Equipment vibration parameter synchronous transmission system and method based on cloud platform, processing equipment and storage medium

Abstract

The invention relates to an equipment vibration parameter synchronous transmission system and method based on a cloud platform, processing equipment and a storage medium, the system comprises the cloud platform and a mobile terminal processing module, and a data acquisition module and a data transmission module are arranged in the cloud platform; the data acquisition module is used for acquiring vibration parameters of application programs in the mobile equipment and standardizing the vibration parameters into a uniform format; the data transmission module is used for compressing and encrypting the standardized vibration parameters, and transmitting the compressed and encrypted vibration parameters to the mobile terminal processing module based on the dynamically adjusted transmission parameters; and the mobile terminal processing module is used for decompressing and decrypting the compressed and encrypted vibration parameters, adapting and processing the decompressed and decrypted vibration parameters by adopting a self-adaptive mapping algorithm, dynamically adjusting the vibration parameters, and feeding back and presenting vibration to the mobile equipment. The method can be widely applied to the technical field of cloud computing and Internet of Things cooperation.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing and Internet of Things collaboration, and particularly to a device vibration parameter synchronous transmission system, method, processing device and storage medium based on a cloud platform. Background Art

[0002] In the fields of modern industrial production and equipment monitoring, the real-time monitoring and analysis of device vibration parameters are crucial for ensuring the safe operation of equipment, predictive maintenance, and optimizing equipment performance. Traditional vibration parameter acquisition systems usually adopt wired transmission methods to transmit the vibration data collected by sensors to the data processing center. However, the wired transmission method has disadvantages such as complex wiring, high cost, and poor flexibility. Especially in large-scale equipment or complex industrial environments, it is difficult to meet the requirements of real-time monitoring.

[0003] With the development of wireless communication technology, wireless sensor networks (WSNs) have gradually become the mainstream solution for device vibration parameter acquisition. The wireless transmission method has advantages such as flexible deployment, low cost, and strong scalability, but also faces challenges such as high data transmission delay, high packet loss rate, and low synchronization accuracy. Especially in the scenario of multi-sensor collaborative work, how to achieve high-precision synchronous transmission has become a technical difficulty.

[0004] At present, some technical solutions have been attempted to solve the synchronization problem in wireless transmission. However, there are still many problems in the synchronous transmission of device vibration parameters in the existing technologies: 1) Traditional solutions rely on fixed TCP (Transmission Control Protocol) transmission, resulting in an average delay greater than 200 ms; 2) Existing adaptive algorithms do not consider multi-dimensional network quality indicators; 3) The differences in vibration interfaces between Android and iOS systems lead to inaccurate parameter mapping. Summary of the Invention

[0005] In view of the above problems, the object of the present invention is to provide a device vibration parameter synchronous transmission system, method, processing device and storage medium based on a cloud platform that consider multi-dimensional network quality indicators and have low and accurate parameter mapping.

[0006] To achieve the above object, the present invention adopts the following technical solutions: In the first aspect, a device vibration parameter synchronous transmission system based on a cloud platform is provided, including a cloud platform and a mobile terminal processing module. Among them, a data acquisition module and a data transmission module are provided in the cloud platform;

[0007] The data acquisition module is used to collect the vibration parameters of applications in the mobile device and standardize them into a unified format;

[0008] The data transmission module is used to compress and encrypt the standardized vibration parameters, and transmit the compressed and encrypted vibration parameters to the mobile terminal processing module based on dynamically adjusted transmission parameters;

[0009] The mobile device processing module is used to decompress and decrypt the compressed and encrypted vibration parameters, and adopts an adaptive mapping algorithm to adapt and process the decompressed and decrypted vibration parameters, dynamically adjust the vibration parameters, and present the vibration feedback to the mobile device.

[0010] Further, the data acquisition module includes a virtual haptic acquisition agent unit, wherein the virtual haptic acquisition agent unit includes:

[0011] The DX12 haptic data extraction unit is used to obtain the original vibration waveform of the application in the mobile device from the graphics rendering pipeline;

[0012] The graphics engine hook unit is used to intercept the haptic output interface of the DX12 haptic data extraction unit by means of hooks, process the original vibration waveform, convert the vibration parameters in different formats of the application into a unified format, and output the standardized vibration parameters.

[0013] Further, the data transmission module includes a bitrate adaptive controller based on network quality, wherein the bitrate adaptive controller includes:

[0014] The channel establishment unit is used to establish a dual-channel transmission architecture;

[0015] The protocol stack unit is used to adopt the QUIC protocol and combine it with the BBR congestion control algorithm to optimize the network transmission efficiency;

[0016] The transmission parameter adjustment unit is used to adopt a multi-dimensional QoS evaluation model, calculate the network quality index according to the network quality, and then dynamically select the compression level and dynamically adjust the transmission parameters;

[0017] The compression scheme unit is used to select different compression strategies according to the dynamically selected compression level and the data type of the dynamically adjusted vibration parameters, and compress the vibration parameters in the unified format;

[0018] The security mechanism unit is used to encrypt the compressed vibration parameters by using the national cryptographic SM4 encryption algorithm with dynamic key rotation, and transmit the compressed and encrypted vibration parameters to the mobile device processing module based on the dynamically adjusted transmission parameters.

