WO2023028778A1

WO2023028778A1 - Methods, systems and storage medium for industrial app development

Info

Publication number: WO2023028778A1
Application number: PCT/CN2021/115441
Authority: WO
Inventors: Peng Zhang; Zhu NIU; Shun Jie Fan; Bin Zhang
Original assignee: Siemens Aktiengesellschaft; Siemens Ltd., China
Priority date: 2021-08-30
Filing date: 2021-08-30
Publication date: 2023-03-09

Abstract

Examples of the present disclosure provide a method, system and computer readable storage medium for industrial App development. The method includes: encapsulating industrial skills into corresponding industrial micro-services in advance, each industrial micro-service has attribute information, and the attribute information of the industrial micro-services is stored in the form of knowledge graph; the knowledge graph comprises nodes respectively representing each attribute in the attribute information and connecting lines representing the relationship between nodes; collecting application requirements of a user; matching and obtaining at least one industrial micro-service combination that meet the application requirements based on the attribute information of the industrial micro-services stored in the form of knowledge graph, and taking each industrial micro-service combination as an App corresponding to the application requirements. The technical solutions of the present disclosure can reduce the development difficulty of industrial Apps.

Description

A METHOD, SYSTEM AND STORAGE MEDIUM FOR INDUSTRIAL APP DEVELOPMENT

FIELD

The present disclosure relates to industrial technologies, and more particularly, to a method, system and computer readable storage medium for industrial application (App) development.

BACKGROUND

At present, when developing Apps, some software platforms will provide users with some App development tools, but when using these App development tools for industrial App development, either program developers need to have sufficient technical knowledge such as industrial automation, or technicians with sufficient industrial technical knowledge need to have strong program development ability. In fact, few people can have these two abilities at the same time.

Patent application CN112261080A discloses an Internet of things (IoT) edge agent applied to an electronic Internet of things. The IoT edge agent adopts a micro-service layered architecture, which realizes micro-service by using a Docker container technology and carries out communication interaction of the framework by providing MQTT services. Under the framework of micro-service, applications are divided into many different micro-services. Developers only need to select and configure corresponding micro-services according to required functions, and then call different micro-services through MQTT protocol according to the execution logic. However, selecting and configuring micro-services still requires a high level of experience and professional knowledge. Users without professional knowledge are difficult to select the correct micro-services or configure parameters in micro-services.

Therefore, those skilled in the art are also committed to finding other industrial App development solutions.

SUMMARY

According to examples of the present disclosure, a method, system and computer readable storage medium for industrial App development is provided to reduce the development difficulty of industrial Apps.

The method for industrial App development provided by examples of the present disclosure includes: encapsulating industrial skills into corresponding industrial micro-services in advance, each industrial micro-service has attribute information, and the attribute information of the industrial micro-services is stored in the form of knowledge graph; the knowledge graph comprises nodes respectively representing each attribute in the attribute information and connecting lines representing the relationship between nodes; collecting application requirements of a user; matching and obtaining at least one industrial micro-service combination that meet the application requirements based on the attribute information of the industrial micro-services stored in the form of knowledge graph, and taking each industrial micro-service combination as an App corresponding to the application requirements.

In an example, the attribute information of each industrial micro-service includes: at least one function that the industrial micro-service can achieve; wherein, matching and obtaining at least one industrial micro-service combination that can meet the application requirements based on the attribute information of the industrial micro-services stored in the form of knowledge graph, includes: analyzing the application requirements to determine every function category to be achieved and a logical relationship between function categories; for each function category, determining at least one specific function involved in the function category; matching industrial micro-services with corresponding functions for all determined specific functions based on the attribute information of industrial micro-services stored in the form of knowledge graph, and obtain at least industrial one micro-service combination arranged according to the logical relationship.

In an example, wherein, matching and obtaining at least one industrial micro-service combination that can meet the application requirements based on the attribute information of the industrial micro-services stored in the form of knowledge graph, includes: according to the application requirements, matching and obtaining at least one industrial micro-service combination that can achieve the application requirements based on the attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph; or includes: analyzing the application requirements to determine every function category to be achieved and a logical relationship between function categories, and based on the attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph according to every function category, matching to obtain at least one industrial micro-service composition capable of achieving the application requirements.

