CN114288631B - Data processing method, data processing device, storage medium, processor and electronic device - Google Patents
Data processing method, data processing device, storage medium, processor and electronic device Download PDFInfo
- Publication number
- CN114288631B CN114288631B CN202111664440.3A CN202111664440A CN114288631B CN 114288631 B CN114288631 B CN 114288631B CN 202111664440 A CN202111664440 A CN 202111664440A CN 114288631 B CN114288631 B CN 114288631B
- Authority
- CN
- China
- Prior art keywords
- riding
- data
- sensor
- data processing
- event
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a data processing method, a data processing device, a storage medium, a processor and an electronic device. Wherein the method comprises the following steps: receiving riding status data from a plurality of riding devices, wherein the plurality of riding devices comprises: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene; based on the riding state data, a competition result of the riding event is obtained. The invention solves the technical problems of complex operation process and low accuracy of the acquisition of the competition result of the riding event in the related technology.
Description
Technical Field
The present invention relates to the field of wireless communications technologies, and in particular, to a data processing method, a data processing device, a storage medium, a processor, and an electronic device.
Background
In outdoor riding events, more fair and effective racing results are obtained by monitoring the riding state of each contestant. In the related scheme, the riding data are obtained by installing the positioning device in the racing vehicle. However, when the competition result is counted, riding data of contestants need to be sequentially input, so that a final competition result is obtained, the operation process is complicated, and errors can occur in the finally obtained competition result, so that the competition experience is affected.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the application provides a data processing method, a data processing device, a storage medium, a processor and an electronic device, which are used for at least solving the technical problems of complex operation process and low accuracy of obtaining the competition result of a riding event in the related technology.
According to one embodiment of the present application, there is provided a data processing method, including: receiving riding status data from a plurality of riding devices, wherein the plurality of riding devices comprises: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene; based on the riding state data, a competition result of the riding event is obtained.
Optionally, the plurality of riding devices are configured with a plurality of sensors, the plurality of sensors comprising: motion class sensor and biological class sensor, riding status data includes: riding process data and riding health data, wherein, the process data of riding utilizes motion class sensor to gather, and the process data of riding includes: the speed of riding of every equipment of riding, the equipment of riding of every step on frequently, the tire running number of turns of every equipment of riding, the coefficient of friction of every equipment of riding, the healthy data of riding utilizes biological class sensor to gather, and the healthy data of riding includes: heart rate, blood oxygen value, and respiratory rate of the rider of each riding device.
Optionally, the motion class sensor comprises: acceleration sensor, geomagnetic sensor, rotational speed sensor and coefficient of friction sensor.
Optionally, the biological sensor comprises: heart rate sensor, blood oxygen sensor and respiratory rate sensor.
Optionally, based on the riding status data, acquiring the competition result includes: analyzing riding state data by using a neural network model to determine a competition result, wherein the neural network model is obtained by machine learning training by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: an electronic map of a riding event and riding sample data of the riding event.
Optionally, the data processing method further comprises: determining a target packet mode from a plurality of candidate packet modes, wherein the plurality of candidate packet modes includes: gender grouping mode, quantity grouping mode, cloud service grouping mode, mixed tag grouping mode; the method comprises the steps that first user information is locally obtained at a cloud server, and second user information uploaded by at least part of riding equipment in a plurality of riding equipment is obtained; and carrying out grouping processing on the first user information and/or the second user information according to the target grouping mode to obtain grouping results of a plurality of riding devices, wherein the grouping results are used for distinguishing groups to which the competition results belong.
According to one embodiment of the present application, there is also provided a data processing apparatus including: the system comprises a receiving module for receiving riding status data from a plurality of riding devices, wherein the plurality of riding devices comprise: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene; and the processing module is used for acquiring the competition result of the riding event based on the riding state data.
Optionally, the plurality of riding devices are configured with a plurality of sensors, the plurality of sensors comprising: motion class sensor and biological class sensor, riding status data includes: riding process data and riding health data, wherein, the process data of riding utilizes motion class sensor to gather, and the process data of riding includes: the speed of riding of every equipment of riding, the equipment of riding of every step on frequently, the tire running number of turns of every equipment of riding, the coefficient of friction of every equipment of riding, the healthy data of riding utilizes biological class sensor to gather, and the healthy data of riding includes: heart rate, blood oxygen value, and respiratory rate of the rider of each riding device.
