SENSOR TO SENSOR EDGE TRAFFIC INFERENCE, SYSTEM AND METHOD

Abstract

The invention discloses a system for monitoring a railway network infrastructure, the system comprising: at least one sensor node configured to obtain at least one sensor data; at least one processing component configured to: process the at least one sensor data, and generate at least one processed sensor data; at least one analyzing component configured to generate at least one railway network infrastructure hypothesis based on at least one of: the at least one sensor data, and the at least one processed sensor data. The invention also discloses a method for monitoring a railway network infrastructure, the method comprising: obtaining at least one sensor data from at least one sensor node; processing the at least sensor data to generate at least one processed sensor data; and generating at least one railway infrastructure hypothesis comprising at least one data related to the railway network infrastructure, wherein the at least one railway infrastructure hypothesis is based on at least one of the at least one sensor data, and the at least one processed sensor data.

Claims

1. A system for monitoring a railway network infrastructure, the system comprising at least one sensor node configured to obtain at least one sensor data; at least one processing component configured to process the at least one sensor data, and generate at least one processed sensor data; at least one analyzing component configured to generate at least one railway network infrastructure hypothesis based on at least one of the at least one sensor data, and the at least one processed sensor data.

2. The system according to claim 1, wherein the at least one processing component is configured to retrieve at least one user data from at least one user device configured to be in a proximity of the at least one sensor node.

3. The system according to claim 1, wherein the system comprises at least one server comprising at least one storage component; and at least one base station configured to exchange data with the at least one sensor node, wherein the at least one base station comprises a machine learning architecture.

4. The system according to claim 1, wherein the at least one analyzing component is configured to retrieve sensor data from the at least one processing component.

5. The system according to claim 3, wherein the at least one analyzing component is configured to retrieve raw user data from the at least one user device; retrieve the at least one processed sensor data from the at least one sensor node; exchange data with the at least one base station; and aggregate data sourced by the at least two of: the at least one sensor node, the at least one base station, the at least one processing component, and the at least one user device.

6. A method for monitoring a railway network infrastructure, the method comprising obtaining at least one sensor data from at least one sensor node; processing the at least sensor data to generate at least one processed sensor data; and generating at least one railway infrastructure hypothesis comprising at least one data related to the railway network infrastructure, wherein the at least one railway infrastructure hypothesis is based on at least one of the at least one sensor data, and the at least one processed sensor data.

7. The method according to claim 6, wherein obtaining the at least one sensor data from the at least one sensor node comprises obtaining at least one first sensor data from at least one first sensor node arranged on the railway network infrastructure at a first position, and obtaining at least one second sensor data from at least one second sensor node on the railway network infrastructure at a second position; and processing the at least one sensor data comprises processing at least one of the at least one first sensor data, and the at least second sensor data.

8. The method according to claim 6, wherein the method comprises predicting at least one finding for at least one unmonitored railway network infrastructure, wherein the at least one finding is based on the at least one railway infrastructure hypothesis; and comprises at least one of tonnage data, train count data, and axel count data.

9. The method according to claim 6, wherein at least one railway network infrastructure comprises at least one railway network infrastructure direction, the method comprising using at least one direction data; at least one railway network infrastructure comprises at least one switch; and at least one track segment, wherein the method comprises automatically retrieving at least one sensor data from at least one sensor processing component; aggregating data obtained by the at least two of the at least one sensor node with at least one data sourced from at least one of base station, processing component, and at least one input data; and generating at least one aggregated dataset based on at least one of base station, processing component, and at least one input data.

10. The method according to claim 6, wherein the method comprises generating at least one sensor installing data; retrieving at least one used data from at least one user device; establishing a bidirectionally communication with at least one server comprising at least one storage component; establishing a bidirectional communication with at least one base station; exchanging data between the at least one base station and the at least one sensor node; and exchanging data between the at least one user device and the at least one base station.

11. The method according to claim 6, wherein the at least one base station comprises a machine learning architecture comprising at least one neural network, wherein the method comprises teaching to the at least one neural network at least one of the at least one first sensor data, the at least one second data, the at least one processed sensor data, and the at least one aggregated dataset; and labelling at least one of the at least one first sensor data, the at least one second data, the at least one processed sensor data, the at least one aggregated dataset, and the at least one input data comprising at least one of schedule data, and at least one load data, preferably from the weighing stations.

