Simulation system interoperation implementation method and device

Abstract

The invention discloses a simulation system interoperation realization method and device. The method comprises the following steps: acquiring 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 interoperation information. Therefore, the bridging code can be automatically generated, so that interoperation among the data sources of different simulation systems is realized, and integration and exchange among the data sources of the simulation systems can be improved.

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 a simulation system. Background Art

[0002] During the simulation process, 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 the diverse data sources, the data formats and structures of different data sources are often different, which leads to difficulties in data integration and exchange. In order to solve this problem, a method that can automatically generate bridge code is needed to achieve integration and exchange between different data sources. Existing heterogeneous interconnected simulations usually manually generate bridge code and systems based on the implementation of each project, and manually connect the data that each system needs to interact with on-site. The development cycle is long. In addition, the error rate of manually written code conversion is also high, there is a lack of standard guidance, and the development level is different, which has a great 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 bridge codes to realize interoperability between data sources of different simulation systems, thereby facilitating the integration and exchange between data sources of various simulation systems.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a method for implementing interoperability of a simulation system, 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 pre-processed 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 of the simulation information to be processed to obtain the 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 pre-processed simulation information.

[0013] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the analyzing and processing the pre-processed simulation information to obtain the simulation interoperability information includes:

[0014] S31, training 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: Process the pre-processed simulation information using the first simulation result model and the second simulation result model to obtain simulation interoperability information.

[0016] As an optional implementation, in the first aspect of the embodiment of the present invention, the training process of 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:

[0017] S311, obtaining a simulation training sample set; the simulation training sample set includes a plurality of simulation training samples;

[0018] S312, 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;

[0019] S313: Using the simulation training annotated sample set, the first simulation initial model and the second simulation initial model are trained to obtain a first simulation result model and a second simulation result model.

[0020] As an optional implementation, in the first aspect of the embodiment of the present invention, the use of the simulation training labeled sample set to train 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:

[0021] S3131, preset t=1;

[0022] S3132, 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;

[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, using a simulation matching model, calculating and processing the first training result information and the first label information to obtain a simulation matching value;

[0025] S3135: train the second simulation initial model using the first training result information to obtain second training result information and a second simulation training model;

[0026] S3136, 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;

[0027] S3137, using a 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;

[0028] Among them, 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] 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;

[0033] S3138, determining whether the target loss function value is less than a preset loss function threshold, and obtaining a first determination result;

[0034] 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;

[0035] When the second judgment result is no, increase the 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, 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;

[0037] When the first judgment result is yes, the first simulation training model is determined to be the first simulation result model, and the second simulation training model is determined to be the second simulation result model.

[0038] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the simulation matching model is:

[0039]

[0040] 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.

[0041] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the using the first simulation result model and the second simulation result model to process the pre-processed simulation information to obtain simulation interoperability information includes:

[0042] S321, using the first simulation result model, processing the pre-processed simulation information to obtain simulation intermediate result information;

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

[0044] A second aspect of an embodiment of the present invention discloses a simulation system interoperability implementation device, the device comprising:

[0045] An acquisition module, used for acquiring simulation information to be processed;

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

[0047] The analysis module is used to analyze and process the pre-processed simulation information to obtain simulation interoperability information.

[0048] A third aspect of an embodiment of the present invention discloses another simulation system interoperability implementation device, the device comprising:

[0049] 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 to execute part or all of the steps of the simulation system interoperability implementation method disclosed in the first aspect of the embodiment of the present invention.

[0052] The fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps of the simulation system interoperability implementation method disclosed in the first aspect of the embodiment of the present invention.

