WO2024111118A1 - Système de surveillance, procédé de surveillance et support lisible par ordinateur - Google Patents

Système de surveillance, procédé de surveillance et support lisible par ordinateur Download PDF

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Publication number
WO2024111118A1
WO2024111118A1 PCT/JP2022/043586 JP2022043586W WO2024111118A1 WO 2024111118 A1 WO2024111118 A1 WO 2024111118A1 JP 2022043586 W JP2022043586 W JP 2022043586W WO 2024111118 A1 WO2024111118 A1 WO 2024111118A1
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WO
WIPO (PCT)
Prior art keywords
data
oscillation data
waterfall
transportation infrastructure
time
Prior art date
Application number
PCT/JP2022/043586
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English (en)
Inventor
Murtuza Petladwala
Yoshiyuki Yajima
Takahiro Kumura
Original Assignee
Nec Corporation
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Publication date
Application filed by Nec Corporation filed Critical Nec Corporation
Priority to PCT/JP2022/043586 priority Critical patent/WO2024111118A1/fr
Publication of WO2024111118A1 publication Critical patent/WO2024111118A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H9/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
    • G01H9/004—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means using fibre optic sensors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/02—Detecting movement of traffic to be counted or controlled using treadles built into the road
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

Definitions

  • the distributed acoustics sensing technology acquires surrounding vibration signals (acoustic signals) around an optical fiber cable.
  • a pulse light in the optical fiber cable is transmitted, where the optical fiber cable is installed along a road infrastructure.
  • the distributed acoustics sensing device acquires the infrastructure vibration signal around the road by analyzing a Rayleigh backscattered light of the pulse light.
  • the vibration patterns generated from vehicle traffic are measured by the optical fiber cable laid along the road.
  • the traffic monitoring applications observe these vibration patterns in a real-time to maintain the smooth flow of traffic by continuously observing the traffic properties like vehicle speed and vehicle traffic counting.
  • the trajectory features are estimated from the 3D waterfall dataset obtained from the post-processing of the measured vibration signals. This is a commonly used way to represent vibration signals along a sensing fibre cable.
  • the 3D waterfall dataset is extracted by post-processing the multi-point vibration signals in a combination of low and high frequency bands and filtering parameters. A combined filter is applied to filter out noise information at those frequency bands.
  • the 3D waterfall dataset is processed to obtain trajectory features that are used for traffic monitoring applications and traffic properties calculations.
  • Patent Literature 1 discloses applying spectral filtering to vibration signals.
  • Patent Literature 2 discloses specifying bridge sections from the waterfall dataset.
  • An exemplary object of the invention is to provide a monitoring system, a monitoring method and a non-transitory computer readable medium capable of decreasing noise in a waterfall dataset.
  • a monitoring method that includes: acquiring oscillation data at each of the plurality of sensing points in an optical fiber cable, the optical fiber cable being installed along a transportation infrastructure including a plurality of sections with structures different from each other; obtaining time-distance oscillation data of the transportation infrastructure based on the oscillation data; obtaining a filtering parameter for the time-distance oscillation data of each section of the transportation infrastructure based on a structural property of the section; obtaining waterfall data by removing noise from the time-distance oscillation data of each section of the transportation infrastructure using the filtering parameter; and identifying a trajectory of a moving object passing on the transportation infrastructure from the waterfall data.
  • a non-transitory computer readable medium storing a program for causing a computer to execute: acquiring oscillation data at each of the plurality of sensing points in an optical fiber cable, the optical fiber cable being installed along a transportation infrastructure including a plurality of sections with structures different from each other; obtaining time-distance oscillation data of the transportation infrastructure based on the oscillation data; obtaining a filtering parameter for the time-distance oscillation data of each section of the transportation infrastructure based on a structural property of the section; obtaining waterfall data by removing noise from the time-distance oscillation data of each section of the transportation infrastructure using the filtering parameter; and identifying a trajectory of a moving object passing on the transportation infrastructure from the waterfall data.
