Modeling method and device of intelligent manufacturing system, electronic equipment and medium

Abstract

The invention relates to the technical field of industrial system simulation, and provides a modeling method and device of an intelligent manufacturing system, electronic equipment and a medium. The method comprises the steps of obtaining a model library, wherein the model library comprises a preset model; when a model expansion instruction is received, taking a preset model corresponding to the model expansion instruction as an initial model, expanding the initial model according to the model expansion instruction to obtain a new expansion model, and adding the new expansion model as a new preset model into the model library; when a model instantiation instruction is received, determining a target model corresponding to the model instantiation instruction from all preset models in the model library, and generating a model instance of the target model; an intelligent manufacturing system is determined based on each model instance. The intelligent manufacturing system is determined according to the model instance generated by instantiating the preset model in the model library, the efficiency is greatly improved, in addition, the preset model in the model library has an efficient and rapid expansion approach, and the modeling efficiency of the intelligent manufacturing system based on the preset model is improved.

Description

Technical Field

[0001] The present application relates to the technical field of industrial system simulation, and in particular to a modeling method, device, electronic equipment and medium for an intelligent manufacturing system. Background Art

[0002] Smart factories are intelligent manufacturing systems with complex networks, consisting of production equipment, monitoring systems, information management systems, etc. This manufacturing system has the characteristics of real-time information sharing, dynamic self-optimization, and flexible response, which brings new opportunities and challenges to the manufacturing industry. How to effectively model and analyze such complex smart factories has become a primary and critical issue.

[0003] The commonly used smart factory modeling methods are usually based on discrete event simulation, system dynamics and other theories, and are modeled with single production units or fixed process flows for existing equipment at the production line, workshop and other levels. This modeling approach builds smart factories based on various specific unit equipment instances. There are a large number of repeated unit equipment instances in smart factories, and each unit equipment instance must be set up one by one. The construction of smart factories cannot be realized quickly. Furthermore, if there is a need to modify the local attributes of a certain unit equipment instance, all unit equipment instances must be modified in batches, which is costly and error-prone.

[0004] Therefore, how to provide a solution to the above technical problems is a problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a modeling method, device, electronic device and medium for an intelligent manufacturing system to solve the problem of complex and time-consuming modeling of intelligent factories in the prior art.

[0006] A first aspect of an embodiment of the present application provides a modeling method for an intelligent manufacturing system, comprising:

[0007] Obtain a model library, the model library including one or more preset models;

[0008] When a model extension instruction is received, the preset model corresponding to the model extension instruction is used as the initial model, the initial model is extended according to the model extension instruction to obtain a new extended model, and the new extended model is added to the model library as a new preset model;

[0009] When a model instantiation instruction is received, a target model corresponding to the model instantiation instruction is determined from all preset models in the model library, and a model instance of the target model is generated;

[0010] The intelligent manufacturing system is determined based on each model instance.

[0011] A second aspect of the embodiments of the present application provides a modeling device for an intelligent manufacturing system, comprising:

[0012] A model library acquisition module is used to acquire a model library, where the model library includes one or more preset models;

[0013] The extension module is used for, when receiving a model extension instruction, taking the preset model corresponding to the model extension instruction as the initial model, extending the initial model according to the model extension instruction to obtain a new extended model, and adding the new extended model as a new preset model to the model library;

[0014] The instance module is used to determine the target model corresponding to the model instantiation instruction from all preset models in the model library when a model instantiation instruction is received, and generate a model instance of the target model. It is also used to determine the intelligent manufacturing system based on each model instance.

[0015] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0016] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0017] Compared with the prior art, the embodiments of the present application have at least the following beneficial effects: the embodiments of the present application instantiate the preset model in the model library to generate the required model instance and determine the intelligent manufacturing system based on the model instance. The model instances do not need to be built one by one. This solution determines the intelligent manufacturing system by the model instance generated by instantiating the preset model in the model library, which greatly improves the efficiency. In addition, the preset models in the model library include the preset models that already exist inside and the new extended models generated based on the extension of the original preset models. The preset models have an efficient and fast extension setting method, which improves the efficiency of modeling the intelligent manufacturing system based on the preset models and realizes the rapid construction of the intelligent manufacturing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 It is a schematic diagram of an application scenario of an embodiment of the present application;