12. The method according to claim 6, wherein the at least one sensor installing data comprises at least one of an optimized geographical location for sensor node installation data, and an optimized number of sensor nodes to be installed, and wherein the method comprises generating at least one sensor activation data, wherein the at least one sensor activation data comprises at least one of at least one optimized time period for activation of the at least one sensor node, and at least one given sensor node to be activated from the at least one senor node, wherein the method comprises activating the at least one given sensor node at a pre-determined time; and generating the at least one of sensor installing data and the at least one sensor activation data based on at least one historical data.

13. The method according to claim 6, wherein the method comprises obtaining the at least one first sensor data from the at least one first sensor node arranged on the railway network infrastructure the at a first position; processing the at least one first sensor data; obtaining at least one n-th sensor data from at least one n-th sensor node arranged on the railway network infrastructure at n-th position; processing the at least one n-th sensor data; generating a railway network infrastructure data difference finding, wherein the data difference finding is based on at least one parameter difference between the at least one first sensor data and the n-th sensor data; and outputting at least one interpreted railway network infrastructure data difference finding, wherein the interpreted railway network infrastructure data is based on the railway network infrastructure data difference finding.

14. The method according to claim 13, wherein the method comprises predicting the at least one finding for the at least one unmonitored railway infrastructure using the at least one railway infrastructure based on the at least one interpreted railway network infrastructure data difference finding.

15. The method according to claim 6, wherein the method comprises automatically aggregating at least one sensor data between at least two sensor nodes; generating at least one aggregated sensor data based on the at least one sensor data between the at least two sensor nodes; and inferring the at least one finding based on the at least one aggregated sensor data.

16. The method according to claim 13, wherein the method comprises automatically aggregating at least one sensor data between at least two sensor nodes; generating at least one aggregated sensor data based on the at least one sensor data between the at least two sensor nodes; and inferring the at least one finding based on the at least one aggregated sensor data.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0260] The present invention will now be described with reference to the accompanying drawings, which illustrate embodiments of the invention. These embodiments should only exemplify, but not limit, the present invention.

[0261] FIG. 1 depicts a sensor node routing in a railway infrastructure according to embodiments of the present invention,

[0262] FIG. 2 depicts a system embodiment according to embodiments of the present invention,

[0263] FIGS. 3a-f depict an exemplary operation of the system according to embodiments of the present invention;

[0264] FIGS. 4A-C schematically depict an exemplary railway network infrastructure according to embodiments of the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

[0265] In the following description, a series of features and/or steps are described. The skilled person will appreciate that unless explicitly required and/or unless requires by the context, the order of features and steps is not critical for the resulting configuration and its effect. Further, it will be apparent to the skilled person that irrespective of the order of features and steps, the presence or absence of time delay between steps can be present between some or all of the described steps.

[0266] It is noted that not all the drawings carry all the reference signs. Instead, in some of the drawings, some of the reference signs have been omitted for sake of brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to the accompanying drawings.

[0267] FIG. 1 depicts a sensor node 1-9 routing in a railway infrastructure according to embodiments of the present invention. There is shown an example of a railway section with the railway itself, comprising rails and sleepers. Instead of the sleepers also a solid bed for the rails can be provided. Moreover, a mast that is just one further example of constructional elements that are usually arranged at or in the vicinity of railways. A sensor node 1-9 can be arranged on one or more of the sleepers. The sensor 10 can comprise an acceleration sensor and/or any other kind of railway specific sensor. The sensor node 1-9 can further comprise a wireless sensor network. The sensor node can transmit data to a base station (not shown here). The at least one base station can be installed to the railway infrastructure. The at least one base station can also be installed in the surroundings of the railway infrastructure. The at least one base station can also be a remote base station. The communication module between the at least one base station and the sensor node (s) can comprise, for example Xbee with a frequency of 868 MHz.