Optionally, the motion class sensor comprises: acceleration sensor, geomagnetic sensor, rotational speed sensor and coefficient of friction sensor.
Optionally, the biological sensor comprises: heart rate sensor, blood oxygen sensor and respiratory rate sensor.
Optionally, the processing module is further configured to: analyzing riding state data by using a neural network model to determine a competition result, wherein the neural network model is obtained by machine learning training by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: an electronic map of a riding event and riding sample data of the riding event.
Optionally, the data processing apparatus further comprises: a determining module, configured to determine a target packet mode from a plurality of candidate packet modes, where the plurality of candidate packet modes includes: gender grouping mode, quantity grouping mode, cloud service grouping mode, mixed tag grouping mode; the acquisition module is used for locally acquiring the first user information at the cloud server and acquiring second user information uploaded by at least part of the riding equipment in the plurality of riding equipment; the processing module is further used for carrying out grouping processing on the first user information and/or the second user information according to the target grouping mode to obtain grouping results of the plurality of riding devices, wherein the grouping results are used for distinguishing groups to which the competition results belong.
According to one embodiment of the present application, there is also provided a non-volatile storage medium in which a computer program is stored, wherein the computer program is arranged to perform the data processing method of any one of the above when run.
According to an embodiment of the present application, there is also provided a processor for running a program, wherein the program is arranged to execute the data processing method of any of the above when run.
According to one embodiment of the present application, there is also provided an electronic device including a memory, a processor, the memory having stored therein a computer program, the processor being arranged to run the computer program to perform the data processing method of any of the above.
In an embodiment of the present application, by receiving riding status data from a plurality of riding devices, wherein the plurality of riding devices includes: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene; based on the riding state data, the racing result of the riding event is obtained, the purpose of efficiently and accurately obtaining the racing result of the riding event is achieved, the technical effects of simplifying the operation flow of obtaining the racing result and improving the accuracy of the racing result are achieved, and the technical problems that the operation process of obtaining the racing result of the riding event in the related technology is complex and the accuracy is low are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flow chart of a data processing method according to one embodiment of the present application;
FIG. 2 is a flow chart of yet another data processing method according to one embodiment of the present application;
fig. 3 is a block diagram of a data processing apparatus according to one embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to one embodiment of the present application, there is provided an embodiment of a data processing method, it being noted that the steps shown in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.
The method embodiments may be performed in a data processing system. The data processing system includes: the cloud server and the plurality of terminal devices. The plurality of terminal devices can be riding devices, and can also be mobile terminals such as mobile phones (such as Android mobile phones and iOS mobile phones), tablet computers, palm computers, mobile internet devices (Mobile Internet Devices, MID) and the like.
Through the method, the cloud server receives riding state data from a plurality of riding devices, wherein the plurality of riding devices comprise: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene; based on the riding state data, a competition result of the riding event is obtained.
The internal body structure of the terminal device will be described as an example.
The terminal device may include one or more processors (which may include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processor (GPU), a Digital Signal Processing (DSP) chip, a Microprocessor (MCU), a programmable logic device (FPGA), a neural Network Processor (NPU), a Tensor Processor (TPU), an Artificial Intelligence (AI) type processor, etc.) and a memory for storing data. Optionally, the mobile terminal may further include a transmission device, an input/output device, and a display device for a communication function. It will be appreciated by those of ordinary skill in the art that the foregoing structural descriptions are merely illustrative and are not intended to limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than the above structural description, or have a different configuration than the above structural description.
The memory may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to the method described in the embodiments of the present application, and the processor executes the computer program stored in the memory, thereby performing various functional applications and data processing, that is, implementing the method described in the embodiments of the present application. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the memory may further include memory remotely located with respect to the processor, the remote memory being connectable to the mobile terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through the base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.
Display devices may be, for example, touch screen type Liquid Crystal Displays (LCDs) and touch displays (also referred to as "touch screens" or "touch display screens"). The liquid crystal display may enable a user to interact with a user interface of the mobile terminal. In some embodiments, the mobile terminal has a Graphical User Interface (GUI), and the user may interact with the GUI by touching finger contacts and/or gestures on the touch-sensitive surface, where the man-machine interaction functions optionally include the following interactions: executable instructions for performing the above-described human-machine interaction functions, such as creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving electronic mail, talking interfaces, playing digital video, playing digital music, and/or web browsing, are configured/stored in a computer program product or readable storage medium executable by one or more processors.