[0020] Figure 2 It is a flow chart of a modeling method for an intelligent manufacturing system provided in an embodiment of the present application;

[0021] Figure 3 It is a schematic diagram of an extended relationship of a preset model in a model library provided in an embodiment of the present application;

[0022] Figure 4 It is a structural schematic diagram of a modeling device of an intelligent manufacturing system provided in an embodiment of the present application;

[0023] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0025] A modeling method, device, electronic device and medium for an intelligent manufacturing system according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0026] Figure 1 1 is a schematic diagram of an application scenario of an embodiment of the present application. The application scenario may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a server 104 and a network 105.

[0027] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be hardware or software. When the first terminal device 101, the second terminal device 102, and the third terminal device 103 are hardware, they can be various electronic devices with display screens and support communication with the server 104, including but not limited to vehicle systems, smart phones, tablet computers, laptop portable computers, and desktop computers; when the first terminal device 101, the second terminal device 102, and the third terminal device 103 are software, they can be installed in the above electronic devices. The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be implemented as multiple software or software modules, or as a single software or software module, which is not limited in the embodiment of the present application. Further, various applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0028] The server 104 may be a server that provides various services, for example, a background server that receives a request sent by a terminal device that establishes a communication connection with the server, and the background server may receive and analyze the request sent by the terminal device, and generate a processing result. The server 104 may be a single server, or a server cluster composed of several servers, or a cloud computing service center, which is not limited in the present embodiment of the application.

[0029] It should be noted that the server 104 can be hardware or software. When the server 104 is hardware, it can be various electronic devices that provide various services for the first terminal device 101, the second terminal device 102, and the third terminal device 103. When the server 104 is software, it can be multiple software or software modules that provide various services for the first terminal device 101, the second terminal device 102, and the third terminal device 103, or it can be a single software or software module that provides various services for the first terminal device 101, the second terminal device 102, and the third terminal device 103, and the embodiment of the present application does not limit this.

[0030] The network 105 can be a wired network connected by coaxial cable, twisted pair and optical fiber, or it can be a wireless network that can interconnect various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), infrared, etc., which is not limited in the embodiments of the present application.

[0031] It should be noted that the specific types, quantities and combinations of the first terminal device 101, the second terminal device 102, the third terminal device 103, the server 104 and the network 105 can be adjusted according to the actual needs of the application scenario, and the embodiments of the present application do not limit this.

[0032] Figure 2 It is a flow chart of a modeling method for an intelligent manufacturing system provided in an embodiment of the present application. Figure 2 The modeling method can be obtained by Figure 1 The first terminal device, the second terminal device, the third terminal device or the server executes. Figure 2 As shown, the modeling method includes:

[0033] S201: Acquire a model library, where the model library includes one or more preset models;

[0034] S202: when a model extension instruction is received, taking a preset model corresponding to the model extension instruction as an initial model, extending the initial model according to the model extension instruction to obtain a new extended model, and adding the new extended model as a new preset model to the model library;

[0035] S203: When a model instantiation instruction is received, a target model corresponding to the model instantiation instruction is determined from all preset models in the model library, and a model instance of the target model is generated;

[0036] S204: Determine an intelligent manufacturing system based on each model instance.

[0037] It can be understood that the model library is set in a database, and the method of this embodiment needs to first call the database to obtain the model library, and then use the preset model in the model library to perform subsequent operations.

[0038] Specifically, the preset model in the model library can be used as an initial model to provide an expansion basis for the new model. When a model expansion instruction is received, the structure and attributes of the initial model are adjusted, added or deleted based on the initial model in accordance with the model expansion instruction to generate a new model. The new model is called a new extended model relative to the initial model. The new extended model is actually a derivative inheritance of the initial model. The new extended model is added to the model library as a new preset model. The model library will include the original preset model and the new preset model expanded based on the preset model. The preset models in the model library can be increased through this step.

[0039] Specifically, the preset models in the model library serve as the modeling basis for the intelligent manufacturing system. Any preset model in the model library can be instantiated to generate a model instance. During instantiation, the name of the preset model is set to the name actually defined by the user. The attribute description of the model instance is consistent with that in the preset model. The user can complete the initialization of multiple actual devices by setting the instance name of the model instance.

