CN115658021B - Determination method and device of dynamic model, storage medium and electronic equipment - Google Patents
Determination method and device of dynamic model, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention provides a method and a device for determining a dynamic model, a storage medium and electronic equipment, wherein the method is applied to an industrial software architecture driven by the model and comprises the following steps: dividing the corresponding industrial behaviors of the industrial system to obtain behavior units; determining parameter information corresponding to the behavior unit based on a system layer, a parameter layer and a data layer corresponding to the industrial system; wherein the system layer is used for defining objects; the parameter layer is used for defining parameters of the object; the data layer is used for defining the value of the parameter; based on the parameter information and the established algorithm information, a functional relation between independent variable parameters and dependent variable parameters in the parameter information is established to obtain a dynamic model, and the dynamic model can realize unified solution of parameters in an industrial system and meet various operation requirements of users.
Description
Technical Field
The present invention relates to the field of software construction, and more particularly, to a method and apparatus for determining a dynamic model, a storage medium, and an electronic device.
Background
Along with the rapid development of industrial software, an industrial software system is more and more complex and has more and more powerful functions, so that the design requirement on the industrial software is higher and higher, the industrial software has specificity, and general software cannot meet the specific requirements of engineers, so that specialized and customized industrial software architecture has important significance. However, the specialized and customized industrial software architecture faces a complex object, and the difficulty of obtaining the industrial software which can be used for users is high.
Disclosure of Invention
The invention provides a method and a device for determining a dynamic model, a storage medium and electronic equipment, and aims to solve the technical problem that the difficulty of acquiring industrial software which can be used for users is high in the prior art.
According to a first aspect of the present invention, there is provided a method for determining a dynamic model, applied to a model-driven industrial software architecture, comprising:
dividing the corresponding industrial behaviors of the industrial system to obtain behavior units;
determining parameter information corresponding to the behavior unit based on a system layer, a parameter layer and a data layer corresponding to the industrial system; wherein the system layer is used for defining objects; the parameter layer is used for defining parameters of the object; the data layer is used for defining the value of the parameter;
And based on the parameter information and the created algorithm information, establishing a function relation between the independent variable parameter and the dependent variable parameter in the parameter information to obtain a dynamic model.
Optionally, in the case that the behavior unit is a design unit, the establishing a functional relationship between an independent variable parameter and a dependent variable parameter in the parameter information based on the parameter information and the created algorithm information to obtain a dynamic model includes:
determining a design input parameter serving as an independent variable parameter and a design output parameter serving as the dependent variable parameter from the parameter information;
and in the created algorithm information, configuring a first target algorithm for the design input parameters and the design output parameters so as to establish a dynamic link library which stores the functional relation between the design input parameters and the design output parameters, and obtaining a dynamic model which corresponds to the design unit and comprises the parameter information and the dynamic link library.
Optionally, after the step of obtaining the dynamic model corresponding to the design unit and including the parameter information and the dynamic link library, the method further includes:
extracting operation input parameters from the parameter information;
Obtaining an operation output parameter based on the operation input parameter and the dynamic link library;
and updating the parameter information based on the operation output parameter.
Optionally, when the behavior unit is a manufacturing unit, the establishing a functional relationship between an independent variable parameter and a dependent variable parameter in the parameter information based on the parameter information and the created algorithm, to obtain a dynamic model corresponding to the behavior unit includes:
determining control parameters serving as independent variable parameters and detection parameters serving as the independent variable parameters from the parameter information;
configuring a second target algorithm operated on the edge equipment for the control parameter and the detection parameter in the created algorithm information; and establishing an edge computing program library which stores the functional relation between the control parameters and the detection parameters, and obtaining a dynamic model which corresponds to the manufacturing unit and comprises the parameter information and the edge computing program library.
Optionally, the obtaining the dynamic model corresponding to the manufacturing unit and including the parameter information and the edge calculation program library includes:
extracting operation control parameters from the parameter information;
Obtaining operation detection parameters based on the operation control parameters, the edge calculation program library and the edge equipment;
and updating the parameter information based on the operation detection parameter.
Optionally, the method further comprises:
constructing a data warehouse based on the updated parameter information;
determining statistical information corresponding to the industrial system based on the data warehouse;
optionally, the method further comprises: training a machine learning model based on the parameter information and the updated parameter information.
Optionally, the argument parameter is a plurality of; the method further comprises the steps of:
determining sequence information corresponding to the plurality of independent variable parameters;
and orderly combining the independent variable parameters, the dependent variable parameters, the functional relation among the independent variable parameters and the dependent variable parameters according to the sequence information to obtain flow template information.