[0019] Further, the security mechanism unit uses the Diffie-Hellman key exchange algorithm for key negotiation, and the dynamic key rotation mechanism updates the session key every few minutes or when the network is abnormal.

[0020] Further, the mobile device processing module includes a multi-modal vibration synthesizer, wherein the multi-modal vibration synthesizer includes:

[0021] A data processing unit, configured to receive the compressed and encrypted vibration parameters, query the current session key for decryption calculation and then decompress to obtain the decompressed and decrypted vibration parameters;

[0022] A device capability database, configured to store parameter mapping tables of several types of mobile devices, wherein the parameter mapping table defines a unified format of vibration parameters, including device fingerprints, performance parameters, and security thresholds;

[0023] A parameter adaptation unit, configured to query the parameter mapping table stored in the device capability database and generate a vibration drive signal according to the device fingerprint and performance parameters of the mobile device;

[0024] A dynamic adaptation engine unit, configured to adopt an adaptive mapping algorithm, based on the vibration drive signal, adjust the output drive waveform through a PID controller, dynamically adjust the decompressed and decrypted vibration parameters, and present the vibration feedback to the mobile device;

[0025] A fault fallback mechanism unit, configured to switch to the preloading mode of the local pre-stored waveform library when the network delay exceeds a preset time threshold.

[0026] In a second aspect, a method for synchronously transmitting device vibration parameters based on a cloud platform is provided, including:

[0027] A data acquisition module acquires the vibration parameters of the application program in the mobile device and standardizes them into a unified format and sends them to the data transmission module;

[0028] The data transmission module adopts a multi-dimensional QoS evaluation model to dynamically adjust the transmission parameters;

[0029] The data transmission module compresses and encrypts the standardized vibration parameters based on the dynamically adjusted transmission parameters and transmits them to the mobile terminal processing module;

[0030] The mobile terminal processing module decompresses and decrypts the compressed and encrypted vibration parameters, adopts an adaptive mapping algorithm, adapts and processes the decompressed and decrypted vibration parameters, dynamically adjusts the vibration parameters, and presents the vibration feedback to the mobile device.

[0031] Further, the data transmission module adopts a multi-dimensional QoS evaluation model to dynamically adjust the transmission parameters, including:

[0032] Real-time monitoring of network quality;

[0033] Calculating a network quality index according to the network quality;

[0034] Dynamically selecting a compression level and dynamically adjusting the transmission parameters according to the calculated network quality index;

[0035] Repeat the above steps to continuously monitor the network quality in a loop.

[0036] Further, the mobile device processing module decompresses and decrypts the compressed and encrypted vibration parameters, and uses an adaptive mapping algorithm to adapt and process the decompressed and decrypted vibration parameters, dynamically adjust the vibration parameters, and present the vibration feedback to the mobile device, including:

[0037] The data processing unit receives the compressed and encrypted vibration parameters, queries the current session key for decryption calculation and then decompresses them to obtain the decompressed and decrypted vibration parameters;

[0038] The device capability database stores parameter mapping tables of several types of mobile devices;

[0039] The parameter adaptation unit queries the parameter mapping tables stored in the device capability database, and generates a vibration drive signal according to the device fingerprint and performance parameters of the mobile device;

[0040] The dynamic adaptation engine unit uses an adaptive mapping algorithm, based on the vibration drive signal, adjusts the output drive waveform through a PID controller, dynamically adjusts the decompressed and decrypted vibration parameters, and presents the vibration feedback to the mobile device;

[0041] When the network delay exceeds the preset time threshold, the fault fallback mechanism unit switches to the preloading mode of the local pre-stored waveform library.

[0042] In a third aspect, a processing device is provided, including computer program instructions, wherein when the computer program instructions are executed by the processing device, they are used to implement the steps corresponding to the above-mentioned method for synchronously transmitting device vibration parameters based on a cloud platform.

[0043] In a fourth aspect, a computer-readable storage medium is provided, on which computer program instructions are stored, wherein when the computer program instructions are executed by a processor, they are used to implement the steps corresponding to the above-mentioned method for synchronously transmitting device vibration parameters based on a cloud platform.

[0044] Due to the above technical solutions adopted by the present invention, it has the following advantages:

[0045] 1. Low latency: The data transmission module of the present invention uses the QUIC (Quick UDP Internet Connections) protocol and combines the BBR (Bottleneck Bandwidth and Round-trip propagation time) congestion control algorithm to optimize the network transmission efficiency. After laboratory tests, the average transmission latency of the present invention is 38 ms, which is reduced by 68% compared with the prior art.

[0046] 2. High compatibility: The mobile device processing module of the present invention uses an adaptive mapping algorithm to dynamically adjust the vibration parameters, and can support 94% of Android devices and 100% of iOS devices.

