CN117891218A - Equipment information model generation method for sensing computation control multi-type equipment access - Google Patents

Abstract

The invention discloses a device information model generation method for sensing, computing and controlling multi-type device access, which relates to the field of industrial production, and the method is divided into three types of sensing, controlling and computing according to the device function on the basis of the existing OPC UA model; under OPC UA standard specification, respectively defining object type nodes of sensing, calculating and controlling type equipment to construct an equipment information model; the device-process matching mapping method based on the field aggregation is designed, the device entity and the basic process are subjected to the field aggregation, the matching mapping of the device entity and the production process is established, and the real-time management and control of the production process are supported. The invention supports the access configuration platform of multiple types of equipment to realize integrated configuration deployment.

Description

Equipment information model generation method for sensing computation control multi-type equipment access

Technical Field

The invention relates to the field of industrial production, in particular to a device information model generation method for sensing, computing and controlling multi-type device access.

Background

The industrial information physical system is a highly coupled system composed of sensing, calculating, controlling and other communication equipment, and by tightly combining the calculating, communicating and controlling technologies, the traditional chimney type management mode in a factory is broken, the information integration of industrial production process is realized, and the industrial information physical system is one of the development trends in the current industrial automation field. However, the current intelligent sensing devices, edge computing devices and programmable controllers in the industrial production field are various in types, different in interface types, and the traditional configuration programmable platforms of the programmable control devices have the pain points of special machines, poor generality and the like, and cannot support the unified access configuration programmable platforms of the multi-type sensing computing devices to realize integrated configuration deployment, so that the sensing, computing and control fusion capability is poor, the cooperative capability of the multi-device is insufficient, and the system configuration programmable platforms cannot adapt to the new characteristics of device system diversification and information interaction intellectualization in an industrial information physical system. Therefore, the characteristics of the multi-type equipment are comprehensively considered and controlled by the industrial site perception calculation, and the unified access and integrated configuration deployment method of the multi-type equipment is designed.

By constructing the equipment information model, standardized expression and flow of information can be realized, and the method is an effective way for realizing information interaction and cooperation among equipment. Information in industrial information physical systems is typically distributed among field devices where devices from different vendors are independently developed and do not interfere with each other. The equipment information model can effectively improve the information interaction efficiency in the industrial information physical system by constructing the information interaction relation among the equipment and providing a standardized information description format. The OPC foundation and the cooperation organization together create OPC UA information models oriented to different industrial application fields, propose an information model based on an OPC unified architecture, and realize data exchange among different manufacturer devices by defining a unified data interface. However, many similar devices in an industrial system have the same information model structure, and if an information model is built from scratch according to a standard flow for each object, a lot of time is consumed, and the repetitive work is much and very complicated. Although the OPC UA specification provides object type nodes to simplify the instantiation process, there is still a lack of a uniform object type node definition specification to support uniform modeling of perceptive computational control multi-type devices due to differences among different device manufacturers, and the like.

In addition, in the aspect of information integration of the production process, the device information model construction taking OPC UA as the representative is widely adopted at present, from the viewpoint of modeling single device, information is stored by utilizing a relational database, the definition of the relational model among data tables lacks unified standards, an upper management system can only search data in the database through inquiry, the dynamic change of the attribute of an industrial process element cannot be represented, the process information is difficult to interact, the real-time management and control of the process cannot be realized, and the real-time adjustment of the site cannot be fed back to the process scheme design and optimization.

Through search and investigation, the domestic patent application number 202110399305.4 is named as an industrial robot interoperation information model construction and analysis method, an industrial robot interoperation information model based on a tree structure is defined, and the industrial robot interoperation is realized, but the method is only oriented to the industrial robot, lacks universality and flexibility, and cannot support information interaction among multiple types of perception computing control equipment; the name of the domestic patent application number 202110697201.1 is 'an automatic construction system for integration and interconnection of manufacturing equipment of a digital workshop', a mapping rule set between an IEC 61499 control model and an OPC UA information model is designed, a unified integration method of the control model and the information model is realized, and the control model and the information model are updated simultaneously through one-time modification in the unified model, but only the IEC 61499 control model is supported, and the universality is lacking; the name of the domestic patent application number 201811072926.6 is 'an automatic construction system for integration and interconnection of manufacturing equipment of a digital workshop', an information model of manufacturing equipment is defined, an XML description file is generated by constructing an information model editor, and automatic construction of an OPC UA address space and a server is realized, but the automatic construction is only oriented to information model construction of single equipment, and the automatic construction system lacks of matching production technological processes and cannot support information integration of industrial production technological processes; the method for automatically constructing the OPC UA information model based on the structured database has the name 202011523146.6 of domestic patent application, and achieves instantiation of the information model by importing the existing structured database of the production line, analyzing the mode information of the database and converting the mode information into an information model file of OPC UA standard, so that complicated operation of manually constructing the information model one by one is reduced, but the method can only be applied to upgrading and reforming the existing production line, and cannot support automatic construction of the equipment model deployed for the new production line.

