CN117437564B

CN117437564B - Unmanned aerial vehicle data processing method and device for bridge construction monitoring

Info

Publication number: CN117437564B
Application number: CN202311752937.XA
Authority: CN
Inventors: 牛洪强; 孙廷鑫; 陈世超; 张晓鹏; 巫祖伟; 吴琛; 杨明; 朱晨; 程海玲
Original assignee: China Railway No 3 Engineering Group Co Ltd; Guangdong Construction Engineering Co Ltd of China Railway No 3 Engineering Group Co Ltd
Current assignee: China Railway No 3 Engineering Group Co Ltd; Guangdong Construction Engineering Co Ltd of China Railway No 3 Engineering Group Co Ltd
Priority date: 2023-12-20
Filing date: 2023-12-20
Publication date: 2024-03-12
Anticipated expiration: 2043-12-20
Also published as: CN117437564A

Abstract

The invention discloses an unmanned aerial vehicle data processing method and device for bridge construction monitoring, wherein the method comprises the following steps: before the construction of a construction area of a pier in bridge water is started, acquiring a plurality of first image information of the construction area at a plurality of angles through a plurality of unmanned aerial vehicles; determining construction feasibility parameters corresponding to the construction areas according to the first image information and a preset image similarity algorithm model; acquiring a plurality of second image information of the cofferdam engineering at a plurality of angles through a plurality of unmanned aerial vehicles in the process of carrying out the cofferdam engineering of the construction area; and determining the current engineering construction quality corresponding to the cofferdam engineering based on a neural network algorithm according to the current construction stage of the cofferdam engineering and the plurality of second image information. Therefore, the invention can effectively improve the monitoring effect, realize more intelligent feasibility analysis and construction monitoring of bridge engineering and improve engineering efficiency and quality.

Description

Unmanned aerial vehicle data processing method and device for bridge construction monitoring

Technical Field

The invention relates to the technical field of unmanned aerial vehicle data processing, in particular to an unmanned aerial vehicle data processing method and device for bridge construction monitoring.

Background

Along with the large-scale bridge construction and wide coverage, higher requirements are put forward on the lower structure of the bridge when crossing the river, and the underwater pier construction technology is widely applied to the bridge construction from the aspect of the current bridge development condition, and has important roles in the bridge construction field. The application of the underwater pier construction technology is helpful for enhancing the stability of the pier and even the full bridge.

Aiming at the monitoring technology of underwater pier construction, most of the current monitoring technology is still realized in a manual supervision mode, camera equipment is probably disclosed in some prior art to monitor the field, but the flexibility and low cost of the unmanned aerial vehicle are not fully considered to improve the frequency of image acquisition and the monitoring effect by combining algorithm technology. It can be seen that the prior art has defects and needs to be solved.

Disclosure of Invention

The invention aims to solve the technical problem of providing the unmanned aerial vehicle data processing method and the unmanned aerial vehicle data processing device for bridge construction monitoring, which can effectively improve the monitoring effect, realize more intelligent feasibility analysis and construction monitoring of bridge engineering and improve engineering efficiency and quality.

In order to solve the technical problems, the first aspect of the invention discloses an unmanned aerial vehicle data processing method for bridge construction monitoring, which comprises the following steps:

Before the construction of a construction area of a pier in bridge water is started, acquiring a plurality of first image information of the construction area at a plurality of angles through a plurality of unmanned aerial vehicles;

determining construction feasibility parameters corresponding to the construction areas according to the first image information and a preset image similarity algorithm model;

acquiring a plurality of second image information of the cofferdam engineering at a plurality of angles through a plurality of unmanned aerial vehicles in the process of carrying out the cofferdam engineering of the construction area;

and determining the current engineering construction quality corresponding to the cofferdam engineering based on a neural network algorithm according to the current construction stage of the cofferdam engineering and the plurality of second image information.

As an optional implementation manner, in the first aspect of the present invention, the current construction stage is a steel sheet pile hole guiding construction stage, a steel sheet pile construction stage, a cofferdam folding construction stage, a mounting support construction stage, a foundation pit excavation dredging construction stage, a bottom sealing concrete pouring construction stage, a bearing platform pier column construction stage or a cofferdam dismantling construction stage; and before the current construction quality of the cofferdam project is determined according to the current construction stage of the cofferdam project and the plurality of second image information based on an image analysis algorithm, the method further comprises:

Acquiring the current time, the construction starting time and the construction progress plan corresponding to the cofferdam engineering;

calculating a time difference between the start construction time and the current time;

and determining the current construction stage corresponding to the cofferdam engineering according to the time difference and the construction progress plan.

