Method and apparatus for realizing interoperability of a simulation system

Abstract

The present invention discloses a method and device for realizing interoperability of simulation systems. The method includes obtaining simulation information to be processed; preprocessing the simulation information to be processed to obtain preprocessed simulation information; and analyzing and processing the preprocessed simulation information to obtain simulation interoperability information. It can be seen that this application can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of various simulation systems.

Description

Technical Field

[0001] The present invention relates to the field of simulation, and in particular to a method and device for realizing interoperability of simulation systems. Background Art

[0002] In the process of simulation, there is a type of simulation activity that involves cross-domain interconnection of multiple heterogeneous simulation systems. Due to the different mechanisms of each simulation system and diverse data sources, the data formats and structures between different data sources are often different, which leads to difficulties in data integration and exchange. To solve this problem, a method capable of automatically generating bridging code is required to achieve integration and exchange between different data sources. Existing heterogeneous interconnection simulations usually, according to the implementation of each project, interface with the data that needs to be interacted between each system on-site, manually generate bridging code and systems, with a long development cycle. In addition, manual code conversion has a high error rate, lacks standard guidance, and the development levels vary, which also has a greater impact on system stability. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for realizing interoperability of simulation systems, which can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, and is beneficial to improving the integration and exchange between data sources of each simulation system.

[0004] To solve the above technical problem, a first aspect of an embodiment of the present invention discloses a method for realizing interoperability of simulation systems, the method comprising:

[0005] S1, obtaining simulation information to be processed;

[0006] S2, preprocessing the simulation information to be processed to obtain preprocessed simulation information;

[0007] S3, analyzing and processing the preprocessed simulation information to obtain simulation interoperability information.

[0008] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the preprocessing the simulation information to be processed to obtain preprocessed simulation information includes:

[0009] S21, performing data cleaning processing on the simulation information to be processed to obtain first simulation information;

[0010] S22, performing word segmentation processing on the first simulation information to obtain second simulation information;

[0011] S23, performing format conversion processing on the second simulation information to obtain third simulation information;

[0012] S24, performing data enhancement processing on the third simulation information to obtain preprocessed simulation information.

[0013] As an alternative implementation, in the first aspect of the embodiments of the present invention, the analysis and processing of the preprocessed simulation information to obtain simulation interoperability information includes:

[0014] S31. Perform training processing on the first simulation initial model and the second simulation initial model to obtain a first simulation result model and a second simulation result model;

[0015] S32. Use the first simulation result model and the second simulation result model to process the preprocessed simulation information to obtain simulation interoperability information.

[0016] As an alternative implementation, in the first aspect of the embodiments of the present invention, the training processing of the first simulation initial model and the second simulation initial model to obtain a first simulation result model and a second simulation result model includes:

[0017] S311. Obtain a simulation training sample set; the simulation training sample set includes a number of simulation training samples;

[0018] S312. Perform annotation processing on the simulation training sample set to obtain a simulation training annotated sample set; the simulation training annotated sample set includes a number of simulation training annotated samples;

[0019] S313. Use the simulation training annotated sample set to perform training processing on the first simulation initial model and the second simulation initial model to obtain a first simulation result model and a second simulation result model.

[0020] As an alternative implementation, in the first aspect of the embodiments of the present invention, the training processing of the first simulation initial model and the second simulation initial model using the simulation training annotated sample set to obtain a first simulation result model and a second simulation result model includes:

[0021] S3131. Preset t = 1;

[0022] S3132. Use the t-th simulation training annotated sample in the simulation training annotated sample set to train the first simulation initial model to obtain first training result information and a first simulation training model;

[0023] S3133. Process the first training result information and the first label information corresponding to the first training result information to obtain a first loss function value;

[0024] S3134. Use a simulation matching model to perform calculation processing on the first training result information and the first label information to obtain a simulation matching value;

[0025] S3135. Use the first training result information to train the second simulation initial model, obtaining second training result information and a second simulation training model;

[0026] S3136. Perform calculation processing on the second training result information and the second label information corresponding to the second training result information, obtaining a second loss function value and a simulation similarity value;

