CN114353686B - Intelligent obtaining method and related device for curvature distribution of tunnel lining - Google Patents
Intelligent obtaining method and related device for curvature distribution of tunnel lining Download PDFInfo
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- CN114353686B CN114353686B CN202111064172.1A CN202111064172A CN114353686B CN 114353686 B CN114353686 B CN 114353686B CN 202111064172 A CN202111064172 A CN 202111064172A CN 114353686 B CN114353686 B CN 114353686B
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Abstract
The embodiment of the application provides a method for intelligently acquiring curvature distribution of tunnel lining and a related device, wherein the method comprises the following steps: acquiring the central wavelength variation of a first target grating, wherein the first target grating is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in a tunnel; and determining the first curvature distribution of the tunnel lining according to the central wavelength variation, so that the accuracy of curvature distribution determination can be improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent acquisition method and a related device for curvature distribution of tunnel lining.
Background
With the development of tunneling technology, more and more tunnels are built into use. In the aspect of existing tunnel monitoring, a camera is usually adopted manually or arranged in a tunnel, the tunnel monitoring is carried out by analyzing videos and the like, for example, the tunnel lining curvature is usually obtained by deformation of the whole tunnel image, but the tunnel lining curvature is obtained by deformation analysis through image processing, and the accuracy of the tunnel in the case of smaller deformation is lower, so that the accuracy of the tunnel in the case of detection is lower.
Disclosure of Invention
The embodiment of the application provides an intelligent acquisition method and a related device for curvature distribution of tunnel lining, which can improve the accuracy of curvature distribution determination.
A first aspect of an embodiment of the present application provides a method for intelligently acquiring curvature distribution of a tunnel lining, where the method includes:
acquiring the central wavelength variation of a first target grating, wherein the first target grating is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in a tunnel;
and determining the first curvature distribution of the tunnel lining according to the central wavelength variation.
With reference to the first aspect, in one possible implementation manner, the acquiring a central wavelength variation of the first target grating includes:
acquiring an elastic deformation parameter of the first target grating;
and determining the central wavelength variation according to the elastic deformation parameter.
With reference to the first aspect, in one possible implementation manner, the acquiring an elastic deformation parameter of the first target grating includes:
acquiring a first image of the first target grating at a first moment and acquiring a second image of the first target grating at a second moment, wherein the first moment is the moment before the second moment;
determining a first morphological parameter of the first target grating according to the first image, and determining a second morphological parameter of the first target grating according to the second image;
and determining the elastic deformation according to the first morphological parameter and the second morphological parameter.
With reference to the first aspect, in one possible implementation manner, the acquiring an elastic deformation parameter of the first target grating includes:
and acquiring the elastic deformation parameters according to the fiber grating sensor where the first target grating is positioned.
With reference to the first aspect, in one possible implementation manner, the determining, according to the central wavelength variation, a first curvature distribution of the tunnel lining includes:
acquiring the grating diameter, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber of the first target grating;
and determining the first curvature distribution of the tunnel lining according to the central wavelength variation, the grating diameter of the first target grating, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber.
With reference to the first aspect, in one possible implementation manner, the method further includes:
acquiring a third image of the inner wall of the tunnel where the tunnel lining is located at the first moment and acquiring a fourth image of the inner wall of the tunnel at the second moment;
determining a first distance between the first target grating and a second target grating according to the third image, wherein the second target grating is arranged adjacent to the first target grating;
determining a second distance between the first target grating and a second target grating according to the fourth image;
determining a first adjustment parameter of the first curvature distribution according to the first distance and the second distance;
acquiring vehicle information of a tunnel in which the tunnel lining is positioned according to the third image;
determining a second adjustment parameter of the first curvature distribution according to the vehicle information;
acquiring a first temperature difference between the tunnel lining and the first target grating at the first moment, and acquiring a second temperature difference between the tunnel lining and the first target grating at the second moment;
determining a third adjustment parameter of the first curvature distribution according to the first temperature difference and the second temperature difference;
and adjusting the first curvature distribution according to the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain a second curvature distribution.
With reference to the first aspect, in one possible implementation manner, the method further includes:
judging whether the tunnel where the tunnel lining is located is in a normal state or not according to the second curvature distribution;
and if the tunnel is not in a normal state, sending out alarm information.