[0047] 3. Energy consumption optimization: The data transmission module of the present invention adopts the method of Delta coding + Zstandard compression to optimize efficiency. The network quality index (NQI) is calculated every 50 ms by the code rate adaptive controller to dynamically select the compression level. When the network delay of the mobile terminal processing module exceeds 100 ms, it switches to the preloading mode, which can save 41% of power consumption compared with the traditional solution.

[0048] 4. Latency classification optimization: The present invention adopts a dual-channel transmission architecture (TCP control flow + UDP data stream). The latency of the present invention under 5G network is 28 ms, which is 68% less than the traditional method; the latency of the present invention under Wi-Fi network is 25 ms, which is 30% less than the traditional method.

[0049] 5. Improvement of transmission reliability: The data transmission module of the present invention adopts forward error correction of the QUIC protocol and combines with the BBR congestion control algorithm. The packet loss recovery rate under 5G environment is 92%.

[0050] 6. Extension of hardware life: The multi-modal vibration synthesizer of the present invention adopts the PWM waveform approximation algorithm, which supports the reproduction of 3D Touch waveforms of the Apple Taptic Engine and the resonance frequency matching of Android linear motors.

[0051] 7. Traditional tactile data capture depends on the application layer interface (such as the Android Vibrator API), resulting in information loss. The data acquisition module of the present invention can directly connect to the underlying API of the graphics engine. The DX12 tactile data extraction unit directly extracts data from the graphics rendering pipeline, which can retain the original waveform details, avoid signal distortion at the application layer, and support all tactile applications developed based on DX12.

[0052] In summary, the present invention relates to a low-latency synchronization method for tactile feedback of cross-platform devices, which is applicable to real-time interactive applications in 5G edge computing scenarios and can be widely applied in the field of cloud computing and Internet of Things collaborative technologies. Brief Description of the Drawings

[0053] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered as limiting the present invention. Throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0054] Figure 1 is a schematic diagram of the system structure provided by an embodiment of the present invention;

[0055] Figure 2It is a schematic diagram of the data transmission process provided by an embodiment of the present invention;

[0056] Figure 3 It is a schematic diagram of the parameter adaptive adjustment process provided by an embodiment of the present invention;

[0057] Figure 4 It is a schematic diagram of cross-platform parameter mapping provided by an embodiment of the present invention. Detailed implementation manners

[0058] Hereinafter, the exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be completely conveyed to those skilled in the art.

[0059] It should be understood that the terms used herein are only for the purpose of describing specific exemplary embodiments and are not intended to be limiting. Unless otherwise clearly specified in the context, the singular forms "a", "an" and "the" as used herein may also include the plural forms. The terms "include", "comprise", "contain" and "have" are inclusive and thus specify the presence of the stated features, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be executed in the particular order described or illustrated, unless the execution order is explicitly stated. It should also be understood that additional or alternative steps may be used.

[0060] Currently, there have been some technical solutions attempting to solve the synchronization problem in wireless transmission. However, there are still many problems in the synchronous transmission of device vibration parameters in the prior art: 1) The traditional solution relies on fixed TCP (Transmission Control Protocol) transmission, resulting in an average delay greater than 200 ms; 2) The existing adaptive algorithms do not consider multi-dimensional network quality indicators; 3) The differences in vibration interfaces between Android and iOS systems lead to inaccurate parameter mapping. The data transmission module of the present invention optimizes the network transmission efficiency by using the UDP protocol in combination with the forward error correction algorithm. At the same time, the data transmission module also adopts a multi-dimensional QoS evaluation model, which can dynamically adjust the transmission parameters. The mobile processing module of the present invention adopts an adaptive mapping algorithm to dynamically adjust the vibration parameters, which can adapt to devices of different types of systems and is applicable to real-time interaction applications in the 5G edge computing scenario.

[0061] Embodiment 1

[0062] Such as Figure 1 、 Figure 2As shown in the figure, this embodiment provides a device vibration parameter synchronous transmission system based on a cloud platform, including a cloud platform and a mobile terminal processing module. Among them, a data acquisition module and a data transmission module are provided in the cloud platform.

[0063] The data acquisition module is used to collect the vibration parameters of the applications in the mobile device through virtual sensors and standardize them into a unified format.

[0064] The data transmission module is used to optimize the network transmission efficiency by using the QUIC protocol in combination with the BBR congestion control algorithm; adopt a multi-dimensional QoS evaluation model to dynamically adjust the transmission parameters; and compress and encrypt the standardized vibration parameters, and based on the dynamically adjusted transmission parameters, transmit the compressed and encrypted vibration parameters to the mobile terminal processing module with low latency to optimize the efficiency.

[0065] The mobile terminal processing module is used to decompress and decrypt the compressed and encrypted vibration parameters, adopt an adaptive mapping algorithm to adapt and process the decompressed and decrypted vibration parameters, dynamically adjust the vibration parameters, and present the vibration feedback to the mobile device.

[0066] In a preferred embodiment, a management module is further provided in the cloud platform for managing and coordinating the work of the data acquisition module, the data transmission module, and the mobile terminal processing module.

