CN117634861A - Knowledge base-based industrial software execution flow determination method and device - Google Patents
Knowledge base-based industrial software execution flow determination method and device Download PDFInfo
- Publication number
- CN117634861A CN117634861A CN202311148523.6A CN202311148523A CN117634861A CN 117634861 A CN117634861 A CN 117634861A CN 202311148523 A CN202311148523 A CN 202311148523A CN 117634861 A CN117634861 A CN 117634861A
- Authority
- CN
- China
- Prior art keywords
- industrial
- software
- flow
- knowledge base
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 273
- 238000012795 verification Methods 0.000 claims abstract description 71
- 230000006399 behavior Effects 0.000 claims abstract description 67
- 238000012545 processing Methods 0.000 claims description 29
- 238000003860 storage Methods 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 description 43
- 238000005452 bending Methods 0.000 description 15
- 238000004519 manufacturing process Methods 0.000 description 14
- 238000011156 evaluation Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 10
- 238000010276 construction Methods 0.000 description 9
- 238000011161 development Methods 0.000 description 7
- 238000007726 management method Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000013461 design Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000001364 causal effect Effects 0.000 description 4
- 238000003754 machining Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 238000007792 addition Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000013499 data model Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000000556 factor analysis Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000002620 method output Methods 0.000 description 1
- 238000003801 milling Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000005215 recombination Methods 0.000 description 1
- 230000006798 recombination Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Abstract
The invention provides a method and a device for determining an industrial software execution flow based on a knowledge base, wherein the method comprises the following steps: based on the industrial knowledge in the knowledge base, carrying out flow division on the target industrial system to obtain at least two flow units; determining software behaviors corresponding to the flow units aiming at each flow unit; for each software behavior, driving the industrial software to execute the software behavior so as to call a target method in a method library, acquire input parameters, and transmit the input parameters to a method source program of the target method to obtain an output result; verifying the output result based on the judgment information in the knowledge base, and adjusting the target method and the input parameters based on the verification result and the adjustment rule when the verification result shows that the verification is not passed, so as to acquire a new output result and determine the new output result; and when the verification result shows that the verification is passed, obtaining the execution flow of the industrial software. The technical scheme provided by the invention can rapidly acquire the industrial software with higher performance.
Description
Technical Field
The invention relates to the technical field of industrial software development, in particular to a method and a device for determining an industrial software execution flow based on a knowledge base.
Background
Industrial software is an internalization and an embodiment of industrial processes and process flows, and thus a very deep understanding of industrial processes and process flows is required when developing industrial software. However, industrial processes often involve many industrial systems, resulting in complex industrial mechanisms involved, variable industrial parameters, and difficult uniform characterization of process flows. At present, when industrial software development is carried out, the development efficiency of the industrial software is lower due to the fact that the development of the industrial software is seriously dependent on professional staff.
Disclosure of Invention
The invention provides a method and a device for determining an industrial software execution flow based on a knowledge base, a computer readable storage medium and electronic equipment, and aims to solve the technical problem of low development efficiency of industrial software in the prior art.
According to a first aspect of the present invention, there is provided a method for determining an execution flow of industrial software based on a knowledge base, including:
based on the industrial knowledge in the knowledge base, carrying out flow division on the target industrial system to obtain at least two flow units;
Determining software behaviors corresponding to each flow unit aiming at each flow unit;
for each software behavior, driving industrial software to execute the software behavior so as to call a target method in a method library, acquiring input parameters, and transmitting the input parameters to a method source program of the target method to obtain an output result;
and verifying the output result based on the judgment information in the knowledge base, and adjusting the target method and the input parameters based on the verification result and the adjustment rule when the verification result shows that the verification is not passed, so as to obtain a new output result, and obtaining the execution flow of the industrial software when the verification result shows that the verification is passed.
Optionally, the input parameters include a first parameter and a second parameter, and the obtaining the input parameters includes:
acquiring the first parameter corresponding to the target method in a full-element model corresponding to the target industrial system;
and acquiring the second parameter corresponding to the target method from a working condition library, wherein the working condition library is used for storing historical input and output data.
Optionally, the adjustment rule includes a first adjustment rule and a second adjustment rule, and the adjusting the target method and the input parameter based on the verification result and the adjustment rule includes:
Adjusting the target method based on the verification result and the first adjustment rule in the method library;
and adjusting the second parameter based on the verification result and the second adjustment rule in the knowledge base.
Optionally, before the step of acquiring the first parameter corresponding to the target method in the full-element model corresponding to the target industrial system, the method further includes:
constructing the full-element model, wherein the full-element model comprises a product dimension and a method dimension;
determining first label information of the full-element model at an object layer of the product dimension and a parameter layer of the product dimension;
determining second label information of the full-element model at an object layer of the method dimension and a parameter layer of the method dimension;
the second adjustment rule is determined based on the first tag information and the second tag information.
