WO2012090235A1 - Integrated method and system for detecting and elaborating environmental and terrestrial data - Google Patents

Abstract

The invention relates to a method for detecting environmental data, comprising the steps of: - acquiring at least one audio signal through at least two different audio sensors of an audio analysis module, said sensors being installed at a predetermined reciprocal distance and at a predetermined distance from a travelling route of a vehicle or from an observation area comprising an emitter of said audio signal; - elaborating a correlation between the audio signals detected by the sensors, determining a forecast or instantaneous model of the travelling route or of the observation area, said model comprising at least the data relating to a travelling direction of the vehicle or a travelling speed of the vehicle or a pollution class of the vehicle or a number of vehicles on the travelling route or a monitoring value of the route or of the observation area.

Description

TITLE: Integrated method and system for detecting and elaborating environmental and terrestrial data.

Field of application

The present invention relates to a method and a system for detecting and elaborating environmental and terrestrial data comprising detecting and elaborating acoustic signals and determining traffic, atmospheric and/or acoustic pollution, as well as ordinary and extraordinary meteorological processes, and relations between environment, territory and vehicle traffic.

Prior art

As is known, the extraction of information from the noise detected by the audio sensors positioned in the proximity of the roads is made difficult by the nature of the sound wave, which in the propagation of the transmission means finds echoes and attenuations due to the environment shape, by the audio noise, emitted by other sources, which operate in the same band as the signals to be investigated, by the presence of sources or vehicles that emit signals with similar intensity and harmonic components, in the direction that goes from the vehicle to the sound detector, as well as by the mtrinsic features of the sensor, such as the resolution or directionality thereof.

Such information is useful for representing a so-called environmental matrix or model, according to the provisions of the European Environmental Agency (EEA), required for monitoring the environment status and forecasting the changes thereof, based on multiple factors, such as: human and natural activities, acoustic pollution, electromagnetic fields, waste, industrial waste, urban expansion, infrastructures, deforestation, wood fires, etc.

The technical problem at the basis of the present invention is to devise a method and a system capable of automatically acquiring information present on the territory, and in particular acoustic signals, and elaborating such information accurately determining the identification of the relationships between environment and vehicle traffic, the measurement and monitoring of its effects on air quality and on the noise level, the measurement of pollutants in the air and in water and the momtonng ot meteorological processes that influence the dispersion thereof, overcoming the limitations that still affect known systems, and in particular the difficulty of acquiring and elaborating signals attenuated by the environment shape or covered by noise.

Summary of the invention

The solution idea at the basis of the present invention is to acquire an audio signal, for example generated by a vehicle, through at least two different audio sensors located at a predetermined distance, and to determine information on the vehicle, for example speed, travelling direction, class of acoustic or atmospheric, as well as environmental and territorial pollution, through a correlation or similarity of the signal detected by the two sensors.

In particular, according to the present invention, it is envisaged to determine the data relating to vehicle traffic, and correlate them to the environmental meteorological and/or air andor water pollution, as well as acoustic pollution data, and/or to the data relating to river flow rate and/or seismic data, through the interfacing of multiple territorial information systems.

Information on the vehicles is determined through the acquired audio signals and information on the territory, such as the distribution of pollutants; further on it is determined through other sensors according to the present invention, as well as through the correlation of the signals acquired by such sensors and of further data stored to one or more territorial databases; information on vehicles and territorial information is used for deterniining an instantaneous or forecasting model, or a simulation model.

The audio sensors also detect the audio signals emitted by multiple and different sound sources, such as water courses, rain, wind etc., determining an accurate forecast or instantaneous model of the environment through correlations and similarities. Based on such solution idea, the invention provides for positioning data acquisition and elaboration devices in the proximity of the road and/or of water courses which perform a first local data elaboration and communicate the data to other similar devices connected over a network and or to a central unit, forming a capillary elaboration and correlation system, which allows performing environmental analyses on a larger scale. 2010/000527

The devices are provided with a protective case against atmospnenc agents wmui allows extended operation and autonomy thereof.

In particular, the system comprises multiple devices complementary to the audio analysis module, comprising a Main Unit, one or more Remote Units and Local Units. The Main Unit (MAD-UPRI) is connected to remote units (MAD-URxx) through cable communication lines and/or radio communication channels, and to local units (MAD- ULxx) with direct coupling. Remote Units are powered by the Main Unit or have autonomous power supply, preferably from the mains or solar panels. Local Units are powered by the Main Unit.

The system preferably comprises at least one of the following modules: a Local Air analysis Unit (MAD-ULAR), a Remote Weather Unit (MAD-URMT), A Remote Audio Analysis Unit (MAD-URAD), a Remote Analogue Camera Unit (MAD-URTA), a Remote Digital Camera Unit (MAD-URTD), A Local Water Analysis Unit (MAD- URAQ), a Remote Flow Rate and 3D Acquisition Unit (MAD-UR3D), a Remote Seismic Analysis Unit (MAD-URAS), a Remote Electromagnetic Field Analysis Unit (MAD-UREM), a Remote Power Supply and Backup Unit (MAD-URAB), a Remote Solar Cell Supply Unit (MAD-URAC). At least one of the following antennas is connected to the Main Unit: a GPS antenna (MAD-AGPS), a GSM/UMTS antenna (MAD-AUMT), a WiFi antenna (MAD-AWIF), a Short Range Radio antenna (MAD- ARCR).

Further features of the system and method according to the present invention will appear more clearly from the following description, made by way of a non-limiting example with reference to the annexed drawings.

Brief description of the drawings

Figure 1 shows a graph of a correlation function for detecting environmental data, according to the present invention.

Figure 2 shows a two-dimensional graph of a family C_hk(w,t)pQ of correlation functions. Figure 3 shows a three-dimensional graph of the family of functions of figure 2. Figure 4 shows a block diagram of the audio analysis step of the method accordmg to the present invention.

Figure 5 shows a block diagram of the data flow of the step of figure 4.

Figure 6 shows a block diagram of an audio analysis module of the system according to the present invention.

Figure 7 schematically shows an air analysis module of the system according to the present invention.

Figure 8 schematically shows a connection between the air analysis module of figure 7 and a main module of the system according to the present invention.

Figure 9 shows a block diagram of a video module of the system according to the present invention.

Figure 10 schematically shows a remote flow rate and 3D acquisition unit of the system according to the present invention.

Figures 11 and 16 schematically show multiple remote units connected to a mesh network according to the present invention, respectively, for the flow rate and seismic detection.

Figure 12 schematically shows laser devices of the system according to the present invention.

Figures 13 and 14 schematically show a remote flow rate and 3D acquisition unit of the system according to the present invention.

Figure 15 shows a block diagram of a remote seismic analysis unit of the system according to the present invention.

Figure 16 shows a block diagram of a remote seismic analysis unit of the system according to the present invention.