  • Fig. 1 is a drawing for explaining an example of a 3D waterfall dataset.
  • Fig. 2 is a drawing for explaining the related art.
  • Fig. 3 is a drawing for explaining the monitoring system in accordance with a first example embodiment of the present invention.
  • Fig. 4A is a drawing for explaining a second example embodiment of the present invention.
  • Fig. 4B is a drawing for explaining the second example embodiment of the present invention.
  • Fig. 4C is a drawing for explaining the second example embodiment of the present invention.
  • Fig. 5 is a block diagram illustrating configuration example of the monitoring system in accordance with the second example embodiment of the present invention.
  • Fig. 6 is a drawing for explaining the monitoring system in accordance with the second example embodiment of the present invention.
  • Fig. 1 is a drawing for explaining an example of a 3D waterfall dataset.
  • Fig. 2 is a drawing for explaining the related art.
  • Fig. 3 is a drawing for explaining the monitoring system in accordance with a first example embodiment
  • FIG. 7 is a drawing for explaining the monitoring system in accordance with the second example embodiment of the present invention.
  • Fig. 8 is a table for explaining the filtering parameters in accordance with the second example embodiment of the present invention.
  • Fig. 9 is a flowchart for explaining an operation of the monitoring system in accordance with the second example embodiment of the present invention.
  • the X-axis is distance from the sensing device (box) location
  • Y-axis is time
  • Z-axis corresponds to the amplitude of the vibration at that distance from the sensing device location.
  • the amplitude is normalized.
  • the waterfall dataset includes oscillation data of bridge sections surrounded by dashed boxes.
  • the trajectory features obtained from 3D waterfall dataset are filtered out in the filtering process using a common set of filtering parameters applied to all sensing points that correspond to locations on the road (e.g., a highway) as illustrated in Fig.2.
  • Multi-point vibration signals are input, the vibration signals are filtered out, and accumulated amplitudes are output.
  • the amplitudes correspond to signal intensities.
  • the set of filtering parameters is applied to 4 different sections (e.g., tunnels, usual roads, bridges) of the transportation infrastructure.
  • sensing locations are oscillated according to dynamic and structural frequency responses that depend on each structure and its vibration characteristics upon excited. Therefore, it is possible that the waterfall dataset becomes noisy.
  • the inventor of the present application arrived at the present disclosure according to the embodiments based on the above study.
  • the monitoring system 10 includes a signal acquisition unit 12, a raw dataset processing unit 14, a passband processing unit 16, and a waterfall processing unit 18, and a trajectory detection unit 20.
  • the signal acquisition unit 12 acquires oscillation data at each of a plurality of sensing points in an optical fiber cable.
  • the optical fiber cable is installed along a transportation infrastructure including a plurality of sections with structures different from each other.
  • the transportation infrastructure may be called a road.
  • a plurality of sections may include tunnel sections, bridge sections and usual road sections. Usual road sections are supported by the ground.
  • the raw dataset processing unit 14 obtains time-distance oscillation data of the road and applies pre-processing steps like signal down sampling rate and unit conversion.
  • pre-processing steps like signal down sampling rate and unit conversion.
  • optical phase radian unit maybe converted to micro-strain units.
  • the unit conversion may depend on the input settings of the measurement system.
  • the passband processing unit 16 obtains a filtering parameter for the time-distance oscillation data of each section of the transportation infrastructure based on a structural property of the section.
  • the filtering parameter is also referred to as a structural passband parameter.
  • the waterfall processing unit 18 obtains waterfall data by removing noise from the time-distance oscillation data of each section of the transportation infrastructure using the filtering parameter.
  • the trajectory detection unit 20 identifies a trajectory of a moving object passing on the transportation infrastructure from the waterfall data.
  • the moving object may be a vehicle, a train, a bicycle, or a pedestrian.
  • the monitoring system 10 removes noise from the time-distance oscillation data based on the structural properties, it can decrease noise in the waterfall dataset.
