CN117726830B - Online bow net detection method, system and storage medium based on monocular image - Google Patents
Online bow net detection method, system and storage medium based on monocular image Download PDFInfo
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Abstract
The invention relates to the technical field of pantograph-catenary relation detection, in particular to a method, a system and a storage medium for detecting a pantograph-catenary on line based on monocular images, wherein the method specifically comprises the following steps: simulating a test working condition in a severe weather environment, collecting working data of a pantograph-catenary in a subway vehicle running state, and judging the dynamic current-carrying quality of the pantograph-catenary; predicting and evaluating the abrasion area ratio of the pantograph slide plate and the contact line; extracting working data of the pantograph-catenary, and drawing a statistical image formed by the working data; calculating an evaluation coefficient of the arch gateway system, and carrying out working state identification evaluation on the arch gateway system; and evaluating the bow net relationship on line and outputting fault judgment corresponding to different bow net bad relationships in real time. The pantograph is integrally used as a conductor, has high requirements on the electromagnetic interference resistance and the insulation and isolation performance of a test system, and the actual detection precision of the test system is greatly influenced by the external environment.
Description
Technical Field
The invention relates to the technical field of pantograph-catenary relation detection, in particular to a monocular image-based pantograph-catenary online detection method, a monocular image-based pantograph-catenary online detection system and a storage medium.
Background
With the continuous improvement of urban rail transit operation speed, operation safety has become one of the topics of most concern for all subway companies. In particular to a pantograph-catenary system (called a pantograph-catenary system for short), which is a complex nonlinear system of mutual coupling and interaction of an electric bus and a power supply catenary, and the operation safety of the system is directly related to the operation safety of urban rail transit. The contact net mainly bears the power supply function of the electric bus, and the reliability of the contact net influences the operation safety of the electric bus; the pantograph is electric equipment for acquiring electric energy from the electric bus, good contact of the pantograph and the contact net is a prerequisite for the contact net to transmit the electric energy to the electric bus, and problems in any link of the pantograph can reduce the current-carrying quality of the pantograph net, and even serious driving accidents can be caused. How to effectively master and improve the bow-net relationship is a relatively difficult problem faced in the industry at present. The contact pressure when the pantograph used for the metro vehicle is in static contact with the contact line is usually 120N, and is also called the static lifting force of the pantograph. In the train operation process, because the vehicle, the pantograph and the contact net are stimulated by external excitation, the pantograph slide plate and the contact line form dynamic contact, so that the contact pressure between the pantograph slide plate and the contact line is difficult to maintain at 120N.
Therefore, arch gateway system detection is particularly important. At present, the comprehensive detection system is used for acquiring the bow net data, has high detection efficiency and strong environmental adaptability, and has the functions of real-time data transmission, storage and analysis processing. The true condition of each parameter index of the bow net relation can be accurately judged by analyzing the obtained bow net detection data.
In the prior art, as disclosed in the patent with publication number CN105067158a, a bow-net separated type pantograph pressure detection device is disclosed, which comprises a pressure detection structure, along the length direction of the catenary, two sides of the pressure detection structure are respectively provided with a catenary pre-tightening mechanism for applying the pretightening force of the catenary, the pressure detection structure comprises a connecting arm assembly and a mounting assembly hinged with the connecting arm assembly, one end of the connecting arm assembly is provided with a downward moving contact piece for separating the pantograph from the catenary, the other end of the connecting arm assembly is provided with a detection assembly for detecting the contact pressure between the downward moving contact piece and the pantograph, and the catenary pre-tightening mechanism is electrically connected with the downward moving contact piece.