Optionally, the first tag information of the parameter layer of the product dimension includes derivative tag information, and after the step of determining the first tag information of the full-element model, the method further includes:
Selecting index parameters from the parameters corresponding to the derived label information;
and determining part of judgment information in the knowledge base based on the index parameters.
Optionally, the method further comprises:
for each adjustment, determining a new first adjustment rule and a new second adjustment rule based on the comparison information before and after adjustment;
updating the existing first adjustment rules in the method library based on the new first adjustment rules;
and updating the existing second adjustment rules in the knowledge base based on the new second adjustment rules.
Optionally, the method further comprises:
adding flow information corresponding to the execution flow of the industrial software into the working condition library, wherein the flow information comprises unit names of the flow units, using methods of the flow units, input and output parameters corresponding to the using methods, and obtaining historical input and output data after the industrial software is operated.
According to a second aspect of the present invention, there is provided a knowledge base-based industrial software execution flow determining apparatus, including:
the flow dividing module is used for dividing the flow of the target industrial system based on the industrial knowledge in the knowledge base to obtain at least two flow units;
The behavior determination module is used for determining the software behavior corresponding to each flow unit aiming at each flow unit;
the driving processing module is used for driving the industrial software to execute the software behaviors aiming at each software behavior so as to call a target method in a method library, acquire input parameters, and transmit the input parameters to a method source program of the target method to obtain an output result;
and the verification processing module is used for verifying the output result based on the judgment information in the knowledge base, adjusting the target method and the input parameters based on the verification result and the adjustment rule when the verification result shows that the verification is not passed, so as to obtain a new output result, and obtaining the execution flow of the industrial software when the verification result shows that the verification is passed.
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 the knowledge base-based industrial software execution flow.
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 used for reading the executable instructions from the memory and executing the instructions to realize the method for determining the industrial software execution flow based on the knowledge base.
Compared with the prior art, the method and the device for determining the industrial software execution flow based on the knowledge base, the computer readable storage medium and the electronic equipment provided by the invention at least have the following beneficial effects:
according to the technical scheme, the process of the target industrial system is automatically divided through industrial knowledge in the knowledge base to obtain at least two process units, and corresponding software behaviors are determined for each process unit. And driving the industrial software to execute software behaviors so as to call the target method in the method library, acquiring input parameters corresponding to the target method, and transmitting the input parameters to a method source program of the target method to obtain an output result. And further verifying the output result according to the judgment information in the knowledge base, and adjusting the target method and the input parameters based on the verification result and the adjustment rule under the condition that the verification is not passed, so as to obtain a new output result, and verifying the new output result again until the verification result shows that the verification is passed, so as to obtain the execution flow of the industrial software. According to the technical scheme provided by the invention, the industrial knowledge in the knowledge base is used for guiding the automatic division of the process, the target method and the input parameters are automatically adjusted by utilizing the judgment information in the knowledge base, and the execution process of the industrial software is continuously and automatically optimized, so that the execution process of the industrial software with higher performance can be rapidly obtained, and the development efficiency is higher.
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 to a person skilled in the art.
FIG. 1 is a flowchart illustrating a method for determining a process of executing industrial software based on a knowledge base according to an exemplary embodiment of the invention;
FIG. 2 is a schematic diagram of flow units in a method for determining a flow of an industrial software execution based on a knowledge base according to an exemplary embodiment of the invention;
FIG. 3 is a schematic diagram of software behavior in a method for determining a flow of execution of industrial software based on a knowledge base according to an exemplary embodiment of the invention;
FIG. 4 is a second flow chart of a method for determining the execution flow of knowledge base-based industrial software according to an exemplary embodiment of the invention;
FIG. 5 is a schematic diagram of a robotic elbow system in a method for determining a flow of industrial software execution based on a knowledge base according to an exemplary embodiment of the invention;
FIG. 6 is a flowchart illustrating a method for determining the execution flow of knowledge base based industrial software according to an exemplary embodiment of the invention;
FIG. 7 is a schematic diagram of a method for determining a flow of industrial software execution based on a knowledge base according to an exemplary embodiment of the invention;
FIG. 8 is a schematic diagram of a determination device for an implementation flow of industrial software based on a knowledge base according to an exemplary embodiment of the invention;
fig. 9 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.
Industrial software is an internalization and an embodiment of industrial processes and process flows, and thus a very deep understanding of industrial processes and process flows is required when developing industrial software. However, the industrial process often involves more industrial systems, so that the included industrial mechanism is complex, industrial parameters are changeable, and the process flow is difficult to uniformly characterize, so that the development efficiency of industrial software is low and the performance of the developed industrial software is poor due to the fact that the industrial process still depends on professional staff seriously when data are processed.
Further, when the knowledge base is applied to the field of industrial software, a unified management and informatization organization mode for knowledge is lacking aiming at a single process in a plurality of industrial systems (such as CAD/CAE/CAM), so that the knowledge base is not beneficial to the rapid development of the industrial software, and the requirements of a modern manufacturing mode cannot be met.
