2619 results about "Calculated data" patented technology

The invention discloses a crop growth prediction method based on multi-source data fusion analysis, and the method comprises the steps: carrying out the standardization processing and missing value filling processing of multi-source data, constructing a time window, calculating the correlation between data sources, and generating a current data credibility vector based on historical credibility evaluation data; extracting features by adopting multi-scale decomposition, detecting abnormal points and analyzing abnormal degrees, and constructing an abnormal conduction network to evaluate conduction influence; performing feature importance evaluation and feature fusion; initial weight distribution is calculated, a weight prediction model is constructed, and consistency optimization is carried out on a weight matrix; performing hierarchical prediction by adopting a multi-level prediction model, and performing collaborative integration based on a prediction result and an optimized weight matrix; and calculating a prediction result deviation and carrying out optimization adjustment, and outputting a final prediction result. According to the invention, accurate prediction of the crop growth state is realized, and the reliability and practicability of the prediction result are improved.

The invention discloses a nursing data sharing method and system based on the Internet of Things. The method comprises the steps that S1, multi-modal nursing data are collected in real time through a distributed Internet of Things terminal cluster, and a feature signal set is formed; s2, constructing a data coordination engine, executing federal learning preprocessing operation on the feature signal set, and forming a coordination signal flow; s3, dynamically calculating a data contribution degree weight and generating a verification signal chain; s4, monitoring vital sign abnormal indexes in the coordination signal flow in real time, and generating a trigger signal vector; and S5, updating incentive mechanism parameters according to the contribution degree voucher in the verification signal chain, optimizing a service quality strategy based on a network resource allocation scheme in the trigger signal vector, and forming a closed-loop control signal. According to the invention, the problem of data islanding effect in the medical care data sharing process can be solved.

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

A method for inversing a depth of a subsurface chlorophyll-a maxima (SCM) of an oceanic water body based on remote sensing reflectance is provided. The method includes: band selection, R value calculation, data collection, model construction, and ZSCM calculation. This method can not only reduce the dependence of machine learning on sea surface chlorophyll-a concentrations, sea surface temperature data and sea surface height data, and reduce the calculation resource consumption of running machine learning, but also effectively improve the inversion accuracy, and effectively reduce the calculation resource consumption and inversion difficulty of inversing the SCM.

The invention relates to the field of wind power plants, in particular to a wind power plant edge calculation data cleaning and real-time transmission optimization method and system. The method comprises the following steps: forming a multi-source heterogeneous data stream by collecting SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator in real time; performing standardization processing on the data at an edge computing node, constructing a multi-dimensional feature vector, and generating a fusion data set by adopting a self-adaptive fusion algorithm based on a dynamic weight; and constructing a fault prediction model based on the deep dual-channel convolutional neural network, and outputting a health state evaluation value and a fault risk level in real time. And when the risk level exceeds a threshold value, generating an early warning signal. The system also performs data compression transmission optimization according to the real-time bandwidth state, and dynamically adjusts the weight of the monitoring parameter to realize the closed-loop optimization of the preventive maintenance strategy.

The invention provides an integrated metadata management method and system based on data source expandability, and relates to the technical field of data sources, and the method comprises the steps: executing real-time detection on a plurality of heterogeneous data sources through a PF4J plug-in framework, collecting connection information, and establishing a dynamic connection pool; obtaining a table structure, a field attribute and an association relationship list, and assembling the table structure, the field attribute and the association relationship list into JSON format data; performing analysis and standardization processing on the data, and constructing an association degree calculation matrix; generating a standardized data structure description file; establishing a three-layer index directory, and detecting structure change and updating; and calculating a data table reference dependency relationship, and generating a DAG data flow path. According to the method, efficient integration and management of heterogeneous data sources are realized, and the metadata processing efficiency is improved.

