CN107885747A

CN107885747A - A kind of semantic relation generation method and equipment

Info

Publication number: CN107885747A
Application number: CN201610868517.1A
Authority: CN
Inventors: 余明; 袁勇; 王琪
Original assignee: Siemens AG
Current assignee: Siemens AG
Priority date: 2016-09-29
Filing date: 2016-09-29
Publication date: 2018-04-06
Anticipated expiration: 2036-09-29
Also published as: CN107885747B

Abstract

A kind of semantic relation generation method and equipment are provided, for generating the semantic relation between at least two components in a production system, this method includes：From the data at least one stage of the product life cycle of the production system, at least one target keyword needed for running situation described in extraction and analysis；Based on semanteme coupling rule, the semantic relation between at least two component is generated according at least one target keyword.Wherein, the semantic coupling rule is used to provide when having the semantic relation between at least two component, the condition that at least one target keyword should meet.

Description

A kind of semantic relation generation method and equipment

Technical field

It is related to the life of the semantic relation between the component in technical field of automation in industry, more particularly to a kind of production system Into method and apparatus.

Background technology

In industrial automation, various production systems (production system) be present.Such as：Supply water Pipe network etc..In order to realize the optimization to production system, it is necessary to analyze the various running conditions of production system.With feed pipe Exemplified by net, it may be necessary to the leakage situation of water supply network is analyzed, to prevent pipe leakage；It may also need to feed pipe The Energy Expenditure Levels of net are analyzed, energy-optimised to carry out.

On the one hand, a production system product life cycle (Product Life Cycle, PLC) it is at least one , it is necessary to describe production system using various instruments in stage.It can describe to give birth to using data model generally in these instruments Each component (component) in production system, alternatively, is further described the correlation between component.

On the other hand, the running situation of a production system can be carried out using network system (cyber system) etc. Analysis.

When the running situation to a production system is analyzed, it is to be understood that the information such as composition of production system.Cause This is, it is necessary to which production system is described in all such as above-mentioned network systems.In order to meet the needs of running situation analysis, not only Need to be described in each component to production system, it is also necessary to describe the relation between component, we will be in network system Described in production system component between relation be referred to as " semantic relation ".

So, the semantic relation between the component of production system used in network system how is generated, is just turned into right One urgent problem when the running situation of production system is analyzed.

The content of the invention

In view of this, the present invention proposes a kind of semantic relation generation method and device, for generating in a production system At least two components between semantic relation.

In a first aspect, the embodiment of the present invention provides a kind of semantic relation generation method, methods described is used to generate a life The semantic relation between at least two components in production system, wherein, the semantic relation is used to analyze the production system A kind of running situation, methods described include：

From the data at least one stage of the product life cycle of the production system, feelings are run described in extraction and analysis At least one target keyword needed for condition；

Based on semanteme coupling rule, according between at least one target keyword generation at least two component Semantic relation, wherein, when the semantic coupling rule is used to provide to have the semantic relation between at least two component, The condition that at least one target keyword should meet.

Technical scheme according to embodiments of the present invention, realize and semantic relation is automatically generated by equipment, fully ensured it Validity and accuracy, solve and determine defect caused by semantic relation due to artificial in the prior art.For what is analyzed Running situation extracts target keyword, avoids extracting useless keyword, improves the efficiency of whole semantic relation generating process. The target keyword of extraction is analyzed needed for the running situation, therefore ensure that precision of analysis.Semanteme coupling rule The condition that target keyword should meet when then being used to provide to have semantic relation between at least two components, there is provided automatically generate A kind of feasible program of semantic relation.

According in a first aspect, in the first implementation, at least one target keyword includes being used to describe institute State at least one first keyword of correlation between at least two components, based on semanteme coupling rule, according to it is described at least One target keyword generates the semantic relation between at least two component, including：

When at least one first keyword meets the condition of the semantic coupling specified by rules, according to it is described extremely The correlation of few first keyword description determines the semantic relation.

Technical scheme according to embodiments of the present invention, realize and be based on mutually closing between at least two components of description by equipment The keyword of system automatically generates semantic relation, has fully ensured its validity and accuracy, and improve flexibility.

According in a first aspect, in second of implementation, at least one target keyword is included for described in extremely Each component in few two components is respectively provided with an attribute, for describing second keyword of the attribute value, base In semanteme coupling rule, the semantic relation between at least two component is generated according at least one target keyword, Including：

Correlation between the second keyword value corresponding at least two component meets the semanteme When coupling the condition of specified by rules, according to taking for second keyword of each component of at least two component Value, determines the semantic relation.

Technical scheme according to embodiments of the present invention, realize by the keyword of attribute of the equipment based on description single component Automatically generate semantic relation, the semantic relation between component determined according to the value of attribute, can deep enough excavation semantic relation, The semantic relation of generation more comprehensively, accurately.

According to first aspect, the first of first aspect or second of implementation, in the third implementation, from described In the data at least one stage of the product life cycle of production system, at least one needed for running situation described in extraction and analysis Individual target keyword, including：

From the data of design phase of the production system and/or planning stage, extraction at least one target is closed Keyword.

Technical scheme according to embodiments of the present invention, realize by design phase of the equipment based on production system and/or rule The data in the stage of drawing automatically generate semantic relation.

According to first aspect, first, second or third kind of implementation of first aspect, in the 4th kind of implementation, Based on semanteme coupling rule, the semanteme between at least two component is generated according at least one target keyword and closed Before system, in addition to：

According to the analysis demand of the running situation generation semantic coupling rule.

Technical scheme according to embodiments of the present invention, semantic coupling can be automatically generated according to the analysis demand of running situation Rule, and carry out automatic generative semantics relation according to the semantic coupling rule generated so that for spy during the semantic relation of generation The analysis demand of fixed running situation, semantic relation is more targeted, further increases the validity of institute's generative semantics relation And reliability.