  • the monitoring system 10 includes, as its components, a processor, a memory, and a storage device (none illustrated).
  • the storage device stores a computer program that implements the processes of the monitoring method according to the present example embodiment.
  • the processor loads the computer program from the storage device onto the memory and executes the computer program.
  • the processor implements the functions of the acquiring unit 12, the raw dataset processing unit 14, the passband processing unit 16, the waterfall processing unit 18, and the trajectory detection unit 20.
  • the acquiring unit 12, the raw dataset processing unit 14, the passband processing unit 16, the waterfall processing unit 18, and the trajectory detection unit 20 may each be implemented by a dedicated piece of hardware.
  • a part or the whole of the constituent elements of each device may be implemented by, for example, general-purpose or dedicated circuitry, a processor, or a combination thereof.
  • Such constituent elements may be formed by a single chip or by a plurality of chips connected via a bus.
  • a part or the whole of the constituent elements of each device may be implemented by a combination of the above-described circuitry or the like and a program.
  • a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), or the like can be used.
  • these information processing devices, circuitries, or the like may be disposed centrally or distributed.
  • these information processing devices, circuitries, or the like may be implemented in a mode in which they are connected to each other via a communication network, as in, for example, a client server system or a cloud computing system.
  • the function of the monitoring system 10 may be provided in a Software as a Service (SaaS) format.
  • SaaS Software as a Service
  • Figs. 4A to 4C are drawings for explaining the summary of the second example embodiment.
  • Fig. 4A is an example illustration of raw vibration signal dataset (multi-point raw dataset) measured at every sequential point of the optical fiber cable attached to the road.
  • the three vibration signals are illustrated as an example.
  • Fig. 4B illustrates the vibration signals obtained after applying structural passband filters over all the channels (sensing points) of the optical fiber cable, for illustration example three vibration signal channels are shown.
  • the structural passband filter may be obtained from known structural properties of the given structure for example, a resonance frequency (natural frequency) or low frequencies that corresponds to displacement of the surface of the structure.
  • the signals clearly show the presence of vehicles by the change in amplitude from the baseline amplitudes.
  • Fig.4C illustrates the extracted 3D waterfall dataset.
  • the first axis that is distance axis is the number of channels (sensing points) on the optical fiber cable.
  • the second axis that is time axis is the time of the measurement.
  • the third axis is the processed amplitude of the vibration signal after applying structural passband filter.
  • the number of channels in Fig.4C are greater than three channels, not illustrated in Fig. 4A and 4B.
  • the pixels in the 3D waterfall dataset are converted to white and black color to illustrate the presence and absence of vehicle respectively.
  • the white color pixels of an individual vehicle are known as a trajectory of the vehicle in the time-distance plane. As compared to Fig.1 vehicle trajectories that are continuity of white pixels are maintained even at the bridge sections.
  • a monitoring apparatus 100 includes a signal acquisition unit 102, a raw dataset processing unit 104, a structural passband processing unit 106, a 3D waterfall processing unit 108 and a trajectory detection unit 110.
  • the monitoring apparatus 100 is one specific example embodiment of the monitoring system 10.
  • the monitoring apparatus 100 is also referred to as a Distributed Acoustics Sensing 3D waterfall extraction apparatus.
  • the monitoring apparatus 100 is connected to a distributed acoustic sensor (DAS).
  • DAS distributed acoustic sensor
  • the DAS detects oscillation signals at the plurality of sensing points in the optical fiber cable, and transmits the oscillation signals to the monitoring apparatus 100.
  • the signal acquisition unit 102 is one specific example embodiment of the signal acquisition unit 12.
  • the signal acquisition unit 102 acquires an oscillation signal (acoustics or vibration data) from the Interrogator, the DAS.
  • the DAS is able to detect an oscillation signal of the road induced by vehicles, when the vehicle is passing on any traffic lane.
  • the raw dataset processing unit 104 is one specific example embodiment of the raw dataset processing unit 14.
  • the raw dataset processing unit 104 obtains a vibration/acoustics signal for each of the sequential sensing points forming a time-distance chart (raw dataset in Fig. 4A).
  • the raw dataset maybe used to obtain structural information like usual road section, bridge section and tunnel section from the pre-specified positions on the fiber cables.
  • An example snap of 3D waterfall dataset generated from related art is illustrated in Fig.1, where vehicles are going away and coming towards the sensing device and their vibration intensities are visible, which are proportional to the types of vehicles passing on the road.
  • the dashed box represents the bridge sections present on the road, where the vibration intensities are unclear due to high vibrations of bridge sections.
  • the structural passband processing unit 106 is one specific example embodiment of the raw dataset processing unit 16.
  • the structural passband processing unit 106 obtains the structural passband parameters (filtering parameters) for each of the sections on the road as shown in Fig.6.
  • the raw dataset is used as input for obtaining the structural passband parameters.
  • Fig.7 illustrates the background of structural passband parameter selection.
  • a vehicle (e. g., a truck) 21 with a velocity V is passing over the bridge section 22 of length L.
  • bridge structures tend to displace from rest state, known as bridge deflection.
  • Equation (1) represents the total time taken to cross the bridge section 22, where T is time taken in second, L is length of bridge in meter and V is vehicle speed in meter/second.
  • Equation (2) is the inverse of equation (1) from time T in second to frequency F in Hertz. The frequency from zero Hertz to the cut-off frequency F consists of all deflection information.
  • Fig.8 is the table for cut-off frequency in Hertz obtained from equation (2) by assuming the possible vehicle speed V in kilometer per hour and bridge length L in meters.
  • the 3D waterfall processing unit 108 is one specific example embodiment of the waterfall processing unit 18.
  • the 3D waterfall processing unit 108 obtains the cut-off frequency parameters and applies low pass filter on each of the channels of the corresponding structure section.
  • the signal smoothing method maybe applied in a time axis and maybe in a distance axis to remove if any signal noise.
  • the signal smoothing method like moving average may be used.
  • the smoothed signals are converted to absolute values and accumulated over time axis to obtain waterfall intensity amplitudes.
  • the 3D waterfall amplitude maybe normalized to enhance each of the section vibrations.
  • the trajectory detection unit 110 is one specific example embodiment of the trajectory detection unit 20.
  • the trajectory detection unit110 detects the trajectory that is the higher amplitudes of the passing vehicle observed as in the 3D waterfall dataset.
  • the trajectory detection method may use Artificial Intelligence model (e.g., deep neural network model) to detect the trajectory patterns for each of the structural sections.
  • the estimated trajectories for each of the sections are useful to monitor traffic and structure health conditions that is made possible by using structural passband filters (parameters).
  • Fig. 9 is a flow chart illustrating an operation example of the monitoring apparatus 100 which extracts the 3D intensity amplitudes from the raw vibrations measured from the optical fiber cable.
  • the monitoring apparatus 100 receives from the signal acquisition unit 102, the oscillation signal (acoustics or vibration signal from the DAS).
  • the oscillation signal acoustics or vibration signal from the DAS.
  • the raw dataset processing unit 104 processes the X RAW (the raw dataset) as shown in Fig.4A (S100).
  • X RAW may be time-distance graph.
  • the pre-processing step (S101) removes amplitude offset that is DC (direct current) component (bias component) from measured signals possibly due to phase drift in DAS Interrogator and standardize signal amplitude.
  • the pre-processing step may include IIR (Infinite impulse response) filtering.
  • the structural passband processing unit 106 obtains the structural passband parameters (S102) for each of the sections on the road as shown in Fig.6.
  • the raw dataset is used as input for obtaining the structural passband parameters.
  • the cut-off frequency is obtained from pre-specified structural sections.
  • the step 103 applies the signal smoothing method to the time axis and distance axis, for example moving average method may be used.
  • the step 104 accumulates the vibration signal amplitudes (intensities) from the absolute values of the smooth signals over time axis.