The invention relates to a method for establishing and optimizing a multi-scale prediction model for constitutive parameters of a fiber reinforced composite material. Comprising the following steps: constructing a fiber-matrix microscopic RVE model, applying a boundary condition and a load, determining a failure criterion, and calculating equivalent mechanical parameters of a fiber bundle; a fiber bundle-matrix mesoscopic RVE model is generated, fiber bundle equivalent mechanical parameters serve as material attributes of fiber bundles in the mesoscopic RVE, a viscoelastic model is adopted for a matrix, and fiber reinforced material unit cell equivalent mechanical parameters are calculated; constructing a macroscopic fiber reinforced composite material finite element model, endowing a macroscopic model with unit cell equivalent mechanical parameters, defining an orthotropic material card, simulating an explosion impact process by adopting an explicit dynamics algorithm, outputting structural displacement, a damage area and stress distribution, and comparing calculation and test data. And correcting the microscopic RVE model and the mesoscopic RVE model through reverse optimization iteration to obtain a final macroscopic prediction model. The explosion-proof performance of the fiber composite structure is remarkably improved.

The invention discloses a power grid dynamic modeling method based on a physical information neural network and a related device, and the method comprises the steps: obtaining power grid observation data, inputting the power grid observation data into a neural network for power grid dynamic behavior prediction, and obtaining a power grid state prediction value; calculating data item loss through a power grid state prediction value and a power grid state actual measurement value, and calculating physical residual item loss through power grid observation data; the physical residual item loss comprises current conservation constraint loss, voltage closed-loop constraint loss and generator dynamic response loss; and network parameters of the neural network are updated through the data item loss and the physical residual item loss until the neural network converges, and a power grid state model is obtained. According to the method, the mapping relation between the state variable and the system input is established by using the neural network, and the physical rule of the power grid is introduced into the loss function as a hard constraint term, so that physical consistency control during dynamic behavior modeling of the power system is realized, and the accuracy of dynamic behavior modeling of the power grid is improved.

The invention provides a performance optimization method and system for cross-data-source paging query, and relates to the technical field of data processing, and the method comprises the steps: calculating a data distribution weight and a data source routing table, determining an optimal data access path, and splitting a paging query request into sub-query tasks; dynamically adjusting the number of parallel query threads based on the load state and distributing sub-query tasks; establishing a local index and adaptively adjusting an index structure; dynamically adjusting the connection number of the connection pool and acquiring data in batches; performing feature analysis according to a query result, determining the size of a data fragment, and selecting an optimal merging strategy to merge the data; constructing a cache access mode graph, and aggregating and storing related data based on the access association degree; and optimizing a performance strategy set through reinforcement learning, adjusting a cache updating interval, a data access path and a feature matrix, and finally obtaining a query result. According to the method, the cross-data-source paging query efficiency can be effectively improved, the query delay is reduced, and the overall performance of the system is improved.

The invention relates to the technical field of calibration tests, in particular to an intelligent metering calibration method and system, and the method comprises the following steps: calculating the similarity between data points, constructing a similarity matrix, further calculating the fitting degree, carrying out the spectral decomposition, extracting a feature vector, carrying out the dimension reduction, mapping to a low-dimensional space, carrying out the data point clustering based on a feature vector matrix, and carrying out the calibration of the data points. An error cluster is identified and an error pattern is determined. According to the method, by calculating the similarity between the data points and mapping the similarity to the low-dimensional space, the error modes can be efficiently recognized and classified, the error correction path can be dynamically adjusted, error correction is more flexible and real-time, the error analysis depth is improved through spectral decomposition and feature vector extraction, and the error analysis accuracy is improved. Therefore, the error trends of the equipment under different conditions are accurately identified, the measurement precision and stability are remarkably improved, the error correction path parameters are updated in real time, the adaptability of the system to the operation change of the equipment is enhanced, and the calibration time is effectively shortened.

The invention, which relates to the technical field of social emotion analysis, discloses a university social negative emotion characterization analysis system comprising a data acquisition and preprocessing module, a feature extraction and anomaly detection module, an intelligent perception and negative emotion characterization module, and an anomaly risk assessment and response module. Emotional feature information is acquired in real time based on a multi-dimensional data source, and the acquired emotional feature information is cleaned and preprocessed. According to the method, by calculating the data consistency offset coefficient and the strong correlation coefficient, the multi-collinearity problem among multi-dimensional data features is solved, and instability of the model regression coefficient is avoided. And the system identifies abnormal correlation in real time and combines risk threshold classification processing to ensure robust model output. An analysis set is established by continuously collecting data, long-term emotion trend analysis is supported, personalized responses such as reminding, intervention or emergency measures are provided according to risk levels, and accurate management and timely adjustment of student emotional states are achieved.

