CN118981827B - A semi-physical production simulation system and method - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to a semi-physical production simulation system and method; the method comprises the following steps of S1, constructing a digital-analog platform in a Web application end, collecting building data to obtain a data model and corresponding packaging data of each building data, S2, arranging a model table in a database, inputting the data model into the model table, correspondingly marking the data model and the packaging data of the same building data, and storing and arranging icon templates in the database, S3, acquiring the data model of related data by a Web front end, acquiring the icon templates with the same icon placement number, acquiring the packaging data of the corresponding marks of each related data, S4, acquiring analysis data by the Web front end, inputting the analysis data into the icon templates to obtain a page chart, S5, loading the page chart into a UE engine for display, loosely binding the high coupling of the UE engine and the Web client, and improving the utilization rate of resources.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a semi-physical production simulation system and method.
Background
Semi-physical simulation is a simulation technique combining physical entity and mathematical model, in which part of the object system to be simulated is physically introduced into a simulation loop, while the rest is described by mathematical model, and a joint simulation of real-time mathematical simulation and physical simulation is performed by a simulation calculation model, specifically, a test is performed by combining a controller with a simulation model of a control object implemented on a computer. Therefore, in the semi-physical simulation process, the data needs to be processed at the Web client, and the processed data is displayed through the UE engine to obtain a mathematical model.
The UE engine is used for creating video games and other virtual reality application programs, has wide application in the field of building engineering at present due to strong virtual reality function expression, and is used for displaying a virtual building engineering three-dimensional model by establishing a BIM model in the field of building engineering, wherein the BIM model is often combined with the UE engine and the Unity3D engine to display engineering data information consistent with actual conditions.
In the field of building engineering, information related to engineering projects often has very strong project attributes, the project attributes have specificity, on different engineering projects, information focused by users is different, each time the information focused is changed, the developed product needs to be redeveloped and iterated, for the establishment of a mathematical model of a new product, all data designed by the new product need to be processed through a Web client and then displayed through a UE engine, more data identical to the old product are inevitably present in design data of the new product, the coupling between the UE engine and the Web client is higher, so that the data processing repeatability is higher, and the resource utilization rate is lower.
Therefore, it is needed to provide a semi-physical production simulation system and method, which, compared with the prior art, loosely bind the high coupling between the UE engine and the Web client, and improve the utilization rate of resources.
Disclosure of Invention
The invention solves the technical problems existing in the prior art, and provides a semi-physical production simulation system and a semi-physical production simulation method.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a semi-physical production simulation system and method comprises the following steps:
S1, constructing a digital-to-analog platform in a Web application end, collecting building data through the digital-to-analog platform, and processing the building data to obtain a data model of each building data;
S2, a model table is arranged in a database, the data model obtained in the step S1 is recorded into the model table, the packed data obtained in the step S1 is stored in the database, the data model and the packed data of the same building data are correspondingly marked, and an icon template is stored in the database;
S3, the Web front end obtains a data model of related data through interface service with a digital-to-analog platform, wherein the related data is all data or part of data in building data;
s4, the Web front end analyzes the packed data obtained in the step S3 to obtain analysis data, and the analysis data is input into an icon template to obtain a page chart;
And S5, the Web client loads the page diagram in the step S4 into the UE engine through WebUI plug-in units for display.
Further, in the step S1, the building data includes a plurality of indexes, each index is formed into a plurality of words by using a natural language processing technology, each word of each index is converted into metadata, and the metadata is represented by a field to form a data model of each index.
Further, when the digital model platform obtains new indexes, the new indexes are compared with the indexes in the database, each new index is divided into a plurality of words by using a natural language processing technology, the words divided by the new indexes are compared with the words divided by the original indexes in the same way, and the original indexes are modified according to the comparison result to form a data model of the new indexes.