According to first aspect, the first, second, third of first aspect or the 4th kind of implementation, the 5th kind of realization side In formula, the semantic relation includes physical couplings, subordinate relation, and at least one of control planning.

Technical scheme according to embodiments of the present invention, realize more flexible, diversification semantic relation and automatically generate.

Second aspect, the embodiment of the present invention provide a kind of semantic relation generation equipment, and the equipment is used to generate a life The semantic relation between at least two components in production system, the semantic relation are used for a kind of fortune for analyzing the production system Market condition, including：

One target keyword extraction module, at least one stage for the product life cycle from the production system Data in, at least one target keyword needed for running situation described in extraction and analysis；

One semantic relation generation module, for regular based on semanteme coupling, according to the target keyword extraction module At least one target keyword of extraction generates the semantic relation between at least two component, wherein, the semanteme When coupling rule is used to provide to have the semantic relation between at least two component, at least one target keyword The condition that should meet.

According to second aspect, in the first implementation, the target keyword extraction module extraction it is described at least One target keyword includes being used for describing at least one first keyword of correlation between at least two component, institute Predicate justice relation generation module is specifically used for：

According to second aspect, in second of implementation, the target keyword extraction module extraction it is described at least Each component that one target keyword includes being directed at least two component is respectively provided with an attribute, for describing this One the second keyword of attribute value, the semantic relation generation module are specifically used for：

Technical scheme according to embodiments of the present invention, realize by the keyword of attribute of the equipment based on description single component Semantic relation is automatically generated, has fully ensured its validity and accuracy, the language between component is determined according to the value of attribute Adopted relation, can deep enough excavation semantic relation, the semantic relation of generation more comprehensively, accurately.

According to second aspect, the first of second aspect or second of implementation, in the third implementation, the mesh Mark keyword extracting module is specifically used for：

According to second aspect, first, second or third kind of implementation of second aspect, in the 4th kind of implementation, Also include semantic coupling rule generation module, for being based on semantic coupling rule in the semantic relation generation module, according to institute Before stating the semantic relation that at least one target keyword is generated between at least two component, according to the running situation The analysis demand generation semantic coupling rule.

According to second aspect, the first, second, third of second aspect or the 4th kind of implementation, the 5th kind of realization side In formula, the semantic relation of the semantic relation generation module generation includes physical couplings, subordinate relation, and control At least one of relation.

The third aspect, the embodiment of the present invention provide a kind of semantic relation generation equipment, and the equipment is used to generate a life The semantic relation between at least two components in production system, the semantic relation are used for a kind of fortune for analyzing the production system Market condition, the equipment include：

At least one memory, for storing computer instruction；

At least one processor, for calling the computer instruction, perform first aspect or any realization of first aspect The methods described that mode provides.

Fourth aspect, the embodiment of the present invention provide a kind of computer-readable medium, stored on the computer-readable medium There is computer instruction, the computer instruction makes the computing device first aspect or first party when being executed by processor Method described in any implementation in face.

Brief description of the drawings

The preferred embodiment of this method will be described in detail by referring to accompanying drawing below, make one of ordinary skill in the art more The above and other feature and advantage of this method are understood, in accompanying drawing：

Fig. 1 is shown to be carried out using ArcGIS pipelines data model (ArcGIS Pipeline Data Model, APDM) One example of the network topology of the water supply network of description；

Fig. 2 shows a kind of exemplary number used during the network topology that the water supply network shown in Fig. 1 is described using APDM According to structure；

Fig. 3 uses another exemplary when showing the network topology that the water supply network shown in Fig. 1 is described using APDM Data structure；

Fig. 4 shows an example of the water supply network logical topology chart of reconstruct in analysis software (EPANET)；

Fig. 5 shows a Petri Net example of semantic coupling process generation according to embodiments of the present invention；

Fig. 6 is the flow chart for the semantic relation generation method that one embodiment of the invention provides；

Fig. 7 is the structural representation that the semantic relation that one embodiment of the invention provides generates equipment；

Fig. 8 is the structural representation that the semantic relation that another embodiment of the present invention provides generates equipment；

Fig. 9 is the structural representation that the semantic relation that another embodiment of the invention provides generates equipment.

Reference numerals list：

P1、P2、P3：Pipeline section V1：Valve

21：Supply equipment class 22：Connect class

221：Diameter 222：Material

223：Connection type 31：Build data structure

32：Pipeline closest approach position data structure 33：Pipeline section data structure

311：Build name 312：Building type

313：Whether by people 321 are possessed：Build event id

331：Pipeline ring event id 332：Father station row event id

333：Upper station row event id 334：Upper station row coefficient of connection

335：Next station row event id 336：Next station row coefficient of connection

337：Reference model 338：Stand row name

338：Stand row ID

p1、p2、p3、p4、p5：Resource/behavior t1, t2, t3, t4：The state change of resource

601：From the data at least one stage of the product life cycle of the production system, fortune described in extraction and analysis At least one target keyword needed for market condition

602：Based on semanteme coupling rule, according at least one target keyword generate at least two component it Between semantic relation, wherein, the semantic coupling rule is used to providing having between at least two component described semantic close When being, condition that at least one target keyword should meet

71：Target keyword extraction module

72：Semantic relation generation module 83：Semanteme coupling rule generation module

90：Memory 91：Processor

Embodiment

In the embodiment of the present invention, from the analysis demand of a production system running situation, the production system is generated Semantic relation between component.Required semantic relation there may be difference when being analyzed for different running situation. Such as：When the leakage situation to water supply network is analyzed, more need consider pipeline between annexation, pipeline with The position relationship of water pump, valve etc.；And the Energy Expenditure Levels to water supply network may more need to consider when analyzing Correlation of the energy-dissipating devices such as water pump in whole water supply network between other devices in water supply process in flow.Cause This, the semantic relation of generation is directed to the running situation to be analyzed, it is ensured that the accuracy of running situation analysis.