The adaptability of the above patent to the detection environment is weak, and in actual operation, the device stability and reliability are challenged by the influence of special environments such as bad weather, dust and the like, and meanwhile, because of the pressure detection, the accuracy and the sensitivity of the detection assembly are required to be high.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The whole pantograph is used as a conductor, the requirements on the electromagnetic interference resistance and the insulation and isolation performance of a test system are very high, the actual detection precision of a detection system is greatly influenced by external environments, and in the geometric dimension detection of a pantograph net and a rail, as a portable detection sensor arranged on an electric bus lacks protection outer package, the electric bus is influenced by different light rays on site, partial visible light interference data with similar wavelengths can be mixed in collected data, so that the collected data and the actual data deviate, and the actual detection precision is seriously reduced. In addition, due to the shaking of the vehicle body relative to the steel rail, the geometric parameter data acquisition equipment arranged on the vehicle body can slightly deviate from the acquisition points of geometric parameters such as the pull-out value, the guide height and the like of the contact line, and the accuracy of acquired data is affected. The track detection data is mainly obtained by detection of measuring sensors such as a laser camera assembly, an axle encoder, an acceleration sensor and the like which are arranged on the metro vehicle. In the actual detection process, when any one of a sensor of a detection system fails, calibration errors exist in equipment, interference of external bad environments (such as rainy and snowy weather and strong illumination), a special track structure of a switch point, poor data transmission and the like occurs, the sensor can possibly interfere data acquired by the track detection sensor, further abnormal values of sampling points are generated, and based on the problems in the prior art, the invention provides a monocular image-based bow net online detection method, a monocular image-based bow net online detection system and a storage medium.
In order to achieve the above purpose, the technical scheme of the bow net online detection method based on monocular images of the invention comprises the following steps:
S1: simulating a test working condition in a severe weather environment, performing environment compensation, collecting working data of a pantograph-catenary in the running state of the metro vehicle through a fiber bragg grating sensing technology, and performing dynamic current-carrying quality judgment of the pantograph-catenary;
S2: establishing a machine learning model, and predicting and evaluating the abrasion area ratio of the pantograph slide plate and the contact line;
s3: extracting working data of a pantograph-catenary in the running state of the metro vehicle through a fiber bragg grating sensing technology, and drawing a statistical image formed by the working data;
S4: according to the steps S2-S3, calculating an arch gateway system evaluation coefficient, and carrying out working state identification evaluation on the arch gateway system;
S5: and evaluating the bow net relationship on line and outputting fault judgment corresponding to different bow net bad relationships in real time.
Specifically, in S1, the environment compensation process includes: the method comprises the steps that a 3D camera and a line laser sensor assembly are arranged at the middle position of a roof of a detection vehicle during the detection period of the bow gateway system, the height and the transverse offset of a contact line relative to the camera are calculated through line laser speckles at different imaging positions of the 3D camera, and the geometric parameters of a contact network are dynamically compensated by combining two laser 2D sensors in a vehicle body vibration compensation module, accurately measuring the positions of the center and the line center of the vehicle body and the height of the vehicle body and the rail surface.
Specifically, in S1, the test conditions include: under severe weather, the detection speed of the metro vehicle is; Indoor temperature of test environment is at/>Outdoor temperature is at/>; The altitude of the test environment is less than or equal to 500m.
Specifically, in S1, the collecting, by using a fiber bragg grating sensing technology, working data of a pantograph-catenary in an operation state of a metro vehicle includes:
installing a 20W searchlight near the pantograph;
the method for obtaining the dynamic geometric parameters of the overhead line of the metro vehicle in normal operation through the overhead line geometric parameter testing device comprises the following steps: a contact line pull-out value, a height guiding value and a height difference value among positioning points;
Acquiring dynamic inertia items of a pantograph-catenary through a plurality of mechanical sensors, and acquiring dynamic contact pressure data between a pantograph of a metro vehicle in positive line operation and the catenary;
testing longitudinal, vertical and transverse accelerations of the front sliding plate and the rear sliding plate through a plurality of acceleration sensors to obtain vibration acceleration data of the pantograph;
acquiring monocular images of the height of a pantograph slide plate and the working running state of the pantograph of the metro vehicle in positive line running through 1 high-precision cameras respectively;
Testing the arcing data of offline spark of an arch net through 1 arcing sensor;
All acquired information is transmitted to a data acquisition box through a fiber bragg grating sensing technology, and the working data of the pantograph-catenary acquired through storage analysis of a high-performance computer are stored.
Specifically, in S1, the determining the dynamic current-collecting quality of the bow net includes:
dynamic contact pressure between pantograph and catenary of metro vehicle in positive line operation When the dynamic contact pressure is too large, executing a detection step S2;
dynamic contact pressure between pantograph and catenary of metro vehicle in positive line operation When the dynamic contact pressure is too small, executing a detection step S3;
Wherein, The contact pressure data of the station measuring point at the ith station;
The contact pressure of the station measuring point is the maximum value of the contact pressure of the i-th station measuring point;
The contact pressure of the station is the minimum value of the station.
Specifically, in S2, the machine learning model includes: an input layer, a convolution layer, a sampling layer, a pooling layer, a full connection layer and an output layer, wherein the convolution layer comprises 10 layers with the size ofIs a convolution kernel of (a).
Specifically, S2 includes the following specific steps:
s21: denoising preprocessing is carried out on the monocular image of the working operation state of the pantograph acquired by the high-precision camera, and processed monocular image data is input into an input layer in a machine learning model;
S22: performing feature extraction of edge features, texture features and shape features on monocular image data through a convolution layer in a machine learning model, and integrating abrasion features by utilizing a full-connection layer;
S23: performing gray threshold segmentation processing on the bow net region by using a detection algorithm of template matching based on the features extracted in the step S22, and dividing a monocular image into a background region, a center abrasion region, an edge abrasion region and a lossless region according to gray value gradients;
s24: judging the abrasion area ratio of the pantograph in the severe weather environment through an image recognition algorithm, wherein the calculation formula of the abrasion area ratio is as follows: ; g is the total number of wear monitoring points; /(I) The area of the central abrasion area of the g abrasion monitoring point; /(I)The area of the edge abrasion area of the g abrasion monitoring point; s is the area of the monocular image divided into lossless regions.
Specifically, in S3, the statistical image formed by the working data includes: arcing scattered point images and vertical, longitudinal and transverse acceleration time course curve images of a front sliding plate when the metro vehicle runs in a normal opening operation and a closed operation.
Specifically, in S4, the arch gateway system evaluates coefficientsThe calculation strategy of (2) is as follows:
；
Wherein, The arc rate is the arc rate of the bow net; /(I)The number of items meeting the design standard range of the metro vehicle in the dynamic geometrical parameters of the contact net; /(I)For the number of items meeting the design standard range of the metro vehicle in the vertical, longitudinal and transverse accelerations of the front sliding plate during the front line opening running and the closed running of the metro vehicle, wherein/>。
Specifically, the calculation of the bow net arc rate comprises the following steps:
When the arcing times of the bow net is less than or equal to 1/160 m and the maximum offline time of the bow net is less than or equal to 100ms, ；
When the arcing times of the bow net are more than 1/160 m and the maximum offline time of the bow net is less than or equal to 100ms,；
When the arcing times of the bow net is less than or equal to 1/160 m and the maximum offline time of the bow net is more than 100ms,；
When the arcing times of the bow net are more than 1/160 m and the maximum offline time of the bow net is more than 100ms,。
Specifically, in S5, the outputting, in real time, the fault determination corresponding to the different bow net bad relationships includes:
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the dynamic geometrical parameters of the midspan, the locating point, the middle joint, the anchor section joint, the segmented insulator, the rigid-flexible transition, the accelerating section, the cantilever low head and the serious water leakage part are out of limits;
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the slope change and the triangular pit of the subway vehicle running line generate hard points;
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the pantograph and the contact line are offline to cause voltage drop, break through air and generate electric arc; the pantograph is electrically isolated from the contact wire, creating a voltage difference that breaks down instantaneously, creating an arc.
In addition, the bow net online detection system based on the monocular image comprises the following modules:
the device comprises a force module, an acceleration module, a data acquisition box, a high-performance computer, an arcing module, a speed measurement module, a current module and a contact net detection module;
The force module is used for collecting and storing contact pressure data of the subway vehicle;
The acceleration module is used for measuring the vibration acceleration of the pantograph;
the arcing module is used for detecting an offline spark state of the pantograph-catenary;
the speed measuring module is used for detecting real-time running speed of the metro vehicle in the detection zone;
The current module is used for measuring the current of the pantograph to be tested in the running process of the metro vehicle;
the overhead line system detection module is used for simulating a test working condition in a severe weather environment, collecting working data of the pantograph-overhead line system in the running state of the metro vehicle through a fiber bragg grating sensing technology, and judging the dynamic current-carrying quality of the pantograph;
a storage medium having instructions stored therein that, when read by a computer, cause the computer to perform the above-described monocular image-based bow net online detection method.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above-mentioned monocular image based bow net online detection method when executing the computer program.
Compared with the prior art, the invention has the following technical effects:
1. Aiming at the problem of inaccurate detection data of the bow gateway system caused by severe weather environment, the invention adopts a 3D camera and a line laser sensor assembly to be arranged at the middle position of the roof of a detection vehicle during the detection period of the bow gateway system, calculates the height and transverse offset of a contact line relative to the camera at different imaging positions of the 3D camera through line laser speckles, combines two laser 2D sensors in a vehicle body vibration compensation module, accurately measures the positions of the center of the vehicle body and the line center and the heights of the vehicle body and the rail surface, dynamically compensates geometric parameters of a contact network, and improves the accuracy of detection data.
2. In order to solve the problem that the whole pantograph is used as a conductor and has high requirements on the electromagnetic interference resistance and the insulation and isolation performance of a test system, the invention adopts a fiber bragg grating sensing technology and has the following technical characteristics:
(1) Anti-electromagnetic interference: electromagnetic radiation has a lower frequency than light waves, and the optical signals are not affected by electromagnetic interference.
(2) Good electrical insulation performance, safety and reliability: the fiber itself is made of dielectric and does not require power to drive.
(3) Corrosion resistance and stable chemical performance: the quartz, the material of the optical fiber, has extremely high chemical stability and is suitable for being used in severe environments.
(4) Small volume, light weight and plastic geometry.
(5) The transmission loss is small: and (5) remote control monitoring.
(6) The transmission capacity is large: multipoint distributed measurement.
(7) The measuring range is wide: temperature, pressure, strain, stress, flow rate, current, voltage, etc. may be measured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of an online detection method of an arch net based on monocular images;
FIG. 2 is a schematic diagram of the structure of the monocular image-based bow net online detection system of the present invention;
FIGS. 3, 4, 5, 6, 7, 8 are schematic structural views of the installation position of the data acquisition sensor of the present invention;
FIG. 9 is a diagram of the primary hardware apparatus of the present invention;
FIGS. 10 and 11 are exemplary graphs of arcing scatter images during opening and closing operation of a metro vehicle in a statistical image formed by working data of the present invention;
Fig. 12 and 13 are graphs of example images of acceleration time course curves of vertical, longitudinal and transverse directions of a front slide plate in opening and closing operation of a metro vehicle in statistical images formed by working data of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" 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.
Embodiment one:
As shown in fig. 1 and fig. 3 to fig. 11, the method for online detection of the bow net based on the monocular image according to the embodiment of the invention, as shown in fig. 1, comprises the following specific steps:
s1: simulating a test working condition in a severe weather environment, performing environment compensation treatment, acquiring working data of a pantograph-catenary in a running state of the metro vehicle by using a fiber bragg grating sensing technology, and performing dynamic current-carrying quality judgment of the pantograph-catenary;
In S1, the environment compensation process includes: the method comprises the steps that a 3D camera and a line laser sensor assembly are arranged at the middle position of a roof of a detection vehicle during the detection period of the bow gateway system, the height and the transverse offset of a contact line relative to the camera are calculated through line laser speckles at different imaging positions of the 3D camera, and the geometric parameters of a contact network are dynamically compensated by combining two laser 2D sensors in a vehicle body vibration compensation module, accurately measuring the positions of the center and the line center of the vehicle body and the height of the vehicle body and the rail surface.
In S1, the test conditions include: under severe weather, wherein, the severe weather includes: in overcast and rainy days or snow, etc., the detection speed of the metro vehicle is; Indoor temperature of test environment is at/>The outdoor temperature is at; The altitude of the test environment is less than or equal to 500m.
As shown in fig. 3 to 9, in S1, the collecting, by using the fiber bragg grating sensing technology, working data of a pantograph-catenary in an operation state of a metro vehicle includes:
As shown in fig. 7, a 20W searchlight is installed near the pantograph to increase the brightness of the pantograph;
the method for obtaining the dynamic geometric parameters of the overhead line of the metro vehicle in normal operation through the overhead line geometric parameter testing device comprises the following steps: a contact line pull-out value, a height guiding value and a height difference value among positioning points;
as shown in fig. 3, dynamic inertia items of the pantograph and the catenary are acquired through a plurality of mechanical sensors, and dynamic contact pressure data between the pantograph and the catenary of the metro vehicle in positive line operation is acquired;
as shown in fig. 4, longitudinal, vertical and lateral accelerations of the front and rear sliding plates are tested by a plurality of acceleration sensors, and pantograph vibration acceleration data is obtained;
As shown in fig. 6, monocular images of the working operation state of the pantograph of the metro vehicle in normal operation are respectively acquired by 1 high-precision cameras;
as shown in fig. 5, the arc data of the off-line spark of the bowing net was tested by 1 arc sensor;
as shown in fig. 8 and 9, all acquired information is transmitted to a data acquisition box through a fiber bragg grating sensing technology, and the acquired working data of the pantograph-catenary is stored and analyzed through a high-performance computer.
In this embodiment, specifically, a signal acquisition box for dynamic contact pressure of the pantograph net and vibration acceleration of the pantograph is installed at the base of the pantograph, then an electrical signal is converted into an optical signal, the optical signal is transmitted to a low-voltage acquisition area by adopting an optical fiber, and then the optical signal is converted into an electrical signal and processed and stored by adopting a pantograph net test analysis software of a detection host. The operation monitoring of the pantograph and the height detection of the pantograph slide plate are carried out through 1 camera arranged on the roof of the vehicle, and data are transmitted to a video processing host computer by adopting a network cable; the offline spark of the pantograph is detected by 1 spark detector arranged on the roof of the vehicle, and spark data is transmitted to a video host by adopting a CameraLink data line; the height of the pantograph slide plate and the off-line spark of the pantograph net are analyzed and displayed in real time by software installed on a video host.
In S1, the determining the dynamic current-receiving quality of the bow net includes:
dynamic contact pressure between pantograph and catenary of metro vehicle in positive line operation When the dynamic contact pressure is too large, executing a detection step S2;
dynamic contact pressure between pantograph and catenary of metro vehicle in positive line operation When the dynamic contact pressure is too small, executing a detection step S3;
Wherein, The contact pressure data of the station measuring point at the ith station;
The contact pressure of the station measuring point is the maximum value of the contact pressure of the i-th station measuring point;
The contact pressure of the station is the minimum value of the station.
In the present embodiment, in particular,；
Wherein/>The real-time running speed of the subway vehicle at the ith station measuring point is obtained.
S2: establishing a machine learning model, and predicting and evaluating the abrasion area ratio of the pantograph slide plate and the contact line;
In S2, the machine learning model includes: an input layer, a convolution layer, a sampling layer, a pooling layer, a full connection layer and an output layer, wherein the convolution layer comprises 10 layers with the size of Is a convolution kernel of (a).
S2 comprises the following specific steps:
s21: denoising preprocessing is carried out on the monocular image of the working operation state of the pantograph acquired by the high-precision camera, and processed monocular image data is input into an input layer in a machine learning model;
S22: performing feature extraction of edge features, texture features and shape features on monocular image data through a convolution layer in a machine learning model, and integrating abrasion features by utilizing a full-connection layer;
S23: performing gray threshold segmentation processing on the bow net region by using a detection algorithm of template matching based on the features extracted in the step S22, and dividing a monocular image into a background region, a center abrasion region, an edge abrasion region and a lossless region according to gray value gradients;
s24: judging the abrasion area ratio of the pantograph in the severe weather environment through an image recognition algorithm, wherein the calculation formula of the abrasion area ratio is as follows: ; g is the total number of wear monitoring points; /(I) The area of the central abrasion area of the g abrasion monitoring point; /(I)The area of the edge abrasion area of the g abrasion monitoring point; s is the area of the monocular image divided into lossless regions.
S3: extracting working data of a pantograph-catenary in the running state of the metro vehicle through a fiber bragg grating sensing technology, and drawing a statistical image formed by the working data;
As shown in fig. 10, 11, 12, and 13, in S3, the statistical image formed by the working data includes: arcing scattered point images and vertical, longitudinal and transverse acceleration time course curve images of a front sliding plate when the metro vehicle runs in a normal opening operation and a closed operation.