In an example, the method further includes: pushing at least one APP corresponding to the application requirements to the user; or, attribute information of each industrial micro-service further includes: input and output parameters of the industrial micro-service, and the input and output parameters are respectively set with a default value; when the at least one App is two or more Apps, for each App, performing a simulation run on the App based on the default values of the input and output parameters of each industrial micro-service in the App and obtaining a corresponding running performance, and pushing the at least one App and its corresponding running performance to the user, or pushing the App with the best running performance to the user; or, attribute information of each industrial micro-service further includes: a performance parameter of the industrial micro-service; when the at least one App is two or more Apps, for each App, evaluating a comprehensive performance of the App based on the performance parameter of each industrial micro-service in the App; pushing the at least one App and its corresponding comprehensive performance to the user, or pushing the App with the best comprehensive performance to the user.

In an example, wherein the attribute information of each industrial micro-service further includes: input and output parameters of the industrial micro-service; the method further includes: receiving an adjustment or setting command for the input and output parameters of an industrial micro-service in a final App confirmed by the user, and performing corresponding parameter adjustment and setting.

In an example, the method further includes: attribute information of each industrial micro-service of a final App confirmed by the user is stored corresponding to the application requirements in the form of knowledge graph.

The system for industrial App development provided by examples of the present disclosure includes: a skill library, in which industrial micro-services encapsulating industrial skills are set, each industrial micro-service has attribute information, and attribute information of the industrial micro-services is stored in the form of knowledge graph; the knowledge graph includes nodes respectively representing each attribute in the attribute information and connecting lines representing the relationship between nodes; an application requirements collection module, to collect application requirements of a user; an App generation module, to match and obtain at least one industrial micro-service combination that meet the application requirements based on attribute information of industrial micro-services stored in the form of knowledge graph in the skill library, and take each industrial micro-service combination as an App corresponding to the application requirements.

In an example, the attribute information of each industrial micro-service includes: at least one function that the industrial micro-service can achieve; the App generation module includes: a requirement analysis module, to analyze the application requirements to determine every function category to be achieved and a logical relationship between function categories; a function determination module, to determine at least one specific function involved in each function category; a first intelligent matching module , to match micro-services with corresponding functions for all determined specific functions based on the attribute information of industrial micro-services stored in the form of knowledge graph, obtain at least one micro-service combination arranged according to the logical relationship, and take each industrial micro-service combination as an App corresponding to the application requirements.

In an example, the App generation module further includes: a second intelligent matching module, to match and obtain at least one industrial micro-service combination that meets the application requirements based on attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph, and take each industrial micro-service composition as an App corresponding to the application requirements; or further includes: a requirement analysis module, to analyze the application requirements to determine at least one function category to be achieved and a logical relationship between function categories; and a third intelligent matching module, to, according to the at least one function category to be achieved and the logical relationship among the at least one function category, match at least one industrial micro-service composition capable of achieving the application requirements based on the attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph, and take each industrial micro-service composition as an App corresponding to the application requirements.

In an example, wherein the App generation module further includes: an App recommendation module, to push the at least one App corresponding to the application requirements to the user; or, the attribute information of each industrial micro-service further includes: input and output parameters, and the input and output parameters are respectively set with a default value; when the at least one App is two or more Apps, for each App, the App recommendation module is to perform simulation run on the App based on the default values of the input and output parameters of each industrial micro-service in the App to obtain a corresponding running performance, push the at least one App and its corresponding running performance to the user, or push the App with the best running performance to the user; or, the attribute information of each industrial micro-service further includes: a performance parameter; when the at least one App is two or more Apps, for each App, the App recommendation module is to evaluate a comprehensive performance of the App based on the performance parameter of each industrial micro-service in the App; push the at least one App and its corresponding comprehensive performance to the user, or push the App with the best comprehensive performance to the user.

In an example, wherein the attribute information of each industrial micro-service further includes: input and output parameters of the industrial micro-service; the App recommendation module is further to receive an adjustment or setting command for the input and output parameters of an industrial micro-service in a final App confirmed by the user, and perform corresponding parameter adjustment and setting.

In an example, the App recommendation module is further to store attribute information of each industrial micro-service of a final App confirmed by the user corresponding to the application requirements in the form of knowledge graph.

Another system for industrial App development includes: at least one memory, to store a computer program; and at least one processor, to call the computer program stored in the at least one memory to perform a method for industrial App development mentioned above.

The non-transitory computer-readable storage medium, on which a computer program is stored, the computer program is to be executed by a processor to implement a method for industrial App development mentioned above.