Exemplary method
Fig. 1 is a flow chart of a method for determining an execution flow of industrial software based on a knowledge base according to an exemplary embodiment of the invention, which at least includes the following steps:
and step 11, dividing the flow of the target industrial system based on the industrial knowledge in the knowledge base to obtain at least two flow units.
Specifically, a great amount of industrial knowledge in the industrial field is stored in the knowledge base, and the industrial knowledge can include, but is not limited to, a processing flow of the target industrial system, a data flow direction of the target industrial system, and historical division data of the target industrial system, so that reasoning can be performed according to the industrial knowledge in the knowledge base, the flow division can be performed on the target industrial system, at least two flow units are obtained, and the target industrial system can be any object processing system to be established in the industrial field for executing the industrial software. It should be noted that, the industrial knowledge in the knowledge base is not fixed, but is continuously enriched through practical data, so as the data is accumulated, the process of the target industrial system is divided based on the industrial knowledge in the knowledge base, and the accuracy of the obtained process unit is higher and higher.
Illustratively, the target industrial system is a robotic pipe-bending system, and the industrial knowledge includes processing flows, data flow directions, and historical partition data of the robotic pipe-bending system, so that the robotic pipe-bending system may be automatically partitioned according to the industrial knowledge stored in the knowledge base to obtain at least two flow units. When dividing, the robot pipe bending system can be functionally decomposed according to industrial knowledge to obtain a group of achievable low-complexity functional sets, a serialized processing flow is constructed by combining and sequencing the functional sets according to the data flow direction of the processing process of a workpiece, the pipe bending processing is sequentially divided into 8 flow units according to the functions required to be executed by the pipe bending of the workpiece as shown in fig. 2, and the 8 flow units are sequentially pipe fitting CAD model construction (related to CAD industrial system), unit division and parameter extraction (related to CAE/CAM industrial system), mold matching (related to CAE/CAM industrial system), processing technology generation (related to CAE/CAM industrial system), animation simulation (related to CAE/CAM industrial system), interference judgment (related to CAE/CAM industrial system), processing program output (related to CAM industrial system) and processing compensation (related to CAM industrial system).
Step 12, for each flow unit, determining the software behavior corresponding to the flow unit.
The software behavior is used for calling the method and performing parameter transfer to complete function execution, and the software behavior is the conversion from an industrial process to a software process. After determining the software behavior corresponding to the flow unit, initially building an execution process of the industrial software.
Specifically, for each flow unit, determining the software behavior corresponding to the flow unit according to the unit input/output information of the flow unit.
In one possible implementation, after the process division is performed on the target industrial system, determining the unit input/output information of each process unit, and then configuring the software behavior for the process unit according to the unit input/output information, so that the target method pointed by the software behavior is matched with the unit input/output information. When the software behavior of the flow unit is configured, automatic recommendation can be performed, and of course, manual setting can be performed.
In an exemplary embodiment, when determining a software behavior corresponding to a flow element, a target method is configured for the software behavior, an associated product corresponding to the behavior is determined, and behavior parameters of the software behavior are determined based on the target method and the associated product. As shown in fig. 3, configuration software behavior 1 for pipe CAD model construction: constructing a CAD model of the pipe fitting, wherein a target method corresponding to the software behavior is a structurePipe method, the method type is a pipe modeling method, and the input parameters of the method are pipe interface graphics, pipe fitting track and wall thickness information; the output parameters of the method are pipeline CAD files. The associated product of this software behavior is product 1: and (3) determining the behavior parameters of the software behavior when the pipe is to be bent, wherein the behavior input parameters are the section figure parameter value, the track parameter value and the wall thickness parameter value of the pipe to be bent, and the behavior output parameters are CAD files of the pipe to be bent.
And step 13, aiming at each software behavior, driving the industrial software to execute the software behavior so as to call a target method in a method library, acquiring input parameters, and transmitting the input parameters to a method source program of the target method to obtain an output result.
Specifically, after determining the software behavior, driving the industrial software to execute the software behavior so as to call a target method in a method library, acquiring method input information corresponding to the target method (the method input information is an abstract parameter name and is not a parameter specific numerical value) in order to execute the target method, acquiring according to the behavior parameters corresponding to the software behavior when acquiring the method input information, acquiring input parameters (the input parameters are parameter specific numerical values, namely, assigned method input information), transmitting the input parameters to a method source program of the target method, and performing calculation after completing parameter transmission to obtain an output result.
In one embodiment, the construction of the method library is performed in advance. In one possible construction manner, the industrial algorithm information related to various industrial software in the industrial field is added into the method library, i.e. one method library is uniformly used. In one possible implementation, the industrial algorithm information corresponding to each flow unit of the target industrial system is added into a method library, i.e. one target industrial system uses one method library.
