CN115659673A - Bridge construction process safety monitoring system based on unmanned aerial vehicle image - Google Patents
Bridge construction process safety monitoring system based on unmanned aerial vehicle image Download PDFInfo
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Abstract
The invention discloses a bridge construction process safety monitoring system based on unmanned aerial vehicle images, relates to the technical field of bridge construction, and solves the technical problems that in the prior art, dangerous areas of bridge construction areas are difficult to accurately divide, constructors cannot be reminded in time, bridge construction efficiency is low, and construction environment safety is low; the method combines a computer technology and an image recognition technology, and establishes a construction model through three-dimensional modeling software and a plurality of collected construction images; simulating a construction environment through meteorological simulation data of a construction area, and acquiring bridge simulation data so as to acquire a bridge state sequence; then, a dangerous area is defined by combining meteorological prediction data, and early warning is carried out in time by combining the dangerous area; according to the construction method, the construction model is built through the unmanned aerial vehicle image, the dangerous area in the construction area is defined through the simulation result of the construction model, the definition accuracy of the dangerous area can be guaranteed, and a data foundation is laid for early warning.
Description
Technical Field
The invention belongs to the field of bridge construction, relates to a bridge construction safety monitoring technology based on unmanned aerial vehicle images, and particularly relates to a bridge construction process safety monitoring system based on unmanned aerial vehicle images.
Background
The bridge generally consists of an upper structure, a lower structure, a support and an auxiliary structure, and is very important for the modern high-speed developed traffic industry. The bridge construction process is tedious, and most importantly, the safety problem is solved, and potential safety hazards can still be formed due to negligence when each construction point is monitored.
The prior art (patent application with publication number CN111223279 a) discloses a bridge construction safety detection alarm system, which detects bridge displacement in real time through various sensors without referring to external environment, determines a danger area according to the detection result, and gives an early warning to constructors. In the bridge construction process, the construction environment has important influence on the safety of the bridge and constructors, so that in the bridge construction monitoring process, the safety of the bridge is judged only by detecting the states of all components of the bridge through sensors in the prior art, the dangerous area of a bridge construction area cannot be accurately divided, and the constructors cannot be reminded in time; therefore, a bridge construction process safety monitoring system based on unmanned aerial vehicle images is urgently needed.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art; therefore, the invention provides a bridge construction process safety monitoring system based on unmanned aerial vehicle images, which is used for solving the technical problems that in the prior art, dangerous areas of a bridge construction area are difficult to accurately divide, constructors cannot be reminded in time, the bridge construction efficiency is low, and the construction environment safety is low.
In order to achieve the above object, a first aspect of the present invention provides a bridge construction process safety monitoring system based on unmanned aerial vehicle images, which includes a central processing module, and a data acquisition module and an intelligent terminal connected thereto; the data acquisition module is connected with a plurality of unmanned aerial vehicles or meteorological platforms;
the data acquisition module acquires construction images in real time through a plurality of unmanned aerial vehicles, acquires meteorological historical data and meteorological forecast data of a construction area through a meteorological platform, and sends acquired data to the central processing module;
the central processing module is used for establishing a three-dimensional model of a construction area by combining the construction plan and the plurality of construction images, and marking the three-dimensional model as a construction model; reasonably expanding and acquiring meteorological simulation data according to meteorological historical data, acting the meteorological simulation data on a construction model, and extracting bridge simulation data;
the central processing module integrates the bridge simulation data and the corresponding meteorological simulation data to generate a bridge state sequence; and determining bridge state data corresponding to the meteorological prediction data through the bridge state sequence, identifying the bridge state data, defining a dangerous area, and performing safety early warning based on the dangerous area.
Preferably, the central processing module is respectively in communication and/or electrical connection with the data acquisition module and the intelligent terminal; the intelligent terminal comprises an intelligent bracelet, a mobile phone or a computer;
the data acquisition module is respectively in communication connection with the unmanned aerial vehicles and the meteorological platform; and a plurality of unmanned aerial vehicles are controlled by the data acquisition module or the central processing module.