As an optional implementation manner, in the first aspect of the present invention, the determining, according to the plurality of first image information and a preset image similarity algorithm model, a construction feasibility parameter corresponding to the construction area includes:

grouping the plurality of first image information based on a template matching algorithm to obtain a house image group, a trestle image group and a power equipment image group;

determining house positions and the number of houses of a plurality of houses corresponding to the construction area according to the house image group and an image recognition algorithm;

determining the occupying position of the trestle corresponding to the construction area according to the trestle image group and an image recognition algorithm;

determining the equipment positions and the equipment numbers of a plurality of pieces of electric equipment corresponding to the construction area according to the electric equipment image group and an image recognition algorithm;

And determining the construction feasibility parameters corresponding to the construction area according to the construction range of the construction area, the house positions, the house number, the trestle occupying positions, the equipment positions and the equipment number.

As an optional implementation manner, in the first aspect of the present invention, the grouping the plurality of first image information based on the template matching algorithm to obtain a house image group, a trestle image group and a power device image group includes:

acquiring a house image template group, a trestle image template group and an electric equipment image template group;

for each piece of first image information, determining a shooting angle corresponding to the first image information based on an angle identification algorithm;

screening a house template image set, a trestle template image set and a device template image set corresponding to the shooting angle from the house image template group, the trestle image template group and the power device image template group according to the shooting angle;

calculating the average value of the similarity between the first image information and each image in the house template image set to obtain the house similarity corresponding to the first image information;

Calculating the average value of the similarity between the first image information and each image in the trestle template image set to obtain trestle similarity corresponding to the first image information;

calculating the average value of the similarity between the first image information and each image in the equipment template image set to obtain the equipment similarity corresponding to the first image information;

setting an objective function to maximize the number of images in each image group; the image group is a house image group, a trestle image group or a power equipment image group;

the setting of the limiting conditions includes: the house similarity corresponding to the images in the house image group is larger than a preset first similarity threshold; the similarity of the trestle corresponding to the images in the trestle image group is larger than a preset second similarity threshold; the equipment similarity corresponding to the images in the power equipment image group is larger than a preset third similarity threshold; the number of the similarity type corresponding to the type of the image in any one image group is larger than the number of the similarity type of the images in any other image group; the similarity type is house similarity when the type of the image group is a house image group, the similarity type is trestle similarity when the type of the image group is a trestle image group, and the similarity type is equipment similarity when the type of the image group is an electric equipment image group;

And carrying out iterative grouping calculation on the plurality of first image information based on a dynamic programming algorithm according to the objective function and the limiting condition so as to obtain a house image group, a trestle image group and a power equipment image group.

In a first aspect of the present invention, determining the construction feasibility parameter corresponding to the construction area according to the to-be-constructed range of the construction area, and the house position, the house number, the bridge occupying position, the equipment position, and the equipment number includes:

determining the number of houses in the range to be constructed according to the range to be constructed, the positions of the houses and the number of houses;

determining the length of the trestle in the range to be constructed according to the range to be constructed and the position occupied by the trestle;

determining the number of range devices in the range to be constructed according to the range to be constructed, the device positions and the number of devices;

calculating a first difference value between the number of the range houses and a first number threshold value, a second difference value between the number of the range devices and a second number threshold value, and a length difference value between the length of the range trestle and a length threshold value;

Calculating a weighted sum average value of the first difference value, the second difference value and the length difference value to obtain a construction feasibility parameter corresponding to the construction area; the construction feasibility parameters are used for being sent to a construction planning terminal to assist in the establishment of a construction plan; wherein the first difference has a greater weight than the length difference and the second difference has a greater weight than the length difference.

As an optional implementation manner, in the first aspect of the present invention, the determining, based on a neural network algorithm, the current construction quality of the cofferdam according to the current construction stage of the cofferdam, and the plurality of second image information, includes:

acquiring at least one neural network model corresponding to the current construction stage of the cofferdam project;

inputting each piece of second image information into the at least one neural network model to obtain construction quality parameters corresponding to each piece of second image information; the construction quality parameters comprise delay degree parameters and engineering quality parameters;

and determining the current engineering construction quality corresponding to the cofferdam engineering according to all the construction quality parameters corresponding to the second image information and the angle weight corresponding to the shooting angle corresponding to each image information.

As an optional implementation manner, in the first aspect of the present invention, the obtaining at least one neural network model corresponding to a current construction stage of the cofferdam project includes:

acquiring a first neural network model corresponding to the current construction stage of the cofferdam project;

acquiring a second neural network model and a third neural network model which correspond to a previous construction stage and a next construction stage of the current construction stage respectively; the first neural network model, the second neural network model and the third neural network model are all obtained through training of a training data set comprising a plurality of training images corresponding to construction stages and corresponding construction quality labels;

and inputting each piece of second image information into the at least one neural network model to obtain construction quality parameters corresponding to each piece of second image information, wherein the construction quality parameters comprise:

for each piece of second image information, inputting the second image information into the first neural network model, the second neural network model and the third neural network model to obtain a first quality prediction result, a second quality prediction result and a third quality prediction result which are output respectively;

Calculating a first correction weight corresponding to the second quality prediction result; the first correction weight is inversely proportional to the difference between the second quality prediction result and a preset cross-stage prediction quality threshold;

calculating a second correction weight corresponding to the third quality prediction result; the second correction weight is inversely proportional to the difference between the third quality prediction result and the cross-stage prediction quality threshold;

and calculating the product of the first quality prediction result, the first correction weight and the second correction weight to obtain the construction quality parameter corresponding to the second image information.

