CN117270897B - Computer system based on low-code business system - Google Patents
Computer system based on low-code business system Download PDFInfo
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- CN117270897B CN117270897B CN202311550022.0A CN202311550022A CN117270897B CN 117270897 B CN117270897 B CN 117270897B CN 202311550022 A CN202311550022 A CN 202311550022A CN 117270897 B CN117270897 B CN 117270897B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/60—Software deployment
- G06F8/61—Installation
- G06F8/63—Image based installation; Cloning; Build to order
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3024—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a central processing unit [CPU]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3051—Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs
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- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
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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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a computer system based on a low-code service system, which comprises a containerization module, a big data analysis module and a server deployment module, wherein the containerization module is used for carrying out standardized deployment work on an application program in a client server, the big data analysis module is used for estimating the damage condition of a central processor of the server according to the running condition of the client server so as to adapt and custom develop a localized deployment scheme, the server deployment module is used for deploying the application program packaged into a container mirror image, a required dependent library and a configuration file in the client server, the containerization module comprises a containerization engine, a containerization file writing module and a container mirror image uploading module, and the containerization engine is electrically connected with the containerization file writing module which is electrically connected with the container mirror image uploading module.
Description
Technical Field
The invention relates to the technical field of low codes, in particular to a computer system based on a low-code service system.
Background
Low code is an application development method that reduces the need to manually write code by using visual modeling tools and automated code generation. The low code platform provides a graphical interface that allows a user to design the user interface and business logic of an application by dragging and dropping elements, allowing non-professional developers to create and deploy the application without having to go deep into programming language and technical details.
The existing low-code platform generally does not provide local deployment on a client server, which causes an idea that data is not mastered by the client, and trust is difficult to build, if the local deployment is provided, the network conditions of client server hardware and software are different due to different time, and different network conditions need to be re-adapted and tested, so that development difficulty is increased. Therefore, it is necessary to design a computer system based on a low code business system with low development difficulty.
Disclosure of Invention
The present invention is directed to a computer system based on a low-code business system, which solves the above-mentioned problems.
In order to solve the technical problems, the invention provides the following technical scheme: the computer system based on the low-code service system comprises a containerization module, a big data analysis module and a server deployment module, wherein the containerization module is used for carrying out standardized deployment work on an application program in a client server, the big data analysis module is used for estimating the damage condition of a central processor of the server according to the running condition of the client server so as to adapt and custom develop a localized deployment scheme, and the server deployment module is used for deploying the application program packaged into a container mirror image, a required dependency library and a configuration file in the client server.
According to the technical scheme, the containerization module comprises a containerization engine, a containerization file writing module and a container mirror image uploading module, wherein the containerization engine is electrically connected with the containerization file writing module, and the containerization file writing module is electrically connected with the container mirror image uploading module;
the containerization engine is used for configuring a containerization network so that an application program can be successfully containerized, packed, compressed and transmitted, the containerized file writing module is used for writing the application program into a containerized file, and the container mirror image uploading module is used for transmitting the packed container mirror image to a database and a client server;
the big data analysis module comprises a load monitoring unit, a data transmission speed measuring unit, a time accumulating unit, a load span calculating module, an information transmission module, a loss presetting module and a loss calculating module, wherein the load monitoring unit and the data transmission speed measuring unit are electrically connected with the information transmission module, the load monitoring unit is electrically connected with the load span calculating module, and the loss calculating module is electrically connected with the load span calculating module and the loss presetting module;
the loss calculation module is used for estimating the damage degree of the central processor of the server by monitoring the operation data of the client server, the loss preset module is used for retrieving according to the acquired component information of the central processor to obtain the basic damage speed of the central processor, the information transmission module is used for transmitting index information of data monitoring, the load span calculation module is used for calculating the load span of the server within a period of time, the time accumulation unit is used for calculating time, the data transmission speed measurement unit is used for measuring the data transmission speed of the server, and the load monitoring unit is used for detecting the current load condition of the server.