Optionally, there is a target independent variable parameter in the independent variable parameters, where the target independent variable parameter corresponds to a target dependent variable parameter, and the target dependent variable parameter is one of the independent variable parameters.
According to a second aspect of the present invention, there is provided a dynamic model determining apparatus, provided in a model-driven industrial software architecture, comprising:
The behavior dividing module is used for dividing the corresponding industrial behaviors of the industrial system to obtain behavior units;
the parameter determining module is used for determining parameter information corresponding to the behavior unit based on a system layer, a parameter layer and a data layer corresponding to the industrial system; wherein the system layer is used for defining objects; the parameter layer is used for defining parameters of the object; the data layer is used for defining the value of the parameter;
and the model determining module is used for establishing a function relation between the independent variable parameter and the dependent variable parameter in the parameter information based on the parameter information and the established algorithm information so as to obtain a dynamic model.
According to a third aspect of the present invention, there is provided a computer-readable storage medium storing a computer program for executing the above-described determination method of a dynamic model.
According to a fourth aspect of the present invention, there is provided an electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method for determining a dynamic model described above.
Compared with the prior art, the method and the device for determining the dynamic model, the computer-readable storage medium and the electronic equipment provided by the invention at least have the following beneficial effects:
the technical scheme of the invention divides the corresponding industrial behaviors of the industrial system to obtain the behavior units. And then determining a system layer, a parameter layer and a data layer corresponding to the industrial system, wherein the system layer is used for defining the object, the parameter layer is used for defining the parameter of the object, and the data layer is used for defining the value of the parameter, so that the parameter information corresponding to the behavior unit can be determined according to the system layer, the parameter layer and the data layer corresponding to the industrial system. And further establishing a function relation between independent variable parameters and dependent variable parameters in the parameter information according to the parameter information and the established algorithm information to obtain a dynamic model. According to the technical scheme, the method and the system for solving the industrial software parameters in the industrial system can solve the parameters in the industrial system uniformly, simplify the complexity of solving the parameters in the industrial software, simplify the parameter solving process, facilitate the use of users, and effectively reduce the difficulty of obtaining the industrial software which can be used for users by accurately grasping the study objects through a system layer, a parameter layer and a data layer and determining a dynamic model according to parameter information and established algorithm information efficiently and accurately.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for determining a dynamic model according to an exemplary embodiment of the present invention;
FIG. 2 is a schematic diagram of a dynamic model in a method for determining a dynamic model according to an exemplary embodiment of the present invention;
FIG. 3 is a flow chart of a modeling process in a method for determining a dynamic model according to an exemplary embodiment of the present invention;
FIG. 4 is a flow chart of a model operation process in a method for determining a dynamic model according to an exemplary embodiment of the present invention;
FIG. 5 is a flow chart of a data application in a method for determining a dynamic model according to an exemplary embodiment of the present invention;
FIG. 6 is a schematic diagram of a dynamic model determining apparatus according to an exemplary embodiment of the present invention;
fig. 7 is a block diagram of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by one of ordinary skill in the art without creative efforts, based on the embodiments of the present invention are within the protection scope of the present embodiments.
Exemplary method
Fig. 1 is a flow chart of a method for determining a dynamic model according to an exemplary embodiment of the present invention, which is applied to a model-driven industrial software architecture, and at least includes the following steps:
and step 11, dividing the corresponding industrial behaviors of the industrial system to obtain behavior units.
The industrial system is a target object corresponding to industrial software, for example, the industrial system can be an industrial robot system; the industrial behavior is full-flow behavior data throughout the industrial system, and the behavior data is mainly related to industrial software, and particularly corresponds to a dynamic calculation independent model CIM of an industrial product. The corresponding industrial behaviors of the industrial system are divided, and different behavior units can be obtained with different division granularities. By dividing industrial behaviors, research objects are thinned, and complexity is reduced.
In one possible implementation, as shown in fig. 2, the industrial activities are divided into a design unit, a manufacturing unit, a transportation and management unit, where the activity unit corresponds to the design unit, the manufacturing unit and the management unit.
In another possible implementation, as shown in fig. 2, the industrial activities are divided to obtain a design unit, a manufacturing unit, a transportation and management unit; further dividing the design unit to obtain an analysis subunit and a design subunit; the manufacturing unit is further divided to obtain a detection subunit and a control subunit; the operation and management unit is further divided to obtain a big data subunit and an artificial intelligence subunit, and the behavior unit corresponds to the analysis subunit, the design subunit, the detection subunit, the control subunit, the big data subunit and the artificial intelligence subunit.
Step 12, determining parameter information corresponding to the behavior unit based on a system layer, a parameter layer and a data layer corresponding to the industrial system; wherein the system layer is used for defining objects; the parameter layer is used for defining parameters of the object; the data layer is used to define the values of the parameters.
