CN110658795A - Accurate fusion method and system for digital twin data - Google Patents
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Abstract
The invention discloses a method and a system for accurately fusing digital twin data, wherein the method is suitable for Virtex-5 series FPGA chips of Xilinx company and comprises the following steps: designing a digital twin data accurate fusion front-end module, firstly establishing a BRAM cache to finish caching heterogeneous digital twin data, wherein the cache comprises virtual model data, physical entity data, physical attribute data describing physical attributes of physical entities and behavior rule data describing operation rules of the physical entities, and then adding a data type field and a UTC timestamp field to each type of data to form a new data frame; designing a digital twin data accurate fusion rear-end module, firstly establishing four BRAMs to respectively cache received new data frames of different types, and then fusing virtual model data, physical entity data, physical attribute data and behavior rule data by taking a timestamp in a physical entity data frame as a reference. The method can improve the accuracy of the fusion between the heterogeneous data in the digital twin system to a certain extent.
Description
Technical Field
The invention belongs to the field of electronic engineering and computer science, and particularly relates to a method and a system for accurately fusing digital twin data.
Background
The coupling degree of digital twins and intelligent manufacturing is higher and higher, the ground of a digital twins technology is one of key elements for promoting intelligent manufacturing development, heterogeneous data exists in a digital twins system, the data comprises physical entity data generated by physical equipment of a workshop, virtual model data generated by a virtual model corresponding to the physical equipment of the workshop and related to simulation prediction, physical data describing the physical attributes of the physical equipment of the workshop, behavior rule data for describing the process flow and behavior rules of the physical equipment and the like, and how to effectively fuse the heterogeneous data is worthy of research, so that unified service is output to upper-layer users. Therefore, the invention discloses a precise fusion method of digital twin data, which is suitable for Virtex-5 series FPGA chips of Xilinx company and can improve the precision of fusion between heterogeneous data in a digital twin system to a certain extent.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method covers the design of a front-end module for precise fusion of digital twin data and the design of a rear-end module for precise fusion of digital twin data, aims at the requirement of effective fusion of heterogeneous twin data and further outputting unified service for upper-layer users, and can improve the precision of fusion between heterogeneous data in a digital twin system to a certain extent.
The technical problem to be solved by the invention is realized by adopting the following technical scheme: a method for accurately fusing digital twin data comprises the following steps:
(1) designing a digital twin data accurate fusion front-end module, which is specifically realized as follows:
firstly, establishing BRAM to finish caching virtual model data, physical entity data, physical attribute data describing physical attributes of physical entities and behavior rule data describing operation rules of the physical entities in a digital twin system;
adding fields to each different type of data in BRAM, including virtual model data, physical entity data, physical attribute data and behavior rule data, to form a new data frame: firstly, adding a data type field, wherein the field is used for identifying the type of data; then adding a UTC timestamp field, wherein the field is used for identifying the acquisition time of the frame data;
(2) designing a digital twin data accurate fusion rear-end module, and realizing twin data fusion by taking a UTC time stamp in a physical entity data frame as a reference, wherein the method is specifically realized as follows:
firstly, four BRAMs are established to respectively finish caching of different types of new data frames in the step (1), namely a BRAM _1 storage virtual model data frame, a BRAM _2 storage physical entity data frame, a BRAM _3 storage physical attribute data frame and a BRAM _4 storage behavior rule data frame; establishing four FIFOs, namely FIFO _1, FIFO _2, FIFO _3 and FIFO _ 4;
reading a first physical entity data frame in BRAM _2 to obtain a UTC time stamp in the data frame;
reading all virtual model data frames in BRAM _1 in sequence, respectively calculating the difference between the UTC time stamp of the virtual model data frames and the UTC time stamp in BRAM _1, and when the difference is less than or equal to a user set value, showing that the virtual model data frames are consistent with the physical entity data frames in BRAM _1 in the time dimension, and storing the virtual model data frames and the physical entity data frames in FIFO _ 1;
reading all physical attribute data frames in BRAM _3 in sequence, respectively calculating the difference between the UTC time stamp of the physical attribute data frames and the UTC time stamp in BRAM _3, and when the difference is less than or equal to a user set value, indicating that the physical attribute data frames are consistent with the physical entity data frames in BRAM _3 in the time dimension, and storing the physical attribute data frames and the physical entity data frames in FIFO _ 3;
sequentially reading all behavior rule data frames in BRAM _4, respectively calculating the difference between the UTC time stamp of the behavior rule data frames and the UTC time stamp in BRAM _4, and when the difference is less than or equal to a user set value, indicating that the behavior rule data frames are consistent with the physical entity data frames in BRAM _4 in the time dimension, and storing the behavior rule data frames and the physical entity data frames in FIFO _ 4;
storing the first physical entity data frame in BRAM _2 into FIFO _ 2;
seventhly, reading a first physical entity data frame in the FIFO _2, a first virtual model data frame in the FIFO _1, a first physical attribute data frame in the FIFO _3 and a first behavior rule data frame in the FIFO _4 respectively, and recombining the first physical entity data frame, the first virtual model data frame and the first behavior rule data frame into a frame of data to complete the fusion of the digital twin data, wherein the frame format comprises the following steps: frame header, data type, physical entity data frame, data type, virtual model data frame, data type, physical attribute data frame, data type, behavior rule data frame and frame tail; if a certain FIFO is empty, the corresponding field in the fused frame format is filled with zero, and the length is 1 byte, if FIFO _1 in the third FIFO is empty, the fused frame format is expressed as: frame header, data type, physical entity data frame, data type, zero, data type, physical attribute data frame, data type, behavior rule data frame and frame tail;
emptying FIFO _1, FIFO _2, FIFO _3 and FIFO _4 respectively;
and ninthly, returning to read the second physical data frame in BRAM _2, and then executing to the step (b) until all the physical data frames in BRAM _2 are completely read.