According to the technical scheme, the working method of the system comprises the following main steps:
s1, designing a standardized local deployment scheme, wherein the scheme content is the installation and configuration flow of an application program;
s2, analyzing the hardware and software network conditions of a client based on big data, and carrying out adaptation and custom development on a localized deployment scheme;
s3, packaging the application program, the required dependency library and the configuration file into a container mirror image so as to realize cross-platform deployment and quick migration, and adjusting the compression rate of the packaged container mirror image according to the requirements of hardware and software network conditions;
s4, uploading the container mirror image to a client server, and managing and operating the container by using a container arranging tool.
According to the above technical solution, in the step S1, the standardized local deployment solution specifically includes the following steps:
s1-1, installing a containerization engine on a host computer where an application program is located, setting a containerization mirror accelerator and configuring a containerization network;
s1-2, writing a containerized file, and defining a construction process of a container mirror image, wherein the construction process comprises basic mirror image, dependent item installation and application program deployment;
s1-3, uploading the built container mirror image to a database, and managing and operating the container by using a container arranging tool to ensure the normal operation of the application program.
According to the above technical solution, in the step S2, the specific process of adapting and custom-developing the localization deployment scheme is:
step S2-1: in the running process of the client server, the load monitoring unit measures the load value of the running network condition in real time;
step S2-2: the load monitoring unit acquires a time accumulation unit timing value and establishes a load record table;
step S2-3: the load record table records a load change data graph by taking a time value as a horizontal axis and a load value as a vertical axis;
step S2-4: the data transmission speed measuring unit measures the data transmission speed of the client server in real time in the running process;
step S2-5: under the condition of excessive data transmission speed, the occupied space for writing data stream generation is larger than the load value of a client server, and new data is forced to be stored in a discontinuous space on a magnetic disk, so that jump reading is generated on a central processing unit, and the central processing unit is accelerated to break; when the data transmission speed measuring unit measures that the data transmission speed is larger than the value critical data transmission speed value, the data transmission speed measuring unit sends an electric signal to the time accumulating unit;
step S2-6: the second timing channel of the time accumulation unit is used for accumulating and timing the time when the network data transmission speed exceeds the critical data transmission speed and outputting a time value。
According to the above technical solution, the step S2-3 further includes the following steps: the load span calculation module reads a load change data graph; the load span calculation module searches the load value of the highest load point in the unit time of dayLoad value at load nadir->And a time value of load highest point to lowest point +.>The method comprises the steps of carrying out a first treatment on the surface of the The load span calculation module passes the formula: />Calculating the load change speed value in unit time, wherein +.>The load change speed value in unit time; the load calculation module records the daily load change speed value +.>And is>Calculating an average load change speed value during the operation of the client server,/for>Days.
According to the above technical solution, the step S3 further includes the following steps:
step S3-1: the information transmission module acquires network condition data monitored by the client server in running and central processing unit information of a client server core;
step S3-2: the loss preset module searches the acquired component information of the central processing unit, and the basic breaking speed of the component central processing unit is obtained through network searching；
Step S3-3: the loss calculation module calculates the total efficiency value of the CPU according to the current total efficiency valueBasic break speed of CPU>Time of data transmission->Load change speed during operation ∈>Time to exceed critical data transfer rateCalculating the running loss value of the CPU to obtain the running loss value of the server +.>；
Step S3-4: according to the loss value of the CPU of the current serverAdjusting compression ratio of packed container mirror image。
In the step S3-3, the loss calculation module calculates the loss value of the current CPU of the server according to the following formula:
wherein,the loss value of the central processing unit of the current server is calculated by the loss calculation module; />The conversion coefficient between the break speed and the server performance is a constant value; in the formula, when the network data transmission speed exceeds the critical data transmission speed, frequent writing is performedDeletion causes the CPU's break rate to increase exponentially, while new data is forced to be stored in non-contiguous space on disk as the network condition load changes faster during operation, causing jump readings to be generated on the CPU, resulting in faster CPU break rate, and greater CPU wear value for the server as the total performance value base of the CPU is higher.
