CN113450356A - Method, apparatus, and storage medium for recognizing mounting state of target component - Google Patents
Method, apparatus, and storage medium for recognizing mounting state of target component Download PDFInfo
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Abstract
The present disclosure relates to a method, a computing device, and a storage medium for recognizing a target component mounting state. The method comprises the following steps: generating reference simulated mounting image data for simulating mounting of the target component to the mounting position based on at least the size information and the plurality of view data of the target component and the number of mounting environment images; acquiring image characteristics of the target component in the installation state based on the installation environment image data and the installed image data; adjusting the reference simulated installation image data to generate calibration simulated installation image data so as to extract image features of the calibration simulated installation image data; and matching image features of the post-installation image data with image features of the calibration simulation installation image data to generate indication information about the installation state of the target component. The intelligent identification and judgment method can accurately and efficiently carry out intelligent identification and judgment on the field installation quality of the target component.
Description
Technical Field
The present disclosure relates generally to target object recognition, and in particular, to a method, apparatus, and storage medium for recognizing a target component mounting state.
Background
Industrial equipment often requires the installation or replacement of instrumentation (e.g., sensors), for example, a compressed air main in an air compression station requires the installation of a flow meter. Conventional solutions for identifying the installation status of a target component (e.g., without limitation, a meter) rely primarily on human eye identification, i.e., supervision by a supervisory operator arriving at a job site for supervision, in such a manner that the degree of intelligence is low, resulting in inefficient installation quality identification, excessive personnel costs, and inconsistent quality identification standards.
In the industrial internet era, a factory workshop needs to realize digitization, networking and intellectualization, and a solution of the internet of things combining software and hardware is not required. The traditional scheme for identifying the installation state of the target component has the defects of low installation quality identification efficiency, high personnel cost and lack of consistency of identification standards. Thus, there is a need for improvement in the conventional scheme of recognizing the mounting state of the target component.
Disclosure of Invention
The present disclosure provides a method, a computing device, and a computer storage medium for recognizing a target component mounting state, which can intelligently recognize and judge a field mounting quality of a target component accurately and efficiently.
According to a first aspect of the present disclosure, there is provided a method of identifying a target component mounting state. The method comprises the following steps: at a management apparatus, acquiring size information and a plurality of view data of a target component to be mounted; acquiring, via the terminal device, installation environment image data at an installation position of the target device acquired at a predetermined angle and post-installation image data of the target component installed at the installation position; generating reference simulated mounting image data for simulating mounting of the target component to the mounting position based on at least the size information and the plurality of view data of the target component and the number of mounting environment images; acquiring image characteristics of the target component in the installation state based on the installation environment image data and the installed image data; adjusting the reference simulated installation image data to generate calibration simulated installation image data based on a comparison result of the post-installation image data and the reference simulated installation image data so as to extract image features of the calibration simulated installation image data; and matching image features of the post-installation image data and image features of the calibration simulation installation image data to generate indication information about the installation state of the target component based on the matching result.
According to a second aspect of the present invention, there is also provided a computing device comprising: at least one processing unit; at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions when executed by the at least one processing unit, cause the computing device to perform the method of the first aspect of the disclosure.
According to a third aspect of the present disclosure, there is also provided a computer-readable storage medium. The computer readable storage medium has stored thereon machine executable instructions which, when executed, cause a machine to perform the method of the first aspect of the disclosure.
In some embodiments, the target component is a flow meter, the target device is a compressed air main in an air compression station, and the dimensional information includes width information, height information, and depth information of the target component to be installed.
In some embodiments, acquiring the image feature of the target component in the installation state includes: comparing the installation environment image data with the installed image data to obtain difference data; and extracting profile features of the target component of the mounting state based on the acquired difference data.
In some embodiments, generating calibration simulated installation image data comprises: acquiring reference axis information of the installed image data;
acquiring reference axis information of reference simulation installation image data; comparing the reference axis information of the post-installation image data with the reference axis information of the simulated installation image data to generate comparison information about the reference axis information; determining a rotation angle for adjusting the reference simulation installation image data via the prediction model based on the difference information about the reference axis; repeatedly generating the adjusted reference simulated installation image data, the difference information about the reference axis, and the rotation angle via the prediction model based on the rotation angle for adjusting the reference simulated installation image data until the difference information about the reference axis meets a predetermined convergence condition; the reference simulated installation image data based on the current adjustment is determined as calibration simulated installation image data.
In some embodiments, generating reference simulated installation image data for simulating installation of the target component at the installation location comprises: reconstructing virtual model data of the target component based on the size information, the attribute information, the front view, the side view and the reference three-dimensional model of the target component, wherein the target component is a flowmeter, and the target equipment is a compressed air main pipe in an air compression station; reconstructing virtual model data of the installation environment based on the image data of the installation environment; and stitching the target component virtual model data and the installation environment virtual model data to generate reference simulated installation image data.
In some embodiments, matching the image features of the post-installation image data with the image features of the calibration simulation installation image data to generate the indication information about the target component installation state based on the matching result includes: calculating at least one of contour point deviation data, parallelism data, perpendicularity data, and rotational angle deviation data of each axis based on the contour feature of the target component in the mounted state and the contour feature of the calibration simulation mounting image data; and generating indication information indicating that the target component mounting state does not meet the predetermined condition in response to a determination that at least one of the calculated contour point deviation data, parallelism data, perpendicularity data, and each axis rotation angle deviation data is greater than or equal to a corresponding predetermined threshold value.
In some embodiments, the plurality of view data includes at least elevation view data, overhead view data, side view data, and axonometric view data of the target part, the method further comprising: obtaining attribute information and a standard three-dimensional virtual model about a target component via a link associated with the target component; and transmitting indication information on the installation state of the target component to the terminal device.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.
Drawings
Fig. 1 shows a schematic diagram of a system for a method of identifying a target component mounting state according to an embodiment of the present disclosure.
Fig. 2 shows a flowchart of a method for identifying a target component mounting state according to an embodiment of the present disclosure.
FIG. 3 shows a schematic of multiple view data of a target component according to an embodiment of the disclosure.
FIG. 4 illustrates a schematic diagram of installation environment image counts and post-installation image data, according to some embodiments of the present disclosure.
FIG. 5 shows a flow diagram of a method for generating calibration simulation installation image data in accordance with an embodiment of the present disclosure.
FIG. 6 illustrates a schematic diagram of installation environment image counts and post-installation image data according to further embodiments of the present disclosure.
Fig. 7 shows a flowchart of a method of generating indication information on a target component mounting state according to an embodiment of the present disclosure.
FIG. 8 schematically illustrates a block diagram of an electronic device suitable for use to implement embodiments of the present disclosure.
Like or corresponding reference characters designate like or corresponding parts throughout the several views.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object.
As mentioned above, the conventional scheme for recognizing the mounting state of the target component has disadvantages in that: the installation quality recognition efficiency is low, the personnel cost is high, and the recognition standard is lack of consistency.
To address, at least in part, one or more of the above issues and other potential issues, an example embodiment of the present disclosure proposes a scheme for identifying a target component mounting state. In this scheme, reference simulated mounting image data for simulating mounting of the target component at the mounting position is generated by at least based on the obtained size information and the plurality of view data of the target component, and the number of mounting environment images; then, based on the installation environment image data (i.e., pre-installation image data) and the post-installation image data, the image features of the target component in the installation state are acquired, and the image features of the target component in the actual installation state can be accurately extracted by the present disclosure. In addition, the present disclosure can calibrate the simulated mount image data for mount quality evaluation according to the image data of the actual mount state by adjusting the reference simulated mount image data to generate the calibration simulated mount image data based on the comparison result of the post-mount image data and the reference simulated mount image data so as to extract the image feature of the calibration simulated mount image data. Further, the present disclosure can compare the features of the actual mounting image with the features of the virtual mounting image on the basis of the alignment, thereby rapidly judging the eligibility of the mounting state, by matching the image features of the post-mounting image data and the image features of the calibration simulation mounting image data to generate the indication information about the mounting state of the target component based on the matching result. Thus, the present disclosure accurately and efficiently intelligently identifies and determines the field installation quality of a target component.
Fig. 1 shows a schematic diagram of a system 100 for a method of identifying a target component mounting status according to an embodiment of the present disclosure. As shown in fig. 1, the system 100 includes, for example, a terminal device 110, a management device 130, a network 150, a target component 140, and a target device 120. The terminal device 110 may perform data interaction with the management device 130 in a wired or wireless manner through the network 150. The target component 140 is, for example, a meter of an industrial device, such as a flow meter. The target device 120 is, for example, an industrial device. The mark 122 indicates, for example, the installation position of a compressed air main 124 (only a part of the compressed air main is shown in fig. 1) in the air compression station.
Regarding the terminal device 110, it is used to acquire installation environment image data (for example, image data 402 in fig. 4) at the installation position 122 of the target device 120 at a predetermined angle. The terminal device 110 may also be used to capture post-installation image data (e.g., image data 404 in fig. 4) of the target component 140 installed at the installation location 122 at a predetermined angle. The terminal device 110 is for example a mobile phone or other mobile terminal of an installer.
As for the management device 130, it is, for example, a cloud server. The management apparatus 130 is used to acquire the size information and a plurality of view data (for example, image data 302 to 310 in fig. 3) of the target component 140 to be installed, and the installation environment image data (for example, image data 402 in fig. 4). The management apparatus 130 may also be used to generate reference simulation installation image data for simulating the installation of the target component 140 to the installation location 122, and acquire the image characteristics of the target component 140 of the installation state. The management apparatus 130 may also adjust the reference simulated installation image data to generate calibration simulated installation image data based on the comparison result of the post-installation image data and the reference simulated installation image data; and matching the image features of the post-installation image data and the image features of the calibration simulation installation image data to generate indication information about the installation state of the target component 140 based on the matching result. In some embodiments, the management device 130 may have one or more processing units, including special purpose processing units such as GPUs, FPGAs, ASICs, and general purpose processing units such as CPUs. In addition, one or more virtual machines may also be running on each management device 130.
A method for identifying a target component mounting state according to an embodiment of the present disclosure will be described below with reference to fig. 2 and 3, and fig. 4 and 6. Fig. 2 shows a flowchart of a method 200 for identifying a target component mounting status according to an embodiment of the present disclosure. FIG. 3 shows a schematic of multiple view data of a target component according to an embodiment of the disclosure. FIG. 4 illustrates a schematic diagram of installation environment image counts and post-installation image data, according to some embodiments of the present disclosure. FIG. 6 illustrates a schematic diagram of installation environment image counts and post-installation image data according to further embodiments of the present disclosure. It should be understood that the method 200 may be performed, for example, at the electronic device 800 depicted in fig. 8. May also be performed at the management device 130 depicted in fig. 1. It should be understood that method 200 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect.
At step 202, the management apparatus 130 acquires the size information and the plurality of view data of the target component to be mounted. In some embodiments, the management apparatus 130 may further acquire the attribute information about the target component and the reference three-dimensional model data of the target component via a link associated with the target component.
As regards the target component to be installed, it is for example and without limitation a sensor or a meter for measuring the state of the industrial equipment, such as a flow meter.
Dimensional information about the target component, for example and without limitation, includes: the width, height, depth, maximum size information of the outer contour, and minimum size information of the outer contour of the target component to be mounted. Attribute information on the target component, which includes, for example: material information of the target component, and the like.
With respect to the plurality of view data 300, as shown in fig. 3, for example and without limitation, at least a front view data 302, a top view data 306, side view data 308 and 310, a bottom view data 304, and an axonometric view data of the target part, and the like.
At step 204, the management apparatus 130 acquires, via the terminal apparatus 110, installation environment image data at the installation position of the target apparatus acquired at a predetermined angle, and post-installation image data of the target component installed at the installation position.
As for the target device, it is, for example, without limitation, an industrial device. For example, the method comprises the following steps: compressed air main pipe in the air compression station.
As for the installation environment image data, it includes, for example, a plurality of images. The installation environment image data is, for example, installation environment image data at a position to be installed of the target device taken at the same angle as a certain view or views (e.g., a front view, a top view, or a side view) of the target component. Taking the application of installing a flow meter on a compressed air main in an air compression station as an example, installing environment image data includes, for example: image data of a flowmeter mounting base of the compressed air main before mounting the flowmeter, taken at the same angle as the front view of the flowmeter. It should be noted that, after the target component is installed on site, the terminal device is used to collect the installed image data at a predetermined angle, and the collection angle of the installed image data needs to be completely consistent with the predetermined angle when the installation environment image data is collected before installation, so as to facilitate the management device 130 to highly accurately identify and evaluate the installation quality of the target component.
At step 206, the management apparatus 130 generates reference simulation installation image data for simulating installation of the target component at the installation position based on at least the size information and the plurality of view data of the target component and the number of installation environment images.
Regarding the method of generating the reference simulation installation image data, it includes, for example: the management device 130 reconstructs virtual model data of the target component based on the size information, the attribute information, the front view, the side view and the reference three-dimensional model of the target component, the target component is a flow meter, and the target device is a compressed air main pipe in the air compression station; reconstructing virtual model data of the installation environment based on the image data of the installation environment; and stitching the target component virtual model data and the installation environment virtual model data to generate reference simulated installation image data.
Among them, regarding the method of reconstructing the virtual model data of the target component, it includes, for example: the management apparatus 130 determines a reference three-dimensional model of the target part based on the size information, the attribute information, and the front view and the side view of the target part; acquiring front projection data and side projection data of a reference three-dimensional model of a target part; extracting a front profile curve of a front view and a side profile curve of a side view of the target part respectively; then, the management apparatus 130 extracts a plurality of pieces of first characteristic point information of the front profile curve and a plurality of pieces of second characteristic point information of the side profile curve, matches the plurality of pieces of first characteristic point information of the front profile curve of the target part with a plurality of corresponding characteristic points of the front projection profile curve, so as to calculate a front projection conversion matrix; matching a plurality of second characteristic point information of the side profile curve of the target component with a plurality of corresponding characteristic points of the side projection profile curve so as to calculate a side projection conversion matrix; and reconstructing the target component simulation image data based on the front projection conversion matrix, the side projection conversion matrix and the reference three-dimensional model of the target component.
A method of matching the plurality of first characteristic point information of the front profile curve of the target member with the plurality of corresponding characteristic points of the front projection profile curve of the reference three-dimensional model will be described below with reference to formula (1).
In the above-mentioned formula (1),
on a curve representing the front profile of the target part
Coordinate information of the two-dimensional perspective projection points of the characteristic points.
A first on the front projection profile curve representing the reference three-dimensional model with respect to the target part
Coordinate information of the two-dimensional perspective projection points of the characteristic points.
Representing a front projection transformation matrix.
Representing a translation in the horizontal direction.
Representing a translation in the vertical direction.
Representing the scaling factor in the horizontal direction.
Representing the scaling factor in the vertical direction. When the major axis of the front profile curve of the target part is parallel to the major axis of the front projected profile curve of the reference three-dimensional model with respect to the target part,
and
is 0. And matching the plurality of second characteristic point information of the side profile curve of the target component with the plurality of corresponding characteristic points of the side projection profile curve of the reference three-dimensional model based on a method similar to the formula (1).
As for the method of reconstructing the installation-environment virtual model data, there may be a variety of, which may be a method similar to the above-described method of reconstructing the target component virtual model data, that is, reconstructing the installation-environment virtual model data based on the installation-environment image data and the reference three-dimensional model relating to the installation environment. It may also include: based on the installation environment image data, installation environment virtual model data is generated via the neural network model. Specifically, the neural network model includes, for example: an encoder constructed based on a CNN network model, an intermediate layer constructed based on an arrangement of LSTM units into a 3D mesh structure, and a decoder constructed based on a Deconvolution (Deconvolution) network. Wherein, the encoder constructed based on the CNN network model is used for encoding the two-dimensional multi-view data. Each LSTM unit in the middle layer is used to convey the convolution hidden states of the feature vectors output by the encoder and the neighboring units to the decoder. It should be understood that the two-dimensional installation environment image data may be mapped to the installation environment virtual model data in other suitable manners.
Methods for stitching the target component virtual model data and the installation environment virtual model data include various methods, and an existing three-dimensional graphic engine may be used for stitching two three-dimensional models. For example, an iterative closest point algorithm is used to match the partial images of the mutually coupled surfaces in the target component virtual model data and the installation environment virtual model data. Specifically, for example, the set Pl = { x1, x2, x3, …, xn } represents coordinate points of the coupling surface of the target component virtual model data. P2= { y1, y2, y3, …, yn } represents coordinate points of the coupling surface on which the environment virtual model data is installed. The closest corresponding point in Pl and P2 can be solved iteratively using local image registration techniques (ICP); then calculating a transformation matrix based on the corresponding points; and transforming the installation environment virtual model data based on the calculated transformation matrix, repeating the iteration until a convergence condition of the distance between Pl and P2 is reached, stopping the iteration, and forming reference simulated installation image data based on the transformed installation environment virtual model data and the target component virtual model data that meet the convergence condition.
At step 208, the management apparatus 130 acquires image features of the target component after installation based on the installation environment image data and the image data after installation.
Regarding a method of acquiring an image feature of a target component in a mounted state, it includes, for example: comparing the installation environment image data with the installed image data to obtain difference data; and extracting profile features of the target component after installation based on the acquired difference data. For example, the management apparatus 130 calculates a difference between the installation environment image data and the post-installation image data, then takes an absolute value of the difference as data of the target component in the installation state (i.e., image data having the absolute value of the difference as a foreground target), and binarizes for the absolute value of the difference using a predetermined threshold value so as to acquire the contour feature of the target component in the installation state. The contour feature of the target component is, for example, a contour feature of a meter.
At step 210, the management apparatus 130 adjusts the reference simulated installation image data to generate calibration simulated installation image data based on the comparison result of the post-installation image data and the reference simulated installation image data so as to extract the image feature of the calibration simulated installation image data.
Regarding a method of generating calibration simulation installation image data, it includes, for example: acquiring reference axis information of the installed image data; acquiring reference axis information of reference simulation installation image data;
comparing the reference axis information of the post-installation image data with the reference axis information of the simulated installation image data to generate comparison information about the reference axis information; determining a rotation angle for adjusting the reference simulation installation image data via the prediction model based on the difference information about the reference axis; repeatedly generating the adjusted reference simulated installation image data, the difference information about the reference axis, and the rotation angle via the prediction model based on the rotation angle for adjusting the reference simulated installation image data until the difference information about the reference axis meets a predetermined convergence condition; determining whether the difference information about the reference axis meets a predetermined convergence condition; and responsive to determining that the difference information about the reference axis meets a predetermined convergence condition, basing. The method for generating the calibration simulation mounting image data will be specifically described below with reference to fig. 5, and will not be described herein again.
At step 212, the management apparatus 130 matches the image feature of the target component after installation and the image feature of the calibration simulation installation image data to generate indication information on the installation state of the target component based on the matching result.
In the above-described aspect, the reference simulated mounting image data for simulating mounting of the target component at the mounting position is generated by at least based on the obtained size information and the plurality of view data of the target component, and the number of mounting environment images; then, based on the installation environment image data (i.e., pre-installation image data) and the post-installation image data, the image features of the target component in the installation state are acquired, and the image features of the target component in the actual installation state can be accurately extracted by the present disclosure. In addition, the present disclosure can calibrate the simulated mount image data for mount quality evaluation according to the image data of the actual mount state by adjusting the reference simulated mount image data to generate the calibration simulated mount image data based on the comparison result of the post-mount image data and the reference simulated mount image data so as to extract the image feature of the calibration simulated mount image data. Further, the present disclosure can compare the features of the actual mounting image with the features of the virtual mounting image on the basis of the alignment, thereby rapidly judging the eligibility of the mounting state, by matching the image features of the post-mounting image data and the image features of the calibration simulation mounting image data to generate the indication information about the mounting state of the target component based on the matching result. Thus, the present disclosure accurately and efficiently intelligently identifies and determines the field installation quality of a target component.
A method for generating calibration simulation installation image data according to an embodiment of the present disclosure will be described below in conjunction with fig. 5. Fig. 5 shows a flow diagram of a method 500 for generating calibration simulation installation image data in accordance with an embodiment of the present disclosure. It should be understood that the method 500 may be performed, for example, at the electronic device 800 depicted in fig. 8. May also be performed at the management device 130 depicted in fig. 1. It should be understood that method 500 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect.
At step 502, the management apparatus 130 acquires reference axis information of the post-installation image data. In some embodiments, the management apparatus 130 extracts a reference axis of the contour feature of the target component of the installation state.
At step 504, the management apparatus 130 acquires reference axis information of the reference simulation installation image data. For example, the management apparatus 130 extracts a reference axis for calibrating the contour feature of the simulated installation image data. It is noted that the reference axis extracted at step 504 and the reference axis extracted at step 502 are for the same target object.
At step 506, the management apparatus 130 compares the reference axis information of the post-installation image data and the reference axis information of the simulation installation image data to generate comparison information about the reference axis information. For example, the management apparatus 130 compares reference axis information of the contour feature of the target component in the mounting state and reference axis information of the contour feature of the calibration simulation mounting image data, and generates difference information about the reference axes.
At step 508, the management apparatus 130 determines a rotation angle for adjusting the reference simulation installation image data via the prediction model based on the difference information about the reference axis. If the reference axis of the image feature of the target component and the reference axis of the calibration simulation mounting image data coincide, the difference information about the reference axes is 0 or very small, indicating that the image feature of the target component and the reference of the calibration simulation mounting image data coincide, and the rotation angle predicted via the prediction model is 0. If the difference information about the reference axes exceeds a predetermined threshold, it is determined that the reference axes of the image features of the target component and the reference axes of the calibration simulation setup image data do not coincide, and the features of the difference information are extracted via the prediction model to predict a matching rotation angle for use in rotating the reference simulation setup image data.
At step 510, the management apparatus 130 repeats generating the adjusted reference simulation setup image data, the difference information about the reference axis, and generating the rotation angle via the prediction model, based on the rotation angle for adjusting the reference simulation setup image data, until the difference information about the reference axis meets a predetermined convergence condition.
For example, the rotation angle for fine-tuning the reference simulation installation image data is predicted from the difference information about the reference axis via a prediction model constructed based on a neural network model, then, the adjusted reference simulation installation image data is regenerated based on the predicted rotation angle, and the reference axis information of the adjusted reference simulation installation image data is again compared with the reference axis information of the post-installation image data, and so on until the reference axis information of the post-installation image data coincides with the reference axis of the adjusted reference simulation installation image data, that is, the reference axis coincides, and the difference information about the reference axis conforms to a predetermined convergence condition.
A method for repeatedly generating adjusted reference simulation installation image data is described below in conjunction with equation (2).
In the above-mentioned formula (2),
representing rotation of the reference simulated mount image data by a determined rotation angle
The adjusted reference thereafter simulates the installation image data.
Representing the reference simulated mounting image data.
Representing a rotation transformation matrix that rotates the reference analog mount image data about a rotation axis (e.g., the Z-axis).
A method of generating training samples for a pre-trained neural network model, for example, comprising: rotating the reference three-dimensional model of the target component for multiple times according to a specific angle, so as to obtain reference three-dimensional models of the target component corresponding to different rotation angles; calculating deviation information between reference axis information in a reference three-dimensional model of the target component corresponding to different rotation angles so as to generate a plurality of input sample sets regarding the deviation information between the reference axis information; the sets of input samples are labeled with corresponding rotation angles so that a predictive model is trained with the plurality of input samples with deviation information between reference axis information for corresponding rotation angle labels.
At step 512, the reference simulated installation image data based on the current adjustment is determined as calibration simulated installation image data.
By adopting the above means, the reference axis of the reference simulation installation image data can be adjusted, and the actual installation contour and the virtual installation contour of the target component are compared on the basis that the reference axis information of the reference simulation installation image data is consistent with the reference axis information of the installed image data, so that the accurate identification and evaluation of the installation quality of the target component are facilitated.
A method 700 of generating indication information on a target component mounting state according to an embodiment of the present disclosure will be described below with reference to fig. 4, 6, and 7. Fig. 7 illustrates a flow diagram of a method 700 of generating alert information regarding a target component installation status in accordance with an embodiment of the present disclosure. It should be understood that method 700 may be performed, for example, at electronic device 800 depicted in fig. 8. May also be performed at the management device 130 depicted in fig. 1. It should be understood that method 700 may also include additional acts not shown and/or may omit acts shown, as the scope of the present disclosure is not limited in this respect.
At step 702, the management apparatus 130 calculates at least one of contour point deviation data, parallelism data, perpendicularity data, and each axis rotation angle deviation data based on the contour feature of the target component in the mounted state and the contour feature of the calibration simulation mounting image data.
At step 704, the management apparatus 130 determines whether at least one of the calculated contour point deviation data parallelism data, perpendicularity data, and each axis rotation angle deviation data is greater than or equal to a corresponding predetermined threshold value.
At step 706, if the management apparatus 130 determines that at least one of the calculated contour point deviation data, parallelism data, perpendicularity data, and each axis rotation angle deviation data is greater than or equal to a corresponding predetermined threshold value, indication information indicating that the target component mounting state does not meet a predetermined condition is generated.
At step 708, if the management apparatus 130 determines that each of the calculated contour point deviation data, parallelism data, perpendicularity data, and each shaft rotation angle deviation data is less than a corresponding predetermined threshold, indication information indicating that the target component mounting state meets a predetermined condition is generated.
For example, a method of generating the indication information about the mounting state of the target component will be described below by taking the vertical axis rotation angle deviation data as an example. For example, the management apparatus 130 determines whether the horizontal section of the target part is symmetrical with respect to the center of the vertical axis based on the size information of the target part, if it is determined that the horizontal section of the target part is not symmetrical with respect to the center of the vertical axis; it is determined whether a difference between a contour line area of the target part (e.g., a contour line area of the target part in the front image) and a predetermined contour line area is greater than a predetermined area threshold. If it is determined that the difference between the contour line area of the target part and the predetermined contour line area is greater than the predetermined area threshold, it is determined that the target object rotational angle deviation data about the vertical axis exceeds the corresponding installation quality standard, the management apparatus 130 generates warning information that the installation quality is not satisfactory, and correction information about the rotational angle of the vertical axis calculated based on the difference. It should be noted that if it is determined that the horizontal section of the target part is symmetrical with respect to the center of the vertical axis, for example, the horizontal section belonging to the target part is circular, the rotation angle of the target part about the vertical axis does not cause a deviation in the area of the contour line.
For example, the management apparatus 130 generates indication information indicating that the target component mounting state does not meet the predetermined condition, based on the mounting environment image data 402 and the number of mounted images 404 as shown in fig. 4, and the calculated parallelism data and perpendicularity data are greater than or equal to the corresponding predetermined thresholds. For another example, the management apparatus 130 generates indication information indicating that the target component mounting state meets the predetermined condition, based on the mounting environment image data 602 and the number of mounted images 604 shown in fig. 6, and that each of the calculated contour point deviation data, parallelism data, verticality data, and rotational angle deviation data of each axis is smaller than a corresponding predetermined threshold.
For another example, the cloud server matches the corresponding prompt information according to the installation quality determination result and returns the prompt information to the user terminal, such as the mobile terminal. And the application of the user terminal receives and prompts the result returned by the cloud, and the prompt information may be that the installation quality is qualified, or the installation quality is unqualified and corresponding correction guidance is provided.
By adopting the means, the method intelligently evaluates whether the installation quality of the target component meets the requirement or not, and sends the prompt information to a remote installer.
FIG. 8 schematically illustrates a block diagram of an electronic device 800 suitable for use in implementing embodiments of the present disclosure. The device 800 may be a device for implementing the methods 200, 500, and 700 shown in fig. 2, 5, and 7. As shown in fig. 8, device 800 includes a Central Processing Unit (CPU) 801 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 802 or loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM, various programs and data required for the operation of the device 800 can also be stored. The CPU, ROM, and RAM are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a central processing unit 801 perform the respective methods and processes described above, such as performing the methods 200, 500, and 700. For example, in some embodiments, methods 200, 500, and 700 may be implemented as a computer software program stored on a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM and/or communications unit 809. When loaded into RAM and executed by a CPU, the computer program may perform one or more of the operations of methods 200, 500 and 700 described above. Alternatively, in other embodiments, the CPU may be configured by any other suitable means (e.g., by way of firmware) to perform one or more of the acts of methods 200, 500, and 700.
It should be further appreciated that the present disclosure may be embodied as methods, apparatus, systems, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor in a voice interaction device, a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
The above are only alternative embodiments of the present disclosure and are not intended to limit the present disclosure, which may be modified and varied by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (9)
1. A method for identifying a target component mounting state, comprising:
at a management apparatus, acquiring size information and a plurality of view data of a target component to be mounted;
acquiring, via the terminal device, installation environment image data at an installation position of the target device acquired at a predetermined angle and post-installation image data of the target component installed at the installation position;
generating reference simulated mounting image data for simulating mounting of the target component to the mounting position based on at least the size information and the plurality of view data of the target component and the number of mounting environment images;
acquiring image characteristics of the target component in the installation state based on the installation environment image data and the installed image data;
adjusting the reference simulated installation image data to generate calibration simulated installation image data based on a comparison result of the post-installation image data and the reference simulated installation image data so as to extract image features of the calibration simulated installation image data; and
image features of the post-installation image data and image features of the calibration simulation installation image data are matched to generate indication information on the installation state of the target component based on the matching result.
2. The method of claim 1, wherein the target component is a flow meter, the target device is a compressed air main in an air compression station, and the dimensional information includes width information, height information, and depth information of the target component to be installed.
3. The method of claim 1, wherein acquiring image features of the target component in the installed state comprises:
comparing the installation environment image data with the installed image data to obtain difference data; and
based on the acquired difference data, contour features of the target component in the mounted state are extracted.
4. The method of claim 1, wherein generating calibration simulated installation image data comprises:
acquiring reference axis information of the installed image data;
acquiring reference axis information of reference simulation installation image data;
comparing the reference axis information of the post-installation image data with the reference axis information of the simulated installation image data to generate comparison information about the reference axis information;
determining a rotation angle for adjusting the reference simulation installation image data via the prediction model based on the difference information about the reference axis;
repeatedly generating the adjusted reference simulated installation image data, the difference information about the reference axis, and the rotation angle via the prediction model based on the rotation angle for adjusting the reference simulated installation image data until the difference information about the reference axis meets a predetermined convergence condition; and
the reference simulated installation image data based on the current adjustment is determined as calibration simulated installation image data.
5. The method of claim 1, wherein generating reference simulated mounting image data for simulating mounting of a target component at a mounting location comprises:
reconstructing virtual model data of a target component based on size information, attribute information, a front view, a side view and a reference three-dimensional model of the target component, wherein the target component is a flowmeter, and the target equipment is a compressed air main pipe in an air compression station;
reconstructing virtual model data of the installation environment based on the image data of the installation environment; and
the target component virtual model data and the installation environment virtual model data are stitched to generate reference simulated installation image data.
6. The method of claim 1, wherein matching image features of the post-installation image data with image features of the calibration simulation installation image data to generate indication information about the target component installation status based on the matching result comprises:
calculating at least one of contour point deviation data, parallelism data, perpendicularity data, and rotational angle deviation data of each axis based on the contour feature of the target component in the mounted state and the contour feature of the calibration simulation mounting image data; and
in response to a determination that at least one of the calculated contour point deviation data, parallelism data, perpendicularity data, and each shaft rotation angle deviation data is greater than or equal to a corresponding predetermined threshold value, indication information indicating that the target component mounting state does not meet a predetermined condition is generated.
7. The method of claim 1, wherein the plurality of view data includes at least elevation view data, overhead view data, side view data, and axonometric view data of the target part, the method further comprising:
obtaining attribute information and a standard three-dimensional virtual model about a target component via a link associated with the target component; and
and sending indication information about the installation state of the target component to the terminal device.
8. A computing device, comprising:
at least one processing unit;
at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions when executed by the at least one processing unit causing the computing device to perform the method of any of claims 1-7.
9. A computer readable storage medium having stored thereon machine executable instructions which, when executed, cause a machine to perform the method of any one of claims 1 to 7.
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