Specifically, extracting industrial algorithm information corresponding to each flow unit, and storing the extracted industrial algorithm information in a method library in a standardized construction manner. The method information to be stored in the method library comprises a method name, a method type, a method ID, an address of a method source program and method input and output information. The input and output information of the method is uniformly stored by using a dataframe data structure, and parameter analysis can be carried out according to columns when a method library is called so as to realize input and output conversion among different methods. For the result file with a specific storage format and needing to be independently stored, address information of the result file is input and output. And the method library can design a mechanism based on semantic fuzzy calling, and can provide a proper method candidate set when the software acts to call the method.
The method corresponding to the pipe fitting CAD model construction unit is a StructurePipe method, the method type is a pipe modeling method, the input parameters are pipe interface graphics, pipe fitting track and wall thickness information, the output parameters are pipe CAD files, and when the output parameters are CAD result files, the address of the result files needs to be saved instead of the complete files.
In one possible implementation, as shown in fig. 4, after determining a processing flow, i.e., a flow unit, the industrial software is driven to execute a software behavior, a target method is called in a method library according to the software behavior, and the method is linked to a full-element model to search in a database, obtain data, i.e., input parameters, and then perform function execution to obtain an output result.
In an embodiment, the input parameters include a first parameter and a second parameter, and the obtaining, in step 13, the input parameters corresponding to the software behavior includes:
and step 131, acquiring the first parameter corresponding to the target method in a full-element model corresponding to the target industrial system.
The full-element model is a data model which is built for the target industrial system in advance and is used for providing various data related to the target industrial system and carrying out assignment on the behavior parameters. Therefore, according to the full element model, corresponding first parameters can be obtained, wherein the first parameters are fixed parameters, such as pipe fitting size, wall thickness, interference bounding box and the like.
And step 132, acquiring the second parameter corresponding to the target method from a working condition library, wherein the working condition library is used for storing historical input and output data.
Specifically, the working condition library is a pre-constructed database and is used for storing historical input and output data, the historical input and output data is obtained through simulation or actual operation, and the historical input and output data is historical experience data and has referenceability. And acquiring a second parameter corresponding to the target method, wherein the second parameter is a variable parameter, such as a clamping die, a processing mode, a clamping position, a feeding speed and the like, and a value change interval is acquired.
In this embodiment, the first parameter and the second parameter are distinguished, and the first parameter and the second parameter are determined by using different data sources, that is, the first parameter is obtained by using a full-element model, and the second parameter which can be adjusted and changed is obtained by using a working condition library, so that more accurate input parameters are obtained, and the accuracy of an output result is ensured.
In an embodiment, before step 131, the method further comprises:
step 133, constructing the full-element model, wherein the full-element model comprises a product dimension and a method dimension.
Step 134, determining the first label information of the full-element model at the object layer of the product dimension and the parameter layer of the product dimension.
And step 135, determining second label information of the full-element model at the object layer of the method dimension and the parameter layer of the method dimension.
Specifically, a full-element model is constructed that includes two dimensions, one being the product dimension and one being the method dimension. And the label information is used for unified management from two layers of the object and the parameter, namely, part of the first label information is determined at the object layer of the product dimension, and part of the first label information is determined at the parameter layer of the product dimension. And determining part of the second label information at the object layer of the method dimension, and determining part of the second label information at the parameter layer of the method dimension. The first tag information and the second tag information may each be a three-level tag.
Illustratively, for the whole life cycle of the workpiece processing process, a full-element data model of the robot pipe bending processing is constructed from two dimensions of a product and a method, and three-level labels are used for unified management from two levels of objects and parameters.
The object labels of the product dimension are characterized in that, as shown in a product dimension table and a method dimension table, a first level defines object categories (workpieces/devices), a second level defines relatively independent integers (blades, machine tools, milling machines and the like), and a third level defines integral parts (blade bodies, tenons, cooling systems and the like). The three-level labels of the method dimension define industrial processes (design, manufacture, operation and maintenance management and the like), second-level definition process categories (appearance design, production manufacture, data analysis and the like) and third-level definition subclasses (product design, geometric measurement, quality management and the like).
The attribute tags of the product dimension are used for hierarchical classification and management of parameters of industrial system objects, wherein a first level is responsible for defining categories (general parameters, geometric parameters, material parameters and the like) of the parameters, a second level is a stage (design, manufacturing, operation and maintenance management and the like) where the parameters are defined by referring to a process of the object tags in the method dimension, and a third level is a causal (original/derivative) of the defined parameters. The attribute labels of the method dimension are defined and managed for parameters participating in operation in the method dimension, wherein the first-level labels define the method category (algorithm program, manual operation, machine processing and the like), the second-level labels are responsible for defining the input/output relation (input/output) of the parameters, and the third-level labels define the obtaining mode (manual/automatic) of the parameters.
Product dimension table
Method dimension table
Step 136, determining the second adjustment rule based on the first tag information and the second tag information.
The second adjustment rule is a rule stored in the knowledge base for adjusting the second parameter, i.e. the variable parameter.