[0027] S3137. Use a first loss function model to perform calculation processing on the first loss function value, the simulation matching value, the second loss function value, and the simulation similarity value, obtaining a target loss function value;

[0028] Wherein, the first loss function model is:

[0029] MB = α1·DY + α2·DE + α3·(1 - PP) + α4·(1 - XS);

[0030] α1 + α2 + α3 + α4 = 1;

[0031] 0 ≤ α1, α2, α3, α4 ≤ 1;

[0032] In the formula, MB is the target loss function value, DY is the first loss function value, DE is the second loss function value, PP is the simulation matching value, XS is the simulation similarity value, and α1, α2, α3, and α4 are the first weight coefficient, the second weight coefficient, the third weight coefficient, and the fourth weight coefficient respectively;

[0033] S3138. Judge whether the target loss function value is less than a preset loss function threshold, obtaining a first judgment result;

[0034] When the first judgment result is no, judge whether t is equal to the number of simulation training labeled samples in the simulation training labeled sample set, obtaining a second judgment result;

[0035] When the second judgment result is no, increment t by 1, determine the first simulation training model as the first simulation initial model, determine the second simulation training model as the second simulation initial model, and execute S3132;

[0036] When the second judgment result is yes, determine the first simulation training model as the first simulation result model and the second simulation training model as the second simulation result model;

[0037] When the first judgment result is yes, determine the first simulation training model as the first simulation result model and the second simulation training model as the second simulation result model.

[0038] As an alternative implementation, in the first aspect of the embodiments of the present invention, the simulation matching model is:

[0039]

[0040] In the formula, PP is the simulation matching value, CD1 is the length of the first tag information, CD2 is the length of the first training result information, PN n is the n-gram precision, ω n is the nth adjustment factor, α5 is the fifth weight coefficient, and N is the matching length value.

[0041] As an alternative implementation, in the first aspect of the embodiments of the present invention, using the first simulation result model and the second simulation result model to process the preprocessed simulation information to obtain simulation interoperability information includes:

[0042] S321. Using the first simulation result model to process the preprocessed simulation information to obtain simulation intermediate result information;

[0043] S322. Using the second simulation result model to process the simulation intermediate result information to obtain simulation interoperability information.

[0044] The second aspect of the embodiments of the present invention discloses a device for realizing simulation system interoperability, and the device includes:

[0045] An acquisition module, configured to acquire the simulation information to be processed;

[0046] A preprocessing module, configured to preprocess the simulation information to be processed to obtain preprocessed simulation information;

[0047] An analysis module, configured to analyze and process the preprocessed simulation information to obtain simulation interoperability information.

[0048] The third aspect of the embodiments of the present invention discloses another device for realizing simulation system interoperability, and the device includes:

[0049] A processor;

[0050] A memory coupled to the processor and storing executable program code;

[0051] The processor calls the executable program code stored in the memory and executes part or all of the steps of the method for realizing simulation system interoperability disclosed in the first aspect of the embodiments of the present invention.

[0052] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer instructions, which when called, are used to execute some or all of the steps of the method for realizing interoperability of simulation systems disclosed in the first aspect of the embodiments of the present invention.

[0053] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0054] In the embodiments of the present invention, to-be-processed simulation information is obtained; the to-be-processed simulation information is preprocessed to obtain preprocessed simulation information; and the preprocessed simulation information is analyzed and processed to obtain simulation interoperability information. It can be seen that this application can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of various simulation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0056] Figure 1 It is a schematic flowchart of a method for realizing interoperability of a simulation system disclosed in the embodiments of the present invention;

[0057] Figure 2 It is a schematic structural diagram of a device for realizing interoperability of a simulation system disclosed in the embodiments of the present invention;

[0058] Figure 3 It is a schematic structural diagram of another device for realizing interoperability of a simulation system disclosed in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0060] In the description, claims and the above drawings of the present invention, terms such as "first", "second", etc. are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0061] Reference to "embodiment" herein means that a particular feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appearing in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0062] The present invention discloses a method and device for realizing interoperability of simulation systems, which can automatically generate bridging code to realize interoperability between data sources of different simulation systems, and is beneficial to improving the integration and exchange between data sources of each simulation system. The following will be described in detail respectively.