Preferably, the central processing module establishes a construction model by combining the construction plan and the plurality of construction images, and the construction model comprises:
determining a current construction node according to the construction plan; wherein the construction plan comprises construction nodes or construction materials;
identifying a plurality of construction images to determine the construction progress of the current construction node, and constructing a construction model by combining three-dimensional modeling software; the three-dimensional modeling software comprises BIM modeling software or OpenBridge Modler.
Preferably, the expanding the received weather historical data by the central processing module to obtain the weather simulation data includes:
acquiring meteorological historical data of a construction area; wherein the meteorological historical data comprises temperature, air pressure and wind power;
expanding the meteorological historical data according to the set step length to obtain meteorological simulation data; wherein, the setting step lengths corresponding to all factors in the meteorological historical data are different.
Preferably, the central processing module simulates a bridge construction environment based on meteorological simulation data to obtain the bridge state sequence, and the method includes:
sequentially applying meteorological simulation data to the construction model, and recording bridge simulation data of the construction model; the bridge simulation data comprise displacement or vibration of each bridge member;
and after the meteorological simulation data are completely simulated, correspondingly splicing the recorded bridge simulation data and the corresponding meteorological historical data, and integrating a plurality of sets of data obtained by splicing to generate the bridge state sequence.
Preferably, the central processing module combines the bridge state sequence with the weather prediction data to define the dangerous area, and the method includes:
acquiring meteorological prediction data; the content attributes of the weather prediction data and the weather historical data are consistent;
matching and retrieving the meteorological prediction data in the bridge state sequence to obtain bridge simulation data corresponding to each bridge member; and comparing the bridge simulation data with a bridge state threshold value to determine a dangerous area.
Preferably, the central processing module defines the dangerous area based on the artificial intelligence model and the meteorological forecast data, and comprises:
training an artificial intelligence model based on the bridge state sequence; the artificial intelligence model comprises an error reverse propagation neural network model or an RBF neural network model;
integrating and inputting the meteorological prediction data into a trained artificial intelligence model to obtain output bridge simulation data; and comparing the bridge simulation data with a bridge state threshold value to determine a dangerous area.
Preferably, the central processing module is combined with the meteorological forecast data to demarcate a dangerous area, and generates an area dynamic graph according to the duration of the dangerous area; and
and sending an early warning signal to the intelligent terminal according to the regional dynamic graph to realize early warning on constructors.
Compared with the prior art, the invention has the beneficial effects that:
1. the method combines a computer technology and an image recognition technology, and establishes a construction model through three-dimensional modeling software and a plurality of collected construction images; simulating a construction environment through meteorological simulation data of a construction area, and acquiring bridge simulation data so as to acquire a bridge state sequence; then, a dangerous area is defined by combining meteorological prediction data, and early warning is carried out in time by combining the dangerous area; according to the construction method, the construction model is built through the unmanned aerial vehicle image, the dangerous area in the construction area is defined through the simulation result of the construction model, the definition accuracy of the dangerous area can be guaranteed, and a data foundation is laid for early warning.
2. Predicting a dangerous area of a bridge construction area based on meteorological prediction data and a bridge state sequence to obtain an area dynamic graph; identifying a dangerous area to be updated according to the area dynamic graph, and providing early warning information for constructors in time through an intelligent terminal; the invention provides early warning information for constructors in advance, and is beneficial to improving the bridge construction efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the working steps of the present invention;
fig. 2 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-2, in a first aspect of the present invention, a bridge construction process safety monitoring system based on an unmanned aerial vehicle image is provided, including a central processing module, and a data acquisition module and an intelligent terminal connected thereto; the data acquisition module is connected with a plurality of unmanned aerial vehicles or meteorological platforms; the data acquisition module acquires construction images in real time through a plurality of unmanned aerial vehicles, acquires meteorological historical data and meteorological prediction data of a construction area through a meteorological platform, and transmits the acquired data to the central processing module; the central processing module is used for establishing a three-dimensional model of a construction area by combining the construction plan and the plurality of construction images, and marking the three-dimensional model as a construction model; reasonably expanding according to meteorological historical data to obtain meteorological simulation data, acting the meteorological simulation data on a construction model, and extracting bridge simulation data; the central processing module integrates the bridge simulation data and the corresponding meteorological simulation data to generate a bridge state sequence; and determining bridge state data corresponding to the meteorological prediction data through the bridge state sequence, identifying the bridge state data, defining a dangerous area, and performing safety early warning based on the dangerous area.
