, and setting the dependency package as a command executed after the operation of the image through a CMD command. And after the DockFile is written, executing a packaging command, and packaging the Dockfile into a target image file.
The action and format of each of the above commands are explained below:
FROM command: the function is to specify the base image and must be the first instruction. If not based on any mirror image, then the notation is: FROM scratch. Meaning that the next written instruction will start as the first layer of the mirror.
ADD command: a copy command to copy the file to the image. If the virtual machine and container are thought of as two linux servers, then this command is similar to scp, except that scp needs authentication with a username and password, and ADD does not.
RUN command: the function is to run a specified command. The RUN command has two formats:
1.RUN<command>
2.RUN["executable","param1","param2"]
the first type is directly followed by a shell command, and defaults/bin/sh-C on a linux operating system and default cmd/S/C on a windows operating system; the second is similar to function calls. Executable may be understood as an executable file, followed by two parameters.
WORKDIR command: grammar: WORKDIR/path/to/WORKDIR. And setting a working directory to be effective to RUN, CMD, ENGYPOINT, COPY and ADD. If not, it is created, or it may be set multiple times.
CMD command: the function is a command to be run when the container is started. There are three writing methods for grammar:
1.CMD["executable","param1","param2"]；
2.CMD["param1","param2"]；
CMD command param1 param 2; the third is better to understand that with the execution and writing of the shell, the first and second are in essence both executable file plus parameter forms.
S204, copying the target image file to a corresponding server, and loading the target image file to a Docker container of the server.
The servers are physical machines and are used for providing hardware resources, the number of the servers can be one or more, and when the number of the servers is multiple, the target image file is pushed to a Docker container of each server. Copying the target image file to a corresponding server, loading the image through a Docker command, and executing a Docker image loading instruction to load the image into a Docker container by combining the target image file generated in S202.
The process of copying the target image file to the server comprises the following steps: and marking a label on the target image file, and pushing the target image file to a server.
S205, starting the target image file in the Docker container.
The Docker container in the server has a unique Docker name, the loaded target image file is started through the Docker name, and the target image file is started through executing a Docker image starting command.
After the micro application is deployed in the Docker container, the terminal device may access the micro application in the server, where the access process includes: the terminal equipment sends an access request to the server, wherein the access request carries a label of a target image file; and receiving an access response returned by the Docker container from the server.
Further, when the number of the servers is multiple, the terminal device may select a server with the lightest load from the multiple servers based on a load balancing algorithm, and send an access request to the server, for example: and acquiring one or more of the occupancy rate of the CPU, the throughput of the disk and the memory occupancy rate of each server, and determining the server with the lightest load according to the load parameters.
The beneficial effect of this application includes: the method comprises the steps of constructing a Docker container of an operating environment based on ammonia injection prediction codes, constructing micro applications based on the ammonia injection prediction codes, packaging the micro applications into the operating environment through a Docker file to form a mirror image file, starting or stopping the mirror image file through a Docker command, so that the ammonia injection prediction codes can shield the difference between the type and the version of a host operating system, integrating the operating environment with a complex ammonia injection prediction algorithm into the Docker container, fully utilizing the transportability of the Docker container to construct a one-stop pluggable mirror image file, and executing the algorithm model in the Docker container, thereby independently constructing the operating environment according to different operating system types and versions of the host, and enabling the application to be lighter and the resources to be more optimized.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Referring to fig. 3, a schematic structural diagram of a processing apparatus for ammonia injection prediction code according to an exemplary embodiment of the present application is shown. The processing device of the ammonia injection prediction code can be realized by software, hardware or a combination of the software and the hardware to form all or part of the terminal equipment. The device 3 comprises: an acquisition unit 31, a construction unit 32, a packing unit 33, a loading unit 34, and a startup unit 35.
An acquisition unit 31 for acquiring an ammonia injection prediction code;
a construction unit 32, configured to construct a micro application according to the ammonia injection prediction code;
a packaging unit 33, configured to package the micro application into a target image file;
a loading unit 34, configured to copy the target image file to a corresponding server, and load the target image file into a Docker container of the server;
and the starting unit 35 is configured to start the target image file in the Docker container.
In one or more embodiments, packaging the micro-application into a target image file includes:
writing dockfile: selecting a basic image file according to the programming language type of the micro application;
uploading the code of the micro application to the basic mirror image file, and setting a directory for uploading the code of the micro application as a working directory;
installing a dependency package required by the micro-application;
setting a command executed after the mirror image is operated;
and after the dockfile is written, packaging the dockfile into a target mirror image file.
In one or more embodiments, the copying the target image file to the corresponding server includes:
labeling a target mirror image file;
and pushing the target image file to an image warehouse deployed in the server.
In one or more embodiments, further comprising:
a receiving and sending unit, configured to send an access request to the server; wherein the access request carries the tag;
receiving an access response from the server.
In one or more embodiments, the server comprises a plurality of server clusters;
wherein the sending an access request to the server comprises:
determining a server with the lightest load in the plurality of servers based on a load balancing algorithm;
and sending the access request to the server with the lightest load.
In one or more embodiments, the Python-based ammonia injection prediction code builds a micro-application comprising:
configuring parameters of a calling interface for the micro application; the parameters of the calling interface comprise an application identifier and a network address.
In one or more embodiments, the obtaining ammonia injection prediction code comprises:
receiving an application deployment request from a user; wherein the application deployment request carries an equipment identifier;
acquiring a corresponding ammonia spraying prediction code from a code warehouse according to the equipment identifier; wherein the ammonia injection prediction code is written using Python language.