A second aspect of an embodiment of the present application provides an intelligent obtaining device for curvature distribution of tunnel lining, the device including:
the device comprises an acquisition unit, a first target grating and a second target grating, wherein the acquisition unit is used for acquiring the central wavelength variation of the first target grating, the first target grating is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in a tunnel;
and the determining unit is used for determining the first curvature distribution of the tunnel lining according to the central wavelength variation.
With reference to the second aspect, in one possible implementation manner, the acquiring unit is configured to:
acquiring an elastic deformation parameter of the first target grating;
and determining the central wavelength variation according to the elastic deformation parameter.
A third aspect of the embodiments of the present application provides a terminal comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory being interconnected, wherein the memory is adapted to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to execute the step instructions as in the first aspect of the embodiments of the present application.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute some or all of the steps as described in the first aspect of the embodiments of the present application.
A fifth aspect of embodiments of the present application provides a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has at least the following beneficial effects:
the first curvature distribution of the tunnel lining is determined according to the central wavelength variation, so that the curvature distribution of the tunnel lining is determined by acquiring the central wavelength variation of the grating arranged on the outer surface of the tunnel lining, and the accuracy of curvature distribution determination can be improved.
Drawings
In order to more clearly illustrate the embodiments of the application 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 application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1A is a schematic view of an embodiment of the present application for providing an arrangement of an optical fiber sensor in a tunnel;
FIG. 1B is a schematic view of another arrangement of fiber optic sensors in a tunnel according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for intelligently acquiring curvature distribution of a tunnel lining according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of another intelligent obtaining method for curvature distribution of tunnel lining according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an intelligent obtaining device for curvature distribution of tunnel lining according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms first, 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 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, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference in the specification 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 application. 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 described embodiments of the application may be combined with other embodiments.
In order to better understand the intelligent obtaining method for the curvature distribution of the tunnel lining provided by the embodiment of the application, an application scene of the intelligent obtaining method for the curvature distribution of the tunnel lining is briefly described below. The method is applied to tunnel monitoring, and as shown in fig. 1A, an optical fiber grating is arranged in a tunnel, and an optical fiber sensor is taken as an example for illustration, wherein the optical fiber sensor comprises the optical fiber grating, and is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in the tunnel. As shown in fig. 1B, fig. 1B shows a schematic view of an arrangement of optical fiber sensors in a tunnel, where the optical fiber sensors in the tunnel are shown to be arranged in segments and arranged on an outer surface of a tunnel liner.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for intelligently obtaining curvature distribution of a tunnel lining according to an embodiment of the present application. As shown in fig. 2, the method includes:
201. the method comprises the steps of obtaining the central wavelength variation of a first target grating, wherein the first target grating is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in a tunnel.
The first target grating may be any one of gratings arranged on an outer surface of the tunnel lining, and a curvature distribution of the corresponding tunnel lining may be acquired by the first target grating, specifically, for example, a curvature distribution of the tunnel lining in which the first target grating is provided.
The central wavelength variation can be obtained by the elastic deformation parameter of the first target grating.
202. And determining the first curvature distribution of the tunnel lining according to the central wavelength variation.
The first curvature distribution may be determined based on the amount of center wavelength variation, as well as the grating diameter, the center reflection wavelength of the grating, and the effective elasto-optical coefficient of the fiber.
Of course, the central wavelength variation of all gratings set in the tunnel may be obtained, so as to obtain the curvature distribution of the whole tunnel.
In this example, the first curvature distribution of the tunnel lining is determined according to the central wavelength variation, and therefore, the curvature distribution of the tunnel lining is determined by obtaining the central wavelength variation of the grating disposed on the outer surface of the tunnel lining, so that accuracy in determining the curvature distribution can be improved.
In one possible implementation manner, a possible method for obtaining the central wavelength variation of the first target grating includes:
a1, acquiring elastic deformation parameters of the first target grating;
a2, determining the central wavelength variation according to the elastic deformation parameters.
The elastic deformation parameters can be determined by acquiring images corresponding to the first target grating at different moments and according to the morphological parameters of the first target grating in the images.
The elastic deformation parameter may be obtained by a fiber grating sensor.
The axial strain of the first target grating can be determined according to the elastic deformation parameter, and then the central wavelength variation is determined according to the axial strain. The axial strain may be determined specifically by a method shown by the following formula:
wherein phi is a predefined elastic deformation value, delta phi is an elastic deformation amount and epsilon z Is axial strain. The elastic deformation parameter is the elastic deformation delta phi.
And determining the central wavelength variation by a method shown in the following formula:
wherein Deltalambda B As the central wavelength variation, lambda B For the central reflection wavelength of the grating, P e Is the effective elasto-optical coefficient of the optical fiber, P i,j The Proke piezoelectric coefficient of photoelastic tensor, v is Poisson's ratio, epsilon is axial strain distribution, n eff Is the core index of the fiber.
In one possible implementation manner, a possible method for obtaining the elastic deformation parameter of the first target grating includes:
b1, acquiring a first image of the first target grating at a first moment and acquiring a second image of the first target grating at a second moment, wherein the first moment is the moment before the second moment;
b2, determining a first morphological parameter of the first target grating according to the first image, and determining a second morphological parameter of the first target grating according to the second image;
b3, determining the elastic deformation according to the first morphological parameter and the second morphological parameter.
The first image at the first time and the second image at the second time may be acquired by the image capturing device, wherein a time interval between the first time and the second time may be preset, for example, may be 0.01S or the like. The first image and the second image may be optical images or the like, for example, in a visible light optical image, an infrared light optical image or the like. This is by way of example only and is not intended to be limiting.
The first image and the second image may be feature extracted to obtain feature parameters, and the first morphological parameter and the second morphological parameter may be determined according to the feature parameters. The characteristic parameter may be, for example, a gray value or the like.
The difference between the first morphological parameter and the second morphological parameter may be determined as the elastic deformation amount.
In this example, the first shape parameter and the second shape parameter are determined by acquiring the first image and the second image, and the elastic deformation amount is determined according to the first shape parameter and the second shape parameter, so that accuracy in determining the elastic deformation amount is improved.
In one possible implementation manner, one possible method for obtaining the elastic deformation parameter of the first target grating includes:
and acquiring the elastic deformation parameters according to the fiber grating sensor where the first target grating is positioned.
In the example, the elastic deformation parameters are obtained through the fiber grating sensor, so that the elastic deformation parameters can be obtained rapidly, and the efficiency in obtaining is improved.
In one possible implementation, a possible method for determining a first curvature distribution of the tunnel lining according to the central wavelength variation includes:
c1, acquiring the grating diameter, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber of the first target grating;
and C2, determining the first curvature distribution of the tunnel lining according to the central wavelength variation, the grating diameter of the first target grating, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber.
The first curvature distribution may be determined in particular by a method as shown in the following formula:
where k is the first curvature distribution, Δλ B As the central wavelength variation, lambda B For the central reflection wavelength of the grating, P e The effective elasto-optical coefficient of the optical fiber is that h is the grating diameter.
In this example, the accuracy in determining the first curvature distribution can be improved by determining the first curvature distribution of the tunnel lining through the central wavelength variation, the grating diameter of the first target grating, the central reflection wavelength, and the effective elasto-optical coefficient of the optical fiber.
In one possible implementation manner, the embodiment of the application further provides a method for adjusting curvature distribution, which specifically comprises the following steps:
d1, acquiring a third image of the inner wall of the tunnel where the tunnel lining is located at a first moment and acquiring a fourth image of the inner wall of the tunnel at a second moment;
d2, determining a first distance between the first target grating and a second target grating according to the third image, wherein the second target grating is arranged adjacent to the first target grating;
d3, determining a second distance between the first target grating and the second target grating according to the fourth image;
d4, determining a first adjustment parameter of the first curvature distribution according to the first distance and the second distance;
d5, acquiring vehicle information of the tunnel where the tunnel lining is located according to the third image;
d6, determining a second adjustment parameter of the first curvature distribution according to the vehicle information;
d7, acquiring a first temperature difference between the tunnel lining and the first target grating at the first moment, and acquiring a second temperature difference between the tunnel lining and the first target grating at the second moment;
d8, determining a third adjustment parameter of the first curvature distribution according to the first temperature difference and the second temperature difference;
and D9, adjusting the first curvature distribution according to the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain a second curvature distribution.
The third image and the fourth image may be images of the inner wall of the tunnel and may be obtained by shooting with a camera. The position information of the first target grating and the second target grating can be determined by means of feature extraction, and the first distance and the second distance between the first target grating and the second target grating are determined according to the position information. The first distance and the second distance may be understood as a distance average value between corresponding pixel points between the first target grating and the second target grating, etc.