As an optional implementation manner, in the first aspect of the present invention, the determining, according to the construction quality parameters corresponding to all the second image information and the angle weights corresponding to the shooting angles corresponding to each image information, the current engineering construction quality corresponding to the cofferdam engineering includes:

for each piece of second image information, determining a shooting angle corresponding to the second image information based on an angle identification algorithm;

calculating an angle difference value between a shooting angle corresponding to the second image information and a preset clear shooting angle;

Calculating an angular weight inversely proportional to the angular difference;

calculating a product value of the construction quality parameter corresponding to the second image information and the angle weight;

calculating the average value of the product values corresponding to all the second image information to obtain the current engineering construction quality corresponding to the cofferdam engineering; the current engineering construction quality is used for being sent to a construction monitoring terminal to assist construction monitoring.

The invention discloses an unmanned aerial vehicle data processing device for bridge construction monitoring in a second aspect, which comprises:

the first acquisition module is used for acquiring a plurality of first image information of a construction area of a bridge underwater pier at a plurality of angles through a plurality of unmanned aerial vehicles before the construction of the construction area;

the first determining module is used for determining construction feasibility parameters corresponding to the construction area according to the plurality of first image information and a preset image similarity algorithm model;

the second acquisition module is used for acquiring a plurality of second image information of the cofferdam engineering at a plurality of angles through a plurality of unmanned aerial vehicles in the process of carrying out the cofferdam engineering of the construction area;

and the second determining module is used for determining the current engineering construction quality corresponding to the cofferdam engineering based on a neural network algorithm according to the current construction stage of the cofferdam engineering and the plurality of second image information.

As an optional implementation manner, in the second aspect of the present invention, the current construction stage is a steel sheet pile hole guiding construction stage, a steel sheet pile construction stage, a cofferdam folding construction stage, an installation support construction stage, a foundation pit excavation dredging construction stage, a bottom sealing concrete pouring construction stage, a bearing platform pier column construction stage or a cofferdam dismantling construction stage; and the second determining module is further configured to perform the following steps before determining, based on an image analysis algorithm, a current construction quality of the cofferdam project according to a current construction stage of the cofferdam project and the plurality of second image information:

In a second aspect of the present invention, as an optional implementation manner, the determining, by the first determining module, a specific manner of the construction feasibility parameter corresponding to the construction area according to the plurality of first image information and a preset image similarity algorithm model includes:

In a second aspect of the present invention, as an optional implementation manner, the first determining module groups the plurality of first image information based on a template matching algorithm to obtain a specific manner of a house image group, a trestle image group and a power equipment image group, where the specific manner includes:

In a second aspect of the present invention, the specific manner of determining the construction feasibility parameter corresponding to the construction area by the first determining module according to the to-be-constructed range of the construction area, and the house position, the number of houses, the occupied position of the trestle, the equipment position and the number of equipment includes:

In a second aspect of the present invention, the determining, by the second determining module, the concrete manner of determining the current construction quality of the cofferdam based on the neural network algorithm according to the current construction stage of the cofferdam and the plurality of second image information, includes:

As an optional implementation manner, in the second aspect of the present invention, the specific manner of obtaining, by the second determining module, at least one neural network model corresponding to the current construction stage of the cofferdam project includes:

and the second determining module inputs each piece of second image information into the at least one neural network model to obtain a specific mode of construction quality parameters corresponding to each piece of second image information, and the specific mode comprises the following steps:

In a second aspect of the present invention, the second determining module determines, according to the construction quality parameters corresponding to all the second image information and the angle weights corresponding to the shooting angles corresponding to each image information, a specific manner of the current construction quality of the cofferdam project corresponding to the cofferdam project, including:

Calculating an angular weight inversely proportional to the angular difference;

The third aspect of the invention discloses another unmanned aerial vehicle data processing device for bridge construction monitoring, which comprises:

a memory storing executable program code;

a processor coupled to the memory;

the processor invokes the executable program code stored in the memory to execute part or all of the steps in the unmanned aerial vehicle data processing method for bridge construction monitoring disclosed in the first aspect of the invention.

Compared with the prior art, the invention has the following beneficial effects:

therefore, the embodiment of the invention can utilize the unmanned aerial vehicle image acquisition technology and the data algorithm model to perform feasibility analysis before bridge construction and monitor the quality of the bridge construction, thereby effectively improving the monitoring effect, realizing more intelligent feasibility analysis and construction monitoring of bridge engineering and improving engineering efficiency and quality.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of an unmanned aerial vehicle data processing method for bridge construction monitoring according to an embodiment of the invention.

