Method and device for estimating the revolutions of a bearing on an object

Abstract

The present disclosure provides a method and an apparatus for estimating the revolutions of a bearing on an object, the method comprising: receiving multiple measurement data from a sensor, each of the multiple measurement data having a corresponding timestamp, wherein the multiple measurement data are measured by the sensor, which wakes up based on a first time interval, and wherein the first time interval changes during the running of the object based on a running state of the object; determining a total distance traveled by the object based on the multiple measurement data; determining an offset of the distance traveled by the bearing, wherein the offset of the distance traveled by the bearing is a distance traveled by the object when the bearing is installed on the object;and determining the revolutions of the bearing based on the determined total distance traveled by the object and the determined offset of the distance traveled by the bearing.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to a technical field of data processing, and more particularly to a method and apparatus for estimating revolutions of a bearing on an object. BACKGROUND

[0002] To create a comprehensive profile of the wheel bearing usage of an object (e.g., a train, a subway, a vehicle, etc.) or to realize reliable remote diagnostics of the bearings, recording the bearing revolutions (i.e., the accumulated revolutions since the bearings were installed) is a very important input. Measuring such an accumulated metric generally requires continuous monitoring. However, an energy-constrained (e.g., battery-powered) IoT sensor (e.g., vibration and temperature sensors) typically only wakes up and takes a measurement a few times a day, thus saving energy. This means that it is difficult for such an energy-constrained sensor to be constantly powered on to record revolutions. Therefore, there is a need for an effective method that can be used with energy-constrained sensors to guess or estimate the revolutions of a bearing on an object. SUMMARY

[0003] The SUMMARY section is provided to briefly introduce concepts that are described in detail in the subsequent DETAILED DESCRIPTION section. The SUMMARY section is not intended to reveal key features or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0004] Embodiments of the present disclosure provide a method for estimating revolutions of a bearing on an object, comprising: receiving a plurality of measurement data from a sensor, each of the plurality of measurement data having a corresponding timestamp, wherein the plurality of measurement data is measured by the sensor waking up based on a first time interval, and wherein the first time interval changes during the running of the object based on a running state of the object; determining a total traveled distance of the object based on the plurality of measurement data; determining a traveled distance offset of the bearing, wherein the traveled distance offset of the bearing is a distance traveled by the object when the bearing is installed on the object;and determining the revolutions of the bearing based on the determined total distance traveled by the object and the determined offset of the distance traveled by the bearing.;

[0005] According to embodiments of the present disclosure, changing the first time interval based on a running state of the object includes: decreasing the first time interval according to a first change factor in a case where the running state of the object is a first state; and increasing the first time interval according to a second change factor whose rate of change is lower than that of the first change factor in a case where the running state of the object is a second state.

[0006] According to embodiments of the present disclosure, the first change factor is a multiplication factor and the second change factor is an addition factor.

[0007] According to embodiments of the present disclosure, the running state of the object comprises one or more of the following: a fast running state, a slow running state, and a stopped state, and wherein the first state is a fast running state and the second state is a slow running state or a stopped state.

[0008] According to embodiments of the present disclosure, each of the plurality of measurement data includes position information of the object, wherein the position information is used to indicate a position of the object when the measurement data is measured, and wherein determining a total traveled distance of the object based on the plurality of measurement data comprises: determining a first incremental traveled distance of the object based on the position information of the object and the corresponding timestamp; and adding the first incremental traveled distance to a previously traveled distance of the object to determine the total traveled distance.

[0009] According to embodiments of the present disclosure, the sensor comprises a locating module, wherein the position information of the object is acquired via the locating module.

[0010] According to embodiments of the present disclosure, each of the plurality of measurement data includes running state information of the object, wherein the running state information is used to indicate a running state of the object when the measurement data is measured, and wherein determining a total traveled distance of the object based on the plurality of measurement data comprises: determining a running time of the object based on the running state information of the object and the corresponding timestamp; determining a second incremental traveled distance of the object based on the running time and a running speed corresponding to the running time; and adding the second incremental traveled distance to a previously traveled distance of the object to determine the total traveled distance.

[0011] According to embodiments of the present disclosure, the sensor includes a speed detection module, wherein the running state information of the object is detected via the speed detection module.

[0012] According to embodiments of the present disclosure, determining the revolutions of the bearing based on the determined total distance traveled by the object and the determined offset of the bearing comprises: subtracting the offset of the traveled distance from the total distance traveled to obtain a first distance traveled by the bearing; and dividing the first distance traveled by a second distance traveled associated with the bearing to obtain the revolutions, wherein the second distance traveled is a distance traveled by the object when the bearing rotates one revolution.