, can be first from the production system product life cycle (Product Life during generative semantics relation Cycle, PLC) extracting data analysis production system running situation needed for target keyword；It is then based on semantic coupling rule Then, the semantic relation between the component of production system is generated according to the target keyword of extraction.For the running situation to be analyzed Target keyword is extracted, avoids extracting useless keyword, improves the efficiency of whole semantic relation generating process.The mesh of extraction Mark keyword is analyzed needed for the running situation, therefore ensure that precision of analysis.Semanteme coupling rule is used to advise The condition that target keyword should meet when having semantic relation between fixed at least two components, there is provided automatically generate semantic relation A kind of feasible program.

Below, in order to make it easy to understand, by the present embodiments relate to some descriptions, concept explained.Need Bright, these explanations are not construed as limiting the scope of the present invention.

1st, production system

One system that a production requirement is converted into final products and/or service can be considered a kind of production system.

2nd, component

Production system generally includes at least one component.By taking the production system in process industry field as an example, these production systems System generally may include executive module, control assembly, environment components.

By taking water supply network this production system as an example, executive module may include pump (pump) and valve (valve) etc.；Control group Part may include programmable logic controller (PLC) (Programmable Logic Controller, PLC) etc.；Environment components may include Cistern (tank) and pipeline (pipe) etc..

3rd, the product life cycle

Product life cycle refer to from demand analysis, product programming, product design, manufacture, test, deliver until maintenance, The whole process scrapped.

4th, the running situation of production system and in network system to the semantic description of production system.

To realize optimization, control and management to production system, it is necessary to analyze the running situation of production system.Point The data of analysis institute foundation may include the information of the component of production system, in the embodiment of the present invention, include the component of production system Between semantic relation information.

Information needed for analysis production system running situation is storable in network system, passes through the software in network system Running situation based on these information analysis production systems.The field data that network system collects production system (for example senses Data etc. of device collection), to production system send control command, submit analysis result etc. to the information/control system on upper strata.

In the past, the analysis to production system running situation depended on the warp of domain expert, engineer or staff Test.But with being continuously increased for production system complexity, analysis faces increasing challenge.Therefore, occur to production system The network system that system running situation is analyzed.

In order to reach the purpose of Network System Analysis running situation, a basic requirement is to need to allow network system to know Actual conditions of the production system in actual environment.To realize the demand, the mark to production system need to be established in network system Standardization describe, with based on it is this standardization description by the actual environment of production system be converted into network system it will be appreciated that language Justice description.

Specifically, the property of the component can be known in network system by the keyword of the component for describing production system The information such as energy, parameter.In the embodiment of the present invention, network system can also know the semantic relation between the different components of production system (alternatively referred to as " virtual semantic relation "), and the running situation of production system is entered based on the semantic relation between the component known Row analysis.

5th, semantic relation

The semantic relation between production system component described in network system may include but be not limited to following relationship：Physics Annexation, control planning, subordinate relation etc..

In the embodiment of the present invention, when the running situation that production system is carried out using network system is analyzed, network system System not only knows performance, parameter of each system component etc., the semantic relation being also known between component.

Semantic relation during due to analysis demand for different running situation between system component of interest also may be used It can change, therefore, in the embodiment of the present invention, for language between component corresponding to the analysis demand acquisition of different running situations Adopted relation.

6th, description of the production system in each stage of product life cycle

1) description in GIS-Geographic Information System (Geographical Information System, GIS) to production system

As a kind of example, network system can the information based on the production system stored in GIS, the operation to production system Situation is analyzed.Therefore exemplified by this sentences the information of the production system stored in GIS, the describing mode of production system is carried out Explanation.Production system is described using data model for generalized information system, and different production systems is described and used Data model may also be different.When being described for above-mentioned water supply network this production system, generalized information system is for example Use production of the ArcGIS ArcGIS pipelines data model (ArcGIS Pipeline Data Model, APDM) to water supply network At least one stage of product life cycle is described.More specifically, Fig. 1 shows the water supply network being described using APDM Network topology an example.When Fig. 2 and Fig. 3 shows the network topology that the water supply network shown in Fig. 1 is described using APDM The two kinds of example data structures used.Here in connection with Fig. 1 to Fig. 3 to describing the specific descriptions mode of water supply network using APDM Illustrate.

To describe the network topology of water supply network as shown in Figure 1, on the one hand, need to be pointed to two pipes in water supply network Equipment at line, the tie point that certain transition be present is described, and the equipment is, for example, pump in water supply network, valve etc..Its In, pipeline, pump, valve are the components of this production system of water supply network.In APDM, entered using connection (Fitting) class This description of row, the data structure shown in Fig. 2 are to connect the data structure of (Fitting) class.As shown in Fig. 2 water supply network In the connection class 22 of supply equipment class (WaterFacility) 21 can be specifically defined the diameter (Diameter) 221 of component, group The material (Material) 222 of part and the connection type formed between two pipelines of tie point residing for the component (JointType)223.Connection type 223 specifically may include to bend, intersect, couple.If connection type is coupling, anticipate Taste has formed new structure by the simply connection of two small depot sidings, and the characteristic of this two pipelines be it is completely the same, therefore Two pipelines should be considered as a pipeline.Pass through the connection type defined in connection class so that positioned at different connection classes Component at the tie point of two pipelines of type can be described by same connection class data structure, therefore reduce network The quantity of feature class, GIS performances can be improved.

