505 results about "Algorithm Selection" patented technology

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention provides a power grid fault detection and positioning method and system for an intelligent power grid system. The method comprises the following steps: collecting a fault traveling wave signal when a power distribution line has a fault; preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; carrying out traveling wave front edge detection on the target fault traveling wave signal, and determining the signal arrival time of the fault traveling wave signal; fault signal features are extracted from the target fault traveling wave signals through a frequency domain analysis method and a time domain analysis method; estimating the fault line characteristics of the fault line with the fault in combination with the fault signal characteristics and the signal arrival time; outputting the line type and the fault type of the fault line through a feature recognition model; selecting an optimal fault positioning algorithm by using a decision tree algorithm based on the line type and the fault type; and determining the line position of the fault line according to the target fault traveling wave signal by adopting an optimal fault positioning algorithm. The method has the effect of intelligently selecting the optimal positioning algorithm according to the line type.

The invention relates to the technical field of task allocation, in particular to a task allocation method for flexible employment. According to the technical scheme, the method comprises the following steps that a database of related information of tasks and employees is constructed, wherein the database comprises detailed information of the tasks and comprehensive information of the employees; respectively constructing a task model and an employee portrait based on the information in the database; and matching the task with the employee through an intelligent matching algorithm, wherein the intelligent matching algorithm comprises matching algorithm selection and optimization, real-time matching and dynamic adjustment, and multi-objective optimization and tradeoff. According to the method, multiple matching algorithms are combined for optimization, weights are selected and adjusted according to tasks and employee characteristics, accurate matching is achieved, a task model and employee portrait multi-dimension consideration adaptability is achieved, adaptive changes are matched in real time, tasks and multiple aspects of employees are optimized and balanced in a multi-objective mode, the development of rights and interests of the employees is considered, and the matching quality, the working enthusiasm and the stability are improved; flexible employment resources are managed more effectively, and the task allocation effect is improved.

The present invention discloses a post-quantum key negotiation system and method based on the IKE protocol, which relates to the field of network security, especially the design and implementation of key exchange and identity authentication protocols. The present invention provides security for data protection and identity authentication by combining the IKE protocol scheme with the post-quantum cryptographic algorithm; by introducing post-quantum cryptographic algorithms such as ML‑KEM, ML‑DSA and FALCON, the quantum resistance of key exchange and identity authentication is enhanced, and the flexibility and adaptability of the protocol are also improved; in addition, the dynamic algorithm selection mechanism adopted by the present invention enables users to optimize security performance according to specific needs, thereby improving the overall efficiency of the system while ensuring information security.

The invention discloses a self-adaptive encryption system based on AI intelligent security management, which relates to the technical field of information security, and comprises a data acquisition module, a risk assessment module, an encryption algorithm selection module, a key generation and management module, an encryption execution module and an encryption effect feedback module, the data collection module is used for collecting various data in a system operation environment, including but not limited to network flow data, equipment state data and user behavior data; and the risk assessment module analyzes the collected data based on an AI algorithm and assesses the security risk level faced by the current system. The operation environment data is comprehensively collected through the data acquisition module, the security risk level is evaluated in real time through the risk evaluation module based on the AI algorithm, the adaptive encryption algorithm can be dynamically selected according to the risk, the defect that a traditional encryption system is fixed in strategy is overcome, and encryption pertinence and effectiveness are improved.

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous big data processing system, which comprises the steps of dividing data into structural sub-data and non-structural sub-data, performing feature extraction on the non-structural sub-data to obtain multi-modal features, so that the data can be compared in a unified feature space, and then, performing feature extraction on the non-structural sub-data to obtain multi-modal features. The similarity and relevance between the data are identified through pattern matching, it is ensured that the data of the same entity are identified to be directly fused, redundancy is reduced, accuracy is improved, then the most similar data combination is selected through an optimization algorithm, and the actual relation type between the data is determined through relation deduction; and finally, the most suitable fusion mode is selected according to the relationships. According to the method, the accuracy and effectiveness of the data fusion process are ensured, and a reliable basis is provided for deeper analysis and decision making.

The invention discloses a QoS guarantee method and system for a communication network, and relates to the technical field of communication networks, and the method comprises the steps: collecting and preprocessing network state data, and forming a standardized data set; dynamically classifying service types based on an improved random forest algorithm, and predicting a future QoS demand trend of each priority service in combination with an LSTM neural network; establishing a mapping model of QoS demands and resource parameters, converting predicted demands into allocable resource indexes, monitoring the resource utilization rate in real time, and setting an elastic reservation mechanism and conflict early warning; when early warning is triggered, selecting an optimal transmission link by adopting a multi-path collaborative algorithm, and implementing differentiated resource allocation according to service priorities; and a closed-loop feedback mechanism is triggered to dynamically adjust resource allocation by monitoring the deviation between the actual QoS and a predicted value in real time. The method has the advantages that through multi-dimensional perception, LSTM prediction, dynamic resource management and multi-path scheduling, QoS requirements of services with different priorities are accurately matched, and dynamic changes of the network are efficiently coped with.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention relates to a self-adaptive multi-algorithm scheduling method and system based on cloud edge collaboration, and the method comprises the steps: constructing a cloud algorithm knowledge base and a scheduling strategy model, which are used for storing a plurality of algorithms, and providing a unified scheduling rule and optimization criterion; designing an edge node real-time sensing and reporting module, dynamically monitoring the computing power state, task characteristics and operation environment of the node, and transmitting related information to the cloud in real time; a cloud intelligent scheduling decision module is constructed, a knowledge base and a scheduling strategy are combined, a received edge state is comprehensively analyzed, and an optimal algorithm selection and execution position decision is generated; an algorithm dynamic scheduling and heterogeneous execution module is deployed on the edge side, and a target algorithm is flexibly loaded and executed on a local cache or heterogeneous computing resources according to an instruction issued by the cloud; and the cloud edge collaborative closed-loop iteration and adaptive optimization module realizes adaptive iteration and continuous optimization of algorithm scheduling, so that high robustness and high efficiency of task execution in a complex and changeable scene are ensured.

The invention discloses an information retrieval system and method based on semantic normalization, and relates to the technical field of artificial intelligence information, and the method comprises the steps: collecting a semantic query record input by a user, carrying out the preliminary semantic analysis, and generating structured data; on the basis of the structured data, entity disambiguation is carried out by utilizing a knowledge graph, abstract classes are generated through a neural network, calibration and dynamic weight adjustment are carried out, and high-confidence entity abstract classes and confidence scores are generated; entity abstract classes and confidence scores are combined with user contexts, an action-value function is calculated through a value network, and an optimal action is selected by utilizing a-greedy algorithm; executing semantic normalization mapping according to the optimal action, and obtaining an intermediate expression by using a meta-symbol dynamic generator; and performing index retrieval and multi-dimensional sorting based on the intermediate expression to generate a sorted retrieval result list. According to the method, the semantic fragmentation problem of multi-modal query is solved, and deep semantic alignment and dynamic weight calibration of heterogeneous data are realized.

The invention provides an illegal capital collection suspicious subject identification method and system based on reinforcement learning. Comprising an information sorting module, a data analysis module, a data feature extraction module, a data feature processing module, a model construction module, a model selection module, a model training module, an algorithm selection module, an algorithm optimization module, a risk identification module and an early warning module. According to the illegal capital collection suspicious subject identification method based on reinforcement learning, a reinforcement learning agent is trained by using key fund features and data, so that the illegal capital collection suspicious subject can be accurately identified, the accuracy and efficiency of risk identification are improved, implicit information in fund data is fully mined, and the risk identification efficiency is improved. And abnormal modes and risk points which are difficult to detect by a traditional rule model and a supervised learning method are identified.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle simulation method, device and equipment, a medium and a product, and the method comprises the steps: simulating and generating at least one target unmanned aerial vehicle based on target unmanned aerial vehicle parameters; environment parameter information is received, and a task scene is simulated and generated according to the environment parameter information; controlling the target unmanned aerial vehicle to execute the target task in the task scene according to the target algorithm; the algorithm is selected after a corresponding triggering operation is received in the algorithm selection interface; the algorithm interface comprises an algorithm selection control of at least one algorithm type. According to the invention, the simulation effect of the unmanned aerial vehicle is more comprehensive and reliable.

The invention relates to the technical field of data acquisition, in particular to a bus real-time data acquisition method and system, and the method comprises the following steps: collecting data from a plurality of sensors of a bus in real time; dividing the data into a plurality of categories according to the timeliness and importance of the data; multi-level dynamic compression algorithm selection based on data categories: adopting multi-level dynamic compression algorithms for different categories of data; during data transmission, a self-adaptive priority scheduling mechanism is adopted, and the uploading priority of different types of data is dynamically adjusted according to the current network bandwidth and the transmission delay; in a bus network, short-distance wireless communication is established among a plurality of buses, and data cached in one bus is shared to other surrounding vehicles through a bus-bus linkage mechanism; according to the invention, real-time sharing and temporary supplement of data are realized through short-distance wireless communication, the problem of data missing caused by network disconnection of a single vehicle in a traditional data acquisition system is solved, and the reliability of overall data is greatly enhanced.

The invention relates to a data center dynamic energy consumption optimization method based on machine learning, and the method comprises the following steps: S1, deploying a sensor at a key position in a data center for collecting energy consumption, temperature, load and other data, and collecting the operation data of the data center through a system interface; s2, preprocessing the data, including data cleaning and feature engineering, extracting features from the original data, selecting the most important features by using an LASSO algorithm, and constructing a training data set; s3, constructing an energy consumption prediction model based on the machine learning model, and training based on the training data set; s4, performing prediction based on the trained energy consumption prediction model, and dynamically allocating tasks according to the predicted energy consumption and the current load condition of the equipment to obtain a preliminary task allocation scheme; and S5, carrying out strategy training by adopting hierarchical reinforcement learning, optimizing a task allocation scheme of the data center, and carrying out task allocation based on the optimized task allocation scheme. The resource utilization efficiency and energy efficiency of the data center can be improved.

The invention provides an ocean buoy data real-time acquisition method based on low earth orbit satellite communication, and belongs to the technical field of electrical digital data processing.The ocean buoy data real-time acquisition method comprises the steps that firstly, a low earth orbit satellite network communication link is built, data characteristics are analyzed through a compression conversion gain index model, and a low earth orbit satellite network communication link is established; and establishing a multi-stage compression mechanism. The system adopts an optimization equation set to dynamically adjust compression parameters, the optimization equation set comprises a resource consumption equation, a compression quality equation and a transmission efficiency equation, and self-adaptive compression of data is achieved. And meanwhile, through a hierarchical algorithm selection mechanism and a parameter optimization strategy and in combination with real-time feedback control, continuous optimization of the compression efficiency is ensured. According to the compression scheme based on multi-level optimization, the technical problem of low compression efficiency caused by poor algorithm adaptability in the ocean buoy data compression process in the prior art is effectively solved.

The invention relates to the technical field of security auditing, in particular to a security auditing method and system for an industrial control system, and the method comprises the following steps: aiming at a key control task, a communication task and a security task of a real-time operating system, collecting a period, a starting timestamp, a finishing timestamp, a central processing unit occupied time slice and peak memory usage amount data. According to the method, the period, timestamp, central processing unit occupation and memory usage data of a key task of a real-time operating system are collected, a task execution time boundary and a resource consumption envelope are set, and then an expected relation rule set of a task time sequence and resource consumption is constructed by applying historical data statistics and logic rule deduction; and meanwhile, the key length of symmetric and asymmetric encryption, initialization vector generation, hash algorithm selection, key derivation parameters and encryption operation context are stipulated, so that a comprehensive and specific ICS behavior specification baseline is established.

The invention relates to the technical field of embedded data storage, and discloses a method and a system for prolonging the service life of a FLASH simulation EEPROM (Electrically Erasable Programmable Read-Only Memory) in a single chip microcomputer, and the method comprises the following steps: firstly, initializing the FLASH, dividing the FLASH into a main data area, a mapping table area, a backup area and a transaction log area, selecting a physical block through a dynamic wear leveling algorithm when data is written, and writing the data into a database; the method comprises the following steps of: starting a backup area or a transaction log area, safely updating a mapping table by using a transaction mechanism, performing cyclic redundancy check when reading data, if the cyclic redundancy check fails, starting cascade recovery from the backup area or the transaction log area, and finally, updating a transaction log to record operation so as to form a closed-loop management process. According to the invention, through a set of dynamic wear leveling and data classification management mechanism, global and uniform distribution of the write load in the storage array is realized, the physical block with the least erasing times is selected during writing, the access frequency of the data can be identified, and cold data which is not changed frequently can be actively migrated.

The invention relates to a data management method and a data management system based on OpenHarmony. The method comprises the following steps: constructing a cross-device data storage layer by utilizing a distributed soft bus technology, and carrying out fragmentation storage and redundant backup on target data to obtain a distributed data storage architecture; performing feature extraction to obtain a data classification result and a fine-grained data mark; comprehensively analyzing the network condition, the equipment performance and the user behavior to obtain a data synchronization strategy; executing two-stage protocol submission and vector clock concurrency control on the cross-device operation to obtain a distributed transaction processing result; performing structural feature and repetitive mode analysis to obtain a compression algorithm selection result and a compression parameter; and carrying out compression processing, and carrying out adaptive routing selection and load balancing on the compressed data transmission path to obtain a multi-device data flow management scheme. According to the method, data integrity and availability are guaranteed, data are effectively compressed, and storage space and transmission bandwidth are saved.

The invention provides a transformer area line loss intelligent algorithm decision-making method based on a knowledge graph and a large model agent, semantic retrieval in the algorithm calling process is supported, dominant and implicit knowledge such as expert experience, technical standards and historical cases is coded in a unified mode, a mapping relation is established between algorithm nodes and process nodes in the knowledge graph, and the algorithm is optimized. An integrated reasoning path is formed, so that the intelligent agent can automatically combine and call an algorithm according to a task target, a large model is embedded into a transformer area line loss analysis process, and a hierarchical structure of'task analysis intelligent agent-algorithm decision intelligent agent-result analysis intelligent agent 'is formed; the task analysis agent is responsible for converting a problem input by a user into a structured task; the algorithm decision agent is responsible for algorithm selection and adaptive parameter adjustment; the result analysis agent is responsible for report generation and decision recommendation based on the knowledge base; according to the method, organized association can be carried out on scattered algorithm tools, expert experience and business rules, and a unified transformer area line loss agent tool integration framework is constructed.

Disclosed in the present invention are a county power distribution network grid planning method based on a digital resource fusion technology, and a system. The method comprises: acquiring geographic information of a county region, and performing preliminary division of power supply grids; performing secondary division of the power supply grids by means of a K-means clustering algorithm; and by means of an adaptive multi-objective whale optimization algorithm, selecting an optimal planning strategy to perform tertiary division of the power supply grids. By studying the development and evolution mechanism of a county power distribution network across temporal, spatial, and resource dimensions, planning and operation methods for the county power distribution network are integrated for digital resource fusion, enabling grid division of the county power distribution network. Moreover, a multi-objective collaborative planning model of the power distribution network is constructed from the perspectives of economy and security, and an optimal planning solution is selected.

The invention discloses an unmanned aerial vehicle group real-time image recognition method and system based on asynchronous federated learning, and the method comprises the steps: carrying out the local recognition model training of each unmanned aerial vehicle, and uploading a trained local model to a central server; calculating a corresponding Shapley value, and introducing an old degree attenuation factor to adjust the weight to obtain an adjusted Shapley value; updating the global model through weighted aggregation based on the adjusted Shapril value; and after the global model is updated, the unmanned aerial vehicle with the highest utility is selected by adopting a confidence upper limit algorithm to participate in subsequent federated learning, and client optimization scheduling in a dynamic environment is realized. The asynchronous federated learning mechanism based on the Shapley value can effectively measure the contribution of each unmanned aerial vehicle to the global model, reduces the influence of outdated updating by introducing an attenuation factor, improves the accuracy of the global model in a dynamic environment, enables the model to better adapt to the complex environment and data change of the unmanned aerial vehicle group, and improves the robustness of the unmanned aerial vehicle group. Therefore, the accuracy of image recognition is improved.

The invention discloses a quantum router for an ultra-secure communication network, and relates to the technical field of quantum communication and networks. The system comprises a quantum key distribution module which is used for generating and distributing quantum keys between two communication parties, and is used for encrypting and decrypting transmitted information; the quantum entanglement state generation and management module is used for generating and managing quantum entanglement pairs and supporting transmission and management of the quantum entanglement pairs among different network nodes; the quantum routing module is used as a core processing unit of the system and is responsible for analyzing the received quantum information and selecting an optimal transmission path according to a preset algorithm; the quantum error correction and protection module is used for applying a quantum error correction code and a quantum state protection mechanism in the information transmission process to prevent the quantum state from being damaged by external environment and equipment defect interference; and the classic control channel module is used for transmitting classic control signals, cooperating with routing and management of the quantum information, and providing a feedback mechanism to realize real-time monitoring and dynamic adjustment of the quantum information transmission process.

The invention relates to a large model adaptive reasoning method based on Monte Carlo tree search. The method comprises the following steps: organizing a reasoning process of a large language model by adopting a tree structure; selecting a node to be expanded by using a UCT algorithm; when a new node is expanded, all information on the current reasoning path is used for the currently selected node to generate a next reasoning step; simulation is executed, reasoning is continued based on the existing reasoning steps until a final answer is obtained, and whether the final answer is correct or not is judged; carrying out back propagation according to the correct final answer, carrying out reward updating on nodes on the current reasoning path, taking a tree structure before new nodes are generated as cue words, and taking the nodes generated according to the cue words as generated data; finely adjusting the large model according to the generated data; and carrying out reinforcement learning on the large model by using a PPO algorithm. According to the method, the father node can be automatically selected according to the current reasoning state, the new reasoning node is added into the nodes, and finally an accurate and reasonable answer is generated.

The invention relates to a chip data compression method and device, equipment and a storage medium, and the method comprises the following steps: obtaining to-be-compressed data of a target chip, and carrying out the type analysis of the to-be-compressed data, and obtaining the type of the to-be-compressed data; based on the to-be-compressed data type, performing data compression algorithm selection on the to-be-compressed data to obtain a selected data compression algorithm; performing content perception identification on the to-be-compressed data through a preset content perception algorithm to obtain perception content identification data; performing Huffman coding on the perceived content identification data and non-perceived content identification data in the to-be-compressed data through a selected data compression algorithm to obtain compressed coding data; and performing format conversion on the compressed coded data to obtain standardized coded data, and storing the standardized coded data in the target chip, thereby solving the technical problem that the traditional compression efficiency is not high or the quality of the decompressed data is reduced.

The invention discloses an optimal scheduling method for an AC / DC flexible interconnection power distribution network. The method comprises the following steps: firstly, deeply analyzing consideration factors such as uncertainty of a distributed power supply, dynamic change of a load and characteristics of flexible DC equipment; then, targets of minimizing the operation cost, maximizing the energy utilization efficiency, optimizing the electric energy quality and achieving the highest reliability are set; in the aspect of a scheduling strategy, intra-regional balance and cross-regional power exchange are realized by adopting layered and partitioned scheduling, intelligent cooperation of all units is realized by applying multi-agent cooperative scheduling, and a scheme is adjusted according to real-time data through real-time optimization; then, a hybrid intelligent algorithm combining a genetic algorithm and a deep learning algorithm is constructed in algorithm selection, and optimization and improvement are continuously carried out; and finally, establishing a coordination control mechanism to ensure effective cooperation of the flexible DC system and the traditional AC system. According to the invention, new requirements and new challenges faced by the AC / DC flexible interconnection power distribution network can be more effectively handled, so that safe, stable and efficient operation of the AC / DC flexible interconnection power distribution network is realized.

The invention relates to the technical field of data security, in particular to a data desensitization method and system based on combination of an MCP protocol and rule driving. The method comprises the following steps: loading a service rule through an MCP Server and registering a desensitization tool as a standardized tool; user query is received through the MCP Client, and user intention is analyzed by the AI agent; the AI agent dynamically calls a standardized tool to form a desensitization task chain according to a user intention and a business rule; and executing the desensitization task chain to process the target data, wherein the calling process comprises dynamic strategy adjustment based on context information. The system comprises an MCP service layer, a desensitization tool library module and an intelligent decision and integration layer. Through MCP protocol standardization integration, service rule driving and AI agent decision making, dynamic weight distribution and probabilistic algorithm selection of a desensitization strategy are achieved, the flexibility, the intelligent level and the system integration efficiency of data desensitization are effectively improved, and meanwhile the safety and compliance guarantee capacity is enhanced.

The invention belongs to the technical field of intelligent control, and discloses an energy optimization management system and method for hotel guest rooms, and the system comprises a central data pool unit which constructs a time sequence database cluster, and carries out the uploading and interaction of data between all units through a data bus; the existence sensing unit is used for integrating multiple sensors by adopting a sensing layer architecture to form a sensing network, capturing existence sensing data and generating a room existence state map based on the existence sensing data; the existence sensing data comprises guest room reservation information data, guest room environment data and guest information data; the partition environment control unit is used for obtaining customer preference data by analyzing a room existence state map; based on the guest preference data and the guest room environment data, selectively activating environment control of different guest room areas by adopting an intelligent optimization algorithm; and the hotel energy utilization rate and customer satisfaction are improved.

The invention relates to a heterogeneous remote sensing image change detection system and method based on a Mama model. The system comprises a feature extractor, a codec network and a change detector. The feature extractor is used for extracting first feature maps of an optical mode and an SAR mode respectively; the codec network is used for mapping and reconstructing the first feature map based on a Mama model to generate a second feature map of an optical mode and an SAR mode; the change detector subtracts the second feature maps of the same mode and calculates an L2 norm along the channel dimension, and weighted fusion is carried out on calculation results of the L2 norms of different modes, so that a difference map is obtained; the difference map processor optimizes the difference map based on a full-connection conditional random field method, and segments the optimized difference map into a varying region and a non-varying region based on an optimal threshold selected by an adaptive threshold segmentation algorithm. According to the method, a feature alignment process and difference chart generation are coupled into an end-to-end optimization task, and the problem of error accumulation caused by two-stage decoupling in a traditional unsupervised method is avoided.

To provide a system according to an embodiment that quickly selects the optimal generative AI algorithm for a task to which a time limit is set, and efficiently executes the task.SOLUTION: A system according to an embodiment comprises a parallel processing unit, a selection unit, and an acquisition unit. The parallel processing unit simultaneously executes a plurality of generative AI algorithms for a task to which a time limit is set. The selection unit selects a generative AI algorithm on the basis of an execution speed from the plurality of generative AI algorithms executed by the parallel processing unit. The acquisition unit acquires a result of the task executed by the generative AI algorithm selected by the selection unit.SELECTED DRAWING: Figure 1

Methods, devices, and media for encoding and decoding visual information and storing video bitstreams are described. In general, methods for combining IBC with existing motion compensation algorithms for video encoding and decoding are described. In a typical aspect, a method for video encoding using IBC includes: determining whether to use a motion compensation algorithm to encode a current block of a current picture; and based on the determination, encoding the current block by selectively applying intra-frame block copying to the current block. In a typical aspect, another method for video encoding using IBC includes: determining whether to use intra-frame block copying to encode a current block of a current picture; and based on the determination, encoding the current block by selectively applying a motion compensation algorithm to the current block.