[0268] The sensor node(s) 1-9 can also be installed in cases and inserted inside the railway infrastructure, for example inside a special hole carved into the concrete. The case can also be attached to the railway infrastructure using fixers. The sensor node 1-9 can be obtaining sensor data based on acceleration, inclination, distance, etc.

[0269] The sensor node 1-9 may further be divided into group, for example based on the distance. The sensor node 1-9 lying within a pre-determined distance may be controlled by one base station. The sensor node 1-9 can also be installed on the moving railway infrastructure such as on-board of a vehicle. The sensor node 1-9 can comprise an amplifier to amplify any signal received by the at least one base station.

[0270] The sensor nodes 1-9 can be installed such that the sensor node lying within one group can communicate with their base station in one-hop. The at least one base station can receive information from its ‘neighbors’ and retransmit all the information to the at least one server 800.

[0271] The sensor node 1-9 can comprise sensor(s). The sensor can be accelerometers, such as Sensor4PRI for example ADCL 345, SQ-SVS etc. The sensor node 1-9 can comprise inclinometers, such as SQ-SI-360DA, SCA100T-D2, ADXL345 etc.

[0272] The sensor node can further comprise distance sensors. The distance sensors can be configured to at least measure the distance between slab tracks, using infrared and/or ultrasonic. The distance sensor can be for example, MB1043, SRF08, PING, etc.

[0273] The sensor node 1-9 can comprise visual sensors, such as 3D cameras, speed enforcement cameras, traffic enforcement cameras, etc. It may be noted that sensor node 1-9 may comprise sensors to observe the physical environment of the infrastructure the sensor node 1-9 are installed in. For example, temperature sensor, humidity sensor, altitude sensor, pressure sensor, GPS sensor, water pressure sensor, piezometer, multidepth deflectometers (MDD), etc.

[0274] The sensor node 1-9 can be installed to the railway structure depending on the sensor. For example, the strain gauge sensor can be most efficient when installed to the rail. The piezometer can be installed to the sub-ballast. The LVDT sensor can be installed to the sleeper. One sensor node 1-9 can be installed to more than one places.

[0275] The sensor node 1-9 can be installed according to a protocol based on routing trees to be able to transmit information to the at least one base station. Once the information has been received, the UMTS technology can be used to send sensor data to a remote server 800.

[0276] The sensor node 1-9 can comprise an analog-to-digital converter, a micro controller, a transceiver, power and memory. One or more sensor(s) can be embedded in different elements and can be mounted on boards to be attached to the railway infrastructure. The sensor node 1-9 can also comprise materializing strain gauges, displacement transducers, accelerometers, inclinometers, acoustic emission, thermal detectors, among others. The analog signal outputs generated by the sensors can be converted to digital signals that can be processed by digital electronics. The data can then be transmitted to the at least one base station by a microcontroller through a radio transceiver. All devices can be electric or electronic components supported by power supply, which can be provided through batteries or by local energy generation (such as solar panels), the latter mandatory at locations far away from energy supplies.

[0277] The at least one sensor data 101 collected from the sensor nodes 1-9 can be transferred to the at least one base station using wireless communication technology such as CAN, FlexRay, Wi-Fi or Bluetooth. For example, the ZigBee network can be advantageous to consumes less power. On the other hand, for transmitting the input 101 data from the at least one base station to the at least one server 800 long-range communication such as GPRS, EDGE, UMTS, LTE or satellite can be used. Due to the short transmission range, communications from sensor nodes may not reach the at least one base station, a problem that can be overcome by adopting relay nodes to pass the data from the sensor nodes 1-9.

[0278] FIG. 2 depicts a system according to an aspect of the present invention. The at least one server 800, The collected sensor data 101 can be transmitted to the at least one server 800 server through long-range communications such as GPRS, EDGE, UMTS, LTE or satellite. The sensor node 1-9 can also communicate directly with the at least one server 800 without requiring the use of the at least one base station as a gateway.

[0279] The at least one server 800 may comprise a data transmitting component may be configured to establish a bidirectional communication with the at least one base station. In other words, the at least one server 800 may retrieve sensor data 101 from the at least one base station, and further may provide it to the at least one processing component 100, for example, vibrational data.