Fig. 2 is a schematic structural diagram of an unmanned aerial vehicle data processing device for bridge construction monitoring according to an embodiment of the present invention.

Fig. 3 is a schematic structural diagram of another unmanned aerial vehicle data processing device for bridge construction monitoring according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The terms "second," "second," and the like in the description and in the claims and in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

The invention discloses an unmanned aerial vehicle data processing method and device for bridge construction monitoring, which can utilize an unmanned aerial vehicle image acquisition technology and a data algorithm model to perform feasibility analysis before bridge construction and monitor bridge construction quality, so that the monitoring effect can be effectively improved, more intelligent feasibility analysis and construction monitoring of bridge engineering are realized, and engineering efficiency and quality are improved. The following will describe in detail.

Referring to fig. 1, fig. 1 is a schematic flow chart of an unmanned aerial vehicle data processing method for bridge construction monitoring according to an embodiment of the present invention. The unmanned aerial vehicle data processing method for bridge construction monitoring described in fig. 1 is applied to a data processing chip, a processing terminal or a processing server (wherein the processing server can be a local server or a cloud server). As shown in fig. 1, the unmanned aerial vehicle data processing method for bridge construction monitoring may include the following operations:

101. before the construction of a construction area of a pier in bridge water is started, a plurality of first image information of the construction area is acquired through a plurality of unmanned aerial vehicles at a plurality of angles.

102. And determining the construction feasibility parameters corresponding to the construction area according to the plurality of first image information and a preset image similarity algorithm model.

103. And acquiring a plurality of second image information of the cofferdam engineering at a plurality of angles through a plurality of unmanned aerial vehicles in the process of carrying out the cofferdam engineering of the construction area.

104. And determining the current engineering construction quality corresponding to the cofferdam engineering based on a neural network algorithm according to the current construction stage of the cofferdam engineering and a plurality of pieces of second image information.

Therefore, the embodiment of the invention can utilize the unmanned aerial vehicle image acquisition technology and the data algorithm model to perform feasibility analysis before bridge construction and monitor the quality of the bridge construction, so that the monitoring effect can be effectively improved, more intelligent feasibility analysis and construction monitoring of bridge engineering are realized, and the engineering efficiency and quality are improved.

As an optional embodiment, the current construction stage is a steel sheet pile hole guiding construction stage, a steel sheet pile construction stage, a cofferdam folding construction stage, an installation support construction stage, a foundation pit excavation dredging construction stage, a bottom sealing concrete pouring construction stage, a bearing platform pier column construction stage or a cofferdam dismantling construction stage.

In a specific embodiment, the construction process of the cofferdam project comprises the following steps:

step 1, measuring lofting construction: marking trestle construction control points, checking, effectively protecting, positioning by using the existing steel pipe piles and steel pile casings, welding I-steel plates on the steel pipe piles (the existing and later-added enclosing purlin piles) as guide beams by using the outer steel sheet piles, welding I-steel plates on the steel pile casings as guide beams by using the inner steel sheet piles, and ensuring that the beaten steel sheet piles are on a straight line by using the I-steel. As guiding position and elevation control mark during piling.

And 2, hole guiding construction of the steel sheet pile: the hole guiding adopts a pneumatic down-the-hole hammer, the diameter of the hole is 600mm, the distance between the holes is 500mm (the lap joint length between two holes is 20 cm), and the holes are sequentially guided to the elevation of the pile bottom. Before construction, marking is carried out on a drill rod, and the depth of a guide hole is controlled to be smaller than the depth of the designed rock entering (more than or equal to 0.9 m).

And 3, construction of steel sheet piles: the steel sheet pile is hoisted to the trestle by adopting a 50T crawler crane, the pneumatic down-the-hole hammer is mechanically matched with a rattan 2045 steel sheet pile machine to carry out steel sheet pile construction on the rest part of a steel trestle platform (the steel sheet pile is guaranteed to meet the design pile length or rock entering depth is more than or equal to 0.9 meter), welding extension is adopted for the steel sheet pile exceeding 18 meters, inserting and driving are carried out after welding is finished, when the steel sheet piles are welded and spliced, the two steel sheet piles are aligned and tightly propped, clamped in a firm clamp for welding and are firmly welded, meanwhile, the splice joint of the steel sheet pile on the same section of the cofferdam is not more than 50%, the joint of the adjacent piles is staggered up and down by not less than 2 meters, dislocation is adopted after the welding of the steel sheet pile is finished, and special electric welding personnel are adopted for repairing and welding steel sheet pile plugging is adopted at the welding position.

And 4, closure construction of the cofferdam: the plane of the steel sheet pile cofferdam is square, so that corners can be formed in the longitudinal direction and the transverse direction of the cofferdam, and shaping corner piles are used at construction corners. In order to facilitate the folding, two adjacent piles at the folding position are one high and one low, each steel sheet pile is driven to be perpendicular to the tangential direction along the normal line of the guide frame, the folding is selected to be near the corner pile (generally 5 piles are separated from the corner pile), if the distance is different, the distance between the adjacent sides of the folding edge and the guide frame can be adjusted, and in order to prevent the two piles at the folding position from being out of a plane, the direction of the corner pile is necessarily adjusted, and one side locking opening of the corner pile and the opposite steel sheet pile locking opening of the corner pile should be kept parallel as much as possible.

And 5, mounting and supporting construction: pile foundation steel pile casing is cut before installing and welding bracket, and supporting bracket is arranged on the steel sheet pile for setting up the shaped steel purlin, so that the purlin bracket is converted into the steel sheet pile by the steel pile casing. When the supporting brackets of the second, third and fourth layers of enclosing purlins are arranged on the steel sheet pile, a construction line is required to be pulled to ensure that the top surfaces of the brackets are positioned on the same horizontal plane. And after the purlin is installed, a reverse bracket is arranged on the purlin.

Step 6, foundation pit excavation dredging construction: river beds at the cofferdam are mainly composed of mucky soil and middle sand, and the geological condition is poor. The cofferdam is large in size, a 24m long-arm crawler excavator is used for dredging the substrate, and when the excavating equipment cannot be constructed, an air compressor is matched with a mud suction machine for dredging mud.

And 7, pouring construction of bottom sealing concrete: the bottom sealing concrete is intensively fed by adopting a commodity concrete mixing plant, and is pumped to the bottom of the foundation pit by adopting a day pump in a construction site. The bottom sealing concrete pouring adopts an underwater concrete pouring process, the bottom of a hose of a top pump is controlled to be about 20cm away from the bottom of a river bed, after the first concrete is poured, the elevation of a concrete top at the bottom opening of the hose is measured, and the burial depth of the hose is ensured to be more than 0.5 m. In the pouring process, the elevation is measured at intervals according to the pouring quantity, so as to guide the pump pipe of the overhead pump to move, and the concrete is uniformly lifted. In order to monitor the pouring condition of the back cover concrete at any time, 10 measuring points are arranged. When the concrete is poured and approaches to a junction, the elevation of the concrete surface is comprehensively measured, and the pouring quantity is increased near the measuring point with the lower elevation of the concrete surface according to the measuring result until the measured result meets the requirement.

And 8, construction of bearing platform pier columns: and carrying out bearing platform pier column construction according to the conventional construction process of steel bar binding, template installation and concrete pouring.

And 9, cofferdam dismantling construction: and after the pouring of the bearing platform is completed, backfilling coarse sand between the bearing platform and the steel sheet pile to the bearing platform surface in time, dismantling the underwater bearing platform steel sheet pile cofferdam, and dismantling the inner support of the cofferdam and the steel sheet pile after the whole construction of the pier body is finished. The process flow comprises cofferdam water injection, inner support dismantling, inner steel sheet pile pulling out, double-layer steel sheet pile concrete dismantling and outer steel sheet pile pulling out.

Specifically, in the above embodiment, the unmanned aerial vehicle may be used to obtain images of the construction site and analyze the construction quality through the algorithm model in the construction stages corresponding to different procedures, and more specifically, enough images may be obtained in the construction process of the cofferdam engineering of the same type to perform training of the predictive algorithm model, so that the algorithm model obtained by training is used for construction monitoring of the subsequent new engineering.

Specifically, in the above steps, before determining the current construction quality of the cofferdam according to the current construction stage of the cofferdam and the plurality of second image information based on the image analysis algorithm, the method further includes:

Specifically, a plurality of construction stages and corresponding time periods are specified in the construction schedule.

Through the embodiment, the current construction stage corresponding to the cofferdam engineering can be determined by calculating the time difference and the construction progress plan, so that the construction stage can be accurately determined, the current construction quality corresponding to the cofferdam engineering can be accurately determined based on the construction stage, the monitoring effect can be effectively improved, more intelligent feasibility analysis and construction monitoring of the bridge engineering can be realized, and the engineering efficiency and quality can be improved.

As an optional embodiment, in the step, determining the construction feasibility parameter corresponding to the construction area according to the plurality of first image information and the preset image similarity algorithm model includes:

and determining the construction feasibility parameters corresponding to the construction area according to the to-be-constructed range of the construction area, the house positions, the house number, the trestle occupying positions, the equipment positions and the equipment number.

Through the embodiment, the house image group, the trestle image group and the power equipment image group can be obtained by grouping the plurality of first image information, and the house position, the house number, the trestle occupation position, the equipment position and the equipment number of the construction area are determined based on the house image group, the trestle image group and the power equipment image group, so that the construction feasibility parameters corresponding to the construction area are further determined, the feasibility corresponding to the construction area can be accurately analyzed, the monitoring effect is effectively improved, more intelligent feasibility analysis of bridge engineering is realized, and the engineering efficiency and the engineering quality are improved.

As an optional embodiment, in the step, grouping the plurality of first image information based on the template matching algorithm to obtain a house image group, a trestle image group and a power equipment image group, including:

screening a house template image set, a trestle template image set and an equipment template image set corresponding to the shooting angles from the house image template set, the trestle image template set and the power equipment image template set according to the shooting angles;

calculating the average value of the similarity of the first image information and each image in the house template image set to obtain the house similarity corresponding to the first image information;

calculating the average value of the similarity of the first image information and each image in the trestle template image set to obtain trestle similarity corresponding to the first image information;

calculating the average value of the similarity of the first image information and each image in the equipment template image set to obtain the equipment similarity corresponding to the first image information;

the setting of the limiting conditions includes: the house similarity corresponding to the images in the house image group is larger than a preset first similarity threshold; the similarity of trestle corresponding to the images in the trestle image group is larger than a preset second similarity threshold; the device similarity corresponding to the images in the power device image group is larger than a preset third similarity threshold; the number of the similarity type corresponding to the type of the image in any image group is larger than the number of the similarity type of the images in any other image group; the method comprises the steps that when the type of an image group is a house image group, the similarity type is house similarity, when the type of the image group is a trestle image group, the similarity type is trestle similarity, and when the type of the image group is an electric power equipment image group, the similarity type is equipment similarity;

and carrying out iterative grouping calculation on the plurality of first image information based on a dynamic programming algorithm according to the objective function and the limiting condition to obtain a house image group, a trestle image group and a power equipment image group.

Through the embodiment, iterative grouping calculation can be performed on the plurality of first image information through the dynamic programming algorithm, so that a house image group, a trestle image group and a power equipment image group are obtained, the house position, the house number, the trestle occupying position, the equipment position and the equipment number of a construction area can be accurately determined in a follow-up mode, feasibility corresponding to the construction area can be accurately analyzed, monitoring effect is improved, intelligent feasibility analysis of bridge engineering is achieved, and engineering efficiency and quality are improved.

As an optional embodiment, in the step, determining the construction feasibility parameter corresponding to the construction area according to the to-be-constructed range of the construction area, and the house position, the number of houses, the occupied positions of trestle bridges, the equipment position and the number of equipment, includes:

determining the number of houses in the range to be constructed according to the range to be constructed, the positions of the houses and the number of the houses;

determining the length of the trestle in the range to be constructed according to the range to be constructed and the occupied position of the trestle;

determining the number of range equipment in the range to be constructed according to the range to be constructed, the equipment positions and the equipment number;

calculating a first difference value between the number of the range houses and a first number threshold value, a second difference value between the number of the range devices and a second number threshold value, and a length difference value between the range trestle length and a length threshold value;

Through the embodiment, the construction feasibility parameters corresponding to the construction area can be determined by calculating the first difference value, the second difference value and the length difference value, so that the feasibility corresponding to the construction area can be accurately analyzed, the monitoring effect is effectively improved, more intelligent feasibility analysis of bridge engineering is realized, and the engineering efficiency and quality are improved.

As an optional embodiment, in the step, determining, based on the neural network algorithm, the current construction quality of the cofferdam according to the current construction stage of the cofferdam and the plurality of second image information, includes:

acquiring at least one neural network model corresponding to the current construction stage of cofferdam engineering;

inputting each piece of second image information into at least one neural network model to obtain construction quality parameters corresponding to each piece of second image information; the construction quality parameters comprise delay degree parameters and engineering quality parameters;

And determining the current engineering construction quality corresponding to the cofferdam engineering according to the construction quality parameters corresponding to all the second image information and the angle weight corresponding to the shooting angle corresponding to each image information.

Alternatively, the neural network model in the present invention may be a neural network model of a CNN structure, an RNN structure, or an LTSM structure, and may be trained by a corresponding gradient descent algorithm and a loss function until convergence, which is not limited herein.

Through the embodiment, the construction quality parameters corresponding to each piece of second image information can be determined through the neural network model, and the construction quality is estimated based on the angle weight and the quality parameters, so that the current engineering construction quality can be accurately estimated, more intelligent construction monitoring of bridge engineering is realized, and the engineering efficiency and quality are improved.

As an optional embodiment, in the step, obtaining at least one neural network model corresponding to the current construction stage of the cofferdam engineering includes:

acquiring a first neural network model corresponding to the current construction stage of cofferdam engineering;

acquiring a second neural network model and a third neural network model which correspond to a previous construction stage and a next construction stage of the current construction stage respectively; the first neural network model, the second neural network model and the third neural network model are all obtained through training of a training data set comprising a plurality of training images corresponding to construction stages and corresponding construction quality labels.

Through the embodiment, the construction quality parameters corresponding to each second image information can be determined by acquiring the neural network models of a plurality of stages, so that the accuracy of the subsequent construction quality parameters is improved, more intelligent construction monitoring of bridge engineering is realized, and the engineering efficiency and quality are improved.

As an optional embodiment, in the step, inputting each piece of second image information into at least one neural network model to obtain a construction quality parameter corresponding to each piece of second image information, including:

And calculating the product of the first quality prediction result and the first correction weight and the second correction weight to obtain the construction quality parameter corresponding to the second image information.

Through the embodiment, the construction quality parameters corresponding to each second image information can be determined and corrected through the neural network model of three stages, so that the accuracy of the follow-up construction quality parameters is improved, more intelligent construction monitoring of bridge engineering is realized, and engineering efficiency and quality are improved.

As an optional embodiment, in the step, determining the current construction quality of the cofferdam according to the construction quality parameters corresponding to all the second image information and the angle weight corresponding to the shooting angle corresponding to each image information includes:

for each piece of second image information, determining a shooting angle corresponding to the second image information based on an angle recognition algorithm;

calculating an angle weight inversely proportional to the angle difference;

Through the embodiment, the inversely proportional angle weight can be determined through the angle difference between the shooting angle and the preset clear shooting angle, and the construction quality is estimated based on the angle weight and the quality parameter, so that the current engineering construction quality can be accurately estimated, more intelligent construction monitoring of bridge engineering is realized, and the engineering efficiency and quality are improved.

In a second embodiment, referring to fig. 2, fig. 2 is a schematic structural diagram of an unmanned aerial vehicle data processing device for monitoring bridge construction according to an embodiment of the present invention. The unmanned aerial vehicle data processing device for bridge construction monitoring described in fig. 2 is applied to a data processing chip, a processing terminal or a processing server (wherein the processing server can be a local server or a cloud server). As shown in fig. 2, the unmanned aerial vehicle data processing device for bridge construction monitoring may include:

a first obtaining module 201, configured to obtain, by a plurality of unmanned aerial vehicles, a plurality of first image information of a construction area of a pier in bridge water before the construction is started;

the first determining module 202 is configured to determine a construction feasibility parameter corresponding to a construction area according to the plurality of first image information and a preset image similarity algorithm model;

A second obtaining module 203, configured to obtain, by a plurality of unmanned aerial vehicles, a plurality of second image information of the cofferdam engineering at a plurality of angles during a cofferdam engineering process of the construction area;

and the second determining module 204 is configured to determine, based on a neural network algorithm, a current construction quality of the cofferdam according to a current construction stage of the cofferdam and the plurality of second image information.

As an optional embodiment, the current construction stage is a steel sheet pile hole guiding construction stage, a steel sheet pile construction stage, a cofferdam folding construction stage, a mounting support construction stage, a foundation pit excavation dredging construction stage, a bottom sealing concrete pouring construction stage, a bearing platform pier column construction stage or a cofferdam dismantling construction stage; and, the second determining module 204 is further configured to perform the following steps before determining, based on the image analysis algorithm, the current construction quality of the cofferdam according to the current construction stage of the cofferdam, and the plurality of second image information:

As an optional embodiment, the first determining module 202 determines, according to the plurality of first image information and the preset image similarity algorithm model, a specific manner of the construction feasibility parameter corresponding to the construction area, including:

As an alternative embodiment, the first determining module 202 groups the plurality of first image information based on a template matching algorithm to obtain a specific manner of building image group, trestle image group and power equipment image group, including:

As an optional embodiment, the specific manner of determining the construction feasibility parameter corresponding to the construction area by the first determining module 202 according to the to-be-constructed range of the construction area, and the house position, the number of houses, the occupied positions of trestle, the equipment position and the number of equipment includes:

As an optional embodiment, the second determining module 204 determines, based on the neural network algorithm and according to the current construction stage of the cofferdam project and the plurality of second image information, a specific manner of the current construction quality of the cofferdam project, including:

As an optional embodiment, the specific manner of obtaining, by the second determining module 204, the at least one neural network model corresponding to the current construction stage of the cofferdam engineering includes:

acquiring a second neural network model and a third neural network model which correspond to a previous construction stage and a next construction stage of the current construction stage respectively; the first neural network model, the second neural network model and the third neural network model are all obtained through training by training data sets comprising a plurality of training images corresponding to construction stages and corresponding construction quality labels;

And, the second determining module 204 inputs each piece of second image information into at least one neural network model to obtain a specific manner of construction quality parameters corresponding to each piece of second image information, including:

As an optional embodiment, the second determining module 204 determines, according to the construction quality parameters corresponding to all the second image information and the angle weight corresponding to the shooting angle corresponding to each image information, a specific manner of the current engineering construction quality corresponding to the cofferdam engineering, including:

calculating an angle weight inversely proportional to the angle difference;

Specific technical details and technical effects of the modules and steps in the above embodiment may refer to corresponding expressions in the first embodiment, and are not described herein.

In a third embodiment, referring to fig. 3, fig. 3 is a schematic diagram of an unmanned aerial vehicle data processing device for monitoring bridge construction according to another embodiment of the present invention. The unmanned aerial vehicle data processing device for bridge construction monitoring described in fig. 3 is applied to a data processing chip, a processing terminal or a processing server (wherein the processing server can be a local server or a cloud server). As shown in fig. 3, the unmanned aerial vehicle data processing device for bridge construction monitoring may include:

A memory 301 storing executable program code;

a processor 302 coupled with the memory 301;

the processor 302 invokes executable program codes stored in the memory 301 for performing the steps of the unmanned aerial vehicle data processing method for bridge construction monitoring described in embodiment one.

In a fourth embodiment, the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, where the computer program causes a computer to execute the steps of the unmanned aerial vehicle data processing method for bridge construction monitoring described in the first embodiment.

In a fifth embodiment, the present invention discloses a computer program product, which includes a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the unmanned aerial vehicle data processing method for bridge construction monitoring described in the first embodiment.

The foregoing describes certain embodiments of the present disclosure, other embodiments being within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to be in the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-transitory computer readable storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to portions of the description of method embodiments being relevant.

The apparatus, the device, the nonvolatile computer readable storage medium and the method provided in the embodiments of the present disclosure correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have similar advantageous technical effects as those of the corresponding method, and since the advantageous technical effects of the method have been described in detail above, the advantageous technical effects of the corresponding apparatus, device, and nonvolatile computer storage medium are not described herein again.

In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., a field programmable gate array (Field Programmable gate array, FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware DescriptionLanguage), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (RubyHardware Description Language), etc., VHDL (Very-High-SpeedIntegrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.

It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.

Finally, it should be noted that: the embodiment of the invention discloses an unmanned aerial vehicle data processing method and device for bridge construction monitoring, which are disclosed by the embodiment of the invention and are only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims

1. An unmanned aerial vehicle data processing method for bridge construction monitoring, the method comprising:

determining construction feasibility parameters corresponding to the construction areas according to the first image information and a preset image similarity algorithm model; the determining the construction feasibility parameter corresponding to the construction area according to the plurality of first image information and a preset image similarity algorithm model comprises the following steps:

determining a construction feasibility parameter corresponding to the construction area according to the construction range of the construction area, the house positions, the house number, the trestle occupying positions, the equipment positions and the equipment number; the grouping of the plurality of first image information based on the template matching algorithm to obtain a house image group, a trestle image group and a power equipment image group includes:

performing iterative grouping calculation on the plurality of first image information based on a dynamic programming algorithm according to the objective function and the limiting condition to obtain a house image group, a trestle image group and a power equipment image group;

2. The unmanned aerial vehicle data processing method for bridge construction monitoring according to claim 1, wherein the current construction stage is a steel sheet pile hole guiding construction stage, a steel sheet pile construction stage, a cofferdam folding construction stage, a mounting support construction stage, a foundation pit excavation dredging construction stage, a bottom sealing concrete pouring construction stage, a bearing platform pier column construction stage or a cofferdam dismantling construction stage; and before the current construction quality of the cofferdam project is determined according to the current construction stage of the cofferdam project and the plurality of second image information based on an image analysis algorithm, the method further comprises:

3. The unmanned aerial vehicle data processing method for bridge construction monitoring according to claim 1, wherein determining the construction feasibility parameter corresponding to the construction area according to the to-be-constructed range of the construction area, the house position, the house number, the trestle occupying position, the equipment position and the equipment number comprises:

4. The unmanned aerial vehicle data processing method for bridge construction monitoring according to claim 1, wherein the determining the current construction quality of the cofferdam project based on the neural network algorithm according to the current construction stage of the cofferdam project and the plurality of second image information comprises:

5. The unmanned aerial vehicle data processing method for bridge construction monitoring according to claim 4, wherein the obtaining at least one neural network model corresponding to the current construction stage of the cofferdam project comprises:

6. The unmanned aerial vehicle data processing method for bridge construction monitoring according to claim 5, wherein the determining the current engineering construction quality corresponding to the cofferdam engineering according to the construction quality parameters corresponding to all the second image information and the angle weight corresponding to the shooting angle corresponding to each image information comprises:

calculating an angular weight inversely proportional to the angular difference;

7. An unmanned aerial vehicle data processing device for bridge construction monitoring, the device comprising:

the first determining module is configured to determine a construction feasibility parameter corresponding to the construction area according to the plurality of first image information and a preset image similarity algorithm model, and specifically includes:

determining a construction feasibility parameter corresponding to the construction area according to the construction range of the construction area, the house positions, the house number, the trestle occupying positions, the equipment positions and the equipment number; the first determining module groups the plurality of first image information based on a template matching algorithm to obtain a specific mode of a house image group, a trestle image group and a power equipment image group, and the specific mode comprises the following steps:

8. An unmanned aerial vehicle data processing device for bridge construction monitoring, the device comprising:

a memory storing executable program code;

a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform the unmanned aerial vehicle data processing method for bridge construction monitoring as claimed in any one of claims 1 to 6.