[0063] Embodiment 1

[0064] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for realizing interoperability of simulation systems disclosed in an embodiment of the present invention. Among them, Figure 1 the described method for realizing interoperability of simulation systems is applied to a device for realizing interoperability of simulation systems, such as a local server or a cloud server for optimizing management of realizing interoperability of simulation systems, etc., which is not limited in the embodiments of the present invention. As Figure 1 shown, the method for realizing interoperability of simulation systems may include the following operations:

[0065] S1, obtaining simulation information to be processed;

[0066] S2, preprocessing the simulation information to be processed to obtain preprocessed simulation information;

[0067] S3, analyzing and processing the preprocessed simulation information to obtain simulation interoperability information.

[0068] It should be noted that the simulation information to be processed is the user's description information, which is mainly used to generate the core input of XIDL file information and bridge code to ensure that the generated content can accurately reflect the user's needs and data characteristics, thereby achieving effective data integration and processing. For example, the simulation information to be processed is "Please define two interactive topics, which are used as simulation handle objects and entity position status information publishing and subscription respectively. The entity position status topic inherits from the simulation handle object topic, and the data carried by the two topics also have an inheritance relationship accordingly. The simulation handle object data elements include handles and timestamps; the entity position status data elements include position, orientation, pitch, and roll."

[0069] It should be noted that XIDL file information is a file format information that describes data interfaces and interactions. It is usually used to define data structures, types, interface methods, etc., to help achieve data exchange between different systems.

[0070] It should be noted that the above simulation interoperability information is a bridge code, which is code information that connects the data structure or interface of one system with another system to realize the functions of data transmission and processing, thereby realizing interoperability between different simulation systems.

[0071] It should be noted that, in the embodiment of the present invention, XIDL file information is first generated through the user's description information, and then the bridge code is generated through the XIDL file information, mainly to introduce an intermediate layer between the user's description information and the bridge code. This intermediate layer (i.e., XIDL file information) has several important functions and advantages:

[0072] Improve flexibility and scalability, and simplify the complexity of generating bridge codes;

[0073] Provide an abstraction layer to separate the interface definition and implementation of the system, and enhance the maintainability and scalability of the bridge code;

[0074] Improve the accuracy and verifiability of generated bridge code, and enhance cross-platform and multi-language support.

[0075] It can be seen that the simulation system interoperability implementation method described in the embodiment of the present invention can automatically generate bridge code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of various simulation systems.

[0076] In an optional embodiment, preprocessing the simulation information to be processed to obtain preprocessed simulation information includes:

[0077] S21, performing data cleaning processing on the simulation information to be processed to obtain first simulation information;

[0078] S22. Perform word segmentation on the first simulation information to obtain the second simulation information;

[0079] S23. Perform format conversion on the second simulation information to obtain the third simulation information;

[0080] S24. Perform data augmentation on the third simulation information to obtain the preprocessed simulation information.

[0081] It should be noted that the above-mentioned word segmentation of the first simulation information to obtain the second simulation information can be performed through tools or libraries such as jieba, THULAC, or HanLP. Specifically, the embodiments of the present invention do not make limitations.

[0082] It should be noted that the above-mentioned data augmentation of the third simulation information to obtain the preprocessed simulation information can be performed through tools or libraries such as nlpaug, TextAttack, or Transformers. Specifically, the embodiments of the present invention do not make limitations.

[0083] It should be noted that through data cleaning, word segmentation, format conversion, and data augmentation, the data quality can be improved, the model performance can be enhanced, the data format can be unified, and the data diversity can be increased. At the same time, the model robustness can be improved and the overfitting risk can be reduced.

[0084] It can be seen that implementing the method for realizing interoperability of the simulation system described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0085] In another optional embodiment, data cleaning is performed on the simulation information to be processed to obtain the first simulation information, including:

[0086] S211. Remove noise from the simulation information to be processed to obtain the fourth simulation information;

[0087] S212. Remove duplicates from the fourth simulation information to obtain the fifth simulation information;

[0088] S213. Fill in missing values in the fifth simulation information to obtain the first simulation information.

[0089] It should be noted that removing noise from the simulation information to be processed to obtain the fourth simulation information can be performed through one of the tools or libraries such as NLTK, spaCy, or gensim. Specifically, the embodiments of the present invention do not make limitations.

