Modelica-based model atlas construction method and system

Abstract

The invention provides a Modelica-based model atlas construction method and system. The method comprises the steps of obtaining a hierarchical structure tree of an industrial system; obtaining knowledge management attribute fields of a plurality of Modelica numerical models in a Modelica numerical model library; and according to the vocabulary groups corresponding to the knowledge management attribute fields of the plurality of Modelica numerical models and the vocabulary groups corresponding to the plurality of nodes on the hierarchical structure tree, performing search positioning and mapping association of the plurality of Modelica numerical models on the hierarchical structure tree by adopting a vocabulary correlation algorithm so as to form a Modelica numerical model map based on the hierarchical structure tree. According to the method, search positioning and mapping association of a plurality of Modelica numerical models on a hierarchical structure tree are carried out by adopting a vocabulary correlation algorithm, so that the problems that in the prior art, complicated industrial system numerical models are not managed in an effective classification arrangement mode, and a constructed model library faces numerical model repetition, omission, contradiction and the like are solved.

Description

Technical Field

[0001] The present application belongs to the technical field of model graph construction, and specifically relates to a model graph construction method and system based on Modelica. Background Art

[0002] Modelica is an object-oriented, equation-based, and reusable hierarchical component modeling language proposed by the European Simulation Association (EUROSIM) in 1997. It is an extremely important model expression standard in the current industry and is widely used in aerospace, aviation, vehicles, nuclear energy, and other fields. Modelica-based modeling and simulation technology uses mathematical and physical modeling to build a numerical model library corresponding to the industrial system. It can express the multi-professional physical processes of the industrial system, such as thermal fluid, machinery, instrumentation and control, and electrical, as well as the structural topology of the industrial system and the coupling relationship between multiple levels and multiple devices. Therefore, the numerical model based on Modelica has the characteristics of many types of models, large numbers, and complex hierarchical relationships.

[0003] However, there is no effective way to classify and organize such complex numerical models of industrial systems to manage them, and the constructed model library will face problems such as duplication, omissions and contradictions in numerical models. Summary of the invention

[0004] In view of this, the embodiments of the present application are committed to providing a model graph construction method and system based on Modelica, which searches, locates and maps multiple Modelica numerical models on a hierarchical structure tree by adopting a vocabulary relevance algorithm, so as to solve the problem that there is no effective classification and organization method to manage the complex industrial system numerical models in the prior art, and the constructed model library faces problems such as duplication, omission and contradiction of numerical models.

[0005] The first aspect of the present application provides a method for constructing a model graph based on Modelica, and the method for constructing a model graph based on Modelica includes: obtaining a hierarchical structure tree of an industrial system, wherein there are multiple simulation objects on the hierarchical structure tree of the industrial system, each simulation object corresponds to a node, each node is configured with a node name, and the node name is expressed in the form of a vocabulary group; obtaining knowledge management attribute fields of multiple Modelica numerical models in a Modelica numerical model library; and searching, locating and mapping associations of multiple Modelica numerical models on the hierarchical structure tree using a vocabulary correlation algorithm according to the vocabulary groups corresponding to the knowledge management attribute fields of the multiple Modelica numerical models and the vocabulary groups corresponding to the multiple nodes on the hierarchical structure tree, so as to form a Modelica numerical model graph based on the hierarchical structure tree.

[0006] In a specific embodiment of the present application, the above-mentioned search, positioning and mapping association of multiple Modelica numerical models on the hierarchical structure tree is performed using a vocabulary correlation algorithm based on the vocabulary groups corresponding to the knowledge management attribute fields of multiple Modelica numerical models and the vocabulary groups corresponding to the multiple nodes on the hierarchical structure tree to form a Modelica numerical model graph based on the hierarchical structure tree, including: according to the vocabulary groups corresponding to the knowledge management attribute fields of multiple Modelica numerical models and the vocabulary groups corresponding to the multiple nodes on the hierarchical structure tree, using a vocabulary correlation algorithm to obtain the correlation scores of the multiple Modelica numerical models and the multiple nodes in the hierarchical structure tree; determining the corresponding Modelica numerical models for mapping and associating the multiple nodes in the hierarchical structure tree according to the correlation scores to form a Modelica numerical model graph based on the hierarchical structure tree.

