Distributed optical fiber sensing system, distributed optical fiber sensing method and program

Abstract

To provide a dispersion type optical fiber sensing system, a dispersion type optical fiber sensing method and a program.SOLUTION: A dispersion type optical fiber sensing (DFOS) system comprises: a non-temporary computer-readable medium configured to store an instruction; and a processor connected to the non-temporary computer-readable medium. The processor is configured such that: DFOS data is received from a sensor connected to an optical fiber adjacent to a roadway, including a bridge; the DFOS data is filtered based on a frequency range; multiple broaden features are generated, each of which corresponds to one of multiple channels of the filtered DFOS data; a binarization of the broaden features is executed; and an instruction for identifying a location of a bridge along a roadway is performed based on a result of the binarization of the broaden features.SELECTED DRAWING: Figure 1A

Description

[Technical field]

[0001] The present disclosure relates to a distributed optical fiber sensing system, a distributed optical fiber sensing method, and a program. [Background technology]

[0002] Optical fibers exist along many roadways. Distributed acoustic sensors (DAS) attached to these optical fibers can detect vibrations where the optical fibers are located. In some cases, these vibrations are the result of passing vehicles. The DAS can collect data related to the number of vehicles, the lane position of the vehicles, and the vehicle speed.

[0003] The DAS generates waterfall data based on time and distance to determine traffic parameters. The ability of the DAS to detect individual vehicles is related to the amount of noise in the signal detected by the DAS. Summary of the Invention [Problem to be solved by the invention]

[0004] Improved technology for identifying the location of bridges along a roadway is desirable. [Means for solving the problem]

[0005] A distributed fiber optic sensing (DFOS) system according to an embodiment of the present disclosure includes a non-transitory computer-readable medium configured to store instructions; and a processor coupled to the non-transitory computer-readable medium, the processor configured to execute the instructions to receive DFOS data from sensors coupled to optical fibers adjacent a roadway including a bridge, filter the DFOS data based on a frequency range, generate a plurality of spread features, each corresponding to a channel among a plurality of channels of the filtered DFOS data, perform binarization of the plurality of spread features, and identify a location of the bridge along the roadway based on a result of the binarization of the plurality of spread features.

[0006] A distributed optical fiber sensing (DFOS) method according to an embodiment of the present disclosure includes receiving DFOS data from sensors connected to optical fiber adjacent a roadway including a bridge, filtering the DFOS data based on a frequency range, generating a plurality of spread features, each corresponding to a channel of a plurality of channels of the filtered DFOS data, performing binarization of the plurality of spread features, and identifying a location of the bridge along the roadway based on a result of the binarization of the plurality of spread features.

[0007] A program according to an embodiment of the present disclosure is a program for causing a computer to receive DFOS data from a sensor connected to optical fiber adjacent to a roadway including a bridge, filter the DFOS data based on a frequency range, generate a plurality of spread features each corresponding to one of a plurality of channels of the filtered DFOS data, perform binarization of the plurality of spread features, and identify the location of the bridge along the roadway based on the result of the binarization of the plurality of spread features. [Brief description of the drawings]

[0008] Aspects of the present disclosure are best understood from the following detailed description when read in conjunction with the accompanying figures. It should be noted that, in accordance with standard industry practice, various features have not been drawn to scale. In fact, dimensions of various features may be arbitrarily expanded or reduced for clarity of illustration.

[0009] [Figure 1A] 1 is a schematic diagram of a distributed acoustic sensor (DAS) system along a roadway, according to some embodiments. [Figure 1B] 1 is a schematic diagram of a distributed acoustic sensor (DAS) system along a roadway, according to some embodiments. [Diagram 2] 1 is a flowchart of a method for identifying bridges from distributed fiber optic sensing (DFOS) data, according to some embodiments. [Diagram 3] FIG. 1 is a schematic diagram of a DAS system along with waterfall data collected by the DAS system, according to some embodiments. [Figure 4] FIG. 2 is a diagram of filtered DFOS data, according to some embodiments. [Figure 5A] FIG. 13 is a histogram of the bridge channel of filtered DFOS data, according to some embodiments. [Figure 5B] FIG. 1 is a histogram of a non-bridge channel of filtered DFOS data in accordance with some embodiments. [Figure 5C] FIG. 13 is a histogram of the bridge channel of filtered DFOS data, including the spread feature, in accordance with some embodiments. [Figure 6] FIG. 13 is a plot of a spread feature including a threshold, according to some embodiments. [Figure 7] FIG. 13 is a thresholding plot of a spread feature according to some embodiments. [Figure 8]1 is a flowchart of a method for identifying a bridge and an extra fiber portion from distributed optical fiber sensing (DFOS) data, according to some embodiments. [Figure 9] FIG. 2 is a diagram of DFOS data, according to some embodiments. [Figure 10] FIG. 1 is a block diagram of a system for analyzing DFOS data, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The following disclosure provides many different embodiments or examples for implementing different features of the provided subject matter. Hereinafter, specific examples of components, values, operations, materials, arrangements, etc. will be described to simplify the disclosure. Of course, these are merely examples and are not intended to be limiting. Other components, values, operations, materials, arrangements, etc. are contemplated. For example, the formation of a first feature above or on a second feature in the following description can include embodiments in which the first and second features are formed in direct contact, and can also include embodiments in which an additional feature may be formed between the first and second features such that the first and second features are not in direct contact. In addition, the disclosure may repeat reference numbers and / or letters in various examples. This repetition is for simplicity and clarity, and does not in itself dictate a relationship between the various embodiments and / or configurations described.

[0011] Additionally, spatially relative terms such as "below," "below," "lower," "above," "upper," and the like may be used herein for ease of description to describe the relationship of one element or feature to another element or feature as shown in the figures. The spatially relative terms are intended to encompass different orientations of the device during use or operation in addition to the orientation shown in the figures. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein may be similarly interpreted accordingly.

[0012] Utilizing data from optical fibers along the roadway is useful for determining traffic volume, traffic speed, accidents, and other events along the roadway. To increase the usefulness of traffic information obtained based on data from optical fibers, a precise location along the roadway corresponding to the traffic information is determined. Because optical fibers are not always installed exactly parallel to the roadway, simply determining the distance along the optical fiber corresponding to the received traffic information does not provide sufficient accuracy in some cases. Identification of fixed reference points, such as bridges along the roadway, helps to improve accuracy by enabling correlation of distances along the optical fiber with known geographic locations. Utilizing these fixed reference points improves the location accuracy of the traffic information.

[0013] In addition to determining the location of fixed reference points along the optical fiber, identifying the location of additional optical fiber helps improve the location accuracy for traffic information. Optical fiber installed along a roadway often has additional sections of optical fiber, such as optical fiber loops, to provide additional optical fiber to assist in repair or relocation of the installed optical fiber. Knowing the location of the additional portion of optical fiber, e.g., optical fiber loop, helps improve the location accuracy by accounting for differences in optical fiber length and roadway length introduced by such additional portion of optical fiber.

[0014] Increasing the location accuracy of traffic information can further aid urban planning by determining, for example, which locations along roadways are choke points for traffic, where traffic accidents occur more frequently, and how traffic patterns change within a roadway network. This information can aid in planning improvements to existing roadways or construction of new roadways.

[0015] Additionally, the precise location of the traffic information aids in the navigation of the vehicle traveling along the roadway. By providing the driver with more accurate traffic data, the navigation system and / or navigation application becomes more useful to the driver. The improved navigation accuracy is also useful to the autonomous driver or driver assistance features on the vehicle. By precisely determining where a traffic jam or accident has occurred, the autonomous vehicle or driver assistance system can guide the vehicle along a more efficient route.

[0016] 1A is a schematic diagram of a distributed acoustic sensor (DAS) system 100A along a roadway 130A, according to some embodiments. The DAS system 100A includes a traffic monitoring device 111 in communication with a DAS 112. The DAS system 100A further includes an optical fiber 121 connected to the DAS 112. The optical fiber 121 is along the roadway 130A. The roadway 130A includes three lanes. A large number of vehicles are present on the roadway 130A. Some vehicles 140 on the roadway 130A are larger than other vehicles 150 on the roadway 130A. Although the description refers to an optical fiber 121, one skilled in the art will understand that in some embodiments, the optical fiber 121 includes a multi-fiber bundle.

[0017] As vehicles 140 and 150 pass along roadway 130A, they generate vibrations. These vibrations change the way light propagates along optical fiber 121. DAS 112 is connected to optical fiber 121 and transmits optical signals onto and detects return light from optical fiber 121. The resulting data is referred to as waterfall data. The waterfall data provides information related to the number of vehicles, the direction of travel by the vehicles, vehicle speeds, and lane location of the vehicles on roadway 130A.

[0018] The roadway 130A in FIG. 1 is on solid ground. The solid ground does not vibrate at a high enough amplitude to obscure detection of the vehicles 140 and 150 traveling along the roadway 130A. As a result, the DAS 112 can accurately detect the vehicles 140 and 150 traveling along the roadway 130A. In some embodiments, the roadway 130A includes at least one bridge, such as the roadway 130B in FIG. 1B.

[0019] Unlike solid ground, bridges exhibit different vibration characteristics, such as damping. The vibration characteristics of a bridge are affected by the length of the bridge, the materials of construction of the bridge, wind, and other factors. These differences in the vibration characteristics of a bridge can be used to determine where along the optical fiber 121 the bridge is located.

[0020] 1A also includes exemplary measured DAS data that is provided to aid in understanding the waterfall data collected by DAS 112.

[0021] FIG 1B is a schematic diagram of a DAS system 100B along a roadway 130B, according to some embodiments. Similar to the DAS system 100A of FIG 1A, the DAS system 100B includes a DAS 112 and an optical fiber 121. In contrast to FIG 1A, the roadway 130B of FIG 1B includes a first bridge 160A and a second bridge 160B. In addition, FIG 1B includes an additional fiber portion 170.

[0022] Considering the first vehicle 150A and the second vehicle 150B helps to understand the use of fixed reference points such as the first bridge 160A and the second bridge 160B in accurately determining the location corresponding to the traffic information. The distance between the first vehicle 150A and the second vehicle 150B along the roadway 130B is significantly different from the length of the optical fiber 121 between the locations of the first vehicle 150A and the second vehicle 150B. This difference is due to the presence of the additional fiber portion 170, as well as the fact that the optical fiber 121 is not exactly parallel to the roadway 130B. Determining the location of the first bridge 160A along the optical fiber 121 helps to determine the exact location of the first vehicle 150A along the roadway 130B. The location of the first bridge 160A along the roadway 130B is known based on publicly available geographic data. By determining the location of the first bridge 160A relative to the optical fiber 121, the length of the optical fiber 121 from the DAS 112 to the end of the first bridge 160A closest to the first vehicle 150A is determined based on the waterfall data. Then, the length of the optical fiber 121 from the end of the first bridge 160A to the first vehicle 150A is determined based on the waterfall data. By limiting the distance of the optical fiber 121 from the fixed reference point of the first bridge 160A to the first vehicle 150A, errors in the length of the roadway 130 from the end of the first bridge 160A to the DAS 112 are eliminated from the position determination. As a result, the position of the first vehicle 150A along the roadway 130B can be more accurately determined using the fixed reference point of the first bridge 160A.

