221 results about "Algorithm Selection" patented technology

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 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 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.

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 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.

The invention relates to the technical field of IPv6 self-configuration, in particular to an IPv6 network mode self-adaptive configuration method and system based on an RA message. In the method, a finite-state machine establishes a state transition path according to the combination relation of M and O flag bits in an RA message cache set and prefix field content, converts a network mode recognition process into a quantifiable state judgment sequence, and sends the state judgment sequence to the RA message cache set; configuration ambiguity caused by flag bit fluctuation in traditional static judgment is eliminated, pattern recognition under network environment changes has continuity and self-consistency, the classification regression tree constructs a branch judgment path with the duration, the advertisement interval and the prefix consistency of an RA source as characteristic variables, an optimal node is selected through a hierarchical splitting algorithm, and the judgment accuracy is improved. The dynamic mapping of stateful and stateless configuration paths is realized, the misjudgment rate of configuration branches is reduced, and the address allocation delay is optimized, so that the IPv6 self-configuration has a structured decision-making capability and a parameter mapping self-optimization characteristic, and the connection reliability, the address generation speed and the configuration accuracy are improved.

The invention provides a video coding and compression transmission method for intelligent equipment, which belongs to the technical field of video coding and compression transmission, realizes advanced channel quality estimation by establishing a channel prediction model enhanced by a spatial transformation network, configures layered coding parameters according to a prediction result and optimizes motion search in combination with inertial measurement unit data. An optimal coding mode is selected by an application rate distortion optimization algorithm, enhancement layer sending is adjusted according to real-time feedback by adopting a dynamic transmission scheduling strategy, and differential forward error correction protection and a time-space domain error concealment mechanism are matched; the technical problem that video transmission is unstable due to violent fluctuation of channel quality when intelligent equipment moves and works in complex electromagnetic environments such as a transformer substation is solved.

The invention discloses an automobile industry code quality automatic compliance method based on a large language model. The method comprises the following steps: calling a static code analyzer to generate a violation report; the method comprises the following steps: intelligently classifying violation into Tier1, Tier2 and Tier3 based on a multi-feature analysis algorithm of a rule document; for Tier1 violation, performing three times of retry iteration repair by using a large language model, automatically detecting compilation errors and performing rollback; for Tier2 violation, multiple technical schemes are extracted by a large language model for a user to select; a multi-LLM antagonistic cooperation mechanism is adopted for Tier3 violation, automatic restoration of complex violation is achieved through a Generator generation restoration scheme, a Disscriminator review challenge scheme and antagonistic iteration of Arbiter arbitration conflicts, and an optimal scheme is selected through a weighted voting algorithm when conflicts occur. The method obviously reduces the manual intervention rate, improves the repairing quality, and is low in cost and high in universality.

The invention provides a large-scale knowledge graph visualization method and system, and relates to the field of knowledge graph visualization. The method comprises the following steps: acquiring knowledge graph data, and clustering the knowledge graph data through a modularity-based discovery algorithm; obtaining each sub-graph corresponding to the clustered knowledge graph data, and for each sub-graph, selecting a representative node based on a PageRank algorithm or a Leader Rank algorithm; carrying out force-oriented layout on the clustered knowledge graph data through a tree diagram space filling technology; and for the knowledge graph data subjected to the force-oriented layout, distributing priorities and use times of a Barnes-Hut algorithm and a random vertex sampling algorithm according to a preset mode, and dynamically displaying a visualization result in a layered manner through an affine transformation technology. According to the method and the device, the problem that the structural expression clarity of the drawn knowledge graph is greatly reduced due to the fact that semantic clusters and hierarchical organizations in the graph are difficult to accurately present in a traditional visualization method is solved.

The invention discloses a data encryption method, a data decryption method and related equipment, and relates to the technical field of artificial intelligence. In the encryption method of the scheme, after to-be-encrypted original plaintext data is determined, encryption environment perception data is firstly obtained; the original plaintext data and the encryption environment perception data are input into an encryption strategy generation model by calling an encryption agent, and the encryption strategy generation model outputs an encryption algorithm selection result, key information and a key distribution path matched with the current original plaintext data and the encryption environment; and finally, encrypting the original plaintext data by using the encryption strategy to obtain ciphertext data. Based on the scheme, the dynamic determination of the encryption algorithm, the key information and the key distribution path can be realized, and by combining with the corresponding decryption scheme, the scheme can adapt to the data encryption and decryption requirements under different scenes and different situations.