[0007] In a specific embodiment of the present application, the vocabulary groups corresponding to the knowledge management attribute fields of the plurality of Modelica numerical models and the vocabulary groups corresponding to the plurality of nodes on the hierarchical structure tree are used to obtain the correlation scores of the plurality of Modelica numerical models and the plurality of nodes in the hierarchical structure tree using a vocabulary correlation algorithm, including: sequentially extracting the model name and the vocabulary group corresponding to the modeling object of the knowledge attribute management field of the i-th Modelica numerical model in the plurality of Modelica numerical models to be mapped, associated or searched and located, as the A-group vocabulary group, the A-group vocabulary group being composed of a single word or two words; sequentially extracting the vocabulary group corresponding to the node name configured for the j-th node in the plurality of nodes on the hierarchical structure tree, as the B-group vocabulary group, the B-group vocabulary group being composed of a plurality of synonyms or near synonyms, i and j being positive integers; selecting the A-group vocabulary 1 of the A-group vocabulary group and the B-group vocabulary 1 of the B-group vocabulary group for comparison, the comparison being the commonality between the A-group vocabulary 1 and the B-group vocabulary 1 in terms of the arrangement of words and characters. The characters of the vocabulary include single Chinese characters, single capital letters and single English words; the correlation between Group A vocabulary 1 and Group B vocabulary 1 is determined according to the commonality of the comparison; the correlation between Group A vocabulary 1 and Group B vocabulary 2 to Group B vocabulary n of Group A vocabulary group is determined in turn, and an array consisting of several vocabulary correlation values ​​is obtained, and the maximum value of the array is selected as the correlation score between Group A vocabulary 1 and Group B vocabulary group; if Group A vocabulary group has only one vocabulary, then the correlation score between Group A vocabulary 1 and Group B vocabulary group is the correlation score between Group A vocabulary group and vocabulary B; if Group A vocabulary group has multiple vocabulary, then the correlation between Group A vocabulary m of Group A vocabulary group and Group B vocabulary 1 to Group B vocabulary n of Group B vocabulary group is determined in turn, and the correlation score between Group A vocabulary m of Group A vocabulary group and Group B vocabulary group is obtained, and then the maximum value of the correlation scores between Group A vocabulary 1 to Group A vocabulary m and Group B vocabulary group is selected as the correlation score between Group A vocabulary group and vocabulary B, and m and n are both positive integers.

[0008] In a specific embodiment of the present application, the correlation between Group A vocabulary 1 and Group B vocabulary 1 is determined based on the commonalities of the comparison, including: if one character is the same in Group A vocabulary 1 and Group B vocabulary 1, 1 point is given, 2 characters are the same, 4 points are given, 3 characters are the same, and so on; if the order of the same characters in Group A vocabulary 1 and Group B vocabulary 1 is consistent, 15 points are added, otherwise no points are given, and if only one character is the same, no points are given; if there are no other characters in Group A vocabulary 1 and Group B vocabulary 1 except the same character, 10 points are added, and 2 points are subtracted for each additional character until the score is reduced to 0; all scores are summed to obtain the correlation score between Group A vocabulary 1 and Group B vocabulary 1.

[0009] In a specific embodiment of the present application, the above-mentioned determining the corresponding Modelica numerical models associated with multiple node mappings in the hierarchical structure tree according to the correlation score to form a Modelica numerical model map based on the hierarchical structure tree includes: if it is determined according to the correlation score that the same node mapping in the hierarchical structure tree is associated with at least two corresponding Modelica numerical models, then the at least two Modelica numerical models are merged; if there is a Modelica numerical model with a correlation score lower than the target value among the multiple Modelica numerical models, then the Modelica numerical model with the correlation score lower than the target value is deleted; if it is impossible to determine the corresponding Modelica numerical model associated with at least one node mapping in the hierarchical structure tree according to the correlation score, then the corresponding Modelica numerical model is supplemented for at least one node to form a Modelica numerical model map based on the hierarchical structure tree.

[0010] In a specific embodiment of the present application, each node is also configured with at least one of attribute information and file binding, the attribute information includes a functional introduction, design parameters and interface description, and the file binding includes a design atlas, a design report and an instantiated numerical model.

[0011] In a specific embodiment of the present application, the knowledge management attribute fields of the Modelica numerical model include one or more of the model name, modeling object, profession, type, developer, development time, status, keywords and summary.