S4: according to the steps S2-S3, calculating an arch gateway system evaluation coefficient, and carrying out working state identification evaluation on the arch gateway system;
s4, the arch gateway system evaluates the coefficient The calculation strategy of (2) is as follows:
；
Wherein, The arc rate is the arc rate of the bow net; /(I)The number of items meeting the design standard range of the metro vehicle in the dynamic geometrical parameters of the contact net; /(I)For the number of items meeting the design standard range of the metro vehicle in the vertical, longitudinal and transverse accelerations of the front sliding plate during the front line opening running and the closed running of the metro vehicle, wherein/>。
The calculation of the bow net arc rate comprises the following steps:
When the arcing times of the bow net is less than or equal to 1/160 m and the maximum offline time of the bow net is less than or equal to 100ms, ；
When the arcing times of the bow net are more than 1/160 m and the maximum offline time of the bow net is less than or equal to 100ms,；
When the arcing times of the bow net is less than or equal to 1/160 m and the maximum offline time of the bow net is more than 100ms,；
When the arcing times of the bow net are more than 1/160 m and the maximum offline time of the bow net is more than 100ms,。
S5: and evaluating the bow net relationship on line and outputting fault judgment corresponding to different bow net bad relationships in real time.
In S5, the outputting, in real time, the fault judgment corresponding to the different bow net bad relations includes:
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the dynamic geometrical parameters of the midspan, the locating point, the middle joint, the anchor section joint, the segmented insulator, the rigid-flexible transition, the accelerating section, the cantilever low head and the serious water leakage part are out of limits;
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the slope change and the triangular pit of the subway vehicle running line generate hard points;
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the pantograph and the contact line are offline to cause voltage drop, break through air and generate electric arc; the pantograph is electrically isolated from the contact wire, creating a voltage difference that breaks down instantaneously, creating an arc.
Embodiment two:
as shown in fig. 2, the monocular image-based bow net online detection system according to the embodiment of the invention comprises the following modules:
the device comprises a force module, an acceleration module, a data acquisition box, a high-performance computer, an arcing module, a speed measurement module, a current module and a contact net detection module;
The force module is used for collecting and storing contact pressure data of the subway vehicle;
The acceleration module is used for measuring the vibration acceleration of the pantograph;
the arcing module is used for detecting an offline spark state of the pantograph-catenary;
the speed measuring module is used for detecting real-time running speed of the metro vehicle in the detection zone;
The current module is used for measuring the current of the pantograph to be tested in the running process of the metro vehicle;
the overhead line system detection module is used for simulating a test working condition in a severe weather environment, collecting working data of the pantograph-overhead line system in the running state of the metro vehicle through a fiber bragg grating sensing technology, and judging the dynamic current-carrying quality of the pantograph;
embodiment III:
the present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the above-described monocular image-based bow net online detection method by calling a computer program stored in the memory.
The electronic device may have a relatively large difference due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to implement the method for online detection of a monocular image-based bow network provided by the above method embodiment. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Embodiment four:
the present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
The computer program, when run on a computer device, causes the computer device to perform the above-described monocular image-based bow net online detection method.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software 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 invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one, and there may be additional partitioning in actual implementation, e.g., 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, which may be in electrical, mechanical or other form.
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 over 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 the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
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.
In summary, compared with the prior art, the technical effects of the invention are as follows:
1. Aiming at the problem of inaccurate detection data of the bow gateway system caused by severe weather environment, the invention adopts a 3D camera and a line laser sensor assembly to be arranged at the middle position of the roof of a detection vehicle during the detection period of the bow gateway system, calculates the height and transverse offset of a contact line relative to the camera at different imaging positions of the 3D camera through line laser speckles, combines two laser 2D sensors in a vehicle body vibration compensation module, accurately measures the positions of the center of the vehicle body and the line center and the heights of the vehicle body and the rail surface, dynamically compensates geometric parameters of a contact network, and improves the accuracy of detection data.
2. In order to solve the problem that the whole pantograph is used as a conductor and has high requirements on the electromagnetic interference resistance and the insulation and isolation performance of a test system, the invention adopts a fiber bragg grating sensing technology and has the following technical characteristics:
(1) Anti-electromagnetic interference: electromagnetic radiation has a lower frequency than light waves, and the optical signals are not affected by electromagnetic interference.
(2) Good electrical insulation performance, safety and reliability: the fiber itself is made of dielectric and does not require power to drive.
(3) Corrosion resistance and stable chemical performance: the quartz material of the optical fiber has extremely high chemical stability and is suitable for being used in severe environments.
(4) Small volume, light weight and plastic geometry.