[0090] It should be noted that to remove duplicates from the fourth simulation information and obtain the fifth simulation information, one of Pandas or NumPy can be used for duplicate removal. Specifically, the embodiments of the present invention do not make any limitations in this regard.

[0091] It should be noted that to fill in missing values in the fifth simulation information and obtain the first simulation information, one of Pandas, Scikit-learn, or FancyImpute can be used for filling in missing values. Specifically, the embodiments of the present invention do not make any limitations in this regard.

[0092] It should be noted that through the above processes of noise removal, duplicate removal, and filling in missing values, the data quality can be significantly improved, and the performance and adaptability of the model can be enhanced.

[0093] It can be seen that implementing the method for realizing interoperability of the simulation system described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of various simulation systems.

[0094] In another optional embodiment, to perform format conversion processing on the second simulation information to obtain the third simulation information, it includes:

[0095] S231, perform character unified half-width processing on the second simulation information to obtain the sixth simulation information;

[0096] S232, perform simplified compound word processing on the sixth simulation information to obtain the seventh simulation information;

[0097] S233, perform text length standardization processing on the seventh simulation information to obtain the third simulation information. It should be noted that character unified half-width processing is to convert full-width characters (such as full-width letters, numbers, and symbols) into half-width characters to ensure text consistency and avoid processing errors caused by different character formats; simplified compound word processing is to simplify compound words (such as "mobile phone number") into single words (such as "mobile phone" and "number") for easy processing and analysis to improve the model's understanding ability; text length standardization processing is to standardize the length of the text, such as truncating or padding to make the text reach the same length for convenient batch processing and model input.

[0098] It should be noted that to perform character unified half-width processing on the second simulation information to obtain the sixth simulation information, NLTK or Jieba can be used for character unified half-width processing. Specifically, the embodiments of the present invention do not make any limitations in this regard.

[0099] It should be noted that for the sixth simulation information, after performing simplified compound word processing to obtain the seventh simulation information, one or more of Jieba, NLTK, or spaCy can be used for the simplified compound word processing. Specifically, the embodiments of the present invention do not make limitations in this regard.

[0100] It should be noted that for the seventh simulation information, after performing text length standardization processing to obtain the third simulation information, one or more of Keras or Pandas can be used for the text length standardization processing. Specifically, the embodiments of the present invention do not make limitations in this regard.

[0101] It should be noted that through character unified half-width processing, simplified compound word processing, and text length standardization processing, it helps to improve the consistency and usability of text data, making subsequent data analysis and model training more efficient and accurate.

[0102] It can be seen that implementing the method for realizing simulation system interoperability described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0103] In another optional embodiment, the preprocessed simulation information is analyzed and processed to obtain simulation interoperability information, including:

[0104] S31, training the first simulation initial model and the second simulation initial model to obtain the first simulation result model and the second simulation result model;

[0105] S32, using the first simulation result model and the second simulation result model to process the preprocessed simulation information to obtain simulation interoperability information.

[0106] It should be noted that both the above-mentioned first simulation initial model and the second simulation initial model are Transformer models, and deep learning models such as CNN and RNN can also be used. Specifically, the embodiments of the present invention do not make limitations in this regard.

[0107] It can be seen that implementing the method for realizing simulation system interoperability described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0108] In yet another optional embodiment, training the first simulation initial model and the second simulation initial model to obtain the first simulation result model and the second simulation result model includes:

[0109] S311, obtaining a simulation training sample set; the simulation training sample set includes a number of simulation training samples;

[0110] S312. Perform annotation processing on the simulation training sample set to obtain a simulation training annotated sample set. The simulation training annotated sample set includes a number of simulation training annotated samples.

[0111] S313. Use the simulation training annotated sample set to perform training processing on the first simulation initial model and the second simulation initial model to obtain a first simulation result model and a second simulation result model.

[0112] It should be noted that performing annotation processing on the simulation training sample set to obtain a simulation training annotated sample set can be carried out through Label Studio, Prodigy or Doccano. Specifically, the embodiments of the present invention do not make limitations.