[0012] The second aspect of the present application provides a model map construction system based on Modelica, and the model map construction system based on Modelica includes a Modelica numerical model attribute entry and management module, a hierarchical structure tree construction and configuration module, a search and positioning module, and a mapping association module. The Modelica numerical model attribute entry and management module is used to provide a standardized model attribute configuration panel to fill in the knowledge management attribute fields of multiple Modelica numerical models, and is used for the classification management and query of multiple Modelica numerical models. The hierarchical structure tree construction and configuration module is used to build a hierarchical structure tree of an industrial system, and configure multiple simulation objects on the hierarchical structure tree, and the node names of the corresponding nodes of the simulation objects. The search and positioning module is used to extract the vocabulary groups corresponding to the knowledge management attribute fields of multiple Modelica numerical models and the vocabulary groups corresponding to multiple nodes, and use this as a search term, adopt a vocabulary correlation algorithm, and search and locate the corresponding nodes of the hierarchical structure tree of the industrial system. The mapping association module is used to associate and bind the Modelica numerical model with the corresponding nodes of the hierarchical structure tree of the industrial system according to the search and positioning results given by the search and positioning module.

[0013] In a specific embodiment of the present application, the Modelica-based model graph construction system also includes a Modelica numerical model retrieval module, which is used to retrieve the Modelica numerical model from the Modelica-based model graph and view and call it in the Modelica-based modeling and simulation software.

[0014] The third aspect of the present application provides a chip, which includes a processor, and the processor is used to call a program from a memory so that a device equipped with the chip executes any one of the Modelica-based model graph construction methods provided in the first aspect of the present application.

[0015] The fourth aspect of the present application provides a computer device, which includes a processor and a memory. The processor is used to execute any one of the model graph construction methods based on Modelica provided in the first aspect of the present application. The memory is used to store executable instructions of the processor.

[0016] The fifth aspect of the present application provides a computer-readable storage medium on which computer executable instructions are stored. When the executable instructions are executed by a processor, any one of the model graph construction methods based on Modelica provided in the first aspect of the present application is implemented.

[0017] The sixth aspect of the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements any one of the Modelica-based model graph construction methods provided in the first aspect of the present application.

[0018] The beneficial effect of the technical solution of the present application is that: by expressing the node name of each node corresponding to each simulation object on the hierarchical structure tree of the industrial system in the form of a vocabulary group, multiple Modelica numerical models in the Modelica numerical model library are managed in a knowledgeable manner, and a vocabulary correlation algorithm is used to search, locate and map multiple Modelica numerical models on the hierarchical structure tree, so that the Modelica numerical model of a specific simulation object can be quickly retrieved, located and related numerical models can be collected, and simulation object information can be viewed and model instantiation configuration can be performed for a specific Modelica numerical model, thereby realizing rapid retrieval and positioning of Modelica numerical models, and effectively improving the reuse efficiency of Modelica numerical models. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a flow chart of a method for constructing a model graph based on Modelica provided in one embodiment of the present application.

[0020] Figure 2Shown is a hierarchical structure diagram of an industrial system provided by an embodiment of the present application.

[0021] Figure 3 The figure shows a schematic diagram of the configuration of nodes on a hierarchical structure of an industrial system provided by an embodiment of the present application.

[0022] Figure 4 The figure is a flow chart of a method for constructing a model graph based on Modelica provided in another embodiment of the present application.

[0023] Figure 5 The figure is a flow chart of a method for constructing a model graph based on Modelica provided in another embodiment of the present application.

[0024] Figure 6 Shown is a schematic diagram of a value-taking rule provided in an embodiment of the present application.

[0025] Figure 7 The figure is a flow chart of a method for constructing a model graph based on Modelica provided in yet another embodiment of the present application.

[0026] Figure 8 The figure shows a block diagram of a model graph construction system based on Modelica provided in one embodiment of the present application.

[0027] Fig. 9 The figure shows a block diagram of a model graph construction system based on Modelica provided by another embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] At least one embodiment of the present application provides a method for constructing a model graph based on Modelica. The execution subject of the method for constructing a model graph based on Modelica may be a processor or a server, etc. The following description will be made by taking the processor as the execution subject as an example. Figure 1 As shown, the model graph construction method based on Modelica includes the following steps.

[0030] S1: Obtain a hierarchical structure tree of the industrial system. There are multiple simulation objects on the hierarchical structure tree of the industrial system, and each simulation object corresponds to a node. Each node is configured with a node name. The node name is expressed in the form of a vocabulary group.

[0031] Specifically, a user may construct a hierarchical structure tree of an industrial system on a hierarchical structure tree construction and configuration module of a model graph construction system based on Modelica, and configure multiple simulation objects on the hierarchical structure tree, as well as the node names of the nodes corresponding to the simulation objects, and store them in a memory of the system, so that a processor can call and obtain the hierarchical structure tree of the industrial system from the memory.