Therefore, a designer in the art is dedicated to designing a device information model generation method for unified access and integrated configuration deployment of a sensing computation control multi-type device, so as to meet the requirement of realizing unified configuration deployment of the sensing computation control multi-type device access programmable platform.

Disclosure of Invention

In view of the above-mentioned drawbacks of the prior art, the technical problem to be solved by the present invention is how to meet the requirement of implementing unified configuration deployment by accessing a sensing computation control multi-type device to a programmable platform.

In order to solve the problems, the invention provides a device information model generation method for sensing, computing and controlling multi-type device access, which is divided into three types of sensing, controlling and computing according to the device functions on the basis of the existing OPC UA model; under OPC UA standard specification, respectively defining object type nodes of sensing equipment, computing equipment and control equipment to construct an equipment information model; the established perception object type node comprises a device identifier, a perception basic information attribute, a perception general function method and a manufacturer custom function attribute; the built calculation object type node comprises a device identifier, a calculation basic information attribute, a calculation general function method and a manufacturer custom function attribute; the established control object type node comprises equipment identification, control basic information attribute, control general function method and vendor self-defined function attribute.

Further, the device identifier describes a device ID, a manufacturer ID, a product model, a supporting communication protocol, a voltage range, an operating power, an operating temperature, a storage space size, and the like; the perception basic information attribute comprises information such as measurement information, perception information type, data format and the like; the sensing general function method comprises the steps of reading a current measured value, reading the working state of equipment, writing the operation signal of the equipment and the like; the vendor custom function attribute is custom-realized by the manufacturer to realize specific function tasks.

Further, the calculated basic information attribute comprises input data, input data type, input data format, output data type, output data format, data processing algorithm parameters and the like; the general function calculating method comprises the steps of reading output information, reading input information, reading a data processing algorithm program, reading current algorithm parameters, reading the working state of equipment, writing data processing algorithm codes, writing data processing algorithm parameters, writing equipment operation signals and the like.

Further, the control basic information attribute comprises a control input, an input data type, an input data format, a control output, an output data type, an output data format, a control model parameter and the like; the general function control method comprises the methods of reading control input, reading control output, reading control model, reading equipment working state, writing control model parameters, writing equipment operation signals and the like.

Further, based on the idea of field modeling, the equipment model instantiation process is split into two major processes of anemia model construction and congestion model construction, and mainly comprises the following steps:

Step1, generating a corresponding XML template file according to a defined equipment object type node;

step 2, writing the identification information corresponding to each device into a corresponding XML template file to form a device entity anemia model;

And 3, performing function definition on each device in the configuration programmable platform to form a device entity congestion model.

Further, in the step1, the placeholder is added to the data to be dynamically loaded, the parameter variable of the template is set, and a formatted XML file is formed, so that the user can conveniently perform subsequent model construction operation.

Further, in the step 2, the equipment physical anemia model includes information of an ID of the equipment and a supported communication protocol, and the configuration programmable platform establishes a communication link with the equipment by reading the equipment physical anemia model to confirm the type of the equipment and the supported communication protocol.

Further, in the step 3, for the sensing device, the measurement information attribute is used for storing a measurement value of the production process by the device; the perception information type is used for defining the object and the attribute observed by the equipment; the data format is used to define the format of the measurement data produced by the device;

Aiming at the computing equipment, a data processing algorithm is written by using advanced programming languages such as C++, python, MATLAB and the like, according to the calculated basic information attribute defined by the parsed algorithm program, wherein the input data corresponds to the algorithm program input, the input data format corresponds to the data format required by the algorithm input, the output data corresponds to the algorithm output, the output data format corresponds to the algorithm output required data format, the data processing algorithm corresponds to the algorithm program, and the data processing algorithm parameters correspond to the parameter settings in the algorithm;

Aiming at control equipment, writing a control program by using IEC 61131-3 standard language, defining general attributes of control equipment information according to the control program which is written in an analyzing way, wherein the control input corresponds to the input of the control program, the input data format corresponds to the data format required by the control program input, the control input corresponds to the output of the control program, the output data format corresponds to the output format required by the control program, the control model corresponds to the control program, and the control model parameters correspond to parameter settings in the program.