Further, when a new field exists in the new index, that is, the new index acquired by the digital model platform has the same words as the original index, and the words in the original index are all located in the new index, the original index with the same words as the new index is used as a replacement index, metadata fields corresponding to all the words in the replacement index are reserved, words which are different from the replacement index in the new index are converted into metadata, and are represented by the fields, and the metadata fields are added to the back of the last field in the replacement index, so that modification of the new index is completed.
Further, when a replacement field exists in the new index, that is, when the new index acquired by the digital model platform has the same words as the original index, the words in the original index are only partially located in the new index, the original index with the same words as the new index is used as the replacement index, metadata fields corresponding to the words identical to the new index in the replacement index are reserved, the words which are different in the new index are correspondingly replaced to the positions of the words which are different from the new index in the replacement index, the replaced fields are marked as deactivated, the fields marked as deactivated are set and cannot be acquired by the front end of the Web, and the fields marked as deactivated are not packed.
Further, when a field to be deleted exists in the new index, that is, the new index obtained by the digital model platform has the same words as the original index, and all the words in the new index are located in the original index, the original index with the same words as the new index is used as a replacement index, the metadata field corresponding to the words identical to the new index in the replacement index is reserved, and the metadata field corresponding to the words not existing in the new index is marked as inactive.
Further, in the step S3, according to the data type of the related data, the specific method for obtaining the packed data of the corresponding tag of each related data is as follows:
S31, aiming at a first index in the related data, acquiring the packaging data of the corresponding mark, and simultaneously inputting the first index into the correlation model to obtain a related index with strong correlation with the first index and the packaging data of the corresponding mark of the related index;
S32, comparing the related indexes with other indexes in the related data in sequence, directly calling the packaging data of the related indexes when the indexes which are the same as the related data exist, and then carrying out S31 analysis on the related indexes, and carrying out correlation marking on the corresponding packaging data and the packaging data of the first index;
S33, processing the next index by adopting the steps S31-S32, and circulating the steps S31-S33 until the packed data of all indexes are obtained.
Further, in the step S4, when the analysis data is entered into the icon template, the analysis data after the analysis of the packed data with the correlation mark in the step S3 is set at the adjacent position in the icon template, and the same color is used for marking.
Further, the specific construction method of the correlation model in the step S31 is as follows:
S311, acquiring a plurality of groups of historical building data;
S312, constructing a machine learning model, inputting all indexes in a group of historical building data into the machine learning model, performing cluster analysis, and marking the indexes which are classified into one cluster as indexes with correlation by using a DBSCAN algorithm, and marking each index as the index with correlation;
S313, obtaining each index and the index with correlation with the index mark by using the method in S312;
S314, dividing each index and the marked correlation index into a data set, wherein the data set comprises a target index and a marked index, the marked index is the target index, the marked correlation index is the marked index, and when the number of the marked indexes is larger than a set value, all marked indexes in the data set are set to have strong correlation with the target index;
The set value satisfies the following equation:
;
in the above formula, L represents a set value, and N represents all index numbers in the set of historical building data;
s315, training a machine learning model for the historical building data of other groups acquired in the step S311 by using the method in the steps S312-S314, so as to obtain a correlation model.
A semi-physical production simulation system performs a semi-physical production simulation method, and the semi-physical production simulation system comprises a UE engine, a Web client and a database, wherein the Web client comprises a digital-to-analog platform and a Web front end, the digital-to-analog platform interacts with the Web front end, the Web front end interacts with the UE engine and the database respectively, and the digital-to-analog platform interacts with the database.
Compared with the prior art, the invention has the beneficial effects that:
The Web client is provided with the Web front end and the digital-analog platform, the Web front end and the digital-analog platform are highly loosely bound and have no strong correlation, the Web front end acquires the data model of the digital-analog platform through interface service, the process is completed in real time, the real-time interaction of data can be completed without secondary development of original codes, the utilization rate of resources is improved, and in addition, when related data is extracted, the extraction of the related data can be completed as soon as possible according to the related indexes of indexes, so that the processing speed is improved.
Drawings
Fig. 1 is a flow chart of the method of the present invention.