Compared with the prior art, the invention has the following beneficial effects: the invention designs the specific low-code computer system, deploys the application program in the client server in a containerized and packaged form, is convenient for establishing trust with the client, monitors the running condition of the client server, estimates the damage degree of the server, and adjusts the containerized compression rate so as to improve the deployment efficiency under the condition that the server can be effectively utilized.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a schematic view of the overall module structure of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: the computer system based on the low-code service system comprises a containerization module, a big data analysis module and a server deployment module, wherein the containerization module is used for carrying out standardized deployment work on an application program in a client server, the big data analysis module is used for estimating the damage condition of a central processor of the server according to the running condition of the client server so as to adapt and custom develop a localized deployment scheme, and the server deployment module is used for deploying the application program packaged into a container mirror image, a required dependency library and a required configuration file in the client server;
the containerization module comprises a containerization engine, a containerization file writing module and a container mirror image uploading module, wherein the containerization engine is electrically connected with the containerization file writing module, and the containerization file writing module is electrically connected with the container mirror image uploading module;
the containerization engine is used for configuring a containerization network so that an application program can be successfully containerized, packed, compressed and transmitted, the containerized file writing module is used for writing the application program into a containerized file, and the container mirror image uploading module is used for transmitting the packed container mirror image to the database and the client server;
the big data analysis module comprises a load monitoring unit, a data transmission speed measurement unit, a time accumulation unit, a load span calculation module, an information transmission module, a loss preset module and a loss calculation module, wherein the load monitoring unit and the data transmission speed measurement unit are electrically connected with the information transmission module, the load monitoring unit is electrically connected with the load span calculation module, and the loss calculation module is electrically connected with the load span calculation module and the loss preset module;
the loss calculation module is used for estimating the damage degree of the central processor of the server by monitoring the operation data of the client server, the loss preset module is used for searching according to the acquired component information of the central processor to obtain the basic damage speed of the central processor, the information transmission module is used for transmitting index information of data monitoring, the load span calculation module is used for calculating the load span of the server within a period of time, the time accumulation unit is used for calculating time, the data transmission speed measurement unit is used for measuring the data transmission speed of the server, and the load monitoring unit is used for detecting the current load condition of the server;
the working method of the system comprises the following main steps:
s1, designing a standardized local deployment scheme, wherein the scheme content is the installation and configuration flow of an application program;
s2, analyzing the hardware and software network conditions of a client based on big data, and carrying out adaptation and custom development on a localized deployment scheme;
s3, packaging the application program, the required dependency library and the configuration file into a container mirror image so as to realize cross-platform deployment and quick migration, and adjusting the compression rate of the packaged container mirror image according to the requirements of hardware and software network conditions;
s4, uploading the container mirror image to a client server, and managing and operating the container by using a container arranging tool;
in the step S1, the standardized local deployment scheme specifically includes the following steps:
s1-1, installing a containerization engine on a host computer where an application program is located, setting a containerization mirror accelerator and configuring a containerization network;
s1-2, writing a containerized file, and defining a construction process of a container mirror image, wherein the construction process comprises basic mirror image, dependent item installation and application program deployment;
s1-3, uploading the constructed container mirror image to a database, and managing and operating the container by using a container arranging tool to ensure the normal operation of an application program;
in the step S2, the specific process of adapting and custom developing the localization deployment scheme is as follows:
step S2-1: in the running process of the client server, the load monitoring unit measures the load value of the running network condition in real time;
step S2-2: the load monitoring unit acquires a time accumulation unit timing value and establishes a load record table;
step S2-3: the load record table records a load change data graph by taking a time value as a horizontal axis and a load value as a vertical axis;
step S2-4: the data transmission speed measuring unit measures the data transmission speed of the client server in real time in the running process;
step S2-5: under the condition of excessive data transmission speed, the occupied space for writing data stream generation is larger than the load value of a client server, and new data is forced to be stored in a discontinuous space on a magnetic disk, so that jump reading is generated on a central processing unit, and the central processing unit is accelerated to break; when the data transmission speed measuring unit measures that the data transmission speed is larger than the value critical data transmission speed value, the data transmission speed measuring unit sends an electric signal to the time accumulating unit;