[0013] Embodiments of the present disclosure provide an apparatus for estimating revolutions of a bearing on an object, comprising: a transceiver configured to receive a plurality of measurement data from a sensor, each of the plurality of measurement data having a corresponding timestamp, wherein the plurality of measurement data is measured by the sensor waking up based on a first time interval, and wherein the first time interval changes during the running of the object based on a running state of the object; and a processor coupled to the transceiver and configured to determine a total distance traveled by the object based on the plurality of measurement data; determine a traveled distance offset of the bearing, wherein the traveled distance offset of the bearing is a distance traveled by the object when the bearing is installed on the object;and to determine the revolutions of the bearing based on the determined total distance traveled by the object and the determined offset of the distance traveled by the bearing.;

[0014] Embodiments of the present disclosure provide an apparatus for estimating revolutions of a bearing on an object, comprising: a communication module configured to receive a plurality of measurement data from a sensor, each of the plurality of measurement data having a corresponding timestamp, wherein the plurality of measurement data is measured by the sensor waking up based on a first time interval, and wherein the first time interval changes during the running of the object based on a running state of the object; a first determination module configured to determine a total distance traveled by the object based on the plurality of measurement data;a second determination module configured to determine a traveled distance offset of the bearing, wherein the traveled distance offset of the bearing is a distance traveled by the object when the bearing is installed on the object; and a third determination module configured to determine the revolutions of the bearing based on the determined total traveled distance of the object and the determined traveled distance offset of the bearing.

[0015] Embodiments of the present disclosure provide a computer-readable storage medium having stored thereon instructions that, when executed, cause a processor to perform a method for estimating the revolutions of a bearing on an object and / or a method for changing the wake-up time interval of a sensor based on a running state of the object, in accordance with embodiments of the present disclosure.

[0016] The method, apparatus, and device provided by the present disclosure have at least one or more of the following advantages: 1) by utilizing one or more power-limited sensors (rather than relying on any wire-powered "always-on" devices), the revolutions of a wheel bearing can be presumed, and 2) by applying the method provided by the present disclosure, the track of the object equipped with the one or more sensors can also potentially be profiled. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description taken in conjunction with the accompanying drawings. Throughout the drawings, like or similar reference numerals designate like or similar elements. It should be understood that the drawings are schematic, and the original and elements are not necessarily drawn to scale. Fig. 1 shows a schematic diagram for determining a distance traveled by an object based on position information received from a sensor according to embodiments of the present disclosure; Fig. 2 shows a schematic representation of the revision of a distance using a GIS method according to embodiments of the present disclosure; Fig. 3 shows a schematic diagram for determining a distance traveled by an object based on running state information received from a sensor according to embodiments of the present disclosure; Fig. 4 shows an example of the flow of a method for estimating the revolutions of a bearing on an object performed on a computing device according to embodiments of the present disclosure; Fig. 5 shows a schematic scene diagram for changing a wake-up time interval of a sensor based on a running state of an object according to embodiments of the present disclosure; Fig. 6 shows an exemplary process for changing a wake-up time interval of a sensor based on a running state of an object according to embodiments of the present disclosure; Fig. 7 shows a schematic representation of performing a measurement using multiple sensors according to embodiments of the present disclosure; Fig. 8 shows a schematic representation of a method for estimating revolutions of a bearing on an object according to embodiments of the present disclosure; Fig. 9 shows a schematic representation of an apparatus for estimating the revolutions of a bearing on an object according to embodiments of the present disclosure; and Fig. 10 shows a schematic representation of an apparatus for estimating the revolutions of a bearing on an object according to embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure are described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are illustrated in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments shown herein, but these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and should not be used to limit the scope of the present disclosure.

[0019] It should be understood that the steps described in the method implementations of the present disclosure may be performed in a different order and / or in parallel. Furthermore, method implementations may include additional steps and / or omit the illustrated steps. The scope of the present disclosure is not limited in this regard.

[0020] As used herein, the term "comprising" and its variants are open-ended, meaning "including, but not limited to." The term "based on" means "based at least in part on." The term "an embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments." Related definitions of some other terms are provided in the following description.

[0021] It should be understood that the terms "first" and "second" mentioned in the present disclosure are used only to distinguish different means, modules or units and are not intended to limit the order or interdependence of the functions performed by those means, modules or units.

[0022] It should be understood that the modifications of "a" and "a plurality" mentioned in the present disclosure are schematic but not limiting, and those skilled in the art should understand that they are to be understood as "one or more" unless the context clearly indicates otherwise.

[0023] The names of the messages or information exchanged between multiple devices in the implementations of the present disclosure are used for illustrative purposes only and are not intended to limit the scope of those messages or information.

[0024] In the present disclosure, an object may refer to any object that moves with wheels and is equipped with wheel bearings, such as a train, a subway, a vehicle (a car, a truck, etc.), a bicycle, etc.

[0025] In the present disclosure, a power-constrained sensor may refer to a sensor that wakes up and takes measurements only once or a few times in a given period of time to conserve power, e.g., a battery-powered sensor.

[0026] To realize reliable remote diagnostics of bearings through sensors (e.g., vibration and temperature sensors) as one of the most commonly used inputs, it is expected that the accumulated revolutions since the bearings were installed are provided. Measuring such an accumulated metric generally requires continuous monitoring, which poses a significant challenge for energy-constrained (e.g., battery-powered) Internet of Things (IoT) sensors that must be constantly powered on.

[0027] The present disclosure provides a method for estimating the rotations of a wheel bearing using one or more energy-limited sensors installed in an object. The method can be implemented using a computing device and one or more energy-limited sensors. The sensor wakes up to measure measurement data of the object (e.g., position or running state, etc.), record corresponding timestamps, and upload the measurement data to the computing device (e.g., a server, a cloud server, or a gateway, etc.). The next wake-up time (or wake-up time interval) of the sensor can be determined by a scheduling mechanism based on the running state of the object (e.g., speed, acceleration, or other corresponding measurements). The computing device can then estimate a distance traveled by the sensor-equipped object.Based on this, the wheel bearing rotations can be further estimated or guessed. If multiple sensors are present, these sensors can be scheduled sequentially (e.g., wake up sequentially and one after the other, or wake up at different time periods, etc.) to further reduce energy consumption.

[0028] In some embodiments, a sensor installed in an object (e.g., a train or subway, etc.) may provide position information (e.g., it may further include a direction of movement, a speed, etc.) of the object and / or detect the object's running state (e.g., moving fast, moving slowly, or stopped, etc.). Generally, the object's position information may be obtained via a positioning module (e.g., a Global Position System (GPS) / Global Navigation Satellite System (GNSS) module, etc.), and the object's running state may be detected via a speed detection module. In some embodiments, the speed detection module may be an accelerometer module, a gyroscope, or any other existing or future module that may be used to detect the speed of an object.In some embodiments, the speed detection module may also be a receiving module capable of receiving external information (e.g., object speed information, etc.) from outside or from other modules. To simplify the description, an accelerometer module is described below as an example.

[0029] In some embodiments, the position information and / or the running state of the object may be obtained by one or more sensors (e.g., temperature sensors or vibration sensors) already present on the object. For example, an existing (or associated) tracking module and / or accelerometer module in one or more sensors already present on the object may be used to obtain the position information and / or the running state of the object, or a tracking module and / or accelerometer module may be integrated into one or more sensors already present on the object to obtain the position information and / or the running state of the object. Furthermore, the position information and / or the running state of the object may also be obtained by one or more specific sensors that include a tracking module and / or an accelerometer module.In some cases, these sensors may be energy-limited sensors.

[0030] The following is an exemplary description using a sensor scene as an example. For example, to save energy, the sensor may wake up periodically or at a specific time interval Δt to measure the object's measurement data (e.g., position or walking state, etc.), record the corresponding timestamps, and upload the measurement data to the computing device (e.g., a server, a cloud server, or a gateway, etc.). In some embodiments, the measurement data may be uploaded at a preconfigured time. In some embodiments, the measurement data may be uploaded based on a request or trigger from the computing device. In some embodiments, the measurement data may be uploaded along with other data (e.g., temperature data, vibration data, etc.).

[0031] Fig. Figure 1 shows a schematic representation of determining a distance traveled by an object based on position information received from a sensor according to embodiments of the present disclosure. For an application (e.g., a train application) where position information is readily available, as in Fig. As shown in Figure 1, position information (e.g., object positions recorded at times T1, T2, T3, ..., T4, etc.) recorded by the sensor can be stored in a computing device (e.g., a cloud server, etc.). The distance between two position samples (e.g., the distance between position samples at T1 and T2) can be easily estimated by (X1−X2)2+(Y1−Y2)2, where X and Y (i.e., X1, Y1, X2, Y2) represent the position coordinates of the object (e.g., latitude and longitude). To simplify the topic, a more precise conversion of the coordinates from latitude and longitude to distance is omitted here. If higher accuracy is expected, a time interval Δt between adjacent sampling times (e.g., the time interval between T1 and T2) can be shortened, or the distance can be verified using a Geographic Information System (GIS). In some embodiments, the time interval Δt can be a preset constant. In some embodiments, the time interval Δt can have an initial value that is a preset constant and can change during the running of the object based on the running state of the object (e.g., further based on the running speed, etc.).

[0032] Fig. 2 shows a schematic representation of the revision of a distance using a GIS method according to embodiments of the present disclosure. As in Fig. As shown in Figure 2, a trajectory line corresponding to two adjacent position samples can be selected on a given map, and the longitudinal distance of the two adjacent position samples on the trajectory line can be calculated. The distance simply estimated based on the above equation can be expressed as Dsimple=(X1−X2)2+(Y1−Y2)2 be expressed, while the calculations based on the Fig. 2 presented GIS method further revised distance as D GIS(X1, Y1, X2, Y2). Furthermore, based on the distance between any two adjacent position samples, the distance traveled by the object in any given period of time or between any two position samples can be calculated by summing them.

[0033] Fig. 3 shows a schematic diagram for determining a traveled distance of an object based on running state information received from a sensor according to embodiments of the present disclosure.

[0034] For an application (e.g., a subway application) where position information is not readily available, but an average speed may be readily available, as in Fig. As shown in Figure 3, a running state of an object (e.g., a subway train) may be measured or recorded. In some embodiments, the running state of the object may include one or more of the following states: a fast running state, a slow running state, or a stopped state. In this way, a running time of the object may be estimated in a computing device based on the information about the running state of the object and the corresponding timestamps, and the distance traveled by the object may be estimated by multiplying the running time by corresponding average speeds. In some embodiments, a corresponding average speed may be estimated or configured in advance for each running state of the object.For example, a fast running state may correspond to a first speed, a slow running state may correspond to a second speed that is lower than the first speed, and a stopped state may correspond to a zero speed. In other embodiments, the average speed of the object in different running states may be obtained or estimated in any other way, not limited here. Fig. 3 shows only two running states, namely a fast running state and a stop state. In the present disclosure, it can be assumed that the running state of the object does not change within a time interval Δt. Therefore, in the example of Fig. 3 It can be assumed that the object is in a fast running state during the period from T1 to T3 and in a stopped state during the period from T3 to T4.

[0035] In addition, the position information and / or the running state of the object recorded by the sensors can be sent or uploaded to the computing device immediately after recording, or they can be uploaded evenly at a preset time, as indicated by the shaded rectangle in Fig. 1 or Fig. 3. There are no restrictions regarding the timing or method of uploading the data.

[0036] Fig. 4 shows a schematic flow of a method 400 for estimating the revolutions of a bearing on an object performed on a computing device, according to embodiments of the present disclosure.

[0037] As in Fig. 4, the method 400 may begin with step S401, in which a computing device (e.g., a server, a cloud server, etc.) may receive data including position information or running state information of an object from a sensor.

[0038] In step S402, the received data may be sorted in a temporal order (for example, according to the timestamps corresponding to the measurement data) to generate sorted data D sorted to obtain.

[0039] In step S403, it may be determined what information (e.g., position information and / or running state information) the data includes. If position information is included in the data (e.g., in a train application), the method 400 may continue with step S404. In step S404, a first incremental distance traveled by the object may be determined in combination with the Fig. 1 and / or Fig. 2 above. For example, the computing device can be as shown in Fig. 1, calculate the distance (e.g., the segment distance corresponding to each time interval Δt) between two adjacent position samples sorted in a temporal order in the manner described above, and then accumulate the segment distances corresponding to all time intervals Δt to obtain the first incremental traveled distance traveled by the object in the time period corresponding to this position information.

[0040] If running state information is included in the data (e.g., in a subway application), the method 400 may continue with step S405. In step S405, a second incremental distance traveled by the object may be calculated in combination with the Fig. 3 described method. As in Fig. For example, as shown in Figure 3, as mentioned above, it is assumed that the running state of the object does not change within a time interval Δt, so that the running time of the object in each running state can be easily determined based on the multiple running state information and the corresponding timestamps. By multiplying the running time of the object in each running state by a predetermined average speed corresponding to the respective running state, the second incremental distance traveled by the object in the period corresponding to this running state information can be determined.

[0041] In step S406, the first incremental distance d determined in step S404 or the second incremental distance d determined in step S405 can then be added to the total distance d prevous_totalthat the object has previously traveled, thus obtaining the total distance traveled by total of the object.

[0042] Next, in step S407, for an application with K wheel bearings (e.g., K is an integer greater than or equal to 1), all wheel bearings can be traversed, executing steps S408-S411.

[0043] For example, in step S408, it may be determined whether an i-th wheel bearing among the K wheel bearings is newly installed. If the bearing is newly installed, a traveled distance d offset of the camp from the total distance covered d total of the object, and then the result is divided by the circumference Φ*pi of the wheel corresponding to the bearing (where Φ is the diameter of the wheel) to obtain the revolutions of the bearing, revolution i , to determine. As described above, the distance traveled d offsetof the bearing may be the distance that the object has already traveled when the bearing is installed on the object. If the bearing is not newly installed, but was installed, for example, before the object began to move, the method 400 may continue with step S410. In step S410, the revolutions of the bearing, revolution i , can be determined directly by dividing the total distance d traveled by the object by the circumference Φ*pi of the wheel corresponding to the bearing.

[0044] In step S411, based on the number i of the current wheel bearing, it can be determined whether all wheel bearings have been traversed. If not all wheel bearings have been traversed, the method can return to step S407 and steps S408-S411 can be performed again. If all wheel bearings have been traversed, method 400 can end in step S412.

[0045] It should be understood that the Fig. 4 is only one example of a method for estimating revolutions of a bearing on an object according to embodiments of the present disclosure, and the steps in Fig. 4 can be performed in any other order. In addition, one or more steps can be Fig. 4 may be omitted or combined with each other, or any other method or step according to the embodiments of the present disclosure may be added.

[0046] In some embodiments, to improve estimation accuracy and further save energy consumption, multiple measurement data may be measured by a sensor that wakes up based on a wake-up time interval ΔT (here, it may be referred to as a first time interval). During the running of an object, the wake-up time interval ΔT may change based on a running state of the object (e.g., further based on a running speed, etc.). For example, if the object is running fast (i.e., in a fast-running state), the sensor may wake up more frequently to perform measurements. For example, the sensor's wake-up time interval ΔT may be multiplicatively reduced (e.g., by a multiplication factor). If the object is running slowly (i.e., in a slow-running state) or is stationary, the sensor may wake up less frequently to perform a measurement. For example,The sensor's wake-up time interval can be increased additively (e.g. by an addition factor).

[0047] More precisely, Fig. 5 is a schematic scene diagram of a change in a wake-up time interval of a sensor based on a walking state of an object according to embodiments of the present disclosure.

[0048] As shown in Fig. 5, it is assumed that an initial state of the object (e.g., the running state at T1) is a slow-running state. In this case, an initial value Δt corresponding to the slow-running state can be set for the sensor's wake-up time interval ΔT. That is, the next wake-up time of the sensor is T2, and the initial time interval between T2 and T1 is Δt.

[0049] If the object is detected to be in a fast-running state at time T2, a multiplicative decrement operation of the current time interval Δt may be performed to obtain an updated / changed wake-up time interval. For example, the current time interval Δt may be multiplied by a multiplication factor such as 1 / 2 to obtain an updated wake-up time interval ΔT = Δt / 2. In this case, the next wake-up time may be T3 = T2 + Δt / 2, but not T2 + Δt in a fixed time interval. If the object is continuously detected to be in a fast-running state at time T3, the next wake-up time may be T4 = T3 + Δt / 4. In some embodiments, if the object is detected to be in a fast-running state, the multiplicative decrement operation may always be performed until a preset minimum wake-up time interval is reached, e.g., B.the wake-up time interval Δt / 4 between T3 and T4 and between T4 and T5, as in . Fig. 5 shown.

[0050] Returning to Fig. 5. If the object is detected to be in a slow-running state at time T5, an additive increment operation can be performed on the current time interval Δt / 4 to obtain an updated / changed wake-up time interval. For example, the current time interval Δt / 4 can be added to an addition factor such as t' (e.g., 60 seconds, etc.) to obtain an updated wake-up time interval ΔT = Δt / 4+t'. In this case, the next wake-up time T6 = T5 + Δt / 4+t'. If the object is continuously detected to be in a slow-running state at time T6, the next wake-up time T7 = T6 + Δt / 4+2t'. In some embodiments, when the object is detected to be in a slow running state, the additive increment operation may always be performed until a preset maximum wake-up time interval is reached.

[0051] It should be understood that the multiplication factor 1 / 2, the addition factor t', the minimum wake-up time interval, and the maximum wake-up time interval described here are only examples and may have other suitable values ​​depending on the specific application. Furthermore, Fig. 5 only two running states, namely a fast running state and a slow running state. It should be understood that, in order to improve the estimation accuracy, the running state of the object in step S401 may also include one or more other running states, such as a standstill or stopped state, a first-speed running state, a second-speed running state, a third-speed running state, a fourth-speed running state, and the like. Each running state may have a corresponding addition or multiplication factor, and the addition or multiplication factors corresponding to different running states may be the same or different.

[0052] Fig. 6 shows an exemplary process 600 of changing a wake-up time interval of a sensor based on a running state of an object according to embodiments of the present disclosure.

[0053] As in Fig. 6, the example flow 600 may begin with step S601. In step S601, a running state of an object (e.g., a fast running state, a slow running state, or a stopped state) may be detected. In this example, to simplify the description, the fast running state may be assumed to be a first state and the slow running state or the stopped state may be assumed to be a second state.

[0054] In step S602, it may be determined whether a wake-up time interval IW is initialized. If not, the process 600 may proceed to step S603 to initialize the IW. For example, the IW may be initialized to a minimum wake-up time interval MIN_INTERVAL. In other embodiments, the IW may also be initialized to a maximum wake-up time interval MAX_INTERVAL or another suitable initial value IW_INITIAL, which is not limited here. If the IW has been initialized, the process 600 proceeds to step S604.

[0055] In step S604, it can be determined whether the current or recently detected running state of the object is a first state (i.e., a fast running state) or a second state (i.e., a slow running state or a stopped state). If it is the first state, as described above, it is expected that the sensor may wake up more frequently, so that the process 600 can proceed to step S605 to reduce the wake-up time interval. Assuming that the multiplication factor corresponding to the first state is 1 / 2, in step S605, it can first be judged whether the product IW / 2 of the current wake-up time interval IW and the multiplication factor 1 / 2 is smaller than the minimum wake-up time interval MIN_INTERVAL. If the product IW / 2 of the current wake-up time interval IW and the multiplication factor 1 / 2 has been smaller than the minimum wake-up time interval MIN_INTERVAL, i.e.,If the minimum wake-up time interval MIN_INTERVAL has been reached, IW can be set as the minimum wake-up time interval MIN_INTERVAL in step S606. If the product IW / 2 of the current wake-up time interval IW and the multiplication factor 1 / 2 is not less than the minimum wake-up time interval MIN_INTERVAL, IW can be set as the product IW / 2 of the current wake-up time interval IW and the multiplication factor 1 / 2 in step S607.

[0056] If it is determined in step S604 that the running state of the object is the second state, it is expected that the sensor may wake up less frequently at this time, so that the process 600 may proceed to step S608 to increase the wake-up time interval. Assuming that the addition factor corresponding to the second state is t', it may first be judged in step S608 whether the sum IW+t' of the current wake-up time interval IW and the addition factor of t' is greater than the maximum wake-up time interval MAX_INTERVAL. If the sum IW+t' of the current wake-up time interval IW and the addition factor of t' is already greater than the maximum wake-up time interval MAX_INTERVAL, i.e., the maximum wake-up time interval MAX_INTERVAL has been reached, IW may be set as the maximum wake-up time interval MAX_INTERVAL in step S610.If the sum IW+t' of the current wake-up time interval IW and the addition factor of t' is not greater than the maximum wake-up time interval MAX_INTERVAL, IW can be set in step S609 as the sum IW+t' of the current wake-up time interval IW and the addition factor of t'.

[0057] The updated or changed current wake-up time interval IW may be determined by steps S606, S607, S609 or S610, after which the process 600 may end in step S611.

[0058] In some embodiments, the exemplary process or method described herein for changing the wake-up time interval of a sensor based on the running state of an object may be executed on a computing device or on the sensor, for example, by a scheduler running on the sensor. Fig. 6 is only one example of the method for changing the wake-up time interval of a sensor based on the running state of an object according to the embodiments of the present disclosure, and the steps in Fig. 6 can be performed in any other order. In addition, one or more steps can be Fig. 6 may be omitted or combined with each other, or any other method or step may be added according to embodiments of the present disclosure.

[0059] When multiple sensors are installed on an object and can be used to perform the method for estimating revolutions of a bearing on the object and / or the method for changing a wake-up time interval of a sensor based on the running state of the object according to embodiments of the present disclosure, these sensors can further save energy by operating in a time-division multiplexed manner.

[0060] Fig. Figure 7 shows a schematic representation of performing a measurement with multiple sensors according to embodiments of the present disclosure. Two sensors, i.e., sensor #1 and sensor #2, are shown in Fig. 7. Sensor #1 and sensor #2 do not have to wake up at the same time to measure the position and / or state of the object, but can operate in a time-multiplexed manner. For example, sensor #1 can wake up and perform the measurement according to the above-mentioned procedures during a period from T1 to T N while sensor #2 does not need to operate; while sensor #2 is operating for a period of T N+1 are N+N night N wake up and perform the measurement according to the above procedures, while sensor #1 is not working. After that, the two sensors can work alternately according to their set working hours, further saving energy.

[0061] Fig. 8 shows a schematic representation of a method 800 for estimating the revolutions of a bearing on an object according to embodiments of the present disclosure.

[0062] As in Fig. 8, the method 800 for estimating the revolutions of a bearing on an object according to embodiments of the present disclosure may include: in step S801, receiving a plurality of measurement data from a sensor; in step S802, determining a total distance traveled by the object based on the plurality of measurement data; in step S803, determining a traveled distance offset of the bearing, wherein the bearing traveled distance offset is a distance traveled by the object when the bearing is installed on the object; and in step S804, determining the revolutions of the bearing based on the determined total distance traveled by the object and the determined bearing traveled distance offset. In some embodiments, each of the plurality of measurement data may have a corresponding timestamp.In some embodiments, as described above, the plurality of measurement data may be measured by the sensor waking up based on a first time interval, and during the running of the object, the first time interval may change based on the running state of the object (e.g., further based on the running speed of the object, etc.).

[0063] In some embodiments, the first time interval may be decreased according to a first change factor when the running state of the object is a first state. In a case where the running state of the object is a second state, the first time interval may be increased according to a second change factor whose rate of change is lower than that of the first change factor. For example, in some embodiments, as described above, the first change factor may be a multiplication factor and the second change factor may be an addition factor. Generally, the rate of change of an addition factor is lower than the rate of change of a multiplication factor. In some embodiments, the first change factor may also be an addition factor with a larger absolute value, such as -60s, while the second change factor may be an addition factor with a smaller absolute value, such as30s, so that a faster deceleration and a slower acceleration of the first time interval can still be achieved. Similarly, the first change factor and the second change factor can also be realized by multiplication factors with different numerical values. For example, the first change factor can be a multiplication factor of 1 / 10 and the second change factor a multiplication factor of 5.

[0064] In some embodiments, the running state of the object may include one or more of the following: a fast running state, a slow running state, and a stopped state.

[0065] In some embodiments, the high speed state may be considered the first state described above and the low speed state and / or the stop state may be considered the second state described above.

[0066] In some embodiments, each of the plurality of measurement data may include position information of an object, which may be used to indicate the position of the object when the measurement data is measured. In some embodiments, determining a total distance traveled by the object based on the plurality of measurement data in step S802 may further comprise: determining a first incremental distance traveled by the object based on the position information of the object and the corresponding timestamp; and adding the first incremental distance traveled to a previously traveled distance of the object to determine the total distance traveled.

[0067] For example, a computing device as in Fig. 1, receive multiple measurement data comprising position information of an object from sensors and sort the multiple pieces of position information according to the corresponding timestamps. The computing device can calculate the distance (e.g., the segment distance corresponding to each time interval Δt) between two adjacent position samples sorted in time order in the manner described above, and then accumulate the segment distances corresponding to all time intervals Δt to obtain the first incremental distance traveled by the object in the time period corresponding to this position information. Subsequently, the total distance traveled by the object is obtained by adding the first incremental distance to the total distance previously traveled by the object.

[0068] In some embodiments, each of the plurality of measurement data may include running state information of the object, which may be used to indicate a running state of the object when the measurement data is measured. In some embodiments, determining a total distance traveled by the object based on the plurality of measurement data in step S802 may further comprise: determining a running time of the object based on the running state information of the object and the corresponding timestamp; determining a second incremental distance traveled by the object based on the running time and a running speed corresponding to the running time; and adding the second incremental distance traveled to a previously traveled distance of the object to determine the total distance traveled.

[0069] For example, in Fig. 3, a computing device may, for example, receive a plurality of measurement data including information about the running state of the object from the sensors and sort the plurality of running state information items according to the corresponding timestamps. As mentioned above, it is assumed that the running state of the object does not change within a time interval Δt, so that the running time of the object in each running state can be easily determined based on the plurality of running state information items and the corresponding timestamps. By multiplying the running time of the object in each running state by a predetermined average speed corresponding to the respective running state, the second incremental distance traveled by the object in the period corresponding to this running state information can be determined.The total distance traveled by the object is then obtained by adding the second incremental distance traveled to the total distance previously traveled by the object.

[0070] In some embodiments, determining the revolutions of the bearing based on the determined total distance traveled by the object and the determined offset of the bearing in step S804 may further comprise: subtracting the offset of the traveled distance from the total distance traveled to obtain a first traveled distance of the bearing; and dividing the first traveled distance by a second traveled distance associated with the bearing to obtain the revolutions, where the second traveled distance may be a distance traveled by the object when the bearing rotates one revolution. The second traveled distance may be, for example, the circumference of a wheel corresponding to the bearing.In addition, as described above, the offset of the traveled distance of the bearing can be the distance that the object has already traveled when the bearing is installed on the object.

[0071] Fig. 9 shows a schematic diagram of an apparatus 900 for estimating the revolutions of a bearing on an object according to embodiments of the present disclosure.

[0072] The device 900 may be any computing device suitable for data processing, e.g., a local server, a cloud server, a data processing center, etc. As in Fig. As shown in Figure 9, the device 900 may include a transceiver 910 and a processor 920. The transceiver 910 may be configured to receive a plurality of measurement data items from a sensor. In some embodiments, each of the plurality of measurement data items may have a corresponding timestamp. In some embodiments, as described above, the plurality of measurement data items may be measured by the sensor waking up based on a first time interval, and during the running of the object, the first time interval may change based on a running state of the object (e.g., further based on the running speed of the object, etc.).The processor 920 may be communicatively coupled to the transceiver 910 and may be configured to determine a total distance traveled by the object based on the plurality of measurement data; determine a traveled distance offset of the bearing, where the traveled distance offset is a distance traveled by the object when the bearing is installed on the object; and determine the revolutions of the bearing based on the determined total distance traveled by the object and the determined traveled distance offset of the bearing.

[0073] Moreover, the apparatus 900 may also perform any other method or step according to the above-described embodiments of the present disclosure, which are not repeated in detail here.

[0074] Fig. 10 shows a schematic representation of an apparatus 1000 for estimating the revolutions of a bearing on an object according to embodiments of the present disclosure.

[0075] As in Fig.10, the device 1000 may include a communication module 1001, a first determination module 1002, a second determination module 1003, and a third determination module 1004. The communication module 1001 may be configured to receive a plurality of measurement data items from a sensor. In some embodiments, each of the plurality of measurement data items may have a corresponding timestamp. In some embodiments, as described above, the plurality of measurement data items may be measured by the sensor, which wakes up based on a first time interval, and during the running of the object, the first time interval may change based on a running state of the object (e.g., further based on the running speed of the object, etc.). The first determination module 1002 may be configured to determine a total distance traveled by the object based on the plurality of measurement data items.The second determination module 1003 may be configured to determine a traveled distance offset of the bearing, wherein the traveled distance offset of the bearing is a distance traveled by the object when the bearing is installed on the object. The third determination module 1004 may be configured to determine the revolutions of the bearing based on the determined total traveled distance of the object and the determined traveled distance offset of the bearing.

[0076] In addition, the device 1000 may also include other modules that perform other methods or steps according to the above-described embodiments of the present disclosure, which are not repeated in detail here.

[0077] In particular, according to embodiments of the present disclosure, the methods or processes described above in connection with the embodiments of the present disclosure or the drawings may be implemented as a computer software program. For example, one embodiment of the present disclosure includes a computer program product comprising computer programs on a non-transitory, computer-readable medium that includes program codes for carrying out the methods depicted in the flowcharts. In such an embodiment, the computer programs may be downloaded and installed from the network via a communication device, or they may be installed from a storage device or from a read-only memory.When the computer programs are executed by a processing device, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0078] Furthermore, embodiments of the present disclosure are provided with a computer-readable storage medium having stored thereon instructions that, when executed, cause a processor to perform any method for estimating the revolutions of a bearing on an object and / or any method for changing a wake-up time interval of a sensor based on a running state of the object in accordance with embodiments of the present disclosure.

[0079] The flowcharts and block diagrams in the drawings illustrate the architecture, functions, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this context, each block in the flowchart or block diagram may represent a module, program segment, or portion of code containing one or more executable instructions for implementing specific logical functions. It should also be noted that in some alternative implementations, the functions represented in the blocks may occur in a different order than that shown in the drawings. For example, two blocks shown one after the other may actually execute substantially in parallel, and sometimes they may execute in reverse order, depending on the functions involved.It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a special purpose hardware-based system that performs specific functions or operations, or may be implemented by a combination of special purpose hardware and computer instructions.

[0080] The units involved in the embodiments described herein may be implemented in software or hardware. In some cases, the names of the units do not imply any limitation on the units themselves.

[0081] The functions described herein may be performed, at least in part, by one or more hardware logic components. Example types of hardware logic components that may be used include, for example, but are not limited to, Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), etc.

[0082] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store programs for use by or in connection with an instruction execution system, device, or apparatus. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the above.More specific examples of the machine-readable storage medium may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a convenient compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0083] The above description merely describes preferred embodiments of the present disclosure and explains the technical principles employed. It should be understood by those skilled in the art that the scope of the present disclosure is not limited to the technical schemes formed by the specific combination of the above technical features, but also includes other technical schemes formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept, for example, technical schemes formed by replacing the above features with technical features (but not limited to) having similar functions to those disclosed in the present disclosure.

[0084] Although the operations are presented in a particular order, this should not be understood to mean that these operations must be performed in the order presented or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Although several specific implementation details are included above, they should not be understood as limiting the scope of the present disclosure. Some features described in connection with individual embodiments may also be combined in a single embodiment. Conversely, various features described in connection with a single embodiment may also be implemented in multiple embodiments individually or in any suitable subcombination.

[0085] Although the subject matter has been described in language referring to structural features and / or methodological-logical acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are merely exemplary forms for implementing the claims.

Claims

[1] A method for estimating the revolutions of a bearing on an object, comprising: Receiving a plurality of measurement data from a sensor, each of the plurality of measurement data having a corresponding timestamp, wherein the plurality of measurement data is measured by the sensor waking up based on a first time interval, and wherein during the running of the object the first time interval changes based on a running state of the object; Determining a total distance traveled by the object based on the plurality of measurement data; Determining a bearing traveled distance offset, wherein the bearing traveled distance offset is a distance traveled by the object when the bearing is installed on the object; and Determining the revolutions of the bearing based on the determined total distance traveled by the object and the determined offset of the distance traveled by the bearing. [2] The method of claim 1, wherein changing the first time interval based on a running state of the object comprises: Reducing the first time interval according to a first change factor in the event that the running state of the object is a first state; and Increasing the first time interval according to a second change factor whose rate of change is smaller than that of the first change factor when the running state of the object is a second state. [3] The method of claim 2, wherein the first change factor is a multiplication factor and the second change factor is an addition factor. [4] The method of claim 2, wherein the running state of the object comprises one or more of the following: a fast running state, a slow running state, and a stop state, and wherein the first state is a fast running state and the second state is a slow running state or a stop state. [5] The method according to claim 1, wherein each of the plurality of measurement data comprises position information of the object, the position information being used to indicate a position of the object when the measurement data is measured, and wherein determining a total distance traveled by the object based on the plurality of measurement data comprises: Determining a first incremental distance traveled by the object based on the position information of the object and the corresponding timestamp; and Adding the first incremental distance traveled to a previously traveled distance of the object to determine the total distance traveled. [6] The method according to claim 5, wherein the sensor comprises a locating module, and wherein the position information of the object is acquired via the locating module. [7] The method according to claim 1, wherein each of the plurality of measurement data comprises running state information of the object, the running state information being used to indicate a running state of the object when the measurement data is measured, and wherein determining a total distance traveled by the object based on the plurality of measurement data comprises: Determining a runtime of the object based on the run state information of the object and the corresponding timestamp; Determining a second incremental distance traveled by the object based on the travel time and a travel speed corresponding to the travel time; and Adding the second incremental distance traveled to a previously traveled distance of the object to determine the total distance traveled. [8] The method according to claim 7, wherein the sensor comprises a speed detection module, and wherein the running state information of the object is detected via the speed detection module. [9] The method of claim 1, wherein determining the revolutions of the bearing based on the determined total distance traveled by the object and the determined offset of the distance traveled by the bearing comprises: Subtracting the offset of the distance traveled from the total distance traveled to obtain a first distance traveled by the bearing; and Dividing the first distance traveled by a second distance traveled associated with the bearing to obtain the revolutions, where the second distance traveled is a distance the object travels when the bearing makes one revolution. [10] A device for estimating the revolutions of a bearing on an object, comprising: a transceiver designed to: receive a plurality of measurement data from a sensor, each of the plurality of measurement data having a corresponding timestamp, wherein the plurality of measurement data is measured by the sensor waking up based on a first time interval, and wherein the first time interval changes during the running of the object based on a running state of the object; and a processor coupled to the transceiver and configured to: to determine a total distance traveled by the object based on the multiple measurement data; determine a bearing traveled distance offset, where the bearing traveled distance offset is a distance traveled by the object when the bearing is installed on the object; and to determine the revolutions of the bearing based on the determined total distance traveled by the object and the determined offset of the distance traveled by the bearing.

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