[0067] In a preferred embodiment, the data acquisition module includes a Virtual Haptic Capture Agent (VHCA) unit. Among them, the Virtual Haptic Capture Agent unit includes a DX12 Haptic Data Extraction unit and a Graphics Engine Hook unit.

[0068] The DX12 Haptic Data Extraction unit is used to obtain the original vibration waveform of the applications in the mobile device from the graphics rendering pipeline (DX12 API).

[0069] The Graphics Engine Hook unit is used to intercept the haptic output interface of the DX12 Haptic Data Extraction unit by means of hooks, perform 128-point sampling quantization on the original vibration waveform, convert the vibration parameters in different formats of the application into a unified format such as the JSON Schema format, and output the standardized vibration parameters for subsequent processing and transmission.

[0070] In a preferred embodiment, the data transmission module includes a Bitrate Adaptive Controller based on Network Quality (BRAC). Among them, the Bitrate Adaptive Controller includes a channel establishment unit, a protocol stack unit, a transmission parameter adjustment unit, a compression scheme unit, and a security mechanism unit.

[0071] The channel establishment unit is used to establish a dual-channel transmission architecture. Among them, the dual channels include a control channel TCP and a data channel QUIC over UDP. The control channel TCP is used to send instructions such as key update requests to the mobile terminal processing module, and the data channel QUIC over UDP is used to send vibration parameters to the mobile terminal processing module.

[0072] The protocol stack unit is used to adopt the QUIC (Quick UDP Internet Connections) protocol and combine it with the BBR (Bottleneck Bandwidth and Round-trip propagation time) congestion control algorithm to optimize the network transmission efficiency. Among them, the QUIC protocol has built-in forward error correction (FEC) features.

[0073] The transmission parameter adjustment unit is used to adopt a multi-dimensional QoS evaluation model, calculate the network quality index according to the network quality, and then dynamically select the compression level and dynamically adjust the transmission parameters to receive vibration parameters in a unified format.

[0074] The compression scheme unit is used to select different compression strategies according to the dynamically selected compression level and the data type of the dynamically adjusted vibration parameters, and compress the vibration parameters in a unified format. For example, time series data uses Delta coding + Zstandard compression, and waveform data uses FFT dimensionality reduction processing.

[0075] The security mechanism unit is used to encrypt the compressed vibration parameters using the national cryptography SM4 encryption algorithm with dynamic key rotation to ensure the security of data transmission, and based on the dynamically adjusted transmission parameters, transmit the compressed and encrypted vibration parameters to the mobile terminal processing module with low latency.

[0076] Specifically, the working logic of the bitrate adaptive controller includes: calculating the network quality index (NQI) every 50 ms; dynamically selecting the compression level (levels 1-5) according to the NQI. For example, when NQI < 0.4, directly adopt low-resolution parameters.

[0077] Specifically, the security mechanism unit uses the Diffie-Hellman key exchange algorithm for key negotiation, and the dynamic key rotation mechanism updates the session key every 30 minutes or when the network is abnormal.

[0078] In a preferred embodiment, the mobile terminal processing module includes a multi-modal vibration synthesizer (MMVS). Among them, the multi-modal vibration synthesizer supports the 3D Touch waveform reproduction of the Apple Taptic Engine and the resonance frequency matching of the Android linear motor. The multi-modal vibration synthesizer includes a data processing unit, a device capability database, a parameter adaptation unit, a dynamic adaptation engine unit, and a fault fallback mechanism unit.

[0079] The data processing unit is used to receive the compressed and encrypted vibration parameters, query the current session key for decryption calculation, and then decompress to obtain the decompressed and decrypted vibration parameters.

[0080] The device capability database is used to store parameter mapping tables of several types of mobile devices for device identification and adaptation. Among them, the parameter mapping table defines the unified format of vibration parameters, including device fingerprints, performance parameters, and safety thresholds. The device fingerprint includes the device model and motor type (such as iPhone Taptic Engine). The performance parameters include the maximum amplitude, resonant frequency, and response time. The safety thresholds include the allowable maximum current limit and the allowable maximum temperature limit.

[0081] The parameter adaptation unit is used to query the parameter mapping table stored in the device capability database and generate a vibration drive signal based on the device fingerprint and performance parameters of the mobile device.

[0082] The dynamic adaptation engine unit is used to adopt an adaptive mapping algorithm, based on the vibration drive signal, adjust the output drive waveform through a PID controller, dynamically adjust the decompressed and decrypted vibration parameters, and present the vibration feedback to the mobile device. The PID controller compensates for vibration deviations caused by device hardware differences (such as aging of the vibration motor) and suppresses parameter fluctuations caused by network jitter.

[0083] The fault fallback mechanism unit is used to switch to the preloading mode of the local pre-stored waveform library (such as basic modes like continuous vibration and pulse vibration) when the network delay exceeds 100 ms to ensure the user experience and avoid the loss of haptic feedback due to network interruption. Among them, the safety threshold in the device capability database is called during the switch. The PID controller is still executed in the preloading mode (to prevent overload), and the matching pre-stored waveform is selected according to the device model of the mobile device.

[0084] The beneficial effects of the device vibration parameter synchronous transmission system based on the cloud platform of the present invention are described in detail below through specific embodiments:

[0085] I. Transmission delay test

[0086] ① Test configuration:

[0087] Table 1: Test configuration table

[0088]

[0089] ② Test steps:

[0090] A) Environment setup:

[0091] 5G group: Downlink 1 Gbps, uplink 100 Mbps, fixed jitter 5 ms;

[0092] 4G group: Downlink 100 Mbps, uplink 10 Mbps, random jitter 10 - 30 ms;

[0093] Wi-Fi group: 100 Mbps bandwidth, fixed jitter 2 ms.

[0094] B) Data acquisition:

[0095] a Transmitter: Record vibration parameters to generate timestamp T1;

[0096] b Receiver: Record the timestamp T2 when the haptic feedback is completed;

[0097] c Single - time delay = T2 - T1 - device local processing time (deducted through laboratory calibration).

[0098] C) Exception handling:

[0099] Reject outliers outside 3 standard deviations (proportion < 2%).

[0100] After the transmission delay test, using the present invention, it is 28 ms under 5G network, 50 ms under 4G network, and 25 ms under Wi-Fi network. Compared with the traditional solution, the average delay of the present invention is reduced by 68% under 5G network, 40% under 4G network, and 30% under Wi-Fi network.

[0101] II. Cross - platform compatibility test

[0102] ① Test matrix:

[0103] Table 2: Test matrix table

[0104]

[0105] ② Judgment criteria:

[0106] The successful conditions are: correctly parse the JSON parameters sent from the cloud; the output error of the device vibrator ≤ 15% (compared with the target amplitude); no hardware overload alarm (the current / temperature threshold is defined by the device capability database).

[0107] ③ Test toolchain:

[0108] Automated test framework:

[0109] Antutu haptic compatibility test suite; high - precision accelerometer (range ±20 g, error ±0.5%); Data acquisition: 1000 samples per second, lasting for 30 seconds.

[0110] ④ Typical failure cases:

[0111] Redmi Note 9: The PWM driver chip does not support 0.1ms-level timing control, resulting in distorted pulse waveforms;

[0112] iPhone SE 2020: The maximum amplitude of the Taptic Engine is insufficient, triggering the safety downgrade mechanism.

[0113] ⑤ Energy consumption calculation model:

[0114] Total energy consumption = ∫(voltage × current)dt

[0115] Comparison benchmark: Traditional solution (TCP transmission + no compression + fixed encryption).

[0116] ⑥ Verification of key power-saving technologies is shown in Table 3 below:

[0117] Table 3: Verification Table of Key Power-saving Technologies

[0118] Technical components Independent power-saving contribution (patent implicit data) LZ4 compression Reduce the energy consumption of the RF module by 29% Dynamic key rotation Reduce the amount of encryption operations by 17% BBR congestion control Reduce the network retransmission power consumption by 32%

[0119] After cross-platform compatibility testing, the present invention supports 94% of Android devices and 100% of iOS devices. The test sample size is 200 Android devices and 50 iOS devices. And it saves 41% of power consumption compared to the traditional solution. The traditional solution does not adopt data compression and efficient transmission protocols, resulting in high energy consumption.

[0120] It should be noted that in actual applications, the effect of energy consumption optimization may vary due to different specific scenarios and devices. For example: Device hardware differences: The hardware performance and power consumption characteristics of different devices are different. Some high-end devices may be more energy-efficient when processing complex algorithms, while low-end devices may benefit from simplified processing logic. Network condition changes: In different network environments, the energy consumption optimization effect of the system will also be different. In a 5G network, the system may show better energy-saving effects, while in a 4G or Wi-Fi network, the energy-saving effects may be slightly different.

[0121] Example 2

[0122] As Figure 2 shown, this embodiment provides a method for synchronous transmission of device vibration parameters based on a cloud platform, including:

[0123] (1) The data acquisition module collects the vibration parameters of the applications in the mobile device through virtual sensors and standardizes them into a unified format and sends them to the data transmission module. Specifically:

[0124] (1.1) The DX12 haptic data extraction unit obtains the original vibration waveform of the applications in the mobile device from the graphics rendering pipeline (DX12 API).

[0125] (1.2) The graphics engine hook unit intercepts the haptic output interface of the DX12 haptic data extraction unit through hooks, samples and quantizes the original vibration waveform at 128 points, converts vibration parameters in different formats of the application program into a unified format such as JSON Schema format, and outputs the standardized vibration parameters.

[0126] (2) The data transmission module establishes a dual-channel transmission architecture and adopts the QUIC protocol, combined with the BBR congestion control algorithm to optimize the network transmission efficiency. Specifically:

[0127] (2.1) The channel establishment unit establishes a dual-channel transmission architecture.

[0128] (2.2) The protocol stack unit adopts the QUIC protocol, combined with the BBR congestion control algorithm, to optimize the network transmission efficiency.

[0129] Specifically, the process of the protocol stack unit optimizing the network transmission efficiency is as follows:

[0130] ① Connection establishment: Perform 0-RTT handshake, reuse the existing connection context, with a 67% reduction in the first packet delay.

[0131] ② Data transmission:

[0132] Perform multiplexing, transmit the vibration parameters and control instructions in independent streams to avoid blocking of critical data;

[0133] Perform BBR bandwidth detection, where calculate once every 10ms: actual bandwidth = min(maximum receiver throughput, bottleneck link bandwidth), target sending rate = actual bandwidth × BBR gain coefficient (default 1.25);

[0134] Dynamically adjust the QUIC send window.

[0135] ③ Packet loss recovery:

[0136] Perform forward error correction (FEC), add 1 redundant packet for every 5 data packets to increase the recovery rate to 92%;

[0137] BBR packet loss response, perform non-loss-based congestion judgment (based on the bandwidth / delay model rather than the packet loss rate) to reduce retransmission.

[0138] ④ Dynamic degradation:

[0139] When the network deteriorates (NQI < 0.4), BBR switches to the PROBE_RTT mode to limit the sending rate to the lowest feasible value;

[0140] QUIC enables header compression (HPACK algorithm) to reduce protocol overhead.

[0141] (3) The data transmission module adopts a multi-dimensional QoS evaluation model to dynamically adjust transmission parameters, such as Figure 3 shown as follows:

[0142] (3.1) System initialization.

[0143] (3.2) Monitor the network quality in real time, including the network's RTT (round-trip time), jitter, and packet loss rate.

[0144] (3.3) Calculate the network quality index (NQI) based on the network quality:

[0145]

[0146] where RTT is the round-trip time; Jitter is the jitter; PacketLossRate is the packet loss rate.

[0147] (3.4) Dynamically select the compression level and dynamically adjust the transmission parameters according to the calculated network quality index to optimize the transmission efficiency.

[0148] (3.5) Enter step (3.2), continuously monitor the network quality, and dynamically adjust the transmission parameters to ensure the best transmission effect.

[0149] (4) Based on the dynamically adjusted transmission parameters, the data transmission module compresses and encrypts the standardized vibration parameters and transmits them to the mobile terminal processing module with low latency, specifically as follows:

[0150] (4.1) The compression scheme unit selects different compression strategies according to the dynamically selected compression level and the data type of the dynamically adjusted vibration parameters to compress the vibration parameters in a unified format. For example, time-series data uses Delta coding + Zstandard compression, and waveform data uses FFT dimensionality reduction processing.

[0151] (4.2) The security mechanism unit uses the national cipher SM4 encryption algorithm with dynamic key rotation to encrypt the compressed vibration parameters to ensure the security of data transmission, and based on the dynamically adjusted transmission parameters, transmits the compressed and encrypted vibration parameters to the mobile terminal processing module with low latency.

[0152] Specifically, the process of the security mechanism unit for encryption is as follows:

[0153] ① Key negotiation stage:

[0154] The cloud platform and the mobile terminal processing module generate the initial session key K0 through the DH protocol and derive the SM4 session key based on the initial session key K0.

[0155] ② Data encryption stage: After the vibration parameters are compressed, the compressed vibration parameters are grouped and encrypted.

[0156] ③ Key rotation stage:

[0157] The triggering conditions include time conditions: ≥ 30 minutes since the last rotation, network anomaly: NQI < 0.3 for 3 consecutive times;

[0158] The rotation process is as follows: The cloud platform initiates a key update request through the control channel TCP; a new round of DH key exchange is executed to generate a new session key K1; parallel period of old and new keys: lasts for 10 seconds to ensure that in-transit data packets can be decrypted; the switch takes effect.

[0159] More specifically, as shown in Table 4 below, it is a verification and comparison of the technical effects of the encryption method of the present invention and the traditional scheme (AES-256 fixed key):

[0160] Table 4: Technical effect verification table

[0161]

[0162] (5) The mobile device processing module decompresses and decrypts the compressed and encrypted vibration parameters, and uses an adaptive mapping algorithm to adapt and process the decompressed and decrypted vibration parameters, dynamically adjusts the vibration parameters, and presents the vibration feedback to the mobile device. Specifically:

[0163] (5.1) The data processing unit receives the compressed and encrypted vibration parameters, queries the current session key for decryption calculation and then decompresses to obtain the decompressed and decrypted vibration parameters.

[0164] (5.2) The device capability database stores parameter mapping tables of several types of mobile devices for device identification and adaptation.

[0165] Specifically, the establishment process of the parameter mapping table is as follows:

[0166] ① Establish a parameter mapping table, define the unified format of vibration parameters, including vibration ID, mode type, frequency range, amplitude, and duration.

[0167] ② Obtain information of Android devices, including device model, motor type, maximum amplitude, resonance frequency, response time, etc.

[0168] ③ Obtain information of IOS devices, similar to Android devices, including device model, motor type, maximum amplitude, resonance frequency, response time, etc.

[0169] ④ As Figure 4As shown, based on the information of Android devices and iOS devices, a parameter mapping table is established to relate to Android devices and iOS devices, so as to map standardized vibration parameters to devices on different platforms.

[0170] ⑤ Devices on different platforms adapt and process the vibration parameters according to their own characteristics.

[0171] (5.3) The parameter adaptation unit queries the parameter mapping table stored in the device capability database, and generates a vibration drive signal according to the device fingerprint and performance parameters of the mobile device.

[0172] (5.4) The dynamic adaptation engine unit adopts an adaptive mapping algorithm. Based on the vibration drive signal, it adjusts the output drive waveform through a PID controller, dynamically adjusts the decompressed and decrypted vibration parameters, and presents the vibration feedback to the mobile device.

[0173] Specifically, the output drive waveform is adjusted through a PID controller:

[0174]

[0175] Among them, u(t) is the control quantity, that is, the output waveform adjusted according to the current error; K p is the proportional coefficient, used to adjust the gain of the proportional term; K i is the integral coefficient, used to adjust the gain of the integral term; K d is the differential coefficient, used to adjust the gain of the differential term; e(t) is the error, that is, the difference between the target vibration parameter and the actual virtual sensor feedback value.

[0176] (5.5) When the network delay exceeds 100 ms, the fault fallback mechanism unit switches to the preloading mode of the local pre-stored waveform library to ensure the user experience and avoid the loss of haptic feedback due to network interruption. Among them, the safety threshold in the device capability database is called during the switch, and the control of the PID controller is still executed in the preloading mode (to prevent overload), and the matching pre-stored waveform is selected according to the device model of the mobile device.

[0177] Embodiment 3

[0178] This embodiment provides a processing device corresponding to the device vibration parameter synchronous transmission method based on the cloud platform provided in Embodiment 2. The processing device can be a processing device applicable to the client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of Embodiment 2.

[0179] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, the memory, and the communication interface are connected through the bus to complete communication with each other. The memory stores a computer program that can run on the processing device. When the processing device runs the computer program, it executes the method for synchronously transmitting device vibration parameters based on a cloud platform provided in Embodiment 2 of the present invention.

[0180] In some implementations, the memory may be a high-speed random access memory (RAM: Random Access Memory), and may also include non-volatile memory, such as at least one disk memory.

[0181] In other implementations, the processor may be various types of general-purpose processors such as a central processing unit (CPU) or a digital signal processor (DSP), which is not limited herein.

[0182] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks, etc., which can store program codes.

[0183] Those skilled in the art can understand that the structure of the above-mentioned computing device is only a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computing device to which the solution of the present invention is applied. The specific computing device may include more or fewer components, or combine certain components, or have different component arrangements.

[0184] Embodiment 4

[0185] This embodiment provides a computer program product corresponding to the method for synchronously transmitting device vibration parameters based on a cloud platform provided in Embodiment 2. The computer program product may include a computer-readable storage medium, on which there are computer-readable program instructions for executing the method for synchronously transmitting device vibration parameters based on a cloud platform described in Embodiment 2.

[0186] A computer-readable storage medium can be a tangible device that retains and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing.

[0187] For a computer-readable storage medium provided in the foregoing embodiments, its implementation principle and technical effects are similar to those of the foregoing method embodiments, and will not be elaborated herein.

[0188] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0189] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0191] The foregoing embodiments are only used to illustrate the present invention. The structures, connection manners, manufacturing processes, etc. of the components can all be changed. Any equivalent transformation and improvement made on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A cloud platform-based equipment vibration parameter synchronous transmission system, characterized in that: It includes a cloud platform and a mobile terminal processing module, wherein the cloud platform is provided with a data acquisition module and a data transmission module; The data acquisition module is used to collect vibration parameters of applications in the mobile device and standardize them into a unified format; The data transmission module is used to compress and encrypt the standardized vibration parameters, and transmit the compressed and encrypted vibration parameters to the mobile terminal processing module based on the dynamically adjusted transmission parameters; The mobile terminal processing module is used to decompress and decrypt the compressed and encrypted vibration parameters, use an adaptive mapping algorithm to adapt and process the decompressed and decrypted vibration parameters, dynamically adjust the vibration parameters, and present vibration feedback to the mobile device.

2. A cloud platform-based device vibration parameter synchronous transmission system as claimed in claim 1, characterized in that: The data acquisition module includes a virtual tactile acquisition agent unit, wherein the virtual tactile acquisition agent unit includes: DX12 Haptic Data Extraction Unit, which is used to obtain the raw vibration waveforms of applications in mobile devices from the graphics rendering pipeline; The graphics engine hook unit is used to intercept the tactile output interface of the DX12 tactile data extraction unit by hooking, process the original vibration waveform, convert the vibration parameters of different formats of the application program into a unified format, and output the standardized vibration parameters.

3. The device vibration parameter synchronous transmission system based on a cloud platform as claimed in claim 1, characterized in that: The data transmission module includes a rate adaptive controller based on network quality, wherein the rate adaptive controller includes: A channel establishment unit, used to establish a dual-channel transmission architecture; The protocol stack unit is used to optimize network transmission efficiency by using the QUIC protocol and combining it with the BBR congestion control algorithm; A transmission parameter adjustment unit, used to adopt a multi-dimensional QoS evaluation model to calculate a network quality index according to network quality, and then dynamically select a compression level and dynamically adjust transmission parameters; A compression scheme unit, for selecting different compression strategies according to the dynamically selected compression level and the data type of the dynamically adjusted vibration parameters, to compress the vibration parameters in a unified format; The security mechanism unit is used to encrypt the compressed vibration parameters using the national secret SM4 encryption algorithm with dynamic key rotation, and transmit the compressed and encrypted vibration parameters to the mobile terminal processing module based on the dynamically adjusted transmission parameters.

4. A cloud platform-based device vibration parameter synchronous transmission system as claimed in claim 3, characterized in that: The security mechanism unit adopts the Diffie-Hellman key exchange algorithm to perform key negotiation, and the dynamic key rotation mechanism updates the session key every several minutes or when the network is abnormal.

5. The device vibration parameter synchronous transmission system based on a cloud platform as claimed in claim 1, characterized in that: The mobile terminal processing module includes a multi-modal vibration synthesizer, wherein the multi-modal vibration synthesizer includes: A data processing unit, used for receiving the compressed and encrypted vibration parameters, querying the current session key to perform decryption calculation and then decompressing, to obtain the decompressed and decrypted vibration parameters; A device capability database, used to store parameter mapping tables of several types of mobile devices, wherein the parameter mapping table defines a unified format of vibration parameters, including device fingerprints, performance parameters, and safety thresholds; A parameter adaptation unit, used to query the parameter mapping table stored in the device capability database, and generate a vibration drive signal according to the device fingerprint and performance parameters of the mobile device; A dynamic adaptation engine unit, which is used to use an adaptive mapping algorithm, adjust the output drive waveform through a PID controller based on the vibration drive signal, dynamically adjust the decompressed and decrypted vibration parameters, and present the vibration feedback to the mobile device; The fault fallback mechanism unit is used to switch to the preloading mode of the locally pre-stored waveform library when the network delay exceeds a preset time threshold.

6. A method for synchronous transmission of device vibration parameters based on a cloud platform, characterized in that: include; The data acquisition module collects vibration parameters of the application in the mobile device, standardizes them into a unified format and sends them to the data transmission module; The data transmission module adopts a multi-dimensional QoS evaluation model to dynamically adjust transmission parameters; The data transmission module compresses and encrypts the standardized vibration parameters based on the dynamically adjusted transmission parameters, and transmits them to the mobile terminal processing module; The mobile terminal processing module decompresses and decrypts the compressed and encrypted vibration parameters, uses an adaptive mapping algorithm to adapt and process the decompressed and decrypted vibration parameters, dynamically adjusts the vibration parameters, and presents the vibration feedback to the mobile device.

7. A method for synchronously transmitting vibration parameters of a device based on a cloud platform as claimed in claim 6, characterized in that: The data transmission module adopts a multi-dimensional QoS evaluation model to dynamically adjust transmission parameters, including: Real-time monitoring of network quality; According to the network quality, calculate the network quality index; Dynamically select compression level and dynamically adjust transmission parameters based on calculated network quality index; Repeat the above steps to continuously monitor the network quality.

8. The method for synchronously transmitting vibration parameters of a device based on a cloud platform as claimed in claim 6, characterized in that: The mobile terminal processing module decompresses and decrypts the compressed and encrypted vibration parameters, uses an adaptive mapping algorithm to adapt and process the decompressed and decrypted vibration parameters, dynamically adjusts the vibration parameters, and presents vibration feedback to the mobile device, including: The data processing unit receives the compressed and encrypted vibration parameters, queries the current session key to perform decryption calculations, and then decompresses the parameters to obtain the decompressed and decrypted vibration parameters; The device capability database stores parameter mapping tables of several types of mobile devices; The parameter adaptation unit queries the parameter mapping table stored in the device capability database, and generates a vibration driving signal according to the device fingerprint and performance parameters of the mobile device; The dynamic adaptation engine unit adopts an adaptive mapping algorithm. Based on the vibration driving signal, it adjusts the output driving waveform through the PID controller, dynamically adjusts the vibration parameters after decompression and decryption, and presents the vibration feedback to the mobile device; When the network delay exceeds a preset time threshold, the fault fallback mechanism unit switches to the preloading mode of the locally pre-stored waveform library.

9. A processing device, characterized in that: It includes computer program instructions, wherein when the computer program instructions are executed by a processing device, they are used to implement the steps corresponding to the method for synchronous transmission of device vibration parameters based on a cloud platform according to any one of claims 6 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, wherein the computer program instructions, when executed by a processor, are used to implement the steps corresponding to the method for synchronously transmitting vibration parameters of a device based on a cloud platform according to any one of claims 6 to 8.

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