Specifically, after the first tag information and the second tag information are determined, an association relationship between an original parameter (determined according to the first tag information) in a product dimension parameter layer and a method input parameter (determined according to the second tag information) in a method dimension parameter layer in the full-element model and a method output parameter (determined according to the first tag information) in the method dimension parameter layer can be determined according to the first tag information, so that unit input/output information of each flow unit, association relationship between the original parameter and the derivative parameter in the full-element model and index parameters (such as processing efficiency, processing precision and the like for evaluation) in the derivative parameter are established, and the association relationship is stored in a knowledge base to be used as a second adjustment rule.
In one possible implementation manner, the determination of the association relationship is divided into two steps, wherein the first step is to determine the parameter association relationship between objects of different industrial systems by corresponding the input and output information of units of each flow behavior to the full-element model, namely, the association relationship exists between the input and output of the method, and the causal relationship exists between the input and output. Illustratively, as shown in fig. 5, in the pipe bending process, a first flow unit outputs a pipe CAD file, and a fifth flow unit inputs a pipe CAD file in addition to a second flow unit, so that from the perspective of direct parameter association, output content changes caused by changes in node input content in the first flow unit can affect the two subsequent flow units in an associated manner. And the second step is to modify the variable parameters one by using a single factor analysis method, obtain the influence of the variable parameters on the targets of the flow units according to the simulation result, and provide a reference for the subsequent second parameter adjustment, thus obtaining a second adjustment rule.
In an embodiment, the first tag information of the parameter layer of the product dimension includes derivative tag information, and after the step 134, the method further includes:
step 137, selecting index parameters from the parameters corresponding to the derived label information;
And 138, determining part of judgment information in the knowledge base based on the index parameters.
Specifically, because the original attribute of the product and the input of the method have a corresponding relationship with the output of the method, the index parameter can be selected from the parameters corresponding to the derived label information, and the selected index parameter can be used as part of the judgment information in the knowledge base. Therefore, the corresponding relation between the judgment information in the knowledge base and the index parameters in the full-element model can be established, and the accuracy of part of the judgment information is ensured.
And step 14, verifying the output result based on the judgment information in the knowledge base, and adjusting the target method and the input parameters based on the verification result and the adjustment rule when the verification result shows that the verification is not passed, so as to obtain a new output result, and obtaining the execution flow of the industrial software when the verification result shows that the verification is passed.
Specifically, the knowledge base stores evaluation information, the evaluation information is used for verifying an output result, so that after the output result is determined, the output result is verified according to the evaluation information, if the verification result indicates that the verification is not passed, a target method or input parameters pointed by the software behavior are adjusted according to the verification result and an adjustment rule, the adjusted new output result is determined, then the new output result is verified again based on the evaluation information of the knowledge base, if the new output result is not passed, the adjustment and verification processes are repeated until the verification result indicates that the verification is passed, the software behavior corresponding to each flow unit passing the verification, the target method pointed by the software behavior and the input parameters are determined as an execution process of the industrial software, and the execution process of the industrial software is a result of continuously optimizing the execution process of the initially built industrial software, so that the performance of the industrial software is higher.
Illustratively, the knowledge base includes a series of industrial knowledge such as evaluation criteria (pipe bending degree, machining efficiency, feeding speed, etc.) of each flow unit, process constraints (clamping angle, feeding speed, collision interference, etc.), variable association relationships (parameter coupling, causal association, etc.), and possible machining conditions can be constructed by adopting a rule reasoning mode based on the industrial knowledge, and the possible machining conditions are stored in a working condition library as candidate working conditions, wherein the evaluation criteria (pipe bending degree, machining efficiency, feeding speed, etc.) of each flow unit can be used as evaluation information to verify the output result.
It should be noted that, since the knowledge base stores the evaluation criteria for each flow unit, when performing verification, the verification may be performed after the software behaviors of one flow unit are executed, or the unified verification may be performed after the software behaviors corresponding to all flow units are executed sequentially, which is not limited in this embodiment.
In an embodiment, the adjustment rules include a first adjustment rule and a second adjustment rule, and the adjusting the target method and the input parameter in step 14 based on the verification result and the adjustment rule includes:
And step 141, adjusting the target method based on the verification result and the first adjustment rule in the method library.
And step 142, adjusting the second parameter based on the verification result and the second adjustment rule in the knowledge base.
The first adjustment rule is an adjustment rule pre-constructed in a method library and is used for adjusting a target method called when executing software behaviors. The second adjustment rule is an adjustment rule pre-constructed in the knowledge base and is used for adjusting the second parameter acquired when the software behavior is executed.
Specifically, the adjustment is divided into two layers: condition adjustment (method adjustment) and parameter adjustment (second parameter adjustment). The working condition adjustment refers to adjustment of a method in a flow unit, comprising replacement, addition and sequential adjustment of the method, and is automatically completed by an established method library, and mainly aims to treat serious conditions such as missing of key attributes of an elbow pipe and unreachable targets, for example, when the length of the pipe is found to be unable to be extracted in CAD model construction behaviors of the pipe in the elbow pipe processing flow, other model construction methods are needed. The parameter adjustment mainly refers to an adjustable parameter, namely a second parameter, in the method parameters, parameter selection is carried out in a value change interval of the adjustable parameter, parameters to be adjusted are determined by targets of all flow units, and the conditions of parameter coupling and causal association between all flows are considered, so that the association relation in a knowledge base is utilized for adjustment, namely the association relation is utilized for selecting and adjusting the association parameter and carrying out assignment, and then re-verification is carried out.
