Data processing method, system and device of accelerometer and storage medium

Abstract

The invention discloses a data processing method, system and device of an accelerometer and a storage medium. The method comprises the following steps: acquiring acceleration data of a target object through a target accelerometer; according to an acquisition sequence, performing sliding filtering processing on the acceleration data to obtain first data; grouping the first data, and performing mean filtering processing on the grouped first data to obtain second data; and determining a motion track of the target object according to the second data. Through the sliding filtering processing and the mean filtering processing, the original data interference of the low-cost accelerometer is relieved, and the accuracy of the motion trail is improved. The method can be widely applied to the technical field of computers.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a data processing method, system, device, and storage medium for an accelerometer. Background Technology

[0002] As elevator usage increases, so does the rate of elevator malfunctions. More and more cities are requiring the installation of elevator IoT devices to monitor elevator status in real time, enabling timely rescue in case of malfunctions. Accelerometers have numerous applications in elevator IoT. Depending on their installation location, they can acquire information such as elevator operating status and car door operation. However, due to cost constraints, elevator IoT devices typically use low-cost MCUs and accelerometers. The raw data collected by accelerometers is highly susceptible to interference and cannot accurately reconstruct the true trajectory of an object. Summary of the Invention

[0003] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0004] Therefore, the purpose of this invention is to provide an efficient data processing method, system, device, and storage medium for accelerometers.

[0005] To achieve the above-mentioned technical objectives, one aspect of this invention provides a data processing method for an accelerometer, comprising the following steps: acquiring acceleration data of a target object using a target accelerometer; performing sliding filtering on the acceleration data according to the acquisition order to obtain first data; grouping the first data and performing mean filtering on the grouped first data to obtain second data; and determining the motion trajectory of the target object based on the second data. This application, through sliding filtering and mean filtering, helps to mitigate interference from the raw data of low-cost accelerometers and improves the accuracy of the motion trajectory.

[0006] In some embodiments, the step of performing sliding filtering on the acceleration data according to the acquisition order to obtain first data includes:

[0007] Determine the sliding threshold;

[0008] If the number of current acceleration data is less than the sliding threshold, the current acceleration data will be used as the current first data.

[0009] If the number of current acceleration data is equal to the sliding threshold, the collected acceleration data with a number equal to the sliding threshold will be averaged to obtain the current first data.

[0010] Collect new acceleration data, discard the earliest acceleration data, obtain a new number of acceleration data equal to the sliding threshold, calculate the average, and obtain a new first data; until sampling is complete.

[0011] In some embodiments, grouping the first data and performing mean filtering on the grouped first data to obtain the second data includes:

[0012] Determine the grouping threshold;

[0013] The first data is grouped according to the grouping threshold;

[0014] The first data point of each group is averaged to obtain the second data point.

[0015] In some embodiments, determining the grouping threshold includes:

[0016] The sampling frequency of the accelerometer and the acceleration change frequency of the target object are obtained;

[0017] The grouping threshold is determined based on the quotient of the sampling frequency and the acceleration change frequency.

[0018] In some embodiments, the method further comprises:

[0019] Obtain the acceleration change frequency of the target object;

[0020] The sampling frequency of the accelerometer is set such that the sampling frequency is greater than or equal to the acceleration change frequency.

[0021] In some embodiments, the method further comprises:

[0022] Displays the real-time trajectory interface;

[0023] In response to a first editing operation on the first control received on the real-time trajectory interface, a first sliding threshold is determined, and a first motion trajectory is obtained based on the first sliding threshold;

[0024] In response to a second editing operation on the first control received on the real-time trajectory interface, a second sliding threshold is determined, and a second motion trajectory is obtained based on the second sliding threshold;

[0025] In response to a trigger operation on the second control received on the real-time trajectory interface, the first motion trajectory and the second motion trajectory are displayed;

[0026] Based on the displayed first motion trajectory and second motion trajectory, a new sliding threshold is determined as the first sliding threshold, or a new sliding threshold is determined as the second sliding threshold.