The invention discloses a two-stage algorithm selection and hyper-parameter joint optimization method, which comprises the following steps of: in the first stage, processing a training set and a test set through row sampling operation and column dimension reduction operation to form a reduced data set; randomly sampling a certain number of configurations in the hyper-parameter space of each candidate algorithm, evaluating the performance of each candidate algorithm by using the reduced data set, and extracting an optimal performance score; in the second stage, a previous algorithm is screened according to the optimal performance score to form a candidate set, and a pruned hyper-parameter search space is formed so as to reduce the calculation complexity of processor hyper-parameter search; and performing hyper-parameter optimization on the pruned hyper-parameter search space by using the original data set, and outputting an optimal algorithm adaptive to the target technical task and hyper-parameter configuration thereof. Algorithm screening and hyper-parameter tuning adaptive to a specific scene are realized through a two-stage optimization strategy, and meanwhile, the method is suitable for a traditional table type dichotomy task and aims at improving the deployment efficiency and performance of a machine learning model.

The invention belongs to the technical field of computer vision and three-dimensional reconstruction, and particularly relates to a building intelligent three-dimensional reconstruction method and system based on point cloud, and the method comprises the steps: obtaining the point cloud data of a building, and carrying out the preprocessing of the point cloud data; constructing an intelligent three-dimensional reconstruction model based on an intelligent algorithm selection mechanism, an adaptive surface reconstruction technology and an anti-mold-penetration processing algorithm; and three-dimensional reconstruction of the building is realized based on the preprocessed point cloud data and the intelligent three-dimensional reconstruction model. Through an intelligent algorithm selection mechanism, a self-adaptive surface reconstruction technology and an anti-mold-penetration processing algorithm, the technical problems of blind algorithm selection, unstable reconstruction quality, geometric mold penetration and the like in the prior art are solved, and high-quality and high-efficiency building three-dimensional reconstruction of point cloud data of various formats is realized.

The invention relates to the technical field of load segmented transfer, in particular to a 10kV distribution line load segmented transfer method and device based on an electric energy meter and a storage medium. Comprising the steps that S1, electrical parameters of key nodes of a 10kV distribution line are monitored in real time based on an intelligent electric energy meter, and the electrical parameters at least comprise voltage, current and a power factor; s2, mapping the monitored electrical parameters to a predefined fuzzy set by using a fuzzy logic algorithm, and reasoning and quantitatively evaluating the overload degree of the equipment based on a fuzzy rule; selecting a to-be-transferred load path based on a Dijkstra algorithm; and after it is confirmed that the load verification of the to-be-transferred path is passed, carrying out load transfer. According to the method, the electrical parameters on the key nodes are monitored in real time, overload evaluation is carried out in combination with the fuzzy logic algorithm and the optimized defuzzification algorithm, and the overload condition can be recognized more accurately even under the condition that current and power factors fluctuate sharply due to frequent starting and stopping of large industrial equipment.

The invention provides a zero-trust-based dynamic data secure transmission and processing channel construction device and method, and the method comprises the steps: S1, collecting to-be-transmitted data, and classifying the data to obtain a data classification result; s2, based on the data classification result, the user identity information and the environmental risk information, a dynamic security policy is generated, and the dynamic security policy comprises an encryption algorithm selection rule, a trust evaluation threshold and a data operation authority policy; according to the invention, the fine-grained security policy is generated and continuously adjusted by dynamically sensing the data sensitivity and the real-time trust state, so that the full-link self-adaptive security protection from transmission to processing is realized; according to the method, the security multi-party calculation is seamlessly integrated in a high-sensitivity scene, so that the end-to-end security and privacy protection level of data in a cross-domain and multi-participant environment are remarkably improved while the data mobility is guaranteed, and the problem that a traditional static security mechanism is difficult to adapt to dynamic risks is effectively solved.

The invention relates to the technical field of computer vision and deep learning, in particular to a signature and seal verification method and system based on target recognition. The method comprises the following steps: collecting signature and seal image data in an image file, and generating a structured data set containing category labels and position information; processing the structured data set; a convolutional neural network is adopted to construct a feature extraction network, local features of a target are captured through a convolutional layer, downsampling dimensionality reduction is realized through a pooling layer, and high-dimensional features are mapped through a full connection layer; a YOLO algorithm and an SSD algorithm are introduced as target identification core algorithms; setting unified training parameters and evaluation indexes, and training and testing the optimized YOLO algorithm and SSD algorithm; the system comprises a data set construction module, a data enhancement module, a feature extraction module, an algorithm selection and optimization module and an experiment verification module. The purpose of improving the accuracy and efficiency of signature and seal verification is achieved.