(5) The transmission loss is small: and (5) remote control monitoring.
(6) The transmission capacity is large: multipoint distributed measurement.
(7) The measuring range is wide: temperature, pressure, strain, stress, flow rate, current, voltage, etc. may be measured.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (12)
1. The bow net on-line detection method based on the monocular image is characterized by comprising the following steps of: the method comprises the following specific steps:
s1: simulating a test working condition in a severe weather environment, performing environment compensation treatment, acquiring working data of a pantograph-catenary in a running state of the metro vehicle by using a fiber bragg grating sensing technology, and performing dynamic current-carrying quality judgment of the pantograph-catenary;
s2: establishing a machine learning model, predicting and evaluating the abrasion area ratio of a pantograph slide plate and a contact line ；
S3: extracting working data of a pantograph-catenary in the running state of the metro vehicle through a fiber bragg grating sensing technology, and drawing a statistical image formed by the working data;
S4: according to the steps S2-S3, calculating an arch gateway system evaluation coefficient, and carrying out working state identification evaluation on the arch gateway system;
s5: evaluating the bow net relationship on line and outputting fault judgment corresponding to different bow net bad relationships in real time;
s4, the arch gateway system evaluates the coefficient The calculation strategy of (2) is as follows:
；
Wherein, The arc rate is the arc rate of the bow net; /(I)The number of items meeting the design standard range of the metro vehicle in the dynamic geometrical parameters of the contact net; /(I)For the number of items meeting the design standard range of the metro vehicle in the vertical, longitudinal and transverse accelerations of the front sliding plate during the front line opening running and the closed running of the metro vehicle, wherein/>；
In S5, the outputting, in real time, the fault judgment corresponding to the different bow net bad relations includes:
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the dynamic geometrical parameters of the midspan, the locating point, the middle joint, the anchor section joint, the segmented insulator, the rigid-flexible transition, the accelerating section, the cantilever low head and the serious water leakage part are out of limits;
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the slope change and the triangular pit of the subway vehicle running line generate hard points;
When (when) And when the fault corresponding to the bow net bad relation output in real time comprises the following steps: the pantograph and the contact line are offline to cause voltage drop, break through air and generate electric arc; the pantograph is electrically isolated from the contact wire, creating a voltage difference that breaks down instantaneously, creating an arc.
2. The method for online detection of a monocular image based bow net according to claim 1, wherein in S1, the environment compensation process includes: the method comprises the steps that a 3D camera and a line laser sensor assembly are arranged at the middle position of a roof of a detection vehicle during the detection period of the bow gateway system, the height and the transverse offset of a contact line relative to the camera are calculated through line laser speckles at different imaging positions of the 3D camera, and the geometric parameters of a contact network are dynamically compensated by combining two laser 2D sensors in a vehicle body vibration compensation module, accurately measuring the positions of the center and the line center of the vehicle body and the height of the vehicle body and the rail surface.
3. The method for online detection of a monocular image based bow net according to claim 2, wherein in S1, the test conditions include: under severe weather environment, the detection speed of the metro vehicle is as follows; Indoor temperature of test environment is at/>Outdoor temperature is at/>; The altitude of the test environment is less than or equal to 500m.
4. The method for online detection of a pantograph-catenary based on monocular images according to claim 3, wherein in S1, the collecting, by a fiber bragg grating sensing technology, working data of a pantograph-catenary in an operation state of a metro vehicle comprises:
installing a 20W searchlight near the pantograph;
the method for obtaining the dynamic geometric parameters of the overhead line of the metro vehicle in normal operation through the overhead line geometric parameter testing device comprises the following steps: a contact line pull-out value, a height guiding value and a height difference value among positioning points;
Acquiring dynamic inertia items of a pantograph-catenary through a plurality of mechanical sensors, and acquiring dynamic contact pressure data between a pantograph of a metro vehicle in positive line operation and the catenary;
testing longitudinal, vertical and transverse accelerations of the front sliding plate and the rear sliding plate through a plurality of acceleration sensors to obtain vibration acceleration data of the pantograph;
acquiring monocular images of the height of a pantograph slide plate and the working running state of the pantograph of the metro vehicle in positive line running through 1 high-precision cameras respectively;
Testing the arcing data of offline spark of an arch net through 1 arcing sensor;
All acquired information is transmitted to a data acquisition box through a fiber bragg grating sensing technology, and the working data of the pantograph-catenary acquired through storage analysis of a high-performance computer are stored.