Fig. 2 is a block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly described below with reference to the accompanying drawings, and it is obvious that the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by a person skilled in the art without making any inventive effort are within the scope of protection of the present invention.
As shown in FIG. 1, the invention provides a semi-physical production simulation method, which comprises the following steps:
S1, constructing a digital-analog platform in a Web application end, collecting building data through the digital-analog platform, processing the building data to obtain a data model of each building data, packaging the obtained data models to obtain packaging data of the building data, wherein the packaging data are in a JSON format, and all the data models are stored in the digital-analog platform.
The building data comprises a plurality of indexes, wherein the indexes comprise equipment names, online time lengths, last offline time lengths and last maintenance time, a natural language processing technology is used for forming a plurality of words by each index, each word of each index is converted into metadata, and the metadata is represented in a field mode to form a data model of each index.
When the digital-analog platform obtains new indexes, comparing and analyzing the new indexes with the original indexes in the database, dividing each new index obtained by the digital-analog platform into a plurality of words by using a natural language processing technology, comparing the words divided by the new indexes with the words divided by the original indexes in an identical manner by using the natural language processing technology, and then dividing the comparison into the following cases to be discussed:
(1) When a new field exists in the new index, namely, the new index acquired by the digital model platform has the same words as the original index, all the words in the original index are positioned in the new index, the original index with the same words as the new index is used as a replacement index, metadata fields corresponding to all the words in the replacement index are reserved, words which are different from the replacement index in the new index are converted into metadata, the metadata fields are used for representing the metadata, and the metadata are added to the rear of the last field in the replacement index, so that the modification of the new index is completed.
(2) When a replacement field exists in the new index, namely, the new index acquired by the digital model platform has the same words as the original index, the words in the original index are only partially positioned in the new index, the original index with the same words as the new index is used as the replacement index, metadata fields corresponding to the words identical to the new index in the replacement index are reserved, the words which are different in the new index are correspondingly replaced to the positions which are different from the new index in the replacement index, the replaced fields are marked as deactivated, the fields marked as deactivated are not acquired by the front end of the Web, and the fields marked as deactivated are not packed.
(3) When a field to be deleted exists in the new index, namely, the new index acquired by the digital model platform has the same words as the original index, all the words in the new index are positioned in the original index, the original index with the same words as the new index is used as a replacement index, metadata fields corresponding to the words with the same words as the new index in the replacement index are reserved, metadata fields corresponding to the words which do not exist in the new index are marked as deactivated, the fields marked as deactivated are not acquired by the front end of the Web, and the fields marked as deactivated are not packed.
S2, a model table is arranged in the database, the data model obtained in the step S1 is recorded into the model table, the packed data obtained in the step S1 is stored in the database, the data model and the packed data of the same building data are correspondingly marked, icon templates are stored and arranged in the database, the icon templates are provided with a plurality of types, and the icon templates are classified according to the icon placement quantity.
S3, the Web front end obtains a data model of related data through interface service with a digital-analog platform, wherein the related data is all data or part of data in building data, icon templates with the same icon placement number are obtained according to the number of the related data, and package data corresponding to marks of each related data are obtained according to the data type of the related data.
The specific method for acquiring the package data of the corresponding mark of each related data according to the data type of the related data comprises the following steps:
s31, aiming at the first index in the related data, acquiring the packaged data of the corresponding mark, and simultaneously inputting the first index into the correlation model to obtain the related index with strong correlation with the first index and the packaged data of the corresponding mark of the related index. The specific construction method of the correlation model comprises the following steps:
s311, acquiring a plurality of groups of historical building data.
S312, constructing a machine learning model, inputting all indexes in a group of historical building data into the machine learning model, performing cluster analysis, and particularly marking the indexes which are classified into one cluster as indexes with correlation by using a DBSCAN algorithm, and marking each index as the index with correlation.
S313, using the method in S312, each index and an index having a correlation with the index mark are obtained.
