Data processing device and method applied to VR sports equipment, equipment and medium

Abstract

The invention relates to a data processing device and method applied to VR sports equipment, equipment and a storage medium. The method comprises the steps that a main control module collects various types of motion data corresponding to VR motion equipment through a VR data collection interface module according to a preset collection frequency and sends the VR motion data to a synchronous clock module, the synchronous clock module adds a timestamp to each type of VR motion data to obtain data with the timestamps added, and the data is sent to the VR motion equipment through the VR data collection interface module. The edge fusion module is used for executing fusion operation on the data added with the timestamp to obtain fusion data and sending the VR fusion data to the caching and uploading module, the VR fusion data represents the motion state of the user, and the caching and uploading module is used for compressing the VR fusion data and then uploading the compressed VR fusion data to a preset platform. The fusion data generated by the method can reflect the real motion state of the user more accurately, and accurate synchronization of the user motion and the virtual scene is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a data processing device and method applied to a VR motion device, a device and a storage medium. BACKGROUND

[0002] At present, data collection in the current VR motion system mostly depends on a single device, such as a treadmill or a bracelet, and it is difficult to realize the fusion and synchronization of multi-device data, especially in the case of complex scenes or multi-user participation, there are problems such as high delay, data asynchronization, and the like, which affect the immersion and the fairness of competition. For example, in a multi-player VR competitive motion game, due to the asynchronization of device data, the player's actions have differences in the display of the virtual scene, resulting in a big discount in game experience.

[0003] Therefore, how to realize the accurate synchronization of user motion and a virtual scene has become a technical problem to be solved by those skilled in the art. SUMMARY

[0004] In view of the above, the present application provides a data processing device and method applied to a VR motion device, a device and a storage medium, which aims to solve the above technical problems.

[0005] In a first aspect, the present application provides a data processing device applied to a VR motion device, the device comprising: a master control module, a data collection interface module, a synchronization clock module, an edge fusion module, a cache and uploading module;

[0006] The data collection interface module is in communication connection with the VR motion device, the master control module is in communication connection with the data collection interface module, the synchronization clock module and the edge fusion module respectively, and the cache and uploading module is in communication connection with the synchronization clock module and the edge fusion module respectively;

[0007] The master control module is configured to collect a plurality of types of motion data corresponding to the VR motion device through the data collection interface module according to a preset collection frequency, and send the motion data to the synchronization clock module;

[0008] The synchronization clock module is configured to add a timestamp to each type of the motion data to obtain data after adding the timestamp;

[0009] The edge fusion module is configured to perform a fusion operation on the data after adding the timestamp to obtain fusion data, and send the fusion data to the cache and uploading module, wherein the fusion data represents a motion state of a user;

[0010] The cache and uploading module is configured to compress and upload the fusion data to a preset platform.

[0011] In a second aspect, the application provides a data processing method applied to a VR motion device, the method comprising:

[0012] According to a preset acquisition frequency, acquiring a plurality of types of motion data corresponding to the VR motion device;

[0013] Adding a timestamp to each type of the motion data to obtain data after adding the timestamp;

[0014] Performing a fusion operation on the data after adding the timestamp to obtain fusion data, the fusion data representing a motion state of a user;

[0015] Uploading the fusion data to a preset platform after compression.

[0016] In a third aspect, the application provides an electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;

[0017] The memory is used to store a computer program;

[0018] The processor is used to execute the program stored on the memory to implement the steps of the data processing method applied to the VR motion device according to any one of the embodiments of the second aspect.

[0019] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, the computer program being executed by a processor to implement the steps of the data processing method applied to the VR motion device according to any one of the embodiments of the second aspect.

[0020] Compared with the prior art, the above technical solution provided by the embodiments of the application has the following advantages:

[0021] According to the preset acquisition frequency, the master control module acquires a plurality of types of motion data corresponding to the VR motion device through a VR data acquisition interface module, and sends the VR motion data to a synchronous clock module, the synchronous clock module adds a timestamp to each type of the VR motion data to obtain data after adding the timestamp, an edge fusion module is used to perform a fusion operation on the data after adding the timestamp to obtain fusion data, and the VR fusion data is sent to a cache and upload module, the VR fusion data representing a motion state of a user, the cache and upload module is used to upload the VR fusion data to a preset platform after compression. The generated fusion data can more accurately reflect the real motion state of the user, realize accurate synchronization of the user motion and the virtual scene, and enhance the immersion and reality of the user in the VR motion. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 This is a schematic diagram of a data processing device applied to VR sports equipment in this application;