Figure 17 shows a graph relating to the size and position of audio antennas of the system according to the present invention.

Figures 18 and 19 schematically show audio antennas of the system according to the present invention. 010 000527

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Figures 20-22 show one of the monitoring graphs ot tne oil aucts oi me system according to the present invention.

Figures 23-26 show a box body comprising a control unit and the antennas of the system according to the present invention.

Figure 27 schematically shows an acoustic radar of the system according to the present invention.

Detailed description

The present invention relates to a method and a system for detecting road traffic and/or a plurality of descriptive values of environmental conditions. Such descriptive values are preferably but not exclusively obtained from a noise sound analysis module which, based on the signals detected, determines both the vehicle traffic and the presence of physical or atmospheric phenomena of great interest for managing ordinary public administration but also in the case of extraordinary events and in particular, in the event of an emergency. The system comprises at least one of the following modules: a Main Unit, a Main Elaboration Module, an Audio Analysis Module, a Local Air Analysis Unit, a Video Module and a Power Supply Module.

The sound analysis module is described hereinafter.

The environmental monitoring system according to the present invention comprises one or more audio analysis modules (MAAD), hereinafter also referred to as audio modules, which may remotely be connected to one another; each audio module comprises at least one audio antenna that acquires the sound signals emitted by the vehicles in the proximity of an installation seat of the audio module; the signals are elaborated for obtaining information relating to the number, travelling direction, speed and acoustic pollution class of the vehicles. The audio analysis module communicates with the main elaboration module of the monitoring system, which performs further elaborations based on the data received from the audio module. In particular, the audio module comprises sensors powered by the audio signal emitted by the vehicles, i.e. by an energy emitted by the signal. Preferably, the sensors are positioned at a distance between 4 and 50 from the road. According to the present invention, the extraction of information relating to the vehicle traffic, an in general relating to noise sources moving along the streets and in the proximity of microphones, is based on analogies oi tne signals detected by multiple audio signals of the audio module. In particular, the signal detected over time by an audio sensor is the result of the noises emitted by multiple sound sources and signal segments substantially correspond to signal segments detected by another audio sensor of the audio module, temporally spaced from the first sensor. The differences in the two signals detected are mainly due to the differences between the transfer functions of the microphones associated to each audio sensor and between the relative control circuits, to the position of the sources, to their motion, and to the differences in the path of the sound waves that reach each audio sensor; such path difference affects the intensity and the phase of the audio signal components.

The audio module determines a nature of the sound sources passing in the proximity of the audio antennas through:

- a search of the similarity features between the signals generated by the microphones, i.e. the signals acquired by the relative audio sensor associated to the microphone; such search of similarities is indicated hereinafter as correlation of the microphone signals associated to the audio sensors.

- a graphical representation of the change of such similarities over time;

- a comparison of the graphs represented with predetermined graphs characteristic of the movement of sound sources; in a preferred embodiment, the graphs are two- dimensional curves.

In particular, the correlation of the microphone signals is determined in the audio module through a correlation function that in input has the signals generated by two different microphones M_h and M_k of the audio module. Given S_h (t) and S_k (t) as signals generated by the microphones as a function of time t, ΔΤ as sampling period of Sj, (t) and S_k (t), Q as observation time interval (Q = m-ΔΤ), P as observation time interval centred at instant t (P = 2η·ΔΤ), with t defined every ΔΤ, the correlation function is defined in interval Ρ=2η·ΔΤ as:

C_hkCw q =∑_i=o_..m S_h (t - wAT + ίΔΤ) S_k (t + ίΔΤ), where w and C_hk have values every ΔΤ in interval Ρ=2η·ΔΤ. IT2010/000527

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Function C_hk(w) describes around P = 2η·ΔΤ ot t a level oi similarity ui me iwu intervals Q=m AT of signals S_h (t) and S_k (t) of microphones Mi and Mj. For example, with n = 10, P = 500 μβεο, ΔΤ=45 sec, function C_hk(w) at a given instant t is schematically shown in figure 1. The two maximums, in n = -4 that is w = t-4 ΔΤ μ≤εϋ and in n = 6 that is w = t+όΔΤ psec, indicate that, considering signal time segment with length Q of sensor S^ it is "similar" to a portion of same length Q of sensor S_h, located at a distance 6ΔΤ μεεϋ from the considered instant t, and to a portion of same length Q of sensor S_h temporally located at a distance -4ΔΤ μ8εο from instant t.

In the practice, if wc 8xamine a portion Q of signal S_k at instant t and it is compared with a portion Q of the signal genεΓatεd by sensor S_h after δΔΤμεεε, and with the signal generated by sensor S_h 4ΔΤ μ5εο before, it is possible to identify signal similarities or analogies, above a pred8termined similarity threshold.

Function C_hk(w) τερΓεεεηίβ, on the observation int8rvals, ί ε Ιενεΐ or degre8 of similarity of the two portions Q of signal around P of instant t; substantially, it is possibte to d8t8rm re which translations of portions Q of a signal it is possibk to perform for obtaining an analogous or similar signal portion.

As time t varies, function C_hk(w)tPQ constructs a family of curves C _k(w,t)pQ whereon ονεΓ time it is possible to 8valuate the similarity of two signals, as schematically shown in figure 2.

Given the features of the signals to be analysed, a further time interval AS multiple of ΔΤ is defined, wherein variable t of C_hk(w,t)p_Q has to be defined, that is, the instants at which correlations C_hk(w) are evaluated.

Setting the observation intervals P and Q, the number and distances between the microphones, the sampling intervals ΔΤ and AS, the correlation function generates families of curves representative of the movement of sound sources, also in the presence of complex and contemporary movements of multiple sound sources. The curves in the figure are shown as uninterrupted for simplicity but they have discrete values, defined upon every ΔΤ. The movement of sound sources, and in the case of vehicle traffic, direction and speed of vehicles, are determined through the elaboration of the families of curves C_hk(w,t)p_Q. In particular, it is provided to use a plurality of micropnones asswiaicu iu

audio sensors of the audio analysis module, and to determine a plurality of correlations.

The temporal evolution of the translations required to make two portions of signal Q, each one generated by a respective microphone, more similar corresponds to the spatial evolution of the sound source, which relative to the 2 microphones, is located in positions that make the reception of the same signal segment substantially advanced or delayed relative to one microphone or the other.

For an accurate calculation of the correlations it is therefore necessary to set the observation interval P, the observation interval Q but also the number of microphones, the distance between microphones, the sampling interval ΔΤ and the sampling interval AS.

Advantageously, given the location of the information content of the audio signals emitted by the vehicles on the frequency spectrum, the present invention finds a compromise between processing load and resolution with a signal sampling frequency equal to 22Khz and thus a sampling period ΔΤ of l/22Khz.