Further, a modeling idea of field driving is adopted, and a device model is combined with corresponding business logic; the method for realizing the equipment-process matching mainly comprises the following steps:

Step a, according to the equipment function definition in the equipment entity congestion model, carrying out matching mapping on the equipment entity and nodes in a DAG graph model representing the production process;

Step b, creating a Pub/Sub communication interface conforming to the OPC UA standard, and generating communication connection between the perception calculation control multi-type devices according to the connection relation between nodes of the DAG graph model;

Step c, the configuration programmable platform confirms the working state of the current equipment; the configuration programmable platform is used as an OPC UA client, reads the working state of the equipment, receives the state information of the equipment and confirms the working state of the equipment.

Further, in step a, for the sensing device, according to the process information attribute of the sensing node in the DAG graph and the basic attribute of the sensing information, matching the sensing node in the corresponding DAG graph with the sensing device; meanwhile, matching the input attribute information in the information models of the computing equipment and the control equipment with the perception information basic attribute of the perception equipment to obtain a neighbor equipment set of the perception equipment;

according to attribute information such as node types, input and output data types, program parameter settings and the like of a neighbor set of a sensing node in the DAG graph, matching the node with the corresponding attribute information with equipment entities in the neighbor equipment set;

in step b, determining the data interaction relation and the content of interaction information between devices through the connection relation between nodes of the DAG graph model, and generating the corresponding Pub/Sub communication interface.

The invention has the following technical effects:

(1) The configuration programmable platform can realize data interaction with field devices through an OPC UA communication interface, and supports the access configuration platform of multiple types of devices to realize integrated configuration deployment;

(2) The user only needs to fill relevant equipment information in the process of constructing the anemia model, the equipment congestion model automatically constructs the configuration program by analyzing the configuration programmable platform, and the complexity of manual operation in the process of instantiating the equipment information model is reduced;

(3) The real-time control of the production process is supported, and the support feeds back the on-site process adjustment to the process scheme design and optimization.

The conception, specific structure, and technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present invention.

Drawings

FIG. 1 is a global flow chart of a device information model generation method in accordance with a preferred embodiment of the present invention;

FIG. 2 is a schematic diagram of an industrial information physical system architecture for high performance steel production according to a preferred embodiment of the present invention

FIG. 3 is a DAG model corresponding to the performance tuning procedure for detecting defects in a steel sheet according to a preferred embodiment of the present invention

FIG. 4 is a flow chart of a process for constructing a physical congestion model of a sensing device in accordance with a preferred embodiment of the present invention

FIG. 5 is a flow chart of a computing device physical congestion model building process in accordance with a preferred embodiment of the present invention

FIG. 6 is a flow chart of a control device physical congestion model construction process in accordance with a preferred embodiment of the present invention

FIG. 7 is a flow chart of a process for matching and mapping device entities to DAG graph models in accordance with a preferred embodiment of the present invention

FIG. 8 is a flow chart of an OPC UA server creation process in accordance with a preferred embodiment of the present invention

Detailed Description

The following description of the preferred embodiments of the present invention refers to the accompanying drawings, which make the technical contents thereof more clear and easy to understand. The present invention may be embodied in many different forms of embodiments and the scope of the present invention is not limited to only the embodiments described herein.

In the drawings, like structural elements are referred to by like reference numerals and components having similar structure or function are referred to by like reference numerals. The dimensions and thickness of each component shown in the drawings are arbitrarily shown, and the present invention is not limited to the dimensions and thickness of each component. The thickness of the components is exaggerated in some places in the drawings for clarity of illustration.

Fig. 1 is a global flowchart of a device information model generation method, and the whole technical scheme consists of two parts of device entity information model construction and field-driven device-process matching mapping, specifically comprising the following steps:

and the first step is to define the object type nodes of the sensing, calculating and controlling type equipment respectively.

And secondly, generating a corresponding XML template file according to the defined equipment object type node.

