Vehicle information estimation system, vehicle information estimation device, vehicle information estimation method, and computer program

Abstract

Provided are a vehicle information estimation system, a vehicle information estimation device, a vehicle information estimation method, and a computer program with which it is possible to estimate information pertaining to a vehicle traveling at a location to be measured in order to compare different measurement results. A vehicle information estimation system according to an embodiment includes a sensor and a vehicle number estimation unit. The sensor detects an elastic wave generated from a structure. The vehicle number estimation unit estimates the number of vehicles passing through the structure using the elastic waves detected by the sensor.

Description

[0001] This application is a divisional application of the invention patent application with application number 202110978998.2, application date August 25, 2021, and invention name "Vehicle information inference system, vehicle information inference device, vehicle information inference method and computer program". Technical Field

[0002] Embodiments of the present invention relate to a vehicle information estimation system, a vehicle information estimation device, a vehicle information estimation method, and a computer program. Background Art

[0003] When the load generated by traffic is applied to the concrete deck of a bridge, AE (Acoustic Emission) occurs due to the development of cracks in the concrete deck, friction, etc. By installing an AE sensor on a surface different from the surface to which the load is applied (for example, the lower surface of the deck), it is possible to detect AE occurring in the concrete deck. Conventionally, an AE sensor is installed on the lower surface of the concrete deck, and the elastic waves occurring as vehicles pass by are detected in the AE sensor. The soundness of the concrete deck is evaluated based on the density of the sources of the detected multiple elastic waves.

[0004] However, when comparing the measurement results of different concrete bridge decks, it is effective to compare the measurement results obtained under the same traffic volume. For example, the longer the measurement is, the higher the density of the source becomes, so it is impossible to compare when the measurement time is different. Furthermore, even if the measurement time is the same, the density of elastic wave sources tends to increase in places with heavy traffic or at times with heavy traffic, so it is impossible to obtain effective results by comparing the measurement results under different conditions. Therefore, in order to compare different measurement results under the same conditions, it is necessary to grasp the information related to the vehicles traveling in the place of the measurement object.

[0005] Prior art literature

[0006] Patent Literature

[0007] Patent Document 1: International Publication No. 2019 / 167137 Summary of the invention

[0008] The problem to be solved by the present invention is to provide a vehicle information estimation system, a vehicle information estimation device, a vehicle information estimation method and a computer program capable of estimating information about a vehicle traveling at a location to be measured in order to compare different measurement results.

[0009] A vehicle information estimation system according to an embodiment includes a sensor and a vehicle number estimation unit. The sensor detects elastic waves generated from a structure. The vehicle number estimation unit estimates the number of vehicles passing the structure using the elastic waves detected by the sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a diagram showing the configuration of the vehicle information estimation system in the first embodiment.

[0011] Figure 2 is a diagram showing the distribution of elastic waves detected by a vehicle.

[0012] Figure 3 This is a schematic block diagram showing the functions of the signal processing unit in the first embodiment.

[0013] Figure 4 This is a diagram showing the flow of the number estimation process performed by the signal processing unit in the first embodiment.

[0014] Figure 5 This is a diagram for explaining another method of estimating the number of passing vehicles in the first embodiment.

[0015] Figure 6 This is a schematic block diagram showing the functions of the signal processing unit in the second embodiment.

[0016] Figure 7 This is a diagram showing an example in which sensors are arranged in a plurality of rows in the second embodiment.

[0017] Figure 8 This is a graph showing the transition of elastic wave data when one vehicle passes by.

[0018] Fig. 9 It is a diagram showing the configuration of a vehicle information estimation system in a third embodiment.

[0019] Fig.10 This is a schematic block diagram showing the functions of the signal processing unit in the third embodiment.

[0020] Fig.11 It is a diagram for explaining specific processing of the structure evaluation device in the third embodiment.

[0021] (Explanation of Reference Numerals)

[0022] 10-1~10-n: sensor; 20, 20a, 20b: signal processing unit; 30: structure evaluation device; 31: communication unit; 32: control unit; 33: storage unit; 34: display unit; 201: amplifier; 202: A / D converter; 203: waveform shaping filter; 204: selection generation circuit; 205: arrival time determination unit; 206: feature extraction unit; 207: data recording unit; 208: memory; 209: removal unit; 210: event extraction unit; 211: vehicle number estimation unit; 212: weight estimation unit; 213: speed estimation unit; 214: transmission data generation unit; 321: acquisition unit; 322: position calibration unit; 323: distribution generation unit; 324: correction unit; 325: evaluation unit. DETAILED DESCRIPTION

[0023] Hereinafter, a vehicle information estimation system, a vehicle information estimation device, a vehicle information estimation method, and a computer program according to an embodiment will be described with reference to the drawings.

[0024] (First embodiment)

[0025] Figure 1 1 is a diagram showing the structure of a vehicle information estimation system 100 in the first embodiment. The vehicle information estimation system 100 is a system for estimating information related to a vehicle 12 (hereinafter referred to as a "passing vehicle") passing through a structure 11. The information related to the vehicle is, for example, the number of passing vehicles during a measurement period, the weight of the passing vehicles, and the speed of the passing vehicles.

[0026] In the following description, a bridge made of concrete is used as an example of a structure, but the structure is not limited to a bridge. As for the structure, any example can be used as long as it is a structure that generates elastic waves with the occurrence or development of cracks or external impacts (such as rain, artificial rain, etc.). For example, the structure can also be a bedrock. In addition, bridges are not limited to structures built on rivers, valleys, etc., but also include various structures located above the ground (such as elevated highways).

[0027] Hereinafter, a specific configuration of the vehicle information estimation system 100 will be described.

[0028] The vehicle information estimation system 100 includes a plurality of sensors 10-1 to 10-n (n is an integer greater than or equal to 1) and a signal processing unit 20. Each of the plurality of sensors 10-1 to 10-n is communicatively connected to the signal processing unit 20 via a wired method. In the following description, when the sensors 10-1 to 10-n are not distinguished, they are described as sensors 10.