S314, dividing each index and the marked correlation index into a data set, wherein the data set comprises a target index and a marked index, the marked index is the target index, the marked correlation index is the marked index, and when the number of the marked indexes is larger than a set value, all marked indexes in the data set are set to have strong correlation with the target index.
The set value satisfies the following equation:
;
In the above formula, L represents a set value, and N represents the total index number in the set of historical building data.
S315, training a machine learning model for the historical building data of other groups acquired in the step S311 by using the method in the steps S312-S314, so as to obtain a correlation model.
S32, comparing the related indexes obtained in the step S31 with other indexes in the related data in sequence, directly calling the packaging data of the related indexes when the indexes which are the same as the related data exist, and carrying out no analysis of the step S31 on the related indexes, and carrying out correlation marking on the corresponding packaging data and the packaging data of the first index.
S33, processing the next index by adopting the steps S31-S32, and circulating the steps S31-S33 until the packed data of all indexes are obtained.
S4, the Web front end analyzes the packed data obtained in the step S3 to obtain analysis data, and the analysis data is input into the icon template to obtain a page chart. The process of analyzing the data is to extract data items according to the JSON data structure of the packed data, and because the JSON supports the nested structure, the analyzed data needs to recursively search nested sub-data, and the required specific data items are extracted from the analyzed data to obtain the analyzed data.
When the analysis data is input into the icon template, the analysis data after the analysis of the packed data with the relevance mark in the step S3 is arranged at the adjacent position in the icon template, and the same color is adopted for marking.
And S5, the Web client loads the page diagram in the step S4 into the UE engine through WebUI plug-in units for display.
As shown in FIG. 2, the invention also provides a semi-physical production simulation system, which comprises a UE engine, a Web client and a database, wherein the Web client comprises a digital-to-analog platform and a Web front end, the digital-to-analog platform and the Web front end interact, the Web front end interacts with the UE engine and the database respectively, and the digital-to-analog platform interacts with the database.
The Web client is provided with the Web front end and the digital-analog platform, the Web front end and the digital-analog platform are highly loosely bound and have no strong correlation, the Web front end acquires the data model of the digital-analog platform through interface service, the process is completed in real time, the real-time interaction of data can be completed without secondary development of original codes, the utilization rate of resources is improved, and in addition, when related data is extracted, the extraction of the related data can be completed as soon as possible according to the related indexes of indexes, so that the processing speed is improved.
Finally, it should be noted that the above description is only for illustrating the technical solution of the present invention, and not for limiting the scope of the present invention, and that the simple modification and equivalent substitution of the technical solution of the present invention can be made by those skilled in the art without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. The semi-physical production simulation method is characterized by comprising the following steps of:
S1, constructing a digital-to-analog platform in a Web application end, collecting building data through the digital-to-analog platform, processing the building data to obtain a data model of each building data, packaging the obtained data models to obtain packaged data of the building data, and storing all the data models in the digital-to-analog platform;
S2, a model table is arranged in a database, the data model obtained in the step S1 is recorded into the model table, the packed data obtained in the step S1 is stored in the database, the data model and the packed data of the same building data are correspondingly marked, and an icon template is stored in the database;
s3, the Web front end obtains a data model of related data through interface service with a digital-to-analog platform, wherein the related data is all data or part of data in building data, icon templates with the same icon placement number are obtained according to the number of the related data, package data of corresponding marks of each related data are obtained according to the data type of the related data, and the specific method for obtaining the package data of the corresponding marks of each related data is as follows:
S31, aiming at a first index in the related data, acquiring the packaged data of the corresponding mark, and simultaneously inputting the first index into a correlation model to obtain a related index with strong correlation with the first index and the packaged data of the corresponding mark of the related index, wherein the specific construction method of the correlation model comprises the following steps:
S311, acquiring a plurality of groups of historical building data;
S312, constructing a machine learning model, inputting all indexes in a group of historical building data into the machine learning model, performing cluster analysis, and marking the indexes which are classified into one cluster as indexes with correlation by using a DBSCAN algorithm, and marking each index as the index with correlation;