[0113] It can be seen that implementing the method for realizing simulation system interoperability described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of various simulation systems.

[0114] In an optional embodiment, using the simulation training annotated sample set to perform training processing on the first simulation initial model and the second simulation initial model to obtain a first simulation result model and a second simulation result model includes:

[0115] S3131. Preset t = 1.

[0116] S3132. Use the t-th simulation training annotated sample in the simulation training annotated sample set to train the first simulation initial model to obtain first training result information and a first simulation training model.

[0117] It should be noted that the above training of the first simulation initial model presets the prompt format of the model output. For example, the prompt format is set to json, or it can be other formats. The embodiments of the present invention do not make specific limitations.

[0118] S3133. Perform calculation processing on the first training result information and the first label information corresponding to the first training result information to obtain a first loss function value.

[0119] It should be noted that performing calculation processing on the first training result information to obtain a first loss function value can be carried out through a cross-entropy loss function, or it can be carried out through other loss functions in the deep learning model. Specifically, the embodiments of the present invention do not make limitations.

[0120] It should be noted that the above first label information corresponding to the first training result information is the true output information corresponding to the first training result information, which is used to measure the difference between the model prediction true result and the true result together with the first training result information.

[0121] It should be noted that the first tag information can be set by the user or obtained according to historical data, and the embodiments of the present invention do not make any limitations in this regard.

[0122] It should be noted that the above first training result information is the XIDL file information output by the model during the training process.

[0123] S3134. Using the simulation matching model, perform calculation processing on the first training result information and the first tag information to obtain a simulation matching value.

[0124] It should be noted that to perform calculation processing on the first training result information and the first tag information to obtain a simulation matching value, a BLEU model can also be used for calculation processing. Specifically, the embodiments of the present invention do not make any limitations in this regard.

[0125] S3135. Using the first training result information, train the second simulation initial model to obtain second training result information and a second simulation training model.

[0126] It should be noted that the above training of the second simulation initial model has a pre-set prompt format for the model output. For example, the prompt format is set to json, or it can be other formats. The embodiments of the present invention do not make specific limitations in this regard.

[0127] It should be noted that the above second training result information is the bridging code output by the model during the training process.

[0128] S3136. Perform calculation processing on the second training result information and the second tag information corresponding to the second training result information to obtain a second loss function value and a simulation similarity value.

[0129] S3137. Using the first loss function model, perform calculation processing on the first loss function value, the simulation matching value, the second loss function value, and the simulation similarity value to obtain a target loss function value.

[0130] Wherein, the first loss function model is:

[0131] MB = α1·DY + α2·DE + α3·(1 - PP) + α4·(1 - XS);

[0132] α1 + α2 + α3 + α4 = 1;

[0133] 0 ≤ α1, α2, α3, α4 ≤ 1;

[0134] Wherein, MB is the target loss function value, DY is the first loss function value, DE is the second loss function value, PP is the simulation matching value, XS is the simulation similarity value, and α1, α2, α3, and α4 are the first weight coefficient, the second weight coefficient, the third weight coefficient, and the fourth weight coefficient, respectively;

[0135] It should be noted that the first weight coefficient, the second weight coefficient, the third weight coefficient, and the fourth weight coefficient can be set by the user or obtained from historical data. Specifically, the embodiments of the present invention do not make any limitations.

[0136] It should be noted that both the first loss function value and the second loss function value are used to reflect the result that the model generates a word frequency distribution conforming to the training set, which is beneficial to improving the accuracy of local vocabulary selection. The smaller the first loss function value and the second loss function value are, the higher the accuracy is; the simulation matching value is used to reflect the n-gram matching of the vocabulary combination, and the value of the simulation matching value is in the range of [0, 1]. When the simulation matching value is larger, the language fluency and local expression are better; the simulation similarity value is used to reflect the vocabulary overlap between the predicted text and the actual expected text, and the value of the simulation similarity value is in the range of [0, 1]. When the simulation similarity value is larger, the information of the predicted text is more complete. By superimposing the first loss function value, the second loss function value, the simulation matching value, and the simulation similarity value, the target loss function value can be obtained, which can reflect the degree to which the generated text conforms to the grammatical structure of the training set, and at the same time can reflect the degree of natural fluency and information integrity semantically.