A method of structuring entity data using a machine learning document profiling model for improved information extraction comprises: training a learning document profiling model by applying document data and identification information on different document types to the learning document profiling model; profiling entity document data using the trained learning document profiling model; generating an entity profile using the trained learning document profiling model based on the profiled document data; selecting a subset of documents comprised in the profiled document data based on the entity profile; and tagging the selected subset of documents for further processing comprising one or more of a topic extraction process, a document processing algorithm selection process, a topic importance rating process, a knowledge graph mapping process, a document signature generation process, a document querying process, and a language derivation process.

The invention relates to the technical field of computers, and discloses a heterogeneous computing resource dynamic scheduling method and system and a computer readable storage medium, the method comprises the following steps: firstly, receiving a resource request of a task, the resource request comprising a resource demand and a user preference identifier; secondly, obtaining user preference information based on the user preference identifier, and obtaining real-time state information of all GPUs in the heterogeneous GPU resource pool based on resource requirements; then, based on the user preference information and the real-time state information of all GPUs, selecting an optimal GPU resource through a multi-stage filtering and scoring algorithm; and finally, based on the selected optimal GPU resource, dynamically establishing a connection and allocating the resource when the task is needed. Dynamic allocation and efficient scheduling of computing resources (especially GPUs) are achieved, the computing resources of different suppliers and models can be compatible, task requirements are accurately matched, and the resource utilization rate and the task execution efficiency are improved.

The invention relates to the technical field of power grid data encryption, in particular to a power grid data hybrid security encryption method based on artificial intelligence driving, and the method comprises the steps: generating a control flow label, a metering flow label and an encryption algorithm selection label according to a power grid data flow characteristic through a scene classification model; wherein the scene classification model is constructed based on a multi-output classification SVM framework; the power grid data traffic features are combined with an encryption algorithm to select labels to be input into a key strength decision model, key recommendation strength and traffic abnormal values are generated, and the key strength decision model is constructed based on a hybrid random forest framework; and based on the control flow label, the metering flow label, the encryption algorithm selection label and the key recommendation intensity, calculating the comprehensive prediction encryption overhead, and setting the self-adaptive hybrid security encryption algorithm of the communication. According to the method, the data security and the encryption overhead are balanced through the SVM-based artificial intelligence driving selection algorithm, and a reliable guarantee is provided for the power grid data security.

The invention relates to the technical field of post quantum cryptography, and discloses a multi-algorithm collaborative acceleration system for dynamically reconstructing post quantum cryptography. The system comprises a dynamic parameter library, a password graph indexer, a vectorization scheduling engine and a collaborative execution unit. And after dynamic parameter inventory, the quantum cryptography algorithm operates the parameter set in real time, and feedback update is performed according to the collaborative execution unit. A dynamic cryptographic algorithm index graph is constructed by a cryptographic graph indexer, nodes are algorithm instances, edges are in a cooperative relation, and edge weights are fed back and adjusted according to vectorization scheduling engine path weights. The vectorization scheduling engine carries out vectorization coding on a user password task request to generate a task semantic vector, calculates the similarity between the task semantic vector and an index map node embedding vector, and generates an algorithm selection instruction. And the collaborative execution unit receives the instruction, calls a corresponding algorithm instance for operation, and feeds back the parameter change to the dynamic parameter library. According to the system, dynamic optimization and efficient collaboration of the post-quantum cryptography algorithm are realized, and the adaptability to complex scenes is enhanced.

A design method of high-capacity hydrogen storage alloy, the specific method comprises the following steps: obtaining data, data preprocessing, data set division, establishing a prediction model, determining the optimal prediction model and alloy composition design. By comparing various machine learning algorithms, the optimal prediction model is selected and determined. Compared with the alloy composition design method of a single prediction model, the high-precision prediction of the hydrogen storage capacity of the hydrogen storage alloy can be further improved, and the efficiency is higher than that of the traditional empirical criterion method and the linear fitting method. By using Xgboost algorithm as the fitness function, the average and maximum fitness function values of the population are converged, and after the preparation of the alloy, the relative error between the actual hydrogen storage capacity and the predicted hydrogen storage capacity is as low as 0.54%, that is, the high-precision prediction is achieved.

The invention provides an AI-based multi-node rapid disaster recovery switching method and system, and the method comprises the steps: obtaining multi-dimensional monitoring indexes of the CPU usage rate, the memory occupancy rate, the network IO, the disk IO, the application response time and the error rate of each node, and constructing a time sequence feature matrix; extracting space correlation features among nodes by using a graph convolutional network, extracting time sequence features through a long-short-term memory network, constructing a deep space-time graph neural network to identify a fault precursor, and predicting a node fault 30-120 seconds in advance; generating a node health score based on a node fault prediction result and capacity estimation, executing a target node optimization algorithm, and selecting an optimal disaster recovery backup node; the method comprises the following steps: constructing a hot backup by adopting a CRI U-based lightweight process migration technology, and generating a check point and an incremental snapshot of a source container; and automatic disaster recovery switching is executed before the node fails, the service flow is redirected, and the service is ensured not to be interrupted. According to the method, the disaster recovery switching time can be shortened to a millisecond level, and the system availability is remarkably improved.