It can be seen from above mentioned technical solutions in examples of the present disclosure, various industrial skills are encapsulated into various industrial micro-services, and the attribute information of the industrial micro-service is stored in the form of knowledge graph, so the user only needs to input the application requirements, and then the App will be automatically constructed based on the attribute information of the industrial micro-service stored in the form of knowledge graph, thus the difficulty for users to create Apps is reduced. In addition, Apps based on industrial micro-services have good reusability.

In addition, the efficiency of App creation is further improved by storing historical App instances in the form of knowledge graph.

In addition, when more than one App is obtained, higher performance App can be recommended for the user by evaluating the comprehensive performance or running performance of each App.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present disclosure, reference should be made to the Detailed Description below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures.

Figure 1 is a flow diagram illustrating a method for industrial App development according to examples of the present disclosure.

Figure 2A is a schematic diagram illustrating attribute information of an industrial micro-service stored in the form of knowledge graph according to an example of the present disclosure.

Figure 2B is a schematic diagram illustrating attribute information of multiple industrial micro-services stored in the form of knowledge graph according to an example of the present disclosure.

Figures 2C and 2D are schematic diagrams respectively illustrating a knowledge graph of an APP instance according to an example of the present disclosure.

Figure 3 is a schematic diagram illustrating a system industrial App development according to examples of the present disclosure.

Figure 4 is a schematic diagram illustrating an industrial edge according to an example of the present disclosure.

Figure 5 is a schematic diagram illustrating another system industrial App development according to an example of the present disclosure.

The reference numerals are as follows:

Reference numeral	Object
S11、S12	processes
310	skill library
320	application requirements collection module
330	App generation module
331	requirement analysis module
332	function determination module
333	first intelligent matching module
334	second intelligent matching module
335	third intelligent matching module
336	App recommendation module
410	connection layer
411	SIMATIC S7 connector
412	OPC UA connector
413	Modbus TCP connector

414	PROFINET IO connector
420	data layer
430	application layer
51	memory
52	processor
53	bus

DETAILED DESCRIPTION

In the embodiment of the present disclosure, in order to reduce the development difficulty of industrial Apps, it is considered to propose an industrial App development scheme that supports requirement-driven. In this scheme, various skills in industrial applications are encapsulated as corresponding industrial micro-services and all skill information is stored in the knowledge graph. For example, each industrial micro-service can be stored as a skill knowledge graph, and further, each historical App instance can be stored as a knowledge graph instance. Then, the user's application requirements can be obtained, at least one App corresponding to the application requirements can be created by using corresponding industrial micro-service based on the skill knowledge graph and the knowledge graph instance, and the at least one App can be recommended to the user for selection and parameter configuration. Further, the performance of each App can be evaluated and the evaluation results can be provided to the user at the same time, or only the App with the best comprehensive performance can be recommended to the user.

Reference will now be made in detail to examples, which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. Also, the figures are illustrations of an example, in which modules or procedures shown in the figures are not necessarily essential for implementing the present disclosure. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the examples.

Figure 1 is a flow diagram illustrating a method for industrial App development according to examples of the present disclosure. As shown in figure 1, the method may include the following processes.

At block S11, various industrial skills are encapsulated into corresponding industrial micro-services in advance, each industrial micro-service has attribute information that can indicate the basic situation of the industrial micro-service, and the attribute information of the industrial micro-services is stored in the form of knowledge graph; the knowledge graph includes nodes respectively representing every attribute in the attribute information and connecting lines representing the relationship between nodes.

The attribute information of each industrial micro-service may include: the name of the industrial micro-service and at least one function that the industrial micro-service can achieve. In addition, the attribute information may also include input and output parameters, and a performance parameter. Among them, the performance parameter may be time-consuming, CPU occupancy, or algorithm complexity, etc., which can be modified according to the actual needs of users.

In specific implementation, as shown in figure 2A, the attribute information of each industrial micro-service may be stored as a knowledge graph. In figure 2A, nodes respectively representing the function F, the performance parameter P, and the input and output parameters I/O are all connected to a node representing the name S of the industrial micro-service. Further, as shown in figure 2B, all or some industrial micro-services may be associated through their common attributes to form a large knowledge graph.