[0040] The intelligent manufacturing system includes one or more model instances. Based on the relationship between the model instances in the intelligent manufacturing system, the connection between the independent model instances is established to obtain the intelligent manufacturing system, that is, the intelligent factory. Therefore, when the model instantiation instruction is received, the corresponding preset model is selected from the model library as the target model, the target model is instantiated to generate the corresponding model instance, and the intelligent manufacturing system is established based on the model instance, so that the modeling of the intelligent manufacturing system can be realized.

[0041] It is understandable that any preset model in the model library may be used as an optional model object for the model instantiation instruction, and both the basic preset model and the new preset model derived and extended subsequently can be instantiated. If the model library does not have a target model corresponding to the model instance required by the user, the corresponding preset model can be first generated through the model extension instruction and added to the model library, and then the preset model can be instantiated as the target model through the model instantiation instruction to generate the corresponding model instance.

[0042] Based on the expansion and instantiation of the preset models in the model library, the method of this embodiment can achieve efficient modeling of the intelligent manufacturing system, and the modeling cost is significantly reduced. Since all model instances are obtained through preset models, the number of preset models is much smaller than the model instances. At the same time, the reliability and stability of the preset models in the model library are significantly higher than those of the directly constructed model instances. Therefore, the probability of modeling errors in this embodiment is significantly reduced.

[0043] Specifically, for the preset models in the model library, each preset model is an abstraction of a specific device, and carries its own model name and its own model description. Each preset model includes various attribute descriptions of the specific device. According to the description direction of the attribute, the attribute description includes communication attributes, parameter attributes, channel attributes and functional attributes. According to the actual situation of the specific device, the preset model includes one or more of the above attribute descriptions, and these attribute descriptions are abstracted as sub-models of the preset model. Therefore, each preset model includes:

[0044] The communication sub-model is used to indicate the communication properties of the preset model;

[0045] and / or, a parameter submodel, for indicating parameter properties of a preset model;

[0046] and / or, a channel sub-model, for indicating channel properties of a preset model;

[0047] and / or, a functional sub-model, used to indicate the functional properties of a preset model.

[0048] Specifically, for the communication sub-model, the communication attribute indicated is the attribute description about communication in the preset model, including at least one of the communication mode, communication protocol and the sub-communication attribute corresponding to the communication mode;

[0049] When the communication mode is a serial port mode, the corresponding sub-communication attribute includes at least one of a baud rate, a data bit, a stop bit, a check attribute, and hardware flow control information;

[0050] When the communication mode is a transmission control protocol mode, the corresponding sub-communication attribute includes at least one of a port and a redundancy mode.

[0051] The optional communication modes include but are not limited to serial port mode or Transmission Control Protocol (TCP) mode; the optional communication protocols include but are not limited to OPCUA (Open Platform Communications Unified Architecture), MQTT (Message Queuing Telemetry Transport), IEC104, SNMP (Simple Network Management Protocol), among which IEC104 is the full name of IEC60870-5-104, which is a commonly used communication protocol in power automation systems. IEC is the abbreviation of International Electrotechnical Commission.

[0052] Furthermore, the communication properties of the communication sub-model also include some communication properties that change with the communication mode. These communication properties become sub-communication properties of the corresponding communication mode. When the communication mode is serial port mode, the sub-communication properties include baud rate, data bits, stop bits, check properties and hardware flow control information; when the communication mode is transmission control protocol mode, the sub-communication properties include port and redundancy mode.

[0053] It can be understood that for the parameter submodel, the parameter attribute indicated is the attribute description of the parameter in the preset model. For each parameter submodel, its attribute parameters usually include name, description, default value, value content, and alarm configuration. Since parameters can be divided into two categories: analog quantity and digital quantity, the parameter submodel can be an analog quantum model or a digital quantum model. For different analog quantum models, there are differences in their value content. For example, the value content of the analog quantum model includes the range and unit, while the value content of the digital quantum model includes the value meaning configuration. Therefore, the parameter submodel includes an analog quantum model and / or a digital quantum model, wherein:

[0054] The parameter attributes of the simulated quantum model include at least one of a name, a description, a default value, a range, a unit, and an alarm configuration;

[0055] The parameter attributes of the digital quantum model include at least one of a name, a description, a default value, a value meaning configuration, and an alarm configuration.