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

[0054] In the embodiment of the present invention, the simulation information to be processed is obtained; the simulation information to be processed is preprocessed to obtain preprocessed simulation information; the preprocessed simulation information is analyzed and processed to obtain simulation interoperability information. It can be seen that the present application can automatically generate bridge code to achieve interoperability between data sources of different simulation systems, which is conducive 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 briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 A schematic diagram of a flow chart of a simulation system interoperability implementation method disclosed in an embodiment of the present invention;

[0057] Figure 2 A schematic diagram of the structure of a simulation system interoperability implementation device disclosed in an embodiment of the present invention;

[0058] Figure 3 The present invention is a schematic diagram of the structure of another simulation system interoperability implementation device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

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

[0062] The present invention discloses a simulation system interoperability implementation method and device, which 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. Detailed descriptions are given below.

[0063] Embodiment 1

[0064] See also Figure 1 , Figure 1 1 is a flow chart of a simulation system interoperability implementation method disclosed in an embodiment of the present invention. Figure 1 The described simulation system interoperability implementation method is applied to a simulation system interoperability implementation device, such as a local server or cloud server used for optimizing management of simulation system interoperability, and the embodiments of the present invention do not limit this. Figure 1 As shown, the simulation system interoperability implementation method 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 pre-processed 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, performing word segmentation processing on the first simulation information to obtain second simulation information;

[0079] S23, performing format conversion processing on the second simulation information to obtain third simulation information;

[0080] S24, performing data enhancement processing on the third simulation information to obtain pre-processed simulation information.

[0081] It should be noted that the word segmentation processing of the first simulation information to obtain the second simulation information can be performed by tools or libraries such as Jieba, THULAC or HanLP, and the specific details are not limited in the embodiment of the present invention.

[0082] It should be noted that the data enhancement processing is performed on the third simulation information to obtain the preprocessed simulation information, and the data enhancement processing can be performed by tools or libraries such as nlpaug, TextAttack or Transformers, etc., which is not limited in the embodiments of the present invention.

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

[0084] 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.

[0085] In another optional embodiment, performing data cleaning processing on the simulation information to be processed to obtain first simulation information includes:

[0086] S211, performing noise removal processing on the simulation information to be processed to obtain fourth simulation information;

[0087] S212, performing deduplication processing on the fourth simulation information to obtain fifth simulation information;

[0088] S213, performing missing value filling processing on the fifth simulation information to obtain first simulation information.

[0089] It should be noted that the noise removal process is performed on the simulation information to be processed to obtain the fourth simulation information, and the noise removal process can be performed by using one of the NLTK, spaCy or gensim tools or libraries, which is not limited in the embodiments of the present invention.

[0090] It should be noted that the fourth simulation information is deduplicated to obtain the fifth simulation information, and the deduplication process may be performed using either Pandas or NumPy, which is not limited in detail in the embodiment of the present invention.

[0091] It should be noted that the missing values ​​of the fifth simulation information are filled to obtain the first simulation information, and one of Pandas, Scikit-learn or FancyImpute can be used to fill the missing values. Specifically, the embodiment of the present invention does not limit this.

[0092] It should be noted that the above-mentioned processing through noise removal, deduplication and missing value filling can significantly improve data quality and enhance model performance and adaptability.

[0093] 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.

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

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

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

[0097] S233, perform text length normalization processing on the seventh simulation information to obtain third simulation information. It should be noted that character uniform half-width processing is to convert full-width characters (such as full-width letters, numbers and symbols) into half-width characters to ensure the consistency of the text 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") to facilitate processing and analysis and improve the understanding ability of the model; text length normalization processing is to normalize the length of the text, such as by truncating or padding to make the text reach the same length, which is convenient for batch processing and model input.

[0098] It should be noted that the second simulation information is processed with a unified half-width character to obtain the sixth simulation information. The unified half-width character processing can be performed by NLTK or Jieba, and the specific embodiment of the present invention is not limited thereto.

[0099] It should be noted that the sixth simulation information is simplified and compound words are processed to obtain the seventh simulation information. One or more of Jieba, NLTK or spaCy can be used to simplify the compound words. The specific details are not limited in the embodiment of the present invention.

