460 results about "Adaptive method" patented technology

The invention discloses a self-adaptive NL2SQL method and system based on a large language model and retrieval enhancement. The method comprises four key steps of mode linking, example enhancement and SQL generation, multi-path thinking chain fusion reasoning and multi-round self-correction. The system comprises the following modules: a knowledge base management module, an input analysis and preprocessing module, a mode link module, an example enhancement module, an SQL (Structured Query Language) generation module, an SQL fusion and optimization module, an SQL execution and feedback module, a multi-round self-correction module, a Prompt construction and context management module, a system interface module and a content generation module. The method has the advantages that multiple large models are supported, a data source is quickly accessed, a user-defined prompt structure is configured, good engineering maintainability and scene adaptability are achieved, and SQL generation accuracy, performability and universality are improved.

The invention discloses an offshore wind turbine foundation scouring full-period intelligent monitoring method and system based on multi-source data fusion and a dynamic coupling model, and relates to the technical field of intelligent monitoring, and the method comprises the steps: deploying a multi-source monitoring module, and constructing a finite element model; carrying out load calculation and parameter inversion; and training full-cycle dynamic updating of the washout failure function model. According to the method, a self-adaptive Kriging-Bayesian method is adopted, a Bayesian inversion framework and a self-adaptive agent model are fused to solve optimal soil body parameters, full-period model dynamic updating based on dynamic monitoring data is achieved, a multi-fidelity deep kernel learning model is adopted, three types of data are fused into a training set, full-period intelligent monitoring of offshore wind turbine foundation scouring is achieved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The dynamic identification of soil parameters is realized by combining a self-adaptive inversion framework with a displacement error closed-loop optimization mechanism, and the technical problem that a traditional static model cannot adapt to the spatial-temporal variability of seabed geology is solved.

The invention provides a power distribution network single-high-frequency resonance rapid tracking and adaptive suppression method, a multi-mode dynamic cooperative adaptive suppression system is established based on a PCCVF adaptive method, and the damping characteristics of a power distribution network are remodeled by injecting compensation current into a power distribution network system so as to realize broadband resonance suppression of the power distribution network. The compensation current injection method comprises the following steps: step 1, detecting resonant frequency deviation of a power distribution network through real-time FFT (Fast Fourier Transform); 2, performing dynamic phase angle correction based on a phase prediction residual error of real-time frequency deviation; step 3, constructing a layered impedance remodeling module, and ensuring stable power transmission; 4, establishing a frequency-variable impedance model of the power distribution network based on resonance energy spectral density analysis, dynamically optimizing parameters through fuzzy logic of a bell-shaped membership function, and feeding harmonic compensation current into the power distribution network by using a space vector pulse width modulation technology; according to the invention, single high-frequency resonance can be effectively suppressed, the response and tracking performance of the system is improved, and the spectrum analysis efficiency and bandwidth occupation are optimized.

The invention provides a hierarchical ethical adaptive method and system based on a development stage, and the method comprises the steps: collecting the multi-modal behavior data of a user, and recognizing the cognitive development stage of the user; constructing an ethical rule knowledge graph with a hyponym inheritance and context activation mechanism; fusing user acceptability and ethical conflict strength, and performing strategy balance based on a game model; calling the generative language model and the hierarchical template library to generate matched ethical feedback content; different culture expression styles are adapted through a culture migration network; and a user stage transition window is predicted based on the cognitive evolution trend, and an intervention mechanism and strategy adjustment are triggered. The system supports personalized ethical guidance strategy configuration, is compatible with various user types and cross-culture situations, realizes adaptive optimization and expression intellectualization of ethical decision, and improves user understanding degree, acceptability and ethical guidance effect. The method is suitable for multiple scenes such as education guidance, value intervention and man-machine ethical interaction.

