CN111897220A - Engineering project control method and control system based on neural network operation mode - Google Patents
Engineering project control method and control system based on neural network operation mode Download PDFInfo
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Abstract
The invention provides an engineering project control method based on a neural network operation mode, which comprises the following steps: simulating a neural system signal transmission process in a vector graph mode based on the description of the engineering project, and establishing a neural network model for engineering control; supporting Web access, database access and mapping access to a physical interface service program of the simulation process through a plurality of service programs; and the arithmetic program is used for finishing the operation and the transmission of the signals in real time, and the arithmetic program dynamically selects an optimal signal path to finally realize various control functions and control flows set by a control engineering project. The invention abstracts the control process into a neural network operation vector graph mode, adopts the mathematical model and the algorithm framework of the artificial neural network directed graph, can draw the programming mode more intuitively and efficiently, supports free drawing of the control operation graph, reduces the programming difficulty and simplifies the programming process.
Description
Technical Field
The invention relates to the field of intelligent control, in particular to a control method and a control system based on a neural network operation mode.
Background
Currently, the world is undergoing a new revolution of high integration of science and technology and industry, the formation of big data, the innovation of theoretical algorithm, the improvement of computing power and the application of artificial intelligence enter the peak period of a new revolution of innovation and development, the new technology continuously obtains breakthrough progress, and presents new characteristics of cross-border integration, man-machine cooperation, crowd-sourcing, autonomous control and the like which are oriented by application. The artificial intelligence technology and the application have new breakthrough progress, the artificial intelligence technology based on the simulation of the human neural network operation mode becomes the most prominent characteristic and hot spot, the control system based on the artificial intelligence neural network form and algorithm is produced, and the industry urgently needs to develop a control language based on the neural network operation mode and a control method thereof in theory and practice.
Disclosure of Invention
The invention aims to provide an engineering project control method based on a neural network operation mode, which has the advantages of intuitive control method and easiness in operation and brings brand new experience for people to use.
In order to achieve the above object, the present invention provides an engineering project control method based on a neural network operation mode, including:
1) simulating a neural system signal transmission process in a vector graph mode based on the description of the engineering project, and establishing a neural network model for engineering control;
2) supporting Web access, database access and mapping access to a physical interface service program of the simulation process in the step 1) through a plurality of service programs;
3) and (3) the operation and the transmission of the signals in the step 1) are completed in real time through an algorithm program, and the algorithm program dynamically selects an optimal signal path to finally realize various control functions and control flows set by a control engineering project.
In a preferred scheme of the invention, in the process of simulating the neural system signal transmission in a vector graph mode in the step 1), a functional module or a transfer function is taken as an information processing node, and all the information processing nodes are connected through a directed signal line, so that an arbitrary reticular control flow is laid. The information processing node is similar to a skeleton of a neural network model and is used for processing received information or data based on the setting of self parameters. The information processing node can be a traditional condition quantity module, and is selected from any one or more of an AND or NOR logic module, a network protocol stack module, a clock timer module, an analog quantity operation module and a JSON data analysis module; or a function module customized by a user according to a certain rule. The self-defined function module is obtained by abstracting a physical control interface and/or a common function; the physical interface module includes: various network protocol ports (such as tcp, udp, telnet, http, snmp, etc.), a plurality of standard serial ports, infrared/unidirectional serial ports, relay ports, I/O ports, etc.
In a further preferred aspect of the present invention, the information processing node includes a general module and/or a macro module; the macro module is formed by packaging and encapsulating the common module and/or the macro module.
In a further preferred scheme of the invention, the step 1) further comprises the step of packaging and encapsulating any local or whole neural network control operation model to form a macro module through editing, compiling, encapsulating and debugging of a typical computer high-level language, wherein the packaged macro module can be called, rewritten, expanded and shared.
In the preferable scheme of the invention, the back-end data storage in the step 2) adopts a MongoDB database to provide extensible high-performance data storage for the integrated development environment of the neural network control method based on WEB application.