[0025] Figure 2 This is a schematic diagram of a flow chart of the data processing method applied to VR sports equipment in this application;

[0026] Figure 3 A schematic diagram of a preferred embodiment of the electronic device of the present application;

[0027] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0029] It should be noted that the descriptions of "first", "second", etc. in this application are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0030] Reference Figure 1 , which is a schematic diagram of a data processing device applied to VR sports equipment in this application, comprising: a main control module, a data acquisition interface module, a synchronization clock module, an edge fusion module, and a cache and upload module;

[0031] The data acquisition interface module is in communication connection with the VR motion device, the main control module is in communication connection with the data acquisition interface module, the synchronous clock module and the edge fusion module respectively, and the cache and uploading module is in communication connection with the synchronous clock module and the edge fusion module respectively;

[0032] The main control module is used for collecting the motion data of the VR motion device according to a preset acquisition frequency through the data acquisition interface module, and sending the motion data to the synchronous clock module;

[0033] The synchronous clock module is used for adding a timestamp to each type of motion data to obtain data after adding a timestamp;

[0034] The edge fusion module is used for performing a fusion operation on the data after adding a timestamp to obtain fusion data, and sending the fusion data to the cache and uploading module, wherein the fusion data represents the motion state of the user;

[0035] The cache and uploading module is used for compressing and uploading the fusion data to a preset platform.

[0036] In this embodiment, a high-performance microprocessor is used as the main control module, which has powerful computing and multitasking capabilities, and is internally integrated with a high-speed data processing engine, which can quickly preprocess the received multi-channel sensing data, such as data filtering and format conversion. The main control module exchanges data with other modules through a high-speed bus. According to a preset acquisition frequency, the main control module sends instructions to the data acquisition interface module to start collecting motion data of the VR motion device, receives the collected data, and then sends the data to the synchronous clock module.

[0037] Suppose the preset acquisition frequency is 10 times per second, the main control module will accurately send collection instructions to the data acquisition interface module at this frequency. When the treadmill and the bracelet are connected as the two VR motion devices, the main control module can process data from the two devices simultaneously, collect speed and distance data from the treadmill, collect heart rate and step count data from the bracelet, and send these different types of data to the synchronous clock module.

[0038] Since the VR motion system contains various types of motion devices, the data transmission methods of these devices are different, so a data acquisition interface module with strong compatibility is needed to realize communication connection with these motion devices to obtain various motion data. The data acquisition interface module of the embodiment supports multiple methods such as Bluetooth, Wi-Fi and wired access. The Bluetooth interface adopts Bluetooth Low Energy (BLE) technology, which can quickly connect various Bluetooth-enabled motion devices such as wristbands and heart rate monitors, and realize real-time data acquisition. The Wi-Fi interface supports 802.11ac and above standards to ensure high speed of data transmission, and is suitable for devices that require large amounts of data transmission, such as smart treadmills. The wired access provides a stable and reliable data transmission method, such as the USB interface, which is used to connect some professional motion sensors that require very high data transmission stability.

[0039] When the user uses the wristband and treadmill for VR exercise, the data acquisition interface module connects with the wristband through Bluetooth, uses Bluetooth Low Energy (BLE) technology for fast and stable connection, and collects real-time data such as heart rate and step count of the wristband. At the same time, the data acquisition interface module connects with the treadmill through Wi-Fi and collects a large amount of data such as speed and distance of the treadmill, realizing the collection of multi-source motion data and the compatibility of motion devices of different types and different data transmission methods, and meeting the user's diversified motion data collection needs.

[0040] In the VR exercise scene, when multiple devices collect motion data at the same time, if the time between the data is inconsistent, it will cause the motion display in the virtual scene to have a difference in sequence, affecting the user experience and game fairness. Therefore, a synchronization clock module is needed to ensure that the data collected by different devices has consistency in time, and the synchronization clock module can achieve microsecond-level synchronization based on local clock and network clock dual mechanisms. The local clock uses a high-precision crystal oscillator to provide a stable clock signal, but it may drift over time, so it needs to be calibrated regularly. The network clock obtains standard time through an NTP (Network Time Protocol) server.