The invention relates to the technical field of wireless communication, and discloses a wireless communication adaptive method and system based on a data transmission state, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the multi-source data, obtaining a CSI compression matrix, predicting the channel coherence time, dynamically adjusting the CSI sampling interval, and defining a load-channel coupling factor; obtaining cross-layer data based on the CSI compression matrix, performing fusion through a rotation matrix to obtain a fusion matrix, and extracting a physical layer fusion feature and an application layer fusion feature to calculate a multi-target state score; establishing a 5G power compensation mechanism based on cross-layer data, calculating a four-dimensional influence tensor, performing tensor decomposition and optimal action selection, and decomposing T into a core tensor and a factor matrix; selecting an optimal parameter combination through modular product calculation; based on cross-layer data, a quantum entanglement feedback mechanism is introduced, data is fed back, entanglement state association cross-layer indexes are designed, a quantum gate is adjusted through entanglement state design, and a model is updated in real time in combination with incremental learning.

The invention discloses a passive domain self-adaptive encrypted traffic detection method based on self-training Mama. The method comprises the following steps: detecting encrypted traffic by adopting a detection model of the encrypted traffic; the domain adaptation of the detection model comprises offline adaptation and online adaptation. The off-line adaptive method is used for carrying out robust self-adaption on encrypted traffic offset in a static traffic data set under a passive domain and semi-supervised domain adaptive SF-SSDA normal form. The method comprises the following steps of: 1) pre-training a source domain based on a mask auto-encoder MAE; and 2) target domain adaptation training. According to the online adaptation method, on the basis of offline adaptation, an offline batch screening mechanism is replaced with an online cache queue, and online adaptation of the flow type threat flow is achieved. Experimental results on a real world and a public reference data set show that the method realizes faster reasoning with fewer parameters, and is significantly superior to a representative passive baseline method in the aspect of cross-domain detection accuracy.

The application discloses a short video code rate self-adaptive method based on meta learning, relates to the technical field of streaming media, and comprises the following steps: S1, offline training, a model is established to represent user characteristics and network prediction information; S2, online learning, according to the characteristics of the current user environment, the model parameters are adjusted and optimized. The short video code rate self-adaptive method based on meta learning is adopted, a new SABR framework based on meta learning is successfully realized, the framework can quickly adapt to different user demands, the practicability and the calculation speed of the system are improved, and the framework has industrial application; the offline training and the online learning technology are successfully combined, the generalization and the stability of the model are enhanced; the idea of action masking is introduced in pre-training, the rationality and the reliability of decision are enhanced, the data amount required by meta learning is effectively reduced, the learning efficiency and the accuracy are improved, and the data demand and the training time in the industrial environment are significantly reduced.

The invention provides a low-rank personalized blood pressure estimation method, which comprises the following steps of: performing pre-training on a large-scale PPG-blood pressure data pair on a group level based on a UniTS model of a Transform backbone network, and learning a mapping relation from a PPG signal to blood pressure; carrying out personalized fine tuning on the pre-trained model by adopting a low-rank adaptive technology to realize model adaptation under the condition of few samples; in the personalized fine tuning process, a pulse pressure segmented penalty loss function is introduced, total training loss is formed by combining mean square error loss, and prediction results of the systolic pressure and the diastolic pressure are restrained to conform to the physiological law; the problem of sampling rate difference is solved by adopting a low-rank self-adaptive method with a stable sampling rate, and stable blood pressure estimation across equipment is realized; the invention aims to realize high-precision and cross-device robust blood pressure estimation according with physiological rules under the condition of few-sample calibration by introducing a physiological constraint loss function and sampling rate robust adaptation mechanism only depending on PPG signals and through a framework combining group pre-training and low-rank personalized fine tuning.

A domain adaptation procedure, such as fine-tuning training, is required to utilize a large-capacity PLM for a specific domain. Attempts in existing research have been made to improve performance of a PLM through domain adaptor technology based on an N-gram in order to reduce errors on the basis of the results of domain text error analysis of the PLM. Proposed is a method of selecting a semantic chunk through a domain semantic chunk graph and PageRank based on the existing domain adaptor research, with an N-gram as the semantic chunk. Proposed is also a method of domain-adapting a large-capacity PLM using semantic chunk dynamic weight masking, which reflects an output value of a PLM rather than simply integrating embedding values of semantic chunks, in a semantic chunk domain adaptor technology.