In the prior art, when bridge construction safety monitoring is carried out, part of the monitoring is realized through various sensors arranged in a bridge construction area, namely, whether the bridge has abnormal displacement or vibration is detected through a displacement sensor, a vibration sensor and the like, and then whether the bridge is safe is judged; the other part is judged by image data, for example, whether the constructor is configured with necessary safety measures is judged by images. In the prior art, the safety is judged only by detecting whether a bridge member is normal through a sensor, and once a construction area is unsafe, construction personnel can not be warned in time, and environmental factors of the construction area are not taken into consideration, so that the accuracy of safety judgment can not be ensured, and the bridge construction efficiency can be influenced at some time.
The method combines a computer technology and an image recognition technology, and establishes a construction model through three-dimensional modeling software and a plurality of collected construction images; simulating a construction environment through meteorological simulation data of a construction area, and acquiring bridge simulation data so as to acquire a bridge state sequence; and then, a dangerous area is defined by combining with meteorological prediction data, and early warning is carried out in time by combining with the dangerous area. According to the construction method, the construction model is built through the unmanned aerial vehicle image, the dangerous area in the construction area is defined through the simulation result of the construction model, the definition accuracy of the dangerous area can be guaranteed, and a data foundation is laid for early warning.
The central processing module is respectively in communication and/or electrical connection with the data acquisition module and the intelligent terminal; the data acquisition module is respectively in communication connection with the unmanned aerial vehicles and the meteorological platform; and a plurality of unmanned aerial vehicles are controlled by data acquisition module or central processing module.
The intelligent terminal comprises an intelligent bracelet, a mobile phone or a computer and is used for displaying the simulation process and the detection and identification result; the central processing module is mainly responsible for data processing and performs data interaction with the data acquisition module and the intelligent terminal; the data acquisition module is mainly responsible for data acquisition, for example, the unmanned aerial vehicle acquires construction images, and the meteorological prediction data and meteorological historical data are acquired through a meteorological platform.
In a preferred embodiment, the central processing module combines the construction plan and the plurality of construction images to build a construction model, comprising: determining a current construction node according to the construction plan; and identifying a plurality of construction images to determine the construction progress of the current construction node, and constructing a construction model by combining three-dimensional modeling software.
The construction node at which it is currently located, i.e. which component is specifically constructed, is determined based on the construction plan. After the construction nodes are determined, a three-dimensional model of the whole bridge (at which time the bridge is not completely constructed) is built based on BIM modeling software or OpenBridge Modler. When the safety analysis is performed, all the components in the bridge are analyzed instead of a certain component, so that the constructed components also need to be represented in the three-dimensional model. Each construction node also comprises construction progress, for example, when the pier is built to which step, the pier which is not built is sometimes a very dangerous area. It should be understood that the construction plan includes construction nodes or construction materials, and the construction materials should be considered as much as possible when performing three-dimensional modeling so as to ensure the accuracy of the simulation.
In a preferred embodiment, the central processing module expands the received weather historical data to obtain weather simulation data, and the method comprises the following steps: acquiring meteorological historical data of a construction area; and expanding the meteorological historical data according to the set step length to obtain meteorological simulation data.
The meteorological historical data acquired by the meteorological platform are not necessarily satisfactory in quantity, and therefore, the meteorological historical data are necessarily expanded by a data method. The embodiment is expanded by setting step lengths corresponding to all factors, and the setting step lengths corresponding to all factors in the meteorological historical data are different, for example, the setting step length of the temperature is 1 ℃, and the setting step length of the wind power is 0.5 grade. It should be noted that the main function of the meteorological simulation data is to ensure that the simulation environment of the construction model is comprehensive in the simulation process, and not only the conventional environment needs to be simulated, but also the extremely severe environment needs to be included, so that the accuracy of the division of the dangerous area can be ensured.
In a preferred embodiment, the central processing module simulates a bridge construction environment based on meteorological simulation data to obtain a bridge state sequence, and the method comprises the following steps: sequentially applying meteorological simulation data to the construction model, and recording bridge simulation data of the construction model; and after the meteorological simulation data are completely simulated, correspondingly splicing the recorded bridge simulation data and the corresponding meteorological historical data, and integrating a plurality of sets of data obtained by splicing to generate a bridge state sequence.
The basic data of the dangerous area is predicted and defined, the meteorological simulation data are applied to the construction model, and the meteorological simulation data are obtained according to the change of the construction model and are the most basic data. The meteorological historical data includes data that temperature, atmospheric pressure, wind power and the like can influence the bridge construction, and of course, other data such as humidity, climate type and the like can also be included.
When each set of meteorological simulation data is applied to the construction model, changes of each bridge member in the construction model can be influenced, such as displacement or vibration, and the changes are bridge simulation data; the set of meteorological simulation data and the corresponding bridge simulation data are spliced to form a bridge state sequence. The bridge simulation data includes changes of each member of the bridge, and the bridge simulation data is recorded as 0 if no change occurs in the bridge member.
In an alternative embodiment, the central processing module combines the bridge state sequence with the weather forecast data to define the danger zone, including: acquiring meteorological prediction data; matching and retrieving the meteorological prediction data in the bridge state sequence to obtain bridge simulation data corresponding to each bridge member; and comparing the bridge simulation data with a bridge state threshold value to determine a dangerous area.
And searching the meteorological forecast data in the bridge state sequence to obtain bridge simulation data which best accords with the meteorological forecast data, wherein the searching process can refer to a lookup table in remote sensing correction. And comparing each element in the acquired bridge simulation data with a corresponding bridge state threshold, and if the vibration data of the bridge member is compared with the corresponding vibration threshold, judging whether the bridge member is abnormal or not under the action of the meteorological prediction data, and if so, dividing the bridge member, even the associated area into a dangerous area.
In another preferred embodiment, the central processing module demarcates the hazardous area based on the artificial intelligence model and the meteorological forecast data, comprising: training an artificial intelligence model based on the bridge state sequence; integrating and inputting the meteorological prediction data into a trained artificial intelligence model to obtain output bridge simulation data; and comparing the bridge simulation data with a bridge state threshold value to determine a dangerous area.
According to the method, an artificial intelligence model is obtained through training of a bridge state sequence, then weather prediction data are used as input of the artificial intelligence model, output bridge simulation data are obtained, and then the bridge simulation data are compared with a bridge state threshold value, so that a dangerous area is defined. It should be noted that the artificial intelligence model in this embodiment has a strong nonlinear fitting capability, and the output accuracy is higher than the result retrieved in the bridge state sequence.
In a preferred embodiment, the central processing module is used for dividing the dangerous area by combining with the meteorological forecast data and generating an area dynamic graph according to the duration of the dangerous area; and sending an early warning signal to the intelligent terminal according to the regional dynamic graph to realize early warning on constructors.
The meteorological forecast data is meteorological data of a bridge construction area in a period of time in the future, and the corresponding bridge simulation data is also the change of the bridge member in the period of time in the future, and the change is changed along with the meteorological data, namely, the defined dangerous area is also updated along with the change of the time, so that timely early warning can be provided for constructors according to the regional dynamic diagram.
The working principle of the invention is as follows:
the data acquisition module acquires construction images in real time through a plurality of unmanned aerial vehicles, acquires meteorological historical data and meteorological forecast data of a construction area through a meteorological platform, and sends acquired data to the central processing module.
The central processing module is used for establishing a three-dimensional model of a construction area by combining the construction plan and the plurality of construction images, and marking the three-dimensional model as a construction model; and reasonably expanding and acquiring meteorological simulation data according to the meteorological historical data, acting the meteorological simulation data on the construction model, and extracting bridge simulation data.
The central processing module integrates the bridge simulation data and the corresponding meteorological simulation data to generate a bridge state sequence; and determining bridge state data corresponding to the meteorological prediction data through the bridge state sequence, identifying the bridge state data, defining a dangerous area, and performing safety early warning based on the dangerous area.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. The bridge construction process safety monitoring system based on the unmanned aerial vehicle image comprises a central processing module, and a data acquisition module and an intelligent terminal which are connected with the central processing module; the data acquisition module is connected with a plurality of unmanned aerial vehicles or meteorological platforms; the method is characterized in that:
the data acquisition module acquires construction images in real time through a plurality of unmanned aerial vehicles, acquires meteorological historical data and meteorological prediction data of a construction area through a meteorological platform, and transmits the acquired data to the central processing module;
the central processing module is used for establishing a three-dimensional model of a construction area by combining the construction plan and the plurality of construction images, and marking the three-dimensional model as a construction model; reasonably expanding and acquiring meteorological simulation data according to meteorological historical data, acting the meteorological simulation data on a construction model, and extracting bridge simulation data;
the central processing module integrates the bridge simulation data and the corresponding meteorological simulation data to generate a bridge state sequence; and determining bridge state data corresponding to the meteorological prediction data through the bridge state sequence, identifying the bridge state data, defining a dangerous area, and performing safety early warning based on the dangerous area.
2. The bridge construction process safety monitoring system based on unmanned aerial vehicle images as claimed in claim 1, wherein the central processing module is respectively in communication and/or electrical connection with the data acquisition module and the intelligent terminal; the intelligent terminal comprises an intelligent bracelet, a mobile phone or a computer;
the data acquisition module is respectively in communication connection with the unmanned aerial vehicles and the meteorological platform; and a plurality of unmanned aerial vehicles are controlled by the data acquisition module or the central processing module.
3. The unmanned aerial vehicle image-based bridge construction process safety monitoring system of claim 2, wherein the central processing module establishes a construction model in combination with a construction plan and a plurality of construction images, comprising:
determining a current construction node according to the construction plan; wherein the construction plan comprises construction nodes or construction materials;
identifying a plurality of construction images to determine the construction progress of the current construction node, and constructing a construction model by combining three-dimensional modeling software; the three-dimensional modeling software comprises BIM modeling software or OpenBridge Modler.
4. The bridge construction process safety monitoring system based on unmanned aerial vehicle image of claim 1, wherein the central processing module expands the received meteorological historical data to obtain the meteorological simulation data, and comprises:
acquiring meteorological historical data of a construction area; wherein the meteorological historical data comprises temperature, air pressure and wind power;
expanding the meteorological historical data according to the set step length to obtain meteorological simulation data; wherein, the setting step lengths corresponding to all factors in the meteorological historical data are different.
5. The bridge construction process safety monitoring system based on unmanned aerial vehicle image of claim 4, wherein the central processing module simulates a bridge construction environment based on meteorological simulation data, and obtains the bridge state sequence, including:
sequentially applying meteorological simulation data to the construction model, and recording bridge simulation data of the construction model; the bridge simulation data comprise displacement or vibration of each bridge member;
and after the meteorological simulation data are completely simulated, correspondingly splicing the recorded bridge simulation data with the corresponding meteorological historical data, and integrating a plurality of sets of data obtained by splicing to generate the bridge state sequence.
6. The bridge construction process safety monitoring system based on unmanned aerial vehicle image of claim 5, wherein the central processing module combines the bridge state sequence with meteorological prediction data to define a danger zone, comprising:
acquiring meteorological prediction data; the content attributes of the weather prediction data and the weather historical data are consistent;
matching and retrieving the meteorological prediction data in the bridge state sequence to obtain bridge simulation data corresponding to each bridge member; and comparing the bridge simulation data with a bridge state threshold value to determine a dangerous area.
7. The bridge construction process safety monitoring system based on unmanned aerial vehicle image of claim 5, wherein the central processing module demarcates the danger area based on artificial intelligence model and meteorological prediction data, comprising:
training an artificial intelligence model based on the bridge state sequence; the artificial intelligence model comprises an error reverse propagation neural network model or an RBF neural network model;
integrating and inputting the meteorological prediction data into a trained artificial intelligence model to obtain output bridge simulation data; and comparing the bridge simulation data with a bridge state threshold value to determine a dangerous area.
8. The bridge construction process safety monitoring system based on unmanned aerial vehicle images as claimed in claim 6 or 7, wherein the central processing module is used for demarcating a dangerous area in combination with meteorological forecast data, and generating a dynamic area map according to the duration of the dangerous area; and
and sending an early warning signal to the intelligent terminal according to the regional dynamic graph to realize early warning on constructors.
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