The invention provides an HVAC system temperature control and energy consumption optimization method based on deep reinforcement learning. Through an entropy-based data screening method, a strategy evaluation module and a design reward function, HVAC system temperature control and energy consumption optimization are realized under external condition interference. According to the entropy-based data screening method, the value of data for agent training is calculated through an error formula, high-value data is repeatedly used for agent training, and the training effect is enhanced; the strategy evaluation module is used for further solving the problem of overestimation of a Q value on a double-Q structure, correcting the output of the Q value, reducing the influence of a random Q value, and adding regularization of a strategy and an actual action into a loss function updating process, so that the influence of random noise on the Q value is eliminated, and the accuracy of strategy output is ensured; a reward function for controlling the temperature and optimizing the energy consumption is designed, so that the intelligent agent can balance the contradiction among multiple tasks, and the optimal solution of multi-target task intersection is searched, so that the HVAC system can control the temperature to be always kept in the temperature range, and meanwhile, unnecessary energy consumption is reduced.

The invention relates to the technical field of traffic data processing, and discloses a traffic edge computing data security sharing method and system, and the method comprises the steps: collecting traffic edge computing network multi-source heterogeneous data, and carrying out the classification standardization processing to generate a structured data set; designing a dynamic data sharing security protocol based on a multi-party security computing protocol and a homomorphic encryption algorithm; verifying the authority of a requester in a multi-level manner by using an attribute-based access control model and a zero-knowledge proof mechanism, and generating a dynamic access token; storing data by adopting a fragmentation storage and redundancy encryption strategy, and recording storage information through a hash chain; and dynamically adjusting the encryption strength and the sharing strategy according to the network threat level and the data sensitivity. The method effectively guarantees safe sharing of traffic data, accurately controls access authority, improves storage and sharing efficiency, adapts to complex network environment changes, and provides powerful support for development of an intelligent traffic system.

The invention discloses a financing guarantee business digital intelligence risk control method and system, and belongs to the field of financial science and technology, and the method comprises the steps: obtaining multi-source heterogeneous data of government affairs, supply chains, Internet and the like; calculating a data type comprehensive score D and judging a data type through a dynamic weight model based on a unified data parameter system comprising a source identifier, a structure specification, a time sequence, entity association and content characteristics; for different types of data, technologies such as block chain evidence storage, federated learning, a graph neural network and natural language processing are adopted to carry out differentiated risk assessment, guarantee limit output and risk early warning and other decisions. The system comprises a four-layer architecture of data acquisition, processing, modeling and decision support, and supports real-time access of multi-source data and automatic risk control. The method effectively deals with emerging scenes such as false trade and public opinion risks, meets the requirements of data security compliance, and provides an efficient and accurate digital intelligent risk control scheme for financing guarantee.

The invention relates to the technical field of network communication, and discloses a vehicle-mounted edge computing data recording system and method based on protocol adaptive analysis, and the system comprises a multi-protocol network access controller which is used for capturing an original data frame and extracting a physical feature triple; the identification analysis engine is used for executing hash operation on the triple to generate a physical feature code and retrieving a physical logic address mapping table; the direct memory access controller is used for responding to a target memory address pointer hit by retrieval and directly writing a data load into an input buffer area of the functional operation module, and by constructing a direct addressing mechanism based on Hash mapping, thorough decoupling of vehicle-mounted heterogeneous network physical topology and edge computing logic is achieved.