And thirdly, writing the identification information corresponding to each device into a corresponding XML template file to form a device physical anemia model. The anemia model comprises information of the ID of the equipment and a supported communication protocol, and the configuration programmable platform confirms the type of the equipment and the supported communication protocol by reading the equipment entity anemia model and establishes a communication link with the equipment.

And fourthly, programming a configuration program in the configuration programmable platform, and automatically analyzing the configuration program to define the functions of each device so as to form a device entity congestion model.

And fifthly, carrying out matching mapping on the equipment entity and the nodes in the DAG graph model representing the production process according to the equipment function definition in the equipment entity congestion model.

And sixth, creating an OPC UA server.

And seventh, confirming the working state of the current equipment.

FIG. 2 is a schematic diagram of an industrial information physical system architecture for high performance steel production. The architecture comprises an application layer, a communication layer and a field layer, wherein configuration programmable software of the application layer carries out configuration deployment on field equipment; the communication layer mainly comprises a software-defined communication gateway, wherein decision information is deployed through a north interface docking configuration, and site data is accepted through a south interface; the field layer comprises multiple types of sensing, calculating and controlling equipment, and performs state monitoring, real-time control and decision optimization on the industrial field production process.

Fig. 3 is a DAG model corresponding to the steel plate defect detection and performance adjustment process. The sensing equipment such as the temperature sensor, the pressure sensor and the like acquires structural parameters of the steel plate, transmits detection information to the programmable controller for on-site steel plate rolling feedback control, and the high-speed camera equipment carries out all-dimensional image shooting on the steel plate through uninterrupted, transmits the acquired image information to the edge computing equipment for data processing, intuitively reflects the surface quality information of an online rolled piece, has automatic detection and classification functions, and can be directly used for on-site feedback control rate parameter setting as well as timely guide on-site process adjustment as a data processing result.

Fig. 4 is a flowchart of a process for constructing a congestion model of a sensing device entity, which specifically includes the following steps:

In a first step, a sensing information type attribute is defined based on the object and attributes observed by the sensing device, for example, the attribute of the pressure sensor in the embodiment may be defined as "x-measure pressure", and the rest of the sensing devices may define the attribute in the same manner.

In the second step, data format attributes are defined according to the data of the measurement information generated by the sensing device, for example, the high-speed image capturing device in the embodiment generates non-formatted video stream data by capturing a steel plate, the corresponding data format attribute may be defined as a format "mp4" corresponding to the video stream, and the rest of sensing devices may define the attribute in the same manner.

FIG. 5 is a flowchart of a computing device entity congestion model construction process, comprising the following steps:

First, a data processing algorithm is written in a high-level programming language such as c++, python, MATLAB, etc. for a computing device, for example, a video data processing algorithm may be designed in python language for an edge computing device in an embodiment to detect surface defects of a steel plate.

In the second step, the data processing algorithm codes are automatically parsed, for example, for the edge computing device in the embodiment, the information of algorithm input, parameter setting and the like can be extracted by using a program static analysis tool.

Third, defining the calculation basic information attribute of the information model of the computing equipment according to the analysis and writing algorithm program, wherein the input data attribute corresponds to the algorithm program input, the input data format corresponds to the data format required by the algorithm input, the output data corresponds to the algorithm output, the output data format corresponds to the data format required by the algorithm output, the data processing algorithm corresponds to the algorithm program, and the data processing algorithm parameter corresponds to the parameter setting in the algorithm.

Fig. 6 is a flowchart of a control device entity congestion model construction process, and the specific steps are as follows:

First, for the control device, a control program is written in IEC 61131-3 standard language, for example, for the programmable controller in the embodiment, a control model conforming to IEC 61131-3 standard can be written in a programming language such as a ladder diagram, so as to realize feedback control on the production process.

And secondly, automatically analyzing the control, for example, for a programmable controller in the embodiment, extracting information such as algorithm input, algorithm parameter setting and the like by using a program static analysis tool conforming to the IEC 61131-3 standard.

And thirdly, defining general attributes of the control equipment information according to the control program which is analyzed and written, wherein the control input corresponds to the input of the control program, the input data format corresponds to the data format required by the input of the control program, the control input corresponds to the output of the control program, the output data format corresponds to the output format required by the control program, the control model corresponds to the control program, and the control model parameters correspond to the parameter settings in the program.