S313, obtaining each index and the index with correlation with the index mark by using the method in S312;
S314, dividing each index and the marked correlation index into a data set, wherein the data set comprises a target index and a marked index, the marked index is the target index, the marked correlation index is the marked index, and when the number of the marked indexes is larger than a set value, all marked indexes in the data set are set to have strong correlation with the target index;
The set value satisfies the following equation:
;
in the above formula, L represents a set value, and N represents all index numbers in the set of historical building data;
S315, training a machine learning model for the historical building data of other groups acquired in the step S311 by using the method in the steps S312-S314, so as to acquire a correlation model;
S32, comparing the related indexes with other indexes in the related data in sequence, directly calling the packaging data of the related indexes when the indexes which are the same as the related data exist, and then carrying out S31 analysis on the related indexes, and carrying out correlation marking on the corresponding packaging data and the packaging data of the first index;
S33, processing the next index by adopting the steps S31-S32, and circulating the steps S31-S33 until the packed data of all indexes are obtained;
s4, the Web front end analyzes the packed data obtained in the step S3 to obtain analysis data, and the analysis data is input into an icon template to obtain a page chart;
And S5, the Web client loads the page diagram in the step S4 into the UE engine through WebUI plug-in units for display.
2. The semi-physical production simulation method of claim 1, wherein in step S1, a plurality of words are formed by using a natural language processing technology for each index, each word of each index is converted into metadata, and a data model of each index is formed by field representation.
3. The semi-physical production simulation method of claim 2, wherein when the digital model platform obtains new indexes, the new indexes are compared with indexes existing in a database, each new index is divided into a plurality of words by using a natural language processing technology, the words divided by the new indexes are compared with the words divided by the original indexes in terms of identity, and the original indexes are modified according to a comparison result to form a data model of the new indexes.
4. A semi-physical production simulation method according to claim 3, wherein when a new field exists in a new index, that is, when the new index acquired by the digital model platform has the same words as those in the original index, the words in the original index are all located in the new index, the original index with the same words as the new index is used as a replacement index, metadata fields corresponding to all the words in the replacement index are reserved, words which are different from the replacement index in the new index are converted into metadata, and are represented by the fields, and the metadata fields are added to the back of the last field in the replacement index to complete modification of the new index.
5. The semi-physical production simulation method of claim 3, wherein when a replacement field exists in a new index, namely, a word which is the same as an original index exists in the new index obtained by the modeling platform, the word in the original index is only partially positioned in the new index, an original index which is the same as the new index is used as the replacement index, metadata fields corresponding to the word which is the same as the new index in the replacement index are reserved, the word which is different from the new index in the replacement index is correspondingly replaced to a position which is different from the new index in the replacement index, the replaced field is marked as deactivated, the field which is marked as deactivated is set to be not obtained by a Web front end, and the field which is marked as deactivated is not packed.
6. The semi-physical production simulation method of claim 4, wherein when a field to be deleted exists in a new index, namely, a word identical to an original index exists in the new index obtained by the digital model platform, the words in the new index are all located in the original index, the original index with the same word as the new index is used as a replacement index, metadata fields corresponding to the word identical to the new index in the replacement index are reserved, and metadata fields corresponding to the word not existing in the new index are marked as inactive.
7. The semi-physical production simulation method of claim 1, wherein in the step S4, when the analysis data is input into the icon template, the analysis data after the analysis of the packed data with the correlation mark in the step S3 is set at adjacent positions in the icon template, and the same color is used for marking.
8. A semi-physical production simulation system, characterized in that a semi-physical production simulation method according to any one of claims 1-7 is executed, comprising a UE engine, a Web client and a database, wherein the Web client comprises a digital-to-analog platform and a Web front end, the digital-to-analog platform interacts with the Web front end, the Web front end interacts with the UE engine and the database respectively, and the digital-to-analog platform interacts with the database.
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| CN117874886A (en) * | 2024-01-16 | 2024-04-12 | 郑州宝冶钢结构有限公司 | Construction and use method of BIM safety measure family library |
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