[0029] The sensor 10 detects the elastic wave 13 generated from the inside of the structure 11. The sensor 10 is set at a position where the elastic wave 13 can be detected. For example, the sensor 10 is set on a surface different from the surface to which the load is applied with respect to the structure 11. In the case where the surface to which the load is applied is the surface of the structure 11 (hereinafter referred to as the "road surface"), the sensor 10 is set on any of the side and bottom surfaces of the structure 11. The sensor 10 converts the detected elastic wave 13 into an electrical signal. In the following description, the case where the sensor 10 is set on the bottom surface of the structure 11 is taken as an example.

[0030] In the sensor 10, a piezoelectric element having sensitivity in the range of, for example, 10 kHz to 1 MHz is used. The sensor 10 includes a resonance type having a resonance peak in a frequency range, a broadband type suppressing resonance, and the like, but the type of the sensor 10 may be any. The sensor 10 may detect the elastic wave 13 by a voltage output type, a resistance change type, an electrostatic capacitance type, and the like, but any detection method may be used.

[0031] An acceleration sensor may be used instead of the sensor 10. In this case, the acceleration sensor detects the elastic wave 13 generated inside the structure 11. Then, the acceleration sensor converts the detected elastic wave 13 into an electrical signal by performing the same processing as the sensor 10.

[0032] The signal processing unit 20 receives the electrical signal output from the sensor 10 as input. The signal processing unit 20 performs signal processing on the input electrical signal. The signal processing performed by the signal processing unit 20 is, for example, noise removal, parameter extraction, and estimation of information related to a passing vehicle. The signal processing unit 20 is constructed using an analog circuit or a digital circuit. The digital circuit is implemented by, for example, an FPGA (Field Programmable Gate Array) or a microcomputer. The digital circuit can also be implemented by a dedicated LSI (Large-Scale Integration). In addition, the signal processing unit 20 can also be equipped with a non-volatile memory such as a flash memory or a removable memory. The signal processing unit 20 is one form of a vehicle information estimation device.

[0033] like Figure 1 As shown, when a vehicle 12 passes over a structure 11, a load is applied to the road surface due to the contact between the tires of the vehicle 12 and the road surface. Due to the bending caused by the load, a large number of elastic waves 13 are generated in the structure 11. The sensor 10 provided on the lower surface of the structure 11 can detect the elastic waves generated in the structure 11.

[0034] Figure 2 1 is a diagram showing a general distribution of elastic waves detected by the sensor 10 in response to the passage of the vehicle 12 . Figure 2 The multiple points 25 shown represent the time during the measurement (in Figure 2 The elastic wave detected by the sensor 10 within 0 to 15 seconds. Figure 2 It can be seen that every time the vehicle 12 passes, a large number of elastic waves are generated as surrounded by the circle 26. The large number of elastic waves are elastic waves that occur in a relatively short period. In the following, the elastic waves that occur in a relatively short period are regarded as elastic waves included in one event. The relatively short period is determined by, for example, the distance between vehicles. The specific content will be described later. Here, the event refers to a group of multiple elastic waves obtained in a relatively short period as described above as a cluster. In this embodiment, by counting a cluster of the large number of elastic waves (a cluster shown by the circle 26) as one group, the number of passing vehicles can be estimated only based on the elastic waves. For example, in Figure 2 In the example of , there are 12 convergences of elastic waves, so the count is 12 groups, from which it can be inferred that 12 vehicles 12 passed during the measurement period.

[0035] Here, the reason why the amplitude of the elastic wave detected for each group is different is that the size of the passing vehicle is different. The larger the vehicle, the greater the load applied to the structure 11. Therefore, a large number of elastic waves with large amplitudes are generated. Figure 2 The distribution of elastic waves shown can be used to estimate not only the number of passing vehicles but also the size, that is, the weight, of the passing vehicles.

[0036] In the first embodiment, a specific configuration for estimating the number of passing vehicles in the signal processing unit 20 will be described.

[0037] Figure 3 1 is a schematic block diagram showing the functions of the signal processing unit 20 in the first embodiment. The signal processing unit 20 includes an amplifier 201, an A / D converter 202, a waveform shaping filter 203, a gate generation circuit 204, an arrival time determination unit 205, a feature quantity extraction unit 206, a data recording unit 207, a memory 208, a removal unit 209, an event extraction unit 210, and a vehicle number estimation unit 211.

[0038] The amplifier 201 amplifies the elastic wave output from the sensor 10, and outputs the amplified elastic wave to the A / D converter 202. The amplifier 201 amplifies the elastic wave by a predetermined amount (for example, 10 to 100 times), for example.

[0039] The A / D converter 202 quantizes the amplified elastic wave and converts it into a digital signal. The A / D converter 202 outputs the digital signal to the waveform shaping filter 203 .

[0040] The waveform shaping filter 203 removes noise components outside a predetermined frequency band from the input digital signal. The waveform shaping filter 203 is, for example, a digital bandpass filter (BPF). The waveform shaping filter 203 outputs a digital signal after the noise component has been removed (hereinafter referred to as a "noise-removed signal") to the gate generation circuit 204, the arrival time determination unit 205, and the feature quantity extraction unit 206.

[0041] The gate generation circuit 204 receives the noise elimination signal output from the waveform shaping filter 203 as an input. The gate generation circuit 204 generates a gate signal based on the input noise elimination signal. The gate signal is a signal indicating whether the waveform of the noise elimination signal is continuous.

[0042] The gate generation circuit 204 is implemented by, for example, an envelope detector and a comparator. The envelope detector detects the envelope of the noise-removed signal. The envelope is extracted by, for example, squaring the noise-removed signal and performing a predetermined process (e.g., processing using a low-pass filter, Hilbert transform) on the squared output value. The comparator determines whether the envelope of the noise-removed signal is greater than a predetermined threshold.

[0043] When the envelope of the noise-removed signal becomes equal to or greater than a predetermined threshold, the gate generation circuit 204 outputs a first gate signal indicating that the waveform of the noise-removed signal continues to the arrival time determination unit 205 and the feature quantity extraction unit 206. On the other hand, when the envelope of the noise-removed signal is less than a predetermined threshold, the gate generation circuit 204 outputs a second gate signal indicating that the waveform of the noise-removed signal does not continue to the arrival time determination unit 205 and the feature quantity extraction unit 206.

