308 results about "Data heterogeneity" patented technology

The invention discloses an intelligent garden management method and system based on big data, and belongs to the technical field of garden management. Microclimate region division is carried out on a garden through a clustering algorithm, and the region representative weight of each sensor is calculated in combination with a data heterogeneity response factor and a microtopography interference factor; weighted fusion of sensor data is realized, and a more representative environment estimation value is generated; according to the method, prediction and anomaly detection are carried out on a plant growth environment trend based on time sequence analysis, a garden regulation and control instruction is automatically generated, actual plant state feedback is introduced to realize dynamic optimization and adjustment of a weight, and the method effectively improves the accuracy of environment perception and the intelligent level of decision response, and improves the user experience. And resource misuse and plant health risks caused by information distortion are obviously reduced.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention discloses a personalized federated learning method based on knowledge fusion distillation and a storage medium, and belongs to the technical field of personalized federated learning, and the method comprises the steps: a server carries out the pre-training and distributes a diffusion model to each client, and generates a local synthesis data set, initializing a global model as a student model of a client to perform knowledge fusion distillation training, and guiding the student model training by the teacher model optimized in the last round; and after training is completed, the client uploads student model parameters to the server for federal aggregation to update the global model, and meanwhile, the personalized feature extraction capability and reliability of the next round of teacher model are enhanced by using synthetic data, so that a closed-loop learning framework of global cooperation and local personalized collaborative optimization is formed. According to the method, the problems of client model drifting, performance attenuation and convergence rate slowing caused by data heterogeneity can be solved.

The invention discloses a truss structure wind-induced dynamic response prediction method and system based on physical enhancement. The method comprises the following steps: carrying out feature extraction and alignment fusion on input data containing condition parameters and wind speed time sequence data by utilizing a long short-term memory network and a physical enhancement attention mechanism; extracting multi-scale features from the fusion features through expansion convolution, and performing weighted aggregation on the multi-scale features; the physical priori knowledge of structural vibration is fused into position coding and a self-attention mechanism so as to carry out response prediction; and integrating physical model information of the truss structure and a dynamic control equation into a loss function, and calculating physical information residual loss so as to improve the physical interpretability of a prediction result. According to the method, data heterogeneity can be eliminated, complementary information can be fused, the multi-scale characteristic of wind-induced response is coped with, the accuracy and efficiency of wind-induced dynamic response prediction of the truss structure are effectively improved, and the physical interpretability and generalization ability are enhanced.

The invention discloses a federal large model knowledge collaborative training method supporting multi-modal heterogeneous clients, which comprises the following steps: each client receives a model initialization parameter issued by a central server, and applies adaptive differential privacy noise to independently train a heterogeneous lightweight model based on local private data; updating the model to which the noise is applied and uploading a modal identifier of the model to a central server side; after model updating and modal identification of each client are received, based on a modal perception weighted consensus fusion mechanism, knowledge of each client is fused to update a global large model; and the central server side issues the updated presentation layer parameters of the global large model to the client side for initialization of the next round of local training. According to the method, on the premise that a public data set or specific task setting is not needed, comprehensive compatibility of data isomerism, client dynamic participation, model diversity and privacy protection requirements is achieved, and the adaptability, stability and knowledge utilization efficiency of large model federation training are remarkably improved.

The invention belongs to the technical field of asynchronous federated learning, and discloses a data isomerism-oriented knowledge alignment asynchronous federated learning method. According to the method, a data quality perception aggregation strategy is introduced, and a knowledge distillation mechanism based on the old degree is combined, so that a global model is subjected to balanced training on heterogeneous data of different devices, and the generalization ability of the model is improved. Meanwhile, a self-adaptive learning rate adjustment mechanism based on aggregation frequency and weight is designed, and it is ensured that contribution of different devices to the global model is more fair. According to the method, the training deviation in asynchronous federated learning is effectively relieved, the accuracy and stability of a global model are improved, and the method has a considerable application value for a real federated environment.

The invention provides a geological reservoir fracture segmentation method fusing a 3DU-Net graph attention mechanism and graph theory connectivity detection and a system thereof, and aims at solving the technical problems of multi-modal data registration, 3D U-Net graph attention feature extraction, graph theory connectivity detection and the like. The problem of insufficient geological reservoir fracture segmentation precision caused by data heterogeneity and connectivity abnormity is effectively solved, high-precision segmentation of a complex geological reservoir fracture network is realized through accurate alignment of multi-modal data and a graph attention feature extraction and graph theory connectivity detection technology based on 3D U-Net, and the method has the advantages of high precision and high efficiency. The problems of local misjudgment and abnormal connectivity caused by the fact that a traditional method only depends on amplitude or texture features are effectively solved, and therefore segmentation precision and robustness are remarkably improved.

The invention discloses a geotechnical engineering investigation and analysis method and system based on multi-source data fusion, and belongs to the technical field of geological prospecting, and the method comprises the steps: collecting multi-source heterogeneous data of drilling holes in a karst area, and generating a comprehensive feature vector of the drilling holes; according to the drilling comprehensive feature vector, establishing a graph attention network, and generating an update feature; according to the updated features, generating an exploration prediction result of geotechnical engineering; establishing a dynamic adjustment model, and generating a model weight set value of the graph attention network; adjusting the model weight of the graph attention network according to the model weight set value of the graph attention network; according to the method, the multi-source heterogeneous data of the drill holes are converted into the comprehensive feature vectors of the drill holes, so that the problems of data isomerism and information islands are solved; in combination with a dynamic graph attention network, the model weight is optimized in real time, and the accuracy and adaptivity of karst cave distribution prediction under the complex karst geological condition are remarkably improved; and meanwhile, continuous iterative optimization can be carried out on the model, and the geotechnical engineering investigation accuracy of the karst area is improved.

The invention relates to a medical health big data exploration system, and the system comprises a demand understanding module which is used for converting a data exploration demand into a structured exploration task; the compliance review module is used for performing compliance review on the probing task; the code generation module is used for generating a query code according to the probing task and the target database characteristics; the query execution module is used for querying query result data corresponding to the query code from a target database according to the query code; the query result analysis module is used for performing multi-dimensional analysis on the query result data to obtain a multi-dimensional analysis result; a report generation module; and the result presentation module is used for presenting the exploration report in an interactive mode in a user interface and supporting iterative improvement of the report by providing a feedback mechanism, so that the problems of data isomerism, difficulty in quality evaluation, low exploration efficiency, high professional threshold, insufficient privacy protection and the like in the existing medical data exploration process are solved.

A federated byte latent transformer platform utilizing homomorphically-compressed and encrypted byte-level data. The system integrates dynamic entropy-based patching into federated learning to enable efficient, robust, privacy-preserving collaborative learning across distributed nodes. Client devices convert local data into dynamically sized patches based on entropy thresholds, encrypt these patches, and send them to a central server that processes them without decryption. The system offers improved robustness to input noise, enhanced character-level understanding, and better adaptation to low-resource languages compared to token-based approaches. It enables simultaneous scaling of both patch size and model size while maintaining fixed inference budgets, allowing efficient deployment on resource-constrained devices. These innovations address critical challenges in federated learning: efficiency, robustness to data heterogeneity, and privacy preservation.

The invention belongs to the technical field of electric power automation, and particularly relates to an automatic modeling method of power grid dispatching knowledge based on a large model and a related system.The electric power knowledge is decomposed into an explainable intermediate reasoning path through the thinking chain technology, and adaptive small models are selected according to the path to form a modeling link; a modeling link is represented as a triple form, edges and nodes are complemented, a complete knowledge graph is obtained, problems in the knowledge graph are further decomposed to construct a reasoning link, meanwhile, input data are monitored in real time, newly added entities are associated with the reasoning link, and unified representation and processing of heterogeneous power system data are achieved. Due to the fact that data from different sources are different in format, precision and semantics, according to the method, through thinking chain decomposition and small model link processing, sub-module optimization and dynamic adaptation aiming at different data types are achieved, and precision loss and adaptation difficulty caused by data heterogeneity are effectively solved.