step S2-6: the second timing channel of the time accumulation unit is used for accumulating and timing the time when the network data transmission speed exceeds the critical data transmission speed and outputting a time value；
The step S2-3 further comprises the following steps: the load span calculation module reads a load change data graph; the load span calculation module searches the load value of the highest load point in the unit time of dayLoad value at load nadir->And a time value of load highest point to lowest point +.>The method comprises the steps of carrying out a first treatment on the surface of the The load span calculation module passes the formula:calculating the load change speed value in unit time, wherein +.>The load change speed value in unit time; the load calculation module records the daily load change speed value during the operation of the client server through a calculation formulaAnd is>Calculating an average load change speed value during the operation of the client server,/for>Days;
the step S3 further includes the steps of:
step S3-1: the information transmission module acquires network condition data monitored by the client server in running and central processing unit information of a client server core;
step S3-2: the loss preset module searches the acquired component information of the central processing unit, and the basic breaking speed of the component central processing unit is obtained through network searching；
Step S3-3: the loss calculation module calculates the total efficiency value of the CPU according to the current total efficiency valueBasic break speed of CPU>Time of data transmission->Load change speed during operation ∈>Time to exceed critical data transfer rateCalculating the running loss value of the CPU to obtain the running loss value of the server +.>；
Step S3-4: according to the loss value of the CPU of the current serverAdjusting compression ratio of packed container mirror image。
In the step S3-3, the loss calculation module calculates the loss value of the current CPU of the server according to the following formula:
wherein,the loss value of the central processing unit of the current server is calculated by the loss calculation module; />The conversion coefficient between the break speed and the server performance is a constant value; when the network data transmission speed exceeds the critical data transmission speed, the frequent writing and deleting causes the damage speed of the central processing unit to exponentially increase, and simultaneously when the network condition load change speed is higher during operation, new data is forced to be stored in a discontinuous space on a magnetic disk, so that jump readings are generated on the central processing unit, the damage speed of the central processing unit is increased, and when the total efficiency value base of the central processing unit is higher, the loss value of the central processing unit of the server is also larger.
As the central processor of the server wears out, the benefits of high compression in packaging applications into container images are as follows: the compressed container image can reduce the occupied space in the disk and network transmission, and can effectively utilize a small amount of server storage space, and the consumption of a central processing unit often has the inverse proportion of the available storage space. When the loss of the central processing unit of the server is large, the network condition is poor or the scene requiring frequent deployment is more frequent, the deployment process can be quickened by compressing the container mirror image, the downloading and loading time is reduced, and the problem of messy codes caused by long-time transmission is prevented.
The server deployment module comprises a decompression module and a server receiving module, wherein the server receiving module is positioned in the client server and is used for receiving the packed compressed container image, and the decompression module is used for decompressing the compressed container image into an application program capable of running.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A computer system based on a low code business system, characterized by: the system comprises a containerization module, a big data analysis module and a server deployment module, wherein the containerization module is used for carrying out standardized deployment work on an application program in a client server, the big data analysis module is used for estimating the damage condition of a central processor of the server according to the running condition of the client server so as to adapt and custom develop a localized deployment scheme, and the server deployment module is used for deploying the application program packaged into a container mirror image, a required dependency library and a configuration file in the client server;
the containerization module comprises a containerization engine, a containerization file writing module and a container mirror image uploading module, wherein the containerization engine is electrically connected with the containerization file writing module, and the containerization file writing module is electrically connected with the container mirror image uploading module;
the containerization engine is used for configuring a containerization network so that an application program can be successfully containerized, packed, compressed and transmitted, the containerized file writing module is used for writing the application program into a containerized file, and the container mirror image uploading module is used for transmitting the packed container mirror image to a database and a client server;
the big data analysis module comprises a load monitoring unit, a data transmission speed measuring unit, a time accumulating unit, a load span calculating module, an information transmission module, a loss presetting module and a loss calculating module, wherein the load monitoring unit and the data transmission speed measuring unit are electrically connected with the information transmission module, the load monitoring unit is electrically connected with the load span calculating module, and the loss calculating module is electrically connected with the load span calculating module and the loss presetting module;