For example, in one example, the industrial micro-services may include: basic function micro-services, data acquisition micro-services, data processing micro-services, model micro-services, algorithm micro-services, etc. Among them, the main functions provided by basic function micro-services include: 1) building web services and providing web pages for displaying data; 2) establishing a database according to the data; and so on. The main functions provided by the data acquisition micro-services include: 1) PROFINET protocol driver, used for data interaction with industrial device supporting PROFINET protocol; 2) OPC UA protocol driver, used for data interaction with industrial device supporting OPC UA protocol; and so on. The main functions of data processing micro- services include: 1) data cleaning; 2) data compression and decompression, such as compressing and decompressing data according to algorithms; 3) feature extraction, such as extracting data features according to the algorithm; and so on. The main functions of model micro-services include: dynamic modeling of key components of transmission system, such as ball screw, synchronous belt, reducer, etc. The main functions of algorithmic micro-services include: 1) expert knowledge library; 2) optimize control strategy; 3) fault classification; and so on.

In another example, the micro-services mentioned above can be subdivided into different micro-services. For example, 1. The basic function micro-services may include a micro-service for building web services, a micro-service for displaying data and a micro-service for collecting user information. 2. The data acquisition micro-services may include a micro-service based on OPC UA, a micro-service based on PROFINET and a micro-service based on other industrial communication protocols. 3. The data processing micro-services may include a micro-service for data cleaning, a micro-service for data conversion, a micro-service for data enhancement, a micro-service for data statistics, a micro-service for data display, etc. 4. The model micro-services may include a dynamic model describing friction, a dynamic model describing tension, a fault diagnosis model, a predictive maintenance model, various data modeling micro-service, a micro-service for model import, a micro-service for model transformation, a micro-service for model training, and various artificial intelligence (AI) models. 5. The algorithms micro-services may include predictive maintenance an algorithm based on a model or data, a fault diagnosis algorithm, an optimization control algorithm, etc.

In addition, in other examples, there may be other industrial micro-services with different implementation modes, which can be set according to requirements, and they are not limited here

At block S12, application requirements of a user are collected, and at least one industrial micro-service combination that can meet the application requirements is matched and obtained based on the attribute information of the industrial micro-services stored in the form of knowledge graph, and each industrial micro-service combination is taken as an App corresponding to the application requirements.

There are many methods to implement this block, only a few of which are listed below:

A first one: the application requirements are analyzed, and then corresponding function categories to be achieved and a logical relationship between the function categories are determined.

In specific implementation, the application requirements of the user may be collected through questionnaires or other methods, and the application requirements can be analyzed based on machine learning algorithms or trained models to determine the various function categories to be implemented for the application requirements and the logical relationship between the various function categories. For example, according to current application requirements, t function categories are determined, namely C1, C2, ..., Ct. Where t is a positive integer greater than or equal to 1.

For each function category, the specific function (s) involved in the function category can be determined based on a pre stored corresponding relationship between function category and specific function, or based on the learning of a machine learning algorithm. For example, if it is determined that the function category C1 includes functions F1 and F2, ..., the function category Ct includes functions fn-1 and Fn. Then, based on the industrial micro-service attribute information stored in the form of knowledge graph, the corresponding industrial micro-services for all determined functions are matched to obtain at least one industrial micro-service combination arranged according to the logical relationship. For example, suppose there are n industrial micro-service combinations, in which the first industrial micro-service combination contains m industrial micro-services, i.e. S1 + Sx+... +Sm, and the nth industrial micro-service combination contains r industrial micro-services, i.e. Sn1 + Snx +... + Snr. Then, each industrial micro-service composition can be taken as an App corresponding to the application requirements. Where n, m and r are positive integers greater than or equal to 1.

A second one: in this example, the attribute information of each industrial micro-service contained in a historical industrial App and its corresponding user application requirements can be taken as an application instance and stored as a knowledge graph, that is, industrial micro-service attribute information of the application instance can be stored in the form of a knowledge graph. Then, when similar user applications are collected, at least one industrial micro-service combination that can realize the application requirements can be matched based on the industrial micro-service attribute information of each application instance stored in the form of knowledge graph, and each industrial micro-service combination can be used as an App corresponding to the application requirements.

A third one: in this example, the attribute information of each industrial micro-service contained in the historical industrial App and each function category determined according to its corresponding user application requirements can be taken as an application instance and stored as a knowledge graph, that is, another implementation method of storing the industrial micro-service attribute information of the application instance in the form of knowledge graph. At this time, the application requirements can be analyzed first to determine every function category to be achieved for the application requirements and the logical relationship between function categories, and then based on stored attribute information of industrial micro-services of each App instance stored in the form of knowledge graph, at least one industrial micro-service composition capable of realizing the application requirements is matched and obtained, and each industrial micro-service composition is regarded as an App corresponding to the application requirements.