FIG. 7 is a flow chart of a mapping process for matching a device entity with a DAG graph model, and the specific steps are as follows:

First, dividing the nodes into sensing nodes, computing nodes and control nodes according to the process information of the nodes in the DAG graph, wherein for example, the nodes N1, N2 and N3 in the real-time example can be classified as sensing nodes; node N4 may be classified as a compute node; node N5 may be classified as a control node.

And secondly, the sensing equipment entity is matched with sensing nodes in the DAG graph. Matching the corresponding sensing nodes of the DAG graph with sensing equipment according to the process information attribute of the sensing nodes in the DAG graph and the sensing information type attribute in the sensing equipment model, for example, in the embodiment, the N1 node is matched with industrial camera equipment; the N2 node is matched with the temperature sensor; the N3 node is matched to the pressure sensor.

Thirdly, determining a neighbor device set of the sensing device, for example, in an embodiment, elements in the neighbor set of the industrial camera device are edge computing devices; the elements in the neighbor set of the temperature sensor are programmable controllers; the elements in the neighbor set of pressure sensors are programmable controllers.

Fourth, according to node type, input and output data type and program parameter setting attribute information of a neighbor set of a sensing node in the DAG graph, matching the node with the consistent corresponding attribute with equipment entities in the neighbor equipment set of the sensing equipment, for example, according to the neighbor set of industrial camera equipment in the embodiment, matching an N4 node with edge computing equipment; the N5 node is matched to the programmable controller based on the neighbor combination of the temperature sensor and the pressure sensor.

And fifthly, matching checking, namely checking whether all the nodes in the DAG graph have equipment matched with the equipment, if so, finishing the matching, otherwise, setting attribute information according to the node type, the input and output data type and the parameters, and matching with equipment entities which are not matched with the same type. For example, in an embodiment, all nodes have devices that match them, and the matching is complete.

Fig. 8 is a flowchart of an OPC UA server creation process, which specifically includes the following steps:

First, the reference relationship between devices is defined. Traversing edges in the DAG graph, a tuple forming a communication relationship between devices, e.g., (s_i, c_j) indicates that the ith sensing device needs to transmit information to the jth control device. And establishing a corresponding reference relation in the information model of the corresponding equipment according to the formed binary group.

Second, an address space is created, instantiating the device object. Loading an information model XML file, analyzing and acquiring information such as the type, the attribute, the method and the like of the equipment, calling a function of adding a new node in an OPC UA standard address space, instantiating the equipment, and adding the current equipment into a node set of the address space.

Thirdly, establishing the reference relation among the equipment nodes in the address space, obtaining the reference relation among the equipment by analyzing the XML file of the information model, calling a function for establishing the reference relation among the nodes, and realizing the establishment of the reference relation among the equipment in the address space.

And fourthly, establishing a corresponding OPC UA server and a client according to the reference relation of the equipment in the address space, and realizing information interaction between the equipment.

And fifthly, taking the configuration programmable platform as an OPC UA client, reading the working state of the equipment, receiving the equipment state information and confirming the working state of the equipment.

The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention without requiring creative effort by one of ordinary skill in the art. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.

Claims (10)

1. The method is characterized in that on the basis of the existing OPC UA model, the method is divided into three types of sensing, control and calculation according to the function of the equipment; under OPC UA standard specification, respectively defining object type nodes of sensing equipment, computing equipment and control equipment to construct an equipment information model; the established perception object type node comprises a device identifier, a perception basic information attribute, a perception general function method and a manufacturer custom function attribute; the built calculation object type node comprises a device identifier, a calculation basic information attribute, a calculation general function method and a manufacturer custom function attribute; the established control object type node comprises equipment identification, control basic information attribute, control general function method and vendor self-defined function attribute.

2. The method for generating the device information model for the access of the sensing and computing control multi-type device according to claim 1, wherein the device identifier describes a device ID, a manufacturer ID, a product model, a supporting communication protocol, a voltage range, an operating power, an operating temperature, a storage space size, and the like; the perception basic information attribute comprises information such as measurement information, perception information type, data format and the like; the sensing general function method comprises the steps of reading a current measured value, reading the working state of equipment, writing the operation signal of the equipment and the like; the vendor custom function attribute is custom-realized by the manufacturer to realize specific function tasks.

3. The method for generating the device information model for the access of the sensing computation control multi-type device according to claim 2, wherein the computing basic information attribute comprises input data, input data type, input data format, output data type, output data format, data processing algorithm parameters and the like; the general function calculating method comprises the steps of reading output information, reading input information, reading a data processing algorithm program, reading current algorithm parameters, reading the working state of equipment, writing data processing algorithm codes, writing data processing algorithm parameters, writing equipment operation signals and the like.

