阅读说明：本技术 基于数据平台的传感器数据处理方法、装置、系统及存储介质 (Sensor data processing method, device and system based on data platform and storage medium ) 是由 何伟 丛刚 夏迪 申佳 于 2021-01-11 设计创作，主要内容包括：本发明提供了一种基于数据平台的传感器数据处理方法、装置、系统及存储介质,方法包括：获取与所述数据平台关联的传感器的原始传感器数据；根据所述传感器的误差信息对所述原始传感器数据进行校正,得到校正传感器数据；对所述校正传感器数据进行标准化处理,得到标准化传感器数据；基于用户指令对所述标准化传感器数据进行特征提取,得到数据处理结果。根据本发明的方法、装置、系统及存储介质,通过对传感器的原始传感器数据进行校正和标准化,能够准确且高效地提供各个传感器的数据用于数据处理,极大提高了数据处理的效率。(The invention provides a sensor data processing method, a device, a system and a storage medium based on a data platform, wherein the method comprises the following steps: obtaining raw sensor data for a sensor associated with the data platform; correcting the original sensor data according to the error information of the sensor to obtain corrected sensor data; standardizing the corrected sensor data to obtain standardized sensor data; and performing feature extraction on the standardized sensor data based on a user instruction to obtain a data processing result. According to the method, the device, the system and the storage medium, the original sensor data of the sensors are corrected and standardized, the data of each sensor can be accurately and efficiently provided for data processing, and the data processing efficiency is greatly improved.)

1. A sensor data processing method based on a data platform is characterized by comprising the following steps:

obtaining raw sensor data for a sensor associated with the data platform;

correcting the original sensor data according to the error information of the sensor to obtain corrected sensor data;

standardizing the corrected sensor data to obtain standardized sensor data;

and performing feature extraction on the standardized sensor data based on a user instruction to obtain a data processing result.

2. The method of claim 1, wherein correcting the raw sensor data based on the error information of the sensor to obtain corrected sensor data comprises:

correcting the original sensor data based on the error information of the sensor to directly obtain corrected sensor data;

or the like, or, alternatively,

correcting the original sensor data based on the error information of the sensor to obtain intermediate correction data;

and obtaining the corrected sensor data based on the intermediate corrected data and a preset mapping rule.

3. The method of claim 1, wherein the raw sensor data may include pose data and corresponding timestamps.

4. The method of claim 3, wherein correcting the raw sensor data based on the error information of the sensor to obtain corrected sensor data comprises:

taking the initial attitude data as a calibration point of the attitude data;

obtaining relative displacement between the original sensor data of the calibration point and the other original sensor data based on the calibration point and the other original sensor data;

and correcting the other original sensor data based on the relative displacement to obtain corrected sensor data.

5. The method of claim 1, wherein normalizing the corrected sensor data to obtain normalized sensor data comprises:

converting the corrected sensor data to the normalized sensor data in reference units based on reference units and actual units of the raw sensor data.

6. The method of claim 1, wherein performing feature extraction on the normalized sensor data based on a user instruction to obtain a data processing result comprises:

determining a corresponding algorithm based on the user instruction;

and performing feature extraction on the standardized sensor data based on the corresponding algorithm to obtain the data processing result.

7. A sensor data processing apparatus based on a data platform, the apparatus comprising:

a data acquisition module for acquiring raw sensor data of sensors associated with the data platform;

a data processing module to:

correcting the original sensor data according to the error information of the sensor to obtain corrected sensor data;

standardizing the corrected sensor data to obtain standardized sensor data;

and performing feature extraction on the standardized sensor data based on a user instruction to obtain a data processing result.

8. The apparatus of claim 7, wherein the data processing module comprises a local data processing module and/or a cloud data processing module.

9. A sensor data processing system based on a data platform, comprising a memory, a processor and a computer program stored on the memory and running on the processor, the processor implementing the steps of the method according to any one of claims 1-6 when executing the computer program.

10. A computer storage medium having a computer program stored thereon, which when executed by a computer implements the steps of the method of any of claims 1-6.

Technical Field

The invention relates to the technical field of data processing, in particular to data processing of a data platform.

Background

Data of various hardware is generally required to be collected by a data platform for data processing, and each hardware has a corresponding SDK (software development kit) to collect raw data, but the raw data may form a complex data format due to a plurality of factors such as different hardware manufacturers, different models, different default settings, and the like, and cannot be unified. In addition, due to factors such as field environment and installation error, the original data acquired by the sensor is inaccurate and cannot be directly used after the original data is acquired.

Therefore, the problem that different types of sensor data cannot be used in a data platform exists in the prior art, and the data processing efficiency is low.

Disclosure of Invention

The present invention has been made in view of the above problems. The invention provides a sensor data processing method, a sensor data processing device, a sensor data processing system and a computer storage medium based on a data platform, and at least solves the problems.

According to a first aspect of the present invention, there is provided a sensor data processing method based on a data platform, including:

obtaining raw sensor data for a sensor associated with the data platform;

correcting the original sensor data according to the error information of the sensor to obtain corrected sensor data;

standardizing the corrected sensor data to obtain standardized sensor data;

and performing feature extraction on the standardized sensor data based on a user instruction to obtain a data processing result.

Optionally, the correcting the raw sensor data according to the error information of the sensor to obtain corrected sensor data includes:

correcting the original sensor data based on the error information of the sensor to directly obtain corrected sensor data;

or the like, or, alternatively,

correcting the original sensor data based on the error information of the sensor to obtain intermediate correction data;

and obtaining the corrected sensor data based on the intermediate corrected data and a preset mapping rule.

Optionally, the raw sensor data may include pose data and corresponding timestamps.

Optionally, the correcting the raw sensor data according to the error information of the sensor to obtain corrected sensor data includes:

taking the initial attitude data as a calibration point of the attitude data;

obtaining relative displacement between the original sensor data of the calibration point and the other original sensor data based on the calibration point and the other original sensor data;

and correcting the other original sensor data based on the relative displacement to obtain corrected sensor data.

Optionally, normalizing the corrected sensor data to obtain normalized sensor data includes:

converting the corrected sensor data to the normalized sensor data in reference units based on reference units and actual units of the raw sensor data.

Optionally, performing feature extraction on the normalized sensor data based on a user instruction to obtain a data processing result, which may include:

determining a corresponding algorithm based on the user instruction;

and performing feature extraction on the standardized sensor data based on the corresponding algorithm to obtain the data processing result.

According to a second aspect of the present invention, there is provided a sensor data processing apparatus based on a data platform, comprising:

the data acquisition module is used for acquiring original hardware data of hardware equipment associated with the data platform;

a data processing module to:

correcting the original sensor data according to the error information of the sensor to obtain corrected sensor data;

standardizing the corrected sensor data to obtain standardized sensor data;

and performing feature extraction on the standardized sensor data based on a user instruction to obtain a data processing result.

Optionally, the data processing module includes a local data processing module and/or a cloud data processing module.

According to a third aspect of the present invention, there is provided a sensor data processing system based on a data platform, comprising a memory, a processor and a computer program stored on the memory and running on the processor, the processor implementing the steps of the method according to the first aspect when executing the computer program.

According to a fourth aspect of the present invention, there is provided a computer storage medium having a computer program stored thereon, the computer program, when executed by a computer, implementing the steps of the method according to the first aspect.

According to the sensor data processing method, device and system based on the data platform and the storage medium, the original sensor data of the sensors are corrected and standardized, the data of each sensor can be accurately and efficiently provided for data processing, and the data processing efficiency is greatly improved.

Drawings

The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail embodiments of the present invention with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings, like reference numbers generally represent like parts or steps.

FIG. 1 is a schematic flow diagram of a data platform based sensor data processing method according to an embodiment of the present invention;

FIG. 2 is an example of obtaining corrected sensor data according to an embodiment of the invention;

FIG. 3 is an example of feature extraction according to an embodiment of the present invention;

FIG. 4 is an example of data processing according to an embodiment of the present invention;

FIG. 5 is a schematic block diagram of a data platform based sensor data processing apparatus according to an embodiment for implementing the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the invention described herein without inventive step, shall fall within the scope of protection of the invention.

In the process of collecting the original data of a plurality of pieces of hardware, the data platform collects the original hardware data through an SDK (software development kit) corresponding to each piece of hardware, but the original hardware data can form a complex data format due to a plurality of factors such as different hardware manufacturers, different models, different default settings and the like, and cannot be unified. Due to factors such as the field environment and installation error of each device, original hardware data are inaccurate and cannot be directly used or normally used.

The sensors include various types according to different angles, for example, according to application occasions, the sensors may include sensors of a mobile phone, sensors of a handle, and the like, the collection mode of the sensors may be different due to different application occasions or different hardware, but a user needs to use sensor data of a plurality of devices when performing subsequent operations, and the data formats of the sensor data are not uniform, so that the sensor data cannot be directly used after being collected, and the efficiency of data processing is greatly reduced.

In view of the above, an embodiment of the present invention provides a sensor data processing method based on a data platform, and a schematic flow chart of the sensor data processing method based on the data platform according to the embodiment of the present invention will be described with reference to fig. 1. As shown in fig. 1, a sensor data processing method 1 based on a data platform includes:

step S1-1, acquiring raw sensor data of a sensor associated with the data platform;

step S1-2, correcting the original sensor data according to the error information of the sensor to obtain corrected sensor data;

step S1-3, standardizing the corrected sensor data to obtain standardized sensor data;

and step S1-4, performing feature extraction on the standardized sensor data based on a user instruction to obtain a data processing result.

The method comprises the steps of correcting and standardizing original sensor data collected by a sensor to be standard data, and then carrying out feature extraction, analysis and calculation on the standard data based on a user instruction to obtain a corresponding processing result. Therefore, the data processing method provided by the embodiment of the application can accurately and efficiently provide the data of each sensor in the data platform for subsequent data processing, and greatly improves the data processing efficiency. The method is suitable for being widely applied to any occasions of data acquisition and processing.

It should be understood that at least one of the steps S1-1, S1-2, S1-3, and S1-4 may be implemented locally, in a cloud, partially locally and partially in the cloud, which is not limited herein.

Optionally, the sensor may include: and an attitude sensor.

In some embodiments, the attitude sensor may include a three-axis, six-axis, nine-axis sensor, or the like. In other embodiments, acceleration sensors, magnetic sensors, gyroscopes, etc. may be included, depending on the type of attitude sensor. Because the data acquisition modes of the attitude sensors are different due to different hardware in application occasions, but the data can be in the same data format when the user finally uses the data, all the attitude sensors are unified into one type of format type for unified processing, and the data processing efficiency of a subsequent processing program is facilitated. For example, raw sensor data of the attitude sensor can only provide attitude information, corresponding movement tracks and specific track identification associated with the attitude sensor need to further extract the data, different sensors can use different length units, such as different lengths from millimeter to meter, and measurement units of the sensors need to be unified, so that when a subsequent user needs to acquire relevant data such as the movement tracks, compared with the traditional method, the method provided by the embodiment of the invention can directly use standardized sensor data, and the data processing efficiency is greatly improved.

According to the embodiment of the present invention, in the step S1-1, the sensor associated with the data platform may be any sensor that is in communication with the data platform. The sensors may transmit at least a portion of the collected raw sensor data to the data platform in real-time or periodically.

It can be known from the foregoing that the attitude sensor data cannot be directly used after being collected, because the facing azimuth of the attitude sensor data cannot be exactly consistent with the north-south pole of the geomagnetism, therefore, an initial attitude data can be used as a calibration point of the attitude data, on the basis, all relative displacements can be directly used, and accordingly, the initial hardware data can be uniformly standardized, so as to facilitate the use during subsequent processing, and improve the data processing efficiency.

According to an embodiment of the present invention, in step S1-2, the correcting the raw sensor data according to the error information of the sensor to obtain corrected sensor data includes:

correcting the original sensor data based on the error information of the sensor to directly obtain corrected sensor data;

or the like, or, alternatively,

correcting the original sensor data based on the error information of the sensor to obtain intermediate correction data;

and obtaining the corrected sensor data based on the intermediate corrected data and a preset mapping rule.

After the raw sensor data is collected, there may be some errors in the collected raw sensor data due to the deviation between the sensors, and in order to improve the accuracy of data processing, the raw hardware data may be corrected by using a corresponding deviation correction coefficient for each sensor or each type of sensor (e.g., the same model), so as to ensure the accuracy of the data. And then, the data is standardized to form standardized sensor data, and compared with the traditional method in which various original sensor data are directly adopted for data processing, the method not only improves the data processing efficiency, but also improves the data processing accuracy.

In some embodiments, the raw sensor data may include at least one of attitude data, acceleration velocity, or angular velocity data, and a corresponding timestamp.

In some embodiments, the sensor may comprise an accelerometer, a gyroscope, or a magnetometer.

In some embodiments, for some sensors, the error information is convenient to obtain, measure or calculate, and then the correction can be directly performed according to the error information (such as error coefficients, correction formulas and the like) of the hardware device, so as to obtain corrected sensor data.

In some embodiments, for some sensors, if error information cannot be obtained, measured, or calculated, a mapping relationship before and after correction may be obtained according to an empirical value or a fitting method, and then raw sensor data of the devices may be corrected according to the mapping relationship as a preset mapping rule to obtain corrected sensor data.

In some embodiments, referring to fig. 2, fig. 2 illustrates an example of obtaining corrected sensor data in accordance with an embodiment of the present invention. As shown in fig. 2, after the sensor acquires the raw sensor data and the corresponding timestamp thereof, the initial attitude data may be a calibration point of the attitude data, and based on the calibration point and the other raw sensor data, the relative displacement between the raw sensor data of the calibration point and the other raw sensor data is obtained; and correcting the other original sensor data based on the relative displacement to obtain corrected sensor data.

According to an embodiment of the present invention, in step S1-3, the normalizing the corrected sensor data to obtain normalized sensor data includes:

converting the corrected sensor data to the normalized sensor data in reference units based on reference units and actual units of the raw sensor data.

In some embodiments, the actual units used by the sensors may include millimeters, centimeters, or meters, and centimeters may be used as the reference units, and the corrected sensor data in millimeters or meters may be converted to centimeters, and all sensors may be consolidated into normalized sensor data. Therefore, the corrected data are standardized, the accuracy of subsequent data processing is guaranteed, and meanwhile, the data structure is optimized, so that all sensor data have unified standards, the subsequent data processing is greatly facilitated, and the data processing efficiency is improved.

In some embodiments, the step S1-4 of performing feature extraction on the normalized sensor data based on a user instruction to obtain a data processing result may include:

determining a corresponding algorithm based on the user instruction;

and performing feature extraction on the standardized sensor data based on the corresponding algorithm to obtain the data processing result.

The user instruction may be an operation executed based on a function that the user needs to complete. For example, if the user needs to complete the function a on the data platform, the user may trigger an event corresponding to the function a (e.g., click a preset area corresponding to the function a), and the user trigger function a is the user instruction.

In some embodiments, referring to fig. 3, fig. 3 illustrates an example of feature extraction according to an embodiment of the present invention. As shown in fig. 3, the user command may include track recognition, and the raw sensor data collected by the sensor may be corrected and normalized to obtain normalized sensor data such as normalized relative attitude data, normalized relative acceleration data, and normalized relative angular velocity data; then, the standardized sensor data are sent to a data processing module (which may be local or cloud, and is not limited herein), and the data processing module can perform feature extraction and analysis by using a corresponding algorithm based on the standardized sensor data and a corresponding timestamp thereof to obtain a movement track as a data processing result. When a user instruction about track recognition is detected, the data processing module directly (or after encapsulation) transmits the data of the movement track to the corresponding component corresponding to the user instruction, and the data is used as a data base provided by the corresponding component to the user, for example, the data of the movement track can be received by the corresponding component and displayed to the user, and the like.

In the above-described embodiments, referring to fig. 4, fig. 4 shows an example of data processing according to an embodiment of the present invention. As shown in fig. 4, the data processing module may perform feature extraction and analysis by using a corresponding algorithm based on the normalized sensor data and the corresponding timestamps thereof to obtain a movement trajectory as a data processing result, which may specifically include: based on the normalized sensor data (e.g., normalized 9-axis sensor data) for the current pose and the time interval between previous poses, integration is performed to obtain corresponding accumulated data, thereby obtaining the position between each pose and finally forming the movement trajectory.

Because the data processing or completed functions executed in the data platform can be based on the corrected standardized data, the data is not accurate due to factors such as field environment, installation errors and the like, the data cannot be processed due to different data formats of different hardware, the data of each hardware can be accurately and efficiently provided for data processing by standardizing the original hardware data of the hardware equipment, and the data processing efficiency is greatly improved.

Referring to fig. 5, fig. 5 shows a schematic block diagram of a data platform based sensor data processing apparatus according to an embodiment for implementing the present invention. As shown in fig. 5, the apparatus 5 includes:

a data acquisition module 5-1 for acquiring raw sensor data of sensors associated with the data platform;

a data processing module 5-2 for:

correcting the original sensor data according to the error information of the sensor to obtain corrected sensor data;

standardizing the corrected sensor data to obtain standardized sensor data;

and performing feature extraction on the standardized sensor data based on a user instruction to obtain a data processing result.

Optionally, the data processing module 5-2 includes a local data processing module 5-2-1 and/or a cloud data processing module 5-2-2.

Only the main functional modules of the sensor data processing device 5 based on the data platform are described here, and the sensor data processing device 5 based on the data platform according to the embodiment of the present invention is used to implement the sensor data processing method based on the data platform according to the embodiment of the present invention, and repeated parts are not described here again.

According to another aspect of the present invention, there is provided a sensor data processing apparatus system based on a data platform, comprising a memory, and a processor;

the memory stores program code for implementing respective steps in a data platform based sensor data processing method according to an embodiment of the present invention;

the processor is configured to execute the program codes stored in the memory to perform the corresponding steps of the data platform-based data processing method according to the embodiment of the present invention.

In one embodiment, the program code when executed by the processor performs the respective steps of the aforementioned data platform based sensor data processing method according to an embodiment of the present invention.

Furthermore, according to another aspect of the present invention, there is also provided a computer-readable storage medium on which program instructions are stored, which when executed by a computer or a processor are used for executing the respective steps of the data platform based sensor data processing method according to the embodiment of the present invention, and for implementing the data platform based sensor data processing system according to the embodiment of the present invention.

Illustratively, the computer-readable storage medium may be any combination of one or more computer-readable storage media.

In one embodiment, the computer program instructions may, when executed by a computer, implement the aforementioned data platform-based sensor data processing method according to an embodiment of the present invention.

Therefore, according to the sensor data processing method, the sensor data processing device, the sensor data processing system and the storage medium based on the data platform, the raw sensor data of the sensors are corrected and standardized, the data of each sensor can be accurately and efficiently provided for data processing, and the data processing efficiency is greatly improved.

Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the foregoing illustrative embodiments are merely exemplary and are not intended to limit the scope of the invention thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as set forth in the appended claims.

It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Various component embodiments of the invention may be implemented in hardware, or in data modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some of the modules in an item analysis apparatus according to embodiments of the present invention. The present invention may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

The above description is only for the specific embodiment of the present invention or the description thereof, and the protection scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.