[0032] In some embodiments, reference Figure 2 The hierarchical structure tree uses the form of system, subsystem, device to component to structure the composition of the industrial system. Each system, each subsystem, each device, and each component is a simulation object. In this way, the mesh structure of the industrial system is formed by the hierarchical relationship between nodes. For example, each subsystem may include subsystem 1 and subsystem 2. Subsystem 1 may include device 1.1, device 1.2, etc. Device 1.1 may include component 1.1.1 and component 1.1.2. Device 1.2 may include component 1.2.1 and component 1.2.2. Subsystem 2 may include device 2.1 and device 2.2. Device 2.1 may include component 2.1.1 and component 2.1.2. Device 2.2 may include component 2.2.1 and component 2.2.2.

[0033] For example, if the industrial system is a nuclear reactor system, the subsystems of the nuclear reactor system include the reactor coolant system, the secondary circuit system, the chemical and volume control system, and the waste heat removal system. The equipment of the secondary circuit system includes the steam generator, feed water pump, feed water regulating valve, main steam isolation valve, main steam safety valve, steam turbine and condenser. The components of the steam generator include the inlet and outlet chambers on the primary side and the feed water ring, downcomer, heat exchange tube bundle and steam-water separator on the secondary side. The equipment of the nuclear reactor system refers to the equipment included in the subsystem. The components of the nuclear reactor system refer to the components included in the equipment.

[0034] In some embodiments, reference Figure 3 Each node is also configured with at least one of attribute information and file binding. Attribute information includes function introduction, design parameters and interface description. File binding includes design atlas, design report and instantiated numerical model.

[0035] In some embodiments, reference Figure 3 , the vocabulary group is composed of professional terms, synonyms and antonyms. Taking steam generator as an example, the vocabulary group includes steam generator, evaporator, SG and Steam Generator, etc.

[0036] S2: Obtain knowledge management attribute fields of multiple Modelica numerical models in the Modelica numerical model library.

[0037] Specifically, a user may fill in the knowledge management attribute fields of multiple Modelica numerical models on the Modelica numerical model attribute entry and management module of the Modelica-based model graph construction system and automatically store them in the system's memory, so that the processor can call and obtain the knowledge management attribute fields of multiple Modelica numerical models from the memory.

[0038] In at least one embodiment of the present application, the knowledge management attribute fields of the Modelica numerical model include one or more of the model name, modeling object, specialty, type, developer, development time, status, keywords, and summary. In this way, classification management and query can be performed based on the knowledge management attribute fields.

[0039] S3: Based on the vocabulary groups corresponding to the knowledge management attribute fields of multiple Modelica numerical models and the vocabulary groups corresponding to multiple nodes on the hierarchical structure tree, a vocabulary relevance algorithm is used to search, locate and map the multiple Modelica numerical models on the hierarchical structure tree to form a Modelica numerical model graph based on the hierarchical structure tree.

[0040] According to the technical solution provided in the embodiment of the present application, the node names of the nodes corresponding to each simulation object on the hierarchical structure tree of the industrial system are expressed in the form of vocabulary groups, and multiple Modelica numerical models in the Modelica numerical model library are managed in a knowledge-based manner. A vocabulary correlation algorithm is used to search, locate and map multiple Modelica numerical models on the hierarchical structure tree. This allows for rapid retrieval, locating and aggregation of related numerical models for Modelica numerical models of specific simulation objects, as well as for viewing simulation object information and configuring model instantiation for specific Modelica numerical models, thereby achieving rapid retrieval and locating of Modelica numerical models and effectively improving the reuse efficiency of Modelica numerical models.

[0041] It should be noted that the following scenarios can be realized by constructing a model graph construction system based on Modelica.

[0042] Scenario 1: Through the Modelica numerical model map based on the hierarchical structure tree, the Modelica numerical model of interest can be queried according to the knowledge management attribute field of the Modelica numerical model, and then the corresponding hierarchical structure tree node can be located through the Modelica numerical model search. According to the node, various information and files of the simulation object of the Modelica numerical model of interest can be viewed, providing data support for the parameter instantiation and test verification of the Modelica numerical model. At the same time, various information and files of the upper and lower nodes of the simulation object on the hierarchical structure tree can be further viewed, providing data support for the decomposition and integration of the Modelica numerical model, thereby effectively improving the reuse efficiency of the Modelica numerical model.