5. The method for online detection of a bownet based on monocular images according to claim 4, wherein in S1, the determining of the dynamic current-carrying quality of the bownet comprises:
dynamic contact pressure between pantograph and catenary of metro vehicle in positive line operation When the dynamic contact pressure is too large, executing a detection step S2;
dynamic contact pressure between pantograph and catenary of metro vehicle in positive line operation When the dynamic contact pressure is too small, executing a detection step S3;
Wherein, The contact pressure data of the station measuring point at the ith station;
The contact pressure of the station measuring point is the maximum value of the contact pressure of the i-th station measuring point;
The contact pressure of the station is the minimum value of the station.
6. The method for online detection of a monocular image-based bow net according to claim 1, wherein in S2, the machine learning model comprises: an input layer, a convolution layer, a sampling layer, a pooling layer, a full connection layer and an output layer, wherein the convolution layer comprises 10 layers with the size ofIs a convolution kernel of (a).
7. The method for online detection of a monocular image-based bow net according to claim 6, wherein S2 comprises the specific steps of:
s21: denoising preprocessing is carried out on the monocular image of the working operation state of the pantograph acquired by the high-precision camera, and processed monocular image data is input into an input layer in a machine learning model;
S22: performing feature extraction of edge features, texture features and shape features on monocular image data through a convolution layer in a machine learning model, and integrating abrasion features by utilizing a full-connection layer;
S23: performing gray threshold segmentation processing on the bow net region by using a detection algorithm of template matching based on the features extracted in the step S22, and dividing a monocular image into a background region, a center abrasion region, an edge abrasion region and a lossless region according to gray value gradients;
s24: judging the abrasion area ratio of the pantograph in the severe weather environment through an image recognition algorithm, wherein the calculation formula of the abrasion area ratio is as follows: ; g is the total number of wear monitoring points; /(I) The area of the central abrasion area of the g abrasion monitoring point; /(I)The area of the edge abrasion area of the g abrasion monitoring point; s is the area of the monocular image divided into lossless regions.
8. The method for online detection of a monocular image based bow net according to claim 7, wherein in S3, the statistical image formed by the working data includes: arcing scattered point images and vertical, longitudinal and transverse acceleration time course curve images of a front sliding plate when the metro vehicle runs in a normal opening operation and a closed operation.
9. The method for online detection of a bow net based on monocular images according to claim 8, wherein the calculation of the bow net arc rate comprises:
When the arcing times of the bow net is less than or equal to 1/160 m and the maximum offline time of the bow net is less than or equal to 100ms, ；
When the arcing times of the bow net are more than 1/160 m and the maximum offline time of the bow net is less than or equal to 100ms,；
When the arcing times of the bow net is less than or equal to 1/160 m and the maximum offline time of the bow net is more than 100ms,；
When the arcing times of the bow net are more than 1/160 m and the maximum offline time of the bow net is more than 100ms,。
10. A monocular image-based bow net online detection system, which is realized based on a monocular image-based bow net online detection method according to any one of claims 1 to 9, wherein the system comprises the following modules:
the device comprises a force module, an acceleration module, a data acquisition box, a high-performance computer, an arcing module, a speed measurement module, a current module and a contact net detection module;
The force module is used for collecting and storing contact pressure data of the subway vehicle;
The acceleration module is used for measuring the vibration acceleration of the pantograph;
the arcing module is used for detecting an offline spark state of the pantograph-catenary;
the speed measuring module is used for detecting real-time running speed of the metro vehicle in the detection zone;
The current module is used for measuring the current of the pantograph to be tested in the running process of the metro vehicle;
the overhead line system detection module is used for simulating a test working condition in a severe weather environment, collecting working data of the pantograph-overhead line system in the running state of the metro vehicle through a fiber bragg grating sensing technology, and judging dynamic current-carrying quality of the pantograph.
11. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the monocular image based bow net online detection method according to any one of claims 1 to 9.
12. An electronic device, comprising:
a memory for storing instructions;
A processor configured to execute the instructions to cause the apparatus to perform operations to implement the monocular image-based bow net online detection method of any one of claims 1-9.
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