The invention specifically relates to the technical field of edge computing, and discloses a multi-platform concurrent transmission edge computing data acquisition method, which comprises the following steps: S1, concurrent access of multi-platform equipment: establishing a mapping relationship between equipment identity and a communication link; s2, data concurrent collection and preprocessing: obtaining a preprocessed data stream; s3, concurrent transmission priority scheduling: allocating a transmission bandwidth for each data stream; s4, edge node parallel computing: distributing the subtasks to a plurality of computing nodes; s5, cloud collaboration and global optimization: generating a global optimization strategy; s6, carrying out full-link monitoring and dynamic regulation and control; concurrent access of heterogeneous equipment is realized by generating data streams in a uniform format, multi-platform compatibility is effectively enhanced, bandwidth is dynamically allocated to the data streams according to priority weight indexes by calculating the priority weight indexes of the data streams, data transmission efficiency is improved, and the data transmission efficiency is improved by allocating sub-tasks to a plurality of computing nodes. And the resource utilization rate of the edge computing gateway is improved.

The invention provides an industrial data online migration method, medium and system of a productivity middle platform, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, historical access records of an industrial data source are collected, and a deep neural network model comprising a multi-head attention layer, a time sequence feature extraction layer and a residual connection layer is constructed; and the prediction of the data change rate is realized. And based on a prediction result, dynamically calculating a data change rate demarcation point by utilizing an optimization equation set, and dividing a data table into different change frequency categories. And then constructing a data dependency relationship matrix, and determining a migration sequence by adopting an improved topological sorting algorithm. Batch migration, incremental migration and real-time synchronization mechanisms are adopted for different types of data, and model updating is triggered through real-time monitoring, so that dynamic optimization of a migration strategy is ensured. Finally, data consistency verification is carried out, migration accuracy is guaranteed, and the technical problem that the online migration efficiency of industrial data is low due to real-time dynamic change of the data change rate is solved.

The invention relates to the field of data processing, in particular to an operation state monitoring method and system for battery automatic short circuit antipole detection equipment, and the method comprises the steps: obtaining the current, voltage and temperature historical data of the battery detection equipment, calculating the data fluctuation degree through employing an HWKS algorithm, setting an initial k'value, and calculating the initial k 'value; clustering the historical operation data of different levels, analyzing the aggregation degree and local density of a cluster, correcting an initial k'value to obtain a corrected significant k value, taking the significant k value as a parameter value in an HWKS algorithm, obtaining the abnormal degree of the data of each level, performing weighted summation on the abnormal degree of the data of each level, and obtaining the abnormal degree of the data of each level. And the data processing module is used for acquiring the comprehensive abnormal degree of the data of each level, judging whether the data point is abnormal data based on the comprehensive abnormal degree, and monitoring the detection equipment. According to the method, the k value is adjusted in a self-adaptive mode, abnormal points in the data are accurately recognized, and therefore the accuracy of operation monitoring is improved.

The invention discloses a data processing system and method, electronic equipment and a storage medium, and relates to the technical field of computers.Original input data are preprocessed into multiple sets of target input data through a data input module, and the multiple sets of target input data are input into multiple iterative calculation units to obtain data to be calculated and code identifiers; when the code identifier represents that the to-be-calculated data needs to be overturned in advance, the to-be-calculated data is overturned in advance to obtain the to-be-calculated inverted data, and a subsequent iterative calculation unit only needs to perform target calculation on the to-be-calculated inverted data to obtain a data processing result; that is, the iterative calculation unit can obtain the data processing result only by performing a small amount of data bit flipping on the to-be-calculated inverted data through the target calculation, the number of data bit flipping in the target calculation is reduced, the dynamic flipping power consumption is reduced, and compared with a power consumption reduction mode in the related technology, the efficiency is improved. High-difficulty chip rear end layout wiring is not needed, and the performance and the power consumption reduction effect of the chip can be ensured.

The invention relates to the technical field of communication, and discloses a communication data transmission and temporary storage method and device and a storage medium, and the method comprises the following steps: S1, receiving and partitioning initial data, and storing the initial data in a cache region; s2, calculating weighting coefficients of the data blocks and performing priority ranking; s3, encrypting the data blocks according to the sequence; s4, transmitting the encrypted data to a target end through a security protocol; s5, the target end decrypts the data and verifies the integrity; s6, temporarily storing the verified data blocks in a temporary storage area until all the data blocks are transmitted; and S7, after the transmission is completed, recombining and storing the data block in the target equipment. According to the method, the data is subjected to blocking, priority ranking, encryption transmission and decryption recombination, so that the security, integrity and transmission efficiency of the data in the network transmission process are ensured, and the method is suitable for a communication system needing high security and high data integrity.

The invention relates to the technical field of message queues, in particular to a message controllable optimization method and system based on kafka, and the method comprises the steps: obtaining a data attribute, and calculating a data priority value according to the data attribute; the method comprises the steps of establishing a position index cache mechanism based on priority data, persisting partition-offset data in a cache pool to an index table according to the position index cache mechanism, and performing priority consumption of high-priority messages based on persistent priority indexes, including setting a consumption control program architecture, performing priority consumption on the high-priority messages, and performing priority consumption on the high-priority messages based on the persistent priority indexes. Isolating the consumption queue and introducing a message de-duplication operation; according to the method, full-link performance adjustment and optimization are carried out on the premise of preferential consumption of high-priority messages, service scene extension is carried out based on the adjusted and optimized full links, the messages are pulled directionally according to the priorities through a consumption control program, the disorder of parallel consumption of Kafka partitions is broken through, the high-priority messages can directly jump to corresponding positions to be processed preferentially, and the service scene extension efficiency is improved. The consumption delay is obviously shortened.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine heterogeneous data visualization processing method combined with artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous data above and under a coal mine to construct a real-time heterogeneous data matrix; feature categories are divided based on data dynamic relevance, and stable data areas are screened through a dynamic analysis window to generate an optimization matrix; performing pattern recognition on the unstable region to extract an abnormal data cluster, and calculating an abnormal feature factor of a data point; analyzing the feature distribution difference to generate a stable feature index, and calculating an abnormal risk value in combination with the distance, the abnormal feature factor and the stable feature index; and mapping the center coordinate of the stable region to a three-dimensional space of the coal mine, and positioning a key information region in combination with the abnormal risk value. An apparatus includes a memory, a processor, and a computer program. The coal mine heterogeneous data processing efficiency and the safety monitoring accuracy are improved, intelligent positioning of the key information area is achieved, and powerful support is provided for coal mine safety production.

The invention discloses a calculation and measurement fused high-fidelity digital twinning dynamic monitoring method for a cantilever beam structure. Firstly, a real-time updated database is constructed based on a physical entity system; secondly, constructing a three-dimensional digital model by using SolidWorks, and performing multi-physics field simulation analysis in combination with Ansys to obtain high-precision mechanism data of a structural key area; thirdly, performing reduced-order calculation on a simulation result by using MWorks, training and optimizing a reduced-order model through a feedforward neural network, and reducing an error between simulation data and reduced-order data; fusing the one-dimensional system data order reduction model and the three-dimensional field data order reduction model based on a Modelica language; and finally, integrating a mechanism-data hybrid model, constructing a dynamic response characteristic and spatial distribution characteristic integrated high-fidelity digital twinborn body, comparing twinborn body calculation data with sensor actual measurement data in real time, and accurately representing geometric morphology, mechanical properties and dynamic behaviors of a physical system. The method is suitable for high-precision monitoring, real-time state evaluation and performance optimization of the cantilever beam structure.

A method is provided for constructing decision trees. The method includes analyzing a dataset to determine data distribution metrics; calculating feature importance scores for features in the dataset; dynamically adjusting tree depth and node pruning criteria based on the determined data distribution metrics and feature importance scores; initializing a decision tree structure based on the adjusted tree depth and node pruning criteria; and iteratively refining the decision tree by applying heuristic adjustments to improve splits based on updated data distribution metrics and feature importance scores.

The invention discloses an accelerator for accelerating the reasoning process of an unstructured sparse large language model and an operation method of the accelerator. The accelerator comprises a processor module, a direct memory access module, a memory controller module, a DDR memory module, an interconnection bus module, a calculation core module and a router module. The interconnection bus module is connected with the direct memory access module, the memory controller module and the computing core module; the processor module is connected with the direct memory access module; the DDR memory module is connected with the memory controller module; the router module is connected with the computing core module and the adjacent router module; and the calculation core module obtains to-be-calculated data from the off-chip DDR memory module through the interconnection bus module. According to the method, the limit that data reuse is carried out only along a single row or a single column on a traditional systolic array is broken through, cross-row and cross-column data reuse is achieved, the pressure of hardware equipment is relieved, the positions of non-zero elements are coded through bit masks, and the calculation efficiency is improved.

The invention discloses a data ownership verification and authorization control method based on zero knowledge proof. The method comprises the following steps: constructing a traceable Merk tree structure on a block chain or a distributed account book; a data owner calculates data fingerprints and packages the data fingerprints into declaration nodes to be inserted into the traceable Merk tree; storing the ownership zero knowledge proof and the traceable Merk tree root hash into a block chain together; the verification party executes the zero-knowledge verifier contract on the chain to complete ownership verification without reading original data or identity plaintext; access control is realized after on-chain verification of the service party; triggering local heavy hash to update the traceable Merk tree root hash, and broadcasting a revocation event on a chain; and confirming legality of evolution and authorization change of all nodes. According to the method, the historical traceability and structural consistency of the data ownership and authorization relationship are ensured, and efficient traceability and anomaly detection of the node evolution chain are also realized.

The invention discloses a dynamic printing data minimization splitting method based on topological entropy mapping. The method comprises the steps that S1, a complete data set of a printing task is acquired; s2, establishing a topological network for the divided data blocks; s3, calculating the three-dimensional fractal dimension of each data block in the topological network by using an improved spatial fractal algorithm; s4, recording topological entropy values of the data blocks and priority indexes of the data blocks in the network; s5, calculating the comprehensive priority of the data blocks in a weighted fusion mode; s6, performing dynamic minimization splitting on the data blocks according to the comprehensive priority; s7, dynamically adjusting the transmission sequence and the splitting granularity; s8, carrying out local compensation on missing data generated by splitting; s9, optimizing subsequent transmission and splitting strategies; and S10, performing supplementary transmission on the data blocks marked as low priorities according to the integrity requirement of the task. According to the invention, efficient resource allocation is realized, and the integrity and accuracy of the printing task are ensured.

The invention relates to the technical field of water conservancy monitoring, in particular to a remote monitoring and early warning system for a water conservancy monitoring terminal, which comprises a data acquisition module, an edge calculation module, an information transmission module, a central analysis module and an execution module. According to the method, the accuracy and continuity of water conservancy monitoring data are ensured through the technologies of timestamp detection, interpolation calculation, data cleaning, median filtering and the like; according to the method, abnormal changes in key water conservancy data such as water level, flow and pressure can be identified through real-time data analysis, and abnormal fluctuations of a trend part or a periodic part can be found in time in combination with seasonal decomposition and abnormal value analysis; according to the invention, the problems of bandwidth limitation and uneven network load in the traditional remote monitoring system are solved by dynamically adjusting the communication bandwidth, frequency and priority management. Particularly, under the conditions of multiple nodes and large data volume, data transmission delay and loss can be effectively avoided, and the real-time performance and stability are ensured.

The invention relates to the technical field of information technology consultation management, and discloses an information technology consultation management system based on big data, and the system comprises a data optimization module which is used for accessing multi-modal data, collecting federal data, expanding a data source, generating a dynamic cleaning rule, calculating the data quality, and carrying out the optimization processing; the security evaluation module is used for dynamically distributing privacy budget through multi-modal data, calculating a consultation risk value of an information technology in combination with a credit evaluation mechanism, performing block chain traceability and reinforcing compliance auditing, and analyzing a delay index to optimize an edge node model; the decision service module performs intelligent interaction based on compliance auditing, analyzes user characteristics, generates dynamic rules, performs personalized recommendation, and optimizes personalized recommendation in combination with reinforcement learning; and the feedback updating module is used for acquiring feedback data based on personalized recommendation, analyzing feedback reliability, updating compliance audit and adjusting privacy budget.