The signal analysis performed by the applicant has shown that an observation interval Q = m -ΔΤ equal to 4096-ΔΤ is a good compromise between the uniqueness of the sample (the correlation value improves with a high m), the processing load (gets worse with a high m), and the correlation n the proximity of the microphones (gets worse with a high m).

The distance between the microphones linearly improves the resolution of the correlation function, since the delay between the components of the audio signals received increases. At the same time, however, it allows generating echoes in the correlation of harmonic components with a shorter wavelength than the distance between the microphones.

The applicant has also noted that, given the energy distribution of the audio signals from vehicle traffic, further filtered by a sampling at 22 Khz, it is preferable to space the microphones to 20-50 cm. In this case, the observation interval P may be larger than the maximum delay that a sound wave undergoes between the microphones. For example, between 2 microphones at a distance ot cm, tne sound wave w m/s has a maximum delay of 7355 μ5∞, which corresponds to 16 times ΔΤη, that is, with n=16, one P = 2η·ΔΤ =32ΔΤ.

The minimum number of microphones of course is 2 but a plurality of microphones may improve the resolution and in general the information relating to the vehicle transit; the applicant has noted that the resolution improves with the movement direction component parallel to a link axis between the microphones. In fact, a plurality of microphones may compose results relating to movements with directions very different from one another and improve the possibility of determining correlations in situations of traffic consisting of many vehicles at the same time on multiple lanes.

The applicant has observed optimal results detecting the signals with 5 or 6 microphones, with a sampling at 22 Khz with sampling interval AT=45 sec, with an observation time interval Q= πι·ΔΤ=4096·ΔΤ, with an observation time interval P = 2η·ΔΤ =32ΑΎ (minimum), and with a sampling interval AS =1024ΔΤ.

The information on the traffic that generates the signals received from the microphones is determined by the comparison of correlations C_hk(w,t)p_Q with ideal curves, extracting them from background noises not generated by the movement of vehicles. Figure 3 schematically shows the evolution of the wave crest along axis t, that is, the evolution over time of the delay that characteristic elements of the audio signal undergo towards the two different microphones during the vehicle movement.

It is possible to trace the ideal curves of the delay undergone by two samples of audio signal emitted by a vehicle in transit in a direction substantially parallel and at a predetermined distance, for example 7m, from the conjunction line between the two microphones, which are positioned at a predetermined distance, for example 25cm from each other.

Similar curves, symmetrical on the horizontal axis, relate to the movement in the opposite direction. Since real curves exhibit variations relative to ideal curves, greater upon the passage of vehicles close to the microphones, due to the dimensions of the vehicles and of Q, the comparison of correlations Chk(w,t)PQ with ideal curves allows obtaining information on the traffic that generates the signals received by tne sensors, extracting them from the background noises not generated by a movement of vehicles.

Figure 3 schematically shows the crest projections of the correlation functions, following them on a plane parallel to plane t-w; curves are detected which are similar to the ideal delay curves.

A real traffic situation and the search for information on the traffic according to the present invention are described hereinafter. A first step comprises information processing and extraction.

The crest temporal pattern in real conditions highlights profiles similar to those of the ideal curves. From the correlation analysis it is possible to obtain information on the pattern of vehicles that have generated noise, in the vicinity ( - + 50m) of the audio antenna. Correlations C_hk(w,t)p_Q relating to various pairs of microphones are shown with greater intensity according to the height of the correlation.

Analysis software included in the main processing unit calculates the correlation functions detected by the audio module connected thereto, obtaining the passages of vehicles, speed, direction based on the comparison with the ideal curves. For example, an inflection point in the envelopes of the correlation curve crests corresponds to the vehicle passage at an intersection with the plane orthogonal to the conjunction line between the microphones. The curve inclination is associated to the vehicle speed; the curve shape as an "S" or an "upturned S" is indicative of the vehicle direction. The curves offer a better resolution when the microphones are orientated with the conjunction line in the direction of the road axes. Preferably, a plurality of microphones is used for detecting noise at traffic lights or junctions.

Figure 4 shows a block diagram of the software architecture of the sound analysis module for audio elaboration and figure 5 shows a specific portion of such architecture, for elaborating the data flow. An exemplary embodiment of the sound detection method is given hereinafter. The recognition of the vehicle traffic is carried out analysing the sound emissions produced by the vehicles, and in particular through

- acquisition of the audio signal from an array of microphones;

- recognition of the vehicle transit direction; - identification of the transit speed;

- identification of the vehicle type.

The acquisition is performed as described hereinafter.

The array of microphones comprises at least three omnidirectional waterproof microphones A, B and C provided with a wind protection and arranged as a "V". The audio signal is simultaneously acquired by the three microphones A, B and C, through an acquisition card and the acquired signal is filtered on the high frequencies (noise removal). The signal sampling is preferably performed at 22KHz, quantised on 8 bits (with sign and average 0) and then transmitted to the central processing unit.

The recognition of the vehicle transit direction is performed as described hereinafter.

An elaboration is performed every 1024 samples acquired (about 46.44ms) for identifying the vehicle passage. The signals from microphones A and B is correlated on samples of predetermined length, for example equal to 4096 samples, for each temporal deviation of signal B relative to signal A of n samples, for example of 40 samples (in advance and in delay), corresponding to a delay of about 1.81 ms. The output of this step comprises a predetermined number of correlation values, for example 81 values, which are identifiable through a respective vector of correlation values. Each vector is normalised, eliminating the offset and rescaling the values in a predetermined interval, for example in interval 0-255. If a difference between the maximum value and the minimum value of a vector, also indicated as correlation value width, is les than a predetermined value, for example 29000, then a denormalisation and a rescaling are performed, setting the minimum value added to the predetermined value (29000)" as maximum value. The normalised value is saved to a matrix, for example a circular buffer. Some vectors are checked in the matrix, for example the last 27 vectors, checking the presence of a recognised passage. The check consists in a two-dimensional correlation of the audio correlation matrix (for example, of the matrix having dimensions 27x81) with a passage model, consisting of a matrix having the same dimensions (another matrix 27x81).

The output VL of the two-dimensional correlation represents a level of likelihood of an identified passage of a vehicle or a probability of passage. Preferably, the method comprises the calculation of a specular value VR obtamed from the correlation with the inverted passage model (obtained by inverting in a specular manner the columns of the model, i.e. of the matrix).

In particular, according to the method, the passage of a vehicle is classified as "probable" when values VL or VR (for one or the other direction) has a relative maximum higher than a predetermined threshold, for example 75,000; once a "probable passage" has been identified, no other maximum values are considered until the value drops below threshold 0. The "probable passages" with VL or VR higher than n the recognition threshold (for example higher than 225,000) are automatically indicated as "recognised passages". The applicant has found that 70% of the actual passages of vehicles are already recognised in this step.

