Mobile data terminal

Abstract

The invention provides a mobile data terminal. Belongs to the technical field of mobile communication and multimedia. The mobile data terminal comprises an audio acquisition unit, a video acquisition unit, a synchronous control unit, a data processing unit, a storage unit, a communication unit, a power management unit and a self-adaptive environment sensing unit, wherein the output ends of the audio acquisition unit and the video acquisition unit are respectively connected with the input end of the synchronous control unit; and the synchronous control unit is bidirectionally connected with the data processing unit. Through high-precision clock synchronization and intelligent data processing, high-quality synchronous recording of audios and videos is realized, and the problem that audios and pictures of a traditional terminal are not synchronous is effectively solved; the self-adaptive environment sensing unit enables the terminal to intelligently adapt to various scenes, and the recording stability and applicability are improved.

Description

A mobile data terminal Technical Field

[0001] This invention proposes a mobile data terminal, belonging to the field of mobile communication and multimedia technology. Background Technology

[0002] With the rapid development of mobile internet and multimedia technologies, mobile data terminals (such as smartphones, law enforcement recorders, and interviewing equipment) are increasingly widely used in news gathering, security monitoring, and education and training. However, traditional mobile terminals have many limitations in audio and video recording: on the one hand, audio and video capture are often completed independently by different hardware modules, leading to frequent audio-visual asynchrony due to clock discrepancies or processing delays; on the other hand, terminals have poor adaptability to different environments, such as significant degradation in recording quality under strong light, high noise, or motion conditions. Furthermore, existing terminals lack intelligent scheduling mechanisms for data compression, storage, and transmission, making it difficult to achieve efficient resource utilization while ensuring quality. Summary of the Invention

[0003] This invention provides a mobile data terminal to solve the problems mentioned in the background section above:

[0004] The present invention proposes a mobile data terminal, the mobile data terminal comprising:

[0005] Audio acquisition unit, video acquisition unit, synchronization control unit, data processing unit, storage unit, communication unit, power management unit, and adaptive environment perception unit;

[0006] The output terminals of the audio acquisition unit and the video acquisition unit are respectively connected to the input terminal of the synchronization control unit; the synchronization control unit is bidirectionally connected to the data processing unit; the data processing unit is bidirectionally connected to the storage unit, the communication unit, and the adaptive environment perception unit; the power management unit is bidirectionally connected to the audio acquisition unit, the video acquisition unit, the synchronization control unit, the data processing unit, the storage unit, the communication unit, and the adaptive environment perception unit.

[0007] The beneficial effects of this invention are as follows: High-quality synchronous recording of audio and video is achieved through high-precision clock synchronization and intelligent data processing, effectively solving the problem of audio-visual asynchrony in traditional terminals; the adaptive environment perception unit enables the terminal to intelligently adapt to various scenarios, improving recording stability and applicability; the power management unit extends battery life through dynamic power consumption control, and the communication unit supports multi-mode transmission to ensure data real-time performance and security; the overall system has high integration, fast response, and efficient resource utilization, making it suitable for various application scenarios such as news interviews, law enforcement recording, and distance education. Attached Figure Description

[0008] Figure 1 is a schematic diagram of the mobile terminal structure described in this invention. Detailed Implementation

[0009] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0010] According to an embodiment of the present invention, as shown in FIG1, a mobile data terminal includes:

[0011] Audio acquisition unit, video acquisition unit, synchronization control unit, data processing unit, storage unit, communication unit, power management unit, and adaptive environment perception unit;

[0012] The output terminals of the audio acquisition unit and the video acquisition unit are respectively connected to the input terminal of the synchronization control unit; the synchronization control unit is bidirectionally connected to the data processing unit; the data processing unit is bidirectionally connected to the storage unit, the communication unit, and the adaptive environment perception unit; the power management unit is bidirectionally connected to the audio acquisition unit, the video acquisition unit, the synchronization control unit, the data processing unit, the storage unit, the communication unit, and the adaptive environment perception unit.

[0013] The operating method of the mobile data terminal includes:

[0014] The audio acquisition unit receives ambient sound signals through a microphone array and performs noise reduction and gain adjustment.

[0015] The video acquisition unit captures image signals through an image sensor and performs exposure compensation and color correction;

[0016] The synchronization control unit receives audio and video signals and uses a high-precision timestamp alignment mechanism to achieve audio-visual synchronization;

[0017] The data processing unit encodes and compresses the synchronized audio and video data, and integrates environmental parameters provided by the adaptive environment perception unit to optimize data quality;

[0018] The storage unit categorizes and stores the processed data, and creates an index;

[0019] The communication unit establishes a connection with the cloud or external devices to upload or receive audio and video data;

[0020] The power management unit dynamically monitors the power consumption of each unit and implements intelligent power supply strategies.

[0021] The adaptive environment perception unit detects ambient light, noise, and motion status in real time and feeds this information back to the data processing unit to adjust the recording parameters.

[0022] The working principle of the above technical solution is as follows: the mobile data terminal realizes the acquisition, processing and transmission of audio and video data through the coordinated work of various functional units. The audio acquisition unit receives ambient sound signals through a microphone array, uses beamforming technology to focus on the target sound source, employs an adaptive noise reduction algorithm to eliminate ambient noise, and adjusts the signal strength through automatic gain control. The video acquisition unit captures image signals through an image sensor, optimizes brightness distribution using an exposure compensation algorithm, and restores true colors using a color correction matrix. The synchronization control unit receives raw data streams from the two acquisition units, adds nanosecond-level timestamps to each frame of data using a high-precision clock synchronization protocol, dynamically adjusts the buffer depth using a buffer management mechanism, and uses interpolation compensation algorithms to handle data loss or jitter, ensuring the continuity of audio-visual synchronization. The data processing unit performs in-depth processing on the synchronized audio and video data. First, it uses deep learning algorithms for scene recognition and content analysis, dynamically selecting an encoding strategy based on the analysis results. It also interacts with the adaptive environment perception unit to dynamically adjust the processing strategy based on parameters such as ambient light and noise level. Finally, it integrates noise suppression and image enhancement algorithms to improve data quality, and encrypts and embeds digital watermarks on the processed data. The storage unit adopts a hierarchical storage architecture, classifying and storing data according to access frequency and importance, and establishing a multi-dimensional index to support fast retrieval. The communication unit supports dynamic switching of multi-mode protocols, performs intelligent packet segmentation, compression, and encryption before data transmission, and employs a multi-channel transmission protocol to ensure transmission reliability. The power management unit monitors the power consumption status of each unit in real time and implements power supply strategies through dynamic voltage and frequency adjustment and intelligent scheduling algorithms; the adaptive environment perception unit collects environmental parameters in real time through multi-sensor fusion technology, constructs an environmental state model, and provides decision support for other units.

[0023] The effects of the above technical solutions are as follows: The microphone array noise reduction and gain adjustment of the audio acquisition unit reduce the interference of environmental noise on recording quality, avoiding sound distortion or uneven volume, and improving the clarity and realism of the audio acquisition; the video acquisition unit uses exposure compensation and color correction technology to reduce the impact of sudden changes in light or color shifts on the image, avoiding overexposure or color distortion, and improving the naturalness and detail of the video image; the synchronization control unit uses a high-precision timestamp alignment mechanism to reduce timing deviations between audio and video data streams, avoiding the embarrassment of audio and video being out of sync during viewing, and enhancing the smoothness and consistency of the playback experience; the data processing unit combines intelligent encoding compression with environmental parameter fusion to reduce the bandwidth consumption of data storage and transmission, avoiding processing delays caused by excessively large files, and improving data processing efficiency. Adaptability; the storage unit implements classified storage and indexing, reducing the chaos and time consumption during data retrieval, avoiding the problem of missing or difficult-to-find important files, and enhancing the convenience and reliability of data management; the communication unit supports dynamic switching of multiple protocols and secure connection, reducing the impact of network environment fluctuations on transmission stability, avoiding the risk of data loss or interruption, and improving the robustness and real-time performance of remote communication; the power management unit reduces unnecessary energy waste through dynamic monitoring and intelligent power supply strategies, avoids equipment interruption due to insufficient power, and extends the battery life and usage flexibility of terminal equipment; the adaptive environment perception unit detects light, sound, and motion status in real time and provides feedback and adjustments, reducing the cumbersomeness of manual intervention, avoiding errors due to parameter settings being out of sync with the environment, and enhancing the device's adaptability and overall ease of use in different scenarios.

[0024] In one embodiment of the present invention, the synchronization method of the synchronization control unit includes:

[0025] The synchronization control unit receives raw data streams from the audio acquisition unit and the video acquisition unit, and uses a global clock synchronization protocol to add a nanosecond-level timestamp to each frame of audio and video data;

[0026] The buffer management mechanism dynamically adjusts the buffer depth of audio and video data to offset differences in transmission latency.

[0027] An interpolation compensation algorithm is used to perform frame compensation when data is lost or jitter occurs, ensuring the continuity of audio-visual synchronization.

[0028] In collaboration with the data processing unit, it provides real-time feedback on synchronization status and triggers parameter reconfiguration.

[0029] The working principle of the above technical solution is as follows: The synchronization control unit achieves precise synchronization of audio and video data by establishing a unified clock reference. First, a high-precision clock generator is used inside the unit to generate a reference clock signal, providing a unified clock reference for the audio and video acquisition units through the PTP precision clock protocol. When the raw data streams from the two acquisition units are received, the synchronization control unit adds a nanosecond-level precision timestamp to each audio and video frame. At this time, the timestamp contains the frame sequence number and precise acquisition time information. During data transmission, the synchronization control unit dynamically adjusts the depth configuration of the dual buffers by monitoring the transmission delay difference between the audio and video data streams in real time. When the audio transmission delay is detected to be greater than that of the video, the system automatically increases the audio buffer depth and correspondingly decreases the video buffer depth. This dynamic balancing mechanism offsets the timing differences caused by different transmission paths. When the system detects data packet loss or timing jitter, the interpolation compensation mechanism is immediately activated. This mechanism first determines the characteristic parameters of the lost frame through correlation analysis of consecutive frames, and then generates compensation frames using motion vector-based video frame interpolation and waveform-similar audio frame reconstruction algorithms. For video data, motion-compensated interpolation is used to generate intermediate frames by analyzing the motion trajectories of preceding and following frames. For audio data, a phase-continuous waveform interpolation algorithm is employed to ensure a smooth and natural audio signal after compensation. A bidirectional feedback channel is established between the synchronization control unit and the data processing unit to transmit synchronization status information in real time. When the synchronization error exceeds a preset threshold, the system triggers a parameter reconfiguration mechanism, automatically adjusting the sampling rate, encoding parameters, or transmission protocol of the acquisition unit to eliminate synchronization deviations at the source. Simultaneously, the system records historical synchronization data and optimizes synchronization parameters using machine learning algorithms, continuously improving the system's adaptive synchronization capabilities.

[0030] The effects of the above technical solution are as follows: By attaching nanosecond-level timestamps to audio and video frames through a global clock synchronization protocol, the timing alignment accuracy of audio and video data is significantly improved, effectively avoiding audio-visual misalignment caused by clock drift; by using a dynamic buffer management mechanism, the audio and video buffer depth is adjusted in real time, significantly reducing the risk of data accumulation or interruption caused by transmission delay differences, and reducing the occurrence of playback stuttering or frame skipping; by adopting an intelligent interpolation compensation algorithm, compensation frames are automatically generated when data is lost or jittered, effectively avoiding audio-visual interruptions caused by network fluctuations, and significantly improving the continuity and integrity of the playback process; through a real-time collaborative feedback mechanism with the data processing unit, synchronization state anomalies are quickly identified and parameter reconfiguration is triggered, greatly reducing the frequency of manual intervention and significantly enhancing the system's adaptive capability and long-term operational stability.

[0031] In one embodiment of the present invention, the use of an interpolation compensation algorithm to perform frame compensation in case of data loss or jitter, thereby ensuring audio-visual synchronization continuity, includes:

[0032] Real-time monitoring of the transmission status of audio and video data streams, identification of data packet loss, frame jitter or timing deviation events, and generation of anomaly event identifier sets;

[0033] Based on the abnormal event identifier set, the data of the associated frames before and after are extracted, and the content of the compensation frame is calculated by an adaptive interpolation algorithm to generate a preliminary compensation frame sequence.

[0034] The initial compensation frame sequence is time-aligned and checked. Combined with the global clock synchronization protocol, the timestamp offset of the compensation frames is adjusted to generate an aligned compensation frame set.

[0035] The alignment compensation frame set is inserted into the original data stream for smoothness detection and synchronization verification, generating an optimized synchronization data stream;

[0036] Based on the feedback results of the optimized synchronous data stream, the interpolation algorithm parameters are dynamically updated to generate an adaptive interpolation strategy for subsequent frame compensation operations.

[0037] The working principle of the above technical solution is as follows: The interpolation compensation algorithm ensures the continuity of audio-visual synchronization by establishing an intelligent monitoring and dynamic repair mechanism. The system first continuously tracks the transmission status of audio and video data streams through a real-time monitoring module, uses an anomaly detection model to identify events such as data packet loss, frame jitter, or timing deviations, and generates an anomaly event identifier set containing event type, severity, and location information. Based on this identifier set, the system extracts related preceding and following frame data from the cache, uses an adaptive interpolation algorithm (such as motion compensation interpolation for video and waveform reconstruction interpolation for audio) to calculate the compensation frame content, and generates a preliminary compensation frame sequence. Subsequently, the system performs timing alignment verification on the preliminary compensation frame sequence, dynamically adjusts the timestamp offset of the compensation frames by combining the high-precision timestamps of the global clock synchronization protocol, ensures its timing consistency with the original data stream, and generates an aligned compensation frame set. The frame set is intelligently inserted into the original data stream. The system evaluates the quality of the inserted data stream using smoothness detection algorithms (such as gradient analysis) and synchronization verification modules (such as cross-correlation calculation), generating an optimized synchronized data stream. Finally, based on the feedback results of the optimized synchronized data stream (such as synchronization error indicators and visual / auditory quality scores), the system dynamically updates the parameter configuration of the interpolation algorithm through machine learning algorithms, generating an adaptive interpolation strategy. This strategy continuously optimizes the accuracy and efficiency of frame compensation, forming a closed-loop learning system, thereby improving the intelligence and robustness of subsequent frame compensation operations and ensuring continuous and stable audio-visual synchronization under various transmission conditions.

[0038] The above technical solution achieves the following effects: By monitoring the audio and video transmission status in real time and generating an abnormal event identifier set, the efficiency of identifying data loss and timing deviations is significantly improved, effectively avoiding missed detections or delayed processing of abnormal situations; by using an adaptive interpolation algorithm to generate compensation frames based on the abnormal event identifier set, the risk of audio-visual breakage due to data loss is significantly reduced, avoiding the problems of image blurring or sound quality distortion that may be caused by traditional fixed interpolation methods; by performing timing alignment verification and timestamp calibration on the compensation frame sequence, the timing misalignment between the compensation frame and the original data stream is greatly reduced, effectively preventing the generation of secondary synchronization errors; by combining the optimized synchronization data stream with the algorithm parameter update mechanism, the accuracy and adaptability of interpolation compensation are continuously improved, significantly enhancing the system's ability to maintain audio-visual synchronization in complex network environments.

[0039] In one embodiment of the present invention, the data processing method of the data processing unit includes:

[0040] The data processing unit receives synchronized audio and video data and uses deep learning algorithms for scene recognition and content analysis.

[0041] Based on the analysis results, the encoding strategy is dynamically selected, including bit rate, resolution and frame rate;

[0042] Integrating noise suppression and image enhancement algorithms improves data quality;

[0043] It interacts with the adaptive environment perception unit to dynamically adjust processing parameters based on ambient light and noise levels.

[0044] The processed data is encrypted and digital watermarked to ensure data security.

[0045] The working principle of the above technical solution is as follows: The data processing unit optimizes audio and video data processing by constructing an intelligent processing pipeline. The unit first receives audio and video data from the synchronization control unit, extracts scene features from the video stream using a deep convolutional neural network, and simultaneously analyzes the semantic content of the audio stream using a recurrent neural network to establish a feature map linking audio and video, completing scene type identification and content importance assessment. Based on the scene analysis results, the system initiates a dynamic encoding strategy selection mechanism. By constructing a bitrate-quality model, a resolution-aware model, and a frame rate smoothness evaluation model, combined with current network bandwidth and device performance parameters, a multi-objective optimization algorithm is used to calculate the optimal combination of encoding parameters. In low-light or high-noise environments, the noise reduction intensity and dynamic range are automatically increased; in static scenes, the frame rate is intelligently reduced to save bandwidth. The noise suppression module uses a combination of frequency domain analysis and time domain processing, eliminating environmental noise through an adaptive filter bank while preserving speech segment features. The image enhancement module performs illumination compensation based on the Retinex algorithm, uses edge enhancement operators to enhance detail representation, and restores true colors through a color mapping model; a bidirectional data channel is established with the environment perception unit to acquire measurement data from the illumination sensor and noise detection module in real time. By using an environmental parameter mapping model, the gamma curve, contrast parameter, and noise reduction threshold of image processing are dynamically adjusted, forming a closed-loop environmental adaptive processing mechanism. Finally, the data stream is encrypted using the AES-256 algorithm, and a digital watermark is generated using hash chain technology. The watermark information includes device identification, timestamp, and integrity check code. The watermark is distributed into frequency domain coefficients using discrete cosine transform domain embedding technology, achieving data traceability and tamper-proof protection while ensuring visual / auditory quality.

[0046] The effects of the above technical solution are as follows: By employing deep learning algorithms for scene recognition and content analysis, the intelligence level of audio and video processing is significantly improved, effectively avoiding the problem of mismatch between traditional fixed parameter processing methods and real-world scenarios; dynamically adjusting the encoding strategy based on the analysis results significantly reduces the resource consumption of data storage and transmission, avoiding image quality loss caused by unreasonable bitrate allocation in high-complexity scenarios; integrating noise suppression and image enhancement algorithms effectively reduces the impact of environmental interference on audio and video quality, significantly improving the clarity and viewing experience of the output content; through intelligent interaction with the adaptive environment perception unit, processing parameters are optimized in real time, greatly reducing the workload of manual debugging and enhancing the device's adaptability in different environments; employing encryption and digital watermarking technologies for dual protection significantly improves the security of data transmission and storage, effectively preventing the risk of content leakage and illegal tampering.

[0047] In one embodiment of the present invention, the dynamic selection of an encoding strategy based on analysis results, including bitrate, resolution, and frame rate, includes:

[0048] Construct a multi-dimensional quality assessment model that comprehensively considers data volume, clarity, and fluency;

[0049] The reinforcement learning algorithm is used to automatically adjust the encoding parameters based on the network state and storage capacity;

[0050] Enable lossless encoding in high-quality mode and lossy compression in transport mode;

[0051] Monitor battery level in real time and switch to low-power encoding mode when the battery is low.

[0052] The working principle of the above technical solution is as follows: The system achieves dynamic optimization of the coding strategy by establishing an intelligent coding decision mechanism. First, a multi-dimensional quality assessment model is constructed, including indicators such as data compression rate, image structure similarity, and motion smoothness. A weighted fusion algorithm is used to calculate the comprehensive quality score and generate a real-time quality assessment system. Based on this assessment system, the system uses a deep reinforcement learning algorithm to construct a coding decision model. This model uses network bandwidth, remaining storage space, and device computing power as environmental state inputs. It explores the quality benefits of different combinations of coding parameters through the Q-learning algorithm, establishes a state-action value mapping table, and generates adaptive coding parameter decisions. The system maintains a dual-mode coding strategy library. When the system detects that the user has enabled high-quality mode, it automatically selects a lossless coding scheme based on intra-frame prediction to preserve complete color information and spatial details. In transmission mode, a lossy compression algorithm is activated, and perceptual coding technology is used to remove visual information that is not sensitive to the human eye, generating an optimized lossy coded stream. The power management module monitors the battery level and power consumption trend in real time through a coulomb counter. When the battery level is lower than a set threshold, a low-power coding mode is triggered. This model significantly reduces computational energy consumption while maintaining basic quality by lowering coding complexity, reducing motion search range, and employing fast algorithms, generating energy-efficient coding configurations. Each module forms a complete self-optimization system through a closed-loop feedback mechanism, with coding performance data continuously fed back to the quality assessment model, driving the reinforcement learning model to update policy weights, achieving continuous adaptation of coding parameters to the usage environment, and ensuring that optimal coding efficiency and quality balance can be maintained under different working conditions.

[0053] The effects of the above technical solutions are as follows: By constructing a multi-dimensional quality assessment model, the balance between image quality and smoothness is significantly improved, effectively avoiding the experience imbalance caused by optimizing a single indicator; the use of reinforcement learning algorithms to dynamically adjust encoding parameters significantly reduces the risk of encoding failure due to network fluctuations or insufficient storage, and improves the device's adaptability in different environments; lossless encoding is enabled in high-quality mode, which better preserves image details and color authenticity, while lossy compression is used in transmission mode, which greatly reduces bandwidth usage and transmission latency; real-time monitoring of power consumption and switching to low-power encoding mode effectively extends the device's battery life and avoids the loss of important data due to power depletion.

[0054] In one embodiment of the present invention, the interaction with the adaptive environment sensing unit to dynamically adjust processing parameters according to ambient light and noise levels includes:

[0055] The ambient light intensity and noise level data are collected in real time from the adaptive environment perception unit to generate the raw environment perception dataset.

[0056] Based on the original environmental perception dataset, scene classification and anomaly detection are performed through a multi-dimensional environmental state analysis model to generate environmental state assessment results.

[0057] Based on the environmental status assessment results, the parameter configuration database is queried to obtain the baseline processing parameters, and the parameters are initially adjusted in combination with the characteristics of real-time audio and video streams to generate a dynamic parameter adjustment plan.

[0058] The dynamic parameter adjustment scheme is simulated and the effect is predicted. The parameter deviation is corrected by iterative optimization algorithm, and the optimized processing parameter set is generated.

[0059] The optimized processing parameter set is applied to the audio and video processing module, and the output quality indicators are monitored to generate parameter application feedback data for model updates of the adaptive environment perception unit.

[0060] The working principle of the above technical solution is as follows: The system collects ambient light intensity and noise level data in real time through an adaptive environment perception unit, forming a raw environmental perception dataset. A multi-dimensional environmental state analysis model is used to classify the dataset into scenes (e.g., indoor, outdoor, low light, high noise) and detect anomalies (e.g., sudden changes in light intensity, noise peaks), generating an environmental state assessment result. Based on this result, the system queries a parameter configuration database to obtain baseline processing parameters and, combined with the characteristics of real-time audio and video streams (e.g., brightness, contrast, signal-to-noise ratio), performs preliminary parameter adjustments, forming a dynamic parameter adjustment scheme. This scheme is verified through simulation (e.g., virtual environment testing) and effect prediction (e.g., quality index simulation). Iterative optimization algorithms (e.g., gradient descent or genetic algorithms) correct parameter deviations, generating an optimized processing parameter set. Finally, the optimized parameter set is applied to the audio and video processing module, while simultaneously monitoring output quality indicators (e.g., PSNR, SSIM, audio signal-to-noise ratio) in real time, generating parameter application feedback data to update the model parameters of the adaptive environment perception unit, forming a closed-loop adaptive adjustment mechanism. This ensures that processing parameters can be dynamically optimized under different environmental conditions, improving audio and video quality.

[0061] The effects of the above technical solutions are as follows: By collecting ambient light and noise data in real time, the system's sensitivity to environmental changes is significantly improved, effectively avoiding parameter setting lag caused by sudden environmental changes; scene classification and anomaly detection based on a multi-dimensional environmental state analysis model significantly improve the accuracy of scene recognition in complex environments, greatly reducing the occurrence of false positives and false negatives; through intelligent matching of parameter configuration database and real-time audio and video features, parameter adjustment schemes are dynamically generated, effectively reducing the workload of manual debugging and improving the accuracy of parameter settings; the parameter correction mechanism combining simulation verification and iterative optimization significantly reduces the deviation between parameter settings and actual effects, avoiding a decline in processing quality due to improper parameters; by establishing a closed-loop feedback system for parameter application and model updates, the judgment ability of the environmental perception unit is continuously optimized, significantly enhancing the adaptability and stability of the system in long-term operation.

[0062] In one embodiment of the present invention, the environment perception method of the adaptive environment perception unit includes:

[0063] Environmental data is collected in real time using optical sensors, gyroscopes, and microphone arrays.

[0064] An environmental state model is constructed using multi-sensor fusion technology.

[0065] Scene type identification based on convolutional neural networks, including indoor, outdoor, moving, and stationary;

[0066] Based on the recognition results, the optimal recording parameters are recommended to the data processing unit.

[0067] When extreme environments are detected, a protection mechanism is triggered to pause or downgrade recording.

[0068] The working principle of the above technical solution is as follows: The adaptive environment perception unit realizes the environmental adaptation function by constructing a multi-source information acquisition and intelligent decision-making system. The unit first collects ambient light intensity and color temperature data through a light sensor, obtains the device motion state and attitude information through a gyroscope, and collects ambient sound field characteristics and noise spectrum through a microphone array to generate a multimodal environment raw dataset. Based on the collected multi-source data, the system uses Kalman filtering and data fusion algorithms to perform spatiotemporal alignment and feature extraction on heterogeneous sensor data to construct a multi-dimensional environmental state model that includes light distribution, motion trajectory, and noise characteristics. This model eliminates measurement errors from individual sensors and improves the accuracy of environmental perception by weighted fusion of data from various sensors. The system utilizes a pre-trained convolutional neural network to perform deep feature analysis on a multi-dimensional environmental state model, extracting spatial features through multi-layer convolution and pooling operations, and combining temporal analysis to identify dynamic patterns. It accurately determines whether the current environment belongs to indoor, outdoor, moving, or static scene types and outputs scene recognition confidence. Based on the scene recognition results, the system calls a parameter recommendation engine to retrieve the optimal combination of recording parameters from a pre-set parameter configuration library. By establishing a mapping relationship between scene types and recording parameters, and combining real-time environmental data to fine-tune parameter values, a personalized recording scheme including exposure parameters, sampling rate, and encoding mode is generated. When extreme environments such as light intensity exceeding the sensor's dynamic range, noise levels exceeding thresholds, or abnormal motion are detected, the system activates a multi-level protection mechanism. First, we attempt to maintain basic recording functionality through adaptive parameter adjustments. If environmental conditions continue to deteriorate, we automatically downgrade the recording quality or activate a safety pause mechanism based on the severity of the situation to ensure device and data security. The entire perception process forms a closed-loop optimization system. By continuously monitoring feedback data on environmental changes and actual recording effects, we continuously correct the environmental state model and decision algorithm parameters to improve the system's adaptability and robustness under different environmental conditions.

[0069] The effects of the above technical solution are as follows: By constructing an environmental state model through multi-sensor fusion technology, the accuracy and comprehensiveness of environmental perception are significantly improved, effectively avoiding misjudgment problems caused by the limitations of single sensor data; based on convolutional neural network intelligent scene type recognition, the distinction accuracy of complex scenes such as indoor and outdoor environments is significantly improved, and parameter setting deviations caused by environmental misclassification are greatly reduced; the optimal recording parameters are automatically recommended based on scene recognition results, effectively reducing the time and experience dependence of manual debugging and improving the adaptive performance of the equipment in different environments; through the intelligent protection mechanism in extreme environments, the recording process is paused or downgraded in a timely manner, significantly reducing the risk of equipment failure under harsh working conditions and enhancing the safety and reliability of equipment use.

[0070] In one embodiment of the present invention, the communication method of the communication unit includes:

[0071] The communication unit supports 5G, Wi-Fi 6 and Bluetooth 5.0 multi-mode protocols, and can dynamically switch according to network conditions;

[0072] Before data transmission, the audio and video streams are packetized, compressed, and encrypted; a multi-channel transmission protocol is used for data transmission.

[0073] Establish a secure link with the cloud server to enable real-time streaming media push or batch data synchronization;

[0074] Enable local caching and resume interrupted download mechanisms in weak network environments.

[0075] The working principle of the above technical solution is as follows: The communication unit achieves efficient and reliable data transmission by constructing an intelligent multi-mode communication system. The system first collects parameters such as signal strength, bandwidth utilization, latency, and packet loss rate of each network interface (5G, Wi-Fi 6, and Bluetooth 5.0) in real time through a multi-protocol monitoring module, generating a network status evaluation matrix. Based on this matrix, a multi-objective decision algorithm is used to calculate the comprehensive priority of each protocol. When the current network quality is detected to be below a threshold, a protocol switching mechanism is automatically triggered, achieving seamless switching through hardware and software collaboration to ensure communication continuity. In the data transmission preparation stage, the system intelligently segments audio and video streams, dynamically determining the segmentation strategy based on the network MTU size and content characteristics. Subsequently, a layered compression engine is started, using H.265-based inter-frame predictive compression for the video stream and Opus encoding for the audio stream, dynamically adjusting the compression rate according to network conditions. The encryption module uses the AES-GCM algorithm to encrypt data packets and adds a digital signature to each data packet. The multi-channel transmission protocol establishes multiple parallel transmission paths, employing data fragmentation and redundant coding techniques to distribute data packets across different network channels for transmission. The system monitors the transmission quality of each channel in real time, dynamically adjusts the data allocation ratio of each channel based on a load balancing algorithm, and restores the original data stream at the receiving end through a data reassembly mechanism. Secure connection with the cloud server employs a two-way authentication mechanism, establishing an encrypted channel via the TLS 1.3 protocol. The system intelligently selects the transmission mode based on data type and real-time requirements: low-latency streaming media is used for real-time audio and video data, while a high-efficiency data synchronization protocol is enabled for batch data. In weak network environments, the system automatically activates an adaptive caching management mechanism, dynamically adjusting the local caching strategy based on network quality and data priority. When transmission is interrupted, the system records the transmission status through a breakpoint resumption controller, resuming transmission from the point of interruption after network recovery. Forward error correction technology is also employed to enhance the robustness of data transmission.

[0076] The above technical solutions achieve the following effects: By supporting intelligent switching between 5G, Wi-Fi 6, and Bluetooth 5.0 multi-mode protocols, network connection stability is significantly improved, effectively avoiding data transmission interruptions caused by single network failures; the data preprocessing method combining packetization, compression, and encryption significantly reduces the risk of data loss during transmission, while effectively preventing the leakage of sensitive information during transmission; parallel data transmission via multi-channel transmission protocols significantly improves data transmission efficiency, effectively avoiding transmission delays caused by network congestion; establishing a secure link with the cloud server for data transmission significantly enhances the reliability of remote communication, effectively preventing the risk of data theft or tampering; and intelligently enabling local caching and breakpoint resumption mechanisms in weak network environments significantly reduces the impact of network fluctuations on data transmission, effectively avoiding data retransmission and resource waste caused by network interruptions.

[0077] In one embodiment of the present invention, the communication unit supports 5G, Wi-Fi 6, and Bluetooth 5.0 multi-mode protocols, and dynamically switches according to network conditions, including:

[0078] Real-time monitoring of signal strength, bandwidth, and latency parameters of each network interface generates a raw dataset of network status.

[0079] Based on the original network state dataset, the priority scores of each protocol are calculated using a multi-protocol evaluation model to generate a protocol priority sequence.

[0080] Based on the protocol priority sequence, execute protocol switching decisions, dynamically activate the optimal communication protocol stack, and generate an active protocol configuration set;

[0081] Perform connection stability testing and throughput verification on the activated protocol configuration set, and generate a protocol performance evaluation report;

[0082] Based on the protocol performance evaluation report, the protocol parameters are optimized through an adaptive adjustment algorithm to generate a stable communication link.

[0083] The working principle of the above technical solution is as follows: The communication unit achieves seamless switching between multiple protocols by constructing an intelligent network sensing and decision-making system. The system first collects real-time parameters of three network interfaces—5G, Wi-Fi 6, and Bluetooth 5.0—in parallel through a multi-interface monitoring module, including signal strength (RSRP / RSSI), available bandwidth (MBps), and transmission delay (ms), generating a three-dimensional network state raw dataset with timestamps. Based on this dataset, the system initiates a multi-protocol evaluation model. This model employs a multi-attribute decision analysis (MADA) algorithm, comprehensively considering the weight allocation of three dimensions: network stability, transmission rate, and energy efficiency. A weighted scoring mechanism is used to calculate the comprehensive priority score of each protocol, generating a protocol sequence arranged in descending order of priority. According to the protocol priority sequence, the switching decision engine adopts a threshold-based dynamic triggering mechanism. When the performance of the current protocol is detected to be lower than a specific threshold of the suboptimal protocol, the protocol switching process is immediately initiated. By employing protocol stack reconstruction technology, new protocol connections are pre-established while maintaining existing connections, generating an active protocol configuration set that includes protocol type, frequency band parameters, and QoS policies. The system then performs dual verification on the active protocol configuration set: continuous ping testing to assess connection stability and large packet throughput testing to verify actual transmission capabilities, generating a protocol performance evaluation report including packet loss rate, jitter rate, and effective bandwidth. Finally, based on the performance evaluation report, the system uses a Q-learning reinforcement learning algorithm to construct a parameter optimization model, iteratively adjusting protocol parameters (such as transmit power and modulation / coding scheme) to ultimately generate a stable communication link with optimal performance. Simultaneously, the system feeds the optimization results back to the evaluation model, forming a closed-loop optimization mechanism for continuous improvement.

[0084] The effects of the above technical solution are as follows: By monitoring multiple network interface parameters in real time and generating a status dataset, the accuracy of network environment perception is significantly improved, effectively avoiding communication quality degradation caused by signal blind spots or network congestion; by generating a priority sequence based on a multi-protocol evaluation model, the scientific nature of protocol selection is significantly improved, greatly reducing subjective misjudgments and response delays caused by manual switching; by dynamically activating the optimal communication protocol stack and generating a configuration set, the intelligence level of network adaptation is effectively improved, avoiding connection interruption problems caused by protocol mismatch; stability testing and throughput verification of the activated protocol significantly enhance the reliability of the communication link and effectively prevent performance fluctuations that may occur in practical applications; and adaptive parameter optimization based on performance evaluation reports continuously improves communication quality while significantly reducing the workload of repetitive configuration caused by changes in the network environment.

[0085] In one embodiment of the present invention, the audio and video streams are packetized, compressed, and encrypted before data transmission; a multi-channel transmission protocol is used to transmit the data, including:

[0086] Perform traffic analysis and feature extraction on audio and video streams to generate a set of stream feature parameters;

[0087] Based on the stream feature parameter set, a packet segmentation strategy is dynamically selected to perform adaptive packet segmentation processing on audio and video streams and generate standardized data packet sequences.

[0088] The standardized data packet sequence is subjected to multi-layer compression encoding, and the compression ratio is adjusted according to the content complexity to generate an optimized compressed data stream.

[0089] Dynamically encrypt the optimized compressed data stream, integrating symmetric and asymmetric encryption algorithms to generate secure encrypted data packets;

[0090] Based on network status assessment results, the optimal combination of transmission channels is selected, and a multi-channel transmission protocol is used to transmit securely encrypted data packets in parallel, generating a transmission acknowledgment signal; including:

[0091] Based on the network status assessment results, real-time quality monitoring and performance analysis are performed on available transmission channels to generate a set of channel performance indicators.

[0092] Based on the channel performance index set, channel weights and priorities are calculated using a multi-objective decision-making algorithm to generate an initial channel combination scheme.

[0093] Simulation tests and load assessments were performed on the initial channel combination scheme. The channel configuration was optimized by combining historical transmission data, and a verification channel configuration set was generated.

[0094] Based on the verification channel configuration set, initialize the multi-channel transmission protocol, distribute and transmit secure encrypted data packets in parallel, and generate transmission session logs.

[0095] Real-time monitoring of transmission session logs; dynamic adjustment of transmission strategies based on packet loss rate and latency data; generation of transmission optimization signals.

[0096] When the transmission optimization signal reaches a stable threshold, the data integrity is verified and a transmission confirmation signal is generated.

[0097] The working principle of the above technical solution is as follows: The communication unit achieves efficient and secure transmission of audio and video streams by constructing an intelligent data processing and transmission pipeline. The system first performs real-time traffic analysis and feature extraction on the audio and video streams. By statistically analyzing traffic patterns, packet size distribution, and content types (such as voice, music, and dynamic video), it generates a set of stream feature parameters containing traffic characteristics, priority identifiers, and temporal relationships. Based on this parameter set, the system dynamically selects an adaptive packet segmentation strategy, intelligently segmenting the audio and video streams according to the network MTU size and content importance, generating standardized data packet sequences to ensure consistent packet structure and ease of processing. Next, the system performs multi-layer compression encoding on the standardized data packet sequences, employing content-based compression algorithms (such as H.265 for video and Opus for audio), dynamically adjusting the compression rate based on content complexity to minimize data volume while ensuring quality, generating an optimized compressed data stream. Subsequently, the optimized compressed data stream undergoes dynamic encryption processing, integrating AES symmetric encryption and RSA asymmetric encryption algorithms to generate a unique encryption key and digital signature for each data packet, thus creating secure encrypted data packets to prevent data leakage and tampering. During the transmission phase, based on network status assessment results, the system performs real-time quality monitoring and performance analysis on available transmission channels (such as 5G, Wi-Fi 6, and Bluetooth), generating a set of channel performance indicators including bandwidth, latency, and packet loss rate. A multi-objective decision-making algorithm (such as TOPSIS) is used to calculate the weight and priority of each channel, generating an initial channel combination scheme. This scheme is then simulated and load-assessed, and channel configurations are optimized using historical transmission data to generate a verification channel configuration set. Based on this configuration set, a multi-channel transmission protocol is initialized, and secure encrypted data packets are distributed in parallel to multiple channels for transmission, generating a transmission session log. The system monitors this log in real time, dynamically adjusting transmission strategies (such as retransmission mechanisms and path switching) based on packet loss rate and latency data, generating a transmission optimization signal. When the transmission optimization signal reaches a stable threshold, the system verifies data integrity (such as CRC check), ultimately generating a transmission confirmation signal to ensure reliable data delivery.

[0098] The above technical solutions achieve the following effects: By generating a stream feature parameter set through traffic analysis and feature extraction, the targeting of data processing is significantly improved, effectively avoiding the mismatch between traditional fixed packet segmentation strategies and content characteristics; dynamically selecting packet segmentation strategies based on the stream feature parameter set and generating standardized data packet sequences significantly reduces transmission efficiency losses caused by unreasonable data packet structures and improves network resource utilization; employing multi-layer compression coding combined with content complexity adjustment of the compression ratio significantly reduces data volume while ensuring quality, effectively alleviating network bandwidth pressure; and integrating symmetric and asymmetric encryption algorithms through dynamic encryption processing greatly enhances data transmission security and effectively prevents... Data may be stolen or tampered with during transmission; selecting the optimal transmission channel combination based on network state assessment results significantly improves the reliability of the transmission path and avoids transmission interruptions caused by single-path failures; generating channel combination schemes through multi-objective decision-making algorithms and verifying them through simulation effectively improves the scientific nature of the transmission strategy and reduces performance fluctuations in practical applications; real-time monitoring of transmission session logs and dynamic adjustment of the transmission strategy significantly improves the system's adaptability to changes in network conditions and effectively reduces the risk of data loss; verifying data integrity when the transmission optimization signal reaches a stable threshold ensures the accuracy and reliability of the received data and avoids data retransmission due to transmission errors.

[0099] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A mobile data terminal, characterized in that, The mobile data terminal includes: an audio acquisition unit, a video acquisition unit, a synchronization control unit, a data processing unit, a storage unit, a communication unit, a power management unit, and an adaptive environment perception unit; the output terminals of the audio acquisition unit and the video acquisition unit are respectively connected to the input terminal of the synchronization control unit; the synchronization control unit and the data processing unit are bidirectionally connected; the data processing unit and the storage unit, the communication unit, and the adaptive environment perception unit are bidirectionally connected; the power management unit and the audio acquisition unit, the video acquisition unit, the synchronization control unit, the data processing unit, the storage unit, the communication unit, and the adaptive environment perception unit are bidirectionally connected.

2. The mobile data terminal according to claim 1, characterized in that, The terminal employs a high-precision clock synchronization circuit and is integrated using system-level packaging technology.

3. A mobile data terminal according to claim 1, characterized in that, The operation method of the mobile data terminal includes: an audio acquisition unit receiving ambient sound signals through a microphone array and performing noise reduction and gain adjustment; a video acquisition unit capturing image signals through an image sensor and performing exposure compensation and color correction; a synchronization control unit receiving audio and video signals and using a high-precision timestamp alignment mechanism to achieve audio-visual synchronization; a data processing unit encoding and compressing the synchronized audio and video data and integrating environmental parameters provided by an adaptive environmental perception unit to optimize data quality; a storage unit classifying and storing the processed data and establishing an index; a communication unit establishing a connection with the cloud or external devices to upload or receive audio and video data; a power management unit dynamically monitoring the power consumption of each unit and implementing an intelligent power supply strategy; and an adaptive environmental perception unit detecting ambient light, noise, and motion status in real time and feeding back to the data processing unit to adjust recording parameters.

4. A mobile data terminal according to claim 1, characterized in that, The synchronization method of the synchronization control unit includes: the synchronization control unit receiving raw data streams from the audio acquisition unit and the video acquisition unit, using a global clock synchronization protocol to add a timestamp to each frame of audio and video data; dynamically adjusting the buffer depth of audio and video data through a buffer management mechanism; using an interpolation compensation algorithm to perform frame compensation when data is lost or jittered; and cooperating with the data processing unit to provide real-time feedback on the synchronization status and trigger parameter reconfiguration.

5. A mobile data terminal according to claim 4, characterized in that, The interpolation compensation algorithm is used to perform frame compensation when data is lost or jittered. This includes: real-time monitoring of the transmission status of audio and video data streams, identifying data packet loss, frame jitter, or timing deviation events, and generating an abnormal event identifier set; based on the abnormal event identifier set, extracting related frame data from before and after the data stream, and using an adaptive interpolation algorithm to calculate the compensation frame content, generating a preliminary compensation frame sequence; performing timing alignment verification on the preliminary compensation frame sequence, and adjusting the timestamp offset of the compensation frames in conjunction with a global clock synchronization protocol, generating an aligned compensation frame set; inserting the aligned compensation frame set into the original data stream, performing smoothness detection and synchronization verification, and generating an optimized synchronized data stream; and dynamically updating the interpolation algorithm parameters based on the feedback results of the optimized synchronized data stream, generating an adaptive interpolation strategy for subsequent frame compensation operations.

6. The mobile data terminal according to claim 1, characterized in that, The data processing method of the data processing unit includes: the data processing unit receiving synchronized audio and video data, and using deep learning algorithms for scene recognition and content analysis; dynamically selecting an encoding strategy based on the analysis results; integrating noise suppression and image enhancement algorithms to improve data quality; interacting with an adaptive environment perception unit to dynamically adjust processing parameters according to ambient light and noise levels; and encrypting and embedding digital watermarks into the processed data to ensure data security.

7. The mobile data terminal according to claim 6, characterized in that, The interaction with the adaptive environment perception unit, dynamically adjusting processing parameters based on ambient light and noise levels, includes: real-time acquisition of ambient light intensity and noise level data from the adaptive environment perception unit to generate a raw environmental perception dataset; based on the raw environmental perception dataset, scene classification and anomaly detection are performed using a multi-dimensional environmental state analysis model to generate an environmental state assessment result; based on the environmental state assessment result, a parameter configuration database is queried to obtain baseline processing parameters, and preliminary parameter adjustments are made in conjunction with real-time audio and video stream characteristics to generate a dynamic parameter adjustment scheme; the dynamic parameter adjustment scheme is simulated and verified for effect prediction, and parameter deviations are corrected through iterative optimization algorithms to generate an optimized processing parameter set; the optimized processing parameter set is applied to the audio and video processing module, and output quality indicators are monitored to generate parameter application feedback data for model updates of the adaptive environment perception unit.

8. The mobile data terminal according to claim 1, characterized in that, The environmental perception method of the adaptive environmental perception unit includes: real-time acquisition of environmental data through light sensors, gyroscopes, and microphone arrays; construction of an environmental state model using multi-sensor fusion technology; identification of scene types based on convolutional neural networks, including indoor, outdoor, moving, and stationary; recommendation of optimal recording parameters to the data processing unit based on the identification results; and triggering a protection mechanism to pause or downgrade recording when extreme environments are detected.

9. The mobile data terminal according to claim 1, characterized in that, The communication method of the communication unit includes: the communication unit supports 5G, Wi-Fi 6 and Bluetooth 5.0 multi-mode protocols and dynamically switches according to network conditions; before data transmission, the audio and video streams are packetized, compressed and encrypted; a multi-channel transmission protocol is used to transmit the data; a secure link is established with the cloud server to realize real-time streaming media push or batch data synchronization; and local caching and breakpoint resume mechanism are enabled in weak network environments.

10. The mobile data terminal according to claim 9, characterized in that, Before data transmission, the audio and video streams are packetized, compressed, and encrypted. A multi-channel transmission protocol is employed for data transmission, including: performing traffic analysis and feature extraction on audio and video streams to generate a stream feature parameter set; dynamically selecting a packet segmentation strategy based on the stream feature parameter set to adaptively segment the audio and video streams into standardized data packet sequences; performing multi-layer compression encoding on the standardized data packet sequences, adjusting the compression rate according to content complexity to generate an optimized compressed data stream; dynamically encrypting the optimized compressed data stream, integrating symmetric and asymmetric encryption algorithms to generate secure encrypted data packets; and selecting the optimal transmission channel combination based on network status assessment results, using the multi-channel transmission protocol to transmit the secure encrypted data packets in parallel, and generating a transmission confirmation signal.

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