[0043] Scenario 2: View the mapped Modelica numerical model according to the nodes of the hierarchical structure tree, including various information of the knowledge management attribute fields of the Modelica numerical model, and retrieve the Modelica numerical model and view and call it in the Modelica-based modeling and simulation software.

[0044] In at least one embodiment of the present application, reference Figure 4 , S31 and S32 are a specific implementation of the above S3.

[0045] S31: according to the vocabulary groups corresponding to the knowledge management attribute fields of the multiple Modelica numerical models and the vocabulary groups corresponding to the multiple nodes on the hierarchical structure tree, a vocabulary correlation algorithm is used to obtain correlation scores between the multiple Modelica numerical models and the multiple nodes in the hierarchical structure tree.

[0046] S32: Determine, according to the correlation scores, to associate corresponding Modelica numerical models with multiple node mappings in the hierarchical structure tree, so as to form a Modelica numerical model graph based on the hierarchical structure tree.

[0047] Specifically, according to the correlation scores, multiple Modelica numerical models in the Modelica numerical model library are mapped and associated on the hierarchical structure tree to form a Modelica numerical model map based on the hierarchical structure tree.

[0048] In an embodiment of the present application, a lexical relevance algorithm is used to obtain the relevance scores of multiple Modelica numerical models and multiple nodes in a hierarchical structure tree, and search positioning and mapping associations are performed according to the order of the relevance scores to form a Modelica numerical model map based on the hierarchical structure tree, thereby making it more accurate to determine the Modelica numerical models corresponding to the mapping associations of multiple nodes in the hierarchical structure tree, and also realizing a visual structured view of the correspondence between the Modelica-based numerical model and the actual industrial system and the hierarchical relationship of multiple Modelica numerical models.

[0049] In at least one embodiment of the present application, reference Figure 5 , S311 to S316 are a specific implementation of the above S31.

[0050] S311: extracting the model name and the vocabulary group corresponding to the modeling object of the knowledge attribute management field of the i-th Modelica numerical model from the multiple Modelica numerical models to be mapped, associated or searched and located in sequence as the A-group vocabulary group, wherein the A-group vocabulary group is composed of a single word or two words.

[0051] S312: extracting the vocabulary groups corresponding to the node names configured at the jth node from the multiple nodes on the hierarchical structure tree in sequence as the vocabulary group B, wherein the vocabulary group B is composed of a number of synonyms or near synonyms, and i and j are both positive integers.

[0052] S313: Select vocabulary group A 1 of vocabulary group A and vocabulary group B 1 of vocabulary group B for comparison, wherein the content of the comparison is the commonality between vocabulary group A 1 and vocabulary group B 1 in terms of character arrangement, wherein the characters of the vocabulary include single Chinese characters, single capital letters and single English words.

[0053] S314: Determine the correlation between vocabulary 1 of group A and vocabulary 1 of group B according to the commonalities of the comparison.

[0054] S315: Sequentially carry out word-by-word correlation evaluation of group A vocabulary one and group B vocabulary two to group B vocabulary n in order to obtain an array consisting of a number of word correlation values, and select the maximum value of the array as the correlation score between group A vocabulary one and group B vocabulary.

[0055] S315: If there is only one word in vocabulary group A, then the correlation score between vocabulary group A and vocabulary group B is the correlation score between vocabulary group A and vocabulary B.

[0056] S316: If the vocabulary group A has multiple words, then the vocabulary group A m of the vocabulary group A and the vocabulary group B 1 to B n of the vocabulary group B are sequentially relevance-valued, and the relevance score of the vocabulary group A m and the vocabulary group B is obtained, and then the maximum value of the relevance scores of the vocabulary group A 1 to A m and the vocabulary group B is selected as the relevance score of the vocabulary group A and the vocabulary B. Both m and n are positive integers.

[0057] In an embodiment of the present application, by taking lexical relevance values ​​for the node names corresponding to each node of the hierarchical structure tree in turn for multiple Modelica numerical models, sorting according to the relevance scores, determining the node with the highest relevance score that matches the Modelica numerical model, and using this node as a recommended node for mapping and associating a specific Modelica numerical model, as well as as a node located by searching the Modelica numerical model, thereby achieving mapping and associating multiple Modelica numerical models on the hierarchical structure tree.