On the other hand, in the embodiment of the present invention, the geographical position also to the equipment such as pipeline in water supply network and pump and valve The relation of confidence breath is described, and Fig. 3 shows that being used to describe the geographical position of pipeline and equipment in water supply network in APDM believes The example data structure of the relation of breath.As shown in figure 3, involved data structure includes building data structure (Structure) 31, pipeline closest approach position data structure (StructureLocation) 32, pipeline section data structure (StationSeries)33。

Wherein, build data structure 31 to be used to describe the geographical position point nearest apart from pipeline, specifically, in water supply network Each equipment such as pump or valve is a geographical position point, and the building data structure is used to describe such as pump and valve on pipeline Geographical position of equipment etc..More specifically, the position vertex type built in data structure 31 is U.S. environment data research institute (Environmental Systems Research Institute, ESRI) geodata point, it is false if having M values, if there is Z Value is then true.As shown in figure 3, building data structure 31 includes：Name 311 is built, it is arranged to ESRI field data class type-words Accord with serial type；Building type 312, building type 312 is arranged to unknown；Whether by people possess 313, option Yes/No is arranged to It is no.

Pipeline section data structure 33 is used for the position for describing the beginning and end of each pipeline section, wherein, pipeline section is in pipeline One section, pipeline is interconnected to constitute by multiple pipeline sections, therefore the position of the beginning and end based on each pipeline section can calculate Go out the position of pipeline.Specifically, the location type in pipeline section data structure 33 is ESRI geodatas point, is vacation if having M values, It is true if having Z values.As shown in figure 3, pipeline section data structure 33 includes：Pipeline ring event identifier (Identity, ID) 331, The ID uses ESRI field data type World Wide IDs；Father station row event id 332, the ID is using the ESRI field datas type whole world ID；Upper station row event id 333, the ID uses ESRI field data type World Wide IDs；Upper station row coefficient of connection 334, It is arranged to ESRI field data type double-precision numbers；Next station row event id 335, the ID uses ESRI field data classes Type World Wide ID；Next station row coefficient of connection 336, it is arranged to ESRI field data type double-precision numbers；Reference model 337, Reference model 337 is arranged to continuous model；Stand row name 338, the station row name 338 uses ESRI field data type character strings； Stand and arrange ID 339, the ID uses ESRI field data Type Integers.

Pipeline section closest approach position data structure 32 be used to describing pipeline and the geographical position nearest from pipeline point (such as pump or The equipment such as valve).Specifically, the position of pipeline, i.e. route building position can be calculated based on the information described in pipeline section data structure (Route Structure Location) is put, can be come based on the information described in building data structure and pipeline section data structure The relation of run of designing and geographical position point, i.e. station row situation of building (Series Structure Location).More specifically Ground, the location type in pipeline section closest approach position data structure 32 is ESRI geodatas point, is false if having M values, if there is Z values It is then true.As shown in figure 3, pipeline section closest approach position data structure 32 includes：Event id 321 is built, the ID is showed using ESRI Field data type World Wide ID.

Above-mentioned connection class and geographical location information, which are stored in, is used for the keyword for describing water supply pipe net system in GIS.

2) description of Petri net (Petri Net) process model to production system

Petri Net process models include place (PLACE) and transition (TRANSITION) two kinds of nodes.To worked When journey is described, wherein conclusive component is resource and behavior (activity).Resource and behavior can map to Petri Place in Net, the transition that the state change of resource can be mapped in Petri Net.With automatic guided vehicle Exemplified by (Automatic Guided Vehicle, AGV) picking, AGV has four kinds of state P1-P4 (i.e. place), and wherein P1 refers to AGV is idle, and P2 refers to goods on buffered station and waits transmission, and the AGV pickings that P3 refer to are sent to warehouse, and P4 refers to the storage platform of goods unloading.Separately The two kinds of transition of outer definable, T1 refer to AGV and take station to get in stocks formal matter part, and T2 refers to AGV unloading goods in storage platform event.

Then whole flow process can be described as with Petri Net：

|--<-P1<-----|

||

P2→T1-->P3-->T2-->P4

Network system can be based on when the course of work to production system is described, such as when being operated flow management Analyzed using Petri Net process models.

7th, target keyword

The different qualities of production system are described using different keywords for network system, due to for different points The characteristic of analysis demand production system of interest may be different, for the target keyword required for different analysis demands May be also different.Therefore, when analyzing production system, target keyword is determined according to specific analysis demand, the mesh It is one or more of keyword used in production system described in network system to mark keyword.

For example, when carrying out leak detection to water supply network as shown in Figure 2, network system need to be based on the confession stored in GIS The physical topology information of grid set up network system it will be appreciated that water supply network logical topology, with logical topology base Hydraulic model is calculated on plinth, has leakage so as to analyze where.Now should be using Fitting and Location as to be extracted Target keyword, to obtain the tie point being located at two pipelines in water supply network, certain transition being present described by connection class The pump valve at place, and geographical position point and the pipeline described by pipeline section relational data structure and the pump valve nearest from pipeline.

For another example during the workflow of the analysis software production system of network system, due to the state change of resource It is the key of analysis, therefore, transition can be defined as target keyword.

8th, semantic coupling rule

When semanteme coupling rule between at least two components of definition for possessing specific semantic relation in actual environment The required relation met, i.e.,：For establishing actual relationship of at least two components in actual environment with network system to life Mapping between the semantic relation that production system is of interest when being analyzed, understands.Therefore during analysis demand difference, semanteme coupling rule It is then generally also different.Semanteme coupling rule is, for example, to be defined by domain expert, senior staff according to analysis demand, or Person is automatically analyzed from the regular collection of corresponding application field according to analysis demand by the information/control system on upper strata, chosen Obtain.