In a more preferred scheme of the present invention, the MongoDB stores the data as a document, and the data structure is a key value (key = > value) pair, similar to a JSON object, and interacts with the front-end data for the Web server service access based on nodjs at the back end.
The back-end interface service program adopts a NodeJS operation system and realizes the program operation by an event trigger mechanism. And calling the C + + algorithm program to obtain an algorithm operation result, and executing the logic code module of a certain logic block by the main program according to the return value of the C + + program. The return value of the logic module code is used for updating other modules related to the logic module code until the interface module is encountered, and the program calls the interface module service program and transmits the value to each physical interface.
The back-end Web server program also adopts a NodeJS operation system, the Web server introduces a local route processing module to respectively respond and process various Ajax requests and SocketIO requests sent by the front end, and sets each shared directory and completes MongoDB database access so that the front end directly accesses resources and data in the directory and the database;
the back end interface service program comprises an entry function of modules such as a serial interface and the like, and creates a timing thread and a thread for receiving the message of the main program by the udp client. And the main program of the interface service program main calls the physical interface module and returns the message to the background main program through the udp client thread according to the return value of the interface module. The physical interface module comprises a plurality of paths of serial communication ports, a plurality of paths of I/O channels, an RTC clock timer interface and the like.
In a preferred scheme of the invention, the algorithm program in the step 3) adopts a real-time dynamic recursive optimization algorithm.
The method realizes the abstract of the control process into a neural network operation vector diagram mode, adopts the mathematical model and the algorithm architecture of the artificial neural network directed graph, takes the Web vector diagram conforming to the HTML5 standard as the expression form of the control operation diagram, and enables a user to freely define the control flow, parameters, time sequence, protocol, feedback and interaction.
The engineering project control method based on the neural network operation mode has a local or remote real-time online debugging function, can display and present data, signals, results and flows of a control process in real time, and can intervene or interrupt the control process.
The invention also provides an engineering project control system based on the neural network operation mode, which comprises the following components:
the front-end description module is used for simulating a neural system signal transmission process in a vector graphics mode based on the description of the engineering project and establishing a neural network model for engineering control;
the back-end support module is used for supporting Web access, database access and mapping access of a physical interface service program of the simulation process of the front-end description module through a plurality of service programs;
and the number of the first and second groups,
and the rear-end algorithm module is used for finishing the operation and transmission of the signals of the front-end description module in real time through an algorithm program, and dynamically selecting an optimal signal path through the algorithm program.
The invention also provides a storage medium, wherein a computer program is stored in the storage medium, and the storage medium is controlled to execute the engineering project control method on the engineering project system when the computer program runs.
The invention also provides a processor for running the computer program, and the computer program executes the engineering project control method in running.
Compared with the prior art, the invention has the following advantages:
the method can draw the programming mode more intuitively and efficiently, supports free drawing of the control operation diagram, reduces the programming difficulty and simplifies the programming process. The edited program software has cross-platform performance and can be run in various versions of operating systems such as Windows, Linux, Mac and the like. The edited program is more flexible and can be a self-built module, various modules can be freely collocated, and signals of the same type can be randomly connected.
Drawings
FIG. 1 is a flow chart of an engineering control method according to the present invention.
Detailed Description
The control method in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments.
The signal types are as follows from small to large according to the information capacity: digital quantity, analog quantity, serial quantity, structural body serial quantity and the like, and different types of signal lines are used for representing information transmission relations among modules.
The neural network control method abstracts a physical control interface and a common function into a module form, establishes a control transfer relation between modules by configuring module parameters, drawing a signal relation line or editing a signal name, and realizes drawing of a control operation diagram.
The physical interface module includes: various network protocol ports such as tcp, udp, telnet, http, snmp, etc., a plurality of standard serial ports, infrared/unidirectional serial ports, relay ports, I/O ports, etc.