Specifically, a model driver corresponding to an industrial system is designed in advance, and the industrial system is corresponding to a system layer, a parameter layer and a data layer in the designed model driver.
The system layer is used to define objects, which are transactions that are objectively present in an industrial system, such as time, products, and personnel.
The parameter layer is used for defining parameters of the object and mainly comprises parameter categories and parameter names. The parameter categories are distinguished by category names, one category name representing a set of parameters of the same type. One type of parameter is a vector space. Taking the parameters of the product as an example, the parameter types of the product mainly comprise geometric parameters, material parameters, performance parameters and the like. The parameter class also contains specific parameter names, for example, geometric parameters, which contain parameter names of length, width, height, etc. Further, in addition to the parameter category and the parameter name, the boundary condition and the association relation of each parameter are defined in the parameter layer.
The data layer is used for defining the values of parameters, the parameter values correspond to parameter names, the parameter values are also vector space structures, and the parameter values and the space vectors of the parameter names are in one-to-one correspondence. Taking the parameter values of the product as an example, for the same type of product, different parameter values correspond to different models of such product.
After the system layer defines the object, the data layer further defines the parameter value of the object, so as to determine the multidimensional data set in a progressive layer-by-layer manner, and the multidimensional data set has the required parameter information, so that the system layer, the parameter layer and the data layer are determined to be further utilized when the behavior unit is researched.
In one possible implementation manner, based on a system layer, a parameter layer and a data layer corresponding to the industrial system, a multidimensional data set determined by the system layer, the parameter layer and the data layer is extracted, and parameter information corresponding to a behavior unit is determined. In another possible implementation, the parameter information corresponding to the behavior units is a multidimensional dataset determined by a system layer, a parameter layer and a data layer.
It should be noted that, when the model driving design is performed, there are not only a system layer, a parameter layer and a data layer, but also a unit layer, the unit layer defines the topology structure of the unit object mainly through a tree structure, membership relations among tree hierarchy elements of the unit object can be defined and stored through naming rules, and except for the final stage of the tree hierarchy, object names in the tree hierarchy actually correspond to object group names.
And step 13, based on the parameter information and the created algorithm information, establishing a functional relation between the independent variable parameter and the dependent variable parameter in the parameter information to obtain a dynamic model.
Specifically, the creation of the algorithm information is performed in advance, and the created algorithm information is stored to obtain created algorithm information, for example, the created algorithm information may correspond to mathematical operations in fig. 2, including, but not limited to, functions, extremum, statistical analysis, neural network, and the like, which corresponds to a dynamic Platform Independent Model (PIM). And configuring the parameter information and the established algorithm information to establish a function relation between the independent variable parameter and the dependent variable parameter in the parameter information so as to obtain a dynamic model. The dynamic model is called to quickly and accurately determine the dependent variable parameters according to the independent variable parameters and the functional relation, so that the unified solution of the parameters in the unified model driving framework is realized.
In an embodiment, in the case that the behavior unit is a design unit, the step 13 includes:
in step 131, in the parameter information, a design input parameter as an independent parameter and a design output parameter as a dependent parameter are determined.
Specifically, the parameter information of the design unit is extracted, and design input parameters and design output parameters are determined, wherein the design input parameters are parameters which need to be input in a design stage, the design input parameters are independent variable parameters, and the design output parameters are dependent variable parameters corresponding to the design input parameters.
And 132, configuring a first target algorithm for the design input parameters and the design output parameters in the created algorithm information to establish a dynamic link library which stores the functional relation between the design input parameters and the design output parameters, and obtaining a dynamic model which corresponds to the design unit and comprises the parameter information and the dynamic link library.
Specifically, in the created algorithm information, a first target algorithm is configured for the design input parameters and the design output parameters, the first target algorithm determines a functional relation between the design input parameters and the design output parameters, a dynamic link library storing the functional relation between the design input parameters and the design output parameters is established, that is, the dynamic link library stores the corresponding relation between the design input parameters, the design output parameters and the first target algorithm, and a dynamic model corresponding to the design unit is obtained, wherein the dynamic model comprises parameter information and the dynamic link library.
In this embodiment, the independent variable parameters and the dependent variable parameters are selected from the parameter information, and a first target algorithm is determined for the independent variable parameters and the dependent variable parameters, so as to establish a dynamic link library, and the fast and unified solution of the parameters can be realized through the dynamic link library.