[0027] On the other hand, embodiments of the present invention propose an elevator that uses the above-described accelerometer data processing method to process data and obtain a motion trajectory.

[0028] On the other hand, embodiments of the present invention propose a data processing system for an accelerometer, comprising:

[0029] The first module is used to collect acceleration data of the target object through the target accelerometer;

[0030] The second module is used to perform sliding filtering on the acceleration data according to the acquisition order to obtain the first data;

[0031] The third module is used to group the first data and perform mean filtering on the grouped first data to obtain the second data.

[0032] The fourth module is used to determine the motion trajectory of the target object based on the second data.

[0033] On the other hand, embodiments of the present invention provide a data processing device for an accelerometer, comprising:

[0034] At least one processor;

[0035] At least one memory for storing at least one program;

[0036] When the at least one program is executed by the at least one processor, the at least one processor implements the above-described accelerometer data processing method.

[0037] On the other hand, embodiments of the present invention provide a storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the above-described accelerometer data processing method.

[0038] The embodiments of this application include at least the following beneficial effects: The method provided by the embodiments of this invention includes: acquiring acceleration data of a target object using a target accelerometer; performing sliding filtering on the acceleration data according to the acquisition order to obtain first data; grouping the first data and performing mean filtering on the grouped first data to obtain second data; and determining the motion trajectory of the target object based on the second data. This application, through sliding filtering and mean filtering, helps to alleviate the interference of raw data from low-cost accelerometers and improves the accuracy of the motion trajectory. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0040] Figure 1 A flowchart illustrating an embodiment of the accelerometer data processing method provided by the present invention;

[0041] Figure 2 A schematic diagram illustrating one embodiment of the raw data from the accelerometer provided by the present invention;

[0042] Figure 3 For the present invention Figure 2 A schematic diagram illustrating one embodiment of the raw data after undergoing sliding filter processing;

[0043] Figure 4 For the present invention Figure 3 A schematic diagram illustrating one embodiment of the first data after grouping;

[0044] Figure 5 For the present invention Figure 3 A schematic diagram illustrating one embodiment of the first data after mean filtering;

[0045] Figure 6 A schematic diagram of an embodiment of the accelerometer data processing system provided by the present invention;

[0046] Figure 7 This is a schematic diagram of one embodiment of the accelerometer data processing device provided by the present invention. Detailed Implementation

[0047] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0048] As elevator usage increases, so does the rate of elevator malfunctions. More and more cities are requiring the installation of elevator IoT devices to monitor elevator status in real time, enabling timely rescue in case of malfunctions. Accelerometers have numerous applications in elevator IoT. Depending on their installation location, they can acquire information such as elevator operating status and car door operation. However, due to cost constraints, elevator IoT devices typically use low-cost MCUs and accelerometers. The raw data collected by accelerometers is subject to significant interference.

[0049] This application uses data processing methods to eliminate interference from the raw data of low-cost accelerometers and restore the true trajectory of the object.

[0050] The data processing method and system for an accelerometer according to embodiments of the present invention will now be described in detail with reference to the accompanying drawings. First, the data processing method for an accelerometer according to embodiments of the present invention will be described with reference to the accompanying drawings.

[0051] Reference Figure 1 This invention provides a data processing method for an accelerometer. This method can be applied to a terminal, a server, or software running on either a terminal or server. The terminal can be a tablet, laptop, desktop computer, etc., but is not limited to these. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The data processing method for an accelerometer in this invention mainly includes the following steps:

[0052] S100: Acquires acceleration data of the target object through the target accelerometer;

[0053] S200: According to the acquisition sequence, the acceleration data is subjected to sliding filtering to obtain the first data;

[0054] S300: Group the first data and perform mean filtering on the grouped first data to obtain the second data;

[0055] S400: Determine the motion trajectory of the target object based on the second data.

[0056] In some possible implementations, the grouping threshold is the number of data points included in each group after grouping. The target object can be any component of the elevator. The sampling order is that the acceleration data is sampled and processed by sliding filtering simultaneously. In elevator IoT devices, due to the performance requirements of the MCU, overly complex filtering algorithms cannot be executed; only simple filtering algorithms can solve the above problems. This technical solution uses a combination of sliding filtering and mean filtering algorithms to basically restore the trajectory curve of the object.