[0100] It should be noted that the text length normalization processing is performed on the seventh simulation information to obtain the third simulation information. The text length normalization processing can be performed by one or more of Keras or Pandas. Specifically, the embodiment of the present invention does not limit this.

[0101] It should be noted that unified half-width processing of characters, simplified compound word processing and standardized text length processing can help improve the consistency and availability of text data, making subsequent data analysis and model training more efficient and accurate.

[0102] 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.

[0103] In another optional embodiment, the pre-processed 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 a first simulation result model and a second simulation result model;

[0105] S32, using the first simulation result model and the second simulation result model, processing the pre-processed simulation information to obtain simulation interoperability information.

[0106] It should be noted that the first simulation initial model and the second simulation initial model are both Transformer models, and deep learning models such as CNN and RNN may also be used. Specifically, the embodiments of the present invention do not limit this.

[0107] 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.

[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 plurality of simulation training samples;

[0110] S312, 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;

[0111] S313, 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.

[0112] It should be noted that the simulation training sample set is labeled to obtain the simulation training labeled sample set, which can be labeled by Label Studio, Prodigy or Doccano, and the specific embodiment of the present invention does not limit this.

[0113] 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.

[0114] In an optional embodiment, the first simulation initial model and the second simulation initial model are trained using the simulation training annotated sample set to obtain the first simulation result model and the second simulation result model, including:

[0115] S3131, preset t=1;

[0116] S3132, 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;

[0117] It should be noted that the above training of the first simulation initial model pre-sets the prompt format of the model output, such as setting the prompt format to json, or other formats, which is not specifically limited in the embodiment of the present invention.

[0118] S3133, calculating and 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;

[0119] It should be noted that the first training result information is calculated and processed to obtain the first loss function value, which can be calculated and processed by a cross entropy loss function or by other loss functions in the deep learning model. Specifically, the embodiment of the present invention does not limit this.

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

[0121] It should be noted that the first tag information may be set by a user or acquired based on historical data, which is not limited in the embodiment of the present invention.

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

[0123] S3134, using the simulation matching model, calculating and processing the first training result information and the first label information to obtain a simulation matching value;

[0124] It should be noted that the first training result information and the first label information are calculated and processed to obtain the simulation matching value, and the BLEU model can also be used for calculation and processing. Specifically, the embodiment of the present invention does not limit this.

[0125] S3135, using the first training result information, training 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 pre-sets the prompt format of the model output, such as setting the prompt format to json, or other formats, which is not specifically limited in the embodiment of the present invention.

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

[0128] S3136, 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;

[0129] S3137, 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;

[0130] Among them, 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, 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 may be set by a user or may be obtained based on historical data, and the embodiments of the present invention do not limit this.

[0136] It should be noted that the first loss function value and the second loss function value are used to reflect the results of model generation that conform to the word frequency distribution of the training set, which is conducive to improving the accuracy of local vocabulary selection. The smaller the first loss function value and the second loss function value, the higher the accuracy; the simulation matching value is used to reflect the n-gram matching of vocabulary combinations, and 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. The simulation similarity value is in the range of [0,1]. When the simulation similarity value is larger, the predicted text information is more complete. The target loss function value is obtained by superposition of the first loss function value, the second loss function value, the simulation matching value and the simulation similarity value, which can reflect the degree to which the generated text conforms to the grammatical structure of the training set, and can also reflect the degree of semantic natural fluency and information completeness.

[0137] It should be noted that 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 can provide flexible control and multi-dimensional quality improvement for model training, making the model perform more stably and better in various tasks, while improving the accuracy, fluency and information coverage of the text.