Specifically, in the modeling process corresponding to the design unit, as shown in the upper half of "design" in fig. 3, the parameter information corresponding to the design unit is a multidimensional dataset of the central area in fig. 3, where the multidimensional dataset includes a product dimension, a person dimension and a time dimension, and the design input parameter (the input parameter in fig. 3) and the design output parameter (the output parameter in fig. 3) are measured at a multidimensional dataset degree, and the design input parameter and the design output parameter are assigned to accurately measure a functional relationship between the design input parameter and the design output parameter, so as to configure a dynamic link library, and establish a correlation between the dynamic link library and the multidimensional dataset through a dimension member name, so that if the same design input parameter is extracted in the multidimensional dataset later, the design output parameter can be calculated through the dynamic link library. Even in the modeling stage of the design unit, the relation among the design input parameters, the design output parameters and the multidimensional data set is cyclic, namely, the new data can be continuously generated by extracting the data, and the multidimensional data set is further updated by utilizing the generated new data.
In one embodiment, after step 132, the method further comprises:
in step 133, the operation input parameters are extracted from the parameter information. And obtaining operation output parameters based on the operation input parameters and the dynamic link library.
Specifically, the operation input parameter is used as an independent variable parameter. According to the corresponding relation among the design input parameters, the design output parameters and the first target algorithms stored in the dynamic link library, the first target algorithm corresponding to the operation input parameters is determined, the first target algorithm is called for operation, the operation output parameters are obtained, and the rapid solving of the design stage of the parameters is realized.
It should be noted that, the nature of the design input parameters and the operation input parameters are parameters input in the design stage, and in the design modeling stage, various possible input parameters are extracted from the multidimensional database, so that the model operation stage is performed, and the operation input parameters extracted from the multidimensional database are one or more of the design input parameters.
And step 134, updating the parameter information based on the operation output parameter.
The operation output parameter is calculated new data, and parameter information is updated by using the operation output parameter. Specifically, as shown in the upper-half design of fig. 4, initial values are extracted from the multidimensional dataset as operational input parameters (input parameters in fig. 4), operational output parameters (output parameters in fig. 4) are determined using a dynamic link library, and the multidimensional dataset is updated using the operational output parameters.
In this embodiment, the operation output parameters are calculated through the operation input parameters and the dynamic link library, and the parameter information is updated through the operation output parameters, so that the data information is enriched, and the diversity of the data is ensured.
In an embodiment, the method further comprises: constructing a data warehouse based on the updated parameter information; and determining statistical information corresponding to the industrial system based on the data warehouse.
In this embodiment, a new multidimensional dataset and a data warehouse are constructed according to the data generated in the design stage, statistical information (statistical data in fig. 5) of the industrial system multidimensional dataset can be obtained by utilizing OLAP (OnLine Analytical Processing, online analysis processing) according to the content of the data warehouse as shown in fig. 5, and the statistical information can be visually displayed. Further utilization of the multi-dimensional dataset of the industrial system is achieved.
In an embodiment, the method further comprises: training a machine learning model based on the parameter information and the updated parameter information.
In this embodiment, a new multi-dimensional dataset may be constructed according to the data generated in the design stage, as shown in fig. 5, the parameter information before updating and the parameter information after updating of the multi-dimensional dataset in each stage may be utilized to perform data mining, a machine learning model is trained by using a machine learning means, and the machine learning model is utilized to perform data judgment or data prediction, so as to further utilize the multi-dimensional dataset of the industrial system.
In an embodiment, in the case that the behavior unit is a manufacturing unit, the step 13 includes:
in the parameter information, a control parameter as an independent parameter and a detection parameter as an independent parameter are determined 135.
Specifically, the parameter information of the manufacturing unit is extracted to determine a control parameter and a detection parameter, wherein the control parameter is a parameter which needs to be input in the manufacturing stage, and the manufacturing stage mainly realizes manufacturing by controlling the parameter, so that the parameter which needs to be controlled is collectively referred to as a control parameter, the control parameter is an independent variable parameter, the detection parameter is a dependent variable parameter corresponding to the control parameter, the detection parameter is an output parameter corresponding to the manufacturing stage, and the output parameter is collectively referred to as a detection parameter.
Step 136, in the created algorithm information, configuring a second target algorithm operated on the edge device for the control parameter and the detection parameter; and establishing an edge computing program library which stores the functional relation between the control parameters and the detection parameters, and obtaining a dynamic model which corresponds to the manufacturing unit and comprises the parameter information and the edge computing program library.
Specifically, in the created algorithm information, a second target algorithm for calculating on the edge device is configured for the control parameter and the detection parameter, the second target algorithm determines a functional relationship between the control parameter and the detection parameter, an edge calculation program library storing the functional relationship between the control parameter and the detection parameter is established, that is, the corresponding relationship between the control parameter, the detection parameter and the second target algorithm is stored in the edge calculation program library, and a dynamic model corresponding to the manufacturing unit is obtained, where the dynamic model includes the parameter information and the edge calculation program library. The edge equipment aims at reducing central operation pressure, improving operation speed and optimizing software operation flow.