[0044] The arrival time determination unit 205 receives as input the noise removal signal output from the waveform shaping filter 203 and the selection signal output from the selection generation circuit 204. The arrival time determination unit 205 determines the arrival time of the elastic wave using the noise removal signal input during the period when the first selection signal is input. The arrival time determination unit 205 outputs the determined elastic wave arrival time as time information to the data recording unit 207. The arrival time determination unit 205 does not perform any processing during the period when the second selection signal is input. The elastic wave arrival time is equivalent to the time when the elastic wave is acquired.

[0045] The feature quantity extraction unit 206 receives the noise-removed signal output from the waveform shaping filter 203 and the strobe signal output from the strobe generation circuit 204 as input. The feature quantity extraction unit 206 uses the noise-removed signal input during the period when the first strobe signal is input, and extracts the feature quantity of the noise-removed signal. The feature quantity extraction unit 206 does not perform processing during the period when the second strobe signal is input. The feature quantity is information indicating the feature of the noise-removed signal.

[0046] The feature quantity is, for example, the amplitude of the waveform [mV], the rise time of the waveform [usec], the duration of the strobe signal [usec], the number of zero crossing counts [times], the energy of the waveform [arb.], the frequency [Hz], and the RMS (Root Mean Square) value. The feature quantity extraction unit 206 outputs the parameters related to the extracted feature quantity to the data recording unit 207. When outputting the parameters related to the feature quantity, the feature quantity extraction unit 206 corresponds the sensor ID to the parameters related to the feature quantity. The sensor ID represents identification information for identifying the sensor 10 installed in the structure 11.

[0047] The amplitude of the waveform is, for example, the value of the maximum amplitude in the noise-removed signal. The rise time of the waveform is, for example, the time T1 from the rise of the selection signal to the time when the noise-removed signal reaches the maximum value. The duration of the selection signal is, for example, the time from the rise of the selection signal to the time when the amplitude becomes smaller than a preset value. The zero crossing count is, for example, the number of times the noise-removed signal crosses a reference line passing through a zero value.

[0048] The energy of a waveform is, for example, a value obtained by squaring the amplitude of the noise-removed signal at each time point and integrating the result over time. In addition, the definition of energy is not limited to the above example, and an example of approximation using the envelope of the waveform is also possible. The frequency is the frequency of the noise-removed signal. The RMS value is, for example, a value obtained by squaring the amplitude of the noise-removed signal at each time point and taking the square root.

[0049] The data recording unit 207 receives the sensor ID, time information, and parameters related to the feature quantity as input. The data recording unit 207 records the elastic wave data including the input sensor ID, time information, and parameters related to the feature quantity into the memory 208. For example, the data recording unit 207 may record the elastic wave data into the memory 208 in the order in which they are obtained, or may record the elastic wave data into the memory 208 in a time series order according to the time information.

[0050] The memory 208 stores one or more elastic wave data. The memory 208 is, for example, a dual-port RAM (Random Access Memory). One elastic wave data is data obtained by one elastic wave.

[0051] The removal unit 209 reads the elastic wave data stored in the memory 208, and removes the elastic wave data whose amplitude value is less than the threshold value from the read elastic wave. For example, the removal unit 209 removes the elastic wave data whose amplitude value is less than 50 dB. On the other hand, the removal unit 209 outputs the elastic wave data whose amplitude value is greater than the threshold value from the read elastic wave to the event extraction unit 210. In addition, the threshold value can also be appropriately set. By removing the low-amplitude portion in this way, it is possible to remove the small noise detected by the sensor 10 and improve the separation of each vehicle 12. For example, by Figure 2 By removing the elastic wave data with an amplitude value less than 50 dB, it is possible to separate each group and improve the estimation accuracy of the number of passing vehicles. Hereinafter, the elastic wave data with an amplitude value less than a threshold value is recorded as low-amplitude elastic wave data.

[0052] The event extraction unit 210 (classification unit) extracts elastic wave data in one event from the plurality of elastic wave data output by the removal unit 209. Specifically, the event extraction unit 210 classifies the plurality of elastic wave data into a plurality of groups according to the time at which the elastic wave is obtained, and extracts the elastic wave data of each event from the plurality of elastic wave data. The event extraction unit 210 outputs the extracted elastic wave data of each event to the vehicle number estimation unit 211.

[0053] The vehicle number estimation unit 211 estimates the number of passing vehicles using the elastic wave data of each event output by the event extraction unit 210. More specifically, the vehicle number estimation unit 211 calculates the number of events (number of groups) and estimates the result of the calculation as the number of passing vehicles. In this way, the vehicle number estimation unit 211 estimates the number of passing vehicles using the plurality of elastic waves detected by the plurality of sensors 10, respectively.

[0054] Figure 4 1 is a diagram showing the flow of the number estimation process performed by the signal processing unit 20 in the first embodiment. Figure 4 The processing shown is executed when an instruction to execute the number estimation processing is given. Figure 4 When the processing starts, a plurality of elastic wave data detected during the measurement period are stored in the memory 208.

[0055] The removal unit 209 obtains elastic wave data stored in the memory 208 (step S101). For example, the removal unit 209 obtains a plurality of elastic wave data stored in the memory 208 in a time series order. The removal unit 209 removes elastic wave data with low amplitude from the obtained elastic wave data (step S102). The removal unit 209 outputs elastic wave data with an amplitude value greater than a threshold value to the event extraction unit 210.

[0056] When the event extraction unit 210 obtains the elastic wave data from the removal unit 209, it starts one event extraction (step S103). First, the event extraction unit 210 obtains the elastic wave data output from the removal unit 209 (step S104). Next, the event extraction unit 210 determines whether the interval between the acquisition time of the acquired elastic wave data and the acquisition time of the previous acquired elastic wave data is greater than T (step S105). T is a preset value, for example, several hundred milliseconds. In addition, at the beginning of the processing, there is no previous elastic wave data, so in this case, the event extraction unit 210 determines that the interval between the acquisition times is not greater than T.