[0280] In one embodiment, the at least one server 800 may comprise a cloud server, a remote server and/or a collection of different type of servers. Therefore, the at least one server 800 may also be referred to as cloud server 800, remote server 800, or simple as servers 500. In another embodiment, the at least one server 800 may also converge in a central server.

[0281] It will be understood that the at least one server 800 may also be in bidirectional communication with at least one storage component and an interface component. The storage component may be configured to receive information from the at least one server 800 for storage. In simple words, the storing component 800 may store information provided by the at least one server 800. The information provided by the at least one server 800 may include, for example, but not limited to, data obtained by sensor nodes 1-9, data processed by the at least one processing component 100 and any additional data generated in the at least one server 800 or the at least one processing component 800,

[0282] It will be understood that the at least one server 800 may be granted access to the storage component comprising, inter alia, the following dictions about future or otherwise unknown events.

[0283] The storage component can comprise comprises a volatile or non-volatile memory, such as a random-access memory (RAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), static RAM (SRAM), Flash Memory, Magneto-resistive RAM (MRAM), Ferroelectric RAM (F-RAM), or Parameter RAM (P-RAM).

[0284] It will also be understood that the term server may also refer to a computer program, and/or a device, and/or a plurality of each or both that may provide functionality for other programs, devices and/or components of the present invention. For instance, at least one server may provide various functionalities, which may

[0285] be referred to as services, such as, for example, sharing data or resources among multiple clients, or performing computation and/or storage functions. It will further be understood that a single server may serve multiple clients, and a single client may use multiple servers. Furthermore, a client process may run on the same device or may connect over a network to at least one server on a different device, such as a remote server or a cloud. The at least one server may have rather primitive functions, such as just transmitting rather short information to another level of infrastructure, or can have a more sophisticated structure, such as a storing, processing and transmitting unit.

[0286] The at least one processing component 100 can comprise a CPU (central processing unit), GPU graphical processing unit), DSP (digital signal processor), APU (accelerator processing unit), ASIC (application-specific integrated circuit), ASIP (application-specific instruction-set processor) or FPGA (field programmable gate array) or any combination thereof.

[0287] The at least one processing component 100 can further be generating the structured database 103 using the at least one sensor data 101. The structured database 103 may comprise. The at least one processing component 100 can be configured to automatically recognize the sensor associated with the at least one sensor data 101 and can further generate structured database 103 based on the type of the sensor.

[0288] The at least one processing component 100 can be configured with machine learning techniques, such as pattern recognition. The at least one processing component can further be configured to generate labeled data using the structured database 103 and/or the at least one sensor data 101.

[0289] The processed data, meaning the data transmitting from the at least one processing component 100 which can comprise the structured database and/or the labeled data. The processed data can be then automatically pulled by the analyzing component 300. The analyzing component 300 can comprise generating trajectory data based on at least the at least one sensor data (temperature, waves, speed, etc.).

[0290] The analyzing component 300 may comprise of a computer program product which can be configured to be programmed based on at least one of dynamical systems, statistical models, differential equations, game theoretic models, logic. The analyzing component 300 can be equipped with neural networks. The analyzing component 300 can further be configured to automatically learn the at least one of governing equations, assumptions, constraints using an existing knowledgebase. The analyzing component 300 can also learn using the at least one sensor data and/or user data and/or input data.

[0291] The trajectory data generated by the analyzing component 300 can be automatically fed to the sensor routine module 501. The sensor routine module 501 can comprise a machine learning classifier. The sensor routine module 501 may be trained using the trajectory data to generate labeled input data. The sensor routine module 501 can be configured to generate the labeled data by using at least one of k-nearest neighbor, case-based reasoning, artificial neural networks, Naïve Bayes, etc.

[0292] The sensor routine module 501 can further be configured to predict at least one infrastructural feature (ballast, frog, geometry, speed, etc.) based on the labeled data and can further transmit the results to a user device.