In this embodiment, the independent variable parameters and the dependent variable parameters are selected from the parameter information, and a second target algorithm is determined for the independent variable parameters and the dependent variable parameters, so as to establish an edge calculation program library, and the edge calculation program library can implement rapid and unified solution of the parameters.
Specifically, the modeling process corresponding to the manufacturing unit is shown in the lower half of "design" in fig. 3, the parameter information corresponding to the manufacturing unit is a multidimensional dataset of the central area in fig. 3, the multidimensional dataset includes a product dimension, a person dimension and a time dimension, the control parameter and the detection parameter are measured in the multidimensional dataset, the control parameter and the detection parameter are assigned to accurately measure the functional relationship between the control parameter and the detection parameter, so as to configure an edge calculation program, and the association between the edge calculation program and the multidimensional dataset is established through the name of the dimension member, so that the detection parameter can be directly calculated through the edge calculation program if the same control parameter is extracted in the multidimensional dataset in the following. Even in the modeling stage of the manufacturing unit, the relationship among the control parameter, the detection parameter and the multidimensional dataset is cyclic, namely, new data can be continuously generated by extracting the data, and the multidimensional dataset is further updated by using the generated new data.
In one embodiment, the step 136 includes:
step 137, extracting operation control parameters from the parameter information; and obtaining operation detection parameters based on the operation control parameters, the edge calculation program library and the edge equipment.
Specifically, the arithmetic control parameter is used as an independent variable parameter. According to the corresponding relation among the control parameters, the detection parameters and the second target algorithms stored by the edge calculation program, determining the corresponding edge equipment and the second target algorithm corresponding to the control parameters, and calling the second target algorithm to operate so as to obtain operation detection parameters, thereby realizing rapid solving of the parameters in the manufacturing stage.
The control parameters and the operation control parameters are input parameters in the manufacturing stage, and various possible control parameters are extracted from the multidimensional database in the manufacturing modeling stage, so that the operation control parameters extracted from the multidimensional database in the model operation stage are one or more of the control parameters.
And step 138, updating the parameter information based on the operation detection parameter.
The operation detection parameter is calculated new data, and the parameter information is updated by using the operation detection parameter. Specifically, as shown in the lower part of fig. 4, initial values are extracted from the multi-dimensional dataset as operation control parameters (control parameters in fig. 4), operation detection parameters (detection parameters in fig. 4) are determined by an edge calculation program, and the multi-dimensional dataset is updated with the operation detection parameters.
In this embodiment, the operation detection parameters are calculated by the operation control parameters and the edge calculation program library, and the parameter information is updated by the operation detection parameters, so that the data information is enriched, and the diversity of the data is ensured.
In an embodiment, the method further comprises: constructing a data warehouse based on the updated parameter information; and determining statistical information corresponding to the industrial system based on the data warehouse.
In this embodiment, a new multidimensional dataset and a data warehouse are constructed based on the data generated during the manufacturing stage, and statistical information (statistical data in fig. 5) of the industrial system multidimensional dataset can be obtained using OLAP (OnLine Analytical Processing, online analysis processing) based on the content of the data warehouse as shown in fig. 5, and the statistical information can be visually displayed. Further utilization of the multi-dimensional dataset of the industrial system is achieved. Of course, new multidimensional datasets and data warehouses may also be constructed using data generated during the design and manufacturing stages.
In an embodiment, the method further comprises: training a machine learning model based on the parameter information and the updated parameter information.
In this embodiment, a new multi-dimensional dataset may be constructed according to the data generated in the manufacturing stage, as shown in fig. 5, the parameter information before updating and the parameter information after updating of the multi-dimensional dataset in each stage may be utilized to perform data mining, a machine learning model is trained by using a machine learning means, and the machine learning model is utilized to perform data judgment or data prediction, so as to further utilize the multi-dimensional dataset of the industrial system. Of course, the data generated in the design stage and the manufacturing stage can be used for constructing a new multidimensional dataset, and the multidimensional dataset in each stage is used for data mining.
In an embodiment, when the behavior unit is an operation and management unit, the parameter information corresponding to the behavior unit is data before and after updating in the design stage and the manufacturing stage, so that a statistical analysis algorithm and a neural network related algorithm in the created algorithm information can be utilized to establish a functional relationship between independent variable parameters and dependent variable parameters in the parameter information, and a corresponding online analysis processing model and a data mining model can be obtained.