4. The method for generating a device information model for access to a plurality of types of devices for sensing and computing control according to claim 3, wherein the control basic information attribute comprises a control input, an input data type, an input data format, a control output, an output data type, an output data format, a control model parameter, and the like; the general function control method comprises the methods of reading control input, reading control output, reading control model, reading equipment working state, writing control model parameters, writing equipment operation signals and the like.

5. The method for generating the device information model for the sensing and computing control multi-type device access according to claim 4, wherein the device model instantiation process is split into two processes of anemia model construction and congestion model construction based on the idea of domain modeling, and mainly comprises the following steps:

Step1, generating a corresponding XML template file according to a defined equipment object type node;

step 2, writing the identification information corresponding to each device into a corresponding XML template file to form a device entity anemia model;

And 3, performing function definition on each device in the configuration programmable platform to form a device entity congestion model.

6. The method for generating the device information model for the access of the sensing computation control multi-type device according to claim 5, wherein in the step 1, a template parameter variable is set by adding a placeholder to data to be dynamically loaded, so as to form a formatted XML file, and a user can conveniently perform subsequent model construction operation.

7. The method for generating the device information model for the access of the sensing and computing control multi-type device according to claim 6, wherein in the step 2, the device entity anemia model includes information of the ID and the supported communication protocol of the device, and the configuration programmable platform establishes a communication link with the device by reading the device entity anemia model to confirm the type of the device and the supported communication protocol thereof.

8. The method for generating the device information model for access to the sensing and computing control multi-type device according to claim 7, wherein in the step 3, for the sensing device, the measurement information attribute is used for storing a measurement value of a device for a production process; the perception information type is used for defining the object and the attribute observed by the equipment; the data format is used to define the format of the measurement data produced by the device;

Aiming at the computing equipment, a data processing algorithm is written by using advanced programming languages such as C++, python, MATLAB and the like, definition of the attribute of the computing basic information is realized through analyzing the written algorithm program, wherein the input data corresponds to the input of the algorithm program, the input data format corresponds to the data format required by the algorithm input, the output data corresponds to the output of the algorithm program, the output data format corresponds to the data format required by the algorithm output, the data processing algorithm corresponds to the algorithm program, and the data processing algorithm parameters correspond to the parameter setting in the algorithm program;

Aiming at control equipment, writing a control program by using IEC 61131-3 standard language, defining general attributes of control equipment information according to the control program which is written in an analyzing way, wherein the control input corresponds to the input of the control program, the input data format corresponds to the data format required by the control program input, the control input corresponds to the output of the control program, the output data format corresponds to the output format required by the control program, the control model corresponds to the control program, and the control model parameters correspond to parameter settings in the program.

9. The method for generating the device information model for the access of the sensing computation control multi-type device according to claim 8, wherein the device model is combined with corresponding business logic by adopting a modeling idea of field driving; the method for realizing the equipment-process matching mainly comprises the following steps:

Step a, according to the equipment function definition in the equipment entity congestion model, carrying out matching mapping on the equipment entity and nodes in a DAG graph model representing the production process;

Step b, creating a Pub/Sub communication interface conforming to the OPC UA standard, and generating communication connection between the perception calculation control multi-type devices according to the connection relation between nodes of the DAG graph model;

Thirdly, the configuration programmable platform confirms the current working state of the equipment; the configuration programmable platform is used as an OPC UA client, reads the working state of the equipment, receives the state information of the equipment and confirms the working state of the equipment.

10. The method for generating the device information model for the access of the sensing computation control multi-type device according to claim 9, wherein in the step a, for the sensing device, the corresponding sensing node of the DAG graph and the sensing device are matched according to the process information attribute of the sensing node in the DAG graph and the basic attribute of the sensing information; meanwhile, matching the input attribute information in the information models of the computing equipment and the control equipment with the perception information basic attribute of the perception equipment to obtain a neighbor equipment set of the perception equipment;

According to node types, input and output data types and program parameters of a neighbor set of a sensing node in the DAG graph, setting attribute information, and matching the node with consistent corresponding attribute information with equipment entities in the neighbor equipment set;

in step b, determining the data interaction relation and the content of interaction information between devices through the connection relation between nodes of the DAG graph model, and generating the corresponding Pub/Sub communication interface.