[0056] Similarly, for a channel sub-model, the channel attribute indicated is the attribute description of the channel in the preset model. The channel attribute usually includes a name, a description, and a default value.

[0057] Similarly, for a function sub-model, the function attribute indicated by it is the attribute description of the function in the preset model. The function is a function action that will be triggered under a certain special function triggering condition. For example, a function sub-model is an alarm function sub-model, which is used to indicate that an alarm action is performed when a certain parameter is within a trigger value range. Specifically, the function attribute is set based on the content of the function. At this time, the function attributes of the function sub-model include name, description, function triggering condition, and function action. The function triggering condition includes the trigger sub-model and the trigger value range. The function action includes the action sub-model and the action value.

[0058] The trigger sub-model is any sub-model among the communication sub-model, parameter sub-model, channel sub-model and function sub-model.

[0059] The action sub-model is any sub-model among the communication sub-model, parameter sub-model, channel sub-model and function sub-model.

[0060] It can be understood that the functional sub-model represents that in the preset model, when the trigger sub-model is within the trigger value range, the actual state value of the action sub-model becomes the action value.

[0061] See also Figure 3 As shown, Figure 3 The default model in the model library is the derivation and inheritance process of the switch, that is, the expansion process of the model library. The switch model of the first layer is the initial default model in the model library. The model itself is called the switch model, and the model itself is described as a first-generation basic switch. The default model includes an analog quantum model named traffic statistics, a digital quantum model named port status, and a communication sub-model named communication. The parameter attributes indicated by the analog quantum model include the range (upper limit of the range, lower limit of the range), and the unit. The parameter attributes indicated by the digital quantum model include normal and unconnected. The communication attributes indicated by the communication sub-model include the communication mode of http (HyperTextTransfer Protocol) and the communication protocol of SNMP. In addition, the switch model can also include a functional sub-model for alarm. Here, the trigger sub-model is the port status, and the trigger value range is unconnected. When the port status is unconnected, the action sub-model generates an alarm.

[0062] Furthermore, the switch model of the first layer is used as the initial model, and the initial model is extended to obtain a new extended model, namely Figure 3In the example, there are two new extended models, whose own model names are Layer 2 switch model and Layer 3 switch model respectively. Specifically, for the Layer 2 switch model, based on the switch model, the existing attributes are further defined, such as setting the upper and lower limits of the range of traffic statistics to 100 and 0 respectively, setting the unit of traffic statistics to Mbps, setting the value meaning configuration of the digital quantum model, where the normal configuration is 0 and the unconnected configuration is 1, and adding new sub-models, such as adding a channel sub-model named MAC address table, the channel attributes of the channel sub-model include MAC address; similarly, for the Layer 3 switch model, based on the switch model, the existing attributes are further defined, such as setting the upper and lower limits of the range of traffic statistics to 1000 and 0 respectively, setting the unit of traffic statistics to Mbps, setting the value meaning configuration of the digital quantum model, where the normal configuration is 0 and the unconnected configuration is 1, and adding new sub-models, such as adding a channel sub-model named routing table, the channel attributes of the channel sub-model include IP address.

[0063] Furthermore, by expanding the second-layer switch model as the initial model, more specific switches can be obtained, such as Figure 3 The A1 switch model is derived from the Layer 2 switch model. Similarly, the A2 switch model, A3 switch model, and A4 switch model can be derived from the Layer 2 switch model. Specifically, for the A1 switch model, a channel sub-model named system log is added based on the Layer 2 switch model, and its channel attributes include operation logs.

[0064] Similarly, by expanding the second-layer three-layer switch model as the initial model, more specific switches can be obtained, such as Figure 3 The B1 switch model is derived from the three-layer switch model. Similarly, the B2 switch model, B3 switch model, and B4 switch model can be derived from the three-layer switch model. Specifically, for the B1 switch model, based on the three-layer switch model, a new simulation quantum model named switch temperature is added. Its parameter attributes include the upper limit of the range, the lower limit of the range, and the unit. Furthermore, the specific values ​​of the parameter attributes of the switch temperature are set. The upper limit of the range and the lower limit of the range are 80 and -10 respectively, and the unit is Celsius ℃.