[0023] Similarly, the position of the second vehicle 150B is more accurately determined by using a fixed reference point on the second bridge 160B. The waterfall data from the DAS 112 can be used to determine the length of the optical fiber 121 from the DAS 112 to the end of the second bridge 160B that is closest to the second vehicle 150B. Then, only the length of the optical fiber 121 between the second vehicle 150B and the second bridge 160B is used to determine the position of the second vehicle 150B along the roadway 130B. By using this short length of optical fiber 121, the length of optical fiber 121 between the second vehicle 150B and the DAS 112, including the additional fiber portion 170, is excluded from the position determination. As a result, the position of the second vehicle 150B is more accurately determined by using a fixed reference point on the second bridge 160B.

[0024] 2 is a flowchart of a method 200 for identifying bridges from distributed optical fiber sensing (DFOS) data, according to some embodiments. Method 200 can be used with DAS system 100A (FIG. 1A), DAS system 100B (FIG. 1B), or another suitable system that provides DFOS data.

[0025] In operation 205, raw DFOS data is received. In some embodiments, the raw DFOS data includes waterfall data, e.g., waterfall data detected by DAS 112 (FIG. 1). The waterfall data includes information related to the time and location along the optical fiber where the vibration data was detected. Further details of the waterfall data are described with respect to FIG. 3 below. The waterfall data includes data along a roadway including both solid ground and at least one bridge.

[0026] In operation 210, a frequency range is estimated for bridges along the roadway. The frequency range is indicative of expected damping response from one or more bridges along the roadway. The estimated frequency range can be used to filter the raw DFOS data to automatically extract bridge locations. In some embodiments, the frequency range is estimated based on known characteristics of one or more bridges along the roadway, such as bridge length, bridge construction material, or other suitable parameters. In some embodiments, the estimated frequency range is received from an operator based on the operator's experience. In some embodiments, the estimated frequency range is determined based on empirical data. In some embodiments, the estimated frequency range is determined using a trained neural network. In some embodiments, the estimated frequency range is between about 3 Hertz (Hz) and about 7 Hz. In some embodiments, the estimated frequency range is between about 3 Hz and about 5 Hz. If the estimated frequency range is too low, there is an increased risk of including portions of the roadway that are not bridges. If the estimated frequency range is too high, there is an increased risk that bridge ends, or bridges that are generally short, are excluded from the filtered data.

[0027] In operation 215, the raw DFOS data is preprocessed to enhance the received data. Preprocessing the data includes normalizing the vibration amplitude of the data at each location along the roadway, e.g., roadway 130B (FIG. 1B), over a predetermined duration. Normalizing the vibration amplitude helps to account for variations in optical fiber sensitivity. Variations in optical fiber sensitivity result from several causes, including, but not limited to, uneven surfaces of the roadway, inconsistent installation of the optical fiber, and misalignment of the optical fiber. Normalizing the vibration amplitude also helps to account for variations in traffic volume. For example, as the number of vehicles on the roadway increases, the magnitude of vibration detected by the DAS increases. Normalizing the vibration amplitude based on a predetermined duration reduces the impact of large vibrations detected during high traffic conditions on periods of low traffic conditions, providing more accurate data for estimating traffic flow properties.

[0028] In some embodiments, pre-processing the data also limits the maximum vibration amplitude at each location along the optical fiber over a predetermined duration. Limiting the maximum vibration amplitude helps prevent vibrations from large vehicles, such as trucks or construction vehicles, from obscuring vibrations generated by smaller vehicles, such as passenger cars.

[0029] The pre-processed data is then filtered by a bandpass filter based on the estimated frequency range from operation 210. Filtering the data removes portions of the roadway from the data that do not exhibit the vibration damping characteristics of the bridge. Figure 4 below provides an example of filtered DFOS data.

[0030] In operation 220, a spread feature is calculated for each channel of the filtered data. The spread feature determines how much the vibration intensity varies within a channel. A channel in DFOS is a data point / pixel in the waterfall data whose length corresponds to the spatial resolution of the DFOS data. For example, 1.6m, 3.2m, 4.1m, etc. This means that the channel holds information about vibrations within this spatial resolution (1.6m, 3.2m, 4.1m, etc.). A histogram is used to determine the spread feature. The histogram shows the number of occurrences with a particular vibration intensity over a predefined period of time. In some embodiments, the predefined period ranges from about 1 minute to about 30 minutes. In some embodiments, a period of 1 to 2 hours provides a sufficient amount of data to classify bridge and non-bridge channels. A predefined period that is too short may, in some cases, increase the risk of false positives and false negatives for bridge identification. A predefined period that is too long may, in some cases, increase the processing load on the DAS system without significantly improving the accuracy of bridge identification. An example of a histogram of a bridge channel is provided below in Figure 5A. An example of a histogram of a non-bridge channel is provided below in Figure 5B.

[0031] Once the histograms are created for each of the fiber optic channels, a width of the curve containing a predetermined incidence is determined for each histogram. This width is the spread feature of the histogram. A wider spread feature is indicative of a bridge portion of the roadway. A narrower spread feature is indicative of a non-bridge portion of the roadway. In some embodiments, the predetermined incidence ranges from about 50% to about 70%. A predetermined incidence that is too low may potentially increase the risk of false positive and false negative bridge identifications. A predetermined incidence that is too high may potentially increase the computational load on the DAS system without significantly improving the accuracy of bridge identification. An example of a histogram of a bridge channel containing a spread feature is provided below in FIG. 5C.

[0032] In operation 225, a binarization technique is applied to the spread feature for each of the channels. The binarization technique is used to perform a thresholding process on the spread feature, such that all channels with spread features equal to or greater than the threshold are determined to be bridge portions of the roadway, and all channels with spread features less than the threshold are determined to be non-bridge portions of the roadway. In some embodiments, the binarization technique includes Otsu's method, Kapur's method, Huang's method, Niblack's method, or another suitable binarization method. An example of binarization of spread features is provided in FIG. 6 below.

[0033] In operation 230, thresholding is performed on the channel based on the binarization. The thresholding is used to digitize the spread features of the channel, with all spread features equal to or greater than the threshold being designated as "1" or positive as being bridge portions, and all spread features below the threshold being designated as "0" or negative as being non-bridge portions. An example of thresholding spread features is provided in FIG. 7 below.

[0034] Bridge channels are identified based on the results of the thresholding in operation 235. Once the bridge channels have been identified, the length of each of the bridges can be determined based on the number of channels that the bridge spans.

[0035] In some embodiments, in optional operation 240, the segments between the identified bridges are resized to the actual length / number of channels by calculating the number of additional channels. In some embodiments, the number of additional channels is estimated by comparing the location of the identified bridges in the DFOS data to the reference / actual location of the corresponding bridges on the road / highway. In some embodiments, optional operation 240 is performed before or after operation 810.

[0036] In some embodiments, method 200 includes additional operations. For example, in some embodiments, method 200 includes determining a length of the identified bridge based on the number of channels spanned by the bridge identified in the DFOS data. In some embodiments, at least one operation of method 200 is omitted. For example, in some embodiments, operation 230 is omitted and the bridge channel is identified immediately after the binarization technique is performed. In some embodiments, the order of operations of method 200 is adjusted. For example, in some embodiments, operation 210 is performed before operation 205.

[0037] Figure 3 is a schematic diagram of a DAS system 100B with waterfall data 300 collected by the DAS system, according to some embodiments. The DAS system 100B is the same as the DAS system 100B of Figure 1B. Similar to Figure 1B, the roadway (not shown) in Figure 3 includes two bridges as shown by the waterfall data 300. The waterfall data 300 is pre-processed waterfall data.

[0038] The waterfall data 300 includes regions 302, 304, 306, 308, and 310. Regions 302, 306, and 310 include discernible lines indicative of vibrations generated by vehicles crossing the roadway. Regions 304 and 308 represent bridges. In comparison to regions 302, 306, and 310, regions 304 and 308 do not include discernible lines because the damped vibrations of the bridge obscure the detected vibrations of vehicles crossing the bridge.

[0039] 4 is a diagram of filtered DFOS data 400, according to some embodiments. The filtered DFOS data 400 includes a region 410 that exhibits higher vibration intensity within an estimated frequency range. Region 410 is likely to be a bridge along the roadway. The filtered DFOS data 400 also includes a region 420 that exhibits lower vibration intensity within an estimated frequency range. Region 420 is likely to be a non-bridge portion of the roadway.

[0040] 5A is an illustration of a histogram 500A of a bridge channel of filtered DFOS data, according to some embodiments. The histogram 500A includes a curve 510 that shows the number of occurrences for different vibration intensities. The curve 510 is broad due to vibration damping of the bridge; that is, the bridge dissipates peak vibrations slower than the non-bridge portion of the roadway. As a result, the curve 510 is broad.

[0041] 5B is an illustration of a histogram 500B of a non-bridge channel of filtered DFOS data, according to some embodiments. Histogram 500B includes a curve 520 that shows the number of occurrences for different vibration intensities. Curve 520 is narrower; that is, the non-bridge portion of the roadway dissipates vibrations faster than the bridge. As a result, curve 520 is narrower.

[0042] 5C is an illustration of a histogram 500C of the bridge channel of filtered DFOS data, including a spread feature, according to some embodiments. Histogram 500C includes curve 510 as described above with respect to FIG. 5A. In comparison to FIG. 5A, histogram 500C includes spread feature 530. Spread feature 530 indicates the width of curve 510 used to capture a given incidence in histogram 500C. The given incidence is as described above with respect to FIG.

[0043] FIG. 6 is an illustration of a plot 600 of spread characteristics including a threshold 620, according to some embodiments. The plot 600 includes a curve 610 showing spread characteristics for different channels. The spread of a histogram for each channel, e.g., histogram 500C (FIG. 5C), is plotted in the curve 610. The threshold 620 is determined based on a binarization technique, e.g., as described above with respect to operation 225. Region 630 shows channels that are determined to be bridge channels. Region 640 shows channels that are determined to be non-bridge channels. That is, region 630 includes all channels that have a spread characteristic equal to or greater than threshold 620, and region 640 includes all channels that have a spread characteristic less than threshold 620. The plot 600 shows which channels are determined as bridge channels. However, the plot 600 does not locate the bridge channels at a corresponding position along the optical fiber.

[0044] 7 is an illustration of a thresholding plot 700 of spread features, according to some embodiments. The thresholding plot 700 includes a first graph 710 including a line 720 and a threshold 730. The line 720 indicates the spread features for each channel along the length of an optical fiber, e.g., optical fiber 121 (FIG. 1B). The threshold 730 indicates a threshold determined by a binarization technique, similar to threshold 620 (FIG. 6). Each of the channels on the line 720 that have a spread feature equal to or greater than the threshold 730 is determined to be a bridge.