In an embodiment, the method further comprises:
step 143, for each adjustment, determining a new first adjustment rule and a new second adjustment rule based on the comparison information before and after the adjustment.
And step 144, updating the existing first adjustment rules in the method library based on the new first adjustment rules.
And step 145, updating the existing second adjustment rules in the knowledge base based on the new second adjustment rules.
Specifically, in order to ensure timeliness and accuracy of data in the method library and the knowledge library, after each adjustment, comparing information before adjustment with information after adjustment, determining comparison information before adjustment and after adjustment, then summarizing new knowledge according to the comparison information before adjustment and after adjustment, determining a new first adjustment rule and a new second adjustment rule, adding the new first adjustment rule into the method library, updating the existing first adjustment rule in the method library, and adding the new second adjustment rule into the knowledge library, thereby updating the existing second adjustment rule in the knowledge library.
Illustratively, when updating, if the new first adjustment rule conflicts with the existing first adjustment rule, the existing first adjustment rule is deleted, and the new first adjustment rule is added. If the new first adjustment rule is a refinement of the existing first adjustment rule, then the existing first adjustment rule and the new first adjustment rule are retained at the same time.
In an embodiment, the method further comprises:
step 146, adding flow information corresponding to the execution flow of the industrial software into the working condition library, wherein the flow information comprises a unit name of the flow unit, a use method of the flow unit, input and output parameters corresponding to the use method, and historical input and output data obtained after the industrial software is operated. Specifically, the tested pipe bending process flow is supplemented into a working condition library.
In one possible application scenario, as shown in fig. 6, for a robot pipe bending process, a processing flow is automatically divided according to industrial knowledge in a knowledge base to obtain a plurality of flow units, each flow unit corresponds to at least one software behavior, a behavior calling method, a full-element model is linked, input parameters are acquired in a database, a target method is called in a method library, the input parameters are transmitted to a method source program of the target method, function execution, namely, target method, simulation verification, and automatic adjustment, namely, method adjustment and parameter adjustment, are performed according to a verification structure, a working condition library is updated according to each adjustment, namely, newly obtained flow information is added into the working condition library, the method library and the knowledge base are supplemented, namely, a new first adjustment rule is added into the method library and a second adjustment rule is added into the knowledge base.
Further, after determining the pipe bending process, the pipe bending process may be optimized once, as shown in fig. 7, where the intelligent optimization system for pipe bending process includes a knowledge base, a method base and a working condition base, and the knowledge base is used for performing flow division, process constraint, i.e. evaluation information, and variable association, i.e. a second adjustment rule. The method library comprises method combination and fuzzy calling, the working condition library is used for parameter transmission, a second parameter is determined, and flow unit information and candidate working conditions are stored.
In the above embodiment, the process of the target industrial system is automatically divided by the industrial knowledge in the knowledge base to obtain at least two process units, and the corresponding software behavior is determined for each process unit. And driving the industrial software to execute software behaviors so as to call the target method in the method library, acquiring input parameters corresponding to the target method, and transmitting the input parameters to a method source program of the target method to obtain an output result. And further verifying the output result according to the judgment information in the knowledge base, and adjusting the target method and the input parameters based on the verification result and the adjustment rule under the condition that the verification is not passed, so as to obtain a new output result, and verifying the new output result again until the verification result shows that the verification is passed, so as to obtain the execution flow of the industrial software. According to the technical scheme provided by the invention, the industrial knowledge in the knowledge base is used for guiding the automatic division of the process, the target method and the input parameters are automatically adjusted by utilizing the judgment information in the knowledge base, and the execution process of the industrial software is continuously and automatically optimized, so that the execution process of the industrial software with higher performance can be rapidly obtained, and the development efficiency is higher.
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 industrial software execution flow based on the knowledge base.
Fig. 8 is a schematic structural diagram of a determining device for an industrial software execution flow based on a knowledge base according to an exemplary embodiment of the present invention, including:
the flow dividing module 81 is configured to divide a flow of the target industrial system based on the industrial knowledge in the knowledge base, so as to obtain at least two flow units;
a behavior determination module 82, configured to determine, for each flow unit, a software behavior corresponding to the flow unit;
the driving processing module 83 is configured to, for each software behavior, drive the industrial software to execute the software behavior, so as to call a target method in a method library, obtain an input parameter, and transmit the input parameter to a method source program of the target method, thereby obtaining an output result;
and the verification processing module 84 is configured to verify the output result based on the evaluation information in the knowledge base, and adjust the target method and the input parameter based on the verification result and an adjustment rule when the verification result indicates that the verification fails, so as to obtain a new output result, and obtain an execution flow of the industrial software when the verification result indicates that the verification fails.