the loss calculation module is used for estimating the damage degree of a central processor of the server by monitoring operation data of the client server, the loss preset module is used for retrieving according to the acquired component information of the central processor to obtain the basic damage speed of the central processor, the information transmission module is used for transmitting index information of data monitoring, the load span calculation module is used for calculating the load span of the server within a period of time, the time accumulation unit is used for calculating time, the data transmission speed measurement unit is used for measuring the data transmission speed of the server, and the load monitoring unit is used for detecting the current load condition of the server;
the working method of the system comprises the following main steps:
s1, designing a standardized local deployment scheme, wherein the scheme content is the installation and configuration flow of an application program;
s2, analyzing the hardware and software network conditions of a client based on big data, and carrying out adaptation and custom development on a localized deployment scheme;
s3, packaging the application program, the required dependency library and the configuration file into a container mirror image so as to realize cross-platform deployment and quick migration, and adjusting the compression rate of the packaged container mirror image according to the requirements of hardware and software network conditions;
s4, uploading the container mirror image to a client server, and managing and operating the container by using a container arranging tool;
in the step S1, the standardized local deployment scheme specifically includes the following steps:
s1-1, installing a containerization engine on a host computer where an application program is located, setting a containerization mirror accelerator and configuring a containerization network;
s1-2, writing a containerized file, and defining a construction process of a container mirror image, wherein the construction process comprises basic mirror image, dependent item installation and application program deployment;
s1-3, uploading the constructed container mirror image to a database, and managing and operating the container by using a container arranging tool to ensure the normal operation of an application program;
in the step S2, the specific process of adapting and custom developing the localization deployment scheme is as follows:
step S2-1: in the running process of the client server, the load monitoring unit measures the load value of the running network condition in real time;
step S2-2: the load monitoring unit acquires a time accumulation unit timing value and establishes a load record table;
step S2-3: the load record table records a load change data graph by taking a time value as a horizontal axis and a load value as a vertical axis;
step S2-4: the data transmission speed measuring unit measures the data transmission speed of the client server in real time in the running process;
step S2-5: under the condition of excessive data transmission speed, the occupied space for writing data stream generation is larger than the load value of a client server, and new data is forced to be stored in a discontinuous space on a magnetic disk, so that jump reading is generated on a central processing unit, and the central processing unit is accelerated to break; when the data transmission speed measuring unit measures that the data transmission speed is larger than the value critical data transmission speed value, the data transmission speed measuring unit sends an electric signal to the time accumulating unit;
step S2-6: time of dayThe second timing channel of the inter-accumulation unit performs accumulation timing on the time when the network data transmission speed exceeds the critical data transmission speed, and outputs a time value；
The step S2-3 further comprises the following steps: the load span calculation module reads a load change data graph; the load span calculation module searches the load value of the highest load point in the unit time of dayLoad minimum point load valueAnd a time value of load highest point to lowest point +.>The method comprises the steps of carrying out a first treatment on the surface of the The load span calculation module passes the formula: />Calculating the load change speed value in unit time, wherein +.>The load change speed value in unit time; the load calculation module records the daily load change speed value +.>And is>Calculating an average load change speed value during the operation of the client server,/for>Days;
the step S3 further includes the steps of:
step S3-1: the information transmission module acquires network condition data monitored by the client server in running and central processing unit information of a client server core;
step S3-2: the loss preset module searches the acquired component information of the central processing unit, and the basic breaking speed of the component central processing unit is obtained through network searching；
Step S3-3: the loss calculation module calculates the total efficiency value of the CPU according to the current total efficiency valueBasic break speed of CPU>Time of data transmission->Load change speed during operation ∈>Time exceeding critical data transmission speed +.>Calculating the running loss value of the CPU to obtain the running loss value of the server +.>；
Step S3-4: according to the loss value of the CPU of the current serverAdjusting the compression ratio of the package to the mirror image of the container>；
In the step S3-3, the loss calculation module calculates the loss value of the current CPU of the server according to the following formula:
wherein,the loss value of the central processing unit of the current server is calculated by the loss calculation module; />The conversion coefficient between the impairment speed and the server performance is a constant value.
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| CN202311550022.0A CN117270897B (en) | 2023-11-21 | 2023-11-21 | Computer system based on low-code business system |
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