CN117973087A - Big data prediction method based on multi-modal digital twin technology - Google Patents
Big data prediction method based on multi-modal digital twin technology Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000005516 engineering process Methods 0.000 title claims abstract description 11
- 238000012544 monitoring process Methods 0.000 claims abstract description 70
- 230000002159 abnormal effect Effects 0.000 claims abstract description 16
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 239000000428 dust Substances 0.000 claims description 87
- 230000004927 fusion Effects 0.000 claims description 39
- 238000004364 calculation method Methods 0.000 claims description 15
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 2
- 230000009286 beneficial effect Effects 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000007499 fusion processing Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 229920000832 Cutin Polymers 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- 241000208125 Nicotiana Species 0.000 description 1
- 235000002637 Nicotiana tabacum Nutrition 0.000 description 1
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000975 dye Substances 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000011152 fibreglass Substances 0.000 description 1
- 235000013312 flour Nutrition 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
- 229920003023 plastic Polymers 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- HBMJWWWQQXIZIP-UHFFFAOYSA-N silicon carbide Chemical compound [Si+]#[C-] HBMJWWWQQXIZIP-UHFFFAOYSA-N 0.000 description 1
- 229910010271 silicon carbide Inorganic materials 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
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Abstract
The invention discloses a big data prediction method based on a multi-mode digital twin technology, which belongs to the technical field of data processing and comprises the following steps: s1, preprocessing a monitoring data set to obtain a standard monitoring data set of an area to be monitored; s2, constructing a virtual working condition model for the area to be monitored according to a standard monitoring data set of the area to be monitored; s3, predicting an abnormal operation area of the area to be monitored. According to the method, multiple aspects of data acquisition and analysis are carried out on the area to be monitored, and a virtual working condition model capable of reflecting the running condition of the whole area data set is constructed; then, the invention analyzes the data of the specific position of the area to be monitored, and determines the specific position coordinates of abnormal data operation, thereby generating an abnormal operation area; the whole process fully considers a plurality of parameters of the area to be monitored, carries out accurate area division and ensures the accuracy of data monitoring.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a big data prediction method based on a multi-mode digital twin technology.
Background
With the development of technology, there are still some problems and challenges in the area of campus facility management. Traditional campus facility management is often based on experience and rules, and lacks scientific data analysis decisions, resulting in difficulty in effectively predicting abnormal areas of the campus. The digital twin is to fully utilize data such as a physical model, sensor updating and operation history, integrate simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities, and complete mapping in a virtual space so as to reflect the full life cycle process of corresponding entity equipment. How to solve the prediction problem of the park by utilizing the digital twin technology becomes an urgent problem to be solved.
Disclosure of Invention
In order to solve the problems, the invention provides a big data prediction method based on a multi-mode digital twin technology.
The technical scheme of the invention is as follows: the big data prediction method based on the multi-modal digital twin technology comprises the following steps:
S1, collecting a monitoring data set of an area to be monitored, and preprocessing the monitoring data set to obtain a standard monitoring data set of the area to be monitored;
S2, acquiring an electronic map of the area to be monitored, and constructing a virtual working condition model for the area to be monitored according to a standard monitoring data set of the area to be monitored;
s3, predicting an abnormal operation area of the area to be monitored according to the virtual working condition model;
In S1, a monitoring data set of a region to be monitored comprises organic dust concentration and inorganic dust concentration at each moment;
in S1, the method for preprocessing the monitoring data set specifically comprises the following steps: performing de-duplication treatment and normalization treatment on the organic dust concentration and the inorganic dust concentration at each moment to obtain a standard organic dust concentration and a standard inorganic dust concentration at each moment;
S2 comprises the following substeps:
s21, acquiring an electronic map of an area to be monitored, and determining a building sub-area, a greening sub-area and an air-ground sub-area of the area to be monitored according to the electronic map of the area to be monitored;
s22, determining a monitoring fusion coefficient between a building sub-region and a greening sub-region, a monitoring fusion coefficient between the building sub-region and an air-ground sub-region and a monitoring fusion coefficient between the greening sub-region and the air-ground sub-region according to a standard monitoring data set of the region to be monitored;
S23, constructing a virtual working condition model for the area to be monitored according to the monitoring fusion coefficient between the building sub-area and the greening sub-area, the monitoring fusion coefficient between the building sub-area and the air-ground sub-area and the monitoring fusion coefficient between the greening sub-area and the air-ground sub-area.
The beneficial effects of the above-mentioned further scheme are: according to the invention, the building distribution, greening area distribution and air-space area distribution of the area can be seen through the electronic map of the area to be monitored, the monitoring data sets between every two areas are subjected to fusion processing, so that the obtained virtual working condition area distribution is obtained, the monitoring data sets between every two areas are subjected to fusion processing, and the obtained virtual working condition model can comprehensively reflect the dust concentration monitoring data condition of the whole area.
Further, in S22, the calculation formula of the monitoring fusion coefficient M b_g between the building sub-area and the greening sub-area is:
; wherein S b represents the area of a building subarea, S g represents the area of a greening subarea, S represents the area of an area to be monitored, e represents an index, U represents the number of buildings in the building subarea, T represents the acquisition time period,/> Represents the standard organic dust concentration of the u-th building in the building sub-area at time t,/>Represents the standard inorganic dust concentration of the u-th building in the building subregion at time t,/>Represents the standard organic dust concentration of the greening subarea at the time t/(The standard inorganic dust concentration of the greening subareas at the time t is represented;
In S22, the calculation formula of the monitoring fusion coefficient M b_o between the building sub-area and the air-ground sub-area is:
; where S o represents the area of the open area region,/> Represents the standard organic dust concentration of the air-ground subarea at the time t,/>The standard inorganic dust concentration of the air-ground subarea at the time t is represented;
In S22, the calculation formula of the monitoring fusion coefficient M g_o between the greening sub-area and the air-ground sub-area is as follows:
。
Further, in S23, the expression of the virtual operating mode model a is:
，/> ; wherein M b_g represents a monitoring fusion coefficient between a building sub-region and a greening sub-region, M b_o represents a monitoring fusion coefficient between a building sub-region and an air-ground sub-region, M g_o represents a monitoring fusion coefficient between a greening sub-region and an air-ground sub-region, X represents a monitoring fusion matrix, I represents an identity matrix, and I.I.I F represents an F norm of the matrix.
Further, S3 comprises the following sub-steps:
s31, setting a normal organic dust concentration operation interval and a normal inorganic dust concentration operation interval of a region to be monitored;
s32, determining virtual organic dust concentration operation values and virtual inorganic dust concentration operation values of all positions in the area to be monitored according to the virtual working condition model of the area to be monitored;
S33, taking the position of the virtual organic dust concentration operation value which does not belong to the normal organic dust concentration operation interval and the position of the virtual inorganic dust concentration operation value which does not belong to the normal inorganic dust concentration operation interval as an abnormal operation area of the area to be monitored.
The beneficial effects of the above-mentioned further scheme are: in the present invention, in S31, the normal operation interval may be set according to a plurality of tests, or may be set manually according to actual conditions. S2 is focused on overall area division of the area to be monitored, S3 is focused on predicting specific positions (detailed to specific coordinates) of abnormal operation of the area to be monitored, so that virtual organic dust operation values and virtual inorganic dust operation values of specific coordinate positions are calculated in the step, and whether the specific position coordinates are abnormal or not can be judged through a set operation interval, and the whole process is convenient and concise.
Further, in S32, the calculation formula of the virtual organic dust concentration running value F x_y at the position with x-axis and y-axis in the area to be monitored is:
; in the/> The method comprises the steps of representing standard organic dust concentration of a position with x abscissa and y ordinate in a region to be monitored at a time T, round (·) representing rounding operation, A representing a virtual working condition model of the region to be monitored, and T representing acquisition time;
In S32, the calculation formula of the virtual inorganic dust concentration running value f x_y at the position with x on the abscissa and y on the ordinate in the area to be monitored is:
; in the/> The standard inorganic dust concentration at time t is indicated for the position x on the abscissa and y on the ordinate in the area to be monitored.
The beneficial effects of the invention are as follows: the method comprises the steps of collecting and analyzing data of dust concentration in a region to be monitored, and constructing a virtual working condition model capable of reflecting the running condition of a data set of the whole region; then, the invention analyzes the data of the specific position of the area to be monitored, and determines the specific position coordinates of abnormal data operation, thereby predicting the abnormal operation area; the whole process fully considers the organic dust concentration parameter and the inorganic dust concentration parameter of the area to be monitored, carries out accurate area division, ensures the accuracy of data prediction and provides effective basis for the exceeding of the dust concentration of the park prediction.
Drawings
FIG. 1 is a flow chart of a big data prediction method based on a multi-modal digital twinning technique.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a big data prediction method based on a multi-modal digital twin technology, which comprises the following steps:
S1, collecting a monitoring data set of an area to be monitored, and preprocessing the monitoring data set to obtain a standard monitoring data set of the area to be monitored;
S2, acquiring an electronic map of the area to be monitored, and constructing a virtual working condition model for the area to be monitored according to a standard monitoring data set of the area to be monitored;
s3, predicting an abnormal operation area of the area to be monitored according to the virtual working condition model;
In S1, a monitoring data set of a region to be monitored comprises organic dust concentration and inorganic dust concentration at each moment;
In S1, the method for preprocessing the monitoring data set specifically comprises the following steps: and carrying out de-duplication treatment and normalization treatment on the organic dust concentration and the inorganic dust concentration at each moment to obtain the standard organic dust concentration and the standard inorganic dust concentration at each moment.
Inorganic dust includes mineral dust (e.g., sand, coal), metallic dust (e.g., iron, tin, lead, and compounds thereof), artificial inorganic dust (e.g., silicon carbide, cement, fiberglass); organic dust includes plant dust (such as wood, tobacco, flour), animal dust (such as hide, cutin, hair), artificial organic dust (such as explosives, organic dyes, plastics, chemical fibers).
S2 comprises the following substeps:
s21, acquiring an electronic map of an area to be monitored, and determining a building sub-area, a greening sub-area and an air-ground sub-area of the area to be monitored according to the electronic map of the area to be monitored;
s22, determining a monitoring fusion coefficient between a building sub-region and a greening sub-region, a monitoring fusion coefficient between the building sub-region and an air-ground sub-region and a monitoring fusion coefficient between the greening sub-region and the air-ground sub-region according to a standard monitoring data set of the region to be monitored;
S23, constructing a virtual working condition model for the area to be monitored according to the monitoring fusion coefficient between the building sub-area and the greening sub-area, the monitoring fusion coefficient between the building sub-area and the air-ground sub-area and the monitoring fusion coefficient between the greening sub-area and the air-ground sub-area.
According to the invention, the building distribution, greening area distribution and air-space area distribution of the area can be seen through the electronic map of the area to be monitored, the monitoring data sets between every two areas are fused, so that the obtained virtual working condition area distribution is obtained, the monitoring data sets between every two areas are fused, and the obtained virtual working condition model can comprehensively reflect the monitoring data condition of the whole area.
In the embodiment of the present invention, in S22, the calculation formula of the monitoring fusion coefficient M b_g between the building sub-area and the greening sub-area is:
; wherein S b represents the area of a building subarea, S g represents the area of a greening subarea, S represents the area of an area to be monitored, e represents an index, U represents the number of buildings in the building subarea, T represents the acquisition time period,/> Represents the standard organic dust concentration of the u-th building in the building sub-area at time t,/>Represents the standard inorganic dust concentration of the u-th building in the building subregion at time t,/>Represents the standard organic dust concentration of the greening subarea at the time t/(The standard inorganic dust concentration of the greening subareas at the time t is represented;
In S22, the calculation formula of the monitoring fusion coefficient M b_o between the building sub-area and the air-ground sub-area is:
; where S o represents the area of the open area region,/> Represents the standard organic dust concentration of the air-ground subarea at the time t,/>The standard inorganic dust concentration of the air-ground subarea at the time t is represented;
In S22, the calculation formula of the monitoring fusion coefficient M g_o between the greening sub-area and the air-ground sub-area is as follows:
。
in the embodiment of the present invention, in S23, the expression of the virtual operating mode model a is:
，/> ; wherein M b_g represents a monitoring fusion coefficient between a building sub-region and a greening sub-region, M b_o represents a monitoring fusion coefficient between a building sub-region and an air-ground sub-region, M g_o represents a monitoring fusion coefficient between a greening sub-region and an air-ground sub-region, X represents a monitoring fusion matrix, I represents an identity matrix, and I.I.I F represents an F norm of the matrix.
In an embodiment of the present invention, S3 comprises the following sub-steps:
s31, setting a normal organic dust concentration operation interval and a normal inorganic dust concentration operation interval of a region to be monitored;
s32, determining virtual organic dust concentration operation values and virtual inorganic dust concentration operation values of all positions in the area to be monitored according to the virtual working condition model of the area to be monitored;
S33, taking the position of the virtual organic dust concentration operation value which does not belong to the normal organic dust concentration operation interval and the position of the virtual inorganic dust concentration operation value which does not belong to the normal inorganic dust concentration operation interval as an abnormal operation area of the area to be monitored.
In the present invention, in S31, the normal operation interval may be set according to a plurality of tests, or may be set manually according to actual conditions. S2 is focused on overall area division of the area to be monitored, S3 is focused on determining specific positions (detailed to specific coordinates) of abnormal operation of the area to be monitored, so that virtual dust operation values, virtual temperature operation values and virtual noise operation values of specific coordinate positions are calculated in the step, and whether the specific position coordinates are abnormal or not can be judged through a set operation interval, and the whole process is convenient and concise.
In the embodiment of the present invention, in S32, the calculation formula of the virtual organic dust concentration running value F x_y at the position with x-axis and y-axis in the area to be monitored is:
; in the/> The method comprises the steps of representing standard organic dust concentration of a position with x abscissa and y ordinate in a region to be monitored at a time T, round (·) representing rounding operation, A representing a virtual working condition model of the region to be monitored, and T representing acquisition time;
In S32, the calculation formula of the virtual inorganic dust concentration running value f x_y at the position with x on the abscissa and y on the ordinate in the area to be monitored is:
; in the/> The standard inorganic dust concentration at time t is indicated for the position x on the abscissa and y on the ordinate in the area to be monitored. In the above process, the parameters may be subjected to a dimension removal process.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (5)
1. The big data prediction method based on the multi-modal digital twin technology is characterized by comprising the following steps of:
S1, collecting a monitoring data set of an area to be monitored, and preprocessing the monitoring data set to obtain a standard monitoring data set of the area to be monitored;
S2, acquiring an electronic map of the area to be monitored, and constructing a virtual working condition model for the area to be monitored according to a standard monitoring data set of the area to be monitored;
s3, predicting an abnormal operation area of the area to be monitored according to the virtual working condition model;
in the step S1, a monitoring data set of a region to be monitored comprises organic dust concentration and inorganic dust concentration at each moment;
in the step S1, the method for preprocessing the monitoring data set specifically comprises the following steps: performing de-duplication treatment and normalization treatment on the organic dust concentration and the inorganic dust concentration at each moment to obtain a standard organic dust concentration and a standard inorganic dust concentration at each moment;
the step S2 comprises the following substeps:
s21, acquiring an electronic map of an area to be monitored, and determining a building sub-area, a greening sub-area and an air-ground sub-area of the area to be monitored according to the electronic map of the area to be monitored;
s22, determining a monitoring fusion coefficient between a building sub-region and a greening sub-region, a monitoring fusion coefficient between the building sub-region and an air-ground sub-region and a monitoring fusion coefficient between the greening sub-region and the air-ground sub-region according to a standard monitoring data set of the region to be monitored;
S23, constructing a virtual working condition model for the area to be monitored according to the monitoring fusion coefficient between the building sub-area and the greening sub-area, the monitoring fusion coefficient between the building sub-area and the air-ground sub-area and the monitoring fusion coefficient between the greening sub-area and the air-ground sub-area.
2. The big data prediction method based on the multi-modal digital twin technology according to claim 1, wherein in S22, a calculation formula of the monitoring fusion coefficient M b_g between the building sub-area and the greening sub-area is:
; wherein S b represents the area of a building subarea, S g represents the area of a greening subarea, S represents the area of an area to be monitored, e represents an index, U represents the number of buildings in the building subarea, T represents the acquisition time period,/> Represents the standard organic dust concentration of the u-th building in the building sub-area at time t,/>Represents the standard inorganic dust concentration of the u-th building in the building subregion at time t,/>Represents the standard organic dust concentration of the greening subarea at the time t/(The standard inorganic dust concentration of the greening subareas at the time t is represented;
In S22, the calculation formula of the monitoring fusion coefficient M b_o between the building sub-area and the air-ground sub-area is as follows:
; where S o represents the area of the open area region,/> Represents the standard organic dust concentration of the air-ground subarea at the time t,/>The standard inorganic dust concentration of the air-ground subarea at the time t is represented;
In S22, the calculation formula of the monitoring fusion coefficient M g_o between the greening subregion and the air-ground subregion is:
。
3. The big data prediction method based on the multi-modal digital twin technique according to claim 1, wherein in S23, the expression of the virtual working condition model a is:
，
; wherein M b_g represents a monitoring fusion coefficient between a building sub-region and a greening sub-region, M b_o represents a monitoring fusion coefficient between a building sub-region and an air-ground sub-region, M g_o represents a monitoring fusion coefficient between a greening sub-region and an air-ground sub-region, X represents a monitoring fusion matrix, I represents an identity matrix, and I.I.I F represents an F norm of the matrix.
4. The big data prediction method based on the multi-modal digital twinning technique according to claim 1, wherein the S3 includes the sub-steps of:
s31, setting a normal organic dust concentration operation interval and a normal inorganic dust concentration operation interval of a region to be monitored;
s32, determining virtual organic dust concentration operation values and virtual inorganic dust concentration operation values of all positions in the area to be monitored according to the virtual working condition model of the area to be monitored;
S33, taking the position of the virtual organic dust concentration operation value which does not belong to the normal organic dust concentration operation interval and the position of the virtual inorganic dust concentration operation value which does not belong to the normal inorganic dust concentration operation interval as an abnormal operation area of the area to be monitored.
5. The method for predicting big data based on multi-modal digital twinning technique according to claim 4, wherein in S32, the calculation formula of the virtual organic dust concentration running value F x_y at the position with x-axis and y-axis in the area to be monitored is:
; in the/> The method comprises the steps of representing standard organic dust concentration of a position with x abscissa and y ordinate in a region to be monitored at a time T, round (·) representing rounding operation, A representing a virtual working condition model of the region to be monitored, and T representing acquisition time;
in S32, the calculation formula of the virtual inorganic dust concentration running value f x_y at the position with x abscissa and y ordinate in the area to be monitored is as follows:
; in the/> The standard inorganic dust concentration at time t is indicated for the position x on the abscissa and y on the ordinate in the area to be monitored.
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|---|---|---|---|---|
| CN109006111A (en) * | 2018-07-16 | 2018-12-18 | 雷学军 | The method of Ecological Civilization Construction |
| CN112378445A (en) * | 2020-11-11 | 2021-02-19 | 合肥猎知科技有限公司 | Building construction environment intelligence real-time monitoring system based on big data analysis |
| CN116821443A (en) * | 2023-06-19 | 2023-09-29 | 沈阳智信佰达科技有限公司 | Digital twin visual irrigation district management platform |
| CN117151652A (en) * | 2023-09-25 | 2023-12-01 | 河南恒辉建设工程有限公司 | Building construction management system and method based on BIM |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109006111A (en) * | 2018-07-16 | 2018-12-18 | 雷学军 | The method of Ecological Civilization Construction |
| CN112378445A (en) * | 2020-11-11 | 2021-02-19 | 合肥猎知科技有限公司 | Building construction environment intelligence real-time monitoring system based on big data analysis |
| CN116821443A (en) * | 2023-06-19 | 2023-09-29 | 沈阳智信佰达科技有限公司 | Digital twin visual irrigation district management platform |
| CN117151652A (en) * | 2023-09-25 | 2023-12-01 | 河南恒辉建设工程有限公司 | Building construction management system and method based on BIM |
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