[0041] For example, when the wristband and treadmill collect data at the same time and send it to the synchronization clock module, the module will first determine the network connection state. The NTP server obtains standard time and calibrates the local clock, then adds a timestamp accurate to the microsecond level to the heart rate and step count data of the wristband and the speed and distance data of the treadmill, so that these data from different motion devices have a unified time reference. Even if the network has a short-term anomaly, the local clock can continue to work to ensure the continuity of the data time, and after the network is restored, it is calibrated again to ensure the accuracy of the timestamp. This improves the user's immersion and fairness in the VR exercise game and competition scene, so that the player's actions can be accurately synchronized and displayed in the virtual scene.

[0042] The collected raw data is usually scattered and contains different types of motion information, so the data can be fused to better analyze the user's motion state or generate a motion report to optimize the motion experience in the virtual scene, while also reducing the amount of data uploaded to the server to reduce server stress. The edge fusion module in this embodiment uses machine learning algorithms to analyze and process the data with timestamps. For example, by analyzing heart rate and step frequency data, the user's exercise intensity and fatigue level can be determined; combining trajectory data with virtual scene information can optimize the user's performance in the virtual scene. Integrating data from different sources into more valuable information, generating a motion state report containing exercise intensity, fatigue level, virtual scene performance, etc. as fusion data, and sending the fusion data to the cache and upload module.

[0043] Taking heart rate data collected by a bracelet and running speed data collected by a treadmill as an example, the edge fusion module uses machine learning algorithms to analyze these two sets of data. When heart rate increases and running speed increases, it is determined that the user is in a high-intensity exercise state; if heart rate continues to rise and running speed decreases, it may indicate that the user is starting to tire. At the same time, the trajectory data of the treadmill is combined with the road information in the virtual scene to adjust the running posture and visual effects of the characters in the virtual scene, making the user's movement in the virtual scene more realistic and smooth. Finally, a motion state report containing exercise intensity, fatigue level, virtual scene performance, etc. is generated as fusion data. This reduces the amount of data uploaded to the server, reduces server stress, allows users to obtain timely exercise feedback, and optimizes the user's exercise experience in the virtual scene.

[0044] During exercise, the user may experience network instability, and direct data upload may result in data loss, affecting data integrity and reliability, so the cache and upload module can ensure that data is uploaded to the main platform completely and stably. The cache and upload module in this embodiment has a dynamic caching mechanism, uses a high-speed flash memory chip as the cache medium, and the cache size can be dynamically adjusted according to actual application requirements. When the data collection rate is high or the network is unstable, the cache space is automatically expanded to avoid data loss. Before data is uploaded, the fusion data is compressed using a data compression algorithm (such as the LZ77 algorithm) to reduce data transmission and improve upload efficiency. The cache and upload module supports the breakpoint resume function, which can continue to upload data from the breakpoint after the network interruption is restored, ensuring data integrity.

[0045] When the network condition is good, the cache and upload module compresses the fusion data sent by the edge fusion module and quickly uploads it to the main platform. If the network is suddenly interrupted during the uploading process, the data will be temporarily stored in the cache composed of high-speed flash memory chips. When the network is restored, the module automatically continues to upload the data from the last interruption position to ensure that the data can be complete and intact to the main platform. For example, in a 30-minute VR running exercise, even if there are several network fluctuations in the middle, the module can ensure the complete recording and uploading of the exercise data, providing reliable protection for the user's exercise data record, while improving the efficiency and success rate of data uploading.

[0046] In one embodiment, the adding a timestamp to each type of the motion data to obtain the data after adding a timestamp comprises:

[0047] If the network state is detected to be normal, a network clock is used to add a timestamp to each type of the motion data to obtain the data after adding a timestamp;

[0048] If the network state is detected to be abnormal, a local clock is used to add a timestamp to each type of the motion data to obtain the data after adding a timestamp.

[0049] In the VR exercise system, the data collected by different exercise devices needs to be consistent in time to accurately restore the user's exercise process and state. The instability of the network state may affect the accuracy of the timestamp, and it is necessary to ensure that accurate timestamps can be added to different types of exercise data even when the network state is abnormal. Specifically, a network state detection module is set up to determine whether the network state is normal by sending periodic test data packets (such as once every second or minute) to a network time server (such as an NTP server) and recording the sending and receiving times of the data packets to calculate network delay and packet loss rate. For example, when the network delay is below a certain threshold (such as 100 milliseconds) and the packet loss rate is 0, it can be determined that the network state is normal; otherwise, it is determined that the network state is abnormal. When the network connection is normal, the network clock is used for synchronization; when the network is abnormal, the local clock continues to work and is calibrated again after the network is restored. The synchronization clock module adds accurate timestamps to each type of motion data sent by the main control module, with a precision of microseconds, so that the user's actions can be more accurately synchronized in the virtual scene.