The invention belongs to the field of computer networks, data compression and mobile internet and wireless communication, and discloses a TCP (Transmission Control Protocol) data packet compression and aggregation self-adaption method, which comprises the following steps of: 1, receiving a TCP data packet and separating a header from a payload; step 2, storing the effective load part; step 3, compressing the head and updating context data according to the serial number, the confirmation number, the mark and the window size field of the head; step 4, combining the compressed header with the effective load to obtain a compressed message; 5, extracting the compressed messages according to a set aggregation threshold value, aggregating the compressed messages, and sending the aggregated compressed messages to a data link layer; and step 6, storing the operation records, comparing the operation records with the previous N operation records, and changing a set aggregation threshold according to a comparison result. According to the invention, the message can be efficiently processed under the condition of a complex network, so that the overall network throughput and the message processing efficiency are improved.

The invention provides a fast charger based on deep learning and a fast charging multi-protocol adaptive method thereof, and the fast charger comprises a signal sensing module which is used for carrying out the multi-mode sensing of an original signal of an access device, and obtaining a sensing vector; the perception decision module is used for inputting the perception vector into a lightweight deep learning model and outputting a perception decision, and the perception decision comprises protocol probability distribution, recommended power gears and safety suggestions; and the protocol matching module is used for carrying out self-adaptive matching of the fast charging protocol according to the perception decision. According to the fast charger based on deep learning and the fast charging multi-protocol adaptive method thereof, the lightweight deep learning model is adopted to adaptively output the protocol probability distribution, the method does not depend on a preset rule base and a sequence attempt mechanism, the protocol matching efficiency is higher, and in addition, the fast charging efficiency is improved. Decision factors such as recommended power gears and safety suggestions are introduced to form constraint conditions, the charging speed and the battery safety are balanced, and the suitability is better.

The invention relates to the technical field of cable manufacturing, in particular to a tension fluctuation suppression and pitch self-adaption method and system in the stranding process of multiple strands of wire harnesses. According to the method, real-time tension signals and equipment state parameters of each strand are obtained, tension fluctuation feature vectors are obtained through frequency domain decomposition, fluctuation source types and position mapping relations thereof are recognized accordingly, and a tension disturbance distribution diagram is generated. And generating a tension compensation instruction for each pay-off mechanism and a pitch adjustment instruction for the twisting main shaft based on the distribution diagram and the target equilibrium constraint, and performing adjustment. Feedback data is collected and used for updating processing parameters and optimizing an instruction generation strategy; according to the invention, accurate suppression of tension fluctuation and self-adaptive adjustment of the stranding pitch are realized, and the uniformity and stability of wire harness stranding are improved.

The invention discloses an interpretable differential privacy parameter adaptive method for a generative medical record, and relates to the technical field of medical data privacy protection and artificial intelligence. The method comprises the following steps: acquiring an original medical record data set, and identifying a field containing sensitive information; constructing a multi-dimensional evaluation model, and respectively calculating the sensitivity score of each field and the utility score of the downstream task; dynamically allocating differential privacy budget parameters for each field by using a constraint optimization algorithm based on a game relationship between sensitivity and utility; in the training process of the generative model, corresponding noise is injected into a gradient or an input layer according to the distributed parameters; and finally, generating a synthetic medical record and outputting an interpretability report of privacy parameter distribution. By automatically adjusting the DP parameters, the data features of the low-sensitivity and high-value fields are reserved while the high-sensitivity fields are ensured to obtain strong privacy protection, the problem that privacy protection and data availability are difficult to consider in medical data sharing is effectively solved, and a transparent auditing basis is provided.

The invention relates to the technical field of robot positioning and map construction, and particularly discloses a self-adaptive LiDAR-IMU SLAM method fusing intensity features and Riemannian manifold ground constraints, and the LiDAR-IMU SLAM method comprises the following steps: a, original measurement; b, data preprocessing; c, feature extraction; d, carrying out ground manifold constraint; e, optimizing the factor graph; and f, outputting data. The method can solve the problems that in the prior art, matching fails in a low-texture environment due to dependence on geometric features, positioning drifting is caused by point cloud distortion and inertial accumulative errors, and Z-axis errors are continuously expanded due to lack of dynamic adaptation to complex terrains. Experimental results show that according to the self-adaptive LiDAR-IMU SLAM method, the Z-axis drift error is remarkably reduced by 67.78%, the minimum absolute pose error is 0.254 m, the environment sensing and mapping capacity of the mobile robot in the complex underground space is remarkably improved, and reliable technical support is provided for intelligent inspection and infrastructure monitoring of the underground space.

The invention discloses a personalized MCI electrical stimulation intervention method based on a dynamic brain network, and belongs to the technical field of electrical stimulation target determination. Individual heterogeneity of a brain function network of a patient is considered, specific frequency band selection is carried out on electroencephalogram data sets of a normal group and a cognitive impairment group, an effective data set is constructed, and the effective data set is determined. The method comprises the following steps: respectively constructing average dynamic brain network connection of total test times of a normal group and a cognitive impairment group based on an adaptive method of dynamic time-varying weight optimization of reaction time, and then determining a main abnormal frequency band through the difference of the average dynamic brain network connection of the two groups; and then target points are determined for the core nodes connected with the average dynamic brain network of the main abnormal frequency band, and compared with traditional single-frequency-band analysis, the application proposes that the effective data set is constructed by the specific frequency band to determine the main abnormal frequency band; in addition, stimulation targets are positioned according to the core nodes connected with the average dynamic brain network of the abnormal frequency band, and the reliability of target selection is improved.

The invention discloses a phase sequence self-adaption method and device for a three-phase grid-connected converter, equipment and a medium, and relates to the technical field of three-phase electricity. The phase sequence adaptive method of the three-phase grid-connected converter is applied to a three-phase grid-connected converter system. The method comprises the following steps: acquiring a phase angle of a voltage of a first detection point and a voltage of a second detection point; and determining a correct phase sequence of the voltage of the second detection point according to the voltage of the second detection point and the phase angle of the voltage of the first detection point. The initial phase sequence of the input voltage signal can be rapidly and accurately detected, the operation logic is simple, the operand is small, and implementation is easy.

A method and a system for performing domain adaptations of social media text by using lexical data transformations are provided. The method includes: receiving a first data set that is usable for training a machine learning (ML) model that is designed to perform natural language processing tasks; training the ML model by using the first data set; receiving a second data set that relates to a social media platform; transforming a subset of the first data set into a third data set that is suitable for the social media platform; and retraining the ML model by using a combination of the first data set, the second data set, and the third data set. The transformations may include injecting emojis, emoticons, user mention indicators, hashtags, retransmission indicators, URLs, and / or inverse lexical normalizations that are often used in social media posts.

The invention provides an autonomous controllable database intelligent adaptation method and system oriented to localization ecology, and belongs to the technical field of database middleware and cloud native resource management. When the request label is missing or wrong, JDBC metadata, a system function return value and system table structure features are collected through detection connection to generate database fingerprints, and the database type and version are recognized in the localized feature library. For an SQL execution request, parsing the SQL into an abstract syntax tree based on a unified syntax parser, rewriting nodes according to a dialect rule base to generate a target dialect SQL, and performing paging, identification column generation, identifier reference, transaction-related semantic verification, execution plan optimization and dynamic routing execution; and thermally loading from the plug-in warehouse when the driver is missing. And for the resource opening request, calling a Kubernetes Operator to automatically create a domestic database instance, and returning connection information. The system integrates a national cryptographic algorithm and behavior auditing, and realizes connection information protection and abnormal SQL detection.

The invention relates to the technical field of moisture prediction, in particular to a 3D reconstruction and illumination adaptive coal surface moisture prediction method, which comprises the following steps of: planning an unmanned aerial vehicle route to collect an image, reconstructing coal pile surface space structure information, forming a local triangular grid, dividing an illumination continuous area, establishing a space corresponding relation between the illumination area and a hyperspectral pixel, and predicting the moisture content of a coal pile. And geometric registration and reflection transition correction are executed, depression points and shoulder positions of a reflection curve are detected in the identification interval, moisture absorption characteristics are constructed, a moisture migration link is established in the slope direction, and a coal surface moisture prediction result is obtained. According to the method, a spatial structure and illumination distribution are coupled, a dynamic correction relation is established to unify reflection information, image registration and spectrum reconstruction are combined to strengthen geometric consistency, a moisture migration trend is extracted by utilizing depression point and slope correlation, link and accumulation distribution is formed, and a corrected reflection characteristic represents a spectrum attenuation rule. The spatial continuity and numerical stability of moisture identification are ensured, and the water content grade division is coherent and comparable.

The invention discloses a multi-label field adaptive method based on prompt driving, which belongs to the technical field of computer vision and comprises the following steps: generating multi-dimensional semantic text description containing visual attributes, semantic levels and category names of common scenes for each type; embedding the semantic text description into an optimizable category vector, and embedding the optimized category vector into a CLIP text prompt; extracting and projecting multi-layer style statistical characteristics of the image, and synchronously realizing image-text cross-modal alignment and source-target domain distribution alignment in a frame through a cross-domain style mapping network and various alignment losses; semantic priori knowledge of CLIP is combined with a label co-occurrence mode of a source domain, so that the model can sense and adapt to a possibly changed label dependency relationship in a target domain; performing semantic propagation on label embedding by using a graph convolutional network; the whole system is jointly optimized through a multi-task loss function, and end-to-end cross-domain multi-label classification is achieved.

The invention relates to the technical field of data processing and artificial intelligence application, and discloses a data quality rule automatic generation and self-adaption method, system and device and a medium. Performing business term inference and semantic modeling on the structured metadata to construct business concepts and relationships; analyzing the processing logic of the structured metadata based on the business concept and the relationship to identify a data consanguinity map; building a power grid domain knowledge graph based on a business concept, a semantic model and a consanguinity graph mining hidden relationship; reasoning based on the power grid domain knowledge graph and historical data to generate data quality rules and business constraints; monitoring the execution effect of the data quality rule, and performing adaptive optimization through quantitative evaluation; and executing the self-adaptively optimized rule, marking abnormal data and feeding back an execution result. According to the method, the automation and intelligence level of quality treatment can be improved, and continuous evolution and vitality of a data quality system are ensured.

The invention relates to the technical field of image reconstruction, in particular to a compressed sensing driven terahertz tomography reconstruction method based on an adaptive strategy. According to the technical scheme, the method comprises the following steps that a terahertz continuous wave imaging system is used for collecting intensity data P of a corresponding view angle at equal-interval sparse angles, and a sinogram of a sparse sampling view angle is obtained; performing data preprocessing on the intensity data to unify the data scale; relevant parameters of the algorithm are initialized, wherein the relevant parameters comprise step length and regularization parameters; through a core parameter dynamic adjustment mechanism, cooperative improvement of imaging quality, reconstruction efficiency and environmental adaptability is successfully realized, clearer and more accurate terahertz tomographic images can be obtained, reconstruction can be completed at a faster calculation speed, and the method is suitable for wider practical application scenes and has good application prospects. And the comprehensive performance is obviously superior to that of a traditional non-adaptive method.

The invention provides a robot automatic operation self-adaption method and system for a dynamic working condition, and the method comprises the steps: anchoring a working condition evolution track, and fusing a historical working condition evolution rule with a current real-time working condition flow, so as to represent a working condition dynamic change trend; secondly, generating a work behavior gene segment, and extracting and combining work behavior core elements matched with working conditions; and then driving the operation behaviors to self-assemble, and forming a dynamic operation behavior sequence in combination with the real-time change of working conditions. In the execution process, behavior and working condition covariant signals are captured, and covariant information of operation behaviors and working conditions is captured. And finally, iteratively optimizing a baseline and a behavior scheme, reversely injecting a covariant signal into a working condition evolution trajectory generation logic, optimizing an operation behavior gene segment evolution rule, and outputting a target operation behavior scheme adaptive to the current dynamic working condition. According to the method, the operation self-adaptive capability of the robot under the dynamic working condition is remarkably improved.

The invention relates to the technical field of sewage treatment, discloses a self-adaptive method and system for operating parameters of an assembled aerobic granular sludge reactor, and aims to solve the problems of unstable treatment efficiency, high energy consumption and substandard effluent due to the fact that existing operation management depends on presetting or empirical adjustment. The method comprises the steps of collecting and preprocessing multi-dimensional operation data in real time to construct a multi-modal feature set; predicting future key performance indexes through a deep learning model; determining optimal operation parameters (a total operation period, a sludge backflow proportion, aeration intensity and sludge-water mixing time) by utilizing a multi-objective optimization algorithm, and executing an instruction; and feeding back monitoring data to iteratively update the model. The system comprises a data acquisition module, a preprocessing module, a feature engineering module, a deep learning prediction module, a multi-objective optimization module, an instruction generation module and a feedback learning module. By adopting the technical scheme, the intelligent and automatic level of the reactor can be remarkably improved, the effluent is ensured to reach the standard, the energy consumption and the carbon source consumption are reduced, and the reactor has long-term stable operation and environment self-adaptive capability.

This disclosure is directed to a method comprising: initiating a first application, a second application, and a third application, which are associated with a secure computing network; routing a first computing input, a second computing input, and third computing input, respectively, to the first application, the second application and the third application; executing, using the first application and based on the first computing input, a first computing operation and thereby generating a first local computing result; executing, using the second application, based on the second computing input, a second computing operation and thereby generating a second local computing result; and executing, using the third application, based on the third computing input, a third computing operation and thereby generating a third local computing result. The method also includes leveraging the first local computing result, the second local computing result, and third local computing result to generate network resolution data.

The invention provides a construction site safety management and control method and system based on enhanced retrieval generation, and belongs to the technical field of data processing. Comprising the following steps: constructing a construction safety knowledge base, and vectorizing text data in the knowledge base; field self-adaptive fine tuning is carried out on the visual language large model through a low-rank self-adaptive method, and perception, cognition and decision-making processes of construction site safety management and control are executed: in a perception stage, construction safety hidden dangers are dynamically identified and positioned, and violation descriptions containing hidden danger types are generated; in the cognition stage, an enhanced retrieval generation process is started, and construction specification information associated with hidden dangers is obtained through multi-level semantic matching and retrieval; in a decision-making stage, based on violation description and construction specification information, a visual language large model is driven to automatically generate a structured security log through a preset structured cue word template. According to the invention, unsafe behaviors of the construction site can be timely and accurately warned, and effective data support is provided for the safety of the construction site.

The invention provides a commodity search recommendation method and device, equipment, a medium and a product, and relates to the technical field of commodity recommendation, and the method comprises the steps: obtaining an attribute text description of a target commodity in a target business scene, mapping the attribute text description into a cue word, and obtaining an attribute grading tag of the target commodity according to the cue word; obtaining corresponding embedded vectors through a low-rank adaptive method, and performing cascade splicing on the embedded vectors to obtain a first embedded vector; performing dimension reduction processing on the first embedded vector to obtain a dimension-reduced first embedded vector, and obtaining target input according to the dimension-reduced first embedded vector; obtaining user information, and predicting an interest tendency score of the user for the target commodity according to the target input and the user information; and performing commodity search recommendation according to the interest tendency score of the target commodity. Through deep matching of user information and commodity attributes and dimension reduction mapping of embedded vectors, consumption of computing resources is reduced, and accuracy of commodity recommendation prediction is improved.