[0065] Understandably, Figure 3By continuously expanding only one switch model as the preset model, we can obtain a two-layer switch model, a three-layer switch model, an A1-A4 switch model, and a B1-B4 switch model. During the expansion process, the sub-models and attributes of the previous initial model are further limited, added, or deleted to quickly generate a new extended model. The content that is not limited, added, or deleted in the initial model will be directly retained in the new extended model. This expansion step can be applied to various preset models, thereby establishing a model library that includes various preset models with rich diversity and clear extension relationships.

[0066] It can be understood that the user interacts with the execution subject of the method of this embodiment through the human-computer interaction device and issues model extension instructions. The model extension instructions include various user extension settings about sub-models and attributes, thereby achieving the effect of obtaining a new extended model and adding it to the model library.

[0067] Furthermore, considering the extended relationship of each preset model, a graph database that represents the dependency relationship between nodes may be selected when storing the model of the model library. Therefore, the method of this embodiment further includes:

[0068] According to the extended relationship between the preset models, all the preset models in the model library are stored in the graph database.

[0069] Furthermore, the generated model instances can be stored in a relational database.

[0070] Furthermore, the preset models, generated model instances and intelligent manufacturing systems in the model library can all be accessed and queried, and can also be actively published to the public. The specific settings can be made according to actual conditions and user needs, and are not restricted here.

[0071] It can be understood that based on the various preset models in the model library, the user can interact with the execution subject of the method of this embodiment through the human-computer interaction device and issue model instantiation instructions, and then instantiate the preset model selected by the model instantiation instruction to generate the corresponding model instance for building an intelligent manufacturing system.

[0072] Furthermore, when the user has a need to update the preset model, the model update instruction can be directly issued to the execution subject of the method of this embodiment through the human-computer interaction device, which is similar to the model extension instruction, and the content of the preset model corresponding to the model update instruction is updated. It should be noted that the model update instruction and the model extension instruction are similar in operation objects and operation actions, but the final retained results are different. The model update instruction is a content update of the same preset model, and the preset model before the update is not retained in the model library after the update; the model extension instruction generates a new preset model based on the previous preset model. The model library retains the original initial model and adds the new extended model.

[0073] It can be understood that in this embodiment, the preset models and model instances in the model library always maintain a corresponding relationship. If the preset model changes, all model instances corresponding to the preset model should also be updated in batches to the changed state. Therefore, when a preset model is updated, the corresponding model instances should also be updated in batches. To ensure that the update of the model instance meets the user's wishes and avoid deviations in the update range, the update of the model and the update of the instance are usually set to asynchronous updates, which are performed after receiving the model update instruction and the instance update instruction, respectively, to improve the reliability of the update. When the user believes that the current situation meets the update conditions of the model instance, the instance update instruction is issued, thereby completing the update of the model instance in this embodiment, and then achieving the purpose of updating the intelligent manufacturing system including the model instance. Therefore, the method of this embodiment also includes:

[0074] When a model update instruction is received, the corresponding preset model in the model library is updated based on the model update instruction;

[0075] When an instance update instruction is received, the model instances corresponding to the instance update instruction are updated in batches based on all preset models in the model library.

[0076] It can be understood that, using the method in this embodiment, for the same model instance, the same preset model can be directly selected for instantiation. When multiple workshops and multiple production lines in a factory use the same equipment and network structure, the preset model instantiation can be used to quickly realize the initialization modeling of the intelligent manufacturing system and improve the modeling efficiency. When the model instance changes, for example, when the communication method changes from http to https, it is only necessary to update the communication method of the preset model through the model update instruction to complete the update of all related model devices. Users can dynamically obtain the corresponding relationship of all devices corresponding to the preset models by accessing the extended relationship of the preset model, and manage similar devices by paying attention to the different model definitions between the preset models.