The invention relates to an engineering project control method based on a neural network operation mode, which is characterized in that firstly, in a module form, a control transmission relation between modules is established by drawing a signal relation line, and the drawing of a control operation diagram is realized. For example, a control model based on the neural network operation mode as shown in fig. 1 is established. A UI module (User Interface), a plurality of mutual rotation modules (Toggle) and a relay control module (Isolated Relays 8) are arranged according to control requirements.
The basic control flow of the model shown in fig. 1 is as follows: the node d1 of the User Interface module triggers and controls a first Toggle module, and the node out of the Toggle module feeds back a control signal to the User Interface module and transmits the control signal to the node r1 of the Isolated Relays 8 module to correspondingly control the first path of relay to be switched on/off. The second and third Toggle modules are respectively connected to nodes r2 and r3 of the Isolated Relays 8 module, and correspondingly control the second and third Relays to be switched on/off.
The specific operation of each module is explained below.
And (5) engineering structure. The engineering structure of the neural network control method consists of macro modules and common modules, wherein the macro modules are formed by packaging and encapsulating the common modules or the macro modules. Writing a computer application program based on the idea of the control method to obtain neural network mode engineering control software, opening the software, clicking 'new engineering', and inputting an engineering name to determine. The project name can be displayed in the project structure, and then various modules can be dragged into the main working area; displaying the project name and the macro module name used in the project structure; during engineering production, the macro module can be unpacked, edited and packaged into the macro module again in an engineering main window; and editing each module at the interface of the macro module to change the definition of the macro module.
And a language module. The control method of this embodiment provides a group of conventional control configuration module libraries, and the user can also add a custom module or a module group (macro module) by himself, and these modules can be classified according to functions as follows: interface module, KNX module, pronunciation AI module, network module.
The logic module comprises a system module, an analog quantity module, a condition quantity module, a counter module, a memory module, a serial quantity module, a real-time clock module and a timer module.
Application macro (standard module). The macro operation process is organized as follows: firstly, clicking a 'grouping macro' function button on a menu bar, then using a left mouse button box to select modules to be grouped in a main working area, and then checking 'one-by-one selection/cancellation' or pressing a 'ctrl' hot key in a pop-up box to independently select some modules. And finally, filling the macro name and the macro module description in the dialog box when the macro module group is created, and displaying the macro module description characters in a help pop-up box after the macro module is generated. The control method of the embodiment has the function of packing, encapsulating and composing any group of modules into a macro module. From another perspective, the macro module is a special module that encapsulates a group of modules that implement specific functions, and presents specific input, output, and parameter signals to the outside.
The back end of the engineering project control method based on the neural network operation mode is realized by adopting a real-time dynamic recursive optimization algorithm and adopting a C + +11 language.
The back-end data storage of the engineering project control method based on the neural network operation mode described in this embodiment adopts a mongoDB database, and the MongoDB can provide extensible high-performance data storage for the integrated development environment based on the WEB application in this embodiment. MongoDB stores data as a document, and the data structure is similar to JSON objects by key value (key = > value), so that the Web server service access based on NodeJS can be conveniently interacted with front-end data for the back end.
In this embodiment, the back-end interface service program of the engineering project control method based on the neural network operation mode adopts a NodeJS operation system, and the program operation is realized by an event-triggered mechanism. And calling the C + + algorithm program to obtain an algorithm operation result, and executing the logic code module of a certain logic block by the main program according to the return value of the C + + program. The return value of the logic module code is used for updating other modules related to the logic module code until the interface module is encountered, and the program calls the interface module service program and transmits the value to each physical interface.
In the engineering project control method based on the neural network operation mode, the back-end Web server program also adopts a nodesjs operation system, the Web server introduces a local route processing module to respectively respond to various Ajax requests and SocketIO requests sent by a processing front end, and sets each shared directory and completes access of a MongoDB database, so that the front end directly accesses resources and data in the directories and the database.
The interface service program of the engineering project control method based on the neural network operation mode includes entry functions of modules such as a serial port interface, and creates a timing thread and a thread of a udp client receiving a message of a main program. And the main program of the interface service program main calls the physical interface module and returns the message to the background main program through the udp client thread according to the return value of the interface module. The physical interface module comprises a plurality of paths of serial communication ports, a plurality of paths of I/O channels, an RTC clock timer interface and the like, and an interface service program is designed by adopting C + + language.
The engineering project control method based on the neural network operation mode described in this embodiment performs various tests on the completeness of an engineering before uploading to the rear end of equipment to operate after the engineering is edited. The inspection result is prompted in the form of warning or error, the uploading operation is not influenced by the warning information, and the uploading operation is forbidden if the error exists.
And uploading and storing the project without problems in compiling and checking to the back end, and analyzing and operating by a back end program.
In summary, by analyzing the control method engineering drawing of the embodiment, the background algorithm dynamically selects the optimal signal path, thereby implementing intelligent and efficient control signal transmission.
In a word, the engineering project control method based on the neural network operation mode disclosed by the invention has the advantages that the front end integration development environment finishes the description and generation of the control engineering project (the neural system signal transmission process is simulated in a vector graphics mode), and the rear end supports the Web access and the database access of the front end and the mapping access of the physical interface service program by using each service program. In addition, the rear-end algorithm program of the engineering project control method is responsible for finishing the operation and transmission of control signals in real time, and finally realizing various control functions and control flows set by the control engineering project.
Claims (10)
1. A method for controlling engineering projects based on a neural network operation mode comprises the following steps:
1) simulating a neural system signal transmission process in a vector graph mode based on the description of the engineering project, and establishing a neural network model for engineering control;
2) supporting Web access, database access and mapping access to a physical interface service program of the simulation process in the step 1) through a plurality of service programs;
3) and (3) the operation and the transmission of the signals in the step 1) are completed in real time through an algorithm program, and the algorithm program dynamically selects an optimal signal path to finally realize various control functions and control flows set by a control engineering project.
2. The method of claim 1, wherein: in the process of simulating the neural system signal transmission in a vector graph mode, a functional module or a transfer function is used as an information processing node, and all the information processing nodes are connected through a directed signal line, so that an arbitrary reticular control flow is arranged.
3. The method of claim 2, wherein: the information processing node is a condition quantity module and is selected from any one or more of an AND or NOR logic module, a network protocol stack module, a clock timer module, an analog quantity operation module and a JSON data analysis module.
4. The method of claim 2, wherein: the information processing node is a functional module which is self-defined by a user according to a certain rule; the self-defined function module is obtained by abstracting a physical control interface and/or a common function; the physical interface module includes: various network protocol ports (such as tcp, udp, telnet, http, snmp, etc.), a plurality of standard serial ports, infrared/unidirectional serial ports, relay ports, I/O ports, etc.
5. The method of claim 1, wherein: step 1) also comprises the step of packaging and encapsulating any local or whole neural network control operation model to form a macro module through editing, compiling, encapsulating and debugging of a typical computer high-level language, wherein the packaged macro module can be called, rewritten, expanded and shared.
6. The method of claim 1, wherein: the back-end data storage in the step 2) adopts a MongoDB database.
7. The method of claim 1, wherein: the algorithm program in the step 3) adopts a real-time dynamic recursive optimization algorithm.
8. Engineering project control system based on neural network mode of operation, its characterized in that includes:
the front-end description module is used for simulating a neural system signal transmission process in a vector graphics mode based on the description of the engineering project and establishing a neural network model for engineering control;
the back-end support module is used for supporting Web access, database access and mapping access of a physical interface service program of the simulation process of the front-end description module through a plurality of service programs;
and the number of the first and second groups,
and the rear-end algorithm module is used for finishing the operation and transmission of the signals of the front-end description module in real time through an algorithm program, and dynamically selecting an optimal signal path through the algorithm program.
9. A storage medium having a computer program stored therein, the computer program when executed controls the storage medium to execute the engineering project control method of claim 1 on the system of claim 8.
10. A processor for executing a computer program which when executed performs the engineering project control method of claim 1.
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Application publication date: 20201106 |