Exemplified by the analysis demand of leak detection is carried out to water supply network as shown in Figure 2, it is based in network system in GIS The physical topology information of the water supply network of storage establish network system it will be appreciated that water supply network logical topology when, definable Following semantic coupling rule：

Rule one：If the connection type of the tie point between two pipelines is bilateral, two pipelines are considered as whole Body；If the connection type of the tie point between two pipelines is threeway (tee), it is considered as between two pipelines with one Node；

Rule two：If the geographical position of a valve overlaps with a pipeline, it is considered as the valve and is connected with each other with the pipeline, and Think that the valve controls the break-make of the pipeline, correspondingly, in the logical topology chart of generation, the pipeline is divided into interconnection Two new pipelines.For example, valve V1 geographical position overlaps (as shown in Figure 1) with pipeline section P1, then will pipe in logical topology chart Section P1 is divided into pipeline section P2 and P3 as shown in Figure 4, and wherein P2 is connected with V1, and V1 is connected to P3.

, it is necessary to which pipe network physical topological structure is converted into component logic topology when doing leak detection in water supply pipe net system Structure, calculates hydraulic model on the basis of logical topology, and analysis has leakage in where.

Rule, is traveled through repeatedly to the whole water supply network shown in Fig. 1 based on more than：

1) target keyword " Fitting " and " Location " of each component are extracted；

2) according to Keywords matching algorithm, the current semantics relation between component is determined, between for example, current each component Connection type and position relationship；

3) whether it is that the geographical position of " bilateral " or three links: link of trade, travel and post and valve is based on the connection type between current each component It is no to be overlapped with pipeline section, judge whether to meet above-mentioned semantic coupling rule one and/or rule two, if satisfied, according to respective rule existing Coupled in logical topology, the semantic relation between formation component.

Traveled through based on more than, such as logical topology as shown in Figure 4 can be established, wherein Fig. 4 is shown in analysis software (EPANET) example of the water supply network logical topology chart of reconstruct in.

In addition, in the course of work of the network system based on Petri Net planned production systems, for two behavior A1 and A2, such as definition have following semantic coupling rule：

Rule one：If A1 output is A2 input, the relation for meaning A1 and A2 is supervention, i.e. A1 and A2 are on time Between order successively occur；

Rule two：If A1 input is identical with A2 input, the relation for meaning A1 and A2 is concurrent；

Rule three：If A1 output is identical with A2 output, the relation for meaning A1 and A2 is synchronous.

When performing semantic coupling, for the resource mapped in PLACE and behavior, the money mapped in TRANSITION is extracted Source state, i.e.,：The target keyword of extraction assembly.Afterwards, according to domain knowledge such as resource status matching algorithm, it is determined that different The relation of the input and output of behavior, so as to the relation of the input of the different behaviors according to defined in semantic coupling rule, output Mapping between semantic relation, generates the semantic relation between different behaviors, for example, A1 and A2 be supervention, it is concurrent or synchronous Semantic relation.

The semantic coupled relation based on more than, such as Petri Net examples as shown in Figure 5 can be generated, wherein figure 5 show One Petri Net example of semantic coupling process generation according to embodiments of the present invention.In Figure 5, p1, p2, p3, p4, p5 For representing resource/behavior, t1, t2, t3, t4 are used for the state change for representing resource, and it, which directly can reflect in flow, provides The logical relation in source/between behavior and resource status modification.Using Petri Net as shown in Figure 5, workflow can be analyzed In whether there is failure, such as whether resource/behavior in Fig. 5 and the operation flow direction of resource status uniformly judge the work Make to whether there is deadlock in flow, so as to be solved for the failure in found out workflow.

Below, the embodiment of the present invention is described in detail with reference to accompanying drawing.

Fig. 6 is the flow chart for the semantic relation generation method that one embodiment of the invention provides.This method is used to generate one The semantic relation between at least two components in individual production system.As shown in figure 1, the flow comprises the following steps：

601st, from the data at least one stage of the product life cycle of the production system, fortune described in extraction and analysis At least one target keyword needed for market condition；

The input information of the step is the title of at least one target keyword to be extracted, output be extracted with The information of the component/component relation for the production system that at least one target keyword of the name-matches is characterized.

Target keyword can be extracted from the Software tool or document of system design stage in the step, the target keyword For characterizing the key performance of component influenceed for present analysis demand, factor for example, interested or important it is System variable etc..

Wherein, in process industry, the simulation and optimization of to the effect that handling process of interest, factor example interested It such as may include operation characteristic, the work schedule of each component, specifically may include such as operating parameter, configuration parameter, system performance； Important system variable for example may include application message, and the value of the system variable can help application program to carry out in advance system environments Survey or infer.

Exemplified by carrying out the application of leak detection of water supply network, important system variable for example may include flow, if one The inflow flow of individual pipeline is identical with outflow flow, can determine that leakage is not present in the pipeline.For another example for paying close attention to feed pipe The application of control planning in net between component, important system variable for example may include can be according between its determination component The geographical position of the variable of control planning, for example, component, wherein valve and pipeline with same position be identified as with Control planning, i.e.,：Assert that the valve is controllable and be located at cut-offfing for the pipeline of same position with it.Normally, target keyword example Geographical position or resource status (alternatively referred to as component states) such as component, wherein resource status may include the operation of component The state parameters such as parameter, operating flux, pressure and liquid level.

In this step, can travel through the product life cycle of the production system with target keyword some/some ranks Section, for example, planning stage of production system and/or design phase etc., to search the relation between component.Pass between component System may include to characterize the logical relation or component of the sequencing of the time or space between component and component in the process of running Physical couplings between component etc..Wherein, the planning stage of production system and/or design phase for example may include relation Data base management system (Relational Database Management System, DBMS), spatial database and XML texts At least one in system architecture in part, the internal information of planning stage and/or design phase to production system are examined Rope and the concrete operations for extracting target keyword, such as query language (Simple Protocol And Rdf Query can be used Language, SPARQL) realize.

, can be from user or external equipment to the executive agent of the semantic relation generation method (i.e. in the step：Semanteme closes System's generation equipment) title of target keyword to be extracted is inputted, and equipment is generated from storage with various passes by semantic relation In the component of production system and/or the information of component relation of keyword description, the specifying information described by target keyword is extracted.

In addition it is also possible to divided by semantic relation generation equipment by the operation data to production system or storage information Analysis obtains the title of target keyword to be extracted, and wherein operation data may include the data obtained during assembly operating, deposit Storage information may include for the information of the advance static configuration of component.For example, semantic relation generates equipment knows all components through retrieval With position (LOCATION) attribute, and which part component has identical LOCATION, then is closed LOCATION as target Keyword, the present embodiment are not limited this.

602nd, based on semanteme coupling rule, according at least one target keyword generate at least two component it Between semantic relation, wherein, the semantic coupling rule is used to providing having between at least two component described semantic close When being, condition that at least one target keyword should meet.

The input information of the step is at least one target keyword institute table that semantic coupling rule and step 601 are extracted The information of component/component relation of sign, output information are the semantic relations between at least two components.

Semanteme coupling rule is, for example, to be defined and inputted to language according to demand by domain expert, senior staff Adopted relation generates equipment, or by upper strata information/control system according to demand from the regular collection of corresponding application field from Dynamic analysis, choose acquisition.

In this step, semantic relation generation equipment extracted according to semantic coupling rule and step 601 at least two The information for component/component relation that the target keyword of component is characterized, this at least two groups are obtained in present analysis demand The semantic relation of part.For example, a specific example of semanteme coupling rule can be：If two components are electrical conduction relation, Two components are then thought of as a new integrated package, i.e. semantic relation is same integrated package.Then in step client, base The information of the component characterized in the target keyword that step 601 is extracted/establishment relation, determine be between at least two components It is no electrical conduction relation to be present, if in the presence of, export at least two component be same integrated package semantic relation.

Wherein, information generative semantics of the component/component relation that foundation target keyword is characterized in actual environment closes System, both may include polymerizeing to the relation in actual environment in as above example, can also be according to specific semantic coupling rule Relation in actual environment is adjusted, and/or increases new relation on the basis of the relation in actual environment.The present invention Embodiment is not restricted to this.

In the technical scheme of the embodiment of the present invention, without artificially determining that the semantic of inter-module closes by Application Engineers System, by semantic relation generate equipment automatically from such as production system extraction assembly target keyword, based on target critical Word and the semanteme of acquisition couple regular generative semantics relation.I.e.：Semantic relation generation method energy base according to embodiments of the present invention In the semantic coupling rule of acquisition, network system semantic relation to understand is provided using known module information.

For example, stored in network system be used for connection is included to the keyword that production system is described (CONNECTING), it characterizes whether at least two components are connected in actual environment.Difference is being carried out to the production system Running state analysis when, " connection " can be used as target keyword, with according to the existing link information, there is provided meet analysis The semantic relation of demand.

For example, when it is the pipeline of water supply network that above-mentioned production system, which is water supply network, component A, B, C, D, E, if needing The reason for analyzing pipeline A overflows, then it may be determined as follows semantic coupling rule：

If component A (hereinafter referred to as A) and component B (hereinafter referred to as B) has annexation, mean that liquid can flow from A To B, vice versa；

If A and B with annexation, mean that liquid can be from annexation and B and component C (hereinafter referred to as C) A flows to C, and vice versa；

According to the semanteme coupling rule and component A, B, C, D, E target keyword CONNECTING described by component A, B, C, D, E information, it is capable of feedback component A, B, C, D, E pipeline flow value, the reason for so as to analyze pipeline A overflows.

For another example if the information/control system on upper strata needs to obtain the system topological of water supply network, based on target critical Word " connection " can feed back whole hardware level pipeline section annexations.

Further, in the semantic relation generation method of above-described embodiment, at least one target keyword includes being used for At least one first keyword of correlation between at least two components is described, based on semanteme coupling rule, according at least one Individual target keyword generates the semantic relation between at least two components, including：

When at least one first keyword meets the condition of the semantic coupling specified by rules, according at least one the The correlation of one keyword description determines the semantic relation.Specifically, target keyword can be used for characterizing at least Semantic relation between two components.For example, target keyword is " connection (CONNECTING) ", and target keyword is specific The content-defined connecting object of the component, such as component A target keyword CONNECTING define A and are connected with B.

Further, in the semantic relation generation method of above-described embodiment, at least one target keyword includes being directed to Each component at least two components is respectively provided with an attribute, for describing second keyword of the attribute value, Based on semanteme coupling rule, the semantic relation between at least two components is generated according at least one target keyword, including：

Correlation between the second keyword value corresponding at least two components meets semantic coupling rule During the condition of defined, according to the value of the second keyword of each component of at least two components, semantic relation is determined.

Specifically, target keyword can be used for characterizing the attribute of single component.When target keyword is used to characterize list During the attribute of individual component, such as target keyword is geographical position, can root according to the semantic relation generation method of above-described embodiment Value according to the target keyword of at least two components is the relation between geographical position, according to corresponding semantic coupling rule really Determine the semantic relation between component.Such as：Semanteme couples rule：If component A and component B geographical position coincide, depending on For component A and B physical connections.

According to the technical scheme of above-described embodiment, different types of target keyword can be based on, it is more neatly automatic Generative semantics relation.

The another aspect of the embodiment of the present invention additionally provides a kind of semantic relation generation equipment.

Fig. 7 is the structural representation that the semantic relation that one embodiment of the invention provides generates equipment.The equipment is used to give birth to Semantic relation between at least two components in a production system, the wherein semantic relation are used to analyze the production system A kind of running situation of system.As shown in fig. 7, the equipment includes：

One target keyword extraction module 71, at least one stage for the product life cycle from production system In data, at least one target keyword needed for extraction and analysis running situation；

One semantic relation generation module 72, for based on semanteme coupling rule, being carried according to target keyword extraction module At least one target keyword taken generates the semantic relation between at least two components, wherein, semanteme coupling rule is used to advise When there is semantic relation between fixed at least two components, condition that at least one target keyword should meet.

Specifically, the input information of target keyword extraction module 71 is the name of at least one target keyword to be extracted Claim, output is component/group of the production system characterized with the name-matches at least one target keywords that is being extracted The information of part relation.As shown in fig. 7, the output of target keyword extraction module 71 is defeated for one of semantic relation generation module 72 Enter.Another input of target keyword extraction module 71 is semantic coupling rule, semanteme coupling rule be, for example, by domain expert, Senior staff is defined and inputted to semantic relation according to demand generates equipment, or information/control system by upper strata System automatically analyzes from the regular collection of corresponding application field, chooses and obtain according to demand.The basis of semantic relation generation module 72 The semantic coupling rule of input and the target keyword institute table of at least two components inputted by target keyword extraction module 71 The information of component/component relation of sign, obtain the semantic relation of at least two component in present analysis demand.

The detailed process of semantic relation generation equipment generative semantics relation provided in an embodiment of the present invention is implemented with the present invention The semantic relation generation method of example is identical, therefore here is omitted.

Further, in the semantic relation generation equipment of above-described embodiment, what target keyword extraction module 71 extracted At least one target keyword includes at least one first keyword for correlation between at least two components of description, language Adopted relation generation module 72 is specifically used for：

When at least one first keyword meets the condition of semantic coupling specified by rules, closed according at least one first The correlation of keyword description determines semantic relation.

Further, in the semantic relation generation equipment of above-described embodiment, what target keyword extraction module 71 extracted Each component that at least one target keyword includes being directed at least two components is respectively provided with an attribute, for describing this One the second keyword of attribute value, semantic relation generation module 72 are specifically used for：

Correlation between the second keyword value corresponding at least two components meets semantic coupling rule and advised During fixed condition, according to the value of the second keyword of each component of at least two components, semantic relation is determined.

Technical scheme according to embodiments of the present invention, realize by the keyword of attribute of the equipment based on description single component Semantic relation is automatically generated, has fully ensured its validity and accuracy, the language between component is determined according to the value of attribute Adopted relation, can deep enough excavation semantic relation, the semantic relation of generation more comprehensively, accurately..

Further, in the semantic relation generation equipment of above-described embodiment, target keyword extraction module 71 is specifically used In：

Further, in the semantic relation generation equipment of above-described embodiment, in addition to semantic coupling rule generation module 83, as shown in Figure 8.

Fig. 8 is the structural representation that the semantic relation that another embodiment of the present invention provides generates equipment.Shown in Fig. 8 In semantic relation generation equipment, semanteme coupling rule generation module 83 is used to be based on semantic couple in semantic relation generation module 72 Rule, before the semantic relation between at least two components is generated according at least one target keyword, according to running situation Analysis demand generative semantics coupling rule.

Further, in the semantic relation generation equipment of above-described embodiment, the language of the generation of semantic relation generation module 72 Adopted relation includes physical couplings, subordinate relation, and at least one of control planning.

Fig. 9 is the structural representation that the semantic relation that another embodiment of the invention provides generates equipment.The equipment is used for The semantic relation between at least two components in a production system is generated, the wherein semantic relation is used to analyze the production A kind of running situation of system.As shown in figure 9, the equipment includes：

At least one memory 90, for storing computer instruction；

At least one processor 91, in the data at least one stage of the product life cycle from production system, At least one target keyword needed for extraction and analysis running situation；Based on semanteme coupling rule, closed according at least one target Semantic relation between keyword at least two components of generation, wherein, semanteme coupling rule is used between at least two components of regulation During with semantic relation, condition that at least one target keyword should meet.

Further, in the semantic relation generation equipment of above-described embodiment, at least one target keyword includes being used for At least one first keyword of correlation between at least two components is described, processor 91 is specifically used for：

Further, in the semantic relation generation equipment of above-described embodiment, at least one target keyword includes being directed to Each component at least two component is respectively provided with an attribute, and one second for describing the attribute value is crucial Word, processor 91 are specifically used for：

Correlation between the second keyword value corresponding at least two components meets semantic coupling rule During the condition of defined, according to the value of second keyword of each component of at least two components, it is determined that semantic close System.

Technical scheme according to embodiments of the present invention, realize by the keyword of attribute of the equipment based on description single component Semantic relation is automatically generated, has fully ensured its validity and accuracy, and improve flexibility.

Further, in the semantic relation generation equipment of above-described embodiment, the processor 91 is specifically used for：

From the data of design phase of production system and/or planning stage, at least one target keyword is extracted.

Further, in the semantic relation generation equipment of above-described embodiment, processor 91 is additionally operable to：Based on semantic coupling Normally, before the semantic relation between at least two components is generated according at least one target keyword, according to running situation The analysis demand generation semantic coupling rule.

Technical scheme according to embodiments of the present invention, semantic coupling can be automatically generated according to the analysis demand of running situation Rule, and carry out automatic generative semantics relation according to the semantic coupling rule generated, further increase institute's generative semantics relation Validity and reliability.

Further, above-described embodiment semantic relation generation equipment in, semantic relation include physical couplings, from Category relation, and at least one of control planning.