[0077] The method of this embodiment breaks away from the traditional perspective of being limited to a single production line or workshop, and uses a mathematical modeling method of a preset model to build a comprehensive network model. This model-based perspective can better reflect the highly interconnected and complex discrete dynamic system of the smart factory. Through the basic model, the definition and instantiation of the complex model are completed, and the rapid replication and use of the network structure are realized. At the same time, the method of this embodiment adopts a modeling framework based on state monitoring, combined with various functional sub-models, which can timely process the real-time response process under different working conditions, and provide support for analyzing the key dynamic characteristics of the system such as elasticity and fault propagation. In general, this modeling and configuration method based on network architecture integrates a number of key technologies, provides a new theoretical basis and method support for building an intelligent manufacturing system, and will definitely play an important role in improving production efficiency, reducing energy consumption, and ensuring product quality.

[0078] The method of the embodiment of the present application instantiates the preset model in the model library to generate the required model instance and determines the intelligent manufacturing system based on the model instance. The model instances do not need to be built one by one. This solution determines the intelligent manufacturing system by the model instance generated by instantiating the preset model in the model library, which greatly improves the efficiency. In addition, the preset models in the model library include the preset models that already exist inside and the new extended models generated based on the original preset models. The preset models have an efficient and fast extension setting method, which improves the efficiency of modeling the intelligent manufacturing system based on the preset models and realizes the rapid construction of the intelligent manufacturing system.

[0079] All the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here. It should be understood that the size of the sequence number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0080] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0081] Figure 4 Schematic diagram of a modeling device for an intelligent manufacturing system provided in an embodiment of the present application. Figure 4 As shown, the modeling device includes:

[0082] A model library acquisition module 401 is used to acquire a model library, where the model library includes one or more preset models;

[0083] The extension module 402 is used for, upon receiving a model extension instruction, taking the preset model corresponding to the model extension instruction as the initial model, extending the initial model according to the model extension instruction to obtain a new extended model, and adding the new extended model as a new preset model to the model library;

[0084] The instance module 403 is used to determine the target model corresponding to the model instantiation instruction from all preset models in the model library when a model instantiation instruction is received, and generate a model instance of the target model. It is also used to determine the intelligent manufacturing system based on each model instance.

[0085] The device of the embodiment of the present application instantiates the preset model in the model library to generate the required model instance and determines the intelligent manufacturing system based on the model instance. The model instances do not need to be built one by one. This solution determines the intelligent manufacturing system by the model instance generated by instantiating the preset model in the model library, which greatly improves the efficiency. In addition, the preset models in the model library include the preset models that already exist inside and the new extended models generated based on the original preset models. The preset models have an efficient and fast extension setting method, which improves the efficiency of modeling the intelligent manufacturing system based on the preset models and realizes the rapid construction of the intelligent manufacturing system.

[0086] In an exemplary embodiment, each preset model includes:

[0087] The communication sub-model is used to indicate the communication properties of the preset model;

[0088] and / or, a parameter submodel, for indicating parameter properties of a preset model;

[0089] and / or, a channel sub-model, for indicating channel properties of a preset model;

[0090] and / or, a functional sub-model, used to indicate the functional properties of a preset model.

[0091] In an exemplary embodiment, the communication attribute includes at least one of a communication method, a communication protocol, and a sub-communication attribute corresponding to the communication method;

[0092] When the communication mode is a serial port mode, the corresponding sub-communication attribute includes at least one of a baud rate, a data bit, a stop bit, a check attribute, and hardware flow control information;

[0093] When the communication mode is a transmission control protocol mode, the corresponding sub-communication attribute includes at least one of a port and a redundancy mode.

[0094] In an exemplary embodiment, the parametric quantum model comprises an analog quantum model and / or a digital quantum model, wherein:

[0095] The parameter attributes of the simulated quantum model include at least one of a name, a description, a default value, a range, a unit, and an alarm configuration;

[0096] The parameter attributes of the digital quantum model include at least one of a name, a description, a default value, a value meaning configuration, and an alarm configuration.

[0097] In an exemplary embodiment, the function attributes of the function sub-model include name, description, function trigger condition, function action, the function trigger condition includes the trigger sub-model and the trigger value range, and the function action includes the action sub-model and the action value;

[0098] The trigger sub-model is any sub-model among the communication sub-model, parameter sub-model, channel sub-model and function sub-model.

[0099] The action sub-model is any sub-model among the communication sub-model, parameter sub-model, channel sub-model and function sub-model.