The invention provides a federated incremental learning method and system based on prompts, and relates to the field of federated incremental learning, and for each incremental learning task in a flow task sequence, the specific steps of performing federated incremental learning are as follows: a global server distributes a latest global model to a plurality of selected clients; based on a local private training sample and a prompt pool, the client optimizes a prompt vector in a local training process by adopting a dynamic prompt selection mechanism based on a key value pair, and guides a model to output a feature with higher discrimination degree; the client uploads the optimized prompt pool and model parameters to a global server; the global server globally fuses the prompt pool and the model parameters, and the fused prompt pool and model parameters form a new global model; according to the method, the problem of data isomerism caused by continuous arrival of new category data and dynamic addition of a new client in the federal increment problem is effectively solved, and the problem of disastrous forgetting of old knowledge is solved.

The invention discloses a federated learning training method and system supporting heterogeneous data, belongs to the technical field of distributed machine learning, and is used for solving the technical problem that an existing federated learning technology cannot solve three major problems of data privacy, data heterogeneity and hierarchical communication bottleneck at the same time and lacks an integrated federated learning training framework. The method comprises the following steps that: a working node acquires a local model update quantity and performs quantitative compression based on local data, a historical update record and a latest global model to obtain a compressed update package and uploads the compressed update package to an affiliated edge node; the edge node aggregates the compressed update packet sent by each working node to obtain a regional aggregation update packet; carrying out second quantization compression on the regional aggregation update packet to obtain a final uploading packet, and uploading the final uploading packet to a central server to which the final uploading packet belongs; the central server globally aggregates the final upload packet sent by each edge node to obtain a global model update packet; and optimizing the global model according to the global model updating package to obtain an optimized model.

The invention relates to the technical field of power system frequency modulation control, in particular to a thermal power generating unit collaborative frequency modulation method and system based on block chain federated learning. According to the method, a decentralized P2P network is constructed through a block chain technology, and model parameters are initialized based on a secret sharing mechanism; carrying out local training by adopting an adaptive federal near-end optimization algorithm, measuring data isomerism through KL divergence and dynamically adjusting a regular coefficient; carrying out noise addition on a sample by adopting differential privacy, and carrying out homomorphic encryption to realize parameter encrypted transmission; selecting representative nodes based on a block chain consensus mechanism to execute parameter aggregation in the encryption domain; and multi-unit collaborative frequency modulation is realized through model prediction control. According to the method, the problems of data privacy protection, heterogeneous data adaptation and decentralized cooperative training are solved, and the stability of power grid frequency regulation and control is improved.

The invention discloses a temperature self-adaptive adjustment control method and system for an automobile sensor, and relates to the technical field of automobile sensors, and the temperature self-adaptive adjustment control method for the automobile sensor comprises the following steps: S1, obtaining data; s2, performing classification and label addition on the data to form a data set; s3, extracting core temperature characteristic parameters, and constructing a four-dimensional temperature mapping matrix; s4, introducing an ant colony algorithm to iteratively optimize a temperature dynamic weighting rule, and obtaining an adjustment target and a change trend; s5, generating a regulation control strategy, and constructing a temperature fusion regulation feedback model at the same time; s6, performing iterative verification on the model, and generating a sensor three-dimensional verification report; and S7, performing security judgment on the three-dimensional verification report of the sensor. According to the method, through multi-source sensing data acquisition and segmented labeling preprocessing, temperature local mutation features and long-time time sequence dependence features are captured, and temperature data heterogeneity of traditional single-model feature extraction is broken.

The invention provides a submarine topography and land elevation matching method based on multi-source data fusion, and belongs to the technical field of geographic information technology and ocean engineering cross fusion. Seamless splicing of sea and land topographies is realized through multi-source data preprocessing, topographic feature correlation extraction, dynamic compensation matching and adaptive weight fusion; according to the method, the problems of data heterogeneity, dynamic environment adaptability and insufficient feature association can be solved, the matching precision can reach + / -3cm, the processing efficiency is also improved, and the method is suitable for coastal zone modeling, ocean engineering and other scenes.

The invention discloses a thinking data-based large model reasoning ability evaluation and analysis method and device. According to the method, a data flow diagram is constructed through a variable use-definition chain during running of a specific language LMCL in the dynamic monitoring field, logic variable LVAR nodes are extracted, redundant edges are eliminated, and a thinking map with a direct dependency relationship is generated. Based on the thinking map, a five-dimensional evaluation system including reasoning efficiency, key node recognition capability, reasoning generality, multi-path reliability and accumulative hierarchical reasoning is designed, and an internal mechanism of a model reasoning process is quantitatively analyzed. A semantic rule is extracted through a frequent mode of mining successful and failed thinking data, reasoning path probability distribution is integrated in combination with a path aggregation strategy, an interpretable cue word optimization strategy is generated, and a large model reasoning process is dynamically injected. Through the rule guidance and path equalization strategy, the model reasoning accuracy is improved, and the problems of thinking data isomerism, single evaluation and black box enhancement in the traditional technology are solved.

The invention belongs to the technical field of Internet data security, and discloses a multi-dimensional Internet data security fusion processing system, which is characterized in that an acquisition module depends on a three-dimensional decision model, combines reinforcement learning dynamic allocation tasks, only acquires threat associated key data in a low-computing-power and high-threat scene, and starts full-amount lightweight acquisition in a high-computing-power and low-threat scene; the data preprocessing module adds scene labels for data, filters logic and unifies formats according to scene design, and eliminates data isomerism. The identification module builds a basic threat library and a scene adaptation layer, adapts a new scene through transfer learning, calculates a risk value in combination with a four-dimensional quantitative model such as propagation probability, generates an attack link map, and effectively eliminates a monitoring blind area caused by edge computing power limitation; the decision-making module constructs a five-dimensional trust evaluation model of cooperation duration, compliance records and the like, calculates weights by using an analytic hierarchy process, establishes a compliance and trust linkage engine, synchronizes industry specifications in real time and adapts to scene adjustment rules.

The invention provides an urban traffic dynamic deduction and real-time planning method and system based on multi-source position data fusion. Time-space references of signaling, MDT and MR data are unified through a dynamic rasterization mechanism, and the problem of data isomerism is solved by adopting anchor point selection of signal weighting and a triple criterion time expansion algorithm; generating a minute-level updated dynamic OD matrix based on the hierarchical road network constraint model; a METANET macroscopic traffic flow model and EKF extended Kalman filtering are fused to realize traffic state deduction, and parameter online calibration is supported; signal timing optimization, path induction and emergency control schemes are generated in combination with real-time deduction results, and a sensing-deduction-decision-evaluation closed loop is formed. The method can improve road network traffic efficiency, and is especially suitable for response of sudden traffic events.

The invention discloses an industrial internet attack and defense situation and risk early warning and sensing method, and particularly relates to the field of internet risk early warning and sensing, which comprises the following steps of: acquiring multi-source heterogeneous data, constructing a basic data set covering an attack, service and equipment ternary space, including attack characteristics, service influence and equipment control vectors, and solving the problems of data heterogeneity and dispersion; constructing a triple function based on the data set, respectively quantifying the attack comprehensive threat degree, the influence degree of the attack on the service and the malicious control risk of the equipment, and retaining the characteristics of each dimension; a dynamic network topology model is constructed, nodes, edges and edge weights are defined, an attack propagation path and influence intensity are described, and node state dynamic updating is achieved; and finally, a risk prediction model is constructed by fusing quantitative indexes and topological information, a global risk value is calculated, graded early warning is realized through triple dimensions, a corresponding response mechanism is matched, and the timeliness and effectiveness of industrial internet security protection are improved.

The invention provides a construction method of an ocean observation and exploration large model, and belongs to the technical field of large models.Multi-mode original data are collected by constructing a multi-source ocean data collection matrix, an environment change degree vector is established, preprocessing and noise reduction are conducted on the original data by adopting a nonlinear matrix mapping algorithm based on a Gaussian kernel function, and the large model is constructed. An ocean observation and exploration large model architecture of a liquid neural network structure is constructed, different branches are made to process input data of different dimensions by means of asymmetric design, a super sparse reconstruction matrix is established, and high-dimensional original data are reconstructed from low-dimensional observation by means of a compressed sensing reconstruction mechanism. And finally, a supervised training process is executed to optimize model parameters so as to complete the construction of an ocean observation and exploration large model, and the technical problem that high-precision fusion modeling of ocean multi-modal observation data is difficult to realize under the conditions of spatial-temporal distribution sparsity and data isomerism is solved.

The invention provides a wake flow control method and system based on multi-source heterogeneous wind field data, and the method comprises the steps: generating a three-dimensional true value wind field according to the observation data of a multi-source heterogeneous sensor in a target wind field region, and carrying out the sampling in the three-dimensional true value wind field through a virtual sensor, and obtaining three-dimensional virtual observation data; based on the three-dimensional virtual observation data, performing style migration by using a conditional generative adversarial network to obtain enhanced observation data; performing wind field prediction on the target wind field area based on the enhanced observation data, and outputting three-dimensional wind field prediction information; according to the three-dimensional wind field prediction information, utilizing a federated average algorithm to generate a wake flow control strategy of the target wind field area; according to the method, the three-dimensional true value wind field and the virtual observation data are generated through the multi-source heterogeneous data, the prediction precision can be improved through enhancement processing, the control strategy is generated in combination with the federal algorithm, the data heterogeneous and sparse problems can be solved, the wake flow control accuracy and efficiency can be improved, and the data privacy can be protected.

The invention discloses a personalized federated graph learning method oriented to equipment resource isomerism, and aims to solve the defects in graph data isomerism, equipment resource adaptation and privacy protection in the prior art. According to the method, collaborative optimization is realized through a closed-loop process of local pre-training, embedding aggregation, personalized training, soft label collaboration and classifier distillation. Each client pre-trains a model based on a local graph data set and generates interlayer embedding, and uploads the model to a server after sampling and privacy enhancement; the server performs aggregation to form a public embedded data set and distributes the public embedded data set to the client to support distillation training and soft label generation; the soft labels are filtered and subjected to weighted aggregation to form global soft labels, and the client completes classifier knowledge distillation by combining the public embedded data set and the global soft labels, and iteratively optimizes the performance of the model. According to the method, the generalization ability and the resource utilization efficiency of the model are remarkably improved while the data privacy is guaranteed, and the method is suitable for distributed graph data training tasks of multiple scenes such as social networks.

The invention discloses a cloud edge federal learning method and system and a storage medium, and belongs to the field of model training optimization. Firstly, the cloud constructs a dynamic clustering mechanism and reduces intra-group statistical heterogeneity based on model features and data distribution information uploaded by an edge terminal, and the edge terminal performs local training and intra-group model aggregation according to a cloud clustering result to improve the consistency and adaptability of a local cluster model; secondly, a decoupling knowledge transfer mechanism is adopted, the global model and the local cluster model are decoupled into a feature layer and a classification layer respectively, hierarchical knowledge alignment is carried out in the distillation process, the learning ability of the local model for intermediate feature expression and classification decision boundaries is enhanced, and the distillation efficiency is improved; therefore, the convergence speed and generalization performance of the model in the heterogeneous data environment are improved. Therefore, the technical problems of model performance reduction and weak generalization ability caused by data heterogeneity in the cloud-edge federation in the prior art are solved.

The invention belongs to the technical field of single-cell multi-omics, and discloses a single-cell multi-omics data analysis system, method and device and a storage medium, a data import module is used for reading sequencing data from different sequencing platforms and storing the sequencing data as S4 objects; the cross-language analysis module is used for calling a single-cell multi-omics analysis tool based on an R language and a Python language to analyze an S4 object according to an analysis process in a unified framework; the deep learning optimization module is used for training a deep learning model based on the cross-language interaction interface and integrating an analysis result of the deep learning model to an object S4; and the interaction and extension module is used for obtaining an analysis process and visualizing an analysis result, constructing a user-defined single-cell multi-omics analysis tool in a plug-in form and loading the tool to the S4 object for analysis. Cross-sequencing platform data compatibility, multi-language tool seamless integration and deep learning framework efficient access are achieved, and the problems of data isomerism, language ecological splitting and algorithm expansibility in the prior art are solved.

The invention discloses a knowledge graph-based cross-industry information system intelligent integration method and system, and relates to the technical field of image processing. The problem of data heterogeneity is solved through the cross-industry data mapping model, the dynamic knowledge graph is constructed in combination with the graph neural network, self-adaptive adjustment is achieved, meanwhile, data interaction safety is guaranteed by means of multiple verification, and the transmission strategy is dynamically adjusted and resource scheduling optimization is achieved. Standardized integration of cross-industry data, dynamic optimization of knowledge association, credible guarantee of interaction security and improvement of transmission efficiency are achieved, and finally intelligent integration and efficient collaboration of a cross-industry information system are achieved.

The invention relates to the technical field of international marine scientific investigation collaborative technology and mutual identification, and discloses an international marine observation data fusion and achievement mutual identification system suitable for joint scientific investigation. Comprising a multi-source data standardization access module, a polar region adaptive intelligent fusion module, an international achievement mutual recognition standard and verification module, a hierarchical sharing and mutual recognition process management module, a collaborative iteration and operation and maintenance management module, a block chain full-process traceability system and an international joint collaborative operation and maintenance mechanism. According to the system, the problem of international data heterogeneity is solved through multi-source data standardization preprocessing, accurate data fusion under polar region / open sea complex working conditions is realized by utilizing an intelligent fusion technology, a mutual recognition system of bidirectional verification is established, and the traceability of the whole process is ensured by adopting a block chain. The system supports hierarchical sharing and dynamic iterative optimization, is cooperatively operated and maintained by two international parties, guarantees long-term adaptation to standard updating and equipment iteration of the two parties, and effectively improves the joint scientific investigation data sharing efficiency and the result mutual recognition credibility.

The invention belongs to the technical field of data mining, and particularly relates to a social media data mining method based on user behavior analysis. The method comprises the following steps: collecting user multi-source behavior data in real time, analyzing unstructured data through natural language processing, extracting key phrases and mapping the key phrases to structured fields; after abnormal data is removed through data cleaning, a behavior sequence is constructed in a time window, a time perception embedded vector is introduced to generate time sequence feature representation, and meanwhile, BERT is utilized to extract behavior content semantic vectors, and features such as behavior types and strength are fused to generate uniform content representation vectors; and finally, fusing the two types of features through a BiGRU-Attention network, and predicting a future behavior probability value of the user to drive personalized recommendation. According to the method, the problems of insufficient behavior semantic motivation capture, difficulty in data isomerism processing, weak dynamic modeling capability and the like in the prior art are solved, and the social media data mining precision is improved.

The invention discloses a heterogeneous data migration method and device, equipment and a storage medium, and relates to the technical field of data migration, and the method comprises the steps: obtaining a heterogeneous mapping rule between a source database and a target database; creating a migration transition table in the target database according to the heterogeneous mapping rule, and synchronizing the to-be-migrated data in the source database to the migration transition table to generate isomorphic mirror image data; performing batch screening on the isomorphic mirror image data based on the target migration factor to generate a batch migration list; and performing format conversion on the batch migration list according to a heterogeneous mapping rule to obtain target format data, and writing the target format data into a target database. The data heterogeneous difference is shielded through the migration transition table, the format conversion process is postposed, and the precision and stability of format conversion are improved; and the risk of single migration is reduced through gradual batch migration, so that the risk of switching between a new system and an old system is reduced. The heterogeneous data migration complexity can be reduced, and the migration efficiency can be integrally improved.