If the passage is not automatically recognised, then there is provided a search for the n (for example 3) relative maximums on the last m correlation vectors (for example, on the last 54 vectors). In this way, some theoretical passages are analysed at various speeds (preferably with arctangent approximation) and the distance from one of the relative maximums identified is evaluated for each curve, for each column of the matrix (for example of matrix 54x81). A passage is deemed as "recognised" if at least one of the following conditions applies:

- in at least N (for example 34) columns of the matrix, the maximum relative value is at a maximum distance of X ( for example 2) elements from the passage point of one of the theoretical curves;

- there exists a sequence of at least M (for example 19) columns wherein all the columns are at most at y (for example 2) elements of distance from the passage point of one of the theoretical curves;

- there exist sequences defined in the previous point, with length at least equal to (for example 5) wherein the total sum of the lengths is equal to or larger than z (for example 26).

If at least one of the three conditions above is verified, a passage previously deemed as "probable" is classified as "recognised passage" and the speed and class analyses are performed; otherwise, the "probable passage" is discarded and the relative elaboration is interrupted.

The identification of the transit speed is described hereinafter.

The speed is calculated as space difference divided by time difference.

The time measurement is performed through the comparison of the signals recorded by the pair of microphones A-C relative to the pair of microphones C-B. Parallel to the identification of the vehicle passage, for example every 1024 samples (about 46.44ms), the correlations are performed on the signals of microphones A-C and of microphones C-B, substantially as already described. Also in this case, the correlation is performed on a predetermined length of samples, for example on (4096 samples) and the deviations are analysed up to t samples (for example 40) (in advance and delay). The resulting correlation vectors (for example of 81 elements) are normalised as in the previous case and inserted in two matrices, for example in circular buffers. The data of these matrices are only used at an identified passage.

In this case, it is provided to search for a maximum of the two-dimensional correlation on the correlation matrices of microphones A-C and C-B with a gap having a predetermined dimension (for example 40x81 elements) and an offset ranging from 10 to 30 elements (in advance or delay according to the direction of the identified passage).

In this way it is possible to evaluate the delay of the vehicle passage recorded by pair A- C relative to pair C-B. The space measurement for calculating the speed depends on the installation height.

The perpendiculars to the conjunction lines of microphones A-C and C-B are not perpendicular to the road plane but form an angle of about 45° therewith; thus, the distance projected by the two perpendiculars on the ground generates a different distance perceived by the control unit depending on the microphone height.

For example, at an installation of about 4 metres, the distance used for the calculations is set to about 8 metres. The speed calculated for the passage is therefore equal to 8m/T, where T is the time interval corresponding to the correlation maximum calculated in advance. Given a movement from 7 to 40 elements, T may range from 0.325s to about 1.858s, for obtaining a calculated speed from 15Km/h to 88Km/h. Speeds below 15Km/h have a maximum of 15Km/h (the most similar value), whereas 88 m/h or the most similar value in correlation are still indicated above 88Km/h.

The identification of the vehicle type is performed as follows.

There is provided to analyse the spectrum of the audio signal obtained from microphones A and B.

The audio signal taken into account is that upon the vehicle passage (that is, about 0.6s before), n samples (for example 4096 samples) are taken from the signal of microphones A and B, wherefrom a subset is selected (for example 1024, i.e. 1/4); it is provided to make an average of the signal values for obtaining a signal X that represents the bearriforming of the two signals A and B on the plane wherein the points are equally spaced from the two microphones. After that, the discrete Fourier transform Y(f)=FDT(X(t)) is calculated for obtaining as output the frequency content of the signal at the time. Due to the selection or decimation, the contents in frequency obtained are up to about 2750Hz (it has been observed that contents at higher frequencies are not significant for traffic analysis).

The frequency spectrum is therefore normalised in power and used for calculating the third-octave bands. The resulting bands are compared with a set of pre-classified spectrums, for searching the most similar spectrum that is indicative of the class of the identified vehicle; the evaluation is carried out with the square minimums.

The imaging is able to provide, at each instant, a picture of the sound sources on the axis perpendicular to the road passing by the control unit. Axis X is the time, axis Y is the distance from the control unit on the road plane, highlighting the borders of the carriageways. According to the tests performed by the applicant, the imaging is not capable of identifying a height of the sound source. Thus, it is provided to have the imaging a few metres on the right or on the left of the straight line passing by the control unit, rather than on the same straight line; in this way it is possible to evaluate the speed of a passage. The resulting imaging is one-dimensional (plus the time).

The operation of the one-dimensional imaging for an instant T is as follows:

an approximation of the acoustic level is calculated on the point for each point of the straight line, as:

where t = time, j = microphone number, fj is the signal of microphone j and djz is the re- phasing variation of microphone j for the beamforming on the point at a distance z.

The beamforming actually re-phases the audio signals over time so that a signal emitted in the search position reaches the microphones in the same instant.

When the first estimate of values Level(z) has been calculated, a filtering is performed to highlight the interesting components (maximums) wherein: yo t iz) = Level{z)

where dz indicates the verified movement (closer to the control unit and farther from the control unit). A good experimental value is 2 metres. In the practice, the formula shows that a high value of sound level is searched, which is much higher than the closer and farther levels of the searched point.

In the initial image it is possible to recognise the passage of vehicles in the various lanes, and for the bottom lane it is possible to recognise the passage of the axes. In the top lane (the one close to the pole) recognising the axes seems more difficult; the applicant believes that the wheel noise is covered by the vehicle body (this does not happen in the farthest one as it is more angled).

The step of determining the pollution level of the vehicle is described hereinafter.

The signal obtained from the multiple microphones is used as a reference for measuring the acoustic pollution. The continuous use of the same system of omnidirectional microphones whereon the vehicle traffic recognition operates allows increasing the reliability of the acoustic pollution detection system. The energy of the signal received is detected for each microphone. The average of the sum of energies of microphone signals, filtered from those deemed abnormal (deviations in the energy received by the single microphone higher than D, with parametric D, for example 10%) is recorded with a configurable resolution (samples/s) to a local database of the sound analysis module and sent to the central operating unit.

The hardware architecture of the sound analysis module is described hereinafter with reference to figure 6. A microcontroller pilots the sampling, through an amplification and filtering block, for example of 6 signal lines corresponding to 6 microphones; λ auxiliary microphones are provided. The microcontroller shares the Ethernet and CANbus bus of the Main Unit and communicates with the Main Elaboration Unit. Firmware is loaded to a memory of the microcontroller for elaborating the sampled signals. The extraction of high level information is performed by the Main Elaboration Module software. The data transfer between Audio Analysis Module and Main Elaboration Module is preferably carried out on Ethernet bus.

The dimensions and the construction features of the antenna are a compromise between measurement resolution, noise influence and overall dimensions. The Audio Sensors antenna consists of a structure of aluminium tubes with a diameter of about 8 mm and lengths of about 20 cm, which support aluminium containers for seating the microphones.

The distance and the position of the microphones allow selecting an interval of frequencies of the signals to be elaborated and increasing the elaboration resolutions along the traffic directrices. The microphones, located in containers, are waterproof and suitable for low frequencies. The connection wires run into microphone holder support tubes. Vibration-proof seals are associated to the microphones. The Audio Sensors Antenna is connected to the Main Unit through a multipolar cable and connector.

The audio sensors are at a height of 4 m from the road plane which allows performing acquisitions coherently with the measurement requirements of traffic sound pollution, and providing such measurements to the operating control units for modelling the pollution distribution and propagation. Figure 17 shows an example of construction dimensions of a typical antenna and an example of measurements for the placement on the road. In the first graph, A B C D E correspond to the positions of the microphones or of the protections wherein the microphones are located. The second graph provides an example of the installation of an antenna on the road.

Figures 18 and 19 schematically show the antenna according to different embodiments. The microphones are seated into aluminium protections supported by aluminium tubes having a diameter of about 8 mm and are connected with multiple wires that slide within the tubes and with a multipolar cable and a multipolar connector. Although the microphones have a high water protection level, their protections in the antenna are orientated so as to prevent the penetration into the microphone seat.

The microphones for example are model MR-28406-000 Knowles Electronics. The main module control unit is assembled through two aluminium half shells that form a box body, having dimensions of about 25x30x13 cm, as shown in figure 23. An aluminium plate closes the box at the bottom and forms a seat for the power supply and input output connectors. The half shells are coupled to the main body through a flange and an O-ring. On the back side of the body there are fixed two aluminium brackets for fixing the body to tubes, walls etc. A manifold of the audio antenna, with a diameter of about 7 cm made of aluminium, is provided with holes for seating steel tubes with a diameter of about 8 mm and length of about 21 cm. An aluminium capsule with a diameter of about 3 cm, which seats an audio sensor (microphone), is inserted at the end of each tube and kept into the seat by flange, O-ring and snap ring. A microphone is seated in the central manifold with flange, O-ring and snap ring. The tubes and the capsules are fixed through steel dowels. The manifold is fixed by screws to the box body of the box, as shown in figure 24. The connection wires of the microphones run inside the tubes. The wires, combined into the manifold, form a multipolar cable in output from the manifold and connectable to the connector plate on the bottom of the control unit. The manifold-tubes-capsules-microphones-cables assembly forms the audio antenna. The antennas for radio communications and GPS are fixed to the top portion of the box body (figure 25). A case consisting of a steel sheet, shaped and worked by laser, is externally coupled to the box body of the control unit and fixed thereto through steel screws and spacers (figure 26).

The invention provides the use of elaborations for the 3D representation of the noise received, with a technique called "acoustic radar".

In particular, the signals of audio antennas of the acoustic radar are elaborated for performing a temporal representation in the 3D space, and detecting the vehicle type and features, as well as the movement thereof. The use of a "beamforming" is provided. The microphone signals are shifted along a time axis for eliminating the delays associated to the configuration of the audio antennas and bring them "to phase" on the planes or points of the 3D space surrounding the control unit. The energy received from the audio antenna at the 3D figure thus deterrninea is caicuiate tnrougn tne "pnasea signals.

The acoustic radar provides digital outputs corresponding to the images in boxes having time t on the horizontal axis and a straight line r on the vertical axis, given by the intersection of a plane passing by a vertical of the antenna, substantially orthogonal to the travelling lanes of the vehicles, and the road plane, as schematically shown in figure 27.

In the boxes of the digital output (not shown in the figures) it is possible to identify the audio associated to the passage of vehicles at the straight line r. The analysis of audio tracks is performed by the transitions through straight line r or through 3D compositions of transitions through other straight lines and planes of the 3D space around the audio antenna. The tracks thus detected provide indications on the vehicle speed, i.e. the passage speeds of the tracks through the straight lines considered, the vehicle sizes, i.e. the heights of the tracks considered on planes parallel to the road plane, and the type of vehicles, i.e. the track distribution of a same vehicle over time and on the planes.

The system of the present invention also comprises an oil duct monitoring module, preferably based on linear acoustic radar or MAD RADAC-L technologies, which allows localising the generation source of noises propagated through the tubing structure. In particular, the noises due to works on the systems or to vandalic actions are associated to unauthorised collection of oils carried, and communicated to the module. The works may be carried out by the repair teams or by supervision systems, for example monitoring through UAV (Unmanned Aerial Vehicles). The system control units are distributed along the oil duct and connected in a mesh network to one another and to the operating control unit. The antennas of audio sensors are positioned in mechanical coupling with the conduits and detect audio signals propagated at a speed (1500-6000 m/s) typical of the material used in the structure. The MAD control units are carefully temporally synchronised through the "PPS" signal generated by the GPS receiver and they record audio signals referring them to a common temporal base. The audio signals, digitally compressed, are sent by a control unit to the following and previous ones, which perform a correlation of the signal received via radio with that recorded locally. The correlation peaks determine the delay undergone by the signals in travelling the different routes between two consecutive control units at the souna speea on the structure. Calibrating the propagation speed and eliminating the echoes due to small distances from the propagation through the fluids, may be auto-calibrated and configured with transmission of test signals between the MAD control units. The correlation analysis carried out by control unit 1 determines the characteristic elements between the audio signals recorded locally and those sent by control unit 2. The delay of any correlation peak is indicative of the distance (at the propagation speed of the audio signal in the structure) of the noise location relative to the half distance between control units 1 and 2.

An analysis of the correlated audio segment and the comparison thereof with characteristic noises selectively enables interventions of a different type. The monitoring of the oil ducts through the system of the present invention is schematically shown in figures 20-22.

The Local Air Analysis Unit of the system according to the present invention, schematically shown in figure 6, comprises pollution sensors and in particular at least one or more pollutant sensors associated to the vehicle traffic, for detecting CO N0₂NO S0₂, at least one sensor of the C0₂ concentration in the air, at least one sensor of <¾ in the air, at least one optical device for measuring the concentration of the PM10 particulate.

The sensors are installed modulated- wise and are lapped by an air flow, filtered and moved by a fan and by the thermal effect. The sensors, and the relevant electronic components, are included in a box, preferably of aluminium, modular and connectable to the Main Unit, wherefrom it receives power, and is controlled, preferably on CANbus line, as shown in figure 8.

The values obtained from the Air sensors may be associated to the traffic flow, to 3D models of the territory, and to weather variables for monitoring and modelling the pollutant patterns based on the traffic and providing migrations and dissipations.

The Main Elaboration Module (in the Main Unit) compensates the thermal drifts, the sensitivity drifts due to the wear and saturation of the sensors, and corrects the detections through cross-sensitivity diagrams of a sensor to the pollutants. The PM10 measurement sensor has no forced air circulation and moves an air flow through thermal effect of a heating element.

A bottom plate for the filters (figure 8), is fixed with quick coupling to the air analysis unit and is protected from water stagnation by a seal.

