Method and device for estimating occurrence position of event of interest and computing equipment

Abstract

The invention provides a method and device for estimating the occurrence position of an event of interest and computing equipment. The method may include: determining a position determination delay time period based on a first time when the event of interest occurs and a second time when motion information is acquired earliest; acquiring a plurality of pieces of motion information at a plurality of sampling time points with preset sampling intervals, wherein each piece of motion information comprises the motion speed, the motion direction and the position of the motion carrier at the corresponding sampling time point; determining a motion change rate of the motion carrier based on the multiple pieces of motion information; and determining a delay time period based on the motion information at the first sampling time point in the plurality of sampling time points, the motion change rate and the position, and estimating the occurrence position of the event of interest.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more particularly, to a method and system for estimating the occurrence location of an event of interest and a computing device. Background Art

[0002] Energy-limited (i.e., battery-powered) sensor devices usually have the advantages of easy deployment and low maintenance costs, so it has become a trend to apply them to various fields to achieve predictive operation and maintenance. For example, in a moving vehicle (e.g., a train, a vehicle, an aircraft, etc.), it is usually necessary to set up various sensor devices to detect the status of various components in the moving vehicle or the internal or external environment of the moving vehicle, etc. By monitoring various conditions in real time, predictive protection can be achieved before a failure occurs.

[0003] The sensor device in the moving vehicle can sense an event of interest (e.g., a large vibration) and determine the location of the event of interest, so that the location of the event of interest can be used to determine how to make the next judgment and processing for the event of interest. For example, if a large vibration is sensed during the running of the train, according to the determined location of the event, further investigation and analysis measures can be taken on the relevant track area.

[0004] It can be seen that accurately determining the location of an event of interest is very important for the operating performance and safety of a sports vehicle. Therefore, a solution is needed that can accurately determine the location of an event of interest. Summary of the invention

[0005] According to one aspect of the present application, a method for estimating the occurrence position of an event of interest in a moving vehicle is provided, comprising: determining a position determination delay time period based on a first time when the event of interest occurs and a second time when the earliest motion information is acquired; acquiring multiple pieces of motion information at multiple sampling time points with a predetermined sampling interval, wherein each piece of motion information includes the motion speed, motion direction and position of the moving vehicle at the corresponding sampling time point; determining the motion change rate of the moving vehicle based on the multiple pieces of motion information; and estimating the occurrence position of the event of interest based on the motion change rate and the position determination delay time period.

[0006] According to another aspect of the present application, there is provided a device for estimating the location of occurrence of an event of interest in a moving vehicle, comprising: a sensing module for sensing sensing data related to the event of interest in a low power consumption mode and sending indication information to a processing module when the event of interest is sensed; a motion detection module for sampling motion information of the moving vehicle at predetermined time intervals after startup is completed; and a processing module for determining a first time when the event of interest occurs based on the indication information, and in response to determining that the event of interest occurs, starting the motion detection module and obtaining the motion information from the motion detection module, and determining a second time when the earliest the motion information is obtained, wherein the processing module is also configured to execute the method as described above.

[0007] According to another aspect of the present application, a computing device is provided, comprising: a processor; and a memory on which a computer program is stored, and when the computer program is executed by the processor, the processor executes the method as described above.

[0008] Through the solution of the present application for estimating the occurrence position of an event of interest in a moving vehicle, the position of the position of interest is estimated based on multiple motion information collected after the motion detection module is started, rather than directly taking the position in the first motion information collected after the motion detection module is started as the occurrence position of the event of interest. While meeting the low power consumption operation of the sensor device, the occurrence position of the event of interest can be determined more accurately, and there is no need to set up other hardware settings, which is more convenient and cost-saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments of the present application or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings of the embodiments of the present application.

[0010] Figure 1A A schematic diagram showing the deviation between the actual occurrence position of an event of interest and the position provided by the motion detection module.

[0011] Figure 1B A schematic diagram showing an application scenario for estimating the occurrence location of an event of interest.

[0012] Figure 2 A schematic flow chart of a method for estimating the occurrence location of an event of interest in a moving vehicle according to an embodiment of the present application is shown.

[0013] Figure 3A schematic diagram showing multiple pieces of motion information detected by a motion detection module at multiple sampling time points is shown.

[0014] Figure 4 is Figure 3 A schematic diagram of multiple selected time points was added based on the .

[0015] Figure 5 The flowchart of the iterative calculation process without obtaining the movement direction from the map information system is shown.