[0058] In at least one embodiment of the present application, reference Figure 6 S3141 to S3144 are a specific implementation of S314. S3141 to S3144 may be a principle of a value selection rule.

[0059] S3141: If one character in Group A vocabulary 1 and Group B vocabulary 1 is the same, 1 point is awarded; if two characters are the same, 4 points are awarded; if three characters are the same, 9 points are awarded, and so on.

[0060] S3142: If the order of the same characters in vocabulary 1 of group A and vocabulary 1 of group B is the same, add 15 points, otherwise no points will be given. If only one character is the same, no points will be given.

[0061] S3143: If there are no other characters in Group A vocabulary 1 and Group B vocabulary 1 except the same characters, 10 points will be added. For each additional character, 2 points will be subtracted until the score is reduced to 0.

[0062] S3144: Sum all the scores to obtain the correlation score between vocabulary 1 of group A and vocabulary 1 of group B.

[0063] In the embodiment of the present application, by setting the value-taking rules, the number of identical characters between different vocabularies, the order, and the presence or absence of other characters besides the identical characters are comprehensively considered, so that the finally obtained correlation score is more reasonable and can better reflect the correlation between different vocabularies, thereby making the mapping association between multiple Modelica numerical models on the hierarchical structure tree more accurate.

[0064] In at least one embodiment of the present application, reference Figure 7 , S321 to S323 are a specific implementation of the above S32.

[0065] S321: If it is determined according to the correlation score that the same node mapping in the hierarchical structure tree is associated with at least two corresponding Modelica numerical models, then the at least two Modelica numerical models are merged.

[0066] S322: If there is a Modelica numerical model with a correlation score lower than the target value among the multiple Modelica numerical models, the Modelica numerical model with a correlation score lower than the target value is deleted.

[0067] S323: If it is not possible to determine the corresponding Modelica numerical model associated with at least one node mapping in the hierarchical structure tree according to the correlation score, then the corresponding Modelica numerical model is supplemented to at least one node to form a Modelica numerical model map based on the hierarchical structure tree.

[0068] In an embodiment of the present application, multiple Modelica numerical models are combined, deleted, and supplemented according to their correlation scores to achieve continuous improvement and mapping of associated Modelica numerical models on a hierarchical structure tree, making the Modelica numerical model map based on the hierarchical structure tree more accurate.

[0069] At least one embodiment of the present application further provides a model graph construction system based on Modelica, referring to Figure 8 The model graph construction system 100 based on Modelica includes a Modelica numerical model attribute entry and management module 110, a hierarchical structure tree construction and configuration module 120, a search and positioning module 130 and a mapping association module 140. The Modelica numerical model attribute entry and management module 110 is used to provide a standardized model attribute configuration panel to fill in the knowledge management attribute fields of multiple Modelica numerical models, and is used for classification management and query of multiple Modelica numerical models. The hierarchical structure tree construction and configuration module 120 is used to build a hierarchical structure tree of the industrial system, and configure multiple simulation objects on the hierarchical structure tree, and the node names of the nodes corresponding to the simulation objects. The search and positioning module 130 is used to extract the vocabulary groups corresponding to the knowledge management attribute fields of multiple Modelica numerical models and the vocabulary groups corresponding to multiple nodes, and use this as a search term, and use a vocabulary correlation algorithm to search and locate the nodes corresponding to the hierarchical structure tree of the industrial system. The mapping association module 140 is used to associate and bind the Modelica numerical model with the corresponding nodes of the hierarchical structure tree of the industrial system according to the search and positioning results given by the search and positioning module.

[0070] In some embodiments, the hierarchical structure tree construction and configuration module 120 is also used to configure attribute information and file binding, etc. Attribute information includes but is not limited to design information, design data and other design parameters, function introduction and interface description, etc. File binding includes but is not limited to design atlas, design report and instantiated numerical model.

[0071] In at least one embodiment of the present application, reference Fig. 9 The Modelica-based model graph construction system 100 further includes a Modelica numerical model calling module 150. The Modelica numerical model calling module 150 is used to call the Modelica numerical model from the Modelica-based model graph and view and call it in the Modelica-based modeling and simulation software.

[0072] At least one embodiment of the present application further provides a chip, the chip comprising a processor. The processor is used to call a program from a memory, so that a device equipped with the chip executes a model graph construction method based on Modelica in any of the above embodiments of the present application.

[0073] It should be noted that the processor may be any type of processor mentioned above. It is understandable that the memory may be independent of the chip or integrated in the chip.

