Portable add-on instrument based on fiber optic inertial navigation and cloud management system

Abstract

The embodiment of the present application relates to the technical field of rail transit, and discloses a portable on-boarding instrument based on optical fiber inertial navigation and a cloud management system, the on-boarding instrument comprising an audio sensor, an inertial navigation system, a GPS positioning system, a central processing unit and a communication module; the audio sensor collects noise signals in a train compartment; the inertial navigation system measures acceleration data and vibration data of the train, and determines real-time speed and inertial navigation mileage of the train according to the acceleration data when there is no GPS signal or when there is a GPS signal; the GPS positioning system obtains longitude and latitude information of the train when there is a GPS signal; the central processing unit uploads the noise signals, the acceleration data, the vibration data, the real-time speed, the inertial navigation mileage and the longitude and latitude information to a predetermined cloud platform through the communication module. In the above manner, the embodiment of the present application can comprehensively collect and detect various data in the train running process, the train positioning data based on the inertial navigation mileage and the longitude and latitude information has high accuracy, and the cloud platform is used to uniformly and efficiently manage the data.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of rail transit technology, specifically to a portable ride-on device and cloud management system based on fiber optic inertial navigation. Background Technology

[0002] With the rapid development of rail transit, the monitoring of train operation status has become increasingly important. Traditional train monitoring equipment mostly uses single-sensor positioning methods such as accelerometers and GPS locators. For example, accelerometers can detect the acceleration and vibration of the train, while GPS locators can detect the train's position, thus playing a certain role in ensuring the safe operation of the train.

[0003] However, existing train positioning systems are ineffective in environments lacking GPS signals, such as tunnels or other signal-blocked areas. Urban rail transit underground tunnels are GPS-free environments, making train location difficult and hindering the matching of detection results with on-site mileage. Furthermore, while existing train detection equipment has data acquisition capabilities, its timely data processing and comprehensive management are inadequate, making it difficult to guarantee the real-time nature and accuracy of the detection data. Therefore, a comprehensive, accurate, and efficient solution for the detection and management of train operation status data is urgently needed. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a portable ride-on device and cloud management system based on fiber optic inertial navigation to solve the problems existing in the prior art.

[0005] According to one aspect of the present invention, a portable ride-on device based on fiber optic inertial navigation is provided. The portable ride-on device is placed in a moving train carriage and includes an audio sensor, an inertial navigation system, a GPS positioning system, a central processing unit, and a communication module.

[0006] The audio sensor collects noise signals inside the train carriage and sends the noise signals to the central processing unit;

[0007] The inertial navigation system measures the acceleration and vibration data of the train. When there is no GPS signal or when there is a GPS signal, it determines the real-time speed and inertial navigation mileage of the train based on the acceleration data, and sends the real-time speed, the inertial navigation mileage, the acceleration data and the vibration data to the central processing unit. The acceleration data includes acceleration, angular acceleration and vibration acceleration.

[0008] The GPS positioning system acquires the latitude and longitude information of the train when a GPS signal is available, and sends the latitude and longitude information to the central processing unit;

[0009] The central processing unit uploads the noise signal, acceleration data, vibration data, real-time velocity, inertial navigation mileage, and latitude and longitude information to a predetermined cloud platform through the communication module.

[0010] In one alternative approach, when the central processing unit receives a zero-speed instruction, it sends the zero-speed instruction to the inertial navigation system, wherein the zero-speed instruction includes at least an arrival / departure instruction.

[0011] When there is no GPS signal or when there is a GPS signal, the inertial navigation system performs zero-speed correction on the acceleration data according to the zero-speed command, and calculates the inertial navigation mileage of the train based on the zero-speed corrected acceleration data.

[0012] In one alternative approach, the inertial navigation system calculates the initial mileage S0 based on the zero-velocity corrected acceleration data;

[0013] The inertial navigation system also measures the head sway angular velocity of the train, determines the curved travel segment based on the head sway angular velocity, and determines the first mileage deviation ΔS1 of each principal point in the curved travel segment based on the head sway angular velocity of the curved travel segment and the curvature of the curved travel segment.

