CN115202347B - Virtual-real synchronization system and method based on digital twinning - Google Patents
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Abstract
The invention discloses a virtual-real synchronization system and method based on digital twins, which comprises a physical space, an Internet of things system platform and a virtual space, wherein the virtual-real synchronization system is formed by connecting the physical space with the virtual space through the Internet of things system platform; the data of a physical space is transmitted to an Internet of things system platform in real time through a digital twin technology, the Internet of things system platform stores, converts and transmits the data of equipment in the physical space, the real-time data is transmitted to a virtual space through a corresponding interface, the virtual space completes preliminary positioning according to initial data, a rule base of twin data is established, the data of the Internet of things system platform is continuously received according to a mode corresponding to joint-data to drive simulation movement, and a virtual-real synchronization system of the space is completed; the invention analyzes the effectiveness evaluation index by combining the data of dynamic change in the virtual space, judges whether the guide color band needs to be overhauled or not and realizes effective calibration of virtual-real synchronization.
Description
Technical Field
The invention relates to the technical field of virtual-real synchronization, in particular to a virtual-real synchronization system and a virtual-real synchronization method based on digital twins.
Background
The digital twin is a technical means which integrates multiple physical, multiple scale and multidisciplinary attributes, has the characteristics of real-time synchronization, faithful mapping and high fidelity, and can realize the interaction and fusion of physical time and information time. In some physical spaces applying monitoring equipment, real-time monitoring and analysis data arrangement can be effectively achieved, but on the premise that how effective guiding color bands of some equipment in the physical spaces, such as a logistics AGV trolley and a corresponding path of the trolley, are combined with a virtual space technology, the real-time monitoring and analysis on whether the guiding color bands need to be replaced and maintained is a problem which is puzzled in a virtual-real synchronous system at present; meanwhile, how to accurately analyze the validity of the data acquired by the AGV car in combination with the real-time dynamic change of the virtual space is also a problem to be solved at present.
Disclosure of Invention
The present invention provides a virtual-real synchronization system and method based on digital twins, so as to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: a virtual-real synchronization system based on digital twins comprises a physical space, an Internet of things system platform and a virtual space, wherein the virtual-real synchronization system is formed by connecting the physical space with the virtual space through the Internet of things system platform; the data of a physical space is transmitted to an Internet of things system platform in real time through a digital twin communication technology, the Internet of things system platform stores, converts and transmits the data of equipment in the physical space to complete data management, the real-time data is transmitted to a virtual space through a corresponding interface, the virtual space completes preliminary positioning according to initial data, a rule base of twin data is established, the data of the Internet of things system platform is continuously received according to a corresponding joint-data mode to drive simulation movement, and virtual-real synchronization of the space is completed.
Furthermore, the physical space comprises an entity set in which a human-machine-object-ring objective exists, and provides real-time space data, wherein the space data comprises mechanical mobile phone joint motion data, logistics AGV data, personnel data and temperature environment data in the physical space;
the logistics AGV trolley is provided with a camera device, the camera device acquires image information in a physical space and transmits the image information to the Internet of things system platform for processing, and the Internet of things system platform constructs a virtual space according to the acquired data; the image information comprises a guide ribbon image shot by the camera device;
the virtual space analyzes effectiveness evaluation indexes of a guide color band in the physical space according to guide color band image data transmitted by the Internet of things system platform, the effectiveness evaluation indexes comprise a conventional effectiveness evaluation index and a twin effectiveness evaluation index, and the virtual space trims the physical space according to the effectiveness evaluation indexes.
Further, the virtual space analyzes an effectiveness evaluation index of a guidance color band in the physical space according to the guidance color band image data transmitted by the internet of things system platform, wherein the conventional effectiveness evaluation index comprises the following evaluation processes:
acquiring a jth path Lij of a guide color band with ith color historically stored in a virtual space, recording the repetition number n (Lij) of the path Lij, and the load m (Lij) k of the logistics AGV in the kth transportation process on the path Lij, wherein k is less than or equal to n (Lij); the method comprises the steps that a trolley path in a virtual space is acquired after a camera device of the trolley runs and captures an image and then is transmitted to a path constructed after being analyzed by an Internet of things system platform, if a guide color band which is already laid exists in a physical space but the trolley does not walk, the guide color band is not reflected in the virtual space, and time and space data records exist in the path corresponding to each guide color band; using the formula:
calculating a conventional effectiveness evaluation index X of the guiding color strips in the virtual space, wherein n (Lij) 0 represents the number of times that the running speed exceeds a system preset standard speed in n paths of the logistics AGV trolley, and m (Lij) 0 represents the average load in the paths corresponding to all the guiding color strips running on the logistics AGV trolley; the average running speed reflects the running speed of the AGV in the process of one-time transportation, the residence time and the comprehensive speed level of the solution time of the problem handling and the like, the running condition on the path corresponding to the same guide color band is reflected by the speed and the average load, because the influence of the AGV on the abrasion degree of the guide color band is most direct generally, the speed reflects the running environment condition of the AGV, the weight reflects the acting force of the AGV on the ground guide color band, and the analysis is based on the data acquisition of the AGV itself, and the analysis is the friction influence analysis which is possibly caused to the guide color band from the AGV itself.
Further, the virtual space analyzes the effectiveness evaluation index of the guide color band in the physical space according to the guide color band image data transmitted by the internet of things system platform, wherein the twin effectiveness evaluation index comprises the following processes:
acquiring a path corresponding to a logistics AGV trolley traveling guide color band in a virtual space and a working point position of the logistics AGV trolley, setting the working point position connected with the path Lij of the logistics AGV trolley as a monitoring area pij, and setting a time interval of all traveling paths of the logistics AGV trolley when the traveling paths are repeatedly recorded in the virtual space for at least one time as a monitoring period T;
acquiring a total monitoring area P in a virtual space, wherein P = { P11 = { P12 · u.. Pij }, and using a formula:
calculating a twin effectiveness evaluation index Y of a guide color band in a virtual space, wherein GT represents the number of persons appearing in a monitoring area in a monitoring period T, G (T-1) represents the number of persons appearing in the monitoring area in the adjacent previous monitoring period, and max (G) represents the maximum value of the number of persons appearing in the monitoring area; SP denotes an area value corresponding to the total monitoring area P, SP0 is an area value of the target monitoring area P0, and P0= { P11 ∞ P12 · · η j };
the effectiveness evaluation index Q = a X + b Y of the guide color band in the physical space, wherein a represents a conventional coefficient, 0 < a < 1, b represents a twin obstacle coefficient, and the twin obstacle coefficient is the influence of the obstacle on the traveling area of the logistics AGV trolley in the virtual space;
when the effectiveness evaluation index Q is larger than the effectiveness evaluation threshold value, the virtual space transmits a signal to the Internet of things system platform, and the Internet of things system platform reminds the guiding color band corresponding to the logistics AGV to overhaul. Because the different monitoring areas are considered in the virtual space, the influence of obstacles of the logistics AGV in the transportation process instead of the working point for loading and unloading goods exists, the number of the analysts not only analyzes the possibility of pollution caused by foreign personnel to the guide color strip, but also analyzes the obstacles of the logistics AGV caused by the occurrence of the personnel, and the fewer the number of the analysts, the higher the automation degree of the monitoring area is, and the smaller the influence degree of the pollution caused by the personnel to the guide color strip is.
Further, the twin barrier coefficient analysis comprises the following processes:
acquiring image data before and after the AGV encounters an obstacle, wherein the image data comprises a maximum visual field range and a focal length d, and an actual path distance of a path displayed in an image in a virtual space; recording the color proportion of a guide color band in image data before encountering an obstacle of the AGV, wherein the color proportion of the guide color band in the image data after encountering the obstacle is b1, the color proportion of the guide color band in the image data after encountering the obstacle is b2, and the color proportion is the proportion of the guide color band occupying a blank area without containing other guide color bands on the ground before encountering the obstacle;
calculating the difference value between the color proportions b1 and b2, if the difference value is greater than or equal to the difference value threshold, calibrating and acquiring the maximum visual field range f2 and the focal length d2 which are corresponding to the difference value between the color proportions b1 and b2 and are satisfied in the image data and the actual path distance h2 of the path displayed in the image in the virtual space when the color proportion is b2, and utilizing a formula:
calculating a twinborn obstacle coefficient r, wherein f1 is the maximum visual field range of the logistics AGV before encountering an obstacle, d1 is the focal length of a camera device before encountering the obstacle, and h1 is the actual path distance of a path displayed in an image before encountering the obstacle in a virtual space;
if the difference is smaller than the difference threshold, let the twin barrier coefficient r =1. Analyzing the processing condition of the logistics AGV trolley when meeting an obstacle in an image, and aiming at reducing the possibility of wrong identification of the AGV trolley when meeting the obstacle, because the guide color bands are laid adjacently with different colors on the basis of convenience and practicability, if the AGV trolley turns after meeting the obstacle, the visual field is unchanged, and the focal length is unchanged, the situation of disordered color of the guide color bands is easily identified, because the integral chromaticity of the guide color bands is kept unchanged in the identification process, the AGV trolley moves on the same guide color band, and if a plurality of color bands suddenly appear, the shutdown of the AGV trolley can exist; so the analysis here extracts the image field of view and focus that do not contain other colors for processing when a distress situation occurs.
Furthermore, the Internet of things system platform mediates the storage, conversion and transmission of data, is responsible for real-time storage of twin data in a physical space to form historical data and complete transmission of a corresponding interface to a virtual space, and monitors and manages the physical space based on the simulation drive of the real-time data to a virtual workshop.
Furthermore, the virtual space model layer maps the physical layer actually, so as to depict production factors of human, machine, object and environment workshops, and in addition, the virtual space continuously stores production data generated by the physical space and restores the physical space in the operation process.
A virtual-real synchronization method based on digital twinning comprises the following operation steps:
real-time data of the physical space are transmitted to an Internet of things system platform, the Internet of things system platform stores and converts the real-time data, and the data are subjected to given processing;
the method comprises the steps that real-time data of equipment are transmitted into a virtual space by an Internet of things system platform, the virtual space simulates the running state of a simulated physical space, the monitoring of the equipment state, the production progress and the real-time scheduling of the space is realized, the simulation analysis result is fed back to the Internet of things system platform, the Internet of things system platform carries out real-time monitoring and data storage analysis on the physical space based on the real-time data, the equipment in the physical space is trimmed and transmitted to a virtual workshop again, and the health management of the workshop is completed;
according to the data storage of the platform of the Internet of things and the mapping of the virtual workshop, the on-site restoration of the virtual space in the appointed time period is completed, and data acquisition and monitoring are carried out in real time.
Compared with the prior art, the invention has the following beneficial effects: the method utilizes a digital twinning technology to transmit and analyze data information of a physical space through an Internet of things system platform, establishes a real-time corresponding virtual space, and accurately analyzes the use effectiveness of the guiding color band of the logistics AGV, not only analyzes the effectiveness evaluation index and judges whether the guiding color band needs to be overhauled by combining data acquired by the AGV and data of real-time dynamic change in the virtual space, but also analyzes the image data of a trolley image acquisition device when the trolley meets the obstacle, so that virtual-real synchronous effective calibration is realized, the error rate of the logistics AGV in capturing the virtual space data is reduced, and the complexity of analyzing the virtual space data when the logistics AGV meets the obstacle is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a virtual-real synchronization system based on digital twinning according to the present invention;
fig. 2 is a schematic diagram of the obstacle handling of the digital twin-based virtual-real synchronization system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: a virtual-real synchronization system based on digital twins comprises a physical space, an Internet of things system platform and a virtual space, wherein the virtual-real synchronization system is formed by connecting the physical space with the virtual space through the Internet of things system platform; the data of a physical space is transmitted to an Internet of things system platform in real time through a digital twin communication technology, the Internet of things system platform stores, converts and transmits the data of equipment in the physical space to complete data management, the real-time data is transmitted to a virtual space through a corresponding interface, the virtual space completes preliminary positioning according to initial data, a rule base of twin data is established, the data of the Internet of things system platform is continuously received according to a corresponding joint-data mode to drive simulation movement, and virtual-real synchronization of the space is completed.
The physical space comprises an entity set objectively existing in a man-machine-object-ring mode, provides real-time space data and comprises mechanical mobile phone joint movement data, data of a logistics AGV (automatic guided vehicle), personnel data and temperature environment data in the physical space;
the logistics AGV trolley is provided with a camera device, the camera device acquires image information in a physical space and transmits the image information to the Internet of things system platform for processing, and the Internet of things system platform constructs a virtual space according to the acquired data; the image information comprises a guide ribbon image shot by the camera device;
the virtual space analyzes effectiveness evaluation indexes of a guide color band in the physical space according to guide color band image data transmitted by the Internet of things system platform, the effectiveness evaluation indexes comprise a conventional effectiveness evaluation index and a twin effectiveness evaluation index, and the virtual space trims the physical space according to the effectiveness evaluation indexes.
The virtual space analyzes the effectiveness evaluation index of the guide color band in the physical space according to the guide color band image data transmitted by the Internet of things system platform, wherein the conventional effectiveness evaluation index comprises the following evaluation processes:
acquiring a jth path Lij of a guide color band with ith color historically stored in a virtual space, recording the repetition number n (Lij) of the path Lij, and the load m (Lij) k of the logistics AGV in the kth transportation process on the path Lij, wherein k is less than or equal to n (Lij); the method comprises the steps that a trolley path in a virtual space is obtained by driving a camera device of the trolley, capturing an image, transmitting the image to an internet of things system platform for analysis, and constructing the path, if a guide color band which is already laid exists in a physical space but the trolley does not walk, the guide color band is not reflected in the virtual space, and time and space data records exist in the path corresponding to each guide color band; using the formula:
calculating a conventional effectiveness evaluation index X of the guiding color strips in the virtual space, wherein n (Lij) 0 represents the number of times that the running speed exceeds a system preset standard speed in n paths of the logistics AGV trolley, and m (Lij) 0 represents the average load in the paths corresponding to all the guiding color strips running on the logistics AGV trolley; the average running speed reflects the running speed of the AGV in the process of one-time transportation, the residence time and the comprehensive speed level of the solution time of the problem handling and the like, the running condition on the path corresponding to the same guide color band is reflected by the speed and the average load, because the influence of the AGV on the abrasion degree of the guide color band is most direct generally, the speed reflects the running environment condition of the AGV, the weight reflects the acting force of the AGV on the ground guide color band, and the analysis is based on the data acquisition of the AGV itself, and the analysis is the friction influence analysis which is possibly caused to the guide color band from the AGV itself.
The virtual space analyzes the effectiveness evaluation index of the guide color band in the physical space according to the guide color band image data transmitted by the Internet of things system platform, wherein the twin effectiveness evaluation index comprises the following processes:
acquiring a path corresponding to a logistics AGV trolley traveling guide color band in a virtual space and a working point position of the logistics AGV trolley, setting the working point position connected with the path Lij of the logistics AGV trolley as a monitoring area pij, and setting a time interval of all traveling paths of the logistics AGV trolley when the traveling paths are repeatedly recorded in the virtual space for at least one time as a monitoring period T;
acquiring a total monitoring area P in a virtual space, wherein P = { P11 = { P12 · u.. Pij }, and using a formula:
calculating a twin effectiveness evaluation index Y of a guide color band in a virtual space, wherein GT represents the number of persons appearing in a monitoring area in a monitoring period T, G (T-1) represents the number of persons appearing in the monitoring area in the adjacent previous monitoring period, and max (G) represents the maximum value of the number of persons appearing in the monitoring area; SP denotes an area value corresponding to the total monitoring area P, SP0 is an area value of the target monitoring area P0, and P0= { P11 ∞ P12 · · η j };
then the effectiveness evaluation index Q = a X + b Y of the guiding color band in the physical space, wherein a represents a conventional coefficient, 0 < a < 1, b represents a twin barrier coefficient, and the twin barrier coefficient is the influence of the barrier on the traveling area of the AGV trolley in the virtual space;
when the effectiveness evaluation index Q is larger than the effectiveness evaluation threshold value, the virtual space transmits a signal to the Internet of things system platform, and the Internet of things system platform reminds the guiding color band corresponding to the logistics AGV to overhaul. Because the difference of the monitoring areas is considered in the virtual space, the influence of obstacles of the logistics AGV in the transportation process rather than the loading and unloading working point exists, the number of the analysts not only analyzes the possibility of pollution caused by foreign personnel to the guide color band, but also analyzes the obstacles of the logistics AGV caused by the occurrence of the personnel, the fewer the number of the analysts is, the higher the automation degree of the monitoring area is, and the influence degree of the pollution caused by the personnel to the guide color band is smaller.
The twin barrier coefficient analysis comprises the following processes:
acquiring image data before and after the AGV encounters an obstacle, wherein the image data comprises a maximum visual field range and a focal length d, and an actual path distance of a path displayed in an image in a virtual space; recording the color proportion of a guide color band in image data before encountering an obstacle of the AGV, wherein the color proportion of the guide color band in the image data after encountering the obstacle is b1, the color proportion of the guide color band in the image data after encountering the obstacle is b2, and the color proportion is the proportion of the guide color band occupying a blank area without containing other guide color bands on the ground before encountering the obstacle;
calculating the difference value between the color proportions b1 and b2, if the difference value is greater than or equal to the difference value threshold, calibrating and acquiring the maximum visual field range f2 and the focal length d2 which are corresponding to the difference value between the color proportions b1 and b2 and are satisfied in the image data when the color proportion is b2, and the actual path distance h2 of the path displayed in the image in the virtual space, and utilizing the formula:
calculating a twinborn obstacle coefficient r, wherein f1 is the maximum visual field range of the logistics AGV before encountering an obstacle, d1 is the focal length of a camera device before encountering the obstacle, and h1 is the actual path distance of a path displayed in an image before encountering the obstacle in a virtual space;
if the difference is smaller than the difference threshold, let the twin barrier coefficient r =1. The processing condition of the AGV trolley in the process of analyzing the image when meeting the obstacle is to reduce the possibility of error identification of the trolley when meeting the obstacle, because the guide color bands are laid adjacently on the basis of convenience and practicability, different colors can be laid adjacently, and if the trolley turns after meeting the obstacle, the visual field is unchanged, and the focal length is unchanged, the condition of disordered color of the guide color bands is easy to identify, because the integral chromaticity of the guide color bands is kept unchanged in the identification process, the guide color bands move forwards on the same guide color band, and if a plurality of color bands suddenly appear, the trolley possibly stops working; so the analysis here extracts the image field of view and focus that do not contain other colors for processing when a distress situation occurs.
Image data of the logistics AGV before encountering an obstacle as shown on the left side of FIG. 2;
s1 indicates a region corresponding to the target guide color band, and S01 and S02 indicate blank regions excluding the guide color band;
image data of the AGV after encountering an obstacle as shown in the right part of the figure 2;
s2 represents a corresponding area of a target guide color band in image data after obstacle encountering processing of the logistics AGV trolley, S3 represents an area corresponding to another guide color band except the target guide color band, and S01 'and S02' represent blank areas except the guide color band;
because the visual field range, the focal length and the actual distance of the logistics AGV trolley are not changed during obstacle handling,
then S1+ S01+ S02= S2+ S3+ S01'+ S02',
b1＝S1/(S01+S02)，b2＝S2/(S01’+S02’)，
and if the difference value of b1-b2 is larger than the difference threshold value, calibrating the visual field after rotation, so that the focal length and the actual distance are changed and calibration is carried out simultaneously.
The Internet of things system platform is used for mediating data storage, conversion and transmission, is responsible for real-time storage of twin data in a physical space to form historical data and completing transmission of corresponding interfaces to a virtual space, and is used for monitoring and managing the physical space based on simulation driving of the real-time data to the virtual workshop.
The virtual space model layer is used for actually mapping the physical layer to depict production factors of people, machines, objects and environment workshops, and in addition, in the operation process, the virtual space continuously stores production data generated by the physical space and restores the physical space.
A virtual-real synchronization method based on digital twinning comprises the following operation steps:
real-time data of a physical space is transmitted to an Internet of things system platform, the Internet of things system platform stores and converts the real-time data, and the data is subjected to given processing;
the system platform of the Internet of things transmits real-time data of equipment into a virtual space, the virtual space simulates the running state of a simulated physical space, monitoring of the equipment state, the production progress and real-time scheduling of the space is achieved, a simulation analysis result is fed back to the system platform of the Internet of things, the system platform of the Internet of things carries out real-time monitoring and data storage analysis on the physical space based on the real-time data, the equipment of the physical space is trimmed and transmitted to a virtual workshop again, and health management of the workshop is completed;
according to the data storage of the platform of the Internet of things and the mapping of the virtual workshop, the on-site restoration of the virtual space in the appointed time period is completed, and data acquisition and monitoring are carried out in real time.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A virtual-real synchronization system based on digital twins is characterized by comprising a physical space, an Internet of things system platform and a virtual space, wherein the virtual-real synchronization system is formed by connecting the physical space with the virtual space through the Internet of things system platform; the data of a physical space is transmitted to an Internet of things system platform in real time through a digital twin communication technology, the Internet of things system platform stores, converts and transmits the data of equipment in the physical space, the management of the data is completed, the real-time data is transmitted to a virtual space through a corresponding interface, the virtual space completes preliminary positioning according to initial data, a rule base of twin data is established, the data of the Internet of things system platform is continuously received according to a corresponding joint-data mode to drive simulation movement, and the virtual-real synchronization of the space is completed;
the physical space is an entity set which comprises human-machine-object-ring objective existence, and provides real-time space data, wherein the space data comprises mechanical mobile phone joint motion data, logistics AGV data, personnel data and temperature environment data in the physical space;
the logistics AGV trolley is provided with a camera device, the camera device acquires image information in a physical space and transmits the image information to an Internet of things system platform for processing, and the Internet of things system platform constructs a virtual space according to the acquired data; the image information comprises a guide ribbon image shot by the camera device;
the virtual space analyzes effectiveness evaluation indexes of a guide color band in a physical space according to guide color band image data transmitted by an Internet of things system platform, wherein the effectiveness evaluation indexes comprise a conventional effectiveness evaluation index and a twin effectiveness evaluation index, and the virtual space trims the physical space according to the effectiveness evaluation indexes;
the virtual space analyzes the effectiveness evaluation index of the guide color band in the physical space according to the guide color band image data transmitted by the Internet of things system platform, wherein the conventional effectiveness evaluation index comprises the following evaluation processes:
acquiring a jth path Lij of a guide color band with ith color historically stored in the virtual space, recording the repetition times n (Lij) of the path Lij, and the load m (Lij) k of the logistics AGV in the kth transportation process on the path Lij, wherein k is less than or equal to n (Lij); using the formula:
calculating a conventional effectiveness evaluation index X of the guiding color strips in the virtual space, wherein n (Lij) 0 represents the number of times that the running speed exceeds a system preset standard speed in n paths of the logistics AGV trolley, and m (Lij) 0 represents the average load in the paths corresponding to all the guiding color strips running on the logistics AGV trolley;
the virtual space analyzes the effectiveness evaluation index of the guide color band in the physical space according to the guide color band image data transmitted by the Internet of things system platform, wherein the twin effectiveness evaluation index comprises the following processes:
acquiring a path corresponding to a logistics AGV trolley traveling guide color band in a virtual space and a working point position of the logistics AGV trolley, setting the working point position connected with the path Lij of the logistics AGV trolley as a monitoring area pij, and setting a time interval of at least one repeated recording of all traveling paths of the logistics AGV trolley in the virtual space as a monitoring period T;
obtaining a total monitoring area P in the virtual space, P = { P11 = { P12 = · u.
Calculating a twin effectiveness evaluation index Y of a guide color band in a virtual space, wherein GT represents the number of persons appearing in a monitoring area in a monitoring period T, G (T-1) represents the number of persons appearing in the monitoring area in the adjacent previous monitoring period, and max (G) represents the maximum value of the number of persons appearing in the monitoring area; SP denotes an area value corresponding to the total monitoring area P, SP0 is an area value of the target monitoring area P0, and P0= { P11 ∞ P12 · · η j };
then the effectiveness evaluation index Q = a X + b Y of the guiding color band in the physical space, wherein a represents a conventional coefficient, 0 < a < 1, b represents a twin barrier coefficient, and the twin barrier coefficient is the influence of the barrier on the traveling area of the AGV trolley in the virtual space;
when the effectiveness evaluation index Q is larger than the effectiveness evaluation threshold value, the virtual space transmits a signal to the Internet of things system platform, and the Internet of things system platform reminds the guiding color band corresponding to the AGV to overhaul.
2. A digital twin based virtual-real synchronization system as claimed in claim 1, wherein: the twin barrier coefficient analysis comprises the following processes:
acquiring image data before and after the AGV encounters an obstacle, wherein the image data comprises a maximum visual field range and a focal length d, and an actual path distance of a path displayed in an image in a virtual space; recording the color proportion of a guide color band in image data before encountering an obstacle of the AGV, wherein the color proportion is b1, the color proportion of the guide color band in the image data after encountering the obstacle is b2, and the color proportion is the proportion of the guide color band occupying a blank area without containing other guide color bands on the ground before encountering the obstacle;
calculating the difference value between the color proportions b1 and b2, if the difference value is greater than or equal to the difference value threshold, calibrating and acquiring the maximum visual field range f2 and the focal length d2 which are corresponding to the difference value between the color proportions b1 and b2 and are satisfied in the image data when the color proportion is b2, and the actual path distance h2 of the path displayed in the image in the virtual space, and utilizing the formula:
calculating a twin obstacle coefficient r, wherein f1 is the maximum visual field range of the logistics AGV before encountering an obstacle, d1 is the focal length of a camera device of the logistics AGV before encountering the obstacle, and h1 is the actual path distance of a path displayed in an image before encountering the obstacle in the virtual space;
if the difference is smaller than the difference threshold, let the twin barrier coefficient r =1.
3. A digital twin based virtual-real synchronization system as claimed in claim 2, wherein: the Internet of things system platform is responsible for real-time storage of twin data in a physical space to form historical data and finish transmission of a corresponding interface to a virtual space, and monitors and manages the physical space based on simulation driving of the real-time data to the virtual workshop.
4. A digital twin based virtual-real synchronization system as claimed in claim 3, wherein: the virtual space model layer is used for actually mapping the physical layer to depict production factors of people, machines, objects and environmental workshops, and in addition, in the operation process, the virtual space continuously stores production data generated by the physical space and restores the physical space.
5. A digital twin based virtual-real synchronization method applying a digital twin based virtual-real synchronization system according to any of claims 1-4, characterized by comprising the following operation steps:
real-time data of the physical space are transmitted to an Internet of things system platform, the Internet of things system platform stores and converts the real-time data, and the data are subjected to given processing;
the physical space is an entity set which comprises human-machine-object-ring objective existence, the physical space provides real-time spatial data, and the spatial data comprises mechanical mobile phone joint motion data, logistics AGV data, personnel data and temperature environment data in the physical space;
the logistics AGV trolley is provided with a camera device, the camera device acquires image information in a physical space and transmits the image information to an Internet of things system platform for processing, and the Internet of things system platform constructs a virtual space according to the acquired data; the image information comprises a guide ribbon image shot by the camera device;
the virtual space analyzes effectiveness evaluation indexes of a guide color band in a physical space according to guide color band image data transmitted by an Internet of things system platform, wherein the effectiveness evaluation indexes comprise a conventional effectiveness evaluation index and a twin effectiveness evaluation index, and the virtual space trims the physical space according to the effectiveness evaluation indexes;
the virtual space analyzes the effectiveness evaluation index of the guide color band in the physical space according to the guide color band image data transmitted by the Internet of things system platform, wherein the conventional effectiveness evaluation index comprises the following evaluation processes:
acquiring a jth path Lij of a guide color band with ith color historically stored in the virtual space, recording the repetition times n (Lij) of the path Lij, and the load m (Lij) k of the logistics AGV in the kth transportation process on the path Lij, wherein k is less than or equal to n (Lij); using the formula:
calculating a conventional effectiveness evaluation index X of the guiding color strips in the virtual space, wherein n (Lij) 0 represents the number of times that the running speed exceeds a system preset standard speed in n paths of the logistics AGV trolley, and m (Lij) 0 represents the average load in the paths corresponding to all the guiding color strips running on the logistics AGV trolley;
the virtual space analyzes the effectiveness evaluation index of the guide color band in the physical space according to the guide color band image data transmitted by the Internet of things system platform, wherein the twin effectiveness evaluation index comprises the following processes:
acquiring a path corresponding to a logistics AGV trolley traveling guide color band in a virtual space and a working point position of the logistics AGV trolley, setting the working point position connected with the path Lij of the logistics AGV trolley as a monitoring area pij, and setting a time interval of at least one repeated recording of all traveling paths of the logistics AGV trolley in the virtual space as a monitoring period T;
acquiring a total monitoring area P in a virtual space, wherein P = { P11 = { P12 · u.. Pij }, and using a formula:
calculating a twin effectiveness evaluation index Y of a guide color band in a virtual space, wherein GT represents the number of people appearing in a monitoring area in a monitoring period T, G (T-1) represents the number of people appearing in the monitoring area in the previous adjacent monitoring period, and max (G) represents the maximum value of the number of people appearing in the monitoring area; SP denotes an area value corresponding to the total monitoring area P, SP0 is an area value of the target monitoring area P0, and P0= { P11 ∞ P12 · · η j };
an evaluation index Q = a X + b Y of the effectiveness of guiding the color band in the physical space, wherein a represents a conventional coefficient, 0 < a < 1, b represents a twin obstacle coefficient, which is the influence of obstacles on the traveling area of the logistics AGV cart in the virtual space;
when the effectiveness evaluation index Q is larger than the effectiveness evaluation threshold value, the virtual space transmits a signal to the Internet of things system platform, and the Internet of things system platform reminds the logistics AGV to carry out maintenance on a corresponding guide color band;
the twin barrier coefficient analysis comprises the following processes:
acquiring image data before and after the AGV encounters an obstacle, wherein the image data comprises a maximum visual field range and a focal length d, and an actual path distance of a path displayed in an image in a virtual space; recording the color proportion of a guide color band in image data before an obstacle is encountered by the AGV, wherein the color proportion of the guide color band in the image data after the obstacle is encountered is b1, and the color proportion of the guide color band in the image data after the obstacle is encountered is b2, wherein the color proportion is the proportion of the guide color band occupying a blank area without other guide color bands on the ground before the obstacle is encountered;
calculating the difference value between the color proportions b1 and b2, if the difference value is greater than or equal to the difference value threshold, calibrating and acquiring the maximum visual field range f2 and the focal length d2 which are corresponding to the difference value between the color proportions b1 and b2 and are satisfied in the image data when the color proportion is b2, and the actual path distance h2 of the path displayed in the image in the virtual space, and utilizing the formula:
calculating a twinborn obstacle coefficient r, wherein f1 is the maximum visual field range of the logistics AGV before encountering an obstacle, d1 is the focal length of a camera device before encountering the obstacle, and h1 is the actual path distance of a path displayed in an image before encountering the obstacle in a virtual space;
if the difference is smaller than the difference threshold, enabling the twin obstacle coefficient r =1;
the method comprises the steps that real-time data of equipment are transmitted into a virtual space by an Internet of things system platform, the virtual space simulates the running state of a simulated physical space, the monitoring of the equipment state, the production progress and the real-time scheduling of the space is realized, the simulation analysis result is fed back to the Internet of things system platform, the Internet of things system platform carries out real-time monitoring and data storage analysis on the physical space based on the real-time data, the equipment in the physical space is trimmed and transmitted to a virtual workshop again, and the health management of the workshop is completed;
according to data storage of the platform of the Internet of things and mapping of the virtual workshop, on-site restoration of the virtual space in the appointed time period is completed, and data acquisition and monitoring are carried out in real time.
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