[0137] It should be noted that by using the first weight coefficient, the second weight coefficient, the third weight coefficient, and the fourth weight coefficient to balance the first loss function value, the second loss function value, the simulation matching value, and the simulation similarity value, flexible control and multi-dimensional quality improvement can be provided for model training, so that the model performs more stably and excellently in various tasks, and at the same time improves the accuracy, fluency, and information coverage of the text.

[0138] S3138. Determine whether the target loss function value is less than a preset loss function threshold to obtain a first judgment result;

[0139] When the first judgment result is negative, determine whether t is equal to the number of simulation training annotation samples in the simulation training annotation sample set to obtain a second judgment result;

[0140] When the second judgment result is negative, increase t by 1, determine the first simulation training model as the first simulation initial model, determine the second simulation training model as the second simulation initial model, and execute S3132;

[0141] When the second judgment result is yes, determine the first simulation training model as the first simulation result model and the second simulation training model as the second simulation result model;

[0142] When the first judgment result is yes, determine the first simulation training model as the first simulation result model and the second simulation training model as the second simulation result model.

[0143] It should be noted that the number of simulation training labeled samples in the simulation training labeled sample set is more than 100,000. Specifically, the embodiments of the present invention do not make limitations.

[0144] It should be noted that the value range of the preset loss function threshold is between [0, 0.5]. Specifically, the embodiments of the present invention do not make limitations.

[0145] It can be seen that implementing the method for realizing simulation system interoperability described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0146] In an optional embodiment, calculating and processing the second training result information and the second label information corresponding to the second training result information to obtain a second loss function value and a simulation similarity value includes:

[0147] S31361, perform word vector processing on the second training result information and the second label information respectively to obtain a second training result word vector set and a second training label word vector set; the second training result word vector set includes a plurality of second training result word vectors; the second training label word vector set includes a plurality of second training label word vectors;

[0148] It should be noted that the above word vector processing can use the Word2Vec model, GloVe or GPT for word vector processing. Specifically, the embodiments of the present invention do not make limitations.

[0149] It should be noted that the second label information corresponding to the above second training result information is the true output information corresponding to the second training result information, which is used to measure the difference between the model prediction true result and the true result together with the second training result information.

[0150] It should be noted that the second label information can be set by the user or obtained according to historical data. The embodiments of the present invention do not make limitations.

[0151] It should be noted that the above second training result information is the bridging code output by the model during the training process.

[0152] S31362. Use the second loss function model to perform calculation processing on the second training result word vector set and the second training label word vector set to obtain a second loss value;

[0153] Among them, the second loss function model is:

[0154]

[0155] α6 + α7 = 1;

[0156] 0 ≤ α6, α7 ≤ 1;

[0157] In the formula, DE is the second loss value, ZS is the second training label word vector set, ZS is the second training result word vector set, ZSC is the number of second training label word vectors in the second training label word vector set, YCC is the number of second training result word vectors in the second training result word vector set, MSE(ZS, YC) represents the mean square error between the second training label word vector set and the second training label word vector set, and α6 and α7 respectively represent the sixth weight coefficient and the seventh weight coefficient;

[0158] It should be noted that the sixth weight coefficient and the seventh weight coefficient can be set by the user or obtained according to historical data, which is not limited in the embodiments of the present invention.

[0159] It should be noted that to perform calculation processing on the second training result word vector set and the second training label word vector set to obtain a second loss value, it can also be obtained by using a cross-entropy loss function for processing, which is not limited in the embodiments of the present invention.

[0160] S31363. Use the ROUGE-N calculation model to perform calculation processing on the second training result information and the second label information to obtain a simulation similarity value.

[0161] It can be seen that implementing the method for realizing simulation system interoperability described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0162] In an optional embodiment, the simulation matching model is:

[0163]

[0164] In the formula, PP is the simulation matching value, CD1 is the length of the label information corresponding to the first training result information, CD2 is the length of the first training result information, PN n is the n-gram precision, ω n is the nth adjustment factor, α5 is the fifth weight coefficient, and N is the matching length value.