After that, the at least one App can be pushed directly to the user. Alternatively, if the attribute information of each industrial micro-service includes a performance parameter, when the at least one App is two or more Apps, a comprehensive performance of the App can be evaluated for each App based on the performance parameter of each industrial micro-service in the App; then the at least one App and its corresponding comprehensive performance may be pushed to the user, or a App with the best comprehensive performance may be pushed to the user. For example, for above industrial micro-service combination S1 +Sx +... + Sm, the comprehensive performance is CP (P1, Px, ..., Pm) , and for above industrial micro-service combination Sn1 + Snx +... + Snr, the comprehensive performance is CP (Pn1, Pnx, ..., Pnr) .

If there are Apps with similar comprehensive performance, if the input and output parameters of each industrial micro-service are set with default values, a simulation run can be performed on each of Apps based on the default values of the input and output parameters of each industrial micro-service in the App with similar comprehensive performance to obtain corresponding running performances, the at least one App and its comprehensive performance and running performance are pushed to the user, or an App with the best running performance is pushed to the user. Alternatively, whether the attribute information of each industrial micro-service includes a performance parameter or not, if the input and output parameters of the industrial micro-service are respectively set with a default value, when the at least one App is two or more Apps, the simulation run may be directly performed on each App based on the default values of the input and output parameters of each industrial micro-service in the App to obtain a corresponding running performance, the at least one App and its corresponding running performance are pushed to the user, or the App with the best running performance is pushed to the user.

Then, the user can confirmed a final App from the at least one App, and can adjust or set the input and output parameters of each industrial micro-service in the final APP. Accordingly, in this example, the method may further includes: receiving an adjustment or setting command for the input and output parameters of each industrial micro-service in the final App, and performing corresponding parameter adjustment and setting.

In addition, in this example, the final App confirmed by the user may be taken as an APP instance corresponding to the application requirements and the attribute information of each industrial micro-service in the final App may be further stored corresponding to the application requirements in the form of knowledge graph.

Figure 2C is a schematic diagram of a knowledge graph of an APP instance according to an example of the present disclosure. As shown in figure 2C, the knowledge graph includes a node representing the application requirements R1, nodes representing the function categories C1 ～ Ct arranged according to a certain logical relationship and determined according to the application requirements R1, nodes representing the specific functions F1 ～ Fn involved in the function categories, and nodes representing the attribute information of each industrial micro-service. At this time, after receiving the application requirements, at least one industrial micro-service combination that can meet the application requirements can be matched directly based on the stored industrial micro-service attribute information of each App instance stored in the form of knowledge graph.

In addition, in another example, as shown in figure 2D, the node representing the application requirements R1 may not be included. At this time, the application requirements can be analyzed first to determine each function category to be realized for the application requirements and the logical relationship between function categories, and then based on the stored industrial micro-service attribute information of each App instance stored in the form of knowledge graph, at least one industrial micro-service composition capable of realizing the application requirements is matched and obtained.

The method for industrial App development according to examples of the present disclosure is described in detail above, and the system for industrial App development according to examples of the present disclosure is described in detail hereinafter. The system for industrial App development according to examples of the present disclosure can be used to implement the method for industrial App development according to examples of the present disclosure. For the details not disclosed in the examples of the system of the present disclosure, please refer to the corresponding description in the examples of the method of the present disclosure, which will not be repeated here.

Figure 3 is a structure diagram illustrating a system for industrial App development according to examples of the present disclosure. As shown in figure 3, the system may include a skill library 310, an application requirements collection module 320 and an App generation module 330.

Industrial micro-services encapsulating various industrial skills are set in the skill library 310, each industrial micro-service has attribute information that can indicate the basic situation of the industrial micro-service, and the attribute information of the industrial micro-service is stored in the form of knowledge graph. The knowledge graph includes nodes respectively representing the attributes in the attribute information and connecting lines representing the relationship between nodes.

The application requirements collection module 320 is configured to collect application requirements of a user.

The App generation module 330 is configured to match and obtain at least one industrial micro-service combination that can meet the application requirements based on attribute information of industrial micro-services stored in the form of knowledge graph in the skill library, and take each industrial micro-service combination as an App corresponding to the application requirements.

The attribute information of each industrial micro-service may include at least one function that the industrial micro-service can achieve.

In one example, as shown in the implementation part in figure 3, the App generation module 330 may include a requirement analysis module 331, a function determination module 332, and a first intelligent matching module 333.