The data processing method in the embodiment of the application can be applied to a cloud server. FIG. 1 is a flow chart of a data processing method according to one embodiment of the present application, as shown in FIG. 1, the flow includes the following steps:
step S11, receiving riding state data from a plurality of riding devices, wherein the plurality of riding devices comprise: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene;
step S12, based on the riding state data, a competition result of the riding event is obtained.
Through the steps S11 to S12, by receiving riding status data from a plurality of riding devices, wherein the plurality of riding devices includes: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene; based on the riding state data, the racing result of the riding event is obtained, the purpose of efficiently and accurately obtaining the racing result of the riding event is achieved, the technical effects of simplifying the operation flow of obtaining the racing result and improving the accuracy of the racing result are achieved, and the technical problems that the operation process of obtaining the racing result of the riding event in the related technology is complex and the accuracy is low are solved.
The data processing method described in the above embodiment is further described below.
Optionally, the plurality of riding devices are configured with a plurality of sensors, the plurality of sensors comprising: motion class sensor and biological class sensor, riding status data includes: riding process data and riding health data, wherein, the process data of riding utilizes motion class sensor to gather, and the process data of riding includes: the speed of riding of every equipment of riding, the equipment of riding of every step on frequently, the tire running number of turns of every equipment of riding, the coefficient of friction of every equipment of riding, the healthy data of riding utilizes biological class sensor to gather, and the healthy data of riding includes: heart rate, blood oxygen value, and respiratory rate of the rider of each riding device.
Optionally, the motion class sensor comprises: acceleration sensor, geomagnetic sensor, rotational speed sensor and coefficient of friction sensor.
Optionally, the biological sensor comprises: heart rate sensor, blood oxygen sensor and respiratory rate sensor.
Based on the above-described alternative embodiments, accurate riding process data and riding health data can be obtained using a plurality of sensors configured on the riding device to obtain accurate riding event racing results based on the riding state data. Meanwhile, riding health data in the competition process can be acquired by utilizing the biological sensor, so that the physical health condition of a user in the competition process can be monitored in time, and better competition experience is provided.
Optionally, in step S12, based on the riding status data, acquiring the competition result includes:
analyzing riding state data by using a neural network model to determine a competition result, wherein the neural network model is obtained by machine learning training by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: an electronic map of a riding event and riding sample data of the riding event.
Based on the above-mentioned alternative embodiment, the riding state data is analyzed by using the neural network model obtained by machine learning training, so that the processing procedure of a large amount of riding state data can be simplified, and the data processing efficiency can be improved. In addition, the training neural network model is preselected by utilizing the electronic map of the riding event and the riding sample data of the riding event, so that an accurate competition result can be quickly obtained based on the training mature neural network model.
Optionally, the data processing method further comprises: determining a target packet mode from a plurality of candidate packet modes, wherein the plurality of candidate packet modes includes: gender grouping mode, quantity grouping mode, cloud service grouping mode, mixed tag grouping mode; the method comprises the steps that first user information is locally obtained at a cloud server, and second user information uploaded by at least part of riding equipment in a plurality of riding equipment is obtained; and carrying out grouping processing on the first user information and/or the second user information according to the target grouping mode to obtain grouping results of a plurality of riding devices, wherein the grouping results are used for distinguishing groups to which the competition results belong.
Based on the embodiment, various grouping results can be obtained quickly, the actual grouping requirements are met, quick setting is facilitated in an outdoor competition grouping scene, and the grouping efficiency of competition results is improved.
FIG. 2 is a flow chart of yet another data processing method according to one embodiment of the present application, as shown in FIG. 2, the flow includes the following steps:
step S21, receiving riding state data from a plurality of riding devices;
s22, analyzing riding state data by using a neural network model to determine a competition result;
step S23, determining a target packet mode from a plurality of candidate packet modes, wherein the plurality of candidate packet modes includes: gender grouping mode, quantity grouping mode, cloud service grouping mode, mixed tag grouping mode;
step S24, locally acquiring first user information at a cloud server, and acquiring second user information uploaded by at least part of the riding devices in the plurality of riding devices;
step S25, grouping the first user information and/or the second user information according to a target grouping mode to obtain grouping results of a plurality of riding devices, wherein the grouping results are used for distinguishing groups to which the competition results belong.
