CN114399159A - Engineering field progress identification method based on full-time-space monitoring - Google Patents
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Abstract
The invention discloses a full-time-space monitoring-based engineering field progress identification method, which is characterized by comprising the following steps of: the project progress is visually displayed in the modes of three-dimensional model display and key project billboard display respectively, on the three-dimensional model display, the three-dimensional design model is used as a carrier, the actual progress is highlighted and displayed on the GIM model, and the project progress states are displayed in different colors, so that the progress visual management is realized. Compared with the prior art, the invention has the advantages that: lean management can be realized on the progress of the infrastructure construction project, the construction period is shortened, the goals of cost reduction and efficiency improvement are achieved, guarantee is provided for early production of the construction project, guarantee is provided for high-quality development of enterprises, information support is provided for lean management of the progress of the infrastructure construction, intelligent construction requirements are met, popularization and application values are realized, lean management is deeper, and intelligent management is enabled to take a new step.
Description
Technical Field
The invention relates to the field of engineering site construction, in particular to an engineering site progress identification method based on full-time-space monitoring.
Background
A new information technology revolution is developed vigorously, and the global acceleration is promoted to enter the digital economic era. The development of digital economy is accelerated, and the integration development of entity economy and digital economy is promoted. In recent years, important deployment has been made to accelerate the construction of new infrastructures represented by 5G networks, large data centers, artificial intelligence, industrial internet and the like.
Digitization is a necessary choice to accommodate the convergence and trend of the energy revolution and the digital revolution. With the deep fusion and wide application of modern information technologies and energy technologies such as the moving intelligence of the cloudiness object and the like, the digitization and intelligent characteristics of energy transformation are further highlighted. No matter the requirements of large-scale high-proportion grid connection and consumption of new energy are met, or interactive and mobile facilities such as distributed energy sources, energy storage and electric vehicles are supported to be widely accessed, digital technology is required to be used as power for a power grid, coordination and interaction of source grid load and storage are promoted, the power grid is promoted to be upgraded to a more intelligent, more ubiquitous and more friendly energy internet, the energy supply cleanness, terminal consumption electrification and system operation high-efficiency level are continuously improved, and the method plays a greater role in guiding energy production and consumption revolution.
Therefore, in order to ensure that the construction of key power grid construction project is completed, the overall process management and control of major power grid projects are enhanced, the rigid execution of power grid construction tasks is promoted, key projects with intelligent management and control of the construction progress are provided, the construction site progress management and control of the construction progress are enhanced, the high-quality construction level of the power grid, the commissioning rate of the power grid construction project and other key indexes are improved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a project site progress identification method based on full-time-space monitoring.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: a project site progress identification method based on full-time-space monitoring is characterized by comprising the following steps: and respectively carrying out visual display on the project progress in the forms of three-dimensional model display and key project billboard display. And on the aspect of displaying the three-dimensional model, the three-dimensional design model is used as a carrier, the actual progress is highlighted and displayed on the GIM model, and the progress is displayed in different colors, so that the visual management of the progress is realized.
Preferably, the method comprises progress planning, intelligent progress identification, entry control and progress analysis.
Preferably, the schedule plan is: and carrying out interface butt joint with the infrastructure platform, acquiring information of each project milestone plan and construction operation ticket, and providing information inquiry of each project milestone plan and construction operation ticket.
Preferably, the progress intelligent recognition comprises the following steps: the intelligent identification method comprises a sample library management system, an algorithm warehouse system, intelligent identification configuration and intelligent identification video display of each project, wherein a large amount of sampling and data preprocessing are carried out on-site equipment, buildings and the like to form a sample library of the business system, intelligent identification algorithm model training is carried out, the trained models are managed in a unified mode, the models are updated periodically according to the updating of the sample library, and in addition, a user can configure intelligent identification objects and frequency of specific projects and observe intelligent identification results in real time.
Preferably, the schedule control: comparing the progress result returned by intelligent recognition with the extracted key information of the construction operation ticket, automatically generating a project progress state, and providing a grading alarm for progress deviations of different degrees by combining a milestone plan and a deviation analysis rule;
a manager provides deviation correction requirements for the planned progress and correction measures for the project with serious deviation by combining the visual display of the three-dimensional model on the project progress in a field viewing and remote video viewing mode, and realizes the management process of plan adjustment, audit and execution.