[0074] At least one embodiment of the present application further provides a computer device, which includes a processor and a memory. The processor is used to execute a model graph construction method based on Modelica in any of the above embodiments of the present application. The memory is used to store executable instructions of the processor.

[0075] At least one embodiment of the present application further provides a computer-readable storage medium having computer executable instructions stored thereon. When the executable instructions are executed by a processor, a model graph construction method based on Modelica in any of the above embodiments of the present application is implemented.

[0076] At least one embodiment of the present application further provides a computer program product, which includes a computer program / instruction. When the computer program / instruction is executed by a processor, a model graph construction method based on Modelica in any of the above embodiments of the present application is implemented.

[0077] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer device can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that contains one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0078] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments of the present application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0080] It should be noted that the combination of the various technical features in the embodiments of the present application is not limited to the combination described in the embodiments of the present application or the combination described in the specific embodiments, and all technical features described in the present application can be freely combined or combined in any way unless there is a contradiction between them.

[0081] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the term "comprising" only indicates that the steps and elements that have been clearly identified are included, and these steps and elements do not constitute an exclusive list. The method or system may also include other steps or elements.

[0082] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for constructing a model graph based on Modelica, characterized in that: include: Obtain a hierarchical structure tree of the industrial system, wherein there are multiple simulation objects on the hierarchical structure tree of the industrial system, each simulation object corresponds to a node, each node is configured with a node name, and the node name is expressed in the form of a vocabulary group; Get the knowledge management attribute fields of multiple Modelica numerical models in the Modelica numerical model library; According to the vocabulary groups corresponding to the knowledge management attribute fields of multiple Modelica numerical models and the vocabulary groups corresponding to multiple nodes on the hierarchical structure tree, a vocabulary relevance algorithm is used to search, locate and map the multiple Modelica numerical models on the hierarchical structure tree to form a Modelica numerical model graph based on the hierarchical structure tree.

2. The method for constructing a model graph based on Modelica according to claim 1, characterized in that: The method uses a vocabulary correlation algorithm to search, locate and map the multiple Modelica numerical models on the hierarchical structure tree according to the vocabulary groups corresponding to the knowledge management attribute fields of the multiple Modelica numerical models and the vocabulary groups corresponding to the multiple nodes on the hierarchical structure tree, so as to form a Modelica numerical model map based on the hierarchical structure tree, including: According to the vocabulary groups corresponding to the knowledge management attribute fields of the plurality of Modelica numerical models and the vocabulary groups corresponding to the plurality of nodes on the hierarchical structure tree, a vocabulary correlation algorithm is used to obtain correlation scores between the plurality of Modelica numerical models and the plurality of nodes in the hierarchical structure tree; According to the correlation scores, multiple node mappings in the hierarchical structure tree are determined to associate corresponding Modelica numerical models, so as to form a Modelica numerical model map based on the hierarchical structure tree.

3. The method for constructing a model graph based on Modelica according to claim 2, characterized in that: The method of obtaining correlation scores between the plurality of Modelica numerical models and the plurality of nodes in the hierarchical structure tree using a vocabulary correlation algorithm according to the vocabulary groups corresponding to the knowledge management attribute fields of the plurality of Modelica numerical models and the vocabulary groups corresponding to the plurality of nodes in the hierarchical structure tree comprises: Sequentially extracting the model name and the vocabulary group corresponding to the modeling object of the knowledge attribute management field of the i-th Modelica numerical model from the multiple Modelica numerical models to be mapped, associated or searched and located as the A-group vocabulary group, wherein the A-group vocabulary group is composed of a single word or two words; Sequentially extracting the vocabulary groups corresponding to the node names configured for the jth node from the multiple nodes on the hierarchical structure tree as the vocabulary group B, the vocabulary group B is composed of a number of synonyms or near synonyms, and i and j are both positive integers; Selecting vocabulary group A 1 and vocabulary group B 1 of vocabulary group A for comparison, the content of comparison is the commonality of word arrangement between vocabulary group A 1 and vocabulary group B 1, and the words of vocabulary include single Chinese characters, single capital letters and single English words; The correlation between vocabulary 1 in group A and vocabulary 1 in group B is determined based on the commonality of the comparison; Sequentially carry out word correlation value analysis of word A 1 of word group A and word B 2 to word B n of word group B, obtain an array consisting of a plurality of word correlation values, and select the maximum value of the array as the correlation score between word A 1 and word group B; If there is only one word in group A, then the correlation score between group A word one and group B word one is the correlation score between group A word one and word B; If vocabulary group A has multiple words, then the correlation between vocabulary group A m and vocabulary group B 1 to vocabulary group B n is taken word by word in turn to obtain the correlation score between vocabulary group A m and vocabulary group B, and then the maximum value of the correlation scores between vocabulary group A 1 to vocabulary group A m and vocabulary group B is selected as the correlation score between vocabulary group A and vocabulary B, where m and n are both positive integers.