[0014] Based on the initial mileage S0 and the first mileage deviation ΔS1, the inertial navigation system performs linear interpolation on straight driving sections and curve interpolation on curved driving sections according to a preset timestamp to obtain the interpolated second mileage deviation ΔS2.

[0015] The inertial navigation mileage is calculated based on the initial mileage S0, the first mileage deviation ΔS1, and the second mileage deviation ΔS2.

[0016] In one alternative approach, the inertial navigation system calculates a first correction velocity in real time and sends the first correction velocity to the central processing unit.

[0017] The inertial navigation system calculates the second corrected velocity based on the zero-velocity corrected acceleration data;

[0018] The central processing unit acquires the train speed from the GPS positioning system, determines a speed error value based on the train speed and the first corrected speed, and feeds back the speed error value to the inertial navigation system; the inertial navigation system calculates a third corrected speed based on the speed error value and sends the third corrected speed to the central processing unit.

[0019] The central processing unit uploads the first correction speed, the second correction speed, and / or the third correction speed to the cloud platform through the communication module.

[0020] In one alternative approach, the noise signal, the acceleration data, the vibration data, the real-time velocity, the inertial navigation mileage, and the latitude and longitude information are all time-series data based on time.

[0021] After acquiring the noise signal, acceleration data, vibration data, real-time velocity, inertial navigation mileage, and latitude and longitude information, the central processing unit further uses a predetermined data alignment algorithm to align the noise signal, acceleration data, vibration data, real-time velocity, inertial navigation mileage, and latitude and longitude information.

[0022] In one alternative approach, the central processing unit calculates a comfort index based on the acceleration data, calculates a stability index based on the vibration data, and uploads the comfort index and the stability index to the cloud platform via the communication module.

[0023] If the comfort index is greater than a predetermined index threshold, and / or if the stability index is greater than a predetermined index threshold, then an early warning signal is uploaded to the cloud platform via the communication module, or an early warning signal is sent to a predetermined terminal.

[0024] In one alternative approach, when there is no inertial navigation mileage or no GPS signal, the inertial navigation system receives input latitude and longitude information;

[0025] Once the calibration is successful based on the input latitude and longitude information, the inertial navigation system performs system alignment.

[0026] According to another aspect of the present invention, a cloud management system is provided, the cloud management system including a cloud platform and a portable ride-on device based on fiber optic inertial navigation as described above.

[0027] In one alternative approach, the cloud platform includes an access control module that receives the user's registered identity information on the cloud platform. The identity information includes at least the unique device code of the ride-sharing device. The module authenticates the identity information when the user logs into the cloud platform and performs hierarchical access control on the data uploaded by the ride-sharing device.

[0028] In one alternative approach, the cloud platform includes a data display module that graphically processes and displays the noise signal, the acceleration data, the vibration data, the real-time velocity, the inertial navigation mileage, and the latitude and longitude information.

[0029] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction, which, when executed on a computer device, causes the computer device to perform the steps of the central processing unit as described above.

[0030] The portable passenger-carrying device based on fiber optic inertial navigation in this invention includes an audio sensor, an inertial navigation system, a GPS positioning system, and a central processing unit. The audio sensor collects noise signals inside the train carriage. The inertial navigation system measures the train's acceleration and vibration data. Whether there is a GPS signal or not, the inertial navigation system can determine the train's real-time speed and inertial navigation mileage based on the acceleration data. The GPS positioning system acquires the train's latitude and longitude information when there is a GPS signal. The central processing unit uploads the noise signal, acceleration data, vibration data, real-time speed, inertial navigation mileage, and latitude and longitude information to a predetermined cloud platform. It can comprehensively collect and detect various data during train operation. The train positioning data based on inertial navigation mileage and latitude and longitude information has high accuracy, providing a guarantee for the safe operation of the train. By uploading the data to the cloud platform, the data can be managed in a unified and efficient manner.