[0016] Figure 6 The figure shows a flow chart of an iterative calculation process when the movement direction is obtained from a map information system.

[0017] Figure 7 An algorithm flow chart showing the iterative calculation process.

[0018] Figure 8 A structural block diagram of a computing device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0020] The sensor device in the moving vehicle may include a sensing module with a sensing function (e.g., a sensing component and its circuit) and a motion detection module (e.g., a GPS / GNSS module) for sensing the occurrence of an event of interest and detecting the motion state of the moving vehicle, thereby determining the location of the event of interest.

[0021] In order to save energy consumption, in addition to the sensing module in the sensor device maintaining normal sensing operation (working in low power consumption mode), the remaining modules (e.g., processing module) are usually in a dormant state, and the motion detection module (e.g., GPS / GNSS module) is generally in a shutdown state due to its high power consumption. Once the sensing module detects an event of interest, the remaining modules (e.g., processing module) in the sensor device are awakened (herein referred to as the sensor device being awakened), thereby starting the motion detection module to provide location information as the location of the detected event of interest (EOI), and the location information is generally absolute location information, such as longitude and latitude. However, it usually takes a long time (e.g., at least tens of seconds) to start (especially cold start) the motion detection module, which means that the sensing module captures the event of interest, but the motion detection module cannot immediately provide location information. The location information provided is actually the location of the moving vehicle after the start-up time of the motion detection module, and is not the accurate location of the event of interest. For example, in the case where the vehicle is a train, the speed of the train is usually above 150km / h, so the location indicated in the location information provided by the motion detection module may be far away from the actual location of the EOI. For example, Figure 1A The actual location of EOI is shown (X EOI ,Y EOI ) and the position (X GPS ,Y GPS )Schematic diagram with large deviation. Figure 1A It is shown that there is a small difference (white area in the figure) between the time point T1 when the event of interest is sensed and the sensor device (e.g., the processing module) is awakened and the time point when the motion detection module is started, and there is also a small difference (white area in the figure) between the time point when the sensor device (e.g., the processing module) enters the sleep state again and the time point T2 when the motion detection module completes the startup (can send motion information). These time differences are very small and can be ignored in subsequent calculations.

[0022] If the actual location of EOI (X EOI ,Y EOI ) and the position (X GPS ,Y GPS ) is too large, it may have certain effects. For example, in the case of great vibration during the running of the train as mentioned above, if Figure 1A (X GPS ,Y GPS ) as the location of the event of interest, but the actual location of EOI is (X EOI ,Y EOI ), which will cause great misleading and inconvenience to the inspection of relevant areas on the track.

[0023] In summary, since the startup process (especially cold startup) of the motion detection module (e.g., GPS / GNSS module) usually takes a long time (e.g., at least tens of seconds), the location indicated by the location information provided by it is not the actual location where the event of interest occurred. In some solutions, the motion detection module may be operated in a dormant state (so that it can be started faster) when it does not need to detect the motion state at the expense of energy consumption. Therefore, the embodiments of the present application provide a solution for more efficiently and accurately determining the location of the event of interest.

[0024] Figure 1B A schematic diagram of an application scenario for estimating the occurrence location of an event of interest is shown, where the application scenario involves a sensor device and optionally a local computing device and / or a cloud computing device (eg, a cloud server with data processing capabilities, etc.).

[0025] The sensor device 10 is mounted in a moving vehicle and Figure 1B As shown, the sensor device 10 may include a sensing module 110 and a motion detection module 120, and may optionally include a processing module 130. For example, if the sensor device has a processing function, the sensor device 10 may also include a processing module 130 (and may correspondingly include a storage module (e.g., a memory) for storing processing-related data); if the sensor device does not have a processing function, the sensor device 10 may not include the processing module 130, and may communicate with other computing devices (local or cloud computing devices) with processing capabilities to send its data to these computing devices for processing. The computing device mentioned may be a server (local server, remote server or cloud server, etc.) or various terminal devices (e.g., mobile devices, personal computers, digital personal assistants, etc.), etc., to communicate with the sensor device 10 to obtain various data.

[0026] The processing module in the sensor device 10 may include a dedicated hardware-based system (e.g., a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components) that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0027] The sensing module 110 is a component in the sensor device 10 for realizing the sensing function, such as a sensor component body or its related circuits, such as a vibration sensor or a temperature sensor and its peripheral circuits, which usually has low power consumption and keeps working to capture events of interest in real time. When the sensing module senses an event of interest, it sends indication information to the processing module (or an external computing device) in the sensor device 10, so that the processing module (or an external computing device) can determine the first time (T1) when the event of interest occurs (for example, the value of the sensing data meets the threshold condition) based on the indication information. For example, the first time can be the time when the processing module (or the external computing device) receives the indication information.