4. The method for constructing a model graph based on Modelica according to claim 3, characterized in that: The step of determining the correlation between vocabulary 1 of group A and vocabulary 1 of group B according to the commonality of the comparison includes: If one character in Group A vocabulary 1 and Group B vocabulary 1 is the same, 1 point is awarded, if two characters are the same, 4 points are awarded, if three characters are the same, 9 points are awarded, and so on; If the order of the same characters in vocabulary 1 of group A and vocabulary 1 of group B is the same, add 15 points, otherwise no points will be given. If only one character is the same, no points will be given. If there are no other words in Group A and Group B, add 10 points. For each additional word, subtract 2 points until the score is reduced to 0. Sum up all the scores to get the correlation score between vocabulary one in group A and vocabulary one in group B.

5. The method for constructing a model graph based on Modelica according to claim 2, characterized in that: The step of determining, according to the correlation scores, to associate corresponding Modelica numerical models with multiple node mappings in the hierarchical structure tree to form a Modelica numerical model graph based on the hierarchical structure tree comprises: If it is determined according to the correlation score that the same node mapping in the hierarchical structure tree is associated with at least two corresponding Modelica numerical models, then the at least two Modelica numerical models are merged; If there is a Modelica numerical model with a correlation score lower than the target value among multiple Modelica numerical models, the Modelica numerical model with a correlation score lower than the target value is deleted; If it is not possible to determine that at least one node in the hierarchical structure tree is mapped to a corresponding Modelica numerical model according to the correlation score, the corresponding Modelica numerical model is supplemented to at least one node to form a Modelica numerical model map based on the hierarchical structure tree.

6. A method for constructing a model graph based on Modelica according to any one of claims 1 to 5, characterized in that: Each node is also configured with at least one of attribute information and file binding, wherein the attribute information includes a functional introduction, design parameters and interface description, and the file binding includes a design atlas, a design report and an instantiated numerical model.

7. A method for constructing a model graph based on Modelica according to any one of claims 1 to 5, characterized in that: The knowledge management attribute fields of the Modelica numerical model include one or more of model name, modeling object, specialty, type, developer, development time, status, keywords and summary.

8. A model graph construction system based on Modelica, characterized in that: include: Modelica numerical model attribute entry and management module, used to provide a standardized model attribute configuration panel to fill in the knowledge management attribute fields of multiple Modelica numerical models, and used for classification management and query of multiple Modelica numerical models; A hierarchical structure tree construction and configuration module is used to construct a hierarchical structure tree of the industrial system and configure multiple simulation objects on the hierarchical structure tree and node names of nodes corresponding to the simulation objects; The search and positioning module is used to extract vocabulary groups corresponding to the knowledge management attribute fields of multiple Modelica numerical models and vocabulary groups corresponding to multiple nodes, and use them as search terms to search and locate the nodes corresponding to the hierarchical structure tree of the industrial system using a vocabulary correlation algorithm; The mapping association module is used to associate and bind the Modelica numerical model with the corresponding nodes of the hierarchical structure tree of the industrial system according to the search and positioning results given by the search and positioning module.

9. The model graph construction system based on Modelica according to claim 8, characterized in that: Also includes: The Modelica numerical model retrieval module is used to retrieve the Modelica numerical model from the Modelica-based model map and view and call it in the Modelica-based modeling and simulation software.

10. A chip, characterized in that: include: A processor is used to call a program from a memory so that a device equipped with a chip executes a model graph construction method based on Modelica as described in any one of claims 1 to 7.

11. A computer device, characterized in that: include: A processor, configured to execute a method for constructing a model graph based on Modelica according to any one of claims 1 to 7; as well as A memory is used to store executable instructions of the processor.

12. A computer-readable storage medium having computer executable instructions stored thereon, characterized in that: When the executable instructions are executed by the processor, a model graph construction method based on Modelica is implemented as described in any one of claims 1 to 7.

13. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, a method for constructing a model graph based on Modelica according to any one of claims 1 to 7 is implemented.

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