[0031] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0032] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0033] Figure 1 A perspective view of a portable ride-on device based on fiber optic inertial navigation provided in an embodiment of the present invention is shown;

[0034] Figure 2 A schematic diagram of the structure of the portable ride-on device based on fiber optic inertial navigation provided in an embodiment of the present invention is shown;

[0035] Figure 3 A schematic diagram of the structure of the cloud management system provided in an embodiment of the present invention is shown;

[0036] Figure 4 A schematic diagram of the display interface of the cloud management system provided in an embodiment of the present invention is shown. Detailed Implementation

[0037] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0038] like Figures 1-2As shown in the figure, the portable passenger-following device based on fiber optic inertial navigation provided in this embodiment of the invention has a box-type structure, which includes a box and an audio sensor, an inertial navigation system, a GPS positioning system, a central processing unit, and a communication module disposed inside the box. The portable passenger-following device is placed in a moving train carriage, and only one carriage of a train needs to be selected to place the portable passenger-following device.

[0039] The audio sensor collects noise signals inside the train carriage and sends these signals to the central processing unit. The noise signals inside the train carriage include sounds emitted by passengers, sounds from the tracks, and other ambient sounds. The audio sensor can be a microphone embedded in the side of the portable passenger information display device, or other types of audio sensors; no specific limitation is made here.

[0040] Preferably, the inertial navigation system is a fiber optic inertial navigation system. The inertial navigation system measures the train's acceleration and vibration data. Acceleration data includes acceleration and angular acceleration; vibration data includes vibration acceleration, vibration frequency, and vibration amplitude. The inertial navigation system can calculate the train's real-time speed and inertial navigation mileage based on the acceleration data. Specifically, the inertial navigation system can calculate the train's real-time speed using a numerical integration algorithm based on the train's initial speed and acceleration, and can calculate the train's inertial navigation mileage based on the acceleration and other data.

[0041] In this embodiment, whether there is a GPS signal or not, the inertial navigation system can be used to determine the real-time speed and inertial navigation mileage of the train based on the acceleration data, and the real-time speed, inertial navigation mileage, acceleration data and vibration data can be sent to the central processing unit.

[0042] When GPS signals are available, the GPS positioning system receives satellite signals and obtains the train's latitude and longitude information, as well as its speed, based on these signals. This information is then sent to the central processing unit. However, satellite signals may be affected by various interferences and errors during transmission, such as multipath delay, noise, and time lag. This embodiment uses filters, such as EKF or UKF, to filter the satellite signals, thus handling these interferences and errors and obtaining more accurate satellite signals.

[0043] The central processing unit (CPU) uploads noise signals, acceleration data, vibration data, real-time velocity, inertial navigation mileage, and latitude and longitude information to a predetermined cloud platform in real time or at regular intervals via a communication module. The communication module can be a 5G communication module, which can quickly upload all data to the cloud platform. The cloud platform can then perform unified analysis, management, and remote storage of the data.

[0044] It should be noted that the steel rails, tracks, and wheel rails referred to in all embodiments of the present invention refer to the tracks on which trains travel.

[0045] In one embodiment, to obtain accurate inertial navigation mileage, the inertial navigation system performs zero-speed correction before calculating the pipeline mileage. Specifically, when the central processing unit receives a zero-speed command, it sends the command to the inertial navigation system. Whether there is no GPS signal or a GPS signal is available, the inertial navigation system performs zero-speed correction on the acceleration data based on the zero-speed correction, and calculates the train's inertial navigation mileage based on the zero-speed corrected acceleration data.