The requirement analysis module 331 is configured to analyze the application requirements to determine every function category to be achieved for the application requirements and a logical relationship between function categories.

The function determination module 332 is configured to determine at least one specific function involved in each function category.

The first intelligent matching module 333 is configured to match micro-services with corresponding functions for all determined specific functions based on the attribute information of industrial micro-services stored in the form of knowledge graph, obtain at least one micro-service combination arranged according to the logical relationship, and take each industrial micro-service combination as an App corresponding to the application requirements.

In another example, as shown in the dotted line part in figure 3, the App generation module 330 may further include a second intelligent matching module 334, which is configured to match and obtain at least one industrial micro-service combination that can realize the application requirements based on the attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph, and take each industrial micro-service composition as an App corresponding to the application requirements.

In another example, as shown in the dotted line part in figure 3, the App generation module 330 may further include a third intelligent matching module 335, which is configured to, according to the at least one function category to be achieved and the logical relationship among the at least one function category, match at least one industrial micro- service composition capable of achieving the application requirements based on the attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph, and take each industrial micro-service composition as an App corresponding to the application requirements.

In addition, the application generation module 330 may further include: an App recommendation module 336 which is configured to push the at least one App corresponding to the application requirements to the user; Alternatively, the attribute information of each industrial micro-service further includes: input and output parameters of the industrial micro-service, and the input and output parameters are respectively set with a default value. The App recommendation module 336 is configured to perform a simulation run on the App based on the default values of the input and output parameters of each industrial micro-service in the App when the at least one App is two or more Apps to obtain a corresponding running performance, push the at least one App and its corresponding running performance to the user, or push the App with the best running performance to the user. Alternatively, the attribute information of each industrial micro-service further includes: a performance parameter of the industrial micro-service. The App recommendation module 336 is configured to evaluate the comprehensive performance of the App based on the performance parameter of each industrial micro-service in the App when the at least one App is two or more Apps; push the at least one App and its corresponding comprehensive performance to the user, or push the App with the best comprehensive performance to the user.

In one example, the attribute information of each industrial micro-service further includes: input and output parameters of the industrial micro-service. The App recommendation module 336 can be further configured to receive the user's adjustment or setting command of the input and output parameters of an industrial micro-service in determined final application, and perform corresponding parameter adjustment and setting.

In addition, the App recommendation module 336 may also be further configured to take the final App confirmed by the user as an APP instance corresponding to the application requirements and store the attribute information of each industrial micro-service in the final App corresponding to the application requirements in the skill library 310 in the form of knowledge graph.

At present, industrial edge has an open software platform, based on which users can deploy, purchase or develop Apps according to their own needs. Typically, the industrial edge works on a layer between the cloud and the controllers. As shown in figure 4, a typical industrial edge device consists of three main parts: a connection layer 410 where the device driver component is located, a data layer 420 where the data acquisition component is located, and an application layer 430. Among them, the connection layer 410 is used to connect industrial edge devices to various devices (such as PLC, motor, converter, cloud, etc. ) with different protocols (such as SIMATIC S7, OPC UA, MODBUS and PROFINET IO, etc. ) , such as SIMATIC S7 connector 411, OPC UA connector 412, Modbus TCP connector 413, PROFINET IO connector 414 in figure 4. The data layer 420 is responsible for collecting data from the connectivity layer and distributing data to the application layer 430. Typical application layer 430 includes data storage, data buffering, digital twin, performance insight, etc.

The system for industrial App development described in figure 3 of this example can be deployed in the application layer 430 as an industrial analysis toolkit 431. In addition, the application layer 430 may also include a third-party application 432, a custom application 433, and the like.

In fact, the system for industrial App development provided by this implementation manner of the present disclosure may be specifically implemented in various manners. For example, the system for industrial App development may be compiled, by using an application programming interface that complies with a certain regulation, as a plug-in that is installed in an intelligent terminal, or may be encapsulated into an application program for a user to download and use.

When compiled as a plug-in, the system for industrial App development may be implemented in various plug-in forms such as ocx, dll, and cab. The system for industrial App development provided by this implementation manner of the present disclosure may also be implemented by using a specific technology, such as a Flash plug-in technology, a RealPlayer plug-in technology, an MMS plug-in technology, a MIDI staff plug-in technology, or an ActiveX plug-in technology.

The method for industrial App development provided by this implementation manner of the present disclosure may be stored in various storage mediums in an instruction storage manner or an instruction set storage manner. These storage mediums include, but are not limited to: a floppy disk, an optical disc, a DVD, a hard disk, a flash memory, a USB flash drive, a CF card, an SD card, an MMC card, an SM card, a memory stick, and an xD card.