It should be noted that, when the processing apparatus for ammonia injection prediction code provided in the foregoing embodiment executes the processing method for ammonia injection prediction code, only the division of the above functional modules is taken as an example, and in practical applications, the above functions may be distributed to different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to complete all or part of the above described functions. In addition, the processing apparatus for the ammonia injection prediction code and the processing method embodiment of the ammonia injection prediction code provided in the above embodiments belong to the same concept, and the details of the implementation process are shown in the method embodiment, which are not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
An embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiment shown in fig. 2, and a specific execution process may refer to a specific description of the embodiment shown in fig. 2, which is not described herein again.
Please refer to fig. 4, which provides a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 4, the terminal device may be the terminal device 12 in fig. 1, and the terminal device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 4, the memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an application program.
In the terminal device 1000 shown in fig. 4, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to call an application program stored in the memory 1005 for configuring the application program interface, and specifically perform the following operations:
acquiring an ammonia injection prediction code;
constructing a micro application according to the ammonia injection prediction code;
packaging the micro application into a target mirror image file;
copying the target image file to a corresponding server, and loading the target image file into a Docker container of the server;
and starting the target image file in the Docker container.
In one or more embodiments, the packaging of the micro application into the target image file by the processor 1001 includes:
writing dockfile: selecting a basic image file according to the programming language type of the micro application;
uploading the code of the micro application to the basic mirror image file, and setting a directory for uploading the code of the micro application as a working directory;
installing a dependency package required by the micro-application;
setting a command executed after the mirror image is operated;
and after the dockfile is written, packaging the dockfile into a target mirror image file.
In one or more embodiments, the copying of the target image file to the corresponding server performed by the processor 1001 includes:
labeling a target mirror image file;
and pushing the target image file to an image warehouse deployed in the server.
In one or more embodiments, processor 1001 is further configured to perform:
sending an access request to the server; wherein the access request carries the tag;
receiving an access response from the server.
In one or more embodiments, the server comprises a plurality of server clusters;
the processor 1001 performs the sending of the access request to the server, including:
determining a server with the lightest load in the plurality of servers based on a load balancing algorithm;
and sending the access request to the server with the lightest load.
In one or more embodiments, the Python-based ammonia injection prediction code is executed by the processor 1001 to construct a micro-application, including:
configuring parameters of a calling interface for the micro application; the parameters of the calling interface comprise an application identifier and a network address.
In one or more embodiments, the processor 1001 executing the obtain ammonia injection prediction code includes:
receiving an application deployment request from a user; wherein the application deployment request carries an equipment identifier;
acquiring a corresponding ammonia spraying prediction code from a code warehouse according to the equipment identifier; wherein the ammonia injection prediction code is written using Python language.
The concept of this embodiment is the same as that of the embodiment of the method in fig. 2, and the technical effects brought by the embodiment are also the same, and the specific process can refer to the description of the embodiment in fig. 2, and will not be described again here.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A method for processing ammonia injection prediction codes, comprising:
acquiring an ammonia injection prediction code;
constructing a micro application according to the ammonia injection prediction code;
packaging the micro application into a target mirror image file;
copying the target image file to a corresponding server, and loading the target image file into a Docker container of the server;
and starting the target image file in the Docker container.
2. The method of claim 1, wherein packaging the micro-application into a target image file comprises:
writing dockfile: selecting a basic image file according to the programming language type of the micro application;
uploading the code of the micro application to the basic mirror image file, and setting a directory for uploading the code of the micro application as a working directory;
installing a dependency package required by the micro-application;
setting a command executed after the mirror image is operated;
and after the dockfile is written, packaging the dockfile into a target mirror image file.
3. The method of claim 1, wherein copying the target image file to a corresponding server comprises:
labeling a target mirror image file;
and pushing the target image file to an image warehouse deployed in the server.
4. The method of claim 3, further comprising:
sending an access request to the server; wherein the access request carries the tag;
receiving an access response from the server.
5. The method of claim 4, wherein the server comprises a plurality of server clusters;
wherein the sending an access request to the server comprises:
determining a server with the lightest load in the plurality of servers based on a load balancing algorithm;
and sending the access request to the server with the lightest load.
6. The method of claim 1, wherein the Python-based ammonia injection prediction code builds a micro-application comprising:
configuring parameters of a calling interface for the micro application; the parameters of the calling interface comprise an application identifier and a network address.
7. The method of claim 1, wherein obtaining the ammonia injection prediction code comprises:
receiving an application deployment request from a user; wherein the application deployment request carries an equipment identifier;
acquiring a corresponding ammonia spraying prediction code from a code warehouse according to the equipment identifier; wherein the ammonia injection prediction code is written using Python language.
8. An ammonia injection prediction code processing apparatus, wherein the generating means comprises:
an acquisition unit configured to acquire an ammonia injection prediction code;
the construction unit is used for constructing the micro application according to the ammonia injection prediction code;
the packaging unit is used for packaging the micro application into a target mirror image file;
the loading unit is used for copying the target image file to a corresponding server and loading the target image file to a Docker container of the server;
and the starting unit is used for starting the target image file in the Docker container.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to carry out the method steps according to any one of claims 1 to 7.
10. A terminal device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 7.
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| CN117170738A (en) * | 2023-09-05 | 2023-12-05 | 中国人民解放军国防科技大学 | Method, system, equipment and storage medium for interaction of Python and Fortran |
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| CN108958927A (en) * | 2018-05-31 | 2018-12-07 | 康键信息技术（深圳）有限公司 | Dispositions method, device, computer equipment and the storage medium of container application |
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