The first adjustment parameter may be determined based on an amount of change in distance between the first distance and the second distance. The larger the distance change amount is, the larger the first adjustment parameter is, and the smaller the distance change is, the smaller the first adjustment parameter is. The magnitude of the first adjustment parameter may be used to reflect the magnitude of the adjustment amount when adjusting the first curvature distribution.
The method for determining the second adjustment parameter according to the vehicle information acquired by the third image may be:
acquiring the category of the vehicle according to the vehicle information; determining a parameter grading value according to the category of the vehicle; a second adjustment parameter is determined based on the parameter score value. The types of vehicles can include cars, trucks, bicycles, tricycles and the like. Different vehicle categories correspond to different parameter grading values, and the larger the load of the vehicle is, the higher the parameter grading value is, and the smaller the load of the vehicle is, the smaller the parameter grading value is. A mean value of the scoring values for the vehicle categories may be determined, and the second adjustment parameter may be determined based on the mean value. The larger the average value is, the larger the second adjustment parameter is, and the smaller the average value is, the smaller the second adjustment parameter is. When different vehicles pass through the tunnel, vibration is caused to the tunnel lining due to running of the vehicles, so that the grating vibrates to deform, a second adjustment parameter is determined according to the category information of the vehicles, and the first curvature distribution is adjusted based on the second adjustment parameter, so that the accuracy of the adjusted second curvature distribution can be improved, and the accuracy of monitoring the tunnel is further improved.
Because the grating and the lining are made of different materials, the sensitivity to temperature is also different, so that the deformation quantity is reflected differently when the grating is deformed. The first temperature difference and the second temperature difference are obtained and a third adjustment parameter is determined according to the first temperature difference and the second temperature difference. The average value of the first temperature difference and the second temperature difference may be determined, and the third adjustment parameter may be determined according to the average value, the smaller the average value is, the larger the third adjustment parameter is, the larger the average value is, and the smaller the third adjustment parameter is.
The first curvature distribution may be adjusted by the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter, respectively, to obtain a second curvature distribution. For example, the first adjustment parameter is adjusted, then the second adjustment parameter is adjusted, and finally the third adjustment parameter is adjusted, so that the second curvature distribution is obtained, and therefore, the accuracy of the second curvature distribution is improved.
In a possible implementation manner, the embodiment of the application further provides an alarm method, which specifically comprises the following steps:
e1, judging whether a tunnel where the tunnel lining is located is in a normal state or not according to the second curvature distribution;
and E2, if the tunnel is not in a normal state, sending out alarm information.
The second curvature distribution can be compared with a preset curvature distribution to obtain similarity, and whether the tunnel is in a normal state or not is judged according to the similarity. For example, it may be determined that the device is in a normal state when the similarity is higher than a preset threshold value, and is not in a normal state when the similarity is lower than or equal to the preset threshold value.
In the example, when the tunnel is not in a normal state, alarm information is sent out, so that timeliness of tunnel monitoring is improved.
Referring to fig. 3, fig. 3 is a schematic flow chart of another intelligent obtaining method for curvature distribution of tunnel lining according to an embodiment of the present application. As shown in fig. 3, the method includes:
301. acquiring the central wavelength variation of a first target grating, wherein the first target grating is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in a tunnel;
302. determining a first curvature distribution of the tunnel lining according to the central wavelength variation;
303. acquiring a third image of the inner wall of the tunnel where the tunnel lining is located at the first moment and acquiring a fourth image of the inner wall of the tunnel at the second moment;
304. determining a first distance between the first target grating and a second target grating according to the third image, wherein the second target grating is arranged adjacent to the first target grating;
305. determining a second distance between the first target grating and a second target grating according to the fourth image;
306. determining a first adjustment parameter of the first curvature distribution according to the first distance and the second distance;
307. acquiring vehicle information of a tunnel in which the tunnel lining is positioned according to the third image;
308. determining a second adjustment parameter of the first curvature distribution according to the vehicle information;
309. acquiring a first temperature difference between the tunnel lining and the first target grating at the first moment, and acquiring a second temperature difference between the tunnel lining and the first target grating at the second moment;
310. determining a third adjustment parameter of the first curvature distribution according to the first temperature difference and the second temperature difference;
311. and adjusting the first curvature distribution according to the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain a second curvature distribution.
The first curvature distribution may be adjusted by the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter, respectively, to obtain a second curvature distribution. For example, the first adjustment parameter is adjusted, then the second adjustment parameter is adjusted, and finally the third adjustment parameter is adjusted, so that the second curvature distribution is obtained, and therefore, the accuracy of the second curvature distribution is improved.