The invention discloses a precise digital twin data fusion method which is suitable for Virtex-5 series FPGA chips of Xilinx company.
The invention also provides a system for accurately fusing the digital twin data, which comprises the following steps: the digital twinborn data precise fusion front-end module and the digital twinborn data precise fusion rear-end module; wherein the content of the first and second substances,
the digital twin data precise fusion front-end module realizes the following functions:
firstly, establishing BRAM to finish caching virtual model data, physical entity data, physical attribute data describing physical attributes of physical entities and behavior rule data describing operation rules of the physical entities in a digital twin system;
adding fields to each different type of data in BRAM, including virtual model data, physical entity data, physical attribute data and behavior rule data, to form a new data frame: firstly, adding a data type field, wherein the field is used for identifying the type of data; then adding a UTC timestamp field, wherein the field is used for identifying the acquisition time of the frame data;
the digital twin data precise fusion rear-end module realizes twin data fusion by taking a UTC time stamp in a physical entity data frame as a reference, and is specifically realized as follows:
firstly, four BRAMs are established to respectively finish caching of different types of new data frames in the step (1), namely a BRAM _1 storage virtual model data frame, a BRAM _2 storage physical entity data frame, a BRAM _3 storage physical attribute data frame and a BRAM _4 storage behavior rule data frame; establishing four FIFOs, namely FIFO _1, FIFO _2, FIFO _3 and FIFO _ 4;
reading a first physical entity data frame in BRAM _2 to obtain a UTC time stamp in the data frame;
reading all virtual model data frames in BRAM _1 in sequence, respectively calculating the difference between the UTC time stamp of the virtual model data frames and the UTC time stamp in BRAM _1, and when the difference is less than or equal to a user set value, showing that the virtual model data frames are consistent with the physical entity data frames in BRAM _1 in the time dimension, and storing the virtual model data frames and the physical entity data frames in FIFO _ 1;
reading all physical attribute data frames in BRAM _3 in sequence, respectively calculating the difference between the UTC time stamp of the physical attribute data frames and the UTC time stamp in BRAM _3, and when the difference is less than or equal to a user set value, indicating that the physical attribute data frames are consistent with the physical entity data frames in BRAM _3 in the time dimension, and storing the physical attribute data frames and the physical entity data frames in FIFO _ 3;
sequentially reading all behavior rule data frames in BRAM _4, respectively calculating the difference between the UTC time stamp of the behavior rule data frames and the UTC time stamp in BRAM _4, and when the difference is less than or equal to a user set value, indicating that the behavior rule data frames are consistent with the physical entity data frames in BRAM _4 in the time dimension, and storing the behavior rule data frames and the physical entity data frames in FIFO _ 4;
storing the first physical entity data frame in BRAM _2 into FIFO _ 2;
seventhly, reading a first physical entity data frame in the FIFO _2, a first virtual model data frame in the FIFO _1, a first physical attribute data frame in the FIFO _3 and a first behavior rule data frame in the FIFO _4 respectively, and recombining the first physical entity data frame, the first virtual model data frame and the first behavior rule data frame into a frame of data to complete the fusion of the digital twin data, wherein the frame format comprises the following steps: frame header, data type, physical entity data frame, data type, virtual model data frame, data type, physical attribute data frame, data type, behavior rule data frame and frame tail; if a certain FIFO is empty, filling the corresponding field in the fused frame format with zero, wherein the length of the field is 1 byte;
emptying FIFO _1, FIFO _2, FIFO _3 and FIFO _4 respectively;
and ninthly, returning to read the second physical data frame in BRAM _2, and then executing to the step (b) until all the physical data frames in BRAM _2 are completely read.
Has the advantages that:
compared with the prior art, the invention has the advantages that:
(1) the UTC timestamp-based data fusion can ensure high consistency of various types of data in time and improve the accuracy of data fusion;
(2) the time stamp in the physical entity data in the digital twin system is taken as a reference, so that the data validity after the data fusion can be ensured, and the physical equipment is the basis of the digital twin system;
(3) the processing mode based on the multiple BRAMs and the multiple FIFO buffers can improve the efficiency of fusing each frame data in each type of data and reduce the omission of the data as much as possible.
Drawings
FIG. 1 is a block diagram of a digital twin data precise fusion system according to the present invention;
FIG. 2 is a flow chart of a digital twin data precise fusion method of the present invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawings.
The invention relates to a precise fusion method of digital twin data, which is suitable for programmable logic chips such as FPGA and the like; such as Xilinx Virtex-5 series FPGA chips. Data of heterogeneous types exist in the digital twin system, and the data comprises physical entity data generated by physical plant equipment, virtual model data generated by a virtual model corresponding to the physical plant equipment and related to simulation prediction, physical data describing physical attributes of the physical plant equipment, behavior rule data used for describing process flows and behavior rules of the physical plant equipment, and the like. The method disclosed by the invention comprises the design of a digital twin data precise fusion front-end module and the design of a digital twin data precise fusion rear-end module, and can improve the precision of the fusion between heterogeneous data in a digital twin system to a certain extent.
The structural block diagram of the invention is shown in fig. 1, and the specific implementation mode is as follows:
(1) fig. 1 shows a digital twin data precision fusion front-end module, which is implemented as follows:
establishing a BRAM (Block Random Access Memory) to finish caching virtual model data, physical entity data, physical attribute data describing physical attributes of physical entities and behavior rule data describing operation rules of the physical entities in a digital twin system;
adding fields to each different type of data in BRAM, including virtual model data, physical entity data, physical attribute data and behavior rule data, to form a new data frame:
firstly, adding a data type field, wherein the field is used for identifying the type of data;
then adding a UTC (coordinated universal time) timestamp field for identifying the acquisition time of the frame data, and the UTC timestamp-based data fusion can ensure high consistency of various types of data in time and improve the accuracy of the data fusion;
(2) 2 in fig. 1 represents a digital twin data accurate fusion backend module, which is implemented as follows, and is shown in fig. 2:
firstly, four BRAMs are established to respectively finish caching of different types of new data frames in the step (1), namely a BRAM _1 storage virtual model data frame, a BRAM _2 storage physical entity data frame, a BRAM _3 storage physical attribute data frame and a BRAM _4 storage behavior rule data frame; establishing four FIFOs, namely FIFO _1, FIFO _2, FIFO _3 and FIFO _ 4; the processing mode based on the multi-BRAM and the multi-FIFO cache can improve the efficiency of fusing each frame data in each type of data and reduce the omission of the data as much as possible;
reading a first physical entity data frame in BRAM _2 to obtain a UTC time stamp in the data frame, and taking the time stamp in the physical entity data as a reference, so that the validity of data fusion can be ensured, and physical equipment is the basis of a digital twin system;
reading all virtual model data frames in BRAM _1 in sequence, respectively calculating the difference between the UTC time stamp of the virtual model data frames and the UTC time stamp in BRAM _1, and when the difference is less than or equal to a user set value, showing that the virtual model data frames are consistent with the physical entity data frames in BRAM _1 in the time dimension, and storing the virtual model data frames and the physical entity data frames in FIFO _ 1;
reading all physical attribute data frames in BRAM _3 in sequence, respectively calculating the difference between the UTC time stamp of the physical attribute data frames and the UTC time stamp in BRAM _3, and when the difference is less than or equal to a user set value, indicating that the physical attribute data frames are consistent with the physical entity data frames in BRAM _3 in the time dimension, and storing the physical attribute data frames and the physical entity data frames in FIFO _ 3;
sequentially reading all behavior rule data frames in BRAM _4, respectively calculating the difference between the UTC time stamp of the behavior rule data frames and the UTC time stamp in BRAM _4, and when the difference is less than or equal to a user set value, indicating that the behavior rule data frames are consistent with the physical entity data frames in BRAM _4 in the time dimension, and storing the behavior rule data frames and the physical entity data frames in FIFO _ 4;
storing the first physical entity data frame in BRAM _2 into FIFO _ 2;
seventhly, reading a first physical entity data frame in the FIFO _2, a first virtual model data frame in the FIFO _1, a first physical attribute data frame in the FIFO _3 and a first behavior rule data frame in the FIFO _4 respectively, and recombining the first physical entity data frame, the first virtual model data frame and the first behavior rule data frame into a frame of data to complete the fusion of the digital twin data, wherein the frame format comprises the following steps: the data type field is used for representing the type of the data frame and comprises a physical entity data frame, a virtual model data frame, a physical attribute data frame and a behavior rule data frame, in order to distinguish the four data frames, the length of the data type field needs to be more than or equal to 2 bits, and taking the length of the data type field as 2 bits as an example, the data type field can be represented by '00', the '01' can represent the virtual model data frame, the '10' can represent the physical attribute data frame and the '11' can represent the behavior rule data frame; if a certain FIFO is empty, the corresponding field in the fused frame format is filled with zero, and the length is 1 byte, if FIFO _1 in the third FIFO is empty, the fused frame format is expressed as: frame header, data type, physical entity data frame, data type, zero, data type, physical attribute data frame, data type, behavior rule data frame and frame tail;
emptying FIFO _1, FIFO _2, FIFO _3 and FIFO _4 respectively;
and ninthly, returning to read the second physical data frame in BRAM _2, and then executing to the step (b) until all the physical data frames in BRAM _2 are completely read.
In summary, the invention discloses a method and a system for accurately fusing digital twin data, which comprises a front-end module design for accurately fusing digital twin data and a rear-end module design for accurately fusing digital twin data, and can improve the accuracy of fusion between heterogeneous data in a digital twin system to a certain extent.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (4)
1. A method for accurately fusing digital twin data is characterized by comprising the following steps:
designing a digital twin data accurate fusion front-end module in the step (1), and specifically realizing the following steps:
firstly, establishing BRAM to finish caching virtual model data, physical entity data, physical attribute data describing physical attributes of physical entities and behavior rule data describing operation rules of the physical entities in a digital twin system;
adding fields to each different type of data in BRAM, including virtual model data, physical entity data, physical attribute data and behavior rule data, to form a new data frame: firstly, adding a data type field, wherein the field is used for identifying the type of data; then adding a UTC timestamp field, wherein the field is used for identifying the acquisition time of the frame data;
designing a digital twin data accurate fusion rear-end module, and realizing twin data fusion by taking a UTC time stamp in a physical entity data frame as a reference, wherein the method is specifically realized as follows:
firstly, four BRAMs are established to respectively finish caching of different types of new data frames in the step (1), namely a BRAM _1 storage virtual model data frame, a BRAM _2 storage physical entity data frame, a BRAM _3 storage physical attribute data frame and a BRAM _4 storage behavior rule data frame; establishing four FIFOs, namely FIFO _1, FIFO _2, FIFO _3 and FIFO _ 4;
reading a first physical entity data frame in BRAM _2 to obtain a UTC time stamp in the data frame;
reading all virtual model data frames in BRAM _1 in sequence, respectively calculating the difference between the UTC time stamp of the virtual model data frames and the UTC time stamp in BRAM _1, and when the difference is less than or equal to a user set value, showing that the virtual model data frames are consistent with the physical entity data frames in BRAM _1 in the time dimension, and storing the virtual model data frames and the physical entity data frames in FIFO _ 1;
reading all physical attribute data frames in BRAM _3 in sequence, respectively calculating the difference between the UTC time stamp of the physical attribute data frames and the UTC time stamp in BRAM _3, and when the difference is less than or equal to a user set value, indicating that the physical attribute data frames are consistent with the physical entity data frames in BRAM _3 in the time dimension, and storing the physical attribute data frames and the physical entity data frames in FIFO _ 3;
sequentially reading all behavior rule data frames in BRAM _4, respectively calculating the difference between the UTC time stamp of the behavior rule data frames and the UTC time stamp in BRAM _4, and when the difference is less than or equal to a user set value, indicating that the behavior rule data frames are consistent with the physical entity data frames in BRAM _4 in the time dimension, and storing the behavior rule data frames and the physical entity data frames in FIFO _ 4;
storing the first physical entity data frame in BRAM _2 into FIFO _ 2;
seventhly, reading a first physical entity data frame in the FIFO _2, a first virtual model data frame in the FIFO _1, a first physical attribute data frame in the FIFO _3 and a first behavior rule data frame in the FIFO _4 respectively, and recombining the first physical entity data frame, the first virtual model data frame and the first behavior rule data frame into a frame of data to complete the fusion of the digital twin data, wherein the frame format comprises the following steps: frame header, data type, physical entity data frame, data type, virtual model data frame, data type, physical attribute data frame, data type, behavior rule data frame and frame tail; if a certain FIFO is empty, filling the corresponding field in the fused frame format with zero, wherein the length of the field is 1 byte;
emptying FIFO _1, FIFO _2, FIFO _3 and FIFO _4 respectively;
and ninthly, returning to read the second physical data frame in BRAM _2, and then executing to the step (b) until all the physical data frames in BRAM _2 are completely read.
2. The method for precisely fusing the digital twin data as claimed in claim 1, wherein the method is applied to Xilinx Virtex-5 series FPGA chips.
3. A digital twin data precision fusion system, comprising: the digital twinborn data precise fusion front-end module and the digital twinborn data precise fusion rear-end module; wherein the content of the first and second substances,
the digital twin data precise fusion front-end module realizes the following functions:
firstly, establishing BRAM to finish caching virtual model data, physical entity data, physical attribute data describing physical attributes of physical entities and behavior rule data describing operation rules of the physical entities in a digital twin system;
adding fields to each different type of data in BRAM, including virtual model data, physical entity data, physical attribute data and behavior rule data, to form a new data frame: firstly, adding a data type field, wherein the field is used for identifying the type of data; then adding a UTC timestamp field, wherein the field is used for identifying the acquisition time of the frame data;
the digital twin data precise fusion rear-end module realizes twin data fusion by taking a UTC time stamp in a physical entity data frame as a reference, and is specifically realized as follows:
firstly, four BRAMs are established to respectively finish caching of different types of new data frames in the step (1), namely a BRAM _1 storage virtual model data frame, a BRAM _2 storage physical entity data frame, a BRAM _3 storage physical attribute data frame and a BRAM _4 storage behavior rule data frame; establishing four FIFOs, namely FIFO _1, FIFO _2, FIFO _3 and FIFO _ 4;
reading a first physical entity data frame in BRAM _2 to obtain a UTC time stamp in the data frame;
reading all virtual model data frames in BRAM _1 in sequence, respectively calculating the difference between the UTC time stamp of the virtual model data frames and the UTC time stamp in BRAM _1, and when the difference is less than or equal to a user set value, showing that the virtual model data frames are consistent with the physical entity data frames in BRAM _1 in the time dimension, and storing the virtual model data frames and the physical entity data frames in FIFO _ 1;
reading all physical attribute data frames in BRAM _3 in sequence, respectively calculating the difference between the UTC time stamp of the physical attribute data frames and the UTC time stamp in BRAM _3, and when the difference is less than or equal to a user set value, indicating that the physical attribute data frames are consistent with the physical entity data frames in BRAM _3 in the time dimension, and storing the physical attribute data frames and the physical entity data frames in FIFO _ 3;
sequentially reading all behavior rule data frames in BRAM _4, respectively calculating the difference between the UTC time stamp of the behavior rule data frames and the UTC time stamp in BRAM _4, and when the difference is less than or equal to a user set value, indicating that the behavior rule data frames are consistent with the physical entity data frames in BRAM _4 in the time dimension, and storing the behavior rule data frames and the physical entity data frames in FIFO _ 4;
storing the first physical entity data frame in BRAM _2 into FIFO _ 2;
seventhly, reading a first physical entity data frame in the FIFO _2, a first virtual model data frame in the FIFO _1, a first physical attribute data frame in the FIFO _3 and a first behavior rule data frame in the FIFO _4 respectively, and recombining the first physical entity data frame, the first virtual model data frame and the first behavior rule data frame into a frame of data to complete the fusion of the digital twin data, wherein the frame format comprises the following steps: frame header, data type, physical entity data frame, data type, virtual model data frame, data type, physical attribute data frame, data type, behavior rule data frame and frame tail; if a certain FIFO is empty, filling the corresponding field in the fused frame format with zero, wherein the length of the field is 1 byte;
emptying FIFO _1, FIFO _2, FIFO _3 and FIFO _4 respectively;
and ninthly, returning to read the second physical data frame in BRAM _2, and then executing to the step (b) until all the physical data frames in BRAM _2 are completely read.
4. The system for precisely fusing digital twin data as claimed in claim 1, wherein the system is adapted to Xilinx Virtex-5 series FPGA chips.
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| CN113111201A (en) * | 2021-04-19 | 2021-07-13 | 北京航空航天大学 | Digital twin model lightweight method and system |
| CN115270213A (en) * | 2022-06-30 | 2022-11-01 | 北京亚控科技发展有限公司 | Twin method, subscription method and system for current state of entity |
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