[0138] S3138, determining whether the target loss function value is less than a preset loss function threshold, and obtaining a first determination result;

[0139] When the first judgment result is no, determine whether t is equal to the number of simulation training labeled samples in the simulation training labeled sample set to obtain a second judgment result;

[0140] When the second judgment result is no, t is increased by 1, the first simulation training model is determined to be the first simulation initial model, the second simulation training model is determined to be the second simulation initial model, and S3132 is executed;

[0141] When the second judgment result is yes, the first simulation training model is determined to be the first simulation result model, and the second simulation training model is determined to be the second simulation result model;

[0142] When the first judgment result is yes, the first simulation training model is determined to be the first simulation result model, and the second simulation training model is determined to be the second simulation result model.

[0143] It should be noted that the number of simulation training annotated samples in the simulation training annotated sample set is greater than 100,000, and the specific embodiment of the present invention does not limit this.

[0144] It should be noted that the preset loss function threshold value ranges from [0, 0.5], and is not limited in the embodiment of the present invention.

[0145] 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.

[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, respectively perform word vector processing on the second training result information and the second label information 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-mentioned word vector processing can use the Word2Vec model, GloVe or GPT to perform word vector processing, and the specific embodiment of the present invention is not limited.

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

[0150] It should be noted that the second tag information may be set by a user or acquired based on historical data, which is not limited in the embodiment of the present invention.

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

[0152] S31362, using a second loss function model, calculating and processing 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] Where 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, α6 and α7 represent the sixth weight coefficient and the seventh weight coefficient respectively;

[0158] It should be noted that the sixth weight coefficient and the seventh weight coefficient may be set by a user or may be obtained based on historical data, which is not limited in the embodiment of the present invention.

[0159] It should be noted that the second training result word vector set and the second training label word vector set are calculated and processed to obtain the second loss value, which can also be obtained by processing using a cross entropy loss function, and the embodiment of the present invention is not limited thereto.

[0160] S31363, using the ROUGE-N calculation model, calculate and process the second training result information and the second label information to obtain a simulation similarity value.

[0161] 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.

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

[0163]

[0164] Where PP is the simulated 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, and 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.

[0165] It should be noted that the fifth weight coefficient and ω n (1≤n≤N) may be set by a user or may be obtained based on historical data, which is not limited in the embodiment of the present invention.

[0166] It should be noted that the value range of the matching length value is a positive integer between [1, 6], and is not limited in the embodiment of the present invention.

[0167] It should be noted that the n-gram accuracy is how many n-grams (i.e., a sequence of n consecutive words) in the first training result information match the n-grams in the first tag information. N-grams can be 1-grams, 2-grams, 3-grams, etc., and n specifically 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 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.

[0172] In an optional embodiment, the pre-processed simulation information is processed 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, processing the pre-processed simulation information to obtain simulation intermediate result information;

[0174] S322, using the second simulation result model, processing 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 the simulation intermediate result information, is to use the preprocessed simulation information as the input of the first simulation result model, and calculate through the first simulation result model to obtain the output result, i.e., the simulation intermediate result information; using the second simulation result model to process the simulation intermediate result information to obtain the simulation interoperability information, is to use the simulation intermediate result information as the input of the second simulation result model, and calculate through the second simulation result model to obtain the output result, i.e., the simulation interoperability information.

[0176] It should be noted that the simulation intermediate result information is the XIDL file information, and the simulation interoperability information is the bridge code. For example, when the preprocessing simulation information (user description information) 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", 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 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.

[0186] Embodiment 2

[0187] See also Figure 2 , Figure 2 Schematic diagram of a simulation system interoperability implementation device disclosed in an embodiment of the present invention. Figure 2 The described simulation system interoperability implementation device is applied to a simulation system interoperability implementation optimization system, such as a local server or cloud server for simulation system interoperability implementation, etc., which is not limited in the embodiments of the present invention. Figure 2 As shown, the simulation system interoperability implementation device includes:

[0188] An acquisition module 201 is used to acquire simulation information to be processed;

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

[0190] The analysis module 203 is used to analyze and process the pre-processed simulation information to obtain simulation interoperability information.

[0191] It can be seen that the simulation system interoperability implementation device described in the embodiment of the present invention can automatically generate bridge codes 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.

[0192] Embodiment 3

[0193] See also Figure 3 , Figure 3 1 is a schematic diagram of the structure of another simulation system interoperability implementation device disclosed in an embodiment of the present invention. 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 cloud server for simulation system interoperability implementation, etc., which is not limited in the embodiments of the present invention. Figure 3 As shown, the simulation system interoperability implementation device includes:

[0194] Processor 301;

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

[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 of the first embodiment.

[0197] It can be seen that the simulation system interoperability implementation device described in the embodiment of the present invention can automatically generate bridge codes 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.

[0198] Embodiment 4

[0199] The embodiment of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps of the simulation system interoperability implementation method of embodiment one.

[0200] Embodiment 5

[0201] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute part or all of the steps in the simulation system interoperability implementation method described in Example 1.

[0202] The system embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative labor.

[0203] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method 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 such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0204] Finally, it should be noted that the simulation system interoperability implementation method and device disclosed in the embodiment of the present invention discloses only the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for implementing interoperability of a simulation system, characterized in that: The method comprises: S1, obtaining simulation information to be processed; S2, preprocessing the simulation information to be processed to obtain preprocessed simulation information; S3, analyzing and processing the pre-processed simulation information to obtain simulation interoperability information.

2. The method for realizing interoperability of simulation systems according to claim 1, characterized in that: The preprocessing of the to-be-processed simulation information to obtain preprocessed simulation information includes: S21, performing data cleaning processing on the simulation information to be processed to obtain first simulation information; S22, performing word segmentation processing on the first simulation information to obtain second simulation information; S23, performing format conversion processing on the second simulation information to obtain third simulation information; S24, performing data enhancement processing on the third simulation information to obtain pre-processed simulation information.

3. The method for realizing interoperability of simulation systems according to claim 1, characterized in that: The analyzing and processing the pre-processed simulation information to obtain simulation interoperability information includes: S31, training the first simulation initial model and the second simulation initial model to obtain a first simulation result model and a second simulation result model; S32: Process the pre-processed simulation information using the first simulation result model and the second simulation result model to obtain simulation interoperability information.

4. The method for realizing interoperability of simulation systems according to claim 3, characterized in that: The training process of 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: S311, obtaining a simulation training sample set; the simulation training sample set includes a plurality of simulation training samples; S312, 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; S313: Using the simulation training annotated sample set, the first simulation initial model and the second simulation initial model are trained to obtain a first simulation result model and a second simulation result model.

5. The method for realizing interoperability of simulation systems according to claim 4, characterized in that: The method of using the simulation training labeled sample set to train 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: S3131, preset t=1; S3132, 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; 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; S3134, using a simulation matching model, calculating and processing the first training result information and the first label information to obtain a simulation matching value; S3135: train the second simulation initial model using the first training result information to obtain second training result information and a second simulation training model; S3136, 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; S3137, using a 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; S3138, 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 the 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; 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, the first simulation training model is determined to be the first simulation result model, and the second simulation training model is determined to be the second simulation result model.

6. The method for realizing interoperability of simulation systems according to claim 5, characterized in that: 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.

7. The method for realizing interoperability of simulation systems according to claim 3, characterized in that: The using the first simulation result model and the second simulation result model to process the pre-processed simulation information to obtain simulation interoperability information includes: S321, using the first simulation result model, processing the pre-processed simulation information to obtain simulation intermediate result information; S322: Using the second simulation result model, process the simulation intermediate result information to obtain simulation interoperability information.

8. A simulation system interoperability implementation device, characterized in that: The device comprises: An acquisition module, used for acquiring simulation information to be processed; A preprocessing module, used for preprocessing the simulation information to be processed to obtain preprocessed simulation information; The analysis module is used to analyze and process the pre-processed simulation information to obtain simulation interoperability information.

9. 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 1-7.

10. 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 1-7.

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