[0057] T can also be set according to the distance between vehicles. Specifically, the maximum value of the interval T can also be set according to the distance between vehicles, the vehicle speed, and the time until the elastic wave converges after the vehicle 12 passes. When a vehicle is generally traveling on the road, it travels at a certain interval from the vehicle traveling in front. As a result, there is a certain degree of distance between the vehicles. Therefore, there is a short time from the passage of one vehicle to the passage of the following vehicle. Therefore, in the same sensor 10, a certain degree of difference is formed in the acquisition time of the elastic wave generated by the passage of one vehicle and the elastic wave generated by the passage of the second vehicle. Therefore, by setting the interval T according to the generally assumed distance between vehicles, it is possible to prevent the elastic waves generated by the passage of other vehicles from being included in the same group.

[0058] When the interval between the acquisition times is not longer than T (step S105 —No), the event extraction unit 210 includes the elastic wave data in one event (step S106 ). Thereafter, the process of step S104 is executed.

[0059] On the other hand, when the interval between the acquisition times is greater than or equal to T (step S105 —Yes), the event extraction unit 210 ends one event extraction (step S107 ).

[0060] The event extraction unit 210 determines whether the number of elastic wave data contained in the information of one event output from the event extraction unit 210 is greater than M (step S108). M is a preset value, for example, 5. The value of M is set to remove noise. When the number of elastic wave data contained in the information of one event is less than M (step S108-"No"), the event extraction unit 210 deletes the elastic wave data contained in the information of one extracted event. The reason is that when the number of elastic wave data contained in the information of one event is less than M, there is a possibility of noise.

[0061] When the number of elastic wave data included in the information of one event is greater than M (step S108-"Yes"), the event extraction unit 210 outputs the information of one event extracted to the vehicle number estimation unit 211. Thereafter, the event extraction unit 210 determines whether the termination condition is satisfied (step S110). The termination condition is a condition for terminating the extraction of an event. For example, the termination condition may be that the elastic wave data obtained within the measurement period has ended, or that the measurement period has passed. When the termination condition is satisfied (step S110-"Yes"), the vehicle number estimation unit 211 estimates the number of passing vehicles based on the information of one event output from the event extraction unit 210 (step S111). Specifically, the vehicle number estimation unit 211 estimates the total number of information of one event as the number of passing vehicles.

[0062] If the termination condition is not satisfied (step S110 —No), the signal processing unit 20 repeatedly executes the processing after step S103. By repeatedly executing the processing from step S103 to step S110, multiple events are extracted. That is, multiple groups are extracted.

[0063] According to the vehicle information estimation system 100 constructed as described above, the number of vehicles passing through the structure 11 is estimated using the elastic waves detected by the sensor 10. Therefore, in order to compare different measurement results, information related to vehicles traveling at the location of the measurement object can be estimated. Thus, elastic wave data measured in different structures 11 can be compared by standardizing them with the number of passing vehicles.

[0064] A modification of the first embodiment will be described.

[0065] In the above embodiment, the signal processing unit 20 estimates the number of passing vehicles based on the number of events. In contrast, the signal processing unit 20 may use other methods to estimate the number of passing vehicles. In such a configuration, the signal processing unit 20 may not include the event extraction unit 210. The signal processing unit 20 may also be configured as follows: Figure 5 As shown in FIG. 1 , the number of vehicles passing by is estimated based on the change in the number of hits per certain period of time. The number of hits refers to the number of times the elastic wave is detected by the sensor 10. That is, when one elastic wave is detected by one sensor 10, it is considered a hit. Figure 5 , the vertical axis represents the hit count and the horizontal axis represents time. The vehicle count estimation unit 211 generates a graph showing the transition of the hit count corresponding to the time of acquisition of the elastic wave using the plurality of elastic wave data output from the removal unit 209. Thereafter, the vehicle count estimation unit 211 estimates the number of passing vehicles by detecting an envelope in the generated graph and counting the peak values ​​of the detected envelope.

[0066] Figure 5Although the diagram shown is similar to Figure 2 Although the accuracy is lower, the approximate number can be estimated.

[0067] The signal processing unit 20 may also be configured to use other feature quantities obtained from the elastic wave to estimate the number of passing vehicles in the same manner as the number of hits. In this case, the signal processing unit 20 may not include the event extraction unit 210. Other feature quantities are features obtained from the elastic wave, such as amplitude, the sum of amplitude or energy per unit time (integral value per unit time), etc. Here, the sum per unit time is not the sum of the measurement time, but the sum of feature quantities obtained for each certain period of time (for example, 0.1 seconds) within the measurement time. In this case, the vehicle number estimation unit 211 generates a graph (the horizontal axis is time and the vertical axis is feature quantity) showing the transition of the feature quantity obtained from the elastic wave, detects an envelope in the generated graph, and counts the peak value of the detected envelope, thereby estimating the number of passing vehicles. For example, when the sum per unit time is used as the feature quantity, the vehicle number estimation unit 211 obtains the value of the sum obtained per unit time within the measurement time. The vehicle number estimation unit 211 generates a graph using the obtained sum value, detects an envelope in the generated graph, and counts the peaks of the detected envelope to estimate the number of passing vehicles. The horizontal axis of the generated graph is time, and the vertical axis is the sum value obtained per unit time.

[0068] In addition to the summation per unit time, the signal processing unit 20 may also use decimation, for example Figure 2 The vehicle number estimation unit 211 can estimate the number of passing vehicles by detecting and counting peaks from the obtained graph.

[0069] (Second embodiment)

[0070] In the second embodiment, a configuration is described in which the weight and speed of a passing vehicle are estimated in addition to the number of passing vehicles. In the second embodiment, the number of sensors 10 provided in the vehicle information estimation system 100 is two or more.

[0071] Figure 6 1 is a schematic block diagram showing the functions of the signal processing unit 20a in the second embodiment. The signal processing unit 20a includes an amplifier 201, an A / D converter 202, a waveform shaping filter 203, a gate generation circuit 204, an arrival time determination unit 205, a feature quantity extraction unit 206, a data recording unit 207, a memory 208, a removal unit 209, an event extraction unit 210, a vehicle number estimation unit 211, a weight estimation unit 212, and a speed estimation unit 213.

[0072] The signal processing unit 20a is different from the signal processing unit 20 in that it is newly provided with a weight estimation unit 212 and a speed estimation unit 213. The other structures of the signal processing unit 20a are the same as those of the signal processing unit 20. Therefore, the overall description of the signal processing unit 20a is omitted, and the weight estimation unit 212 and the speed estimation unit 213 are described.

[0073] The weight estimation unit 212 estimates the weight of the passing vehicle based on the number of elastic wave data of each event output by the event extraction unit 210. The greater the weight of the passing vehicle, the greater the number of elastic wave data included in the event. Therefore, the weight of the passing vehicle and the load on the measurement part of the structure 11 can be estimated using the number of elastic wave data in the event as an index. The weight estimation unit 212 maintains a first table that corresponds the number of elastic wave data and the weight of the vehicle, and estimates the value of the weight corresponding to the number of elastic wave data of information included in one event as the weight of the passing vehicle. Furthermore, the weight estimation unit 212 can also estimate that the greater the weight, the higher the load on the measurement part of the structure 11 caused by the passing vehicle. In addition, the weight estimation unit 212 can also maintain a second table that corresponds the number of elastic wave data and the type of vehicle (large vehicle, medium-sized vehicle, etc.), and estimate the value of the type of vehicle corresponding to the number of elastic wave data of information included in one event as the type of the passing vehicle.

[0074] The speed estimation unit 213 estimates the speed of the passing vehicle based on the time series data of the plurality of elastic waves detected by the plurality of sensors 10 and the installation intervals of the plurality of sensors 10. Figure 7 as well as Figure 8 , describing the specific estimation method of the speed estimation unit 213.

[0075] exist Figure 7 , an example of the arrangement of a plurality of sensors 10 provided on a structure 11 is shown. Figure 7 In the example shown, sensors 10-1 to 10-18 are arranged in 6 rows and 18 in the direction of travel of the vehicle 12 on the lower surface of the structure 11. The branch number of each sensor 10 indicates the channel used by the sensor. That is, each sensor 10 uses a different channel. The sensor interval in the direction of travel of the vehicle 12 is D. Figure 8 The elastic wave data when a vehicle passes by is shown in FIG. Figure 8 As shown in FIG. 1 , it can be seen that at the timing when the vehicle 12 is estimated to be directly above each sensor 10, the elastic wave detected by each sensor 10 has a peak value, and the peak value is sequentially transferred to the sensor 10 in the next row. Figure 8The data of the elastic wave shown in the figure is used to obtain the timing of the peak value obtained by each sensor 10 row, and to confirm whether the transition of the peak value has a timing appropriate for the passage of the vehicle 12. This makes it possible to determine whether each event is based on the passage of the vehicle 12, and to improve the accuracy of the estimation of the number of passing vehicles. The speed estimation unit 213 estimates the speed of the passing vehicle based on the installation interval of the sensor 10 row and the transition speed of the peak value.

[0076] According to the vehicle information estimation system 100 in the second embodiment configured as described above, it is possible to obtain the same effects as those of the first embodiment.

[0077] Furthermore, in the vehicle information estimation system 100 of the second embodiment, in addition to estimating the number of passing vehicles, information related to other passing vehicles can also be estimated. In this way, by introducing the weight and travel speed of the passing vehicles as indicators, more precise comparison can be achieved.

[0078] A modification of the second embodiment will be described.

[0079] The vehicle information estimation system 100 in the second embodiment may be modified in the same manner as in the first embodiment.

[0080] The vehicle number estimation unit 211 may be configured to acquire information on the weight estimated for each event from the weight estimation unit 212 and estimate the number of passing vehicles within a specific weight range.

[0081] In the above embodiment, the weight estimation unit 212 estimates the weight of a passing vehicle based on the number of elastic waves included in the information of one event. The weight estimation unit 212 may also be configured to estimate the weight of a passing vehicle based on the size of the elastic wave data included in the information of one event. Here, the size of the elastic wave data included in the information of one event refers to, for example, the value of the peak value of the characteristic amount of the elastic wave or the value of the peak value of the number of hits. If the characteristic amount of the elastic wave is the amplitude, the maximum value of the amplitude is the value of the peak value, and if the characteristic amount of the elastic wave is the sum of the amplitude and energy per unit time, the value of the sum per unit time becomes the value of the peak value. In the case of such a configuration, the weight estimation unit 212 maintains a third table that associates the value of the peak value of the characteristic amount of the elastic wave or the number of hits with the weight of the vehicle, and estimates the weight value corresponding to the value of the peak value of the characteristic amount of the elastic wave or the number of hits included in the information of one event as the weight of the passing vehicle. The weight estimation unit 212 may also maintain a fourth table that corresponds the characteristic quantity of the elastic wave or the peak value of the hit number and the type of the vehicle, and estimate the value of the vehicle type corresponding to the characteristic quantity of the elastic wave or the peak value of the hit number of the information contained in one event as the type of the passing vehicle.

[0082] (Third embodiment)

[0083] In the third embodiment, a configuration is described in which a measurement result is corrected using the estimated number of passing vehicles and compared with a different measurement result. In the third embodiment, the measurement result refers to an elastic wave source density distribution represented by the density of an elastic wave source that is a source of elastic waves. The different measurement result may be a measurement result obtained at another location of the same structure 11 or a measurement result obtained at a different structure 11.

[0084] Fig. 9 It is a diagram showing the configuration of a vehicle information estimation system 100b in the third embodiment.

[0085] The vehicle information estimation system 100b includes a plurality of sensors 10-1 to 10-n (n is an integer greater than or equal to 3 in the third embodiment), a signal processing unit 20b, and a structure evaluation device 30. Each of the plurality of sensors 10 is communicably connected to the signal processing unit 20b via a wired method. The signal processing unit 20b and the structure evaluation device 30 are communicably connected via a wired method.

[0086] In the third embodiment, a signal processing unit 20 b is provided in place of the signal processing unit 20 , and a structure evaluation device 30 is newly added.

[0087] The basic processing of the signal processing unit 20 b is the same as that of the first embodiment. The signal processing unit 20 b generates transmission data including event information and information on the number of passing vehicles for each event, and transmits the generated transmission data to the structure evaluation device 30 .

[0088] The structure evaluation device 30 includes a communication unit 31 , a control unit 32 , a storage unit 33 , and a display unit 34 .

[0089] The communication unit 31 receives the transmission data output from the signal processing unit 20 b .

[0090] The control unit 32 controls the entire structure evaluation device 30. The control unit 32 is composed of a processor such as a CPU (Central Processing Unit) and a memory. The control unit 32 functions as an acquisition unit 321, a position calibration unit 322, a distribution generation unit 323, a calibration unit 324, and an evaluation unit 325 by executing a program.

[0091] Part or all of the functional units of the acquisition unit 321, the acquisition unit 321, the position calibration unit 322, the distribution generation unit 323, the correction unit 324 and the evaluation unit 325 can be implemented by hardware such as ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), FPGA, etc., or by the collaboration of software and hardware. The program can also be recorded on a computer-readable recording medium. A computer-readable recording medium refers to a non-temporary storage medium such as a removable medium such as a floppy disk, a magneto-optical disk, a ROM, a CD-ROM, a storage device such as a hard disk built into a computer system, etc. The program can also be sent via an electrical communication line.

[0092] Part of the functions of the acquisition unit 321 , the position identification unit 322 , the distribution generation unit 323 , the correction unit 324 , and the evaluation unit 325 do not need to be pre-installed in the structure evaluation apparatus 30 , and may be realized by installing an additional application program in the structure evaluation apparatus 30 .

[0093] The acquisition unit 321 acquires various information. For example, the acquisition unit 321 acquires transmission data received by the communication unit 31. The acquisition unit 321 stores the acquired transmission data in the storage unit 33.

[0094] The positioning unit 322 positions the elastic wave source based on the sensor position information and the sensor ID and time information included in each transmission data.

[0095] The sensor position information includes information related to the installation position of the sensor 10 in correspondence with the sensor ID. The sensor position information includes information related to the installation position of the sensor 10, such as latitude and longitude, or distances in the horizontal direction and the vertical direction from a position that serves as a reference of the structure 11. The position calibration unit 322 stores the sensor position information in advance. The sensor position information can be stored in the position calibration unit 322 at any timing before the position calibration unit 322 performs position calibration of the elastic wave source.

[0096] The sensor position information may also be stored in the storage unit 33. In this case, the position calibration unit 322 obtains the sensor position information from the storage unit 33 at the timing of position calibration. A Kalman filter, a least square method, or the like may be used to calibrate the position of the elastic wave source. The position calibration unit 322 outputs the position information of the elastic wave source obtained during the measurement period to the distribution generation unit 323.

[0097] The distribution generation unit 323 receives the position information of the plurality of elastic wave sources output from the position calibration unit 322 as input. The distribution generation unit 323 generates an elastic wave source distribution using the position information of the plurality of elastic wave sources input. The elastic wave source distribution represents a distribution showing the positions of the elastic wave sources. More specifically, the elastic wave source distribution is a distribution of points showing the positions of the elastic wave sources on virtual data showing the structure 11 to be evaluated, with the horizontal axis being the distance in the passing direction and the vertical axis being the distance in the width direction. The distribution generation unit 323 generates an elastic wave source density distribution using the elastic wave source distribution. For example, the distribution generation unit 323 generates an elastic wave source density distribution by representing the positions of the elastic wave sources using a contour map.

[0098] The correction unit 324 corrects the elastic wave source density distribution using a correction value based on the ratio of the number of passing vehicles to the comparison number of the passing vehicles obtained in the structure to be compared.

[0099] The evaluation unit 325 uses the corrected elastic wave source density distribution and the elastic wave source density distribution obtained in the structure to be compared to evaluate the degradation state of each structure 11. For example, the evaluation unit 325 may compare the corrected elastic wave source density distribution and the elastic wave source density distribution obtained in the structure to be compared to calculate the proportion of damage. The evaluation unit 325 evaluates the area where the density of the elastic wave source is greater than the threshold as a healthy area, and evaluates the area where the density of the elastic wave source is less than the threshold as a damaged area. The evaluation unit 325 may also calculate the proportion of damaged areas using each elastic wave source density distribution.

[0100] The storage unit 33 stores the transmission data and measurement results acquired by the acquisition unit 321. The storage unit 33 is configured using a storage device such as a magnetic hard disk device or a semiconductor storage device. The storage unit 33 may store the transmission data and measurement results acquired in advance in the structure 11 to be compared.

[0101] The display unit 34 displays the evaluation result according to the control of the evaluation unit 325. The display unit 34 is an image display device such as a liquid crystal display, an organic EL (Electro Luminescence) display, etc. The display unit 34 may also be an interface for connecting the image display device to the structure evaluation device 30. In this case, the display unit 34 generates an image signal for displaying the evaluation result, and outputs the image signal to the image display device connected to itself.

[0102] Fig.101 is a schematic block diagram showing the functions of the signal processing unit 20b in the third embodiment. The signal processing unit 20b includes an amplifier 201, an A / D converter 202, a waveform shaping filter 203, a gate generation circuit 204, an arrival time determination unit 205, a feature quantity extraction unit 206, a data recording unit 207, a memory 208, a removal unit 209, an event extraction unit 210, a vehicle number estimation unit 211, a transmission data generation unit 214, and an output unit 215.

[0103] The signal processing unit 20b is different from the signal processing unit 20 in that it newly includes a transmission data generating unit 214. The other structures of the signal processing unit 20b are the same as those of the signal processing unit 20. Therefore, the overall description of the signal processing unit 20b is omitted, and the transmission data generating unit 214 is described.

[0104] The transmission data generation unit 214 generates transmission data including information on the event output from the event extraction unit 210 and information on the number of passing vehicles for each event output from the vehicle number estimation unit 211. The transmission data generation unit 214 outputs the generated transmission data to the output unit 307.

[0105] The output unit 307 sequentially outputs the transmission data output from the transmission data generating unit 214 to the structure evaluation device 30 .

[0106] Next, use Fig.11 Specific processing of the structure evaluation device 30 in the third embodiment will be described. Fig.11 It is a diagram for explaining specific processing of the structure evaluation device 30 in the third embodiment.

[0107] Fig.11 (A) and Fig.11 (B) are 2 different measurement results. Fig.11 (A) and Fig.11 (B) are all elastic wave source density distributions derived from elastic wave data obtained through 1 hour of measurement. Fig.11 (A) and Fig.11 The elastic wave source density distributions shown in (B) are derived from the structure evaluation device 30 in the vehicle information estimation system 100b. It is considered that the lower the density in the elastic wave source density distribution, the more deterioration of cracks that hinder the transmission of elastic waves. When the obtained elastic wave data is displayed as an elastic wave source density distribution using the same standard, Fig.11 (B) Compared to Fig.11 (A), the density is significantly lower. Therefore, when the evaluation is performed in the structure evaluation device 30, the evaluation is Fig.11 (B) Structure 11 compared to Fig.11 The structure 11 in (A) has further deteriorated.

[0108] Fig.11 (C) and (D) are graphs showing a portion of the time series data of each elastic wave. Specifically, Fig.11 (C) Shown in Fig.11 (A) is the time series data of elastic waves obtained in the structure 11, Fig.11 (D) shows Fig.11 (B) Time series data of elastic waves obtained in structure 11. Fig.11 (C) and (D) show that, Fig.11 (C) Compared to Fig.11 (D), more vehicles pass by. When the signal processing unit 20b estimates the total number of vehicles passing by during each measurement period, Fig.11 (C) became 879, in Fig.11 (D) becomes 100. Therefore, in order to compare using the same reference, it is necessary to divide the density distribution of each elastic wave source by the number of vehicles to align the reference. Fig.11 In the example, Fig.11 (A) As a benchmark, Fig.11 (B) Correction is performed by multiplying the number of passing vehicles by 879 / 100. Fig.11 (B) The corrected distribution is Fig.11 (F). In comparison Fig.11 (E) shows the elastic wave source density distribution, and Fig.11 In the case of the elastic wave source density distribution shown in (F), the density of the elastic wave sources is similar, and it can be determined that the degree of deterioration is similar.

[0109] An example of a process flow of the vehicle information estimation system 100b is described. Here, it is assumed that the sensor 10 (hereinafter referred to as the "first sensor") installed in the first structure 11 is different from the sensor 10 (hereinafter referred to as the "second sensor") installed in the second structure 11. During the measurement period, the first sensor outputs the detected elastic wave to the signal processing unit 20b. The signal processing unit 20b uses the elastic wave obtained from the first sensor during the measurement period to estimate the number of passing vehicles. Then, the signal processing unit 20b generates the first transmission data including the information of the event and the information of the number of passing vehicles for each event, and sends the generated first transmission data to the structure evaluation device 30.

[0110] The second sensor outputs the detected elastic wave to the signal processing unit 20b during the measurement period. The signal processing unit 20b estimates the number of passing vehicles using the elastic wave obtained from the second sensor during the measurement period. Then, the signal processing unit 20b generates second transmission data including event information and information on the number of passing vehicles for each event, and transmits the generated second transmission data to the structure evaluation device 30.

[0111] The acquisition unit 321 of the structure evaluation device 30 stores the first transmission data and the second transmission data in the storage unit 33. The position calibration unit 322 performs position calibration of the elastic wave source based on the sensor position information, the sensor ID and the time information included in the first transmission data. The distribution generation unit 323 generates a first elastic wave source distribution based on the position information of the plurality of elastic wave sources output from the position calibration unit 322. The distribution generation unit 323 generates a first elastic wave source density distribution using the generated first elastic wave source distribution.

[0112] The position calibration unit 322 performs position calibration of the elastic wave source based on the sensor position information, and the sensor ID and time information included in the second transmission data. The distribution generation unit 323 generates the second elastic wave source distribution based on the position information of the plurality of elastic wave sources output from the position calibration unit 322. The distribution generation unit 323 generates the second elastic wave source density distribution using the generated second elastic wave source distribution. The correction unit 324 calculates a correction value based on the ratio of the number of passing vehicles included in the first transmission data to the number of passing vehicles included in the second transmission data. For example, the correction unit 324 divides the number of passing vehicles included in the first transmission data by the number of passing vehicles included in the second transmission data, and calculates the value obtained thereby as the correction value. The correction unit 324 corrects the second elastic wave source density distribution using the calculated correction value. Specifically, the correction unit 324 corrects by multiplying each pixel value of the second elastic wave source density distribution by the calculated correction value. The evaluation unit 325 displays the first elastic wave source density distribution and the second elastic wave source density distribution on the display unit 34 .

[0113] According to the vehicle information estimation system 100b in the third embodiment configured as described above, the same effects as those of the first embodiment can be obtained.

[0114] Furthermore, in the vehicle information estimation system 100b of the third embodiment, the elastic wave source density distribution can be corrected according to the number of passing vehicles. Thus, when comparing places with different conditions such as the number of passing vehicles, the evaluation criteria can be aligned. Therefore, different measurement results can be compared under the same conditions.

[0115] A modification of the third embodiment will be described.

[0116] The vehicle information estimation system 100b in the third embodiment can also be modified in the same manner as the first embodiment.

[0117] The vehicle information estimation system 100 b may include a weight estimation unit 212 and a speed estimation unit 213 as in the second embodiment.

[0118] Modifications common to the first to third embodiments will be described.

[0119] In the above-mentioned embodiments, the elastic wave data is stored in the memory 208 at one time, and the inference processing is executed when the inference processing is instructed. However, the inference processing may be executed in real time. In such a configuration, the removal unit 209 reads the elastic wave data each time the elastic wave data is recorded in the memory 208. The processing after reading the elastic wave data is similar to that of Figure 4 The processing shown is the same and therefore omitted.

[0120] According to at least one embodiment described above, there are: a plurality of sensors 10 for detecting a plurality of elastic waves generated from a structure; and a vehicle number estimation unit 211 for estimating the number of vehicles passing through the structure using the plurality of elastic waves respectively detected by the plurality of sensors 10, thereby being able to estimate the traffic status in order to compare different measurement results.

[0121] A part of the processing performed by the signal processing unit 20 in the above-mentioned embodiment (for example, the number estimation processing performed by the vehicle number estimation unit 211, the weight estimation processing performed by the weight estimation unit 212, and the speed estimation processing performed by the speed estimation unit 213) can also be implemented by a computer. In this case, it can also be implemented by recording the program for realizing the function on a computer-readable recording medium, and making the computer system read and execute the program recorded on the recording medium. In addition, it is assumed that the "computer system" referred to here includes hardware such as OS and peripheral devices. In addition, "computer-readable recording medium" refers to removable media such as floppy disks, optical magnets, ROMs, CD-ROMs, and storage devices such as hard disks built into the computer system. Furthermore, "computer-readable recording medium" can also include a medium that dynamically maintains the program for a short period of time, such as a communication line in the case of sending a program via a network such as the Internet, a communication line such as a telephone line, and a medium that maintains the program for a certain period of time, such as a volatile memory inside a computer system that becomes a server or client in this case. In addition, the above-mentioned program can be used to realize a part of the above-mentioned functions, and the above-mentioned functions can be realized by combining with a program already recorded in a computer system, or can be realized using a programmable logic device such as FPGA.

[0122] Although several embodiments of the present invention have been described, these embodiments are merely illustrative and are not intended to limit the scope of the invention. These embodiments can be implemented in various other ways, and various omissions, substitutions, and changes can be made without departing from the gist of the invention. These embodiments and their variations are included in the scope and gist of the invention, and are also included in the invention described in the claims and their equivalents.

Claims

1. A vehicle information estimation system, comprising: a sensor that detects elastic waves generated from the structure; and a vehicle number estimation unit that estimates the number of vehicles that have passed the structure using the elastic wave detected by the sensor, The vehicle information estimation system further includes a classification unit that classifies a plurality of elastic waves acquired during the measurement period into a plurality of groups. The vehicle number estimation unit estimates the number of groups other than a group in which the number of elastic waves included in the group is smaller than a first threshold value as the number of vehicles.

2. The vehicle information estimation system according to claim 1, wherein: The device further includes a weight estimating unit that estimates the weight of the vehicle or the type of the vehicle based on the number of elastic waves included in the group or the size of the group.

3. The vehicle information estimation system according to claim 1 or 2, wherein: The vehicle information estimation system includes a plurality of the sensors. A plurality of sensors are arranged on the structure at predetermined intervals in the traveling direction of the vehicle, The vehicle information estimation system further includes a speed estimation unit that estimates the speed of the vehicle based on time series data of the plurality of elastic waves detected by the plurality of sensors, respectively, and installation intervals of the plurality of sensors.

4. The vehicle information estimation system according to claim 1 or 2, wherein: The method further includes a removing unit that removes elastic waves having an amplitude value smaller than a second threshold value from among the elastic waves detected by the sensor.

5. A vehicle information estimation system comprising: a sensor that detects elastic waves generated from the structure; and The vehicle number estimation unit estimates the number of vehicles passing through the structure using the elastic wave detected by the sensor, wherein: The vehicle number estimation unit depicts the number of detections of multiple elastic waves obtained during the measurement period or the characteristic quantities of each of the multiple elastic waves according to the acquisition time, and detects an envelope based on the depiction, thereby detecting peaks, and estimates the number of detected peaks as the number of vehicles.

6. A vehicle information estimation device, comprising: The vehicle number estimation unit estimates the number of vehicles that have passed the structure using the elastic wave detected by the sensor that detects the elastic wave generated from the structure, The vehicle information estimation device further includes a classification unit that classifies a plurality of elastic waves acquired during the measurement period into a plurality of groups. The vehicle number estimation unit estimates the number of groups other than a group in which the number of elastic waves included in the group is smaller than a first threshold value as the number of vehicles.

7. A vehicle information estimation device, comprising: The vehicle number estimation unit estimates the number of vehicles that have passed the structure using the elastic wave detected by the sensor that detects the elastic wave generated from the structure, in, The vehicle number estimation unit depicts the number of detections of multiple elastic waves obtained during the measurement period or the characteristic quantities of each of the multiple elastic waves according to the acquisition time, and detects an envelope based on the depiction, thereby detecting peaks, and estimates the number of detected peaks as the number of vehicles.

8. A vehicle information estimation method, wherein: using the elastic waves detected by a sensor for detecting elastic waves generated from the structure, estimating the number of vehicles that have passed through the structure, The plurality of elastic waves acquired during the measurement period are classified into a plurality of groups. The number of groups other than the group in which the number of elastic waves included in the group is smaller than the first threshold value is estimated as the number of the vehicles.

9. A vehicle information estimation method, wherein: using the elastic waves detected by a sensor for detecting elastic waves generated from the structure, estimating the number of vehicles that have passed through the structure, The number of detections of a plurality of elastic waves obtained during the measurement period or the characteristic quantities of each of the plurality of elastic waves is plotted according to the acquisition time, and an envelope based on the plot is detected to thereby detect peaks, and the number of the detected peaks is estimated as the number of the vehicles.

10. A computer program product for causing a computer to execute a vehicle number estimation step, In the vehicle number estimation step, the number of vehicles passing through the structure is estimated using the elastic wave detected by a sensor that detects the elastic wave generated from the structure. The computer program product further causes the computer to execute a classification step of classifying a plurality of elastic waves acquired during the measurement period into a plurality of groups. In the vehicle number estimation step, the number of groups other than a group in which the number of elastic waves included in the group is smaller than a first threshold value is estimated as the number of vehicles.

11. A computer program product for causing a computer to execute a vehicle number estimation step, In the vehicle number estimation step, the number of vehicles passing through the structure is estimated using the elastic wave detected by a sensor that detects the elastic wave generated from the structure. In the vehicle number estimation step, the number of detections of multiple elastic waves obtained during the measurement period or the characteristic quantities of each of the multiple elastic waves are depicted according to the acquisition time, and an envelope based on the depiction is detected, thereby detecting the peak value, and the number of the detected peak values ​​is estimated as the number of the vehicles.

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