[0293] The at least one user device can comprise a memory component such as, main memory (e.g. RAM), cache memory (e.g. SRAM) and/or secondary memory (e.g. HDD, SDD). The at least one user device 200 may also comprise at least of an output user interface, such as: screens or monitors configured to display visual data (e.g. displaying graphical user interfaces of the questionnaire to the user), speakers configured to communicate audio data (e.g. playing audio data to the user). The at least one user device 200 can also comprise an input user interface, such as, camera configured to capture visual data (e.g. capturing images and/or videos of the user), microphone configured to capture audio data (e.g. recording audio from the user), and a keyboard configured to allow the insertion of text and/or other keyboard commands (e.g. allowing the user to enter text data and/or another keyboard and mouse, touchscreen, joystick – configured to facilitate the navigation through different graphical user interfaces of the questionnaire.

[0294] FIG. 3 shows an exemplary network layout of the sensor nodes 1-9 in a railway infrastructure. The sensor nodes 1-9 can be installed in a proximity of a switch 701 as shown in a, wherein the 601 is a path of a rail vehicle and 801 is a railway track.

[0295] In FIGS. 3b, c, d, e and f, respectively, different embodiments related to installation of sensor node 1-9 in proximity of the switch 701 and railway track 801 can be seen. This can facilitate a full coverage of the vehicle path 601.

[0296] FIGS. 4A-C schematically depict an exemplary railway network infrastructure according to embodiments of the present invention. In simple terms, FIG. 4A depicts a layout of the sensor nodes 1-9 (not depict inf FIG. 4A) in a railway network infrastructure. The sensor nodes 1-9 may be installed in a proximity of the switch 701 and the railway track 801 is a railway track. In such a layout, it may possible to measure a load on a joint track conceptually identified in FIG. 4A by reference numeral 910. The load on the joint track 910 may, for instance, be measure by of the nodes 1-9, such as a sensor node arranged on the switch 701 (not depict).

[0297] Additionally or alternatively, as schematically depicted in FIG. 4B, it may also be possible to know two arms of a switch, as conceptually identified in FIG. 4B by reference numeral 920. Once two arms 920 are adequately known, it may possible to estimate a third arm, such as for example, via at least one analyzing component and/or processing component. It should be understood that such an approach may also be extended to a plurality of switches comprising at least 3 arms, such for example, comprising at least 5 arms.

[0298] Subsequently, it may be possible to implement an approach comprising a higher-order logic as depicted inf FIG. 4C, wherein remaining track segments (1. and/or 2.) may iteratively be estimated based on an assumed known segment X.

[0299] Moreover, it should be understood that some segment of the railway infrastructure may be over-determined, so that the current approach may further allow to optimize placement of sensors, which may further facilitate to reduce sensor count while maximizing coverage of the railway infrastructure. Furthermore, individual sensors in combination with the current approach may further provide count as well as other characteristics such as train type of at least one train circulating on the railway network infrastructure, which may further allow a more granular analysis by using the iterative approach described above, which may be implemented for a plurality of individual data sub-category. It should be understood that data estimated for the plurality of individual data sub-category may further be summed up, such for example, via averaging approaches, wherein the summing up may selectively be based on a desired metric.

[0300] Additionally or alternatively, the above-described approach may also be combined with a plurality of further approaches, such as, for example, using additional information like schedule, priors in terms of typical train properties e.g. trains tend to go straight whenever possible due to the allowed speeds being higher than on a diverging track, using train trajectory matching and/or making statements about super-segments which may consist of multiple segments. The latter may be particularly advantageous in maintenance cases, where maintenance may often happen on multiple segments simultaneously.

[0301] Reference numbers and letters appearing between parentheses in the claims, identifying features described in the embodiments and illustrated in the accompanying drawings, are provided as an aid to the reader as an exemplification of the matter claimed. The inclusion of such reference numbers and letters is not to be interpreted as placing any limitations on the scope of the claims.

[0302] The term “at least one of a first option and a second option” is intended to mean the first option or the second option or the first option and the second option.

[0303] Whenever a relative term, such as “about”, “substantially” or “approximately” is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., “substantially straight” should be construed to also include “(exactly) straight”.

[0304] Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Y1), ..., followed by step (Z). Corresponding considerations apply when terms like “after” or “before” are used.