[0045] Plot 700 further includes a second graph 740 that digitizes first graph 710. Second graph 740 includes line 750. Line 750 has a value of "0" for each channel that is determined to be a non-bridge channel. Line 750 has a value of "1" for each channel that is determined to be a bridge channel. Using plot 700, an accurate determination is made as to where the bridge is located relative to the optical fiber.

[0046] 8 is a flow chart of a method 800 for identifying bridges and additional fiber sections from distributed optical fiber sensing (DFOS) data, according to some embodiments. Method 800 includes several operations similar to those in method 200 (FIG. 2). Similar operations have the same reference numbers and will not be described in detail with respect to FIG. 8 for brevity.

[0047] Method 800 includes an operation 810 in which an additional fiber portion is identified. Identifying the additional fiber portion includes determining a location of the additional fiber portion, such as a loop of optical fiber, along an optical fiber installed adjacent to the roadway, such as optical fiber 121 (FIG. 1B). The location of the additional fiber portion is determined based on the DFOS data. A readout of the DFOS data, such as waterfall data, at the additional fiber portion has a distinctive pattern. In some embodiments, the distinctive pattern includes a plurality of straight lines. The distinctive pattern is used to identify the location of the additional fiber portion. In some embodiments, the distinctive pattern is manually identified and marked by an operator. In some embodiments, the distinctive pattern is identified using a trained neural network. An example of DFOS data including an additional fiber portion is provided in FIG. 9 below.

[0048] Identifying the additional fiber portion also includes determining a length of the additional fiber portion. Determining the length of the additional fiber portion helps reduce discrepancies between the distance along the roadway and the length of the optical fiber from the DAS, e.g., DAS 112 (FIG. 1B), to a location along the roadway. The length of the additional fiber portion is determined based on the number of channels spanned by the unique pattern and the size of the channels. For example, the number of channels spanned by the unique pattern multiplied by the distance covered by each channel results in a total length of the additional fiber portion.

[0049] In some embodiments, the distinctive patterns are filtered out in operation 215. In some embodiments, operation 810 is performed before operation 215 to reduce the risk that the distinctive patterns are filtered out. In some embodiments, operation 810 is performed using the raw DFOS data, even if operation 810 is performed after operation 215.

[0050] In some embodiments, method 800 includes additional operations. For example, in some embodiments, method 800 includes modifying the DFOS data to exclude the identified additional fiber portions prior to determining the location of the bridge along the roadway. In some embodiments, at least one operation of method 800 is omitted. For example, in some embodiments, operation 240 is omitted. In some embodiments, the order of operations of method 800 is adjusted. For example, in some embodiments, operation 810 is performed before operation 215.

[0051] 9 is a diagram of DFOS data 900, according to some embodiments. DFOS data 900 includes a region 910 that exhibits higher vibration intensity within an estimated frequency range. Region 910 is likely a bridge along the roadway. DFOS data 900 also includes a region 920 that exhibits lower vibration intensity within an estimated frequency range. Region 920 is likely a non-bridge portion of the roadway. DFOS data 900 includes a region 930 that has a distinctive pattern. Region 930 is indicative of an additional fiber portion. The location of region 930 is identifiable based on the distinctive pattern in DFOS data 900. The length of the additional fiber portion is determined based on the number of channels that region 930 spans in the range direction of DFOS data 900.

[0052] FIG. 10 is a block diagram of a system 1000 for analyzing DFOS data, according to some embodiments. The system 1000 includes a hardware processor 1002 and a non-transitory computer-readable storage medium 1004 encoded with, i.e., storing, a computer program code 1006, i.e., a set of executable instructions. The computer-readable storage medium 1004 is also encoded with instructions 1007 for interfacing with an external device. The processor 1002 is electrically coupled to the computer-readable storage medium 1004 via a bus 1008. The processor 1002 is also electrically coupled to an I / O interface 1010 by the bus 1008. A network interface 1012 is also electrically connected to the processor 1002 via the bus 1008. The network interface 1012 is connected to a network 1014 such that the processor 1002 and the computer-readable storage medium 1004 can connect to external elements via the network 1014. The processor 1002 is configured to execute computer program code 1006 encoded on the computer-readable storage medium 1004 to enable the system 1000 to perform some or all of the operations described with respect to DAS system 100A (FIG. 1A), DAS system 100B (FIG. 1B), method 200 (FIG. 2), method 800 (FIG. 8), or another suitable system for analyzing DFOS data.

[0053] In some embodiments, the processor 1002 is a central processing unit (CPU), a multiprocessor, a distributed processing system, an application specific integrated circuit (ASIC), and / or other suitable processing unit.

[0054] In some embodiments, the computer-readable storage medium 1004 is an electronic, magnetic, optical, electromagnetic, infrared, and / or semiconductor system (or apparatus or device). For example, the computer-readable storage medium 1004 includes a semiconductor or solid state memory, a magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk, and / or an optical disk. In some embodiments using an optical disk, the computer-readable storage medium 1004 includes a compact disk-read-only memory (CD-ROM), a compact disk-read / write (CD-R / W), and / or a digital video disk (DVD).

[0055] In some embodiments, storage medium 1004 stores computer program code 1006 configured to cause system 1000 to perform some or all of the operations described with respect to DAS system 100A (FIG. 1A), DAS system 100B (FIG. 1B), method 200 (FIG. 2), method 800 (FIG. 8), or another suitable system for analyzing DFOS data. In some embodiments, storage medium 1004 also stores information necessary to perform some or all of the operations described with respect to DAS system 100A (FIG. 1A), DAS system 100B (FIG. 1B), method 200 (FIG. 2), method 800 (FIG. 8), or another suitable system for analyzing DFOS data, as well as information generated while performing some or all of the operations described with respect to DAS system 100A (FIG. 1A), DAS system 100B (FIG. 1B), method 200 (FIG. 2), method 800 (FIG. 8), or another suitable system for analyzing DFOS data, e.g., sensor data parameters 1016, bridge position parameters 1018, frequency range parameters 1020, additional fiber portion parameters 1022, and / or a set of executable instructions for performing some or all of the operations described with respect to DAS system 100A (FIG. 1A), DAS system 100B (FIG. 1B), method 200 (FIG. 2), method 800 (FIG. 8), or another suitable system for analyzing DFOS data.

[0056] In some embodiments, the storage medium 1004 stores instructions 1007 for interfacing with an external device. The instructions 1007 enable the processor 1002 to generate instructions readable by an external device to effect some or all of the operations described with respect to DAS system 100A (FIG. 1A), DAS system 100B (FIG. 1B), method 200 (FIG. 2), method 800 (FIG. 8), or another suitable system for analyzing DFOS data.

[0057] The system 1000 includes an I / O interface 1010. The I / O interface 1010 is coupled to external circuitry. In some embodiments, the I / O interface 1010 includes a keyboard, a keypad, a mouse, a trackball, a trackpad, and / or cursor direction keys for communicating information and commands to the processor 1002.

[0058] The system 1000 also includes a network interface 1012 coupled to the processor 1002. The network interface 1012 enables the system 1000 to communicate with a network 1014 to which one or more other computer systems are connected. The network interface 1012 includes a wireless network interface, such as BLUETOOTH, WIFI, WIMAX, GPRS, or WCDMA, or a wired network interface, such as ETHERNET, USB, or IEEE-1394. In some embodiments, some or all of the operations described with respect to DAS system 100A (FIG. 1A), DAS system 100B (FIG. 1B), method 200 (FIG. 2), method 800 (FIG. 8), or another suitable system for analyzing DFOS data are performed with two or more systems 1000, and information such as sensor data, bridge locations, frequency ranges, and additional fiber sections are exchanged between the different systems 1000 via the network 1014.

[0059] The system 1000 is configured to receive information related to DFOS data via the I / O interface 1010 or the network interface 1012. The DFOS data is transferred to the processor 1002 via the bus 1008 for frequency range estimation and pre-processing and / or filtering. The frequency range is stored in the computer readable medium 1004 as frequency range parameters 1020. In some embodiments, the estimated frequency range parameters 1020 are received via the I / O 1010 or the network interface 1012. The pre-processed DFOS data is then stored in the computer readable medium 1004 as sensor data parameters 1016. The processor 1002 retrieves the sensor data parameters 1016 from the computer readable medium 1004 and calculates a spread feature for each channel in the sensor data parameters 1016. The processor 1002 performs a binarization technique on the spread feature and determines a threshold. The processor 1002 performs thresholding of the spread feature to determine which channels from the sensor data parameters 1016 indicate the presence of a bridge. The processor 1002 instructs the channels indicative of the presence of a bridge to be stored in the computer readable medium 1004 as bridge location parameters 1018. In some embodiments, the processor 1002 executes the instructions 1007 to identify distinctive patterns in the sensor data parameters 1016 to identify additional fiber portions. In some embodiments, the processor 1002 executes a trained neural network to identify distinctive patterns indicative of additional fiber portions. The locations and lengths of the additional fiber portions are stored in the computer readable medium 1004 as additional fiber portion parameters 1022.

[0060] During operation, the processor 1002 executes a set of instructions for pre-processing DFOS data received from a DAS, e.g., DAS 100A (FIG. 1A) or DAS 100B (FIG. 1B). The processor 1002 further executes instructions for manipulating the pre-processed DFOS data using bridge location parameters 1018, frequency range parameters 1020, additional fiber portion parameters 1022, and / or other information to estimate at least one location of a bridge along the roadway based on the DFOS data. Following the determination of the location of the at least one bridge, the processor 1002 generates instructions for communicating with an external device via the I / O interface 1010 or the network interface 1012.

[0061] In some embodiments, the external device uses data from a DAS, e.g., DAS100A (FIG. 1A) or DAS100B (FIG. 1B), to determine at least one traffic flow characteristic to determine a traffic pattern within a city or town area. In some embodiments, the external device uses the at least one traffic flow characteristic to determine whether a vehicle traveling along a roadway exceeds a weight limit for the roadway, e.g., based on the width and amplitude of a vibration line caused by the vehicle crossing the road. In some embodiments, the external device uses the at least one traffic flow characteristic to generate a navigation plan for the GPS device. In some embodiments, the external device uses the at least one traffic flow characteristic to assist in the routing of emergency vehicles. For example, to specifically identify the location of a vehicle accident by generating a navigation plan and / or by detecting a large vibration amplitude. In some embodiments, the external device uses the at least one traffic pattern to detect a landslide based on extremely high vibration amplitude and / or damage to optical fiber.

[0062] Compared to other approaches, the system 1000 used to implement some or all of the operations described with respect to the DAS system 100A (FIG. 1A), the DAS system 100B (FIG. 1B), the method 200 (FIG. 2), the method 800 (FIG. 8), or another suitable system for analyzing DFOS data, can replace multiple visual monitoring devices, such as cameras, with a single detection system. By using optical fiber as a measurement instrument instead of visual monitoring devices, wireless communication is avoided. In some cases, wireless communication is interrupted or interferes with other wireless communication devices. Wireless communication also introduces more noise into the transmitted signal than the wired connection provided by optical fiber. In addition, the system 1000 can be connected to optical fiber already installed along the roadway. This minimizes the amount of infrastructure used to install the system 1000 and / or implement some or all of the operations described with respect to the DAS system 100A (FIG. 1A), the DAS system 100B (FIG. 1B), the method 200 (FIG. 2), the method 800 (FIG. 8), or another suitable system for analyzing DFOS data.

[0063] The above description refers to an example of identifying bridges and estimating traffic flow properties associated with roadways that include at least one bridge. Those skilled in the art will recognize that the above principles are also applicable to determining traffic flow properties in tunnels. In contrast to bridges, roadways in tunnels damp vibrations. Thus, instead of a large amount of noise obscuring the signal to bridges, tunnels experience reduced signal strength. In both examples, it is difficult to identify individual vehicle information. However, by modifying the patch size as desired and establishing overlap areas, traffic flow properties in tunnels can still be estimated using the above principles.

[0064] Note that some or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes.

[0065] (Appendix 1) 1. A distributed fiber optic sensing (DFOS) system, comprising: a non-transitory computer-readable medium configured to store instructions; a processor coupled to the non-transitory computer readable medium, Receive DFOS data from optical fiber-connected sensors adjacent to roadways, including bridges; filtering the DFOS data based on a frequency range; generating a plurality of spread features, each corresponding to a channel of the plurality of channels of the filtered DFOS data; performing a binarization of the plurality of spread features; Identifying a location of the bridge along the roadway based on the binarization result of the plurality of spread features a processor configured to execute the instructions for: The DFOS system is equipped with

[0066] (Appendix 2) The processor, determining a threshold value using said binarization; thresholding the filtered DFOS data based on the determined threshold; Identifying the location of the bridge further based on a result of the thresholding. 2. The DFOS system of claim 1, further configured to execute the instructions for:

[0067] (Appendix 3) The processor, Receive said frequency range via an input / output (I / O) or network interface 2. The DFOS system of claim 1, further configured to execute the instructions for:

[0068] (Appendix 4) The processor, The frequency range is determined based on empirical data or using a trained neural network. 2. The DFOS system of claim 1, further configured to execute the instructions for:

[0069] (Appendix 5) The processor, Determine the length of the bridge based on the filtered DFOS data. 2. The DFOS system of claim 1, further configured to execute the instructions for:

[0070] (Appendix 6) The processor, Identifying additional fiber portions of the optical fiber based on the DFOS data. 2. The DFOS system of claim 1, further configured to execute the instructions for:

[0071] (Appendix 7) The processor, Using a trained neural network to identify the additional fiber portion. 7. The DFOS system of claim 6, further configured to execute the instructions for:

[0072] (Appendix 8) The processor, Identifying the additional fiber portion prior to filtering the DFOS data based on the frequency range 7. The DFOS system of claim 6, further configured to execute the instructions for:

[0073] (Appendix 9) 1. A distributed optical fiber sensing (DFOS) method, comprising: receiving DFOS data from a sensor connected to an optical fiber adjacent to a roadway including a bridge; filtering the DFOS data based on a frequency range; and generating a plurality of spread features, each corresponding to a channel of the plurality of channels of the filtered DFOS data; performing a binarization of the plurality of spread features; identifying a location of the bridge along the roadway based on the binarization of the plurality of spread features; DFOS methods, including:

[0074] (Appendix 10) determining a threshold using the binarization; and thresholding the filtered DFOS data based on the determined threshold; and identifying the location of the bridge further based on a result of the thresholding; 10. The DFOS method of claim 9, further comprising:

[0075] (Appendix 11) receiving said frequency range via an input / output (I / O) or network interface; 10. The DFOS method of claim 9, further comprising:

[0076] (Appendix 12) Determining the frequency range based on empirical data or using a trained neural network. 10. The DFOS method of claim 9, further comprising:

[0077] (Appendix 13) determining a length of the bridge based on the filtered DFOS data; 10. The DFOS method of claim 9, further comprising:

[0078] (Appendix 14) identifying additional fiber portions of the optical fiber based on the DFOS data; 10. The DFOS method of claim 9, further comprising:

[0079] (Appendix 15) 15. The DFOS method of claim 14, wherein identifying the additional fiber portion includes using a trained neural network.

[0080] (Appendix 16) 15. The DFOS method of claim 14, wherein identifying the additional fiber portion includes identifying the additional fiber portion before filtering the DFOS data based on the frequency range.

[0081] (Appendix 17) Receive DFOS data from optical fiber-connected sensors adjacent to roadways, including bridges; filtering the DFOS data based on a frequency range; generating a plurality of spread features, each corresponding to a channel of the plurality of channels of the filtered DFOS data; performing a binarization of the plurality of spread features; Identifying a location of the bridge along the roadway based on the binarization result of the plurality of spread features A program that makes a computer do something.

[0082] (Appendix 18) determining a threshold value using said binarization; thresholding the filtered DFOS data based on the determined threshold; Identifying the location of the bridge further based on a result of the thresholding. 18. The program according to claim 17, further causing the computer to execute the steps of:

[0083] (Appendix 19) Identifying additional fiber portions of the optical fiber based on the DFOS data. 18. The program according to claim 17, further causing the computer to execute the steps of:

[0084] (Appendix 20) Identifying the additional fiber portion prior to filtering the DFOS data based on the frequency range 20. The program of claim 19, further causing the computer to execute the steps of:

[0085] The above outlines the features of some embodiments so that those skilled in the art can better understand the aspects of the present disclosure. Those skilled in the art should understand that the present disclosure can be easily used as a basis for designing or modifying other processes and structures for carrying out the same purpose and / or achieving the same advantages as the embodiments introduced herein. Those skilled in the art should also recognize that such equivalent constructions do not depart from the spirit and scope of the present disclosure, and that various changes, substitutions, and alterations can be made herein without departing from the spirit and scope of the present disclosure.

[0086] This application claims priority to U.S. Patent Application No. 18 / 066,237, filed December 14, 2022, the disclosure of which is incorporated herein in its entirety.

[0087] (Related Applications) This application is related to Japanese Patent Application No. 2019 / 026131, the disclosure of which is incorporated herein in its entirety. [Explanation of symbols]

[0088] 100A Distributed Acoustic Sensor (DAS) System 100B DAS System 111 Traffic monitoring device 112 DAS 121 Optical Fiber 130A, 130B roadway 140, 150 vehicles 150A First Car 150B Second car 160A First Bridge 160B Second Bridge 170 additional fiber sections 200 ways 205, 210, 215, 220, 225, 230, 235, 240 operation 300 Waterfall Data 302, 304, 306, 308, 310 areas 400 Distributed Fiber Optic Sensing (DFOS) data 410, 420 area 500A, 500B, 500C Histogram 510, 520 curve 530 Spreading characteristics 600 plots 610 curve 620 Threshold 630, 640 area 700 Thresholding Plot 710 First Graph 720 line 730 Threshold 740 Second Graph 750 line 800 ways 810 operation 900 DFOS data 910, 920, 930 area 1000 Systems 1002 Hardware Processor 1004 Non-transitory computer-readable storage medium 1006 Computer Program Code 1007 Instructions 1008 Bus 1010 I / O Interface 1012 Network Interface 1014 Network 1016 Sensor data parameters 1018 Bridge location parameters 1020 Frequency Range Parameter 1022 Additional Fiber Section Parameters

Claims

1. 1. A distributed fiber optic sensing (DFOS) system, comprising: a non-transitory computer-readable medium configured to store instructions; a processor coupled to the non-transitory computer readable medium, receiving DFOS data from sensors connected to optical fiber adjacent to a roadway, including a bridge; filtering the DFOS data based on a frequency range; generating a plurality of spread features, each corresponding to a channel of the plurality of channels of the filtered DFOS data; performing a binarization of the plurality of spread features; Identifying a location of the bridge along the roadway based on the binarization result of the plurality of spread features a processor configured to execute the instructions for: Equipped with The processor, determining a threshold value using said binarization; thresholding the filtered DFOS data based on the determined threshold; Identifying the location of the bridge further based on a result of the thresholding. The DFOS system is further configured to execute the instructions for:

2. The processor, Receive the frequency range via an input / output (I / O) or network interface 2. The DFOS system of claim 1, further configured to execute the instructions for:

3. The processor, The frequency range is determined based on empirical data or using a trained neural network.

2. The DFOS system of claim 1, further configured to execute the instructions for:

4. The processor, Determine the length of the bridge based on the filtered DFOS data.

2. The DFOS system of claim 1, further configured to execute the instructions for:

5. A distributed fiber optic sensing (DFOS) system, comprising: a non-transitory computer-readable medium configured to store instructions; a processor coupled to the non-transitory computer readable medium, receiving DFOS data from sensors connected to optical fiber adjacent to a roadway, including a bridge; filtering the DFOS data based on a frequency range; generating a plurality of spread features, each corresponding to a channel of the plurality of channels of the filtered DFOS data; performing a binarization of the plurality of spread features; Identifying a location of the bridge along the roadway based on the binarization result of the plurality of spread features a processor configured to execute the instructions for: Equipped with The processor, Identifying additional fiber portions of the optical fiber based on the DFOS data. The DFOS system is further configured to execute the instructions for:

6. The processor, Using a trained neural network to identify the additional fiber portion.

6. The DFOS system of claim 5, further configured to execute the instructions for:

7. The processor, Identifying the additional fiber portion prior to filtering the DFOS data based on the frequency range.

6. The DFOS system of claim 5, further configured to execute the instructions for:

8. 1. A distributed optical fiber sensing (DFOS) method, comprising: receiving DFOS data from a sensor connected to an optical fiber adjacent to a roadway including a bridge; filtering the DFOS data based on a frequency range; generating a plurality of spread features, each corresponding to one of the plurality of channels of the filtered DFOS data; performing a binarization of the plurality of spread features; identifying a location of the bridge along the roadway based on the binarization of the plurality of spread features; Including, Identifying a location of the bridge along the roadway based on the result of the binarization of the plurality of spread features includes: determining a threshold using the binarization; and performing thresholding of the filtered DFOS data based on the determined threshold; and identifying the location of the bridge further based on a result of the thresholding; The DFOS method includes:

9. receiving DFOS data from sensors connected to optical fiber adjacent to a roadway, including a bridge; filtering the DFOS data based on a frequency range; generating a plurality of spread features, each corresponding to a channel of the plurality of channels of the filtered DFOS data; performing a binarization of the plurality of spread features; identifying a location of the bridge along the roadway based on a result of the binarization of the plurality of spread features, Identifying a location of the bridge along the roadway based on the result of the binarization of the plurality of spread features includes: determining a threshold using the binarization; and performing thresholding of the filtered DFOS data based on the determined threshold; and identifying the location of the bridge further based on a result of the thresholding; Including, A program that makes a computer do something.

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