In addition, the method for industrial App development provided by this implementation manner of the present disclosure may also be applied to a storage medium based on a flash memory (Nand flash) , such as USB flash drive, a CF card, an SD card, an SDHC card, an MMC card, an SM card, a memory stick, and an xD card.

Moreover, it should be clear that an operating system operated in a computer can be made, not only by executing program code read by the computer from a storage medium, but also by using an instruction based on the program code, to implement some or all actual operations, so as to implement functions of any embodiment in the foregoing embodiments.

For example, figure 5 is a schematic diagram illustrating another system for industrial App development according to examples of the present disclosure. The system may be used to perform the method shown in figure 1, or to implement the system shown in figure 3. As shown in figure 5, the system may include at least one memory 51 and at least one processor 52. In addition, some other components may be included, such as communication port, input/output controller, network communication interface, etc. These components communicate through bus 53, etc.

At least one memory 51 is configured to store a computer program. In one example, the computer program can be understood to include various modules of the system shown in figure 3. In addition, at least one memory 51 may store an operating system or the like. Operating systems include but are not limited to: Android operating system, Symbian operating system, windows operating system, Linux operating system, etc.

At least one processor 52 is configured to call the computer program stored in at least one memory 51 to perform a method for industrial App development described in examples of the present disclosure. The processor 52 can be CPU, processing unit/module, ASIC, logic module or programmable gate array, etc. It can receive and send data through the communication port.

The I/O controller has a display and an input device, which is used to input, output and display relevant data.

It should be understood that, as used herein, unless the context clearly supports exceptions, the singular forms "a" ( "a" , "an" , "the" ) are intended to include the plural forms. It should also be understood that, "and /or" used herein is intended to include any and all possible combinations of one or more of the associated listed items.

The number of the embodiments of the present disclosure are only used for description, and do not represent the merits of the implementations.

The foregoing description, for purpose of explanation, has been described with reference to specific examples. However, the illustrative discussions above are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The examples were chosen and described in order to best explain the principles of the present disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the present disclosure and various examples with various modifications as are suited to the particular use contemplated.

Claims

A method for industrial App development, characterized in that, comprises:

encapsulating industrial skills into corresponding industrial micro-services in advance, each industrial micro-service has attribute information, and the attribute information of the industrial micro-services is stored in the form of knowledge graph; the knowledge graph comprises nodes respectively representing each attribute in the attribute information and connecting lines representing the relationship between nodes;

collecting application requirements of a user;

matching and obtaining at least one industrial micro-service combination that meet the application requirements based on the attribute information of the industrial micro-services stored in the form of knowledge graph, and taking each industrial micro-service combination as an App corresponding to the application requirements.
The method according to claim 1, characterized in that, the attribute information of each industrial micro-service comprises: at least one function that the industrial micro-service can achieve; wherein, matching and obtaining at least one industrial micro-service combination that can meet the application requirements based on the attribute information of the industrial micro-services stored in the form of knowledge graph, comprises:

analyzing the application requirements to determine every function category to be achieved and a logical relationship between function categories;

for each function category, determining at least one specific function involved in the function category;

matching industrial micro-services with corresponding functions for all determined specific functions based on the attribute information of industrial micro-services stored in the form of knowledge graph, and obtain at least industrial one micro-service combination arranged according to the logical relationship.
The method according to claim 1, characterized in that, wherein, matching and obtaining at least one industrial micro-service combination that can meet the application requirements based on the attribute information of the industrial micro-services stored in the form of knowledge graph, comprises:

according to the application requirements, matching and obtaining at least one industrial micro-service combination that can achieve the application requirements based on the attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph; or comprises:

analyzing the application requirements to determine every function category to be achieved and a logical relationship between function categories, and based on the attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph according to every function category, matching to obtain at least one industrial micro-service composition capable of achieving the application requirements.
The method according to any one of claims 1 to 3, characterized in that, further comprises:

pushing at least one APP corresponding to the application requirements to the user; or,

attribute information of each industrial micro-service further comprises: input and output parameters of the industrial micro-service, and the input and output parameters are respectively set with a default value; when the at least one App is two or more Apps, for each App, performing a simulation run on the App based on the default values of the input and output parameters of each industrial micro-service in the App and obtaining a corresponding running performance, and pushing the at least one App and its corresponding running performance to the user, or pushing the App with the best running performance to the user; or,