In accordance with the foregoing embodiments, referring to fig. 4, fig. 4 is a schematic structural diagram of a terminal provided in an embodiment of the present application, where the terminal includes a processor, an input device, an output device, and a memory, and the processor, the input device, the output device, and the memory are connected to each other, where the memory is configured to store a computer program, the computer program includes program instructions, the processor is configured to invoke the program instructions, and the program includes instructions for executing the following steps;
acquiring the central wavelength variation of a first target grating, wherein the first target grating is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in a tunnel;
and determining the first curvature distribution of the tunnel lining according to the central wavelength variation.
The foregoing description of the embodiments of the present application has been presented primarily in terms of a method-side implementation. It will be appreciated that, in order to achieve the above-mentioned functions, the terminal includes corresponding hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the application can divide the functional units of the terminal according to the method example, for example, each functional unit can be divided corresponding to each function, and two or more functions can be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.
In accordance with the foregoing, referring to fig. 5, fig. 5 is a schematic structural diagram of an intelligent obtaining device for curvature distribution of tunnel lining according to an embodiment of the present application. As shown in fig. 5, the apparatus includes:
an obtaining unit 501, configured to obtain a central wavelength variation of a first target grating, where the first target grating is disposed on an outer surface of a tunnel lining, and the outer surface is a surface of the tunnel lining in a tunnel;
a determining unit 502, configured to determine a first curvature distribution of the tunnel lining according to the central wavelength variation.
In one possible implementation manner, the acquiring unit 501 is configured to:
acquiring an elastic deformation parameter of the first target grating;
and determining the central wavelength variation according to the elastic deformation parameter.
In one possible implementation manner, the acquiring unit 501 is configured to:
acquiring a first image of the first target grating at a first moment and acquiring a second image of the first target grating at a second moment, wherein the first moment is the moment before the second moment;
determining a first morphological parameter of the first target grating according to the first image, and determining a second morphological parameter of the first target grating according to the second image;
and determining the elastic deformation according to the first morphological parameter and the second morphological parameter.
In one possible implementation manner, in the acquiring the elastic deformation parameter of the first target grating, the acquiring unit 501 is configured to:
and acquiring the elastic deformation parameters according to the fiber grating sensor where the first target grating is positioned.
In one possible implementation, the determining unit 502 is configured to:
acquiring the grating diameter, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber of the first target grating;
and determining the first curvature distribution of the tunnel lining according to the central wavelength variation, the grating diameter of the first target grating, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber.
In one possible implementation, the apparatus is further configured to:
acquiring a third image of the inner wall of the tunnel where the tunnel lining is located at the first moment and acquiring a fourth image of the inner wall of the tunnel at the second moment;
determining a first distance between the first target grating and a second target grating according to the third image, wherein the second target grating is arranged adjacent to the first target grating;
determining a second distance between the first target grating and a second target grating according to the fourth image;
determining a first adjustment parameter of the first curvature distribution according to the first distance and the second distance;
acquiring vehicle information of a tunnel in which the tunnel lining is positioned according to the third image;
determining a second adjustment parameter of the first curvature distribution according to the vehicle information;
acquiring a first temperature difference between the tunnel lining and the first target grating at the first moment, and acquiring a second temperature difference between the tunnel lining and the first target grating at the second moment;
determining a third adjustment parameter of the first curvature distribution according to the first temperature difference and the second temperature difference;
and adjusting the first curvature distribution according to the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain a second curvature distribution.
In one possible implementation, the apparatus is further configured to:
judging whether the tunnel where the tunnel lining is located is in a normal state or not according to the second curvature distribution;
and if the tunnel is not in a normal state, sending out alarm information.
The embodiment of the application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program makes a computer execute part or all of the steps of any intelligent tunnel lining curvature distribution acquisition method described in the embodiment of the method.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program for causing a computer to perform part or all of the steps of any one of the intelligent acquisition methods of the curvature distribution of tunnel lining as described in the method embodiments above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units described above may be implemented either in hardware or in software program modules.