[0028] The motion detection module 120 is a component in the sensor device 10 for detecting the motion state, such as a GPS / GNSS module, which usually has high power consumption and is usually not working (i.e., in a shutdown state). It is only started after responding to a trigger operation of the processing module (the startup time is relatively long), so it can consume less power. The motion detection module 120 is used to sample the motion information of the moving vehicle at a predetermined time interval (usually 1 second) after the startup is completed.

[0029] In the case where the location of the event of interest is estimated by the sensor device 10, the processing module 130 is usually in a low power consumption mode such as a sleep mode, and is awakened and starts working after obtaining the indication information indicating the occurrence of the event of interest from the sensing module 10, which can save power consumption and extend the use time. The processing module 130 can determine the first time of receiving the indication information, and in response to determining that the event of interest has occurred, start the motion detection module 120 and start to obtain the motion information at each sampling time point from the motion detection module 120 after the motion detection module is started, and determine the second time (T2) when the first motion information is obtained. In addition, the processing module 130 can estimate the location of the event of interest based on the determined first time, the second time, and the multiple motion information obtained by the motion detection module at multiple sampling time points, using the process described in detail later. The estimated location is closer to the actual location of the event of interest. The motion detection module is in a shutdown state before receiving the start signal, so it can also save power consumption.

[0030] In order to further save power consumption, when the position has been estimated for the current event of interest and no new event of interest has occurred, the motion detection module 120 in the sensor device 10 can be shut down again. For example, the processing device can re-enter the sleep state to wait for the indication information from the sensing module that a new event of interest has occurred to wake up, and the motion detection module can be directly shut down to wait for the start signal from the processing device or an external computing device.

[0031] Similarly, a computing device independent of the sensor device 10 may also obtain the above-mentioned indication information from the sensing module of the sensor device 10, and determine the first time when the indication information is received based on the indication information, and in response to determining that an event of interest occurs, start the motion detection module 120 of the sensor device 10 and start to obtain motion information at each sampling time point from the motion detection module 120 after the motion detection module 120 is started, and determine the second time when the first piece of motion information is obtained. Then, according to the determined first time, the second time, and the multiple pieces of motion information obtained by the motion detection module at multiple sampling time points, the occurrence position of the event of interest can be estimated using a process as will be described in detail later.

[0032] When the sensor device 10 includes a processing device and also has a computing device that communicates with the processing device and is independent of the sensor device, the process described above for the processing module 130 may be executed only in the processing module in the sensor device 10 or only in the computing device, or may be executed jointly by the processing module in the sensor device and an external computing device, and the present application does not impose any restrictions on this.

[0033] Figure 2 FIG. 1 is a flow chart showing a method for estimating the location of an event of interest in a moving vehicle according to an embodiment of the present application. The method may be performed as follows: Figure 1B The processing module in the sensor device 10 shown is either executed by a computing device 20 (eg, a cloud) independent of the sensor device 10 or executed by both.

[0034] like Figure 2 As shown, in step S210, a position determination delay period is determined based on a first time when an event of interest occurs and a second time when the earliest motion information is acquired.

[0035] For example, the sensing module (sensing component) in the sensor device may send indication information indicating the occurrence of an event of interest (e.g., temperature exceeds a threshold value) to the processing module or computing device 20, and the processing module or computing device 20 may determine the time (first time) when the event of interest occurs based on the indication information. Then, the processing module or computing device 20 may start the motion detection module in the sensor device, and after the start-up time, the motion detection module may start to collect motion information and send the motion information to the processing module or computing device 20, so the processing module or computing device 20 may determine the second time when the first piece of motion information is obtained from the motion detection module. Since the communication time between each module is very short and can be ignored, the first time may be equivalent to the actual occurrence time of the event of interest, and the second time may be equivalent to the time when the processing module or computing device 20 can determine the occurrence location of the event of interest, so the time period between the first time and the second time is called the position determination delay time period.

[0036] In step S220, a plurality of pieces of motion information at a plurality of sampling time points with a predetermined sampling interval are obtained, wherein each piece of motion information includes the motion speed, motion direction and position of the moving vehicle at the corresponding sampling time point.

[0037] For example, after the motion detection module is started, it can be sampled at a predetermined sampling interval (expressed as T interval ) collects motion information at multiple sampling time points to obtain multiple pieces of motion information, and each piece of motion information includes the motion speed, motion direction and position of the motion vehicle at the corresponding sampling time point. In addition, each piece of motion information may also include time information corresponding to the corresponding sampling time point. The processing module or the computing device 20 may obtain the motion information.

[0038] For example, Figure 3 FIG. 1 shows a schematic diagram of a motion detection module detecting multiple pieces of motion information. Figure 3 In the figure, point A indicates the location where the event of interest occurs (X EOI ,Y EOI ), which is an unknown value that needs to be estimated. Points G1, G2, …, GN represent N sampling time points after the motion detection module is started. The motion information sampled at each sampling time point can be expressed as (X GPSi ,Y GPSi ,V GPSi ,O GPSi ) and optional time information, where i is an integer greater than or equal to 1 and less than or equal to N, X GPSi ,Y GPSi are the coordinates of the position at the i-th sampling time point, for example, longitude and latitude coordinates, V GPSiis the motion speed at the i-th sampling time point, and O GPSi is the direction of motion at the i-th sampling time point, expressed as an angle relative to the earth's true north.

[0039] In addition, the motion direction may not be detected by the motion detection module, but may be provided by a map information system (GIS). The map information system is usually combined with a cloud service, so Figure 2 When the method shown is executed at a computing device in the cloud (e.g., a cloud server), the motion direction can be obtained directly from the map information system without the need for a motion detection module in the sensor device to collect the motion direction, that is, the computing device in the cloud (e.g., a cloud server) obtains the motion speed and position of the moving vehicle at each sampling time point from the motion detection module, and obtains the motion direction of the moving vehicle at each sampling time point from the map information system.

[0040] In step S230, the motion change rate of the moving vehicle is determined based on the plurality of pieces of motion information.

[0041] For example, the motion change rate may include at least one of a motion speed change rate and a motion direction change rate.

[0042] In step S240, the location where the event of interest occurs is estimated based on the motion information, the motion change rate, and the position determination delay period at the first sampling time point among the plurality of sampling time points.

[0043] During the startup period of the motion detection module (the duration is basically the same as the position determination delay period), based on the assumption that the motion change rate of the moving vehicle will not change drastically during the startup period, that is, it will basically remain constant, the motion change rate of the moving vehicle can be used to estimate the motion changes in the position determination delay period, and then can be used to estimate the location of the event of interest.

[0044] For example, the sampling interval (T interval ) determines a plurality of selected time points included in the position determination delay period, for example, by dividing the length of the position determination delay period by a predetermined sampling interval (T interval ) and round it down, for example, by using the function floor((T2-T1) / T interval ), as the number of selected time points. Then, based on the first sampling time point of the plurality of sampling time points (e.g., Figure 3 The motion information at the G1 point shown in FIG. 1 , the motion change rate and the predetermined sampling interval are obtained from the selected time point closest to the first sampling time point (for example, Figure 3The estimated position at each selected time point is determined iteratively, starting from the time point G1 shown in the figure with a predetermined sampling interval difference. Then, the estimated position at the selected time point farthest in time from the first sampling time point among the multiple selected time points is taken as the estimated position of the occurrence of the event of interest.

[0045] For example, Figure 4 is Figure 3 A schematic diagram of selected time points is added on the basis of . In the figure, M selected time points (P1, P2, ..., PM) are included in the position determination delay time period, wherein point P1 is the selected time point closest to the first sampling time point G1 in time, and point PM is the selected time point farthest from the first sampling time point G1 in time, and the duration between the time point and the first time point is less than or equal to the duration of the predetermined sampling interval. The position of point P1 is first determined, and then used to calculate the position of point P1, and then used to calculate the position of point P2, and so on, until the position of point PM is calculated.

[0046] The specific example iterative calculation process will be further described later.

[0047] By reference Figure 2 The described method for estimating the occurrence position of an event of interest in a moving vehicle estimates the position of the position of interest based on multiple motion information collected after the motion detection module is started, rather than directly taking the position in the first motion information collected after the motion detection module is started as the occurrence position of the event of interest. While meeting the low power consumption operation of the sensor device, the occurrence position of the event of interest can be determined more accurately, and no additional hardware settings are required, which is more convenient and cost-saving.

[0048] The following combination Figure 5 and Figure 6 The iterative calculation process for the positions of multiple selected time points described above is described exemplarily respectively.

[0049] Figure 5 The flowchart of the iterative calculation process without obtaining the motion direction from the map information system is shown. At this time, the motion change rate may include both the motion speed change rate and the motion direction change rate.

[0050] like Figure 5 As shown, in step S510, for the time interval from the first sampling time point (for example, Figure 4 The selected time point closest to point G1 in time (e.g., Figure 4 point P1 in the figure), can be based on the first sampling time point (for example, Figure 4 The speed and direction of movement (V GPS1 ,OGPS1 ), the rate of change of motion speed, the rate of change of motion direction and the predetermined sampling interval (T interval ), determine the temporally nearest selected time point (e.g., Figure 4 The estimated movement speed and estimated movement direction at point P1 in the image are determined, and the estimated position at the most recent selected time point is determined based on the position at the first sampling time point, the estimated movement speed and estimated movement direction at the most recent selected time point, and a predetermined sampling interval.

[0051] Optionally, the motion speed change rate and the motion direction change rate are determined based on the N motion speeds and N motion directions in the N pieces of motion information obtained, for example, by using the linear regression algorithm or the average value algorithm (including simple average or weighted average) to determine. During the startup of the motion detection module, based on the assumption that the motion change rate of the moving vehicle will not change dramatically, that is, it will remain basically constant, during the iterative calculation process, the motion speed change rate and the motion direction change rate are fixed values, respectively expressed as Δ V and Δ O .

[0052] In step S520, for other selected time points, based on the first sampling time point (eg, Figure 4 The speed and direction of movement (V GPS1 ,O GPS1 ), the rate of change of motion speed, the rate of change of motion direction and a predetermined sampling interval, determine the estimated motion speed and the estimated motion direction at the currently selected time point, and determine the estimated position at the currently selected time point based on the estimated position at the last selected time point, the estimated motion speed and the estimated motion direction at the currently selected time point and the predetermined sampling interval.

[0053] For example, the estimated motion speed at each selected time point Pi can be expressed as V GPS1 -i*Δ V *T interval , and the estimated motion direction at point Pi can be O GPS1 -i*Δ O *T interval. The estimated position at point Pi can be obtained by determining the travel distance within the predetermined time interval based on the estimated movement speed at point Pi, and the travel distance in the due north and due east directions can also be determined based on the estimated movement direction (expressed as an angle relative to the due north direction). For the convenience of calculation, considering that the time between two adjacent selected time points is very short, the changes in speed and direction will not be too large, and the movement process between the two selected time points can be regarded as uniform motion with unchanged movement direction. However, as mentioned above, there are the change rates of movement speed and movement direction. Therefore, in order to meet the accuracy requirements and complexity requirements of the calculation at the same time, the changes in the estimated movement speed and estimated movement direction at point Pi within the predetermined time interval can be corrected, and based on the corrected estimated movement speed (uniform speed) and estimated movement direction (unchanged) and the predetermined time interval, a more accurate estimated position at point Pi can be obtained.

[0054] For example, the estimated motion speed V at point Pi VPi Corrected to V GPS1 -i*Δ V *T interval -Δ V *T interval / 2, and the estimated motion direction V at point Pi OPi Corrected to O GPS1 -i*Δ O *T interval -Δ O *T interval / 2, then the travel distance V from point P1 to point G1 can be calculated VP1 *T interval According to the estimated movement direction (the angle with the north direction), the travel distance is converted into the travel distance along the north direction (Y) and the travel distance along the east direction, so that the position of point P1 (X P1 ,Y P1 ).

[0055] Optionally, depending on the type of motion detection module and the reference coordinate system, the position or estimated position mentioned herein may be represented by the distance on two axes (e.g., the X-axis and the Y-axis) relative to the origin of the reference coordinate system, where the Y-axis is the due north direction, for example (X exm , Y exm ) indicates that the distance from the origin on the X axis is X exm , the distance from the origin on the Y axis is Y exmTherefore, when determining the estimated position at point Pi, based on the estimated movement speed, estimated movement direction and predetermined sampling interval at point Pi, the travel distance in the due north direction (Y axis) and due east direction (X axis) within the predetermined sampling interval is determined to determine the estimated position of the currently selected time point Pi together with the position at the first sampling time point G1 or the estimated position at the previously determined selected time point Pi-1.

[0056] In other embodiments, the position or estimated position mentioned in this document is expressed in longitude and latitude. Therefore, when determining the estimated position at point Pi, the travel distance in the latitude direction (due north) and the longitude direction (due east) within the predetermined sampling interval is determined based on the estimated movement speed, estimated movement direction and predetermined sampling interval at point Pi, and converted into latitude difference and longitude difference (i.e., angle difference) respectively, to be used together with the position at the first sampling time point G1 (expressed in longitude and latitude) or the estimated position at the previous selected time point Pi-1 that has been determined (expressed in longitude and latitude) to determine the estimated position of the currently selected time point Pi.

[0057] In addition, when calculating the longitude difference, considering that the distances between the two longitudes corresponding to different latitudes are different, compensation can also be performed based on the latitude at point Pi that has been calculated based on the latitude difference to obtain a more accurate longitude difference. In addition, since longitude is divided into east longitude and west longitude, each corresponding to 180 degrees, if the absolute value of the longitude value of the estimated position calculated is greater than 180 degrees, it can be converted to the range of 0 degrees to 180 degrees of east longitude or west longitude. Similarly, similar conversions are required for north and south latitudes and the polar regions of the earth.

[0058] Figure 6 The flowchart of the iterative calculation process in the case of obtaining the motion direction from the map information system is shown. In this case, the method for estimating the occurrence position of the event of interest described herein is usually performed at a computing device outside the sensor device.

[0059] At this time, the motion change rate only includes the motion speed change rate, because the required motion direction at each selected time point can be obtained from the map information system, so there is no need to determine the motion direction change rate by the computing device.

[0060] In this case, in step S610, for the time interval from the first sampling time point (eg, Figure 4 The selected time point closest to point G1 in time (e.g., Figure 4 point P1 in the figure), can be based on the first sampling time point (for example, Figure 4 The speed and direction of movement (V GPS1 ,OGPS1 ), the rate of change of motion speed and the predetermined sampling interval (T interval ), determine the temporally nearest selected time point (e.g., Figure 4 The estimated movement speed at point P1 in the figure is determined, and the estimated position at the most recent selected time point is determined based on the position at the first sampling time point, the estimated movement speed at the most recent selected time point, the movement direction obtained from the map information system, and the predetermined sampling interval.

[0061] In step S620, for other selected time points, based on the first sampling time point (eg, Figure 4 The speed and direction of movement (V GPS1 ,O GPS1 ), the rate of change of movement speed, the rate of change of movement direction and a predetermined sampling interval, determine the estimated movement speed at the currently selected time point, and determine the estimated position at the currently selected time point based on the estimated position at the last selected time point, the estimated movement speed at the currently selected time point, the movement direction obtained from the map information system and the predetermined sampling interval.

[0062] Steps S610-S620 are similar to steps S510-S520, except that in steps S510-S520, it is necessary to calculate the estimated movement direction at each selected time point, while steps S610-S620 do not need to calculate the estimated movement direction, but directly use the movement direction at each selected time point obtained from the map information system. Therefore, it will not be repeated here.

[0063] exist Figure 6 In the case shown, although there is no Figure 5 Instead of performing a median correction on the direction of motion as in the case shown, the direction of motion at each selected sampling point obtained from the map information system is directly used. However, considering that the predetermined time interval between each two selected time points is very small, the direction change within the interval is also very small. Therefore, even if it is directly used to calculate the deviation in the due north and due east directions, the error caused is relatively small and within an acceptable range.

[0064] References Figure 5 and Figure 6 The iterative calculation process is described below. Figure 7 The algorithm flow chart of the iterative calculation process is schematically described.

[0065] exist Figure 7In , i represents the sequence number of the iterative calculation. For the i-th iterative calculation (i.e., the position calculation for the selected time point Pi mentioned above), the estimated position obtained by the previous (i-1-th) iterative calculation is required. If M iterative calculations have been performed, the estimated position obtained by the last M-th iteration can be used as the occurrence position of the event of interest.

[0066] Specifically, Figure 7 As shown, first, i is assigned a value of 1, and the position at the first sampling time point G1 is used as the initial position value (X0 = X GPS1 ,Y0=Y GPS1 ) for the first iteration. Then, determine whether the current i value is less than or equal to M (i.e., floor(T2-T1) / T interval ), if yes, then it needs to continue iterating, if not, it means that the position of the last (Mth) selected time point has been determined, that is, the position of the Mth selected time point can be used as the location of the event of interest.

[0067] During the first iteration, as described above, the estimated motion speed Vp1 at the first selected time point is determined, for example, it can be determined based on the motion speed at the first sampling time point G1, the motion speed change rate and the predetermined time interval as described above.

[0068] Then, the estimated motion direction Op1 at the first selected time point may be determined, or the motion direction at the first selected time point may be obtained from the map information system. For example, the estimated motion direction at the first selected time point may be determined based on the motion direction at the first sampling time point G1, the motion direction change rate, and the predetermined time interval as described above.

[0069] Next, the position at the first selected time point can be determined. For example, the position at the first selected time point (P1) can be determined based on the position at the first sampling time point G1, the estimated movement speed, and the predetermined time interval. The position can be represented by latitude and longitude as an example, so it can include determining the position in the latitude direction and the position in the longitude direction (X1, Y1). For example, the latitude or longitude of the position at the first selected time point can be further determined based on the estimated movement direction / movement direction. Optionally, cosine compensation and longitude value correction can also be used when calculating longitude, as described above.

[0070] After obtaining the position at the first selected time point, the determined position is used as the initial value of the position for the second iteration calculation for the second selected time point, that is, (X0=X1, Y0=Y1), and then i is assigned to i+1, that is, i is assigned to 2, and then it is determined whether the current i value is less than or equal to M (that is, floor(T2-T1) / Tinterval ), if yes, then continue to repeat the above process of determining the estimated motion direction (if any), estimated motion speed and position; if no, then take the position of the Mth (i.e. the i-1th, currently i=M+1) selected time point as the location of the event of interest.

[0071] By reference Figure 5-Figure 7 In the iterative calculation process described, after acquiring various information or data, the processing device and the control device 20 can calculate the estimated position of the moving vehicle at the time point closest to the first time of the event of interest, and can use it as the location where the event of interest occurred. The process is relatively simple and does not require additional hardware circuits.

[0072] According to another aspect of the present application, a computing device is also provided. The computing device can be Figure 1B A computing device is shown (eg, a local computing device or a cloud computing device).

[0073] Figure 8 A structural block diagram of a computing device according to an embodiment of the present application is shown.

[0074] like Figure 8 As shown, computing device 800 may include one or more processors and one or more memories.

[0075] As an example, the processor and the memory can be connected through a system bus, and the computing device of the present application can also include a network interface, an input device, a display screen, etc. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computing device stores an operating system, and can also store a computer (executable) program, which, when executed by the processor, enables the processor to implement various operations performed by the computing device as described above. The internal memory can also store a computer executable program, which, when executed by the processor, enables the processor to implement various operations performed by the computing device as described above.

[0076] The processor can be an integrated circuit chip with signal processing capabilities. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, to implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can be an X84 architecture or an ARM architecture.

[0077] The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable categories of memory.

[0078] The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the terminal shell, or an external keyboard, touchpad or mouse.

[0079] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the methods and devices according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of the code contains at least one executable instruction for realizing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, or the various modules mentioned, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0080] The embodiments of the present application described in detail above are merely illustrative and not restrictive. It should be understood by those skilled in the art that various modifications and combinations may be made to these embodiments or their features without departing from the principles and spirit of the present application, and such modifications should fall within the scope of the present application.

Claims

1. A method for estimating the location of an event of interest in a moving vehicle, comprising: Determining a position determination delay time period based on a first time when the event of interest occurs and a second time when the earliest motion information is acquired; Acquire multiple pieces of motion information at multiple sampling time points with a predetermined sampling interval, wherein each piece of motion information includes the motion speed, motion direction and position of the motion vehicle at the corresponding sampling time point; Determining a motion change rate of the moving vehicle based on the plurality of pieces of motion information; as well as The occurrence position of the event of interest is estimated based on the motion information at a first sampling time point among the multiple sampling time points, the motion change rate, and the position determination delay period.

2. The method according to claim 1, wherein: The method further comprises: estimating the occurrence position of the event of interest based on the motion information at the first sampling time point among the plurality of sampling time points, the motion change rate, and the position determination delay period, comprising: determining a plurality of selected time points included in the position determination delay period based on the predetermined sampling interval; Iteratively determine an estimated position at each selected time point, starting from a selected time point that is closest to the first sampling time point in time among the multiple selected time points, based on the motion information at the first sampling time point, the motion change rate, and the predetermined sampling interval; and The estimated position of the selected time point farthest in time from the first sampling time point among the multiple selected time points is used as the estimated position where the event of interest occurs.

3. The method according to claim 2, wherein: The motion change rate includes the motion speed change rate and the motion direction change rate. Wherein, based on the motion information at the first sampling time point, the motion change rate and the predetermined sampling interval, starting from the selected time point closest to the first sampling time point in time among the multiple selected time points, iteratively determining the estimated position at each selected time point comprises: For the temporally most recent selected time point, determining an estimated motion speed and an estimated motion direction at the temporally most recent selected time point based on the motion speed and motion direction at the first sampling time point, the motion speed change rate, the motion direction change rate, and the predetermined sampling interval, and determining an estimated position at the temporally most recent selected time point based on the position at the first sampling time point, the estimated motion speed and the estimated motion direction at the temporally most recent selected time point, and the predetermined sampling interval; and For other selected time points, the estimated movement speed and estimated movement direction at the currently selected time point are determined based on the movement speed and movement direction at the first sampling time point, the movement speed change rate, the movement direction change rate and the predetermined sampling interval, and the estimated position at the currently selected time point is determined based on the estimated position at the last selected time point, the estimated movement speed and estimated movement direction at the currently selected time point and the predetermined sampling interval.

4. The method according to claim 2, wherein: The movement change rate includes the movement speed change rate, and the movement direction at each selected time point is obtained from the map information system. Wherein, based on the motion information at the first sampling time point, the motion change rate and the predetermined sampling interval, starting from the selected time point closest to the first sampling time point in time among the multiple selected time points, iteratively determining the estimated position at each selected time point comprises: For the most recent selected time point, determining an estimated motion speed at the most recent selected time point based on the motion speed at the first sampling time point, the motion speed change rate, and the predetermined sampling interval, and determining an estimated position at the most recent selected time point based on the position at the first sampling time point, the estimated motion speed and motion direction at the most recent selected time point, and the predetermined sampling interval; and For other selected time points, the estimated movement speed at the currently selected time point is determined based on the movement speed and movement direction at the first sampling time point, the movement speed change rate and the predetermined sampling interval, and the estimated position at the currently selected time point is determined based on the estimated position at the last selected time point, the estimated movement speed and movement direction at the currently selected time point and the predetermined sampling interval.

5. The method according to claim 3, wherein: The position and the estimated position are expressed in longitude and latitude, Among them, when determining the estimated position at the currently selected time point, based on the estimated movement speed, estimated movement direction and the predetermined sampling interval at the currently selected time point, the travel distance in the latitude direction and the longitude direction within the predetermined sampling interval is determined, and converted into latitude difference and longitude difference respectively, so as to be used together with the position at the first sampling time point or the estimated position at the previous selected time point to determine the estimated position of the currently selected time point.

6. The method according to claim 1, wherein: Acquiring a plurality of pieces of motion information at a plurality of sampling time points with a predetermined sampling interval includes: Acquire the motion speed, motion direction and position of the moving vehicle at each of the plurality of sampling time points from a motion detection module configured by a sensor device in the moving vehicle; or The motion speed and position of the moving vehicle at each of the multiple sampling time points are obtained from the motion detection module, and the motion direction of the moving vehicle at each of the multiple sampling time points is obtained from the map information system.

7. The method according to claim 1, wherein: The motion change rate includes the motion speed change rate and / or the motion direction change rate; Wherein, determining the motion change rate of the motion vehicle based on the plurality of pieces of motion information includes: The linear regression algorithm or the average value algorithm is used to respectively calculate the multiple movement speeds and / or the multiple movement directions included in the multiple pieces of movement information to obtain the movement speed change rate and / or the movement direction change rate.

8. The method according to any one of claims 1 to 7, wherein: The method is executed at a sensor device in the sports vehicle, or executed in the cloud.

9. An apparatus for estimating a location at which an event of interest occurs in a moving vehicle, comprising: a sensing module, configured to sense sensing data related to the event of interest in a low power consumption mode and send indication information to a processing module when the event of interest is sensed; A motion detection module, used for sampling the motion information of the moving vehicle at a predetermined time interval after the startup is completed; as well as a processing module, configured to determine a first time when the event of interest occurs based on the indication information, and in response to determining that the event of interest occurs, start the motion detection module and obtain the motion information from the motion detection module, and determine a second time when the motion information is obtained earliest, Wherein, the processing module is further configured to execute the method according to any one of claims 1-8.

10. A computing device comprising: processor; as well as A memory having a computer program stored thereon, wherein when the computer program is executed by the processor, the processor is caused to perform the method according to any one of claims 1 to 8.

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