  • the accumulated intensity amplitudes may be normalized to enhance the trajectory information.
  • the 3D waterfall dataset is obtained.
  • the step 105 detects trajectory for each of the sensing points of the optical fiber cable. Estimated trajectory may indicate the presence of vehicle over the road. Trajectories are estimated and vehicle presence is output.
  • the sensing apparatus 100 removes noise based on the structural properties, it can decrease noise from the time-distance oscillation data.
  • the program includes instructions (or software codes) that, when loaded into a computer, cause the computer to perform one or more of the functions described in the embodiments.
  • the program may be stored in a non-transitory computer readable medium or a tangible storage medium.
  • non-transitory computer readable media or tangible storage media can include a random-access memory (RAM), a read-only memory (ROM), a flash memory, a solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disk (DVD), Blu-ray disc ((R): Registered trademark) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.
  • the program may be transmitted on a transitory computer readable medium or a communication medium.
  • transitory computer readable media or communication media can include electrical, optical, acoustical, or other form of propagated signals.
  • monitoring system 12 signal acquisition unit 14 raw dataset processing unit 16 passband processing unit 18 waterfall processing unit 20 trajectory detection unit 100 monitoring apparatus 102 signal acquisition unit 104 raw dataset processing unit 106 passband processing unit 108 waterfall processing unit 110 trajectory detection unit 21 vehicle 22 bridge section

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

Un système de surveillance (10) comprend une unité d'acquisition de signal (12) qui acquiert des données d'oscillation au niveau de chaque point de détection d'une pluralité de points de détection dans un câble à fibre optique, le câble à fibre optique étant installé le long d'une infrastructure de transport comprenant une pluralité de sections présentant des structures différentes les unes des autres ; une unité de traitement de jeu de données brutes (14) qui obtient des données d'oscillation de temps/distance de la route sur la base des données d'oscillation ; une unité de traitement de bande passante (16) qui obtient un paramètre de filtrage pour les données d'oscillation temps/distance de chaque section de la route sur la base d'une propriété structurale ou dimensionnelle de la section ; une unité de traitement en cascade (18) qui obtient des données en cascade en éliminant le bruit des données d'oscillation temps/distance de chaque section de la route à l'aide du paramètre de filtrage ; et une unité de détection de trajectoire (20) qui identifie une trajectoire d'un objet mobile passant sur la route à partir des données en cascade.
PCT/JP2022/043586 2022-11-25 2022-11-25 Système de surveillance, procédé de surveillance et support lisible par ordinateur WO2024111118A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021121917A (ja) 2020-01-30 2021-08-26 日本電気株式会社 交通監視装置および交通監視方法
US20210312801A1 (en) * 2020-04-07 2021-10-07 Nec Laboratories America, Inc Traffic monitoring using distributed fiber optic sensing
WO2022101959A1 (fr) * 2020-11-10 2022-05-19 日本電気株式会社 Dispositif de correction de distance, dispositif de traitement, dispositif de détection, procédé de correction de distance et support d'enregistrement
WO2022113173A1 (fr) * 2020-11-24 2022-06-02 Nec Corporation Appareil de détection d'événement de la circulation, système de détection d'événement de la circulation, procédé et support lisible par ordinateur

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021121917A (ja) 2020-01-30 2021-08-26 日本電気株式会社 交通監視装置および交通監視方法
US20210312801A1 (en) * 2020-04-07 2021-10-07 Nec Laboratories America, Inc Traffic monitoring using distributed fiber optic sensing
WO2022101959A1 (fr) * 2020-11-10 2022-05-19 日本電気株式会社 Dispositif de correction de distance, dispositif de traitement, dispositif de détection, procédé de correction de distance et support d'enregistrement
WO2022113173A1 (fr) * 2020-11-24 2022-06-02 Nec Corporation Appareil de détection d'événement de la circulation, système de détection d'événement de la circulation, procédé et support lisible par ordinateur

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