The integrated units, if implemented in the form of software program modules, may be stored in a computer-readable memory for sale or use as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or partly in the form of a software product, or all or part of the technical solution, which is stored in a memory, and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned memory includes: a U-disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-only memory, random access memory, magnetic or optical disk, etc.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (8)
1. An intelligent obtaining method for curvature distribution of tunnel lining is characterized by comprising the following steps:
acquiring the central wavelength variation of a first target grating, wherein the first target grating is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in a tunnel;
acquiring the grating diameter, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber of the first target grating;
determining a first curvature distribution of the tunnel lining according to the central wavelength variation, the grating diameter of the first target grating, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber;
the intelligent obtaining method of the curvature distribution of the tunnel lining further comprises the following steps:
acquiring a third image of the inner wall of the tunnel where the tunnel lining is located at the first moment and acquiring a fourth image of the inner wall of the tunnel at the second moment;
determining a first distance between the first target grating and a second target grating according to the third image, wherein the second target grating is arranged adjacent to the first target grating;
determining a second distance between the first target grating and a second target grating according to the fourth image;
determining a first adjustment parameter of the first curvature distribution according to the first distance and the second distance;
acquiring vehicle information of a tunnel in which the tunnel lining is positioned according to the third image;
determining a second adjustment parameter of the first curvature distribution according to the vehicle information;
acquiring a first temperature difference between the tunnel lining and the first target grating at the first moment, and acquiring a second temperature difference between the tunnel lining and the first target grating at the second moment;
determining a third adjustment parameter of the first curvature distribution according to the first temperature difference and the second temperature difference;
and adjusting the first curvature distribution according to the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain a second curvature distribution.
2. The method of claim 1, wherein the obtaining the center wavelength variation of the first target grating comprises:
acquiring an elastic deformation parameter of the first target grating;
and determining the central wavelength variation according to the elastic deformation parameter.
3. The method of claim 2, wherein the obtaining the elastic deformation parameter of the first target grating comprises:
acquiring a first image of the first target grating at a first moment and acquiring a second image of the first target grating at a second moment, wherein the first moment is the moment before the second moment;
determining a first morphological parameter of the first target grating according to the first image, and determining a second morphological parameter of the first target grating according to the second image;
and determining the elastic deformation parameter according to the first morphological parameter and the second morphological parameter.
4. The method of claim 2, wherein the obtaining the elastic deformation parameter of the first target grating comprises:
and acquiring the elastic deformation parameters according to the fiber grating sensor where the first target grating is positioned.
5. The method according to claim 1, wherein the method further comprises:
judging whether the tunnel where the tunnel lining is located is in a normal state or not according to the second curvature distribution;
and if the tunnel is not in a normal state, sending out alarm information.
6. An intelligent acquisition device for curvature distribution of tunnel lining, characterized in that the device comprises:
the device comprises an acquisition unit, a first target grating and a second target grating, wherein the acquisition unit is used for acquiring the central wavelength variation of the first target grating, the first target grating is arranged on the outer surface of a tunnel lining, and the outer surface is one surface of the tunnel lining in a tunnel;
the determining unit is used for obtaining the grating diameter, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber of the first target grating; determining a first curvature distribution of the tunnel lining according to the central wavelength variation, the grating diameter of the first target grating, the central reflection wavelength and the effective elasto-optical coefficient of the optical fiber;
the intelligent curvature distribution acquisition device of the tunnel lining is also used for:
acquiring a third image of the inner wall of the tunnel where the tunnel lining is located at the first moment and acquiring a fourth image of the inner wall of the tunnel at the second moment;
determining a first distance between the first target grating and a second target grating according to the third image, wherein the second target grating is arranged adjacent to the first target grating;
determining a second distance between the first target grating and a second target grating according to the fourth image;
determining a first adjustment parameter of the first curvature distribution according to the first distance and the second distance;
acquiring vehicle information of a tunnel in which the tunnel lining is positioned according to the third image;
determining a second adjustment parameter of the first curvature distribution according to the vehicle information;
acquiring a first temperature difference between the tunnel lining and the first target grating at the first moment, and acquiring a second temperature difference between the tunnel lining and the first target grating at the second moment;
determining a third adjustment parameter of the first curvature distribution according to the first temperature difference and the second temperature difference;
and adjusting the first curvature distribution according to the first adjustment parameter, the second adjustment parameter and the third adjustment parameter to obtain a second curvature distribution.
7. The apparatus of claim 6, wherein the acquisition unit is configured to:
acquiring an elastic deformation parameter of the first target grating;
and determining the central wavelength variation according to the elastic deformation parameter.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1-5.
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| CN114353686A (en) | 2022-04-15 |
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