The system according to the present invention provides for correlating the vehicle traffic data to the concentrations of atmospheric pollutants, to sound pollution and to their evolution over time, in a three-dimensional territory model.

The system of the present invention further provides for incorporating a Video Module comprising one or more environment monitoring cameras, for data collection at ordinary events, such as the recognition of vehicle number plates; in particular, the connection of analogue cameras (Remote Analogue Camera Unit) is provided. The Video Module is an expansion in the MAD Main Unit, having the function of acquiring the camera signal and communicating the data detected to the elaboration module, preferably through the Ethernet bus. As schematically shown in figure 9, a microcontroller controls a video switching matrix whereto the signals are sent by multiple analogue cameras.

It is also provided to build a Short Range Radio Module into the system, which connects the MAD Main Unit via radio or WiFi Modules to sensor networks. Preferably, the Short Range Radio Module implements a radio network of the Mesh type with coverage of an area comprising a Remote Water Analysis Unit, a Remote Flow Rate Measurement and 3D Acquisition Unit, a Remote Seismic Analysis Unit and/or a Remote Electromagnetic Field Analysis Unit. Preferably, the Short Range Radio Module uses a ZigBee standard for low power radio networks, based on the IEEE 802.15.4 standard, and uses 2.4 GHz ISM band, especially suitable for short and/or long range allocations, through the use of signal repeater nodes. Figure 12 schematically shows an implementation of a Mesh network along a bridge.

In this case, areas are covered which comprise up to 200 low consumption or high power devices, with data connection up to 250kbit/s. Advantageously, the Short Range Radio Module ensures low consumption, encoded transmissions (128 bit encryption), data safety and confidentiality, network redundancy, flexibility and dynamicity of the network topology, low complexity, t he monitoring mainiy out noi exclusively remics iu the environment and in particular, air and water, vibrations and pressures, through the use of accelerometers, structural elements like tunnels, dykes, bridges but it also regards safety, control and automation. The Short Range Radio Module comprises a microcontroller that communicates with an XTR ZB1 xLI and XTR ZB1 xHE component and integrates the microcontroller and the component in an expansion board of the MAD control unit. The board is connected to an Ethernet and CANbus expansion bus of a system control unit, communicates with the Main Elaboration Module and is connected to the external antenna on a protection case. A WiFi Module is provided for wide band communications which provides coverage to 802.1 lg nodes of MAD remote units, with access point service to the Ethernet by cable through the Main Ethernet connection.

The WiFi module is seated within the Main Unit on an expansion bus and is connected to an external WiFi antenna. As an alternative there is provided the use of a WiFi USB module connected to a USB port of the Main Unit Mother Module; the WiFi USB module is not provided with an integrated antenna but is inserted in the Main Unit and uses the WiFi antenna located on the control unit protection case.

The system of the present invention also comprises a Power Supply Module; the latter controls the circuit supplies for the Main Unit and a battery recharge circuit of a Remote Power Supply and Backup Unit.

The module receives current in input from the Remote Power Supply Unit:

in the presence of supply from 220ac mains, it receives current from the 15 VDC power supply, generates the power for the Main Unit circuits and recharges the backup batteries of the Remote Power Supply and Backup Unit; on the other hand, in the absence of supply from 220AC mains, it receives power from the backup batteries of the Remote Unit and generates the power for the circuits of the Main Unit.

The system Main Unit may be connected by cable or radio to the Remote Units, located at a distance from the Control Unit whereto they transfer data and information.

The Remote Units managed by the MAD Main Unit comprise at least one among: the Remote Flow Rate and 3D Acquisition Unit, the Remote Water Analysis Unit, the IT2010/000527

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Remote Weather Unit, the Remote Seismic Analysis Unit, the Remote Analogue and Digital Camera Unit, the Remote Power Supply and Backup Unit, the Remote Solar Cell Supply Unit, the Remote Audio Unit (Audio Antenna).

In particular, the Remote Flow Rate and 3D Acquisition Unit acquires information on the flow rate of water basins, 3D ground profiles, in the proximity (up to 200m) of the Remote Unit, 3D profiles of the bottom of water basins, and based on the features of suspensions in water. This unit communicates with the MAD Main Unit (by cable and radio). The acquisition of information on the flow rate and configuration of water basins allows the operating control units to integrate 3D and map models of the territory and know in real time as well as update a database on the availability and on the water flow, and to modify the instantaneous and forecast models of the flow evolution in emergency situations and based on abnormal stresses and events.

The system also comprises cameras sensitive to the infrared radiation that take pictures of the ground and of the water basin. A local digital elaboration determines the profile of basins with water, drawing a distinction between water and ground based on the very low emission of infrared rays from water. The profiles of the curves thus determined are sent by short range radio network, WiFi, o canbus, to the Main Unit and to the operating control unit.

The comparison and the evolution over time of the "spots" corresponding to basins with water may gave the extent of the evolution of the water basin and help tracing forecast and reference models for determining alarm events.

The images may also be used for monitoring and logging particular situations, for example in the case of extraordinary events.

The system also comprises laser devices, preferably in the 532nm and 1064 nm band. The 532 nm band (green) undergoes a low absorption by water and allows the laser beam to propagate in the water in favourable conditions (low levels of suspensions, algae, etc.). Figure 12 schematically shows the implementation of laser devices for detecting the flow rate. The 1064 nm (near infrared) band is not reflected by the inner water layers, has an excellent response from the ground and from the water surface. The market offers a wide availability of laser devices (transmitters and receivers ) wnicn use the bands indicated above. The laser pulses are sent by a transmitter; the time required by the reflected radiation (scattering) from surfaces hit by the transmitted pulses to transmit again is proportional to the distance from the hit target. A sensitive receiver in the band of the signal sent, provided with relevant filters and optics, provides a signal based on the intensity of the radiation received. A circuit measures the times between the pulse transmitted and the characteristic peaks of that received, and provides a linear distribution of the distribution of material in the direction of transmission of the transmitted pulse. The device of the present invention is provided with a very accurate motor driven system for directing the laser beam, capable of performing the scanning of desired solid angle, for example of the area underneath a bridge arcade. The distance of the profiles detected is composed by device software in a 3D model and sent to the Main Unit.

The laser device, preferably in a 532 nm band, is piloted by a modulator which allows emitting a laser ray on a carrier. The speed measurement of the object that has reflected the ray is calculated on the difference between the transmitted ray phase and the received ray phase. In the case of water basins, the speeds refer to the water surface layer or to inner layers selected through intervals of the return signal reception time. The system generates a distribution Model of the water flow within the basin profile that allows approximating the flow rate thereof.

With reference to figure 10, there are described the hardware and software functional blocks of the Remote Flow Rates and 3D Acquisition Unit. In particular, block A performs a modulation and synchronism check, synchronising the movements of motors 12, acting on the control module D and the modulation of laser transmitters 2 and 7, acting on power supplies 1 and 6. Block B modulates laser 532nm in continuous wave, or both lasers in pulses, respectively, if it is provided to measure the speed of the water surface layers (modulation in continuous wave) or a 3D profile (in pulses, preferably with laser 532nm for profiles covered with water and infrared laser for water and ground surfaces). Block C demodulates the phase of the modulating signal received; the speed of the water surface layers is obtained through the analysis with the modulating signal transmitted. The signal received varies over time according to the modulation transmitted and the speed ot trie layer reflecting u e mciueni rauiauon. me myei selection takes place on the observation interval whereat a distance from the transmitter corresponds. Block D performs a motor and encoding check. Block E performs a sampling of the signals in output from the receivers, comparing them with the instants of generation of the signals transmitted; for example, given the light speed, one nanosecond delay corresponds to 30cm distance. Block F calculates the speed; through the calculation of the phase differences of the signals transmitted and received, such block calculates the water layer speed corresponding to the time delay whereat the phase deviation is calculated. Blocks G-H perform an approximation of the basin flow rate based on the correlation between the 3D profiles and the water layer speed. Block L comprising for example a PC allows monitoring the activities of the Remote Flow Rate and 3D Acquisition Unit. Block M manages the radio communication between Remote Flow Rate Unit and the MAD Main Unit, through modules WiFi 802.1 lg or the Mesh ZigBee network coordinated by the Main Unit. Always with reference to figure 10, reference numeral 1 schematically indicates the power supply for the laser transmitter at 532nm; reference numeral 2 indicates a laser transmitter 532nm in the green visible band capable of crossing the water layers and being transmitted back by the bed. Block 3 represents a laser receiver 532nm, suitable for amplifying the photonic energies received and generating an output voltage towards blocks C and E. Block 4 is an optical group for receiver 532nm; the radiation reflected by the targets is gathered in this group and collimated by a group provided with focus. Block 5 is an optical filter for receiver 532nm; given the high bandwidth of the receiver, the narrow band optical filter, centred on 532nm, increases the signal/noise ratio in output from the receiver. Block 6 is the power supply for the laser transmitter at 1064nm. Block 7 is a laser transmitter in the infrared band. Block 8 is a laser receiver 1064nm which amplifies the photonic energies received and generates an output voltage towards blocks C and E. Block 9 is an optical group for receiver 1064nm; the radiation reflected by the targets is gathered and collimated by an optical group with appropriate focus. Block 10 is an optical filter for receiver 1064nm; given the high bandwidth of the receiver, the narrow band optical filter, centred on 1064nm, increases the signal/noise ratio in output from the receiver. Block 11 is a reflector; a swing of the laser rays for sampling the surrounding ground is performed by reflecting optics moved by the high precision motors. Block 12 represents the motors, comprising 2 steepmg motors witti control encoder and reduction blocks with a very low clearance, which allow moving the reflecting optics so as to sample a solid angle of about 90 degrees.

The Remote Flow Rate Unit is seated in a container with a high degree of protection from external agents (IP67). Within the container, a box contains the laser pointing devices and is swung on two axes by two motor units that pilot the movement on a solid angle of, for example, 90 degrees, as shown in figures 13 and 14. At the centre of the spherical cap wall of the box there is fixed a flat laser crossing glass. The spherical cap- outer container seal is ensured by a Teflon or nylon sealing ring. The glass maintenance is performed in local automation through a swing arm that supports a steel blade. The blade is automated with a motor and digital control from the control unit, and performs a glass cleaning moving on the glass. The contact between the blade and the glass is never interrupted, thus preventing the infiltration of foreign bodies between the two bodies; in particular, the stand-by position of the blade decreases the pressure on the glass, but maintains the contact. In an embodiment of the invention, multiple blades are used. A pump emits air for elirriinating deposits and frictions.

The system of the present invention also comprises a Remote Seismic Analysis Unit. The distribution on a structure, for example on a bridge, of remote seismic analysis units is schematically shown in figure 15. A microcontroller acquires, through adaptation circuits, voltages in output from seismic sensors integral to the structure. The features of the sensors are selected for the detection of typical structure stresses. The microcontroller manages a short range communication module compatible with the MAD radio networks (WiFi 802.1 lg and Zigbee). The Remote Units constitute a Mesh network capable of covering complex structures at low radio power with long distances towards the MAD Main Unit.

The cable communication towards the MAD Main Unit may be performed through CANbus. The Remote Seismic Analysis Units can be locally powered by solar panels.

The system of the present invention also comprises a Remote Electromagnetic Field Monitoring and Analysis Unit (EM pollution), provided with communication devices. The transmission of the communication modules is performed in deferred mode and on bands not interfering with the measurements of the pollutants. A microcontroller acquires, through adaptation circuits, the voltages in output from some sensitive antennas. The microcontroller manages a short range communication module compatible with the system radio networks, that is, WiFi 802.1 lg and Zigbee.

Also the Remote Electromagnetic Field Analysis Units can be locally powered by solar panels.

The collection of information on the water quality in water basins is performed through a microcontroller that acquires voltages in output from water quality sensors, comprising sensors of nutrient parameters (nitrates, ammonia and orthophosphates) and/or of chemical-physical parameters (for example conductivity, PH, temperature, ...). The microcontroller manages a short range communication module compatible with the radio networks, WiFi 802.1 lg and Zigbee. The Remote Units constitute a Mesh network capable of covering long distances at low radio power. It is possible to use repeater units of Mesh network without water analysis features, as shown in figure 16. Also the Remote Water Analysis Units may be locally powered by solar panels.

The Remote Power Supply and Backup Unit provides for powering the Main Units (Control Units), preferably in 12- 15 VDC current. An adaptation to the 220V AC network is provided; safety and backup are provided by the Remote Power Supply and Backup Unit. The unit receives input current also by the optional Remote Solar Cell Supply Unit. A 220VAC-15VDC power supply sends current to the Main Unit, which provides recharge power supply to the backup battery block. Given the network power supply, a lead battery is recommended, preferably 7 Ah, in the case of installations not subject to long periods of mains voltage interruption. A metal waterproof cabinet contains 220V AC fuses, a differential magneto-thermal block, an automatic reset or restore block, 15 VDC and 12 VDC fuses, 15 VDC and 12 VDC switches. The cabinet contains, the 220 VAC - 15 VDC 100W power supply, a lead battery (7-90 Ah) on an aluminium case.

A communication subsystem (UMTS or GPRS or cable, where possible) collects information from a portion of road seat and sends it to the operating control unit. The equipped MAD subsystem of connection with the operating control unit may be provided, for example, every 10 base MAD subsystems. The system according to the present invention finds an advantageous application also in road fixtures, for example noise-dampening barriers, carriageway separators, guard rails, etc.

In particular, the system detects the vehicle traffic and identifies extraordinary events associated thereto, for example accidents or vehicle queues. It is provided to couple or incorporate the detection and communication devices of the system according to the present invention in road furniture structures, in particular miniature audio sensors, low consumption elaboration circuits, radio network connection devices, preferably of the mesh type, or cable connection, GPS receivers. Specific protections for the devices are provided for coupling with concrete, wood structures or other materials of road furniture, i preferably provided with a photovoltaic cell or cable power supply system.

Claims

1. An integrated method for detecting and elaborating environmental data, comprising the steps of:

- acquiring at least one audio signal through at least two different audio sensors of an audio analysis module, said sensors being installed at a predetermined reciprocal distance and at a predetermined distance from a travelling route of a vehicle or from an observation area comprising an emitter of said audio signal;

- elaborating a correlation between the audio signals detected by the sensors, determining a forecast or instantaneous model of the travelling route or of the observation area, said model comprising the data relating to a travelling direction of the vehicle and/or a travelling speed of the vehicle and/or a pollution class of the vehicle and/or a number of vehicles on the travelling route and/or a monitoring value of the route or of the observation area comprising said emitter;

- integrating the forecast or instantaneous model with further data acquired by one or more modules, locally or remotely interconnected to said audio module, comprising an air analysis unit, a weather unit, an analogue and/or digital camera unit, a water analysis unit, a flow rate and 3D acquisition monitoring unit, a seismic analysis unit, a remote electromagnetic field analysis unit, and an oil duct monitoring unit, or a territorial database.

2. Method according to claim 1 , characterised in that a step of deterrriining said data relating to the travelling direction comprises: - a concurrent acquisition of a plurality of samples, preferably 1024 samples, of said audio signal through two audio sensors of said audio module, and transmission of said samples to an elaboration unit; - for each plurality (i.e. of 1024 samples) of samples, calculation of a correlation function of the samples acquired by the sensors and saving of respective values to a plurality of correlation vectors; - normalisation of the vectors and saving to a correlation matrix; - two-dimensional correlation of the correlation matrix with a predetermined matrix, corresponding to a model of vehicle transitions, resulting in a value identifying a probability of transition of said vehicle in said travelling direction.

3. Method according to claim 2, characterised in that it further comprises - a two- dimensional correlation of said matrix with an inverted matrix of said predetermined matrix, resulting in a second value identifying a probability of transition of said vehicle in said travelling direction, said vehicle being classified as actually passing if at least said identifying value and/or said second identifying value are greater than a predetermined threshold value.

4. Method according to claim 2, characterised in that a step of determining said data relating to the travelling speed comprises: - a concurrent acquisition, through two different pairs of said sensors, of said audio module of a plurality of samples, preferably 1024 samples, of said audio signal and transmission of said samples to an elaboration unit; - calculation of a plurality of correlation functions of the samples acquired by said pairs of sensors and saving of the respective values to a plurality of correlation vectors; - normalisation of the vectors and saving into correlation matrices; - calculation of the time delay or advance of the vehicle passage detected through a first pair of sensors relative to a second pair of sensors; - calculation of the vehicle distances from a respective installation of the pairs of sensors; - determination of the speed as comparison between the difference of the distances between the vehicle and the installations and the difference of the detection times by the respective pairs of sensors.

5. Method according to claim 2, characterised in that a step of deteirruning said data relating to the vehicle type comprises: - an analysis of the spectrum of the signal detected by a pair of said sensors, comprising: - acquisition of a predetermined number of samples, preferably 4096 samples; - selection of a subset of the acquired samples, preferably one sample out of four, and calculation of an average of the sample values; - determination of a frequency through a Fourier transform on the average value; - comparison of the frequency with predetermined frequencies identifying a respective vehicle type.

6. Method according to claim 1, characterised in that said audio analysis module receives the signals (Sh(t), Sk(t)) acquired by the two different audio sensors (Mh, Mk), detected in a sampling period ΔΤ (Q=m-AT) in input, and provides a correlation value (Chk(w)tPQ) in output corresponding to a level of similarity between two portions (Q) of said audio signals around a P of a detection time t Chk(w)tPQ =∑i=0..m Sh (t - wAT + ιΔΙ ) t + ιΔ Ι )

where P is an observation interval centred on time t (P = 2η·ΔΤ)

7. Method according to claim 6, characterised in that said instantaneous or forecast model comprises a step of comparison between said audio signal correlation (Chk(w)tPQ) and an ideal correlation between audio signals that said vehicle or emitter would emit without noise.

8. Method according to claim 7, characterised in that said correlation function (Chk(w)tPQ) is calculated as time (t) varies, determining a family of correlation curves (Chk(w,t)PQ) associated to said direction, way, speed and number of vehicle or of the emitter in said route or area.

9. Method according to claims 7-8 wherein the detection is performed by at least three microphones of said audio analysis module, preferably with a sampling at 22 Khz, in a sampling interval

in an observation time interval Q (m-ΔΤ) of 4096-ΔΤ, in an observation time interval centred on time t (P = 2η·ΔΤ) of at least 32ΔΤ and in a sampling interval AS =1024ΔΤ.

10. Method according to claims 7 to 9 comprising the determination of inflection points of the correlation curves (Chk(w,t)PQ) and the association of said inflection points with the passage of said vehicle at an intersection with a plane orthogonal to a conjunction line of said microphones.

11. Method according to claims 7 to 10 comprising the association of a slope of the correlation curves to a corresponding speed of the vehicles.

12. Method according to claims 7 to 11 comprising the association of a shape, respectively "S" or "upturned S", and of said travelling direction of the vehicle.

13. Method according to claim 1, characterised by - mechanically coupling a plurality of linear acoustic radars of said oil duct monitoring unit for identifying noise sources propagated on the oil duct; - connecting said radars over a network; - transmitting the signals acquired by a radar to the radars connected thereto and temporally synchronising such signals; - correlating the signals received with the signals acquired locally by a radar and identifying the correlation maximums; - associating such correlation maximums to a distance ot the noise source, for ideniirymg a possiDie uamage iu mc uu duct.

14. Method according to claim 1, characterised in that a microcontroller of said flow rate monitoring unit acquires voltages in output from water quality sensors, comprising nutrient parameter (nitrates, ammonia and orthophosphates) and/or chemical-physical parameters (e.g. conductivity, PH, temperature, ...) sensors, and communicates the data detected to a control unit through a radio or WiFi 802.1 lg or Zigbee network.

15. Method according to claim 1, characterised in that said flow rate monitoring unit performs a check of modulation and synchronism of the movements of at least two associated laser devices, modulating a laser in continuous wave or both lasers in pulses, respectively, for measuring the speed of the surface layers and inside layers of water (modulation in continuous wave) or a 3D profile; and calculating the flow rate based on the correlation between the 3D profiles and the speed of the water layers.