The another further aspect of the embodiment of the present invention additionally provides a kind of computer-readable medium, is deposited on the computer-readable medium Computer instruction is contained, the computer instruction provides computing device any embodiment of the present invention when being executed by processor Semantic relation generation method.

In addition, the embodiment of the present invention also provides a kind of computer-readable recording medium for being stored with computer program instructions, The computer program instructions are used to perform the semantic coupling process according to any embodiment of the present invention.

Term as used herein "and/or" includes any of one or more relational languages enumerated or all combinations. In addition, singulative "one", " one ", " described " should be interpreted that " at least one ", therefore may also include multiple similar entities, separately Except clearly stating.Further it is appreciated that term "comprising", " comprising ", " containing " and/or " including ", use The feature, operation, entirety, step, operation, element and/or part be present in indicating, but do not exclude the presence of or separately have one or Other multiple features, operation, entirety, step, operation, element, part and/or its combination.Some features it is mutually different from It is described in category claim, but this does not imply that these measures can not be applied in combination, to reach effect of optimization.Computer Program can be stored/distributed on the medium of suitable non-transient, such as together with other hardware or as hardware a part and On the optical storage medium or solid state medium of offer, internet or other wired or wireless communication systems etc. can also be passed through Other forms distribution.

The preferred embodiment of this method is the foregoing is only, not limiting this method, all essences in this method God any modification, equivalent substitution and improvements made etc., should be included within the protection domain of this method with principle.

Claims

1. a kind of semantic relation generation method, methods described is used between at least two components in one production system of generation Semantic relation, it is characterised in that the semantic relation is used for a kind of running situation for analyzing the production system, methods described bag Include：

From the data at least one stage of the product life cycle of the production system, running situation institute described in extraction and analysis At least one target keyword needed；

Based on semanteme coupling rule, the semanteme between at least two component is generated according at least one target keyword Relation, wherein, it is described when the semantic coupling rule is used to provide to have the semantic relation between at least two component The condition that at least one target keyword should meet.

2. the method as described in claim 1, it is characterised in that at least one target keyword includes described for describing At least one first keyword of correlation between at least two components, based on semanteme coupling rule, according to described at least one Individual target keyword generates the semantic relation between at least two component, including：

When at least one first keyword meets the condition of the semantic coupling specified by rules, according to described at least one The correlation of individual first keyword description determines the semantic relation.

3. the method as described in claim 1, it is characterised in that at least one target keyword is included for described at least Each component in two components is respectively provided with an attribute, for describing second keyword of the attribute value, is based on Semanteme coupling rule, the semantic relation between at least two component, bag are generated according at least one target keyword Include：

Correlation between the second keyword value corresponding at least two component meets the semantic coupling During the condition of specified by rules, according to the value of second keyword of each component of at least two component, really The fixed semantic relation.

4. the method as described in any one of claims 1 to 3, it is characterised in that from the product life cycle of the production system At least one stage data in, at least one target keyword needed for running situation described in extraction and analysis, including：

From the data of design phase of the production system and/or planning stage, at least one target keyword is extracted.

5. the method as described in any one of Claims 1-4, it is characterised in that based on semanteme coupling rule, according to it is described extremely Before a few target keyword generates the semantic relation between at least two component, in addition to：

6. the method as described in any one of claim 1 to 5, it is characterised in that the semantic relation include physical couplings, Subordinate relation, and at least one of control planning.

7. a kind of semantic relation generates equipment, the equipment is used between at least two components in one production system of generation Semantic relation, it is characterised in that the semantic relation is used for a kind of running situation for analyzing the production system, including：

One target keyword extraction module (71), at least one stage for the product life cycle from the production system Data in, at least one target keyword needed for running situation described in extraction and analysis；

One semantic relation generation module (72), for regular based on semanteme coupling, according to the target keyword extraction module (71) at least one target keyword of extraction generates the semantic relation between at least two component, wherein, it is described When semanteme coupling rule is used to provide to have the semantic relation between at least two component, at least one target is closed The condition that keyword should meet.

8. equipment as claimed in claim 7, it is characterised in that the target keyword extraction module (71) extraction it is described extremely A few target keyword includes being used for describing at least one first keyword of correlation between at least two component, The semantic relation generation module (72) is specifically used for：

9. equipment as claimed in claim 7, it is characterised in that the target keyword extraction module (71) extraction it is described extremely Each component that a few target keyword includes being directed at least two component is respectively provided with an attribute, for describing One the second keyword of the attribute value, the semantic relation generation module (72) are specifically used for：

10. the equipment as described in any one of claim 7 to 9, it is characterised in that target keyword extraction module (71) tool Body is used for：

11. the equipment as described in any one of claim 7 to 10, it is characterised in that also include semantic coupling rule generation module (83), for being based on semantic coupling rule in the semantic relation generation module (72), according at least one target critical Before word generates the semantic relation between at least two component, institute's predicate is generated according to the analysis demand of the running situation Justice coupling rule.

12. the equipment as described in any one of claim 7 to 11, it is characterised in that the semantic relation generation module (72) is raw Into the semantic relation include physical couplings, subordinate relation, and at least one of control planning.

13. a kind of semantic relation generates equipment, the equipment is used between at least two components in one production system of generation Semantic relation, it is characterised in that the semantic relation is used to analyze a kind of running situation of the production system, the equipment Including：

At least one memory (90), for storing computer instruction；

At least one processor (91), for calling the computer instruction, perform claim requires the side described in 1 to 6 any one Method.

14. a kind of computer-readable medium, it is characterised in that computer instruction, institute are stored with the computer-readable medium Computer instruction is stated when being executed by processor, makes the method any one of the computing device claim 1 to 6.