[0165] It should be noted that the fifth weight coefficient sum ω n (1 ≤ n ≤ N) can be set by the user or obtained according to historical data, and the embodiments of the present invention do not make any limitations in this regard.

[0166] It should be noted that the value range of the matching length value is a positive integer between [1, 6]. Specifically, the embodiments of the present invention do not make any limitations in this regard.

[0167] It should be noted that the n-gram accuracy is the number of n-grams (i.e., sequences composed of n consecutive words) in the first training result information that match the n-grams in the first label information. The n-gram can be 1-gram, 2-gram, 3-gram, etc. Specifically, n represents the number of consecutive words. For example:

[0168] 1-gram: a single word;

[0169] 2-gram: two consecutive words;

[0170] 3-gram: three consecutive words.

[0171] It can be seen that implementing the method for realizing interoperability of the simulation system described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0172] In an optional embodiment, the preprocessed simulation information is processed by using the first simulation result model and the second simulation result model to obtain simulation interoperability information, including:

[0173] S321, using the first simulation result model to process the preprocessed simulation information to obtain simulation intermediate result information;

[0174] S322, using the second simulation result model to process the simulation intermediate result information to obtain simulation interoperability information.

[0175] It should be noted that using the first simulation result model to process the preprocessed simulation information to obtain simulation intermediate result information means taking the preprocessed simulation information as the input of the first simulation result model and calculating through the first simulation result model to obtain the output result, that is, the simulation intermediate result information; using the second simulation result model to process the simulation intermediate result information to obtain simulation interoperability information means taking the simulation intermediate result information as the input of the second simulation result model and calculating through the second simulation result model to obtain the output result, that is, the simulation interoperability information.

[0176] It should be noted that the simulation intermediate result information is XIDL file information, and the simulation interoperability information is bridge code. For example, when the preprocessed simulation information (user's description information) is "Please define two interaction topics, which are used as the simulation handle object and the entity position status information for publishing and subscribing respectively. Among them, the entity position status topic inherits from the simulation handle object topic, and the data carried by the two topics also have an inheritance relationship accordingly. The simulation handle object data elements include a handle and a timestamp; the entity position status data elements include position, orientation, pitch, and roll", the obtained simulation intermediate result information (XIDL file information) should be as follows:

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184]

[0185] It can be seen that implementing the method for realizing simulation system interoperability described in the embodiments of the present invention can automatically generate bridge code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0186] Embodiment 2

[0187] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a device for realizing simulation system interoperability disclosed in the embodiments of the present invention. Among them, Figure 2 the device for realizing simulation system interoperability described is applied to an optimization system for realizing simulation system interoperability, such as a local server or a cloud server for realizing simulation system interoperability, etc., which is not limited in the embodiments of the present invention. As Figure 2 shown, the device for realizing simulation system interoperability includes:

[0188] An acquisition module 201, configured to acquire simulation information to be processed;

[0189] A preprocessing module 202, configured to preprocess the simulation information to be processed to obtain preprocessed simulation information;

[0190] An analysis module 203, configured to analyze and process the preprocessed simulation information to obtain simulation interoperability information.

[0191] It can be seen that implementing the simulation system interoperability implementation device described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0192] Embodiment Three

[0193] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of another simulation system interoperability implementation device disclosed in the embodiments of the present invention. Among them, Figure 3 the described simulation system interoperability implementation device is applied to a simulation system interoperability implementation optimization system, such as a local server or a cloud server for simulation system interoperability implementation, etc., which is not limited in the embodiments of the present invention. As Figure 3 shown, the simulation system interoperability implementation device includes:

[0194] A processor 301;

[0195] A memory 302 coupled to the processor 301 and storing executable program code;

[0196] The processor 301 calls the executable program code stored in the memory 302 to execute part or all of the steps of the simulation system interoperability implementation method in Embodiment One.

[0197] It can be seen that implementing the simulation system interoperability implementation device described in the embodiments of the present invention can automatically generate bridging code to achieve interoperability between data sources of different simulation systems, which is beneficial to improving the integration and exchange between data sources of each simulation system.

[0198] Embodiment Four

[0199] The embodiments of the present invention disclose a computer-readable storage medium. The computer-readable storage medium stores computer instructions, which are used to execute part or all of the steps of the simulation system interoperability implementation method in Embodiment One when the computer instructions are called.