[0046] The zero-speed command includes at least entry and exit commands. The entry command is triggered by pressing the "Enter" button, causing the train to enter the station and enter "Enter Mode," where the train is stationary at zero speed. The exit command is triggered by pressing the "Exit" button, causing the train to exit the station and enter "Exit Mode," where the train is still stationary at zero speed before starting to move. The train triggers zero-speed correction in the inertial navigation system (INS) while at zero speed. Zero-speed correction is used to correct the acceleration bias of the gyroscope online, suppressing drift, thus allowing the INS to calculate more accurate INS mileage. Of course, the zero-speed command can also be other high-confidence zero-speed commands, such as emergency braking commands; this is not limited here. In this embodiment, the train's INS mileage can be calculated by the INS regardless of the presence or absence of GPS signal.

[0047] In this embodiment, when the train triggers zero-speed correction at zero speed, the filter injects zero speed into the observation equation, which is an expression equation describing the relationship between sensor measurements and system state in the inertial navigation system; updates the state covariance, corrects the gyroscope / accelerometer bias; and clears the speed drift to zero and corrects the position drift according to the filter gain.

[0048] Furthermore, the inertial navigation system calculates the initial mileage S0 based on the acceleration data after zero-speed correction; the inertial navigation system also measures the train's head-swing angular velocity, determines the curved driving segment based on the head-swing angular velocity, and determines the first mileage deviation ΔS1 of each principal point in the curved driving segment based on the head-swing angular velocity and the curvature of the curved driving segment; based on the initial mileage S0 and the first mileage deviation ΔS1, the inertial navigation system performs linear interpolation on the straight driving segment and curve interpolation according to a preset timestamp on the curved driving segment to obtain the interpolated second mileage deviation ΔS2; the inertial navigation mileage is calculated based on the initial mileage S0, the first mileage deviation ΔS1, and the second mileage deviation ΔS2.

[0049] In this embodiment, the inertial navigation system integrates the zero-velocity corrected acceleration data twice to obtain an initial mileage S0 with a significantly smaller value.

[0050] S0(t)=∫∫a(t)dt,

[0051] Where a(t) is the acceleration after zero velocity correction, and t is time.

[0052] Integrating the acceleration data after zero-velocity correction enables rapid convergence of velocity drift and significantly suppresses integral drift.

[0053] This embodiment matches the angular velocity of the head tilt on the curved driving segment with the curvature of the curved driving segment to obtain the first mileage deviation ΔS1 for each principal point in the curved driving segment. The first mileage deviation ΔS1 is the mileage deviation calculated based on the angular velocity and curvature. The principal points in the curved driving segment include straight-to-gradient points, gradual-to-round points, round-to-gradient points, and gradual-to-straight points. Even in the absence of GPS signal, this embodiment can still obtain accurate inertial navigation mileage by correcting the remaining drift through geometric reference.

[0054] This embodiment performs interpolation on the corresponding line positions for {S0(t), ΔS1}, where interp is the corresponding interpolation algorithm. Linear interpolation is used for straight lines, and the mileage fitting function is compared with the logbook for curve segments. It is assumed that the number of principal points of the line curve is n, and the timestamp θ ​​when the train passes a certain feature point of the curve is... t Curve interpolation is performed at timestamp intervals to obtain the second mileage deviation ΔS2 at any time t. The second mileage deviation ΔS2 is the mileage deviation before and after interpolation.

[0055] ΔS2(t)=interp(θ t ,ΔS1 i ,t);

[0056] In this embodiment, the inertial navigation mileage S(t) is calculated based on the initial mileage S0, the first mileage deviation ΔS1, and the second mileage deviation ΔS2:

[0057] S(t)=S0(t)+ΔS1+ΔS2(t).

[0058] In this embodiment, the inertial navigation mileage calculated using the initial mileage S0, the first mileage deviation ΔS1, and the second mileage deviation ΔS2 is further improved by zero-speed correction. The angular velocity of the head swing on the curved driving section is matched with the curvature of the curved driving section to eliminate the error of the curved driving section. The deviation is also obtained through interpolation, thus achieving smooth output of the mileage. Therefore, the inertial navigation mileage calculated in this embodiment is an accurate mileage, achieving accurate mileage positioning.