[0100] In an exemplary embodiment, the apparatus further includes a storage module, configured to:

[0101] According to the extended relationship between the preset models, all the preset models in the model library are stored in the graph database.

[0102] In an exemplary embodiment, the apparatus further includes an updating module, configured to:

[0103] When a model update instruction is received, the corresponding preset model in the model library is updated based on the model update instruction;

[0104] When an instance update instruction is received, the model instances corresponding to the instance update instruction are updated in batches based on all preset models in the model library.

[0105] Figure 5 Schematic diagram of an electronic device 5 provided in an embodiment of the present application. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program 503, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 501 executes the computer program 503, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0106] The electronic device 5 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 5 may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that Figure 5The electronic device 5 is merely an example and does not limit the electronic device 5 , and may include more or less components than those shown in the figure, or different components.

[0107] The processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0108] The memory 502 may be an internal storage unit of the electronic device 5, for example, a hard disk or memory of the electronic device 5. The memory 502 may also be an external storage device of the electronic device 5, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. The memory 502 may also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0109] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0110] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0111] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A modeling method for an intelligent manufacturing system, characterized in that: include: Acquire a model library, wherein the model library includes one or more preset models; When a model extension instruction is received, the preset model corresponding to the model extension instruction is used as the initial model, the initial model is extended according to the model extension instruction to obtain a new extended model, and the new extended model is added to the model library as a new preset model; When a model instantiation instruction is received, a target model corresponding to the model instantiation instruction is determined from all the preset models in the model library, and a model instance of the target model is generated; An intelligent manufacturing system is determined based on each of the model instances.

2. The method according to claim 1, characterized in that Each of the preset models includes: A communication sub-model, used to indicate the communication properties of the preset model; and / or, a parameter submodel, used to indicate parameter properties of the preset model; and / or, a channel sub-model, used to indicate channel properties of the preset model; And / or, a functional sub-model, used to indicate the functional attributes of the preset model.

3. The method according to claim 2, characterized in that The communication attribute includes at least one of a communication method, a communication protocol, and a sub-communication attribute corresponding to the communication method; When the communication mode is a serial port mode, the corresponding sub-communication attribute includes at least one of a baud rate, a data bit, a stop bit, a checksum attribute and hardware flow control information; When the communication mode is a transmission control protocol mode, the corresponding sub-communication attribute includes at least one of a port and a redundancy mode.

4. The method according to claim 2, characterized in that: The parametric quantum model includes an analog quantum model and / or a digital quantum model, wherein: The parameter attributes of the simulated quantum model include at least one of a name, a description, a default value, a range, a unit, and an alarm configuration; The parameter attributes of the digital quantum model include at least one of a name, a description, a default value, a value meaning configuration, and an alarm configuration.

5. The method according to claim 2, characterized in that: The function attributes of the function sub-model include name, description, function trigger condition, and function action. The function trigger condition includes a trigger sub-model and a trigger value range. The function action includes an action sub-model and an action value. The trigger sub-model is any sub-model among the communication sub-model, the parameter sub-model, the channel sub-model and the function sub-model. The action sub-model is any sub-model among the communication sub-model, the parameter sub-model, the channel sub-model and the function sub-model.

6. The method according to claim 1, characterized in that Also includes: According to the extended relationship between the preset models, all the preset models in the model library are stored in a graph database.

7. The method according to any one of claims 1 to 6, characterized in that Also includes: When a model update instruction is received, updating the corresponding preset model in the model library based on the model update instruction; When an instance update instruction is received, the model instances corresponding to the instance update instruction are updated in batches based on all the preset models in the model library.

8. A modeling device for an intelligent manufacturing system, characterized in that: include: A model library acquisition module, used to acquire a model library, wherein the model library includes one or more preset models; An extension module, for, upon receiving a model extension instruction, taking the preset model corresponding to the model extension instruction as an initial model, extending the initial model according to the model extension instruction to obtain a new extended model, and adding the new extended model as a new preset model to the model library; The instance module is used to determine the target model corresponding to the model instantiation instruction from all the preset models in the model library when a model instantiation instruction is received, and generate a model instance of the target model. It is also used to determine the intelligent manufacturing system based on each of the model instances.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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