Preferably, the progress analysis: and providing progress signboards for project progress execution condition analysis, project deviation correction, execution condition analysis and key projects for project centers and construction management units at all levels of province and city.
Preferably, when the method is applied to engineering, the transformer substation engineering needs to provide all three-dimensional digital design models in the construction drawing stage, and the line engineering provides a tower model. The loading of the three-dimensional model in the network platform GIS can realize the following technical goals:
(1) supporting the main design tool model format and GIM format conversion;
(2) setting a coordinate system of the GIS during exporting;
(3) the 3d tile data is directly exported in a design tool and loaded in a system, and the model and the material are not lost;
(4) and in order to control display and model search, the method supports the realization of professional-based conversion loading and transparentization control.
Preferably, the three-dimensional model is converted into webbl data by weight reduction, and the following technical requirements are met:
(1) supporting a three-dimensional model format of a main design platform;
(2) direct export in a design tool is supported, intermediate format conversion is not carried out any more, and loading is carried out in a system platform;
(3) batch conversion is supported, and the model, material and extended attribute information are not lost;
(4) the lightweight model is supported to be converted and loaded in different specialties and different parts, and the lightweight model organizes the model, the loading model and the isolation display model according to the modes of projects, specialties and parts;
(5) for quickly positioning and checking important equipment, the camera position and the visual angle preset in the BIM model can be loaded and controlled in the lightweight model.
Compared with the prior art, the invention has the advantages that: lean management can be realized on the progress of the infrastructure construction project, the construction period is shortened, the goals of cost reduction and efficiency improvement are achieved, guarantee is provided for early production of the construction project, guarantee is provided for high-quality development of enterprises, information support is provided for lean management of the progress of the infrastructure construction, intelligent construction requirements are met, popularization and application values are realized, lean management is deeper, and intelligent management is enabled to take a new step.
Drawings
FIG. 1 is a business flow chart of an engineering progress intelligent early warning and deviation rectifying platform based on a full-time-space monitoring engineering field progress identification method.
Fig. 2 is a service architecture diagram of an engineering field progress identification method based on full-time-space monitoring.
FIG. 3 is a Darknet-53 network structure diagram of a project site progress identification method based on full-time-space monitoring.
FIG. 4 is a Yolov3 framework structure diagram of a project site progress identification method based on full-time-space monitoring.
FIG. 5 is a frame structure diagram of a target segmentation algorithm of a project site progress identification method based on full-time-space monitoring.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention relates to a full-time-space monitoring-based engineering field progress identification method in specific implementation, which is characterized by comprising the following steps of: and respectively carrying out visual display on the project progress in the forms of three-dimensional model display and key project billboard display. And on the aspect of displaying the three-dimensional model, the three-dimensional design model is used as a carrier, the actual progress is highlighted and displayed on the GIM model, and the progress is displayed in different colors, so that the visual management of the progress is realized.
Preferably, the method comprises progress planning, intelligent progress identification, entry control and progress analysis.
Preferably, the schedule plan is: and carrying out interface butt joint with the infrastructure platform, acquiring information of each project milestone plan and construction operation ticket, and providing information inquiry of each project milestone plan and construction operation ticket.
Preferably, the progress intelligent recognition comprises the following steps: the intelligent identification method comprises a sample library management system, an algorithm warehouse system, intelligent identification configuration and intelligent identification video display of each project, wherein a large amount of sampling and data preprocessing are carried out on-site equipment, buildings and the like to form a sample library of the business system, intelligent identification algorithm model training is carried out, the trained models are managed in a unified mode, the models are updated periodically according to the updating of the sample library, and in addition, a user can configure intelligent identification objects and frequency of specific projects and observe intelligent identification results in real time.
Preferably, the schedule control: comparing the progress result returned by intelligent recognition with the extracted key information of the construction operation ticket, automatically generating a project progress state, and providing a grading alarm for progress deviations of different degrees by combining a milestone plan and a deviation analysis rule;
a manager provides deviation correction requirements for the planned progress and correction measures for the project with serious deviation by combining the visual display of the three-dimensional model on the project progress in a field viewing and remote video viewing mode, and realizes the management process of plan adjustment, audit and execution.
Preferably, the progress analysis: and providing progress signboards for project progress execution condition analysis, project deviation correction, execution condition analysis and key projects for project centers and construction management units at all levels of province and city.
Preferably, when the method is applied to engineering, the transformer substation engineering needs to provide all three-dimensional digital design models in the construction drawing stage, and the line engineering provides a tower model. The loading of the three-dimensional model in the network platform GIS can realize the following technical goals:
(1) supporting the main design tool model format and GIM format conversion;
(2) setting a coordinate system of the GIS during exporting;
(3) the 3d tile data is directly exported in a design tool and loaded in a system, and the model and the material are not lost;
(4) and in order to control display and model search, the method supports the realization of professional-based conversion loading and transparentization control.
Preferably, the three-dimensional model is converted into webbl data by weight reduction, and the following technical requirements are met:
(1) supporting a three-dimensional model format of a main design platform;
(2) direct export in a design tool is supported, intermediate format conversion is not carried out any more, and loading is carried out in a system platform;
(3) batch conversion is supported, and the model, material and extended attribute information are not lost;
(4) the lightweight model is supported to be converted and loaded in different specialties and different parts, and the lightweight model organizes the model, the loading model and the isolation display model according to the modes of projects, specialties and parts;
(5) for quickly positioning and checking important equipment, the camera position and the visual angle preset in the BIM model can be loaded and controlled in the lightweight model.
The working principle of the invention is as follows: aiming at the characteristics of different identification objects in the power transmission and transformation project site, a target detection identification method is adopted for identification objects such as main transformers, parallel capacitor devices, cable trenches, main control buildings, line foundations, tower tips of towers, insulator strings and the like, and a target segmentation identification method is adopted for identification objects such as enclosure foundations, enclosures, in-station roads, cable trenches and the like. For both types of identification methods, the embodiments are as follows:
a Darknet-53 model design idea is adopted by a main network of the power transmission and transformation project site fixed point area target detection algorithm. The structure is mainly composed of 53 convolutional layers, and the network can correspond to input images with any size because a full connection layer is not used. The global coordinate regression of Yolov3 is similar to Yolov2, and the logistic regression function is still used to predict the anchor frame target. And the Yolov3 performs frame prediction on 3 different feature maps by using the SDD algorithm idea as a reference, and realizes multi-scale detection by adopting an FPN framework. To support multi-labeling, independent logical classification is used for classification prediction, and a binary cross entropy loss function is used for training. The loss function of YOLOv3 primarily includes an object localization offset loss, an object confidence loss, and an object classification loss. The DarkNet-53 network structure is shown in FIG. 3, and the Yolov3 framework structure is shown in FIG. 4.
Aiming at detection of an enclosing wall, an enclosing wall foundation and a road, ResNet50 is adopted as a main network of a segmentation algorithm, an encoder-decoder is adopted as an integral framework structure, wherein the encoder structure comprises a five-layer down-sampling network structure, the network firstly adopts convolution operation on input, mainly adopts convolution kernels of 7x7, then reduces the size of a characteristic diagram of an input image by using pooling operation of 3x3, and then adopts a residual error structure formed by convolution kernels of 1x1 and 3x3 as a 4-layer encoder structure, and then a plurality of residual error structures are superposed for extracting target texture information such as buildings in remote sensing images. The bottom layer of the target extraction algorithm such as buildings is added with a hollow space convolution pooling pyramid, and hollow convolutions with different sampling rates are parallelly sampled for given input, which is equivalent to capturing the context of images in a plurality of proportions. In the semantic segmentation task: the filter field of view can be adjusted, a powerful tool to control the resolution of the feature response computed by the convolutional neural network. In order to solve the target segmentation problem under multiple scales, a hole convolution cascade and hole convolution parallel architectures with different sampling rates are designed. The void space pyramid module can obtain convolution characteristics on multiple scales, and performance is further improved.
The secondary network adopts a VGG16 pre-training model, utilizes the building facility change attention intention output by the main network SE _ ResNet50, and achieves the purposes of utilizing the building change attention to restrain the content loss of the whole network, reducing the building information loss and improving the building target segmentation performance through a difference module and a sigmoid activation function. The main network adopts a binary cross entropy loss function, and the secondary network uses a mean square error loss function. The mean square error loss function is used for recording content loss of the change attention map, restricting network training and avoiding the network from falling into a local minimum value.
After the model training is finished, a corresponding weight file is generated, classification prediction can be carried out by using the test set, a prediction result is obtained, and then the model performance is verified. In the whole prediction process of the model, parameters stored by model training are utilized, then the classification result of the target is calculated through back propagation, and the accuracy is obtained through calculating the pixel value, so that the purpose of intelligent identification of the identification object in the construction site is achieved.
The loss function of YOLOv3 is largely divided into three parts: target position offset penalty Lloc (I, g), target confidence penalty Lconf (O, C), and target classification penalty Lcla (O, C), where λ 1, λ 2, λ 3 are balancing coefficients.
L(O，o，C，c，l，g)＝λ1Lconf(o，c)+λ2Lcla(O，C)+λ3Lloc(l，g)
The target confidence coefficient is the probability of the existence of a target in the predicted target rectangular frame, and the target confidence coefficient loss Lconf (o, c) adopts Binary Cross Entropy loss (Binary Cross Entropy), wherein oi belongs to {0, 1}, which indicates whether the target actually exists in the predicted target bounding frame i, 0 indicates that the target does not exist, and 1 indicates that the target exists.
And (4) the Sigmoid probability of whether the target exists in the predicted target rectangular box i or not is shown.
The target class loss Lcla (O, C) also adopts binary cross entropy loss (adopts binary cross entropy loss, wherein Oi is in a range of {0, 1}, which indicates whether the jth class target really exists in the predicted target boundary box i, 0 indicates that the jth class target does not exist, and 1 indicates that the jth class target exists.
And (4) representing the Sigmoid probability of the j-th class target in the network prediction target boundary box i.
The target location loss Lloc (l, g) is the sum of the squares of the difference between the true deviation value and the predicted deviation value, where
Indicating the predicted rectangular box coordinate offset,
indicating coordinate offset between the GTbox matched with the GTbox and the default frame, (bx, by, bw, bh) is a predicted target rectangular frame parameter, (cx, cy, cw, ch) is a default rectangular frame parameter, and (gx, gy, gw, gh) is a real target rectangular frame parameter matched with the GTbox and the default rectangular frame parameter, wherein the parameters are mapped on the prediction feature map.
Through plan and the actual engineering time of contrast intelligent recognition object and construction operation ticket, can reverse the actual construction progress:
when the actual identification time of the intelligent identification object is within the actual construction time of the construction operation ticket and the actual time is consistent with the planned time, the site progress is proved to be in a normal construction state;
when the actual identification time of the intelligent identification object is within the actual construction time of the construction operation ticket and the actual time is inconsistent with the planned time, the on-site progress is proved to be in an abnormal construction state, and progress early warning information can be given according to the difference value of the actual time and the planned time;
and when the actual identification time of the intelligent identification object is not in the corresponding actual construction time of the construction operation ticket, highlighting the inconsistency of the actual identification time and the corresponding actual construction time of the construction operation ticket at the front end of the system, and reminding a manager to manually determine the progress state.
The application of the artificial intelligence technology in power grid construction and power grid digital transformation is promoted. Aiming at multi-class and multi-feature identification objects in the construction progress process of the power transmission and transformation project, a deep learning framework adaptive to task data is selected by utilizing the performance of hardware equipment, and a deep learning algorithm based on a target identification detection and segmentation detection task is designed. Finally, a technical route of intelligent identification is opened, the artificial intelligence identification technology is guaranteed to be applied to a test point on a construction site and is enabled to be initially effective, a healthy sample library and a model library are built to share and operate cooperatively for subsequently and comprehensively building a infrastructure artificial intelligence identification platform, a batch of high-precision and high-value electric power infrastructure special models are formed, and a foundation is laid for greatly improving the service performance.
The level of the accurate management and control of the engineering field progress is further improved. By combining the construction operation ticket and AI intelligent identification, the on-site infrastructure progress is finely controlled through multidimensional data, the dependence on single-source data is reduced, and the phenomena that the construction operation ticket is partially falsely reported and the like due to manual reporting are avoided. In addition, real-time monitoring and algorithm detection through the field control ball greatly improve the timeliness of progress control. By means of artificial intelligence technology aided decision analysis, the lean management and control level and the management efficiency of the construction site of the power transmission and transformation project are comprehensively improved.
And the application of the digital design result in the engineering construction link is promoted through the engineering progress three-dimensional model display. The three-dimensional model of the transformer substation and the tower model are displayed in colors step by step, visual management and control of the progress of the power transmission and transformation project are achieved, project digital transfer is promoted, a digital twin system of the power transmission and transformation infrastructure project is created, and the intelligent level of infrastructure project construction is improved.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the invention, "plurality" means two or more unless explicitly specifically defined otherwise.
In the present invention, unless otherwise specifically stated or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, "above" or "below" a first feature means that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact with each other via another feature therebetween. Also, the first feature being "on," "above" and "over" the second feature includes the first feature being directly on and obliquely above the second feature, or merely indicating that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature includes the first feature being directly under and obliquely below the second feature, or simply meaning that the first feature is at a lesser elevation than the second feature.
In the description herein, reference to the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (8)
1. A project site progress identification method based on full-time-space monitoring is characterized by comprising the following steps: the project progress is visually displayed in the modes of three-dimensional model display and key project billboard display respectively, on the three-dimensional model display, the three-dimensional design model is used as a carrier, the actual progress is highlighted and displayed on the GIM model, and the project progress states are displayed in different colors, so that the progress visual management is realized.
2. The engineering field progress identification method based on full-time-space monitoring as claimed in claim 1, wherein: the method comprises progress planning, intelligent progress identification, entry control and progress analysis.
3. The engineering site progress identification method based on full-time-space monitoring as claimed in claim 2, wherein: and (3) scheduling: and carrying out interface butt joint with the infrastructure platform, acquiring information of each project milestone plan and construction operation ticket, and providing information inquiry of each project milestone plan and construction operation ticket.
4. The engineering site progress identification method based on full-time-space monitoring as claimed in claim 2, wherein: and (3) intelligently identifying the progress: the intelligent identification method comprises a sample library management system, an algorithm warehouse system, intelligent identification configuration and intelligent identification video display of each project, wherein a large amount of sampling and data preprocessing are carried out on-site equipment, buildings and the like to form a sample library of the business system, intelligent identification algorithm model training is carried out, the trained models are managed in a unified mode, the models are updated periodically according to the updating of the sample library, and in addition, a user can configure intelligent identification objects and frequency of specific projects and observe intelligent identification results in real time.
5. The engineering site progress identification method based on full-time-space monitoring as claimed in claim 2, wherein: progress management and control: comparing the progress result returned by intelligent recognition with the extracted key information of the construction operation ticket, automatically generating a project progress state, and providing a grading alarm for progress deviations of different degrees by combining a milestone plan and a deviation analysis rule;
a manager provides deviation correction requirements for the planned progress and correction measures for the project with serious deviation by combining the visual display of the three-dimensional model on the project progress in a field viewing and remote video viewing mode, and realizes the management process of plan adjustment, audit and execution.
6. The engineering site progress identification method based on full-time-space monitoring as claimed in claim 2, wherein: and (3) progress analysis: and providing progress signboards for project progress execution condition analysis, project deviation correction, execution condition analysis and key projects for project centers and construction management units at all levels of province and city.
7. The engineering field progress identification method based on full-time-space monitoring as claimed in claim 1, wherein: when the transformer substation is applied to a project, all three-dimensional digital design models in a construction drawing stage need to be provided for the transformer substation project, and a tower model needs to be provided for a line project. The loading of the three-dimensional model in the network platform GIS can realize the following technical goals:
(1) supporting the main design tool model format and GIM format conversion;
(2) setting a coordinate system of the GIS during exporting;
(3) the 3d tile data is directly exported in a design tool and loaded in a system, and the model and the material are not lost;
(4) and in order to control display and model search, the method supports the realization of professional-based conversion loading and transparentization control.
8. The engineering field progress identification method based on full-time-space monitoring as claimed in claim 1, wherein: the three-dimensional model is converted into WEBGL data in a light weight mode, and the following technical requirements are met:
(1) supporting a three-dimensional model format of a main design platform;
(2) direct export in a design tool is supported, intermediate format conversion is not carried out any more, and loading is carried out in a system platform;
(3) batch conversion is supported, and the model, material and extended attribute information are not lost;
(4) the lightweight model is supported to be converted and loaded in different specialties and different parts, and the lightweight model organizes the model, the loading model and the isolation display model according to the modes of projects, specialties and parts;
(5) for quickly positioning and checking important equipment, the camera position and the visual angle preset in the BIM model can be loaded and controlled in the lightweight model.
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