[0200] Embodiment Five

[0201] The embodiments of the present invention disclose a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of the simulation system interoperability implementation method described in Embodiment One.

[0202] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.

[0203] Through the above specific descriptions of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, and the storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium that can be used to carry or store data.

[0204] Finally, it should be noted that: What is disclosed in a method and device for realizing interoperability of a simulation system disclosed in an embodiment of the present invention is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention and is not intended to limit it; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for implementing interoperability of a simulation system, characterized in that: The method comprises: Acquire the simulation information to be processed, preprocess and analyze it, and obtain simulation interoperability information, including: Acquire a simulation training sample set; the simulation training sample set includes a plurality of simulation training samples; Performing labeling processing on the simulation training sample set to obtain a simulation training labeling sample set; the simulation training labeling sample set includes a plurality of simulation training labeling samples; Using the simulation training annotated sample set, training the first simulation initial model and the second simulation initial model to obtain the first simulation result model and the second simulation result model specifically includes: H1, preset t = 1; H2, using the tth simulation training labeled sample in the simulation training labeled sample set to train the first simulation initial model to obtain first training result information and a first simulation training model; H3, processing the first training result information and the first label information corresponding to the first training result information to obtain a first loss function value; H4, using a simulation matching model, calculating and processing the first training result information and the first label information to obtain a simulation matching value; Wherein, the simulation matching model is: Where PP is the simulated matching value, CD1 is the length of the first label information, CD2 is the length of the first training result information, PN n is the n-gram accuracy, ω n is the nth adjustment factor, α5 is the fifth weight coefficient, and N is the matching length value; H5, using the first training result information, training the second simulation initial model to obtain second training result information and a second simulation training model; H6, calculating and processing the second training result information and the second label information corresponding to the second training result information to obtain a second loss function value and a simulation similarity value; H7, using the first loss function model, calculating and processing the first loss function value, the simulation matching value, the second loss function value and the simulation similarity value to obtain a target loss function value; Among them, the first loss function model is: MB=α1·DY+α2·DE+α3·(1-PP)+α4·(1-XS); α1+α2+α3+α4＝1； 0≤α1,α2,α3,α4≤1； Wherein, MB is the target loss function value, DY is the first loss function value, DE is the second loss function value, PP is the simulation matching value, XS is the simulation similarity value, α1, α2, α3 and α4 are the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient respectively; H8, determining whether the target loss function value is less than a preset loss function threshold, and obtaining a first determination result; When the first judgment result is no, judging whether t is equal to the number of the simulation training labeled samples in the simulation training labeled sample set, and obtaining a second judgment result; When the second judgment result is no, increase t by 1, determine the first simulation training model as the first simulation initial model, determine the second simulation training model as the second simulation initial model, and execute H2; When the second judgment result is yes, determining the first simulation training model as a first simulation result model, and determining the second simulation training model as a second simulation result model; When the first judgment result is yes, determining the first simulation training model as the first simulation result model, and determining the second simulation training model as the second simulation result model; The preprocessed simulation information to be processed is processed using the first simulation result model and the second simulation result model to obtain simulation interoperability information.

2. The method for realizing interoperability of simulation systems according to claim 1, characterized in that: The pre-processing comprises: The simulation information to be processed is subjected to data cleaning processing, word segmentation processing, format conversion processing and data enhancement processing to obtain pre-processed simulation information.

3. The method for realizing interoperability of simulation systems according to claim 1, characterized in that: The using the first simulation result model and the second simulation result model to process the preprocessed simulation information to be processed to obtain simulation interoperability information includes: Using the first simulation result model, the preprocessed simulation information to be processed is processed to obtain simulation intermediate result information; The simulation intermediate result information is processed using the second simulation result model to obtain simulation interoperability information.

4. A simulation system interoperability implementation device, characterized in that: The device comprises: processor; a memory coupled to the processor and storing executable program code; The processor calls the executable program code stored in the memory to execute the simulation system interoperability implementation method as described in any one of claims 1-3.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the simulation system interoperability implementation method as described in any one of claims 1-3.

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