200 results about "Adaptive encoding" patented technology

The invention provides a fragment data cross-modal label generation system and method based on deep transfer learning. The method comprises the following steps: extracting first high-dimensional feature vectors in different modes; mapping the first high-dimensional feature vectors of different modals into the same semantic space through a cross-modal comparison loss function to realize multi-modal alignment and fusion to obtain second high-dimensional feature vectors; labeling semantic tags corresponding to the second high-dimensional feature vectors based on the fragmented data components by adopting a small sample transfer learning algorithm; a multi-channel Hash encoder is adopted, a self-adaptive encoding strategy is called according to different modal data combinations, and the second high-dimensional feature vector is encoded into a multi-channel binary Hash code; in combination with an incremental graph neural network, the binary hash codes and the corresponding semantic tags are dynamically expanded into the historical knowledge graph; matched fine-grained tags are established for semantic differentiation features of different entity combinations in the target knowledge graph, and a cross-modal tag tree is obtained by combining three-matrix hierarchical construction.

The invention discloses a wireless public network communication data transmission system based on multiple central stations, which relates to the technical field of data transmission and comprises a load sensing and balancing module, a path reconstruction module, a self-adaptive coding modulation module and a safety tunnel management module. The load sensing module realizes load balancing and stable transmission by monitoring indexes of a central station in real time and performing dynamic migration connection; the path reconstruction module adjusts and optimizes path parameters by using federal learning, dynamically adjusts a transmission path and optimizes path quality; the adaptive coding modulation module dynamically adjusts a modulation scheme according to channel conditions, generates a pre-coding matrix in combination with environment information, and improves the transmission efficiency; the security tunnel module adopts a plurality of encryption technologies and dynamic port hopping mechanisms to ensure the security of data transmission; according to the invention, the load balancing capability, the transmission efficiency and the security of the system are effectively improved, and the method is suitable for a complex and changeable wireless public network environment.

The invention discloses an HPLCamp (High Performance Liquid Chromatography) based on deep learning. The invention discloses an HRF dual-mode communication adaptive coding modulation and anti-noise method. The method comprises the following steps: acquiring an optical radio frequency signal amplitude-phase change rate and synchronously sampling and normalizing; calculating a node amplitude-phase residual error to generate a nonlinear mapping coefficient; monitoring coherent change to solve a drift trend, adjusting a modulation coding optimization scheme, compensating distortion and outputting an anti-noise result. According to the method, the instantaneous amplitude and phase of the optical radio frequency dual-mode signal are extracted, a multi-dimensional amplitude-phase characteristic matrix is formed in combination with time domain synchronization and a normalization template, differential residual modeling and nonlinear mapping coefficient calculation are carried out between impedance nodes, and dynamic compensation of amplitude-phase mismatch and envelope offset is achieved. A drift trend quantity is generated based on coherent offset parameter differentiation, feedback is provided for modulation format and coding strategy optimization, amplitude equalization and phase correction are completed, the signal synchronization degree and amplitude-phase consistency are improved, and the steady-state response and anti-disturbance performance of a transmission link are enhanced.

The invention relates to the technical field of audio coding and decoding, and discloses an audio coding and decoding method and system, and the method comprises the steps: carrying out the time domain analysis and frequency domain analysis of an audio input signal of a Bluetooth earphone, and generating an audio feature matrix; performing feature extraction and fusion processing to generate a target feature vector containing frequency band energy distribution and user preference information; inputting the target feature vector into a preset neural network model for dynamic parameter calculation, and generating an adaptive coding matrix; performing layered division on the audio input signal according to the adaptive coding matrix to obtain basic tone quality layer data and enhanced tone quality layer data; and encoding the basic tone quality layer data and the enhanced tone quality layer data through a heterogeneous computing unit to generate a target encoded data stream, and the method realizes double optimization of audio processing precision and processing time.

The embodiment of the invention provides a network state sensing and adaptive coding control method and device, equipment and a computer readable storage medium. The method comprises the following steps: acquiring network index data; inputting the network index data into a trained target AI model to generate a network prediction result; executing a target regulation and control operation according to the target network prediction result; the target regulation and control operation comprises an FEC proportion regulation and control operation, a video parameter regulation and control operation and / or an ICE path switching operation. In this way, higher-quality, more stable and more intelligent real-time audio and video communication can be realized in a complex public network environment.

The invention discloses an enterprise intelligent compliance auditing method based on data driving. The method comprises the following steps: S1, automatically collecting auditing data of various heterogeneous data sources in an enterprise in real time through a cross-domain data access interface; s2, performing feature automatic identification and standardization processing on the audit data by adopting a semantic adaptive coding method; s3, a cross-domain collaborative characterization model is constructed by extracting and fusing shared features through a sub-domain multi-expert structure; s4, generating a visual feature heat map by using a hierarchical attention mechanism of the model, and outputting an anomaly detection result; s5, extracting long and short period correlation mode features, inputting the features into a contrast learning framework, and outputting an abnormal risk score; s6, constructing a dynamic enterprise compliance risk knowledge graph; s7, strategy training is carried out, and risk rating is output; and S8, generating an audit report according to the risk rating. According to the invention, efficient and accurate enterprise compliance risk identification and audit decision support are realized.

The invention relates to the technical field of body intelligence, and discloses a body intelligence-oriented sensing active control method and system, and the method comprises the steps: obtaining multi-modal sensing data, and determining a feature extraction unit and a control center node based on a heterogeneous data fusion framework; selecting a target coding module according to the spatial-temporal correlation index of the adaptive coding module, and generating a feature fusion path; determining a signal synchronization moment by combining the dynamic response delay and the sub-layer coupling degree, and marking a real-time fusion topological graph; and detecting a signal phase conflict, and planning a multi-modal control instruction based on a conflict result. The technology also relates to perception mode priority grading, feature fusion path dynamic generation, spectrum interference detection, path re-planning and the like. Microsecond-level time-space stamp marking and modal feature decoupling are realized through a distributed heterogeneous sensor array. According to the invention, the multi-modal data fusion precision, the signal synchronization control and the multi-modal coordination decision ability are improved, and the environmental adaptability and robustness of the intelligent system are enhanced.

The invention provides a multi-source data convolution fusion TBM electric drive system fault diagnosis method, and relates to the technical field of TBM electric drive control. The method comprises the following steps of: filtering and denoising operation data of an electric drive system by using variational mode decomposition (VMD), extracting time domain features, and constructing a multi-mode fusion data set; secondly, extracting short-term, medium-term and long-term time sequence features in parallel through a residual causal convolutional network, and enhancing key features by using a convolutional block attention module (CBAM) to highlight early weak fault signals; furthermore, working condition parameters are embedded into a feature space by adopting working condition adaptive coding, and cross-working-condition feature consistency constraint is realized based on a maximum mean difference (MMD) criterion. According to the method, the weak fault identification capability and the cross-working-condition diagnosis generalization performance are effectively improved, and the TBM operation stability and the tunnel construction safety are guaranteed.

The invention provides an array image demosaicing method based on dynamic convolution and adaptive coding, which relates to the technical field of image processing, and comprises the following steps: acquiring single-channel original image data and a corresponding color filtering array arrangement type identifier; converting the arrangement type identifier into a multi-dimensional physical feature vector, and inputting the multi-dimensional physical feature vector into a feature processor of a neural network model to generate a weighting coefficient vector; carrying out weighted combination on the plurality of special arrangement transformation matrixes through a weighting coefficient vector to obtain a transformation component, adding the transformation component and a basic convolution kernel parameter to obtain a dynamic convolution kernel parameter, and carrying out directional modulation on a specific spatial position of the dynamic convolution kernel parameter based on a direction weight component in a multi-dimensional physical feature vector; and performing convolution operation on the original image data by using the dynamic convolution kernel parameters, extracting multi-scale features, reconstructing image features, and outputting multi-channel color image data, so that the method can be adaptive to different color filter array types, and the demosaicing precision and generalization capability are improved.

The invention discloses a data full-link dynamic anti-interference optimization transmission method. The method comprises the following steps: S1, constructing a data transmission system architecture comprising a sending end, a transmission link and a receiving end; the sending end detects environment disturbance characteristics in real time, and the receiving end collects receiving signals containing background noise and link distortion; s2, designing a self-adaptive waveform coding strategy at a sending end; s3, performing active channel compensation on a transmission link; channel characteristics are extracted, and channel interference is counteracted in real time through pre-distortion waveform adjustment; s4, deploying an intelligent noise reduction decoding algorithm at a receiving end; a depth feature separation network is constructed, and link residual interference is filtered out by analyzing the time-frequency domain difference between a target signal and a noise component. Through a three-level anti-interference mechanism of adaptive coding of the sending end, active compensation of a transmission link and intelligent noise reduction of the receiving end, the problem that the transmission reliability of signals is reduced due to environment disturbance, channel distortion and noise pollution in a complex environment is solved.

The invention discloses a self-adaptive coding modulation method and system for relieving sun-borne interference in an ultra-dense low-orbit satellite network, and aims to improve the reliability and the spectrum utilization rate of satellite network communication. The method specifically comprises the following steps: modeling satellite-ground and inter-satellite communication links of the ultra-dense low-orbit satellite network, and modeling and calculating time generated by the sun and interference to the low-orbit satellite network; the ground station estimates and predicts the satellite-ground channel state, and returns the predicted SNR to the satellite; and according to the SNR returned by the ground station and the SNR generated by the sun, a deep reinforcement learning method is adopted to select a coding modulation scheme to perform downlink data transmission, and whether a lower satellite is selected to perform data unloading and then transmit the data to the ground station is determined. Simulation results show that the bit error rate of the ultra-dense low-orbit satellite network can be effectively reduced, the spectrum utilization rate can be remarkably improved, real-time response and efficient resistance to sun interference can be achieved, the satellite calculation load can be reduced, the comprehensive performance of the system can be improved, and the method has high practical application value.

A satellite adaptive modulation method based on deep reinforcement learning: 1) Perform initialization operations; initialize the state space, action space, and greedy parameter; 2) The signal receiving end on the ground receives the signal from the satellite downlink and extracts the pilot information in the current frame for signal-to-noise ratio (SNR) estimation. After the receiving end calculates the SNR estimation result, it is transmitted to the ground sending end through the ground feedback link; 3) The sending end translates the selected action into the corresponding modulation method and coding rate; 4) Determine whether the current iteration number is an integer multiple of the preset network update step number. If so, go to step 5) for network update; if not, update the SNR state and return to 2) to enter the next round of iteration; 5) Introduce the concept of a dual network and improve the learning effect and accelerate the convergence of the results by optimizing the structure of the neural network. 6) Update the SNR state, increment the greedy parameter, and return to 2) for the next round of iteration.

The invention discloses a scene electromagnetic texture mapping method and system based on an autonomous mobile robot. The method comprises robot operation and development, radar sensor operation and DART implementation. The robot carries out data acquisition through a sensor and carries out data fusion and processing on the acquired information; the radar sensor operates to detect the surrounding environment through a signal transceiving mechanism, and the obtained signals are fused and processed; in the DART implementation step, visual results of different angles are provided through a visual angle synthesis technology and are finally output in a multi-visual-angle mode. Through a strong coupling mechanism of robot body motion control and radar scanning parameters, a personal error source is eliminated, high-precision motion control is realized based on a robot-radar cooperative control architecture, and based on a DART-NeRF hybrid modeling engine and a lightweight network architecture, model parameters are compressed through adaptive coding, and training and reasoning processes are accelerated.

The invention provides a control panel optimization method based on multi-task scheduling, which relates to the field of data processing, and adopts a self-adaptive coding technology to carry out characteristic analysis on each task in a task queue and dynamically distribute priority codes for each task; constructing a dynamic state matrix for the real-time data flow of the control panel by applying a dynamic matrix technology based on the priority coding; tensor decomposition is carried out, potential correlation features between tasks are extracted, and a decomposition result is input into a reinforcement learning model to generate a real-time scheduling strategy; and according to a scheduling execution result, reversely calculating a weight compensation coefficient, adjusting priority codes and state parameters in the dynamic state matrix, generating an updated scheduling strategy, and feeding back the updated scheduling strategy. Through continuous monitoring and dynamic updating of the data flow state, resource distribution in the task scheduling process is optimized, high-priority tasks are executed preferentially, and the response speed of the system and the accuracy of task execution are effectively improved.

The invention discloses a pneumatic actuator remote intelligent monitoring system based on the Internet of Things, and relates to the technical field of actuator control. Traditional, quantum and biosensors are used for data acquisition, adaptive coding is adopted, transmission is combined with quantum encryption and ultra-wideband, quantum machine learning and causal inference are analyzed and applied, and remote control comprises brain-computer, holographic and intelligent agent technologies; naked-eye 3D and AR are used for visualization, system management depends on a block chain and a quantum key, and a self-repairing function is achieved. The advanced technology is creatively applied, data are comprehensively collected, transmission safety is guaranteed, faults are accurately analyzed and predicted, convenient remote control and visual display are achieved, system safety and stability are improved, and efficient operation of the pneumatic actuator is powerfully guaranteed.

The invention provides an LDPC (Low Density Parity Check) optimization method based on dynamic parameter adaptive coding and a Tanner graph neural network. The method comprises the following steps: acquiring a data set through a channel sensing module; the data set comprises channel state information, bit error rate statistics and QoS (Quality of Service) demand information; the collected data set is preprocessed; dividing the preprocessed data set according to a preset proportion; constructing an LSTM (Long Short Term Memory) prediction model; training and optimizing the LSTM prediction model by using the training set based on the loss function, and storing an optimal model; inputting the test set into the optimal model to predict the coding parameters of the channel transmission to obtain a prediction result of the adaptive coding parameters of the channel transmission; the method comprises the following steps: establishing an LDPC encoder and a Tanner-GNN decoder; building a joint optimization controller; the joint optimization controller is used for dynamically adjusting coding parameters through the LSTM prediction model and receiving the bit error rate BER counted by the Tanner-GNN decoder, the technical limitation of traditional coding and decoding separation optimization is broken through, and the performance of the LDPC code in a complex channel environment is remarkably improved.

The invention discloses an AI-based motion subject tracking and streaming media optimization live broadcast system, and particularly relates to the technical field of motion subject tracking and streaming media optimization, AI-driven motion subject detection and tracking and streaming media coding adaptive control are closely fused, and code rate fluctuation of a coding end and bandwidth change of a network end are sensed in real time, so that the accuracy of the motion subject tracking and streaming media optimization live broadcast system is improved. A continuous and explainable live broadcast jitter risk analysis index is constructed, potential image quality jitter and delay risks are identified in advance through fusion of causal rule scoring and time sequence prediction, and the live broadcast jitter risk analysis index is directly mapped into an adaptive coding and transmission adjustment instruction. Priority guarantee, code rate distribution smoothing and delay control optimization of a key area are realized, the definition and playing smoothness of a live broadcast picture are remarkably improved, and sudden jitter and jamming phenomena are reduced.

The invention relates to the field of hybrid coding of video engines, and provides a hybrid coding processing method, system and equipment based on a video engine, which comprises the step of intelligently switching an H.265 inter-frame predictive coding mode and an MJPEG (Multijoint Joint Photographic Experts Group) intra-frame compression coding mode by dynamically monitoring the proportion of a motion area in a video picture. When a scene is static, discrete cosine transform is adopted to compress space redundancy, motion vector compensation is started to eliminate time redundancy when motion is violent, and meanwhile, key frames are doubly screened by using pixel difference analysis and a perceptual hash algorithm, and repeated frames of visual redundancy are eliminated. And finally, space-time association is established for the optimized double-code-stream data through a timestamp index system, and efficient storage and accurate reconstruction of the mixed code stream are realized. The problems that the coding efficiency is low, no self-adaptive coding mode exists, redundant frames are not optimized, and code stream management is insufficient are solved.

The invention provides an on-site video return coding method and system, and the method comprises the steps: collecting a video stream of an on-site scene, judging whether a current video frame in the video stream responds to a scene switching video frame or not, and marking the current video frame as a key frame when the current video frame responds to the scene switching video frame; after the current video frame is identified as a key frame, visual saliency analysis is performed on the key frame, and an entropy weight matrix representing regional information importance distribution in the key frame is constructed according to a visual saliency analysis result and texture information entropies of different blocks in the key frame; adjusting code rate allocation weights of different blocks in the key frame according to the entropy weight matrix and a code rate regulation and control strategy of the key frame to obtain adaptive coding configuration adaptive to content characteristics of the key frame; and performing optimization coding on the key frame through adaptive coding configuration to obtain a target code stream in response to a field video return demand. By adopting the scheme of the invention, the dynamic differential coding of the key video frame in a complex scene can be realized.

The invention relates to a variable input measuring point-oriented physical field prediction neural operator model and a construction method thereof, the model constructed by the method adopts a two-layer architecture of a backbone network and a branch network, the backbone network encodes query coordinates of an output function by using a full-connection neural network, and the branch network encodes the query coordinates of the output function by using the full-connection neural network; and the branch network realizes adaptive coding of the measuring points at different positions by fusing the permutation invariance and the geometric feature extraction capability of the point cloud network. According to the model and the construction method disclosed by the invention, high-precision solution is kept, meanwhile, the change of the position of an input function measuring point can be flexibly adapted, and the strict limitation of a traditional neural operator model on the spatial consistency of input data is effectively overcome.

A method and system are provided for scheduling data transmission in a Multiple-Input Multiple-Output (MIMO) system. The MIMO system may comprise at least one MIMO transmitter and at least one MIMO receiver. Feedback from one or more receivers may be used by a transmitter to improve quality, capacity, and scheduling in MIMO communication systems. The method may include generating or receiving information pertaining to a MIMO channel metric and information pertaining to a Channel Quality Indicator (CQI) in respect of a transmitted signal; and sending a next transmission to a receiver using a MIMO mode selected in accordance with the information pertaining to the MIMO channel metric, and an adaptive coding and modulation selected in accordance with the information pertaining to the CQI.

The invention discloses a self-adaptive link control method for a multi-dimensional identification system, and belongs to the technical field of link control. The method comprises the following steps: embedding an environment sensing module in a multi-dimensional identification system to obtain network state information; introducing a reinforcement learning algorithm to carry out adaptive coding; the self-adaptive waveform is realized through discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT); a self-adaptive waveform is realized through discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT); and establishing a link layer frame format of the multi-dimensional identification system to realize communication link management. According to the invention, through combination of a multi-dimensional identification system and an environment perception technology, a network state and channel characteristics can be perceived in real time, and a link control strategy is dynamically adjusted; the data transmission efficiency can be dynamically optimized according to the network state and the channel condition by introducing the adaptive coding modulation and adaptive waveform technology; the data transmission time delay is obviously reduced, and the data packet disorder and packet loss rate are reduced, so that the user experience is improved.

The invention relates to the technical field of electronic information data processing, in particular to an electronic information data AI anomaly detection and repair method, which comprises the steps of preprocessing multi-modal data, automatically identifying multi-format data and adaptively encoding and normalizing; fusing a convolutional neural network, a bidirectional long-short-term memory network and a Transform architecture to extract multi-modal features, and judging abnormality through an improved deep isolation forest model; the exceptions are subdivided through a lightweight convolutional neural network in combination with a rule base, and service scene labels are automatically matched; based on the abnormal type, grade, scene and semantic four-dimensional degree, a reinforcement learning model intelligently matches a repair strategy such as filling, correction or manual intervention; a data, model and repair three-level parallel architecture and a sliding window mechanism are adopted to realize real-time stream processing; incremental learning closed-loop optimization is driven through validity and rationality dual verification, and the method is suitable for high-reliability data governance scenes such as finance and communication.

The invention relates to the technical field of intelligent costume design, and discloses an intelligent costume pattern generation system based on enhanced multi-modal generation, which comprises a multi-module input module, a layered attention fusion module, a self-adaptive position coding module and a hierarchical decoding module, the multi-module input module is used for receiving a clothing reference image, a version design demand text and structured data, and performing feature extraction respectively to obtain image features, semantic features and structural features; the layered attention fusion module adopts a three-layer attention mechanism to perform fusion processing on the image features, the text features and the structural features to obtain final fusion features; the self-adaptive coding module carries out self-adaptive coding on the final fusion feature and the model parameter of the clothing template, and outputs a coding result; and the hierarchical decoding module is used for decoding the coding result to generate a complete garment pattern.

The invention relates to a ship mail adaptive coding and dynamic bandwidth allocation method, belongs to mobile voice service, mobile data communication service and other telecommunication services, and particularly relates to the field of digital information transmission. The method comprises the following steps: intelligently predicting the total number of transmitted mails and the total number of transmitted mail data of a target ship in a current time segment by using an AI network data prediction model according to various basic data which are fully and comprehensively selected; and determining an adaptive coding strategy and a dynamic bandwidth allocation strategy for the current time segment of the target ship based on the intelligent prediction result. According to the invention, in order to solve the technical problems that effective and smooth transmission of all mail data of each future time segment of each ship is difficult to consider and the utilization rate of network transmission resources is difficult to improve, A I network data prediction models of different structures customized and designed for different ships are used; and intelligent prediction of the mail transmission state of each ship in the future time segment is completed, so that the technical problem is solved.

The invention provides a self-adaptive coding image transmission system and method based on lunar surface field intensity prediction.The self-adaptive coding image transmission system comprises a lunar surface channel simulation module and an image UEP coding and decoding receiving and transmitting module, the lunar surface channel simulation module is responsible for conducting field intensity prediction in combination with lunar surface real geographic information and electromagnetic parameters to obtain the receiving end signal intensity, and the image UEP coding and decoding receiving and transmitting module is responsible for conducting image UEP coding and decoding. And the image UEP coding and decoding transceiver module is responsible for realizing data classification by adopting discrete wavelet transform, decomposing an input original image into different sub-bands, and then constructing an unequal error protection scheme by combining a RaptorQ coding technology to ensure that important information obtains higher priority protection in transmission, so that the transmission reliability is improved. The method has the beneficial effects that: 1, the transmission efficiency is maximized while the data transmission reliability is ensured; 2, adaptive adjustment can be carried out according to communication conditions of different areas, and excellent adaptability and flexibility are shown;

The invention discloses a real-time video stream transmission method and device based on an unmanned aerial vehicle and electronic equipment, and relates to the technical field of unmanned aerial vehicle communication.The method comprises the steps that network state data, position information and historical transmission records of the unmanned aerial vehicle are obtained, and the historical transmission records of the unmanned aerial vehicle are obtained according to the network state data and the position information; adjusting a coding parameter of the to-be-transmitted video stream by adopting a self-adaptive coding strategy to obtain a target coding parameter, and coding the to-be-transmitted video stream based on the target coding parameter to obtain a target video stream; and screening the transmission path set based on the network state data, the position information and the historical transmission record to obtain a plurality of optimal transmission paths, and transmitting the target video stream by adopting the plurality of optimal transmission paths. According to the invention, the technical problem that the transmission delay is increased and the image quality is reduced due to the instability of the network state and the unicity of the transmission path in the prior art is solved.

The invention relates to the technical field of subway security and protection monitoring and video transmission, in particular to a subway security and protection monitoring video quick calling and multi-channel transmission optimization system, which comprises an analysis module used for extracting a feature set of multi-source heterogeneous metadata in a subway station and carrying out matching calculation on the feature set and an abnormal event template to obtain a matching confidence coefficient; if the matching confidence exceeds a preset threshold, judging that an abnormal event occurs and outputting an event feature identifier; determining an influence range and an association path of the event by combining space and scene information in the metadata according to the event feature identifier; and based on the influence range and the association path, obtaining a target equipment calling list containing the equipment identifier, the space-time weight and the initial priority. Intelligent sensing is realized through multi-source data fusion, dynamic resource allocation is realized through scheduling and channelization, and stable delivery of streaming media is guaranteed through adaptive coding and reliable transmission, so that the emergency response capability of a subway security monitoring system is comprehensively improved.

The invention discloses an industrial Internet of Things equipment collaborative management and control system based on data and knowledge driving. The system comprises six units including a heterogeneous multi-source data fusion interaction unit, an improved knowledge graph construction reasoning unit and an optimized hypergraph neural network feature extraction unit. Heterogeneous data acquisition and fusion are realized through a customized industrial-grade data interface protocol and a self-adaptive coding conversion mechanism; constructing a multi-level knowledge graph by using a semantic relationship mining algorithm and performing logical reasoning; deeply extracting data features by means of an optimized hypergraph neural network; combining equipment collaborative parameter generation, executing and dynamically adjusting a strategy; and monitoring the running state of the system in real time and performing feedback optimization. The method corresponds to six steps of the system, and equipment collaborative full-process management and control are achieved. The problems that a traditional system is difficult in data fusion, low in strategy making and execution efficiency and the like are effectively solved, the industrial Internet of Things equipment collaboration efficiency and production benefits are remarkably improved, and the system is suitable for various industrial production scenes.

An example device for processing a point cloud includes: a memory configured to store at least a portion of the point cloud; and one or more processors implemented in circuitry and configured to: obtain planar information of a reference block of the point cloud; determine, based on the planar information of the reference block, a context; context-adaptive code, based on the context, a syntax element that indicates whether a current node is coded using a planar mode; code, based on the current node being coded using the planar mode, the current node using the planar mode.