[0057] In some embodiments, the acceleration data is subjected to sliding filtering according to the acquisition order to obtain first data, including:

[0058] Determine the sliding threshold;

[0059] If the number of current acceleration data points is less than the sliding threshold, the current acceleration data point will be used as the first data point.

[0060] If the number of current acceleration data is equal to the sliding threshold, the average of the collected acceleration data that equals the sliding threshold is calculated to obtain the current first data.

[0061] Collect new acceleration data, discard the earliest acceleration data, obtain a new number of acceleration data equal to the sliding threshold, calculate the average, and obtain a new first data; until sampling is complete.

[0062] The first few sliding threshold acceleration data points are left unprocessed and used as the first data point at the corresponding position. The next sliding threshold acceleration data point is averaged with the previously collected acceleration data and used as the first data point at the corresponding position (i.e., sorted by the sliding threshold). When new acceleration data is collected, the first acceleration data point is deleted, and the average of the first few sliding threshold acceleration data points is calculated each time to obtain the first data point at the corresponding position.

[0063] In some embodiments, the first data is grouped, and the grouped first data is subjected to mean filtering to obtain the second data, including:

[0064] Determine the grouping threshold;

[0065] The first data is grouped according to the grouping threshold;

[0066] The first data point of each group is averaged to obtain the second data point.

[0067] The grouping threshold is the number of first data points included in each group after grouping; the grouping threshold is related to the accelerometer's sampling frequency and the acceleration change frequency of the target object. Second data can also be obtained by simultaneously acquiring and calculating the first data. After acquiring the grouping threshold number of first data points, the average is calculated to obtain the second data; then, the acquisition of first data continues, and when the number of newly acquired first data points reaches the grouping threshold, the average is calculated again to obtain the second data. The second data reflects the motion of the target object.

[0068] In some embodiments, determining the grouping threshold includes:

[0069] Obtain the accelerometer sampling frequency and the acceleration change frequency of the target object;

[0070] The grouping threshold is determined based on the quotient of the sampling frequency and the acceleration change frequency.

[0071] In some embodiments, the method further includes:

[0072] Obtain the frequency of acceleration changes of the target object;

[0073] Set the accelerometer's sampling frequency so that it is greater than or equal to the acceleration change frequency.

[0074] In some embodiments, the method further includes:

[0075] Displays the real-time trajectory interface;

[0076] In response to a first editing operation on the first control received on the real-time trajectory interface, a first sliding threshold is determined, and a first motion trajectory is obtained based on the first sliding threshold;

[0077] In response to a second editing operation on the first control received on the real-time trajectory interface, a second sliding threshold is determined, and a second motion trajectory is obtained based on the second sliding threshold;

[0078] In response to a trigger operation on the second control received on the real-time trajectory interface, the first motion trajectory and the second motion trajectory are displayed;

[0079] Based on the displayed first and second motion trajectories, a new sliding threshold is determined as the first sliding threshold, or a new sliding threshold is determined as the second sliding threshold.

[0080] The motion trajectory in this application can be displayed visually. This application can compare and display motion trajectories obtained under different sliding thresholds to determine a better sliding threshold. Specifically, a real-time trajectory interface is displayed, and different sliding thresholds are determined by editing the first control. The first control is the control corresponding to the specific value of the sliding threshold.

[0081] The second control is a display control for the motion trajectory. Triggering the second control displays the first and second motion trajectories. It is understood that the first and second motion trajectories in this application are motion trajectories with different sliding thresholds under the same acceleration dataset. Those skilled in the art can determine a new sliding threshold based on the comparison of the motion trajectories. In subsequent accelerometer data processing, data processing can be performed using the new sliding threshold. The triggering operation in this application can be a click operation, touch operation, selection operation, etc., and this application does not impose specific limitations. Similarly, those skilled in the art can adjust the positional relationship between the real-time trajectory interface and the first and second controls, as well as the shapes of the first and second controls, according to actual needs, and this application does not impose specific limitations.

[0082] The data processing method provided in this application will be described in detail below with a specific embodiment:

[0083] Accelerometer principle and raw data:

[0084] An accelerometer is a sensor that collects the acceleration values ​​of an object as it moves; the acceleration can roughly reflect the object's trajectory. The raw data collected by the accelerometer (such as...) Figure 2 It is subject to significant fluctuations and interference, making it unusable directly.

[0085] The solution provided in this application:

[0086] 1. The accelerometer's sampling frequency must be greater than the object's acceleration change frequency to ensure that no details are lost. The more data collected, the better the data analysis results.

[0087] Acceleration change frequency: The time difference between two different acceleration values ​​is t (unit: seconds), and the frequency = 1 / t (unit: Hz Hertz).

[0088] Assuming the object's acceleration changes at a frequency of 100Hz, and the accelerometer's sampling frequency is set to 1600Hz, this can be approximated as the accelerometer collecting 16 consecutive data points representing a single valid point in the object's acceleration.

[0089] In this application, the accelerometer's sampling frequency is adjustable; the higher the multiplier, the better the trajectory reconstruction. If the sampling frequency is modified, N in Scheme 1 (i.e., sliding filter) does not change, while 16 in Scheme 2 (i.e., mean filter) does change accordingly, meaning the number of groups is based on the multiplier.

[0090] 2. Apply moving average filtering to accelerometer data.

[0091] Moving average filtering: N data points are collected sequentially. Each time a new data point is collected, the oldest data point is discarded. The arithmetic mean of the N data points, including the new data point, is then calculated. In this way, a new average is calculated with each sampling. The advantage of moving average filtering is its ability to suppress periodic interference and improve smoothness.

[0092] (like Figure 3 (Figure 1 shows the original value, and Figure 2 shows the value after moving average filtering). After moving average filtering, the accelerometer value only becomes smoother, but the error caused by accelerometer sampling cannot be eliminated.

[0093] For example, 1. Assuming N is 3, the specific calculation is as follows: Data S1, S2, S3, S4… are collected sequentially. When S4 is collected, S1 is discarded, and Y1 = (S2 + S3 + S4) / 3 is obtained from the first data. When S5 is collected, S2 is discarded, and Y2 = (S3 + S4 + S5) / 3 is obtained, and so on. The final processed first data is S1, S2, S3, Y1, Y2…

[0094] The sliding filter scheme involves simultaneous data acquisition and computation; grouping and mean filtering also employ this method. The data in Scheme 2 is the processed data from Scheme 1, and they are used concurrently. The "16 groups" refers to the 16 data points acquired in Scheme 1 being directly fed into Scheme 2 for mean filtering calculation, yielding one effective point. Of course, the number 16 here is adjusted based on the sampling frequency.

[0095] 3. Grouping and Mean Filtering

[0096] Grouping refers to processing accelerometer data into fixed groups (e.g., ...). Figure 4 Finally, the mean filter is applied to each set of data to obtain the effective values, and all the effective values ​​are concatenated to obtain the final data curve (e.g., ...). Figure 5 ).

[0097] As demonstrated in step 1, the accelerometer's sampling frequency is far higher than the object's acceleration change frequency. Using the example from step 1 again, the accelerometer's sampling frequency is 16 times the object's acceleration change frequency. Therefore, the accelerometer data is grouped into sets of 16. After mean filtering, each group of data can approximate the object's current acceleration value.

[0098] 4. After data grouping and mean filtering, the data can basically restore the motion trajectory of the object, thus simplifying the difficulty of judging the object's running state.

[0099] This invention can accurately reconstruct the trajectory of an object using a low-cost accelerometer; the filtering algorithm of this data processing scheme is simple and applicable to MCUs with poor performance; this data processing scheme is not only for accelerometers, but can be used for data from virtually all sensors.

[0100] On the other hand, embodiments of the present invention propose an elevator that uses the above-described accelerometer data processing method to process data and obtain a motion trajectory.

[0101] On the other hand, refer to Figure 6 This invention provides a data processing system for an accelerometer, comprising:

[0102] The first module 610 is used to collect acceleration data of the target object through the target accelerometer;

[0103] The second module 620 is used to perform sliding filtering on the acceleration data according to the acquisition sequence to obtain the first data;

[0104] The third module 630 is used to group the first data and perform mean filtering on the grouped first data to obtain the second data;

[0105] The fourth module 640 is used to determine the motion trajectory of the target object based on the second data.

[0106] It is evident that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0107] Reference Figure 7 This invention provides a data processing device for an accelerometer, comprising:

[0108] At least one processor 410;

[0109] At least one memory 420 is used to store at least one program;

[0110] When the at least one program is executed by the at least one processor 410, the at least one processor 410 implements the accelerometer data processing method.

[0111] Similarly, the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0112] This invention also provides a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the above-described accelerometer data processing method.

[0113] Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0114] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0115] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0116] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0118] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0119] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0120] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0121] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0122] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A data processing method for an accelerometer, characterized in that, Includes the following steps: Acceleration data of the target object is collected using a target accelerometer; According to the acquisition order, the acceleration data is subjected to sliding filtering to obtain the first data; The first data is grouped, and the grouped first data is subjected to mean filtering to obtain the second data; Based on the second data, the motion trajectory of the target object is determined.

2. The data processing method for an accelerometer according to claim 1, characterized in that, The step of performing sliding filtering on the acceleration data according to the acquisition order to obtain the first data includes: Determine the sliding threshold; If the number of current acceleration data is less than the sliding threshold, the current acceleration data will be used as the current first data. If the number of current acceleration data is equal to the sliding threshold, the collected acceleration data with a number equal to the sliding threshold will be averaged to obtain the current first data. Collect new acceleration data, discard the earliest acceleration data, obtain a new number of acceleration data equal to the sliding threshold, calculate the average, and obtain a new first data; until sampling is complete.

3. The data processing method for an accelerometer according to claim 1, characterized in that, The step of grouping the first data and performing mean filtering on the grouped first data to obtain the second data includes: Determine the grouping threshold; The first data is grouped according to the grouping threshold; The first data point of each group is averaged to obtain the second data point.

4. The accelerometer data processing method according to claim 3, characterized in that, The determination of the grouping threshold includes: The sampling frequency of the accelerometer and the acceleration change frequency of the target object are obtained; The grouping threshold is determined based on the quotient of the sampling frequency and the acceleration change frequency.

5. The data processing method for an accelerometer according to claim 1, characterized in that, The method further includes: Obtain the acceleration change frequency of the target object; The sampling frequency of the accelerometer is set such that the sampling frequency is greater than or equal to the acceleration change frequency.

6. The data processing method for an accelerometer according to claim 2, characterized in that, The method further includes: Displays the real-time trajectory interface; In response to a first editing operation on the first control received on the real-time trajectory interface, a first sliding threshold is determined, and a first motion trajectory is obtained based on the first sliding threshold; In response to a second editing operation on the first control received on the real-time trajectory interface, a second sliding threshold is determined, and a second motion trajectory is obtained based on the second sliding threshold; In response to a trigger operation on the second control received on the real-time trajectory interface, the first motion trajectory and the second motion trajectory are displayed; Based on the displayed first motion trajectory and second motion trajectory, a new sliding threshold is determined as the first sliding threshold, or a new sliding threshold is determined as the second sliding threshold.

7. An elevator, characterized in that, Data processing is performed using the accelerometer data processing method as described in any one of claims 1 to 7.

8. A data processing system for an accelerometer, characterized in that, include: The first module is used to collect acceleration data of the target object through the target accelerometer; The second module is used to perform sliding filtering on the acceleration data according to the acquisition order to obtain the first data; The third module is used to group the first data and perform mean filtering on the grouped first data to obtain the second data. The fourth module is used to determine the motion trajectory of the target object based on the second data.

9. A data processing device for an accelerometer, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the accelerometer data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement the accelerometer data processing method as described in any one of claims 1 to 7.

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