After the functional relationships between the independent variable parameters and the dependent variable parameters in the parameter information corresponding to the behavior unit, the manufacturing unit, the operation and management unit respectively are established, a dynamic link library, an edge calculation program, an OLAP model and a data mining model shown in the software process shown in fig. 1 can be obtained, and the dynamic link library, the edge calculation program, the OLAP model and the data mining model correspond to a dynamic platform correlation model (PSM). By establishing the functional relation between the independent variable parameters and the dependent variable parameters, the complex flow in the industrial system is clearly represented, parameter solving is rapidly realized, and the use difficulty of a user is reduced, so that the difficulty of acquiring industrial software which can be used for the user is reduced.
In the above embodiment, the industrial behaviors corresponding to the industrial system are divided to obtain the behavior units. And then determining a system layer, a parameter layer and a data layer corresponding to the industrial system, wherein the system layer is used for defining the object, the parameter layer is used for defining the parameter of the object, and the data layer is used for defining the value of the parameter, so that the parameter information corresponding to the behavior unit can be determined according to the system layer, the parameter layer and the data layer corresponding to the industrial system. And further establishing a function relation between independent variable parameters and dependent variable parameters in the parameter information according to the parameter information and the established algorithm information to obtain a dynamic model. According to the technical scheme, the method and the device for solving the parameters in the industrial system accurately determine the dynamic model efficiently and accurately according to the parameter information and the established algorithm information by splitting the industrial behaviors, refining the study object, reducing the complexity, accurately grasping the study object through the system layer, the parameter layer and the data layer, and determining the function relation among the independent variable parameters, the dependent variable parameters, the independent variable parameters and the dependent variable parameters in the parameter information in the dynamic model.
In an embodiment, the argument parameter is a plurality of; the method further comprises the steps of:
Step 14, determining sequence information corresponding to the plurality of independent variable parameters;
and step 15, orderly combining the independent variable parameters, the dependent variable parameters, the functional relation among the independent variable parameters and the dependent variable parameters according to the sequence information to obtain flow template information.
In this embodiment, after the functional relationship between the independent variable parameters and the dependent variable parameters is established, the dependent variable parameters can be solved according to the independent variable parameters and the corresponding functional relationship, and in the case that the independent variable parameters are multiple, the different independent variable parameters have sequential solving sequences, so that sequence information corresponding to the multiple independent variable parameters needs to be determined. According to the sequence information, the independent variable parameters, the dependent variable parameters and the function relation are orderly combined to obtain flow template information, and according to the flow model information, the sequential solving of a plurality of independent variable parameters can be realized, so that the complexity of parameter solving is further simplified. After the flow template information is determined, even if the parameter values are different in different industrial systems, the same flow template can be used for solving the same problem.
Further, the argument parameters mentioned in the present embodiment may be behavior units of different sources, for example, the argument parameters may be from a design unit, a production unit, an operation and management unit. Therefore, independent variable parameters of different independent behavior units are sequentially and orderly combined according to sequence information to form an industrial system whole-flow template, and the flow template can be applied to other suitable industrial systems, so that reusability of a dynamic model is greatly improved, and design difficulty of industrial software is reduced.
In one embodiment, there is a target independent variable parameter among the independent variable parameters, the target independent variable parameter corresponding to a target dependent variable parameter, the target dependent variable parameter being one of the independent variable parameters.
In this embodiment, there is a target independent variable parameter among the independent variable parameters, the dependent variable parameter corresponding to the target independent variable parameter is the target dependent variable parameter, and the target dependent variable parameter is one of the independent variable parameters. That is, there is a relationship between the dependent variable parameters and the independent variable parameters, i.e., one independent variable parameter corresponds to one dependent variable parameter, and the dependent variable parameter is the next independent variable parameter.
Further, if the solution of one independent variable parameter is calculated through a functional relation, the solution of one independent variable parameter is regarded as one flow element in the flow, so that the flow elements can have a sequential relation and a value transmission relation. Each process element in the process follows the independent variable parameter to calculate the dependent variable parameter through the functional relation, and the dependent variable parameter is the independent variable parameter of the next process element. At this time, a flow template can be manufactured according to the value relation and the sequence information of a plurality of independent variable parameters, so that the complexity of the solving process is further simplified.
Exemplary apparatus
Based on the same conception as the embodiment of the method, the embodiment of the invention also provides a device for determining the dynamic model.
Fig. 6 is a schematic structural diagram of a dynamic model determining apparatus according to an exemplary embodiment of the present invention, which is disposed in a model driven industrial software architecture, and includes:
the behavior dividing module 61 is configured to divide an industrial behavior corresponding to the industrial system to obtain a behavior unit;
a parameter determining module 62, configured to determine parameter information corresponding to the behavior unit based on a system layer, a parameter layer, and a data layer corresponding to the industrial system; wherein the system layer is used for defining objects; the parameter layer is used for defining parameters of the object; the data layer is used for defining the value of the parameter;
the model determining module 63 is configured to establish a functional relationship between the independent variable parameter and the dependent variable parameter in the parameter information based on the parameter information and the created algorithm information, so as to obtain a dynamic model.
In an exemplary embodiment of the present invention, in a case where the behavior unit is a design unit, the model determining module includes:
a first parameter determination unit configured to determine, in the parameter information, a design input parameter as an independent parameter and a design output parameter as a dependent parameter;
The first configuration processing unit is used for configuring a first target algorithm for the design input parameters and the design output parameters in the created algorithm information so as to establish a dynamic link library which stores the functional relation between the design input parameters and the design output parameters, and a dynamic model which corresponds to the design unit and comprises the parameter information and the dynamic link library is obtained.
In an exemplary embodiment of the invention, the apparatus further comprises: a parameter updating module;
the parameter updating module is used for extracting operation input parameters from the parameter information; obtaining an operation output parameter based on the operation input parameter and the dynamic link library; and updating the parameter information based on the operation output parameter.
In an exemplary embodiment of the present invention, in the case where the behavior unit is a manufacturing unit, the model determining module includes:
a second parameter determination unit configured to determine, in the parameter information, a control parameter as an independent parameter and a detection parameter as an independent parameter;
a second configuration processing unit, configured to configure a second target algorithm operated at an edge device for the control parameter and the detection parameter in the created algorithm information; and establishing an edge computing program library which stores the functional relation between the control parameters and the detection parameters, and obtaining a dynamic model which corresponds to the manufacturing unit and comprises the parameter information and the edge computing program library.
In an exemplary embodiment of the invention, the apparatus further comprises: a parameter updating module;
the parameter updating module is used for extracting operation control parameters from the parameter information; obtaining operation detection parameters based on the operation control parameters, the edge calculation program library and the edge equipment; and updating the parameter information based on the operation detection parameter.
In an exemplary embodiment of the invention, the apparatus further comprises: a statistics processing module;
the statistical processing module is used for constructing a data warehouse based on the updated parameter information; and determining statistical information corresponding to the industrial system based on the data warehouse.
In an exemplary embodiment of the invention, the apparatus further comprises: training a processing module;
the training processing module is used for training a machine learning model based on the parameter information and the updated parameter information.
In an exemplary embodiment of the present invention, the argument parameter is a plurality of; the apparatus further comprises: a template determination module;
the template determining module is used for determining sequence information corresponding to the plurality of independent variable parameters; and orderly combining the independent variable parameters, the dependent variable parameters, the functional relation among the independent variable parameters and the dependent variable parameters according to the sequence information to obtain flow template information.
In an exemplary embodiment of the present invention, there is a target independent parameter among the independent parameters, the target independent parameter corresponding to a target dependent parameter, the target dependent parameter being one of the independent parameters.
Exemplary electronic device
Fig. 7 illustrates a block diagram of an electronic device according to an embodiment of the invention.
As shown in fig. 7, the electronic device 70 includes one or more processors 71 and memory 72.
Processor 71 may be a Central Processing Unit (CPU) or other form of processing unit having the determining capability of a dynamic model and/or instruction execution capability, and may control other components in electronic device 70 to perform desired functions.
Memory 72 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 71 to implement the method of determining a dynamic model and/or other desired functions of the various embodiments of the present invention described above.
In one example, the electronic device 70 may further include: an input device 73 and an output device 74, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
Of course, only some of the components of the electronic device 70 that are relevant to the present invention are shown in fig. 7 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 70 may include any other suitable components depending on the particular application.
Exemplary computer program product and MeterComputer readable storage medium
In a sixth aspect, embodiments of the invention may be a computer program product comprising computer program instructions, which when executed by a processor, cause the processor to perform the steps in a method of determining a dynamic model according to various embodiments of the invention described in the "exemplary method" section of the specification, in addition to the method and apparatus described above.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform the steps in the method of determining a dynamic model according to various embodiments of the present invention described in the "exemplary methods" section above in this specification.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present invention. Furthermore, the specific details of the invention described above are for purposes of illustration and understanding only, and are not intended to be limiting, as the invention may be practiced with the specific details described above.
The block diagrams of the devices, apparatuses, devices, systems referred to in the present invention are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
It is also noted that in the apparatus, devices and methods of the present invention, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention.
The previous description of the inventive aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the invention to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (11)
1. The method for determining the dynamic model is characterized by being applied to a model-driven industrial software architecture and comprising the following steps of:
dividing the corresponding industrial behaviors of the industrial system to obtain behavior units;
the industrial system is an industrial robot system;
the industrial behavior is corresponding to a dynamic calculation irrelevant model CIM of an industrial product;
determining parameter information corresponding to the behavior unit based on a system layer, a parameter layer and a data layer corresponding to the industrial system; wherein the system layer is used for defining objects; the parameter layer is used for defining parameters of the object; the data layer is used for defining the value of the parameter;
based on the pre-created algorithm information, storing the pre-created algorithm information to obtain created algorithm information, wherein the created algorithm information corresponds to a dynamic platform independent model PIM;
And based on the parameter information and the created algorithm information, establishing a function relation between the independent variable parameter and the dependent variable parameter in the parameter information to obtain a dynamic model.
2. The method according to claim 1, wherein in the case that the behavior unit is a design unit, the establishing a functional relationship between an independent variable parameter and a dependent variable parameter in the parameter information based on the parameter information and the created algorithm information to obtain a dynamic model includes:
determining a design input parameter serving as an independent variable parameter and a design output parameter serving as the dependent variable parameter from the parameter information;
and in the created algorithm information, configuring a first target algorithm for the design input parameters and the design output parameters so as to establish a dynamic link library which stores the functional relation between the design input parameters and the design output parameters, and obtaining a dynamic model which corresponds to the design unit and comprises the parameter information and the dynamic link library.
3. The method according to claim 2, wherein after the step of obtaining the dynamic model corresponding to the design unit and including the parameter information and the dynamic link library, the method further comprises:
Extracting operation input parameters from the parameter information;
obtaining an operation output parameter based on the operation input parameter and the dynamic link library;
and updating the parameter information based on the operation output parameter.
4. The method according to claim 1, wherein, in the case that the behavior unit is a manufacturing unit, the step of establishing a functional relationship between an independent variable parameter and a dependent variable parameter in the parameter information based on the parameter information and the created algorithm, to obtain a dynamic model corresponding to the behavior unit includes:
determining control parameters serving as independent variable parameters and detection parameters serving as the independent variable parameters from the parameter information;
configuring a second target algorithm operated on the edge equipment for the control parameter and the detection parameter in the created algorithm information; and establishing an edge computing program library which stores the functional relation between the control parameters and the detection parameters, and obtaining a dynamic model which corresponds to the manufacturing unit and comprises the parameter information and the edge computing program library.
5. The method of claim 4, wherein the obtaining the dynamic model corresponding to the manufacturing unit and including the parameter information and the edge calculation library includes:
Extracting operation control parameters from the parameter information;
obtaining operation detection parameters based on the operation control parameters, the edge calculation program library and the edge equipment;
and updating the parameter information based on the operation detection parameter.
6. The method according to claim 3 or 5, characterized in that the method further comprises:
constructing a data warehouse based on the updated parameter information;
determining statistical information corresponding to the industrial system based on the data warehouse;
and/or training a machine learning model based on the parameter information and the updated parameter information.
7. The method of claim 1, wherein the argument parameter is a plurality of; the method further comprises the steps of:
determining sequence information corresponding to the plurality of independent variable parameters;
and orderly combining the independent variable parameters, the dependent variable parameters, the functional relation among the independent variable parameters and the dependent variable parameters according to the sequence information to obtain flow template information.
8. The method of claim 1, wherein there is a target independent parameter among the independent parameters, the target independent parameter corresponding to a target dependent variable parameter, the target dependent variable parameter being one of the independent parameters.
9. A dynamic model determining device, which is characterized in that the device is arranged in a model-driven industrial software architecture, and comprises:
the behavior dividing module is used for dividing the corresponding industrial behaviors of the industrial system to obtain behavior units;
the industrial system is an industrial robot system;
the industrial behavior is corresponding to a dynamic calculation irrelevant model CIM of an industrial product;
the parameter determining module is used for determining parameter information corresponding to the behavior unit based on a system layer, a parameter layer and a data layer corresponding to the industrial system; wherein the system layer is used for defining objects; the parameter layer is used for defining parameters of the object; the data layer is used for defining the value of the parameter;
the parameter determining module is further used for storing the pre-created algorithm information based on the pre-created algorithm information to obtain created algorithm information, and the created algorithm information corresponds to the dynamic platform independent model PIM;
and the model determining module is used for establishing a function relation between the independent variable parameter and the dependent variable parameter in the parameter information based on the parameter information and the established algorithm information so as to obtain a dynamic model.
10. A computer-readable storage medium storing a computer program for executing the method of determining a dynamic model according to any one of the preceding claims 1-8.
11. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method for determining a dynamic model according to any of the preceding claims 1-8.
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