[0059] In one embodiment, the portable ride-on device can correct the train speed and mileage according to the actual environment of the train's operation, providing a reference for the train speed and mileage and ensuring the accuracy of the data. Specifically, the inertial navigation system (INS) calculates a first corrected speed in real time and sends it to the central processing unit (CPU); the INS calculates a second corrected speed based on the acceleration data after zero-speed correction; the CPU obtains the train speed from the GPS positioning system, determines a speed error value based on the train speed and the first corrected speed, and feeds the speed error value back to the INS; the INS calculates a third corrected speed based on the speed error value and sends the third corrected speed to the CPU; the CPU uploads the first, second, and / or third corrected speeds to the cloud platform via a communication module.

[0060] In inertial navigation systems (INS), speed is typically calculated by integrating the inertial acceleration. However, due to issues such as zero bias, scaling factor error, and noise in inertial sensors, speed and mileage estimates drift over time. To improve the accuracy of train speed estimation, speed correction can be performed within the INS. A first corrected speed is calculated in real-time. This internal speed correction relies primarily on the INS's own algorithms, which process sensor data and establish error models to correct the speed in real-time. The mileage calculated using this first corrected speed is the corrected mileage. Alternatively, speed correction can be performed using external data, such as zero-speed commands or GPS data. During train operation, inertial sensors continue to operate at low speeds or when the train is stopped at or near a station, causing speed drift. Zero-speed commands provide the train's speed information at a specific location. When the train reaches the station entry or exit point, the equipment management platform can send a corresponding zero-speed command to the central processing unit (CPU) to determine that the current speed should be zero or a specific value. The CPU then sends the command to the inertial navigation system (INS). At this point, the INS can correct the current speed based on the command to obtain a second corrected speed, reducing speed drift caused by inertial sensor errors. The mileage calculated using the second corrected speed is the corrected mileage.

[0061] For GPS data correction speed, when GPS signal is available, the speed error between the GPS positioning system and the inertial navigation system (INS) can be calculated by comparing the speed provided by the GPS positioning system with the speed estimate from the INS. Then, the Kalman filter algorithm is used to feed the speed error back to the INS for speed correction, resulting in a third corrected speed. The mileage calculated using this third corrected speed is the corrected mileage.

[0062] Through the aforementioned velocity correction methods, the inertial navigation system (INS) can effectively utilize internal algorithms and external data to correct velocity in real time, thereby improving navigation accuracy. It is important to note that the effectiveness of external data-based velocity correction depends on the quality and availability of the external data. Therefore, in practical applications, the advantages and disadvantages of both internal and external data corrections need to be considered comprehensively. Optionally, velocity correction can be performed using zero-speed commands when entering or leaving a station; when GPS signals are available, velocity correction can be performed using the velocity provided by the GPS positioning system; and when GPS signals are unavailable, such as in underground tunnel sections, velocity correction can be performed internally within the INS.

[0063] In one embodiment, the noise signal, acceleration data, vibration data, real-time speed, mileage, and latitude and longitude information are all time-series data based on time. After acquiring the noise signal, acceleration data, vibration data, real-time speed, mileage, and latitude and longitude information, the central processing unit also uses a predetermined data alignment algorithm to align the noise signal, acceleration data, vibration data, real-time speed, mileage, and latitude and longitude information.

[0064] In this embodiment, the predetermined data alignment algorithm can be a timestamp-based precise matching algorithm or a signal cross-correlation alignment algorithm, etc., and is not limited here. Preferably, the data alignment algorithm in this embodiment is a data alignment algorithm based on a dynamic time warp algorithm. Since noise signals, acceleration data, vibration data, real-time speed, mileage, and latitude and longitude information are all time-series data based on time, all data collected in the portable ride-on device can be aligned based on time to ensure data consistency and accuracy.

[0065] In one embodiment, the central processing unit calculates a comfort index based on the acceleration data and a stability index based on the vibration data. If the comfort index is greater than a predetermined index threshold, and / or if the stability index is greater than a predetermined index threshold, then an early warning signal is uploaded to the cloud platform via the communication module, or an early warning signal is sent to a predetermined terminal.

[0066] The indicator thresholds and index thresholds are both empirical values ​​derived from historical data, and both can be dynamically adjusted adaptively according to the actual train operation scenario, improving the accuracy of the early warning. Vibration data includes vibration acceleration, vibration frequency, and vibration amplitude. When the train's comfort index exceeds the predetermined indicator threshold, and / or the stability index exceeds the predetermined index threshold, an early warning is triggered, generating an early warning signal. This signal is then uploaded to the cloud platform via the communication module for unified processing, or sent to a designated terminal for timely maintenance by maintenance personnel.

[0067] In one embodiment, the central processing unit calculates a comfort index based on acceleration data and a stability index based on vibration data, and uploads the comfort index and stability index to a cloud platform via a communication module.

[0068] Among them, the comfort index is used to assess the subjective comfort level of passengers, while the stability index can quantify the smoothness level of vehicle operation.

[0069] For comfort indicators, vehicle acceleration data is acquired using a portable passenger-side data acquisition system (acceleration data can be decomposed into longitudinal, lateral, and vertical three-axis data). Preprocessing is performed first, including setting the sampling frequency (typically ≥100Hz), using low-pass filtering to eliminate high-frequency noise, and removing low-frequency trend terms through high-pass filtering or baseline correction. Then, coordinate transformation and component decomposition are performed: the acceleration data is transformed to the vehicle coordinate system, and the longitudinal, lateral, and vertical components are separated to ensure the data direction is consistent with the actual vehicle motion. Finally, comfort indicators are calculated: the three-axis acceleration is frequency-weighted (e.g., using a Wk weighted curve), the root mean square value (RMS) of the weighted acceleration is calculated, and the overall comfort indicator (e.g., total weighted acceleration RMS) is synthesized using a predetermined formula. For example, for standing posture, the comfort indicator is:

[0070]

[0071] Among them, a XP a YP a ZP This represents the acceleration components in the horizontal, vertical, and longitudinal directions. The superscript of each acceleration component indicates frequency weighting, and the subscript indicates the confidence point of the effective acceleration value. For example... The longitudinal acceleration is represented by Wd frequency weighting and the effective value is taken at the 95% confidence point.

[0072] Of course, in addition to standing posture, sitting posture is also included. The method for calculating the comfort index of sitting posture is similar to that of standing posture.

[0073] For the stability index, considering the characteristics of rail vehicles, the stability index W can be calculated by combining the vibration frequency and amplitude:

[0074]

[0075] Where j is the vibration acceleration, f is the vibration frequency, and F(f) is a correction coefficient related to the vibration frequency f, which can be obtained through experimental statistics.

[0076] The central processing unit uploads comfort and stability indices to the cloud platform via the communication module for unified processing or management.

[0077] In addition, such as Figure 1As shown, the portable ride-sharing device also includes a VGA video signal interface, a network port, a power interface, a USB interface, a power display screen, and a battery. The VGA video signal interface can be connected to an external display device, the USB interface is used for data transmission, and the portable ride-sharing device has an intelligent power management system that can automatically adjust power consumption according to usage to extend battery life.

[0078] The portable passenger-carrying device based on fiber optic inertial navigation in this invention includes an audio sensor, an inertial navigation system, a GPS positioning system, and a central processing unit. The audio sensor collects noise signals inside the train carriage. The inertial navigation system measures the train's acceleration and vibration data. Whether there is a GPS signal or not, the inertial navigation system can determine the train's real-time speed and inertial navigation mileage based on the acceleration data. The GPS positioning system acquires the train's latitude and longitude information when there is a GPS signal. The central processing unit uploads the noise signal, acceleration data, vibration data, real-time speed, inertial navigation mileage, and latitude and longitude information to a predetermined cloud platform. It can comprehensively collect and detect various data during train operation. The train positioning data based on inertial navigation mileage and latitude and longitude information has high accuracy, providing a guarantee for the safe operation of the train. By uploading the data to the cloud platform, the data can be managed in a unified and efficient manner.

[0079] In one embodiment, when there is no inertial navigation mileage or no GPS signal, the inertial navigation system receives input latitude and longitude information; when calibration is successful based on the input latitude and longitude information, the inertial navigation system performs system alignment.

[0080] In this embodiment, if the inertial navigation system (INS) cannot automatically locate itself for an extended period or has no GPS signal, latitude and longitude information can be manually input for calibration. If the INS calibration is successful, it will perform system alignment. During system alignment, the ride-on instrument remains stationary, waiting for attitude alignment to complete. System alignment facilitates obtaining accurate data in subsequent testing.

[0081] like Figure 3 As shown, the present invention also provides a cloud management system, which includes a cloud platform and a portable ride-on device based on fiber optic inertial navigation according to any of the above embodiments.

[0082] The cloud platform includes an access control module. This module receives user registration information, including at least the unique device code of the ride-sharing device. When a user logs in, the module authenticates the entered identity information; only those who pass authentication can log in, otherwise the login fails, thus improving security. Furthermore, the access control module implements tiered access control for the data uploaded by the ride-sharing device. Data can be categorized as sensitive or non-sensitive. Sensitive data includes mileage, latitude, and longitude information, etc. Access permissions are set for sensitive data, allowing only authorized personnel to view it, thus enhancing data security.

[0083] The cloud platform includes a data display module, which graphically processes and displays noise signals, acceleration data, vibration data, real-time speed, mileage, and latitude / longitude information. For example... Figure 4 As shown, the cloud platform includes a display interface. Data uploaded by the portable ride-sharing device is processed by the cloud platform and can be displayed in categories. Figure 4 The interface includes a data acquisition and management interface, a real-time monitoring interface, a historical data interface, a trajectory display interface, and a test record interface. The data acquisition and management interface displays and manages uploaded data, the real-time monitoring interface displays graphically processed data, and the historical data interface manages and displays historical data.

[0084] The cloud management system in this embodiment uses end-to-cloud collaborative technology to comprehensively, accurately, continuously and in real time detect train status data, ensuring low latency and high accuracy of train status data. The cloud platform also enables unified and efficient management of train status data.

[0085] This invention provides a computer-readable storage medium storing at least one executable instruction that, when executed on a computer device, causes the computer device to perform the steps of the central processing unit described above.

[0086] This invention provides a computer program that can be called by a central processing unit (CPU) to cause a computer device to execute the steps described above by the CPU.

[0087] This invention provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions that, when executed on a computer, cause the computer to perform the steps of the central processing unit described above.

[0088] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0089] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0090] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0091] Those skilled in the art will understand that modules in the computer device of the embodiments can be adaptively modified and placed in one or more computer devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or computer device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0092] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A portable ride-on device based on fiber optic inertial navigation, characterized in that, The portable ride-on device is placed in a moving train carriage and includes an audio sensor, an inertial navigation system, a GPS positioning system, a central processing unit, and a communication module. The audio sensor collects noise signals inside the train carriage and sends the noise signals to the central processing unit; The inertial navigation system measures the acceleration and vibration data of the train. When there is no GPS signal or when there is a GPS signal, it determines the real-time speed and inertial navigation mileage of the train based on the acceleration data, and sends the real-time speed, the inertial navigation mileage, the acceleration data and the vibration data to the central processing unit. The acceleration data includes acceleration, angular acceleration and vibration acceleration. The GPS positioning system acquires the latitude and longitude information of the train when a GPS signal is available, and sends the latitude and longitude information to the central processing unit; The central processing unit, through the communication module, uploads the noise signal, the acceleration data, the vibration data, the real-time velocity, the inertial navigation mileage, and the latitude and longitude information to a predetermined cloud platform; When the central processing unit receives a zero-speed instruction, it sends the zero-speed instruction to the inertial navigation system, wherein the zero-speed instruction includes at least an entry / exit instruction. When there is no GPS signal or a GPS signal is present, the inertial navigation system performs zero-speed correction on the acceleration data according to the zero-speed command, and calculates the inertial navigation mileage of the train based on the zero-speed corrected acceleration data; The inertial navigation system calculates the initial mileage S0 based on the acceleration data after zero-velocity correction. The inertial navigation system also measures the head-swing angular velocity of the train, determines the curved travel segment based on the head-swing angular velocity, and determines the first mileage deviation ΔS1 of each principal point in the curved travel segment based on the head-swing angular velocity of the curved travel segment and the curvature of the curved travel segment; The inertial navigation system performs linear interpolation on straight driving sections and curve interpolation on curved driving sections according to preset timestamps, based on the initial mileage S0 and the first mileage deviation ΔS1, to obtain the interpolated second mileage deviation ΔS2; The inertial navigation mileage is calculated based on the initial mileage S0, the first mileage deviation ΔS1, and the second mileage deviation ΔS2.

2. The portable ride-on device according to claim 1, characterized in that, The inertial navigation system calculates the first correction velocity in real time and sends the first correction velocity to the central processing unit. The inertial navigation system calculates the second corrected velocity based on the zero-velocity corrected acceleration data; The central processing unit acquires the train speed of the GPS positioning system, determines a speed error value based on the train speed and the first corrected speed, and feeds back the speed error value to the inertial navigation system. The inertial navigation system calculates a third corrected velocity based on the velocity error value and sends the third corrected velocity to the central processing unit. The central processing unit uploads the first correction speed, the second correction speed, and / or the third correction speed to the cloud platform through the communication module.

3. The portable ride-on device according to claim 1, characterized in that, The noise signal, the acceleration data, the vibration data, the real-time velocity, the inertial navigation mileage, and the latitude and longitude information are all time-series data based on time. After acquiring the noise signal, acceleration data, vibration data, real-time velocity, inertial navigation mileage, and latitude and longitude information, the central processing unit further uses a predetermined data alignment algorithm to align the noise signal, acceleration data, vibration data, real-time velocity, inertial navigation mileage, and latitude and longitude information.

4. The portable ride-on device according to claim 1, characterized in that, The central processing unit calculates a comfort index based on the acceleration data and a stability index based on the vibration data, and uploads the comfort index and the stability index to the cloud platform through the communication module. If the comfort index is greater than a predetermined index threshold, and / or if the stability index is greater than a predetermined index threshold, then an early warning signal is uploaded to the cloud platform via the communication module, or an early warning signal is sent to a predetermined terminal.

5. The portable ride-on device according to claim 1, characterized in that, When there is no inertial navigation mileage or no GPS signal, the inertial navigation system receives the input latitude and longitude information; Once the calibration is successful based on the input latitude and longitude information, the inertial navigation system performs system alignment.

6. A cloud management system, characterized in that, The cloud management system includes a cloud platform and a portable ride-on device based on fiber optic inertial navigation as described in any one of claims 1-5.

7. The cloud management system according to claim 6, characterized in that, The cloud platform includes an access control module, which receives the identity information registered by the user on the cloud platform. The identity information includes at least the unique device code of the ride-sharing device. The module authenticates the identity information when the user logs into the cloud platform and performs hierarchical access control on the data uploaded by the ride-sharing device.

8. The cloud management system according to claim 6 or 7, characterized in that, The cloud platform includes a data display module, which graphically processes and displays the noise signal, acceleration data, vibration data, real-time velocity, inertial navigation mileage, and latitude and longitude information.

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