The invention relates to an edge computing application scheduling and issuing method and device based on storage resource awareness, and the method comprises the steps: obtaining the storage resource information of an edge node and the performance data of a heterogeneous storage device, and predicting the storage demand and IO bottleneck of the edge node based on a machine learning algorithm; based on a prediction result, adopting reinforcement learning and a multi-target genetic algorithm to select an optimal edge node; and executing application task scheduling by adopting the optimal edge node, and adjusting resource allocation and task migration according to task requirements and node loads. According to the method, by dynamically sensing the storage capacity and performance of the edge nodes and the characteristics of heterogeneous storage equipment, node selection is optimized in combination with reinforcement learning and a multi-target genetic algorithm, task delay is remarkably reduced, and the resource utilization rate is increased. A dynamic resource allocation and task migration mechanism ensures load balance and adapts to a complex network environment. The feedback optimization based on the execution effect further improves the scheduling accuracy and the system robustness, and has the remarkable advantages of high efficiency and intellectualization.

Systems and processes are disclosed for a multi-level quantum-based vertically classified entropy exploratory analytics tool designed to improve speed, accuracy, and scalability in anomaly detection and data analysis. The tool employs a dynamic algorithm selector for adaptive algorithm choice, a quantum encoder for precise data encoding, and a multi-level splitter and aggregator for efficient data segmentation and result integration. It includes a classification executor for accurate decision-making, an exploratory data analyzer for uncovering hidden patterns, and a multi-dimensional data processor for handling complex data sets. A qubit selector optimizes quantum resource allocation. The tool combines classical and quantum computing methods, enhancing robustness and versatility. This system significantly reduces false positive rates and improves processing efficiency, addressing the limitations of classical methods in handling large-scale, multi-dimensional data sets. The invention is particularly valuable for applications requiring rapid and precise data analysis, such as finance, cybersecurity, and scientific research.

The invention discloses a medical data risk early warning system and method based on AI and big data, and belongs to the technical field of medical information processing of the big data technology, and the system comprises an intelligent denoising processing unit which is used for obtaining historical medical data and analyzing the noise features of the historical medical data to generate denoised data; the data structure analysis unit is used for analyzing the denoised data to extract data structure features; the self-adaptive algorithm selection unit is used for selecting an early warning algorithm combination and configuring algorithm parameters according to the data structure characteristics so as to generate an algorithm selection result; the multi-algorithm fusion detection unit is used for processing the de-noised data by adopting an algorithm selection result so as to identify abnormity and generate an abnormity detection signal; and the early warning result explanation unit is used for carrying out judgment standard tracking on the abnormal detection signal to determine a judgment basis and generating early warning explanation information in combination with the judgment basis, and the stability and robustness of the whole early warning system are enhanced.

The embodiment of the invention discloses a wearing detection method and device, wearable equipment and a computer readable storage medium, and relates to the technical field of wearing detection. The method comprises the following steps: scanning a currently available sensor of the wearable device, and obtaining configuration information of the sensor; inputting the configuration information of the sensor into a pre-trained algorithm selection model to obtain a target algorithm output by the algorithm selection model; the accuracy rate and the power consumption of the wearing detection performed by the target algorithm based on part or all of the data collected by the sensors meet preset conditions; determining a target sensor corresponding to the target algorithm, and obtaining target data collected by the target sensor; and processing the target data based on a target algorithm to obtain a wearing detection result of the wearable device. Therefore, according to the scheme, accurate and low-power-consumption wearing detection can be carried out on various types of wearing equipment at low research and development cost.

The invention relates to the technical field of underwater acoustic communication, in particular to an underwater acoustic communication system and method based on environmental parameter sensing and adaptive matching, and the system comprises an environmental parameter sensing module, a local environmental resource library, a parameter matching and algorithm selection module, an adaptive algorithm optimizer, a communication processing module, and a transmit-receive transducer. Key environment parameters such as submarine topography and temperature-salinity-depth are obtained in real time, a preset optimal algorithm is called through parameter matching or an algorithm adaptive to a new environment is generated in real time, and the defects that the performance of a fixed algorithm is suddenly reduced when the environment changes and traditional adaptive adjustment response is lagged are fundamentally overcome. The reliability, robustness and intelligent level of communication in a complex underwater environment are improved, and high requirements of scenes such as ocean detection and underwater robot communication on communication performance are met.