[0050] In one embodiment, the performing a fusion operation on the data after adding a timestamp to obtain fusion data comprises:

[0051] performing a preprocessing operation on the data after adding a timestamp to obtain preprocessed data;

[0052] extracting feature information corresponding to the preprocessed data;

[0053] The feature information is input into a pre-trained fusion analysis model to obtain fusion data.

[0054] Since the motion data after adding the timestamp can have problems such as noise, missing values, and inconsistent formats, preprocessing operations can be performed to improve data quality. The preprocessing operations can be removing noise points in the data, correcting incorrect data, filling in missing values, and the like. For example, abnormal mutation points in heart rate data are smoothed, and the average of the previous and subsequent data is used to fill in the temporarily lost step data. The data collected by different devices can also be converted into a unified format, including data types, units, time formats, and the like. The data can also be normalized or standardized to make it comparable. For example, the speed data of different brands of treadmills is uniformly converted to km / h, and the data is scaled to the [0, 1] interval, which facilitates subsequent machine learning algorithm processing.

[0055] The feature information is the intrinsic attribute and key information of the data, which can reflect the main characteristics and laws of the motion data. Extracting effective feature information can reduce the data dimension and highlight the important features of the data. Statistical indicators such as mean, variance, maximum, and minimum of the data can be calculated as feature information, and the change characteristics of the data on the time axis, such as sampling frequency, periodicity, and trend, can also be analyzed as feature information. The extracted feature information can highlight the important characteristics and laws of the data, reduce the data dimension, reduce the computational complexity and complexity of subsequent fusion analysis, and improve the analysis efficiency.

[0056] The fusion analysis model can comprehensively consider the relationships and interactions between multiple feature information and perform fusion analysis on different types of motion data to generate more comprehensive, accurate, and valuable fusion data. The fusion analysis model can be a neural network, a support vector machine, a decision tree, or the like. The fusion analysis model calculates and analyzes the input feature information and outputs fusion data. The fusion data can be the user's motion state (such as exercise intensity, fatigue level, and the like), motion performance in a virtual scene (such as the motion trajectory and posture of a virtual character), and the like. Through comprehensive analysis of the feature information by the fusion analysis model, multiple motion data can be fused into more valuable fusion data, realizing deep mining and utilization of data.

[0057] In one embodiment, the motion data includes at least one of heart rate, step frequency, speed, distance, and trajectory data.

[0058] In one embodiment, inputting the feature information into the pre-trained fusion analysis model to obtain fusion data includes:

[0059] The feature information is input into a pre-trained fusion analysis model to analyze the user's exercise intensity and fatigue level.

[0060] According to the user, the trajectory data, the motion intensity and the fatigue degree are generated fusion data.

[0061] A large amount of feature data with motion intensity and fatigue degree labels is collected as a training set, and the model is trained to learn the mapping relationship between different types of data and motion intensity and fatigue degree. For example, data containing heart rate, step frequency, speed and other information, as well as corresponding motion intensity (such as low intensity, medium intensity, high intensity) and fatigue degree (such as mild fatigue, moderate fatigue, severe fatigue) labels can be used for training. Among them, the feature information can be the mean, variance, maximum, minimum and other statistical characteristics of the heart rate, the period, gait rhythm and other time domain characteristics of the step frequency, and the frequency components of the speed data and other frequency domain characteristics. The extracted feature information is converted according to the input requirements of the model, and input into the pre-trained fusion analysis model. The model can output the user's motion intensity and fatigue degree according to the learned knowledge.

[0062] According to the motion intensity and fatigue degree, a corresponding fusion data generation strategy can be developed. For example, when the user is in high-intensity exercise and the fatigue degree is high, the motion speed and action amplitude of the virtual character in the virtual scene can be appropriately reduced to simulate the user's real motion state; when the user is in low-intensity exercise and the fatigue degree is low, the virtual character can maintain normal motion performance. Combine the preprocessed trajectory data with the analyzed motion intensity and fatigue degree, and generate fusion data according to the developed fusion strategy. The fusion data also includes the user's motion trajectory, posture and information related to the motion state in the virtual scene, such as motion speed, fatigue performance, etc. The generated fusion data can more accurately reflect the user's real motion state, realize precise synchronization of user motion and virtual scene, and enhance the user's immersion and realism in VR exercise.

[0063] Referring to Figure 2 The method is applied to a data processing device of a VR exercise equipment, and the method comprises the following steps:

[0064] Step S10: According to the preset acquisition frequency, a plurality of types of motion data corresponding to the VR exercise equipment are acquired;

[0065] Step S20: Adding a time stamp to each type of motion data to obtain data after adding a time stamp;

[0066] Step S30: Performing a fusion operation on the data after adding a time stamp to obtain fusion data, wherein the fusion data represents the motion state of the user;

[0067] Step S40: uploading the fused data after compression to a preset platform.

[0068] In one embodiment, the adding a timestamp to each type of the motion data to obtain the data after adding a timestamp comprises:

[0069] If the network state is detected to be normal, a timestamp is added to each type of the motion data using a network clock to obtain the data after adding a timestamp;

[0070] If the network state is detected to be abnormal, a timestamp is added to each type of the motion data using a local clock to obtain the data after adding a timestamp.

[0071] In one embodiment, the performing a fusion operation on the data after adding a timestamp to obtain fused data comprises:

[0072] performing a preprocessing operation on the data after adding a timestamp to obtain preprocessed data;

[0073] extracting feature information corresponding to the preprocessed data;

[0074] inputting the feature information into a pre-trained fusion analysis model to obtain fused data.

[0075] In one embodiment, the motion data comprises at least one of heart rate, step frequency, speed, distance and trajectory data.

[0076] In one embodiment, the inputting the feature information into a pre-trained fusion analysis model to obtain fused data comprises:

[0077] inputting the feature information into a pre-trained fusion analysis model to analyze motion intensity and fatigue degree of a user;

[0078] generating fused data according to trajectory data, the motion intensity and the fatigue degree of the user.

[0079] The specific embodiments of the data processing method applied to a VR motion device of the present application are substantially the same as the specific embodiments of the data processing device applied to a VR motion device described above, and will not be repeated here.

[0080] Referring to Figure 3 FIG. 1 shows a schematic diagram of a preferred embodiment of an electronic device of the present application.

[0081] The electronic device comprises a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112 and the memory 113 complete communication with each other through the communication bus 114;

[0082] a memory 113, configured to store computer programs, for example, a data processing program applied to the VR motion device;

[0083] The processor 111 may, in some embodiments, be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 111 is generally used to control the overall operation of the electronic device, such as performing data interaction or communication-related control and processing, etc. In the present embodiment, the processor 111 is used to run the program code or process data stored in the memory 113.

[0084] The communication interface 112 may, optionally, include a standard wired interface, a wireless interface (such as a WI-FI interface), and the communication interface 112 can also be used to establish a communication connection between the electronic device and other electronic devices.

[0085] The memory 113 includes at least one type of readable storage medium, including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 113 can be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. In other embodiments, the memory 113 can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. of the electronic device. Of course, the memory 113 can also include both the internal storage unit and the external storage device of the electronic device. In the present embodiment, the memory 113 is generally used to store the operating system and various computer programs installed in the electronic device. In addition, the memory 113 can also be used to temporarily store various data that has been output or will be output.

[0086] Figure 3 Only an electronic device with components 111-114 is shown, but it should be understood that not all of the components shown are required to be implemented, and more or fewer components can be alternatively implemented.

[0087] In an embodiment of the present application, when the processor 111 executes the program stored in the memory 113, the processor 111 implements the large model-based question correction method provided by any one of the method embodiments, including:

[0088] According to a preset acquisition frequency, a plurality of types of motion data corresponding to the VR motion device are acquired;

[0089] A timestamp is added to each type of the motion data to obtain data after the timestamp is added;

[0090] A fusion operation is performed on the data after the timestamp is added to obtain fusion data, and the fusion data represents a motion state of a user;

[0091] The fusion data is compressed and uploaded to a preset platform.

[0092] For detailed descriptions of the above steps, please refer to the above Figure 2 Regarding a flowchart of an embodiment of the data processing method applied to the VR motion device.

[0093] In addition, an embodiment of the present application further proposes a computer readable storage medium, which is non-volatile or volatile. The computer readable storage medium is any one or any combination of a hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, and the like. The computer readable storage medium includes a storage data area and a storage program area, and the storage program area stores a data processing program 10 applied to the VR motion device. When the data processing program 10 applied to the VR motion device is executed by a processor, the following operations are realized:

[0094] According to a preset acquisition frequency, a plurality of types of motion data corresponding to the VR motion device are acquired;

[0095] A timestamp is added to each type of the motion data to obtain data after the timestamp is added;

[0096] A fusion operation is performed on the data after the timestamp is added to obtain fusion data, and the fusion data represents a motion state of a user;

[0097] The fusion data is compressed and uploaded to a preset platform.

[0098] The specific implementation of the computer readable storage medium of the present application is substantially the same as that of the above-mentioned data processing method applied to the VR motion device, and will not be described here.

[0099] It should be noted that the above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. Moreover, the terms "comprise", "contain" or any other variants thereof in this document are intended to cover non-exclusive inclusion, so that the process, device, article or method comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, device, article or method. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of other identical elements in the process, device, article or method comprising the element.

[0100] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software and a necessary general hardware simulation platform, of course, they can also be implemented by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a plurality of instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.

[0101] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data processing device for VR sports equipment, characterized in that: The device includes: a main control module, a data acquisition interface module, a synchronization clock module, an edge fusion module, and a cache and upload module; The data acquisition interface module is communicated with the VR sports device, the main control module is communicated with the data acquisition interface module, the synchronous clock module and the edge fusion module respectively, and the cache and upload module is communicated with the synchronous clock module and the edge fusion module respectively; The main control module is used to collect various types of motion data corresponding to the VR sports device through the data acquisition interface module according to a preset acquisition frequency, and transmit the motion data to the synchronization clock module; The synchronous clock module is used to add a timestamp to each type of the motion data to obtain the data after the timestamp is added; The edge fusion module is used to perform a fusion operation on the data after adding the timestamp to obtain fused data, and send the fused data to the cache and upload module, wherein the fused data represents the user's motion state; The cache and upload module is used to compress the fused data and upload it to a preset platform.

2. The data processing device for VR sports equipment according to claim 1, wherein: Adding a timestamp to each type of the motion data to obtain the timestamp-added data includes: If it is detected that the network status is normal, using the network clock to add a timestamp to each type of the motion data to obtain the timestamped data; If an abnormal network state is detected, a local clock is used to add a timestamp to each type of the motion data to obtain the timestamped data.

3. The data processing device for VR sports equipment according to claim 1, wherein: The performing a fusion operation on the timestamped data to obtain fused data includes: Perform preprocessing on the data after adding the timestamp to obtain preprocessed data; Extracting feature information corresponding to the preprocessed data; The feature information is input into a pre-trained fusion analysis model to obtain fusion data.

4. The data processing device for VR sports equipment according to claim 1, wherein: The motion data includes at least one of heart rate, cadence, speed, distance and trajectory data.

5. The data processing device for VR sports equipment according to claim 3, wherein: Inputting the feature information into a pre-trained fusion analysis model to obtain fusion data includes: Inputting the feature information into a pre-trained fusion analysis model to analyze and obtain the user's exercise intensity and fatigue level; The fusion data is generated according to the user's trajectory data, the exercise intensity and the fatigue level.

6. A data processing method applied to VR sports equipment, characterized in that: The method comprises: Collect various types of motion data corresponding to VR sports equipment according to the preset collection frequency; Adding a timestamp to each type of the motion data to obtain timestamp-added data; Performing a fusion operation on the timestamped data to obtain fused data, where the fused data represents the user's motion state; The fused data is compressed and uploaded to a preset platform.

7. The data processing method for VR sports equipment according to claim 6, wherein: Adding a timestamp to each type of the motion data to obtain the timestamp-added data includes: If it is detected that the network status is normal, using the network clock to add a timestamp to each type of the motion data to obtain the timestamped data; If an abnormal network state is detected, a local clock is used to add a timestamp to each type of the motion data to obtain the timestamped data.

8. The data processing method for VR sports equipment according to claim 7, wherein: The performing a fusion operation on the timestamped data to obtain fused data includes: Perform preprocessing on the data after adding the timestamp to obtain preprocessed data; Extracting feature information corresponding to the preprocessed data; The feature information is input into a pre-trained fusion analysis model to obtain fusion data.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is configured to implement the data processing method for VR sports equipment according to any one of claims 6 to 8 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data processing method applied to a VR sports device according to any one of claims 6 to 8 is implemented.

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