Based on the steps S21 to S22, the riding event competition result can be efficiently and accurately obtained, and the competition result is efficiently grouped, so that the technical effects of simplifying the operation flow for obtaining the competition result and improving the accuracy of the competition result are realized, and the technical problems of complex operation process and low accuracy of obtaining the riding event competition result in the related technology are solved.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method described in the embodiments of the present application.
In this embodiment, a data processing device is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, and will not be described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
Fig. 3 is a block diagram of a data processing apparatus according to one embodiment of the present application, and as shown in fig. 3, the data processing apparatus 300 includes:
a receiving module 301, configured to receive riding status data from a plurality of riding devices, where the plurality of riding devices includes: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene;
the processing module 302 is configured to obtain a competition result of the riding event based on the riding status data.
Optionally, the plurality of riding devices are configured with a plurality of sensors, the plurality of sensors comprising: motion class sensor and biological class sensor, riding status data includes: riding process data and riding health data, wherein, the process data of riding utilizes motion class sensor to gather, and the process data of riding includes: the speed of riding of every equipment of riding, the equipment of riding of every step on frequently, the tire running number of turns of every equipment of riding, the coefficient of friction of every equipment of riding, the healthy data of riding utilizes biological class sensor to gather, and the healthy data of riding includes: heart rate, blood oxygen value, and respiratory rate of the rider of each riding device.
Optionally, the motion class sensor comprises: acceleration sensor, geomagnetic sensor, rotational speed sensor and coefficient of friction sensor.
Optionally, the biological sensor comprises: heart rate sensor, blood oxygen sensor and respiratory rate sensor.
Optionally, the processing module 302 is further configured to: analyzing riding state data by using a neural network model to determine a competition result, wherein the neural network model is obtained by machine learning training by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: an electronic map of a riding event and riding sample data of the riding event.
Optionally, the data processing apparatus 300 further comprises: a determining module 303, configured to determine a target packet mode from a plurality of candidate packet modes, where the plurality of candidate packet modes includes: gender grouping mode, quantity grouping mode, cloud service grouping mode, mixed tag grouping mode; the obtaining module 304 is configured to obtain, locally, the first user information at the cloud server, and obtain second user information uploaded by at least some of the plurality of riding devices; the processing module 302 is further configured to perform grouping processing on the first user information and/or the second user information according to a target grouping mode, so as to obtain grouping results of the plurality of riding devices, where the grouping results are used for distinguishing groups to which the competition result belongs.
It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; alternatively, the above modules may be located in different processors in any combination.
Embodiments of the present application also provide a non-volatile storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
Alternatively, in the present embodiment, the above-described nonvolatile storage medium may be configured to store a computer program for performing the steps of:
s1, receiving riding state data from a plurality of riding devices, wherein the plurality of riding devices comprise: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene;
s2, based on the riding state data, a competition result of the riding event is obtained.
Alternatively, in the present embodiment, the above-described nonvolatile storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
Embodiments of the present application also provide an electronic device comprising a memory, a processor, the memory having stored therein a computer program, the processor being arranged to run the computer program to perform the steps of any of the method embodiments described above.
Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s1, receiving riding state data from a plurality of riding devices, wherein the plurality of riding devices comprise: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is obtained by mapping the real scene;
s2, based on the riding state data, a competition result of the riding event is obtained.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments and optional implementations, and this embodiment is not described herein.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (9)
1. A method of data processing, comprising:
receiving riding status data from a plurality of riding devices, wherein the plurality of riding devices comprises: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is mapped by the real scene;
based on the riding state data, obtaining a competition result of the riding event;
wherein a plurality of sensors are configured on the plurality of riding devices, the plurality of sensors comprising: a motion sensor and a biological sensor, the riding status data comprising: riding process data and riding health data, wherein, the riding process data utilizes motion class sensor gathers, the riding process data includes: the riding speed of each riding device, the riding pedal frequency of each riding device, the tire running circle number of each riding device and the friction coefficient of each riding device, the riding health data are collected by the biological sensor, and the riding health data comprise: heart rate, blood oxygen value, and respiratory rate of the rider of each riding device;
wherein, the data processing method further comprises: determining a target packet pattern from a plurality of candidate packet patterns; the method comprises the steps that first user information is locally obtained at a cloud server, and second user information uploaded by at least part of riding equipment in the plurality of riding equipment is obtained; and carrying out grouping processing on the first user information and/or the second user information according to the target grouping mode to obtain grouping results of the plurality of riding devices, wherein the grouping results are used for distinguishing groups to which the competition results belong.