attribute information of each industrial micro-service further comprises: a performance parameter of the industrial micro-service; when the at least one App is two or more Apps, for each App, evaluating a comprehensive performance of the App based on the performance parameter of each industrial micro-service in the App; pushing the at least one App and its corresponding comprehensive performance to the user, or pushing the App with the best comprehensive performance to the user.
The method according to claim 4, characterized in that, wherein the attribute information of each industrial micro-service further comprises: input and output parameters of the industrial micro-service; the method further comprises:

receiving an adjustment or setting command for the input and output parameters of an industrial micro-service in a final App confirmed by the user, and performing corresponding parameter adjustment and setting.
The method according to claim 4, characterized in that, further comprises: attribute information of each industrial micro-service of a final App confirmed by the user is stored corresponding to the application requirements in the form of knowledge graph.
A system for industrial App development, characterized in that, comprises:

a skill library, in which industrial micro-services encapsulating industrial skills are set, each industrial micro-service has attribute information, and attribute information of the industrial micro-services is stored in the form of knowledge graph; the knowledge graph includes nodes respectively representing each attribute in the attribute information and connecting lines representing the relationship between nodes;

an application requirements collection module, to collect application requirements of a user;

an App generation module, to match and obtain at least one industrial micro-service combination that meet the application requirements based on attribute information of industrial micro-services stored in the form of knowledge graph in the skill library, and take each industrial micro-service combination as an App corresponding to the application requirements.
The system according to claim 7, characterized in that, the attribute information of each industrial micro-service comprises: at least one function that the industrial micro-service can achieve; the App generation module comprises:

a requirement analysis module, to analyze the application requirements to determine every function category to be achieved and a logical relationship between function categories;

a function determination module, to determine at least one specific function involved in each function category;

a first intelligent matching module , to match micro-services with corresponding functions for all determined specific functions based on the attribute information of industrial micro-services stored in the form of knowledge graph, obtain at least one micro-service combination arranged according to the logical relationship, and take each industrial micro-service combination as an App corresponding to the application requirements.
The system according to claim 7, characterized in that, the App generation module further comprises: a second intelligent matching module, to match and obtain at least one industrial micro-service combination that meets the application requirements based on attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph, and take each industrial micro-service composition as an App corresponding to the application requirements; or further comprises:

a requirement analysis module, to analyze the application requirements to determine at least one function category to be achieved and a logical relationship between function categories; and

a third intelligent matching module, to, according to the at least one function category to be achieved and the logical relationship among the at least one function category, match at least one industrial micro-service composition capable of achieving the application requirements based on the attribute information of industrial micro-services of each historical App instance stored in the form of knowledge graph, and take each industrial micro-service composition as an App corresponding to the application requirements.
The system according to claim 8 or 9, characterized in that, wherein the App generation module further comprises:

an App recommendation module, to push the at least one App corresponding to the application requirements to the user; or,

the attribute information of each industrial micro-service further includes: input and output parameters, and the input and output parameters are respectively set with a default value; when the at least one App is two or more Apps, for each App, the App recommendation module is to perform simulation run on the App based on the default values of the input and output parameters of each industrial micro-service in the App to obtain a corresponding running performance, push the at least one App and its corresponding running performance to the user, or push the App with the best running performance to the user; or,

the attribute information of each industrial micro-service further includes: a performance parameter; when the at least one App is two or more Apps, for each App, the App recommendation module is to evaluate a comprehensive performance of the App based on the performance parameter of each industrial micro-service in the App; push the at least one App and its corresponding comprehensive performance to the user, or push the App with the best comprehensive performance to the user.
The system according to claim 10, characterized in that, wherein the attribute information of each industrial micro-service further comprises: input and output parameters of the industrial micro-service; the App recommendation module is further to receive an adjustment or setting command for the input and output parameters of an industrial micro-service in a final App confirmed by the user, and perform corresponding parameter adjustment and setting.
The system according to claim 10, characterized in that, the App recommendation module is further to store attribute information of each industrial micro-service of a final App confirmed by the user corresponding to the application requirements in the form of knowledge graph.
A system for industrial App development, characterized in that, comprises:

at least one memory, to store a computer program; and

at least one processor, to call the computer program stored in the at least one memory to perform a method for industrial App development according to any one of claims 1 to 6.
A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, the computer program is to be executed by a processor to implement a method for industrial App development according to any one of claims 1 to 6.