In an exemplary embodiment of the present invention, the input parameters include a first parameter and a second parameter, and the driving processing module includes:
a first parameter obtaining unit, configured to obtain, in a full-element model corresponding to the target industrial system, the first parameter corresponding to the target method;
the second parameter acquisition unit is used for acquiring the second parameter corresponding to the target method from a working condition library, and the working condition library is used for storing historical input and output data.
In an exemplary embodiment of the present invention, the adjustment rule includes a first adjustment rule and a second adjustment rule, and the verification processing module includes:
a first adjustment processing unit, configured to adjust the target method based on the verification result and the first adjustment rule in the method library;
and the second adjustment processing unit is used for adjusting the second parameter based on the verification result and the second adjustment rule in the knowledge base.
In an exemplary embodiment of the invention, the apparatus further comprises:
the model construction module is used for constructing the full-element model, and the full-element model comprises a product dimension and a method dimension;
The label determining module is used for determining first label information of the full-element model at the object layer of the product dimension and the parameter layer of the product dimension; determining second label information of the full-element model at an object layer of the method dimension and a parameter layer of the method dimension;
and the rule determining module is used for determining the second adjustment rule based on the first tag information and the second tag information.
In an exemplary embodiment of the present invention, the first tag information of the parameter layer of the product dimension includes derivative tag information, and the apparatus further includes:
the index parameter selection module is used for selecting index parameters from the parameters corresponding to the derived tag information;
and the judgment information determining module is used for determining part of judgment information in the knowledge base based on the index parameters.
In an exemplary embodiment of the invention, the apparatus further comprises:
the data updating module is used for determining a new first adjustment rule and a new second adjustment rule based on the comparison information before and after adjustment for each adjustment; updating the existing first adjustment rules in the method library based on the new first adjustment rules; and updating the existing second adjustment rules in the knowledge base based on the new second adjustment rules.
In an exemplary embodiment of the invention, the apparatus further comprises:
the working condition library updating module is used for adding flow information corresponding to the execution flow of the industrial software into the working condition library, wherein the flow information comprises a unit name of the flow unit, a use method of the flow unit, input and output parameters corresponding to the use method, and historical input and output data obtained after the industrial software is operated.
Exemplary electronic device
Fig. 9 illustrates a block diagram of an electronic device according to an embodiment of the invention.
As shown in fig. 9, the electronic device 90 includes one or more processors 91 and memory 92.
Processor 91 may be a Central Processing Unit (CPU) or other form of processing unit having knowledge base based determination capabilities and/or instruction execution capabilities of an industrial software execution flow and may control other components in electronic device 90 to perform desired functions.
Memory 92 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 91 to implement the method of determining a knowledge base based industrial software execution flow and/or other desired functions of the various embodiments of the invention described above.
In one example, the electronic device 90 may further include: an input device 93 and an output device 94, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
Of course, only some of the components of the electronic device 90 that are relevant to the present invention are shown in fig. 9 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 90 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage Medium
In a sixth aspect, in addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the method of determining a flow of knowledge base based industrial software execution flow according to various embodiments of the invention described in the "exemplary methods" section of this specification.
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 that, when executed by a processor, cause the processor to perform steps in a method of determining a flow of industrial software execution based on a knowledge base 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 (10)
1. A method for determining an execution flow of industrial software based on a knowledge base, comprising:
Based on the industrial knowledge in the knowledge base, carrying out flow division on the target industrial system to obtain at least two flow units;
determining software behaviors corresponding to each flow unit aiming at each flow unit;
for each software behavior, driving industrial software to execute the software behavior so as to call a target method in a method library, acquiring input parameters, and transmitting the input parameters to a method source program of the target method to obtain an output result;
and verifying the output result based on the judgment information in the knowledge base, and adjusting the target method and the input parameters based on the verification result and the adjustment rule when the verification result shows that the verification is not passed, so as to obtain a new output result, and obtaining the execution flow of the industrial software when the verification result shows that the verification is passed.
2. The method of claim 1, wherein the input parameters include a first parameter and a second parameter, and wherein the obtaining the input parameters comprises:
acquiring the first parameter corresponding to the target method in a full-element model corresponding to the target industrial system;
and acquiring the second parameter corresponding to the target method from a working condition library, wherein the working condition library is used for storing historical input and output data.
3. The method of claim 2, wherein the adjustment rules include a first adjustment rule and a second adjustment rule, and wherein the adjusting the target method, the input parameter, based on the verification result and the adjustment rule comprises:
adjusting the target method based on the verification result and the first adjustment rule in the method library;
and adjusting the second parameter based on the verification result and the second adjustment rule in the knowledge base.