2. The data processing method according to claim 1, wherein the motion type sensor includes: acceleration sensor, geomagnetic sensor, rotational speed sensor and coefficient of friction sensor.
3. The data processing method according to claim 1, wherein the biological sensor includes: heart rate sensor, blood oxygen sensor and respiratory rate sensor.
4. The data processing method according to claim 1, wherein acquiring the racing result based on the riding state data comprises:
analyzing the riding state data by using a neural network model to determine the competition result, wherein the neural network model is obtained by using a plurality of groups of data through machine learning training, and each group of data in the plurality of groups of data comprises: the electronic map of the riding event and riding sample data of the riding event.
5. The data processing method of claim 1, wherein the plurality of candidate packet patterns comprises:
gender grouping mode, quantity grouping mode, cloud service grouping mode, mixed tag grouping mode.
6. A data processing apparatus, comprising:
the system comprises a receiving module for receiving riding status data from a plurality of riding devices, wherein the plurality of riding devices comprise: the system comprises at least one first riding device and at least one second riding device, wherein the at least one first riding device is applied to a real scene of a riding event, the at least one second riding device is applied to a virtual scene of the riding event, and the virtual scene is mapped by the real scene;
the processing module is used for acquiring the competition result of the riding competition based on the riding state data;
wherein a plurality of sensors are configured on the plurality of riding devices, the plurality of sensors comprising: a motion sensor and a biological sensor, the riding status data comprising: riding process data and riding health data, wherein, the riding process data utilizes motion class sensor gathers, the riding process data includes: the riding speed of each riding device, the riding pedal frequency of each riding device, the tire running circle number of each riding device and the friction coefficient of each riding device, the riding health data are collected by the biological sensor, and the riding health data comprise: heart rate, blood oxygen value, and respiratory rate of the rider of each riding device;
wherein the data processing apparatus further comprises: a determining module for determining a target packet mode from a plurality of candidate packet modes; the acquisition module is used for locally acquiring first user information at the cloud server and acquiring second user information uploaded by at least part of the plurality of riding devices; the processing module is further configured to perform grouping processing on the first user information and/or the second user information according to the target grouping mode, so as to obtain grouping results of the plurality of riding devices, where the grouping results are used for distinguishing a group to which the competition result belongs.
7. A non-volatile storage medium, characterized in that the storage medium has stored therein a computer program, wherein the computer program is arranged to execute the data processing method of any of the claims 1 to 5 when run.
8. A processor, characterized in that the processor is arranged to run a program, wherein the program is arranged to execute the data processing method of any of the claims 1 to 5 at run-time.
9. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the data processing method of any of the claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111664440.3A CN114288631B (en) | 2021-12-30 | 2021-12-30 | Data processing method, data processing device, storage medium, processor and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111664440.3A CN114288631B (en) | 2021-12-30 | 2021-12-30 | Data processing method, data processing device, storage medium, processor and electronic device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114288631A CN114288631A (en) | 2022-04-08 |
| CN114288631B true CN114288631B (en) | 2023-08-01 |
Family
ID=80974501
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111664440.3A Active CN114288631B (en) | 2021-12-30 | 2021-12-30 | Data processing method, data processing device, storage medium, processor and electronic device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114288631B (en) |
Family Cites Families (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE69729202T2 (en) * | 1996-07-02 | 2005-05-04 | Graber Products, Inc., Madison | ELECTRONIC EXERCISE SYSTEM |
| US7217224B2 (en) * | 2003-08-14 | 2007-05-15 | Tom Thomas | Virtual exercise system and method |
| US8172658B2 (en) * | 2008-05-23 | 2012-05-08 | Sony Ericsson Mobile Communications Ab | Methods, devices, and computer program products for participating in an athletic event with a remotely-located competitor using a mobile terminal |
| TWI395600B (en) * | 2009-12-17 | 2013-05-11 | Digital contents based on integration of virtual objects and real image | |
| CN102008811B (en) * | 2010-08-23 | 2011-12-21 | 大连交通大学 | Intelligent monitoring system for track cycling training |
| KR101461817B1 (en) * | 2013-08-12 | 2014-11-13 | 동신대학교산학협력단 | Realistic bike simulator system |
| CN103977559B (en) * | 2014-05-23 | 2017-10-17 | 北京智谷睿拓技术服务有限公司 | Exchange method and interactive device |
| US10188905B2 (en) * | 2015-04-07 | 2019-01-29 | Tri2Focus, Inc. | System for processing exertion data derived from exertion detection devices |
| EP3549646B1 (en) * | 2016-11-30 | 2023-08-16 | Leomo, Inc. | Motion capture system, motion capture program, and motion capture method |
| JP6610626B2 (en) * | 2017-08-03 | 2019-11-27 | カシオ計算機株式会社 | Activity status analysis device, activity status analysis method and program |
| CN107823846A (en) * | 2017-09-13 | 2018-03-23 | 华东师范大学 | A kind of shared virtual reality for building body system and method with physical examinations |
| CN208176826U (en) * | 2018-04-17 | 2018-12-04 | 福州大学 | A kind of riding simulation device based on virtual reality technology |
| CN108804663A (en) * | 2018-06-08 | 2018-11-13 | 北京搜狗科技发展有限公司 | A kind of result method of estimation, model training method and relevant apparatus |
| CN110490978B (en) * | 2019-07-01 | 2023-07-21 | 浙江工业大学 | Live-action riding training method based on mixed reality technology |
| WO2021197444A1 (en) * | 2020-04-01 | 2021-10-07 | 随机漫步(上海)体育科技有限公司 | Bicycle training auxiliary method, server, user terminal and training bicycle |
| CN113393063A (en) * | 2021-08-17 | 2021-09-14 | 深圳市信润富联数字科技有限公司 | Match result prediction method, system, program product and storage medium |
-
2021
- 2021-12-30 CN CN202111664440.3A patent/CN114288631B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN114288631A (en) | 2022-04-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103984763B (en) | A kind of trajectory diagram display device, method and monitor terminal | |
| CN108053838A (en) | With reference to audio analysis and fraud recognition methods, device and the storage medium of video analysis | |
| CN108721898B (en) | Frame rate determination method and apparatus, storage medium, and electronic apparatus | |
| CN107291317B (en) | The selection method and device of target in a kind of virtual scene | |
| CN104684621A (en) | Merchandizing, socializing, and/or gaming via a personal wellness device and/or a personal wellness platform | |
| CN109789337A (en) | For verifying and the system and method for the interaction of third party's interactive media | |
| CN108579060B (en) | Exercise system and application method thereof | |
| CN104181875A (en) | Excise and fitness system based on mode of Internet of Things | |
| CN108245880A (en) | Body-sensing detection method for visualizing and system based on more wearing annulus sensor fusions | |
| CN106774940A (en) | A kind of Intelligent worn device and smart motion interactive system | |
| CN106326242A (en) | Application pushing method and apparatus | |
| CN110188251A (en) | Processing method, device, storage medium and the terminal for data of brushing teeth | |
| CN107169769A (en) | The brush amount recognition methods of application program, device | |
| CN110263871A (en) | Step counting cheat detection method, device, intelligent terminal and storage medium | |
| CN105117608A (en) | Information interaction method and device | |
| CN111464825A (en) | Live broadcast method based on geographic information and related device | |
| CN206421334U (en) | A kind of Intelligent worn device and smart motion interactive system | |
| CN109376065A (en) | A kind of user behavior hot-zone analysis method, device and electronic equipment | |
| CN102254077A (en) | Sport management system and method | |
| CN114288631B (en) | Data processing method, data processing device, storage medium, processor and electronic device | |
| CN109726808B (en) | Neural network training method and device, storage medium and electronic device | |
| CN106650698A (en) | Data acquisition method and system based on intelligent platform | |
| WO2018171196A1 (en) | Control method, terminal and system | |
| CN106730834A (en) | Game data processing method and device | |
| CN109299355B (en) | Recommended book list display method and device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |