CN116665125A - Intelligent recognition system for bridge tunnel diseases - Google Patents
Intelligent recognition system for bridge tunnel diseases Download PDFInfo
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Abstract
The invention discloses an intelligent recognition system for bridge tunnel diseases, which relates to the technical field of bridge detection and comprises an image acquisition module, a disease recognition module and an operation and maintenance management module; the image acquisition module is used for observing bridge diseases through the high-definition objective lens, storing and recording, measuring the size, distance and position of an observation target through the laser calibrator and the laser range finder, and transmitting acquired video stream information to the monitoring host; the disease identification module is used for acquiring images in the video stream frame by frame, inputting the images into the disease type detection model to identify and measure the disease, and obtaining disease measurement data; the monitoring host is used for generating a disease maintenance task according to the disease measurement data; after receiving the disease maintenance task, the operation and maintenance management module is used for intelligently evaluating threat levels of the corresponding bridge areas and assisting in making maintenance and defect elimination strategies according to the threat levels; the overhaul efficiency is effectively improved, and hidden danger of bridge diseases is eliminated; and the maximization of resource allocation utilization is realized.
Description
Technical Field
The invention relates to the technical field of bridge detection, in particular to an intelligent recognition system for bridge tunnel diseases.
Background
In the field of bridge daily inspection and periodic inspection, more traditional detection means and methods are always used. On the one hand, the bridge structure is contacted by hand by means of an escalator or a bridge operation platform car in a short distance, then the surface diseases of the structure are searched, measured and recorded manually by means of a crack observer, a tape measure, a camera and other tools, and finally an electronic record report is formed by means of internal trimming; on the other hand, the bridge structure is observed manually through the telescope, then the diseases are roughly described, and the images cannot be taken. The main disadvantages of the current detection means and methods are: the measuring speed is low; the influence of human factors on the measurement result is large, and the precision is low; disease positioning is inaccurate; the detection tools are more and inconvenient to carry; the artificial ladder has high risk and is limited by the topography environment conditions such as rivers, lakes, seas, ravines and the like; the cost of the detection operation platform truck is high, and the detection operation platform truck is limited by external environmental conditions such as pavement width, trees, high-voltage wires, anti-throwing nets, sound insulation plates and the like; manual recording is not standard; the processing speed of the internal data is low, etc.; based on the defects, the invention provides an intelligent recognition system for bridge and tunnel diseases.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides an intelligent recognition system for bridge and tunnel diseases.
To achieve the above objective, an embodiment according to a first aspect of the present invention provides an intelligent recognition system for bridge tunnel diseases, including a model training module, a monitoring host, an image acquisition module, a disease recognition module, an operation and maintenance management module, and a database;
the model training module is used for acquiring various disease pictures of the bridge for training, obtaining a disease type detection model, and feeding the disease type detection model obtained through training back to the monitoring host;
the image acquisition module is used for observing bridge diseases through the high-definition objective lens, storing and recording, measuring the size, the distance and the position of an observation target through the laser calibrator and the laser range finder, and transmitting acquired video stream information to the monitoring host;
the disease identification module is connected with the monitoring host, and is used for acquiring images in the video stream frame by frame, inputting the images into the disease type detection model to identify and measure the disease, and obtaining disease measurement data; the disease measurement data comprise disease type, size, distance and position;
the disease identification module is used for transmitting disease measurement data to the monitoring host, and the monitoring host is used for generating a disease maintenance task according to the disease measurement data and uploading the disease maintenance task to the operation and maintenance management module;
and after receiving the disease maintenance task, the operation and maintenance management module is used for calling potential threat associated data of the corresponding bridge area to carry out fusion analysis, intelligently evaluating threat level WX of the corresponding bridge area and making maintenance and defect elimination strategies in an auxiliary mode according to the threat level WX.
Further, the specific analysis steps of the operation and maintenance management module are as follows:
obtaining disease measurement data corresponding to a disease maintenance task; setting a corresponding type value of each disease type, and acquiring a corresponding type value LX according to the disease type; marking disease size as Lc;
acquiring a bridge area corresponding to a disease maintenance task, and acquiring potential threat related data of the corresponding bridge area, wherein the potential threat related data comprises traffic flow data and real-time microclimate data;
evaluating a meteorological value QWi of the bridge area according to the real-time microclimate data;
acquiring the traffic flow data of the bridge area every day in a preset time period, and calculating to obtain a traffic heat coefficient CS; calculating threat levels WX of the corresponding bridge areas by using a formula WX=LX×g3+Lc×g4+CS×g5+ QWi ×g6, wherein g3, g4, g5 and g6 are coefficient factors;
the corresponding overhauling and defect eliminating strategy is formulated according to the threat level WX, and specifically comprises the following steps: and the database stores a mapping relation table of threat level ranges and overhaul and defect elimination strategies.
Further, evaluating a meteorological value QWi of the bridge area according to the real-time microclimate data; the method comprises the following steps:
the real-time microclimate data comprise wind speed, wind direction, temperature, humidity, air pressure and rainfall prediction data; QWi is calculated using the formula QWi =f1×a1+f2×a2+f3×a3+f4×a4+f5×a5; wherein a1, a2, a3, a4 and a5 are coefficient factors, F1 represents wind speed, F2 represents temperature, F3 represents humidity, F4 represents air pressure, and F5 represents rainfall prediction data.
Further, the specific calculation method of the traffic heat coefficient CS is as follows:
the traffic flow data comprises traffic flow, vehicle type and people flow; the vehicle types include large-sized vehicles, medium-sized vehicles, and small-sized vehicles; marking the daily traffic flow of the bridge area as L1;
counting the number of large-sized vehicles, medium-sized vehicles and small-sized vehicles, wherein the number of the large-sized vehicles, the medium-sized vehicles and the small-sized vehicles are Zb1, zb2 and Zb3 in sequence; marking the daily people flow of the bridge area as L2; calculating a traffic value LH by using a formula LH=L1× (Zb1×3+Zb2×2+Zb3) ×b1+L2×b2, wherein b1 and b2 are coefficient factors;
comparing the traffic value LH with a preset traffic threshold value, and counting the number of times that the LH is larger than the preset traffic threshold value as Lb; when the LH is larger than a preset traffic threshold value, obtaining a difference value between the LH and the preset traffic threshold value and summing to obtain a superintersection total value ZT; calculating by using a formula CS=Lb×g1+ZT×g2 to obtain a traffic heat coefficient CS; wherein g1 and g2 are coefficient factors.
Further, the specific training steps of the model training module are as follows:
taking various disease pictures acquired from an image acquisition module and a network as a parameter training set, and manually classifying the acquired disease pictures according to disease types;
dividing the parameter training set into a training set, a testing set and a checking set according to a set proportion; the set ratio comprises 2:1:1, 3:1:1 and 4:3:1;
constructing a fusion model: the fusion model is a model constructed by combining at least two fusion modes of a support vector machine, a deep convolutional neural network and an RBF neural network, wherein the fusion modes comprise a linear weighted fusion method, a cross fusion method, a waterfall fusion method, a characteristic fusion method and a prediction fusion method;
and training, testing and checking the fusion model after the training set, the testing set and the checking set are subjected to data normalization, and marking the trained fusion model as a disease type detection model.
Further, the image acquisition module comprises a high-definition objective lens arranged at the front end; two sides of the high-definition objective lens are respectively provided with an auxiliary light source and a high-resolution camera; a laser distance meter is arranged above the high-definition objective lens, and a laser calibrator is arranged beside the laser distance meter;
wherein, the high-definition objective lens and the high-resolution camera are responsible for observing and confirming diseases in detail, and then collecting disease images; the laser distance meter is used for measuring the distance from the disease position to the objective lens; the laser calibrator is used as a conversion basis between the image measurement size and the actual size.
Further, be provided with electronic cloud platform below the high definition objective, detection personnel control the space rotation of electronic cloud platform through operating notebook computer on the bridge floor, realize searching, the image acquisition of bridge board bottom structure outward appearance disease, measure and record, realize disease space location simultaneously.
Compared with the prior art, the invention has the beneficial effects that:
1. the model training module is used for acquiring various disease pictures of the bridge for training to obtain a disease type detection model; the image acquisition module is used for observing bridge diseases through the high-definition objective lens, storing and recording, measuring the size, the distance and the position of an observation target through the laser calibrator and the laser range finder, and transmitting acquired video stream information to the monitoring host; the disease identification module is used for acquiring images in the video stream frame by frame, inputting the images into the disease type detection model to identify and measure the disease, and obtaining disease measurement data; the intelligent level of bridge detection is improved, and a safe and effective means is provided for bridge detection;
2. the monitoring host is used for generating a disease maintenance task according to the disease measurement data and uploading the disease maintenance task to the operation and maintenance management module; after receiving the disease maintenance task, the operation and maintenance management module is used for calling potential threat associated data of the corresponding bridge area to carry out fusion analysis, intelligently evaluating threat levels of the corresponding bridge area and making maintenance and defect elimination strategies in an auxiliary mode according to the threat levels; the overhaul efficiency is effectively improved, and hidden danger of bridge diseases is eliminated; and the maximization of resource allocation utilization is realized.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic block diagram of an intelligent recognition system for bridge tunnel defects of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious 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.
As shown in fig. 1, the intelligent recognition system for bridge tunnel diseases comprises a model training module, a monitoring host, an image acquisition module, a disease recognition module, an operation and maintenance management module and a database;
the model training module is used for acquiring various disease pictures of the bridge for training to obtain a disease type detection model; the specific training steps are as follows:
taking various disease pictures acquired from an image acquisition module and a network as a parameter training set, and manually classifying the acquired disease pictures according to disease types;
dividing the parameter training set into a training set, a testing set and a checking set according to a set proportion; the set proportions include 2:1:1, 3:1:1 and 4:3:1;
constructing a fusion model: the fusion model is a model constructed by combining at least two fusion modes of a support vector machine, a deep convolutional neural network and an RBF neural network, wherein the fusion modes comprise a linear weighted fusion method, a cross fusion method, a waterfall fusion method, a characteristic fusion method and a prediction fusion method;
training, testing and checking the fusion model after the training set, the testing set and the checking set are subjected to data normalization, and marking the trained fusion model as a disease type detection model; the model training module is used for feeding back the disease type detection model obtained through training to the monitoring host;
in this embodiment, the image acquisition module includes a high-definition objective lens disposed at a front end; two sides of the high-definition objective lens are respectively provided with an auxiliary light source and a high-resolution camera; a laser distance meter is arranged above the high-definition objective lens, and a laser calibrator is arranged beside the laser distance meter;
an electric tripod head is arranged below the high-definition objective lens, a detector controls the space rotation of the electric tripod head by operating a notebook computer on a bridge deck, so that searching, image capturing, measuring and recording of appearance diseases of the bottom structure of the bridge deck are realized, and meanwhile, space positioning of the diseases can be realized;
the high-definition objective lens and the high-resolution camera are responsible for observing and confirming diseases in detail, then collecting disease images, the laser distance measuring device is responsible for measuring the distance (namely object distance) from the disease position to the objective lens, and the laser calibrator is used as a conversion basis between the measured size and the actual size of the images; the auxiliary light source is suitable for working in environments with darker light rays;
the image acquisition module is used for observing bridge diseases through the high-definition objective lens, storing and recording, measuring the size, distance and position of an observation target through the laser calibrator and the laser range finder, and transmitting acquired video stream information to the monitoring host;
the disease identification module is connected with the monitoring host and is used for acquiring images in the video stream frame by frame, inputting the images into the disease type detection model to identify and measure the disease, and obtaining disease measurement data; disease measurement data includes disease type, size (area), distance, and position;
the disease recognition module is used for transmitting the disease measurement data to the monitoring host, and the monitoring host is used for generating a disease maintenance task according to the disease measurement data and uploading the disease maintenance task to the operation and maintenance management module;
in this embodiment, after receiving a disease overhaul task, the operation and maintenance management module is configured to invoke potential threat association data of a corresponding bridge area to perform fusion analysis, intelligently evaluate threat levels of the corresponding bridge area, and assist in formulating an overhaul and defect elimination strategy according to the threat levels; the maintenance efficiency is improved,
the specific analysis steps of the operation and maintenance management module are as follows:
obtaining a bridge area corresponding to a disease maintenance task; acquiring potential threat associated data corresponding to the bridge area, wherein the potential threat associated data comprises traffic flow data and real-time microclimate data; real-time microclimate data comprises wind speed, wind direction, temperature, humidity, air pressure and rainfall prediction data;
evaluating a meteorological value QWi of the bridge area according to the real-time microclimate data; the method comprises the following steps:
QWi =f1×a1+f2×a2+f3×a3+f4×a4+f5×a5; wherein a1, a2, a3, a4 and a5 are coefficient factors, F1 represents wind speed, F2 represents temperature, F3 represents humidity, F4 represents air pressure, and F5 represents rainfall prediction data;
acquiring traffic flow data of a bridge area every day in a preset time period; traffic flow data includes traffic flow, vehicle type, and pedestrian flow; vehicle types include large, medium, and small vehicles; the daily traffic flow of the bridge area is marked as L1;
counting the number of large-sized vehicles, medium-sized vehicles and small-sized vehicles, wherein the number of the large-sized vehicles, the medium-sized vehicles and the small-sized vehicles are Zb1, zb2 and Zb3 in sequence; marking the daily people flow of the bridge area as L2; calculating a traffic value LH by using a formula LH=L1× (Zb1×3+Zb2×2+Zb3) ×b1+L2×b2, wherein b1 and b2 are coefficient factors;
comparing the traffic value LH with a preset traffic threshold value, and counting the number of times that the LH is larger than the preset traffic threshold value as Lb; when the LH is larger than a preset traffic threshold value, obtaining a difference value between the LH and the preset traffic threshold value and summing to obtain a superintersection total value ZT; calculating by using a formula CS=Lb×g1+ZT×g2 to obtain a traffic heat coefficient CS; wherein g1 and g2 are coefficient factors;
obtaining disease measurement data corresponding to a disease maintenance task; setting a corresponding type value of each disease type, and acquiring a corresponding type value LX according to the disease type; marking disease size as Lc;
calculating threat levels WX of the corresponding bridge areas by using a formula WX=LX×g3+Lc×g4+CS×g5+ QWi ×g6, wherein g3, g4, g5 and g6 are coefficient factors;
the corresponding overhauling and defect eliminating strategy is formulated according to the threat level WX, and specifically comprises the following steps:
the database stores a mapping relation table of threat level range and overhaul and defect elimination strategies; the greater the threat level WX is, the higher the corresponding overhaul defect eliminating strategy level is, namely the more the specification number of the input overhaul defect eliminating resources is, and the shorter the time limit of the raising is; the overhaul efficiency is effectively improved, and hidden danger of bridge diseases is eliminated; and the maximization of resource allocation utilization is realized.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
the intelligent recognition system for bridge tunnel diseases is used for acquiring various disease pictures of the bridge for training to obtain a disease type detection model when the intelligent recognition system works; the image acquisition module is used for observing bridge diseases through the high-definition objective lens, storing and recording, measuring the size, distance and position of an observation target through the laser calibrator and the laser range finder, and transmitting acquired video stream information to the monitoring host; the disease identification module is used for acquiring images in the video stream frame by frame, inputting the images into the disease type detection model to identify and measure the disease, and obtaining disease measurement data; the intelligent level of bridge detection is improved, and a safe and effective means is provided for bridge detection;
the monitoring host is used for generating a disease maintenance task according to the disease measurement data and uploading the disease maintenance task to the operation and maintenance management module; after receiving the disease maintenance task, the operation and maintenance management module is used for calling potential threat associated data of the corresponding bridge area to carry out fusion analysis, intelligently evaluating threat levels of the corresponding bridge area and making maintenance and defect elimination strategies in an auxiliary mode according to the threat levels; the overhaul efficiency is effectively improved, and hidden danger of bridge diseases is eliminated; and the maximization of resource allocation utilization is realized.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The intelligent recognition system for the bridge tunnel diseases is characterized by comprising a model training module, a monitoring host, an image acquisition module, a disease recognition module, an operation and maintenance management module and a database;
the model training module is used for acquiring various disease pictures of the bridge for training, obtaining a disease type detection model, and feeding the disease type detection model obtained through training back to the monitoring host;
the image acquisition module is used for observing bridge diseases through the high-definition objective lens, storing and recording, measuring the size, the distance and the position of an observation target through the laser calibrator and the laser range finder, and transmitting acquired video stream information to the monitoring host;
the disease identification module is connected with the monitoring host, and is used for acquiring images in the video stream frame by frame, inputting the images into the disease type detection model to identify and measure the disease, and obtaining disease measurement data; the disease measurement data comprise disease type, size, distance and position;
the disease identification module is used for transmitting disease measurement data to the monitoring host, and the monitoring host is used for generating a disease maintenance task according to the disease measurement data and uploading the disease maintenance task to the operation and maintenance management module;
and after receiving the disease maintenance task, the operation and maintenance management module is used for calling potential threat associated data of the corresponding bridge area to carry out fusion analysis, intelligently evaluating threat level WX of the corresponding bridge area and making maintenance and defect elimination strategies in an auxiliary mode according to the threat level WX.
2. The intelligent recognition system for bridge tunnel defect according to claim 1, wherein the specific analysis steps of the operation and maintenance management module are as follows:
obtaining disease measurement data corresponding to a disease maintenance task; setting a corresponding type value of each disease type, and acquiring a corresponding type value LX according to the disease type; marking disease size as Lc;
acquiring a bridge area corresponding to a disease maintenance task, and acquiring potential threat related data of the corresponding bridge area, wherein the potential threat related data comprises traffic flow data and real-time microclimate data;
evaluating a meteorological value QWi of the bridge area according to the real-time microclimate data; acquiring the traffic flow data of the bridge area every day in a preset time period, and calculating to obtain a traffic heat coefficient CS;
calculating threat levels WX of the corresponding bridge areas by using a formula WX=LX×g3+Lc×g4+CS×g5+ QWi ×g6, wherein g3, g4, g5 and g6 are coefficient factors;
the corresponding overhauling and defect eliminating strategy is formulated according to the threat level WX, and specifically comprises the following steps: and the database stores a mapping relation table of threat level ranges and overhaul and defect elimination strategies.
3. The intelligent recognition system for bridge and tunnel defects according to claim 2, wherein the weather values QWi of the bridge area are evaluated according to real-time microclimate data; the method comprises the following steps:
the real-time microclimate data comprise wind speed, wind direction, temperature, humidity, air pressure and rainfall prediction data; QWi is calculated using the formula QWi =f1×a1+f2×a2+f3×a3+f4×a4+f5×a5; wherein a1, a2, a3, a4 and a5 are coefficient factors, F1 represents wind speed, F2 represents temperature, F3 represents humidity, F4 represents air pressure, and F5 represents rainfall prediction data.
4. The intelligent recognition system for bridge and tunnel diseases according to claim 2, wherein the specific calculation method of the traffic heat coefficient CS is as follows:
the traffic flow data comprises traffic flow, vehicle type and people flow; the vehicle types include large-sized vehicles, medium-sized vehicles, and small-sized vehicles; marking the daily traffic flow of the bridge area as L1;
counting the number of large-sized vehicles, medium-sized vehicles and small-sized vehicles, wherein the number of the large-sized vehicles, the medium-sized vehicles and the small-sized vehicles are Zb1, zb2 and Zb3 in sequence; marking the daily people flow of the bridge area as L2; calculating a traffic value LH by using a formula LH=L1× (Zb1×3+Zb2×2+Zb3) ×b1+L2×b2, wherein b1 and b2 are coefficient factors;
comparing the traffic value LH with a preset traffic threshold value, and counting the number of times that the LH is larger than the preset traffic threshold value as Lb; when the LH is larger than a preset traffic threshold value, obtaining a difference value between the LH and the preset traffic threshold value and summing to obtain a superintersection total value ZT; calculating by using a formula CS=Lb×g1+ZT×g2 to obtain a traffic heat coefficient CS; wherein g1 and g2 are coefficient factors.
5. The intelligent recognition system for bridge tunnel disease according to claim 1, wherein the specific training steps of the model training module are:
taking various disease pictures acquired from an image acquisition module and a network as a parameter training set, and manually classifying the acquired disease pictures according to disease types;
dividing the parameter training set into a training set, a testing set and a checking set according to a set proportion; the set ratio comprises 2:1:1, 3:1:1 and 4:3:1;
constructing a fusion model: the fusion model is a model constructed by combining at least two fusion modes of a support vector machine, a deep convolutional neural network and an RBF neural network, wherein the fusion modes comprise a linear weighted fusion method, a cross fusion method, a waterfall fusion method, a characteristic fusion method and a prediction fusion method;
and training, testing and checking the fusion model after the training set, the testing set and the checking set are subjected to data normalization, and marking the trained fusion model as a disease type detection model.
6. The intelligent recognition system for bridge tunnel defect according to claim 1, wherein the image acquisition module comprises a high-definition objective lens arranged at a front end; two sides of the high-definition objective lens are respectively provided with an auxiliary light source and a high-resolution camera; a laser distance meter is arranged above the high-definition objective lens, and a laser calibrator is arranged beside the laser distance meter;
wherein, the high-definition objective lens and the high-resolution camera are responsible for observing and confirming diseases in detail, and then collecting disease images; the laser distance meter is used for measuring the distance from the disease position to the objective lens; the laser calibrator is used as a conversion basis between the image measurement size and the actual size.
7. The intelligent recognition system for bridge tunnel diseases according to claim 6, wherein an electric holder is arranged below the high-definition objective lens, a detector controls the space rotation of the electric holder by operating a notebook computer on a bridge deck, so that searching, image capturing, measuring and recording of bridge plate bottom structure appearance diseases are realized, and meanwhile, disease space positioning is realized.
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