4. A method according to claim 3, wherein prior to the step of obtaining the first parameter corresponding to the target method in a full-element model corresponding to the target industrial system, the method further comprises:
constructing the full-element model, wherein the full-element model comprises a product dimension and a method dimension;
determining first label information of the full-element model at an object layer of the product dimension and a parameter layer of the product dimension;
determining second label information of the full-element model at an object layer of the method dimension and a parameter layer of the method dimension;
the second adjustment rule is determined based on the first tag information and the second tag information.
5. A method according to claim 3, wherein the first tag information of the parameter layer of the product dimension comprises derivative tag information, and wherein after the step of determining the first tag information of the full-element model at the object layer of the product dimension and the parameter layer of the product dimension, the method further comprises:
selecting index parameters from the parameters corresponding to the derived label information;
and determining part of judgment information in the knowledge base based on the index parameters.
6. A method according to claim 3, characterized in that the method further comprises:
for each adjustment, determining a new first adjustment rule and a new second adjustment rule based on the comparison information before and after adjustment;
updating the existing first adjustment rules in the method library based on the new first adjustment rules;
and updating the existing second adjustment rules in the knowledge base based on the new second adjustment rules.
7. The method according to claim 2, wherein the method further comprises:
adding flow information corresponding to the execution flow of the industrial software into the working condition library, wherein the flow information comprises unit names of the flow units, using methods of the flow units, input and output parameters corresponding to the using methods, and obtaining historical input and output data after the industrial software is operated.
8. A knowledge base-based industrial software execution flow determining device, comprising:
the flow dividing module is used for dividing the flow of the target industrial system based on the industrial knowledge in the knowledge base to obtain at least two flow units;
the behavior determination module is used for determining the software behavior corresponding to each flow unit aiming at each flow unit;
the driving processing module is used for driving the industrial software to execute the software behaviors aiming at each software behavior so as to call a target method in a method library, acquire input parameters, and transmit the input parameters to a method source program of the target method to obtain an output result;
and the verification processing module is used for verifying the output result based on the judgment information in the knowledge base, adjusting the target method and the input parameters based on the verification result and the adjustment rule when the verification result shows that the verification is not passed, so as to obtain a new output result, and obtaining the execution flow of the industrial software when the verification result shows that the verification is passed.
9. A computer-readable storage medium storing a computer program for executing the method of determining a knowledge base based industrial software execution flow of any one of the preceding claims 1-7.
10. 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 the execution flow of the knowledge base based industrial software of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311148523.6A CN117634861A (en) | 2023-09-06 | 2023-09-06 | Knowledge base-based industrial software execution flow determination method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311148523.6A CN117634861A (en) | 2023-09-06 | 2023-09-06 | Knowledge base-based industrial software execution flow determination method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117634861A true CN117634861A (en) | 2024-03-01 |
Family
ID=90025945
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311148523.6A Pending CN117634861A (en) | 2023-09-06 | 2023-09-06 | Knowledge base-based industrial software execution flow determination method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117634861A (en) |
-
2023
- 2023-09-06 CN CN202311148523.6A patent/CN117634861A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2017528790A (en) | System and method for searching machining knowledge database | |
| US8346773B2 (en) | Product classification system | |
| EP3726442A1 (en) | Semantic modeling and machine learning-based generation of conceptual plans for manufacturing assemblies | |
| Ahmad et al. | Current trend in computer aided process planning | |
| Abualdenien et al. | Consistent management and evaluation of building models in the early design stages | |
| Kalyakulin et al. | Informational relational models for calculating the cutting conditions in automatic control systems | |
| EP2827240A1 (en) | Method for generating control-code by a control-code-diagram | |
| Guo et al. | An automatic machining process decision-making system based on knowledge graph | |
| US8914141B2 (en) | Template framework for automated process routing | |
| CN107180138A (en) | A kind of part machinability analysis method and system based on MBD models | |
| Letaief et al. | An approach of CAD/CAM data reuse for manufacturing cost estimation | |
| CN117634861A (en) | Knowledge base-based industrial software execution flow determination method and device | |
| CN114722729B (en) | Automatic cutter recommendation method and device, terminal and storage medium | |
| CN116467219A (en) | Test processing method and device | |
| Hofmann et al. | Closed loop geometrical tolerance engineering with measuring data for reverse information processing | |
| CN116227055A (en) | Intelligent design method and system for water chilling unit | |
| Schuh et al. | Data-based improvement of engineering change impact analyses in manufacturing | |
| CN112001047B (en) | Radar key part process design method based on PMI information | |
| Potočnik et al. | An automated design system for compound washer dies | |
| CN111651846B (en) | Automatic optimization method for pipeline design of refrigeration equipment | |
| CN114116773A (en) | Structured Query Language (SQL) text auditing method and device | |
| Shang et al. | Correlation based analysis of parameter change propagation in variant product design | |
| Kacalak et al. | A method of object-oriented symbolical description and evaluation of machine elements using antipatterns | |
| CN114997001B (en) | Complex electromechanical equipment performance evaluation method based on substitution model and knowledge graph | |
| Schiller et al. | Blisk Specific Query Language (BLISQL)–An approach for domain specific data querying in Blisk Manufacturing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |