CN115184563B - Chemical workshop field data acquisition method based on digital twinning - Google Patents

Chemical workshop field data acquisition method based on digital twinning Download PDF

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CN115184563B
CN115184563B CN202211095433.0A CN202211095433A CN115184563B CN 115184563 B CN115184563 B CN 115184563B CN 202211095433 A CN202211095433 A CN 202211095433A CN 115184563 B CN115184563 B CN 115184563B
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王延敦
秦云松
王岩
宋博
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Abstract

The invention discloses a digital twin-based chemical workshop field data acquisition method, which relates to the technical field of combination of artificial intelligence, virtual reality and the Internet of things, and comprises the following steps: establishing a mapping relation between a real space and a virtual space; arranging a gas concentration sensor in a chemical workshop, and collecting a time sequence numerical value of gas concentration; dividing a virtual space into a plurality of subspaces; establishing a risk model of gas concentration; obtaining a cost function through learning; iteratively calculating a risk model to obtain a learned risk model; calculating the probability of risk on the subspace; and when the probability of the risk existing on a certain subspace is greater than the probability of the risk existing on the rest subspaces, taking the subspace as a risk source, and labeling the subspace. The method comprises the steps of establishing a virtual space according to field space data and gas concentration data, establishing a corresponding risk model, analyzing the probability of risk sources existing in different positions on the field by adopting a neural network method, and marking and warning high-probability risk points in the virtual space.

Description

Chemical workshop field data acquisition method based on digital twinning
Technical Field
The invention relates to the technical field of combination of artificial intelligence, virtual reality and the Internet of things, in particular to a chemical workshop field data acquisition method based on digital twins.
Background
The digital twin is a new technology for building a model in a virtual domain to correspond to an entity in a physical domain, thereby simulating and describing the state of the entity in the physical domain, and is being widely used for intelligent manufacturing in various industries. The basis of the digital twin is data acquisition, information related to production in a real environment is converted into electric signals through acquisition equipment such as a sensor, and the electric signals are further sampled into data for analysis and storage, so that the process of virtualizing and digitizing the objective physical world is realized. The data acquisition is a digital twin foundation stone, and the integrity, accuracy and real-time performance of the acquisition determine the application effect of the digital twin.
Production data acquisition based on digital twinning has gained wide attention in recent years, and has been researched and applied in key industries such as chemical industry, machinery, electric power, traffic and the like. The key to digital twin-based production data collection is collection, processing, and virtualization. In the aspect of acquisition, on-site data are acquired based on a large-scale sensor, and efficient acquisition of a data source is realized; in the aspect of processing, a data processing model is established, large-scale data is modeled, and an abstract entity with practical significance is generated; in the aspect of virtualization, abstract entities are displayed by adopting related technologies, so that a user can intuitively feel the real situation of a physical domain to make a decision.
Monitoring of safety is a fundamental requirement of chemical production. The safety monitoring of the chemical production is carried out through the digital twin, abstract monitoring contents and monitoring objects can be specified, monitoring personnel can know the conditions of the chemical production field more visually, and the efficiency and the effect of the safety monitoring are improved. The sensors for collecting gas concentration and the like are distributed and installed on the site of a chemical workshop, and the data collected by each sensor reflects the state of the workshop site in a small range. However, the data generated by the sensors are isolated from each other, and data from different sensors must be integrated in a certain way to comprehensively reflect the field state. Still some collection methods that adopt the camera, for example patent CN110334701a of the invention, provide a data collection method based on deep learning and multi-vision in the digital twin environment, although it can capture the on-site state in a wider range, it cannot capture the invisible key data such as the gas concentration.
Disclosure of Invention
The invention provides a chemical workshop field data acquisition method based on digital twinning, which is used for overcoming at least one technical problem in the prior art.
The embodiment of the invention provides a chemical workshop field data acquisition method based on digital twinning, which comprises the following steps:
establishing a mapping relation between a real space and a virtual space of a chemical workshop;
arranging a plurality of gas concentration sensors at different positions of a chemical workshop site;
collecting time series values of gas concentration at a predetermined sampling rate using the gas concentration sensor
Figure 854932DEST_PATH_IMAGE001
；
Dividing the virtual space into a plurality of subspaces to obtain linear correlation between the real coordinate of the gas concentration sensor and the subspaces; each of the subspaces having a unique identifier
Figure 633533DEST_PATH_IMAGE002
，
Figure 907519DEST_PATH_IMAGE003
Representing the serial numbers of different subspaces on three coordinate axes;
the real space coordinates of the gas concentration sensor and the time sequence value of the gas concentration are used as input, and the defined output is
Figure 679035DEST_PATH_IMAGE004
Establishing a risk model of gas concentration; wherein the content of the first and second substances,
Figure 987657DEST_PATH_IMAGE005
representing a node; the risk model of the gas concentration comprises a first hidden layer, a second hidden layer, a third hidden layer, a fourth hidden layer and a fifth hidden layer, wherein the first hidden layer is
Figure 937158DEST_PATH_IMAGE006
，
Figure 964020DEST_PATH_IMAGE007
The nodes that represent the hidden layer(s),
Figure 775112DEST_PATH_IMAGE008
representing a shared linear weight connection,
Figure 203819DEST_PATH_IMAGE009
for the linear bias parameter, s represents the sensor number,
Figure 324222DEST_PATH_IMAGE010
the dimensions of the vector are represented in the representation,
Figure 572801DEST_PATH_IMAGE011
represents a non-linear function, defined as
Figure 686119DEST_PATH_IMAGE012
，
Figure 234912DEST_PATH_IMAGE013
As the parameter(s) is (are),
Figure 526216DEST_PATH_IMAGE014
representing a real number domain; the second hidden layer is
Figure 996512DEST_PATH_IMAGE015
，
Figure 929833DEST_PATH_IMAGE016
Indicating neutralization of sensors in a first hidden layer
Figure 552706DEST_PATH_IMAGE017
The corresponding node is connected with the corresponding node,
Figure 280491DEST_PATH_IMAGE018
for the corresponding linear weight connection, t represents time,
Figure 503662DEST_PATH_IMAGE019
is linearly biasedThe parameters are set to be in a predetermined range,
Figure 975095DEST_PATH_IMAGE020
representing a vector dimension; the third hidden layer is
Figure 216589DEST_PATH_IMAGE021
，
Figure 584116DEST_PATH_IMAGE022
Indicating sensor
Figure 294583DEST_PATH_IMAGE023
Is a node in the input layer,
Figure 569707DEST_PATH_IMAGE024
、
Figure 167173DEST_PATH_IMAGE025
、
Figure 236760DEST_PATH_IMAGE026
in order to be connected correspondingly,
Figure 168944DEST_PATH_IMAGE027
is a linear bias parameter; the fourth hidden layer is
Figure 982179DEST_PATH_IMAGE028
，
Figure 667107DEST_PATH_IMAGE029
、
Figure 173175DEST_PATH_IMAGE030
Is that
Figure 592655DEST_PATH_IMAGE031
And node
Figure 944002DEST_PATH_IMAGE032
、
Figure 504341DEST_PATH_IMAGE033
The connection of (a) to (b),
Figure 915731DEST_PATH_IMAGE034
in order to be a linear bias parameter,
Figure 822507DEST_PATH_IMAGE035
representing a vector dimension; the fifth hidden layer is
Figure 977545DEST_PATH_IMAGE036
，
Figure 902644DEST_PATH_IMAGE037
Is a node
Figure 219356DEST_PATH_IMAGE038
And hidden layer node
Figure 613428DEST_PATH_IMAGE039
The connection of (a) to (b),
Figure 57310DEST_PATH_IMAGE040
is a linear bias parameter;
obtaining a cost function of the risk model by learning the risk model
Figure 853228DEST_PATH_IMAGE041
；
Iteratively calculating the risk model by using the cost function to obtain a learned risk model;
the gas concentration sensor acquires a time sequence value of gas concentration, the time sequence value and the real space coordinate of the gas concentration sensor are input into the learned risk model, and the learned risk model sequentially calculates the probability of risks existing in the subspace corresponding to the gas concentration sensor;
and when the probability of the risk existing on one subspace is greater than the probability of the risk existing on the rest subspaces, taking the subspace as a risk source, and labeling the subspace in the virtual space.
Optionally, the establishing of the mapping relationship between the real space and the virtual space of the chemical plant specifically includes:
arranging a camera in a real space of the chemical workshop, acquiring an image by using the camera, and acquiring coordinates of a target point in the real space according to the image;
establishing a virtual space through a computer, and setting mapping points corresponding to the target points in the virtual space;
and obtaining the mapping relation between the virtual space and the real space according to the coordinates of the mapping point in the virtual space and the coordinates of the target point in the real space.
Optionally, obtaining, according to the image, coordinates of a target point in the real space, specifically:
calibrating the internal parameter matrix and the external parameter matrix of the camera according to the linear model of the camera imaging
Figure 340841DEST_PATH_IMAGE042
Obtaining the coordinates of the target point in the real space; wherein, the first and the second end of the pipe are connected with each other,
Figure 222209DEST_PATH_IMAGE043
which represents the homogeneous coordinates of the image,
Figure 702738DEST_PATH_IMAGE044
representing world homogeneous coordinates of the target point,
Figure 353162DEST_PATH_IMAGE045
a matrix of intrinsic parameters representing the camera is shown,
Figure 542835DEST_PATH_IMAGE046
a matrix of extrinsic parameters representing the camera is shown,
Figure 645920DEST_PATH_IMAGE047
is a linear calibration parameter.
The innovation points of the embodiment of the invention comprise:
1. in the embodiment, an optical sensor carried by a camera is used for collecting a field scene, a three-dimensional reconstruction technology is used for establishing the mapping of the field scene in a virtual space, and the position, texture and other key information of a field target are recovered to serve as the basis for constructing the virtual space of a chemical workshop, and the method is one of the innovation points of the embodiment of the invention.
2. In the embodiment, data are acquired by a plurality of gas concentration sensors arranged at different positions on the site, an independent sensor for sensing the specific gas concentration is arranged at each position, the position coordinates of the sensor in the real space are recorded for implementing positioning in the real space, and the risk point location is displayed in the virtual space by establishing a large-scale sensor set, so that the method is one of the innovation points of the embodiment of the invention.
3. In the embodiment, a virtual space is established according to field space data and gas concentration data, a corresponding risk model is established, the probability of risk sources existing at different positions on the field is analyzed by adopting a neural network method, and high-probability risk points are marked and warned in the virtual space, which is one of innovation points of the embodiment of the invention.
4. In this embodiment, defining an output layer and each hidden layer, and establishing a structure of an obtained risk model through a neural network is one of innovative points of the embodiment of the present invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a data collection method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a risk model provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a virtual space and a subspace provided in accordance with an embodiment of the present invention;
fig. 4 is a flowchart of a mapping relationship between a real space and a virtual space according to an embodiment of 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 inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
It is to be noted that the terms "comprises" and "comprising" and any variations thereof in the embodiments and drawings of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The embodiment of the invention discloses a chemical workshop field data acquisition method based on digital twinning. The following are detailed descriptions.
Fig. 1 is a flowchart of a data acquisition method according to an embodiment of the present invention, fig. 2 is a schematic view of a risk model according to an embodiment of the present invention, fig. 3 is a schematic view of a virtual space and a subspace according to an embodiment of the present invention, and referring to fig. 1 to fig. 3, the method for acquiring data of a chemical workshop site based on a digital twin according to the embodiment includes:
step 1: establishing a mapping relation between a real space and a virtual space of a chemical workshop;
step 2: arranging a plurality of gas concentration sensors at different positions of a chemical workshop site;
and step 3: collecting time series values of gas concentration using a gas concentration sensor at a predetermined sampling rate
Figure 431605DEST_PATH_IMAGE048
；
And 4, step 4: dividing the virtual space into a plurality of subspaces to obtain the linear correlation between the real coordinate of the gas concentration sensor and the subspaces; each subspace has a unique identity
Figure 202115DEST_PATH_IMAGE049
，
Figure 297110DEST_PATH_IMAGE050
Representing the serial numbers of different subspaces on three coordinate axes;
and 5: the real space coordinates of the gas concentration sensor and the time sequence value of the gas concentration are used as input, and the defined output is
Figure 621912DEST_PATH_IMAGE051
Establishing a risk model of gas concentration; wherein the content of the first and second substances,
Figure 709822DEST_PATH_IMAGE052
representing a node; the risk model of the gas concentration comprises a first hidden layer, a second hidden layer, a third hidden layer, a fourth hidden layer and a fifth hidden layer, wherein the first hidden layer is
Figure 69260DEST_PATH_IMAGE053
，
Figure 335156DEST_PATH_IMAGE054
The nodes that represent the hidden layer(s),
Figure 678412DEST_PATH_IMAGE055
representing a shared linear weight connection,
Figure 540320DEST_PATH_IMAGE056
for the linear bias parameter, s represents the sensor number,
Figure 19843DEST_PATH_IMAGE057
the dimensions of the vector are represented as,
Figure 456641DEST_PATH_IMAGE058
represents a non-linear function, defined as
Figure 270882DEST_PATH_IMAGE059
，
Figure 451328DEST_PATH_IMAGE013
As the parameter(s) is (are),
Figure 785357DEST_PATH_IMAGE060
representing a real number domain; the second hidden layer is
Figure 393056DEST_PATH_IMAGE061
，
Figure 196058DEST_PATH_IMAGE062
Indicating neutralization of sensors in a first hidden layer
Figure 180194DEST_PATH_IMAGE063
The corresponding node is connected with the corresponding node,
Figure 368730DEST_PATH_IMAGE064
for the corresponding linear weight connection, t represents time,
Figure 412910DEST_PATH_IMAGE065
in order to be a linear bias parameter,
Figure 952475DEST_PATH_IMAGE066
representing a vector dimension; the third hidden layer is
Figure 723991DEST_PATH_IMAGE067
，
Figure 767034DEST_PATH_IMAGE068
Indicating sensor
Figure 716535DEST_PATH_IMAGE069
Is a node in the input layer,
Figure 494129DEST_PATH_IMAGE070
、
Figure 554489DEST_PATH_IMAGE071
、
Figure 717617DEST_PATH_IMAGE072
in order to be connected correspondingly,
Figure 369178DEST_PATH_IMAGE073
is a linear bias parameter; the fourth hidden layer is
Figure 617757DEST_PATH_IMAGE074
，
Figure 731076DEST_PATH_IMAGE075
、
Figure 14289DEST_PATH_IMAGE076
Is that
Figure 305593DEST_PATH_IMAGE077
And node
Figure 41468DEST_PATH_IMAGE078
、
Figure 459942DEST_PATH_IMAGE079
The connection of (a) to (b),
Figure 597663DEST_PATH_IMAGE080
in order to be a linear bias parameter,
Figure 59868DEST_PATH_IMAGE081
representing a vector dimension; the fifth hidden layer is
Figure 283039DEST_PATH_IMAGE082
，
Figure 738160DEST_PATH_IMAGE083
Is a node
Figure 464807DEST_PATH_IMAGE084
And hidden layer node
Figure 363493DEST_PATH_IMAGE085
The connection of (a) to (b),
Figure 824693DEST_PATH_IMAGE086
is a linear bias parameter;
step 6: obtaining a cost function of the risk model by learning the risk model
Figure 568658DEST_PATH_IMAGE087
；
And 7: iteratively calculating a risk model by using a cost function to obtain a learned risk model;
and 8: the method comprises the steps that a gas concentration sensor collects a time sequence value of gas concentration, the time sequence value and real space coordinates of the gas concentration sensor are input into a learned risk model, and the learned risk model sequentially calculates the probability of risks existing in subspaces corresponding to the gas concentration sensor;
and step 9: and when the probability of the risk existing on a certain subspace is greater than the probability of the risk existing on the rest subspaces, taking the subspace as a risk source, and labeling the subspace in the virtual space.
Specifically, referring to fig. 1, in the method for acquiring field data of a chemical workshop based on digital twins according to this embodiment, a mapping relationship between a real space and a virtual space of the chemical workshop is first established in step 1, when the mapping relationship is established, an optical sensor carried by a camera is required to acquire a field scene of the chemical workshop, then a three-dimensional reconstruction technology is used to establish mapping of the field scene in the virtual space, and relevant information such as a position and a texture of a field target is recovered. The virtual space is a three-dimensional space, the value of each coordinate in the virtual space is a vector, and the coordinate is a position coordinate in the real space
Figure 680970DEST_PATH_IMAGE088
Linear mapping of
Figure 484978DEST_PATH_IMAGE089
The vector represents texture information of the visual object at a corresponding position in the real space. The virtual space is used as the linear scale reconstruction of the real space, the texture of the visual target in the real space reappears in the virtual space according to a consistent proportion, the visual reconstruction of the field space is realized, and a user can intuitively feel the field state by observing the virtual space on a computer.
In order to collect the gas concentration, a plurality of gas concentration sensors are arranged on the site of the chemical engineering workshop through step 2, the gas concentration sensors are located at different positions, independent gas concentration sensors for sensing specific gas concentration are arranged at different positions, and the position coordinates of the gas concentration sensors in real space are recorded
Figure 932009DEST_PATH_IMAGE090
And the positioning of the gas concentration sensor in the real space is realized. Then in step 3, the gas concentration sensor is enabled to collect the gas concentration at a preset sampling rate, and a group of time sequence numerical values of the gas concentration changing along with the time are obtained
Figure 479665DEST_PATH_IMAGE091
Wherein s represents the sensor number, t is a time stamp, and the acquisition time stamps of all gas concentration sensors are controlled by a unique central control program on the same site to realize synchronous sampling.
Obtaining a time sequence value of the concentration of the collected gas
Figure 446484DEST_PATH_IMAGE092
And then, establishing a risk model according to the gas concentration data, and analyzing the probability of risk sources at different positions on the site. When the risk model is established, in order to present the risk points more intuitively in the virtual space, the virtual space is divided into a plurality of subspaces through step 4, please refer to fig. 3, in order to make each subspace have a unique identifier, the subspace is divided according to three orthogonal coordinate axes of the three-dimensional virtual space
Figure 686972DEST_PATH_IMAGE093
、
Figure 868904DEST_PATH_IMAGE094
、
Figure 220251DEST_PATH_IMAGE095
The virtual space is divided into several subspaces. Each obtained subspace is a three-dimensional cube, namely the boundary of the subspace is parallel to the coordinate axis; no intersection exists between any two subspaces, and the set of all the subspaces is equal to the original space; any face of a subspace cube only intersects with a unique face of another subspace cube; the position coordinates of any gas concentration sensor necessarily belong to a certain subspace, and no more than one gas concentration sensor is included in each subspace. When the above conditions are met, the obtained subspace has a unique identifier
Figure 307155DEST_PATH_IMAGE096
，
Figure 718545DEST_PATH_IMAGE097
Is a three-dimensional matrix of which the matrix is,
Figure 625321DEST_PATH_IMAGE098
is an element of the matrix that is,
Figure 498468DEST_PATH_IMAGE099
indicating matrix element indices, i.e. subspace edges
Figure 439879DEST_PATH_IMAGE100
Relative order of the coordinate axes. By the subspace identification method, a linear correlation between the subspace and the spatial position of the sensor can be established.
And 5, establishing a risk model of the gas concentration through a neural network by taking the real space coordinates of the gas concentration sensor and the time sequence numerical value of the gas concentration as input and the identification of the virtual space subspace as output. In this embodiment, the output is defined as
Figure 22170DEST_PATH_IMAGE101
Wherein, in the step (A),
Figure 166975DEST_PATH_IMAGE102
representing a node.
Referring to FIG. 2, the risk model includes a first hidden layer defined as
Figure 860125DEST_PATH_IMAGE103
，
Figure 390463DEST_PATH_IMAGE104
The nodes in the hidden layer are represented as,
Figure 143655DEST_PATH_IMAGE105
representing a shared linear weight connection,
Figure 274291DEST_PATH_IMAGE106
1 node representing hidden layer
Figure 771132DEST_PATH_IMAGE107
And
Figure 421556DEST_PATH_IMAGE108
the 5 nodes shown are connected in such a way that,
Figure 80070DEST_PATH_IMAGE109
is the time series value of the gas concentration collected in step 3.
Figure 199467DEST_PATH_IMAGE110
For the linear bias parameter, s represents the sensor number, the set of nodes represented by the first hidden layer
Figure 234419DEST_PATH_IMAGE111
Contains S independent vectors, S is the total number of sensors.
Figure 4929DEST_PATH_IMAGE112
Representing a vector dimension of eachThe dimension of each vector is T, and T is the total length of the acquisition time. By acquiring the characteristic of the gas concentration on the independent sensor changing along with the time, the model can identify the diffusion stage of the gas concentration, and the identification precision of the gas concentration distribution is optimized.
Figure 834345DEST_PATH_IMAGE113
Represents a non-linear function, defined as
Figure 673994DEST_PATH_IMAGE114
，
Figure 981478DEST_PATH_IMAGE115
The representation of the real number field is performed,
Figure 340916DEST_PATH_IMAGE116
is a parameter, a parameter
Figure 357544DEST_PATH_IMAGE117
The function of (1) is to enable the function to generate a breakpoint on a value range at the point x =0, which helps to improve the fitting performance of the model and avoid overfitting.
Referring to FIG. 2, the risk model further includes a second hidden layer, which is defined as
Figure 435222DEST_PATH_IMAGE118
，
Figure 811976DEST_PATH_IMAGE119
Indicating neutralization of sensors in a first hidden layer
Figure 540767DEST_PATH_IMAGE066
The corresponding node is connected with the corresponding node,
Figure 977564DEST_PATH_IMAGE120
for the corresponding linear weight connection, t represents a timestamp,
Figure 542538DEST_PATH_IMAGE121
in order to be a linear bias parameter,
Figure 942558DEST_PATH_IMAGE066
representing vector dimensions, i.e.
Figure 276587DEST_PATH_IMAGE122
In the form of a vector, the vector,
Figure 884286DEST_PATH_IMAGE123
refers to the second dimension of the vector. Set of nodes of a second hidden layer
Figure 936555DEST_PATH_IMAGE124
The correlation between the collected values for different sensor gas concentration changes is described.
Referring to FIG. 2, the risk model further includes a third hidden layer, which is defined as
Figure 920692DEST_PATH_IMAGE125
，
Figure 358495DEST_PATH_IMAGE126
、
Figure 825511DEST_PATH_IMAGE127
、
Figure 365077DEST_PATH_IMAGE128
Indicating sensor
Figure 887325DEST_PATH_IMAGE063
Is a node in the input layer,
Figure 195947DEST_PATH_IMAGE129
、
Figure 394716DEST_PATH_IMAGE130
、
Figure 155998DEST_PATH_IMAGE131
in order to be connected correspondingly,
Figure 481937DEST_PATH_IMAGE132
is a linear bias parameter. Set of nodes of third hidden layer
Figure 645065DEST_PATH_IMAGE133
The correlation between the spatial coordinates of different sensors is described. It should be noted that A, B in the second hidden layer and the third hidden layer are merely identifiers for distinguishing different outputs, and have no practical meaning.
Referring to FIG. 2, the risk model further includes a fourth hidden layer, which is defined as
Figure 516201DEST_PATH_IMAGE134
，
Figure 30359DEST_PATH_IMAGE135
、
Figure 894409DEST_PATH_IMAGE136
Is that
Figure 912044DEST_PATH_IMAGE137
And node
Figure 718195DEST_PATH_IMAGE138
、
Figure 454070DEST_PATH_IMAGE139
The connection of (a) to (b),
Figure 121811DEST_PATH_IMAGE140
in order to be a linear bias parameter,
Figure 993952DEST_PATH_IMAGE035
the dimension of the vector is represented as,
Figure 472469DEST_PATH_IMAGE141
is a 1024-dimensional vector in which the parameter k represents the second dimension of the vector. Set of nodes of fourth hidden layer
Figure 695640DEST_PATH_IMAGE142
A correlation between sensor spatial coordinates and gas concentration acquisition values is described.
Referring to fig. 2, the risk model further includes a fifth hidden layer, which is
Figure 901494DEST_PATH_IMAGE143
，
Figure 628141DEST_PATH_IMAGE144
Is a node
Figure 776095DEST_PATH_IMAGE145
And hidden layer node
Figure 220983DEST_PATH_IMAGE146
The connection of (a) to (b),
Figure 230527DEST_PATH_IMAGE147
is a linear bias parameter.
In the risk model diagram shown in fig. 2, the respective parameters are not represented by the superscripts and subscripts, but the meanings thereof are the same as those of the parameters represented by the superscripts and subscripts, and gs (t) in fig. 2 corresponds to, for example, the above-mentioned gs (t)
Figure 342839DEST_PATH_IMAGE148
H3 (k) corresponds to the above
Figure 885861DEST_PATH_IMAGE149
There are other parameters that are not listed here.
In the embodiment, a risk model among sensor collected data, a real space and a virtual space is established through a defined output layer and a defined hidden layer
Figure 349203DEST_PATH_IMAGE150
。
Figure 631280DEST_PATH_IMAGE151
Each element value of (a) represents the probability of the risk source of the gas leak in the virtual subspace, and (b) represents the value of the probabilityIn the range of 0 to 1; when the value is 0, the risk does not exist, and when the value is 1, the risk is determined to exist. Using risk models
Figure 598099DEST_PATH_IMAGE152
Before monitoring, a training sample set is prepared, a probability matrix output by a model is artificially marked, the output value of a virtual subspace where a gas leakage source is located is marked as 1, and the rest are marked as 0. Marking of the sample
Figure 822276DEST_PATH_IMAGE153
Obtaining model outputs from input calculations of samples
Figure 772914DEST_PATH_IMAGE154
。
Then, through step 6, learning the risk model is performed, and a cost function of the risk model can be obtained
Figure 858682DEST_PATH_IMAGE155
，
Figure 945587DEST_PATH_IMAGE156
The function of a natural index is represented,
Figure 107709DEST_PATH_IMAGE157
and the natural logarithmic function is expressed, and the identification performance under small-scale sample data can be improved by adopting an exponential-logarithmic function combination. In step 7, a risk model is iteratively calculated by using the cost function to obtain parameters of each hidden layer, so that a learned risk model can be obtained. During iterative computation, an extreme value of the cost function can be solved by adopting a backward propagation method, so that the learning of each parameter in the risk model is completed.
In step 8, the gas concentration sensor collects a time series value of the gas concentration, it should be noted that the time series value of the gas concentration collected in step 8 may refer to step 3, which is not described herein again. And then inputting the time sequence value and the real space coordinate of the gas concentration sensor into the learned risk model, wherein the learned risk model can calculate the probability of risks in each subspace. Then, step 9, the subspace corresponding to the maximum risk probability is taken as the risk source, and the subspace is labeled in the virtual space by adopting a special display mode.
The digital twin-based chemical workshop field data acquisition method provided by the invention combines the advantages of the gas concentration sensor and the camera optical sensor, efficiently and quickly acquires the data of the chemical production field, monitors the gas leakage risk, and carries out modeling and virtual display on the potential risk points in the form of digital twin, so that the potential risk positions can be quickly positioned in the chemical workshop in a large range, the risk points can be visually displayed in the virtual space, and monitoring personnel can be helped to quickly find the risk.
Optionally, fig. 4 is a flowchart of a mapping relationship between a real space and a virtual space provided in an embodiment of the present invention, please refer to fig. 4, and in step 1, the mapping relationship between the real space and the virtual space of the chemical plant is established, specifically: step 11: arranging a camera in a real space of a chemical workshop, acquiring an image by using the camera, and acquiring the coordinates of a target point in the real space according to the image; step 12: establishing a virtual space through a computer, and setting mapping points corresponding to the target points in the virtual space; step 13: and obtaining the mapping relation between the virtual space and the real space according to the coordinates of the mapping point in the virtual space and the coordinates of the target point in the real space.
Specifically, referring to fig. 4, when a mapping relationship between a real space and a virtual space of a chemical plant is established, a camera is first set in the real space of the chemical plant in step 11, the camera is used to collect images, and then a scene is obtained from a plurality of images collected in the same scene, so as to obtain coordinates of a target point in the real space, for example, information such as spatial positions and textures of various targets in the scene. In step 12, a three-dimensional virtual space is established by the computer, and a mapping point corresponding to the target point is set in the virtual space, that is, a mapping point of the position and texture information of the on-site target is set in the virtual space, the value of each coordinate in the virtual space is a vector, and the coordinate is a position coordinate in the real space
Figure 14485DEST_PATH_IMAGE158
Linear mapping of (2)
Figure 903944DEST_PATH_IMAGE159
The vector represents texture information of the visual object at the corresponding position in the real space.
After the mapping point is obtained, in step 13, the mapping relationship between the virtual space and the real space can be obtained according to the coordinate of the mapping point in the virtual space and the coordinate of the target point in the real space. The virtual space is used as the linear scale reconstruction of the real space by establishing the mapping of the real space and the virtual space, the texture of a visual target in the real space reappears in the virtual space according to a consistent proportion, the visual reconstruction of the field space is realized, and a user can intuitively feel the field state by observing the virtual space on a computer.
Optionally, referring to fig. 4, in step 11, obtaining coordinates of the target point in the real space according to the image, specifically: calibrating the internal parameter matrix and the external parameter matrix of the camera according to the linear model of the camera imaging
Figure 845355DEST_PATH_IMAGE160
Obtaining the coordinates of the target point in the real space; wherein the content of the first and second substances,
Figure 411334DEST_PATH_IMAGE161
which represents the homogeneous coordinates of the image,
Figure 805407DEST_PATH_IMAGE162
representing the world homogeneous coordinates of the target point,
Figure 498556DEST_PATH_IMAGE163
a matrix of intrinsic parameters representing the camera is shown,
Figure 294474DEST_PATH_IMAGE164
a matrix of extrinsic parameters representing the camera is shown,
Figure 798399DEST_PATH_IMAGE165
is a linear calibration parameter.
Specifically, referring to fig. 4, image pixels are projections of objects in a scene onto an imaging plane of a camera, each image pixel corresponds to an object in the scene, coordinates of the image pixels are related to real world coordinates of the object, and values of the image pixels are related to real texture of the object. The intrinsic parameter matrix of the camera represents the basic parameters of the camera imaging, is related to the properties of imaging elements of the camera, such as pixel size, focal length and the like, and is not related to the position of the camera in a field space; the external parameter matrix of the camera represents the relative position of the camera in the field space, including the optical axis direction, three-dimensional space offset and the like. Therefore, when the coordinates of the target point in the real space are acquired, the internal and external parameter matrixes of the camera are calibrated, and then the linear model imaged by the camera is used
Figure 679767DEST_PATH_IMAGE166
The coordinates of the target point in the real space can be obtained, and therefore the position and texture information of the field target can be recovered from the image pixels. Linear model for camera imaging
Figure 911028DEST_PATH_IMAGE167
In (1),
Figure 561452DEST_PATH_IMAGE168
which represents the homogeneous coordinates of the image,
Figure 751125DEST_PATH_IMAGE169
representing the world homogeneous coordinates of the target point,
Figure 369057DEST_PATH_IMAGE045
a matrix of intrinsic parameters representing the camera is shown,
Figure 30108DEST_PATH_IMAGE170
a matrix of extrinsic parameters representing the camera is shown,
Figure 535039DEST_PATH_IMAGE171
is a linear calibration parameter.
Besides the above mode, the internal and external parameter matrixes of the camera can be calibrated by directly utilizing the position and texture information of the field target. Further, combining the internal and external parameter matrices can obtain:
Figure 630034DEST_PATH_IMAGE172
wherein, in the step (A),
Figure 735262DEST_PATH_IMAGE173
。
Figure 308326DEST_PATH_IMAGE174
being a 3*4 matrix, including 12 free variables, theoretically measuring at least 6 on-site targets can solve for the reason that each image pixel homogeneous coordinate includes 2 free variables
Figure 933342DEST_PATH_IMAGE175
Is determined. Measuring more field targets and solving by adopting linear optimization method
Figure 199238DEST_PATH_IMAGE176
The solution accuracy can be improved.
Based on the method provided by the invention, the inventor conducts experimental verification aiming at chemical workshops of different scales to obtain experimental results shown in table 1, and the table 1 is an experimental result schematic diagram.
TABLE 1
Figure 27648DEST_PATH_IMAGE177
Referring to table 1, experimental results show that the digital twin-based chemical workshop field data acquisition method provided by the invention is suitable for chemical workshops of different scales, has high identification and positioning accuracy and quick positioning, can effectively position the point position of the gas leakage risk, and improves the risk discovery efficiency.
Those of ordinary skill in the art will understand that: the figures are schematic representations of one embodiment, and the blocks or processes shown in the figures are not necessarily required to practice the present invention.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (3)

1. A chemical workshop field data acquisition method based on digital twinning is characterized by comprising the following steps:
establishing a mapping relation between a real space and a virtual space of a chemical workshop;
arranging a plurality of gas concentration sensors at different positions of a chemical workshop site;
acquiring a time sequence numerical value of the gas concentration changing along with the time by using the gas concentration sensor at a preset sampling rate
Figure DEST_PATH_IMAGE001
；
Dividing the virtual space into a plurality of subspaces to obtain linear correlation between the real coordinate of the gas concentration sensor and the subspaces; each of the subspaces having a unique identifier
Figure 790690DEST_PATH_IMAGE002
，
Figure DEST_PATH_IMAGE003
Representing the serial numbers of different subspaces on three coordinate axes;
the real space coordinates of the gas concentration sensor and the time sequence value of the gas concentration are used as input, and the defined output is
Figure 573970DEST_PATH_IMAGE004
Establishing a risk model of gas concentration; wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE005
a node representing a fifth hidden layer; the risk model of the gas concentration comprises a first hidden layer, a second hidden layer, a third hidden layer, a fourth hidden layer and a fifth hidden layer, wherein the first hidden layer is
Figure 928859DEST_PATH_IMAGE006
，
Figure DEST_PATH_IMAGE007
A node representing a first hidden layer is shown,
Figure 490290DEST_PATH_IMAGE008
representing a shared linear weight connection,
Figure DEST_PATH_IMAGE009
for the linear bias parameter, s represents the sensor number,
Figure 133892DEST_PATH_IMAGE010
the dimensions of the vector are represented in the representation,
Figure DEST_PATH_IMAGE011
represents a non-linear function, defined as
Figure 189704DEST_PATH_IMAGE012
，
Figure DEST_PATH_IMAGE013
As the parameter(s) is (are),
Figure 648367DEST_PATH_IMAGE014
representing a real number domain; the second hidden layer is
Figure DEST_PATH_IMAGE015
，
Figure 928170DEST_PATH_IMAGE016
For the corresponding linear weight connection, t represents time,
Figure DEST_PATH_IMAGE017
in order to be a linear bias parameter,
Figure 590227DEST_PATH_IMAGE018
representing a vector dimension; the third hidden layer is
Figure DEST_PATH_IMAGE019
，
Figure 715309DEST_PATH_IMAGE020
Real space coordinates representing the sensor, are nodes in the input layer,
Figure DEST_PATH_IMAGE021
、
Figure 44790DEST_PATH_IMAGE022
、
Figure DEST_PATH_IMAGE023
in order to be connected correspondingly,
Figure 687038DEST_PATH_IMAGE024
is a linear bias parameter; the fourth hidden layer is
Figure DEST_PATH_IMAGE025
，
Figure 524806DEST_PATH_IMAGE026
、
Figure DEST_PATH_IMAGE027
Node being a fourth hidden layer
Figure 171688DEST_PATH_IMAGE028
Node with second hidden layer
Figure DEST_PATH_IMAGE029
A node of the third hidden layer
Figure 355676DEST_PATH_IMAGE030
The connection of (a) to (b),
Figure DEST_PATH_IMAGE031
in order to be a linear bias parameter,
Figure 914964DEST_PATH_IMAGE032
representing a vector dimension; the fifth hidden layer is
Figure DEST_PATH_IMAGE033
，
Figure 82771DEST_PATH_IMAGE034
Is a node of a fifth hidden layer
Figure DEST_PATH_IMAGE035
Node with fourth hidden layer
Figure 80814DEST_PATH_IMAGE036
The connection of (a) to (b),
Figure DEST_PATH_IMAGE037
is a linear bias parameter;
obtaining a cost function of the risk model by learning the risk model
Figure 119309DEST_PATH_IMAGE038
；
Iteratively calculating the risk model by using the cost function to obtain a learned risk model;
the gas concentration sensor acquires a time sequence value of gas concentration, the time sequence value and the real space coordinate of the gas concentration sensor are input into the learned risk model, and the learned risk model sequentially calculates the probability of risks existing in the subspace corresponding to the gas concentration sensor;
and when the probability of the risk existing on one subspace is greater than the probability of the risk existing on the rest subspace, taking the subspace as a risk source, and labeling the subspace in the virtual space.
2. The digital twin-based chemical plant site data acquisition method according to claim 1, wherein the establishing of the mapping relationship between the real space and the virtual space of the chemical plant specifically comprises:
arranging a camera in a real space of the chemical workshop, acquiring an image by using the camera, and acquiring coordinates of a target point in the real space according to the image;
establishing a virtual space through a computer, and setting mapping points corresponding to the target points in the virtual space;
and obtaining the mapping relation between the virtual space and the real space according to the coordinates of the mapping point in the virtual space and the coordinates of the target point in the real space.
3. The digital twin-based chemical plant field data acquisition method according to claim 2, wherein coordinates of a target point in the real space are acquired according to the image, specifically:
calibrating the cameraInternal parameter matrix and external parameter matrix, linear model based on camera imaging
Figure DEST_PATH_IMAGE039
Obtaining the coordinates of the target point in the real space; wherein the content of the first and second substances,
Figure 364345DEST_PATH_IMAGE040
which represents the homogeneous coordinates of the image,
Figure DEST_PATH_IMAGE041
representing world homogeneous coordinates of the target point,
Figure 753869DEST_PATH_IMAGE042
a matrix of intrinsic parameters representing the camera is shown,
Figure DEST_PATH_IMAGE043
a matrix of extrinsic parameters representing the camera is shown,
Figure 696549DEST_PATH_IMAGE044
is a linear calibration parameter.
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