110 results about "Time series data analysis" patented technology

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

The system comprises a data acquisition layer, a data processing layer, a model layer and an application layer, the data acquisition layer is used for acquiring multivariate heterogeneous data in real time, the data processing layer is used for processing the acquired data, the model layer is used for training the processed data, and the application layer is used for applying the trained data to the data processing layer. And the application layer is used for performing dual drive by adopting a time sequence processing block and point prediction and probability prediction, the time sequence processing block is a double-branch parallel backbone network integrating a Decoder-Only Transform network and a multi-scale graph neural network, and the application layer is used for evaluating the running state of the electric energy metering device according to a predicted value and a predicted probability calculated by the model layer at a certain moment. According to the charging pile operation state prediction system based on time sequence analysis, the operation state of the electric energy metering device can be monitored more comprehensively and accurately, potential abnormity and fault hidden dangers can be found in time, early warning is carried out in advance, and economic losses caused by metering errors and equipment faults are effectively reduced.

The invention relates to the technical field of computers, and discloses a time series data prediction method and device based on a diffusion model, equipment and a medium, and the method can ensure the consistency of different modal data in time by performing time dimension alignment on target time series data and multi-modal evaluation data. And feature splicing is performed on the multi-modal data corresponding to each time step, so that the multi-modal information can be integrated, and the data features of each time step are enriched. And on the basis of a self-attention mechanism, weighted summation is carried out on the fused feature vectors to obtain a self-attention feature matrix, so that mutual correlation between a time sequence dependency relationship and multi-modal features in a feature sequence can be captured, and richer feature representation is generated. And diffusion simulation is carried out on the self-attention feature matrix by using the pre-training diffusion model, more accurate target prediction data is output, and the accuracy of a prediction result is improved. And the application efficiency of the diffusion model in time series data analysis scenes in the financial field and the medical field is expanded.

The invention relates to the technical field of intelligent control systems, and discloses a multifunctional embedded intelligent cleaning control system which comprises a control execution layer, an internet-of-things transmission layer and a decision analysis layer. The control execution layer comprises an intelligent variable-frequency scraping device, a wind-water sweeping device and the like and executes sweeping and dust removal operation; the Internet of Things transmission layer realizes data interconnection and transmission through an edge computing node, a protocol conversion gateway and the like; and the decision analysis layer adopts a time sequence data analysis and convolutional neural network algorithm, dynamically optimizes the cleaning operation model, constructs a multi-dimensional linkage cleaning control strategy model, and realizes cleaning intensity adjustment, air and water parameter cooperative control and dust removal efficiency dynamic optimization. According to the system, through multi-technology fusion and collaborative optimization, the intelligent, precise and efficient level of sweeping operation is improved, the system is suitable for a conveying belt sweeping scene, the sweeping efficiency can be improved, equipment loss and dust emission can be reduced, and remarkable economic benefits and environmental protection benefits are achieved.

To monitor the state of a vehicle without being affected by the ambient environment such as in the nighttime.SOLUTION: A detector comprises: an acquisition unit that, on the basis of a reflected wave that is reflected on an object moving in the cabin from a transmitted wave transmitted toward the inside of a cabin of a vehicle, acquires time-series data of a point group indicating one or more detection points representing the position of the object moving in the cabin; an operation unit that analyzes the behavior of the point group moving with the lapse of time on the basis of the time-series data of the point group; and a determination unit that determines whether the object enters or exits from the cabin on the basis of the behavior of the point group.SELECTED DRAWING: Figure 5

The invention relates to the technical field of hydrological monitoring, and discloses a regional groundwater reserve change dynamic monitoring method based on gravity satellite time series data analysis. According to the method, the multi-source data scale effect and observation noise are effectively eliminated, the physical reliability and resolution of groundwater signal extraction are improved, and the recognition precision and early warning perspectiveness of the abnormal loss trend are enhanced. A full-chain monitoring closed loop of multi-source satellite observation, high-precision physical inversion and dynamic statistical benchmark is constructed, multi-dimensional environment data is aligned, a component separation strategy based on multi-element physical mechanism constraint is established based on a water balance principle, and anomaly diagnosis is performed in combination with a self-adaptively updated long-time-sequence historical database.

The invention relates to the technical field of finance, medical health and artificial intelligence, and provides an improved Transform-based time series data analysis method, device and equipment and a medium, which can utilize an adversarial network to perform data enhancement and effectively expand a training data set. The Transform model has the ability to perceive a sequence based on adaptive position coding, the multi-head attention mechanism based on a dynamic weight adjustment strategy enables the model to improve the ability to capture key information, and multi-head parallel computing can integrate attention results of different view angles to comprehensively mine dependency relationships in data; multi-task joint training and adversarial training are performed on the model, so that the task execution capability of the model can be improved on the premise of ensuring the training effect; the attention distribution features and the intermediate layer output features are utilized to perform interpretability analysis on the time series data analysis result, the interpretability of the analysis result can be improved, and a more comprehensive data analysis result can be provided.

A photovoltaic power generation associated physical quantity mining method based on historical time series data analysis, and a related apparatus. The method comprises: acquiring historical time series data of multiple dimensions during photovoltaic power generation; calculating degrees of mutual information between the historical time series data of the multiple dimensions of photovoltaic power and the photovoltaic power; selecting the historical time series data of which the degree of mutual information satisfies a set requirement to serve as data related to photovoltaic data; and using a linear discriminant analysis (LDA) method to perform feature dimension reduction on the selected historical time series data to obtain data having undergone dimension reduction processing. In the present invention, by using a maximal information coefficient (MIC) feature selection method, data most related to photovoltaic power generation is selected from original feature variables, and then by using an LDA-based feature dimension reduction method, high-dimensional data is mapped to a lower-dimensional space. By means of the MIC feature selection method and the LDA-based feature dimension reduction method, the accuracy of photovoltaic power generation prediction is effectively improved.

A time series data analysis method, includes: generating a plurality of first feature vectors including feature amounts of a plurality of feature waveforms, based on distances from a plurality of first time series data sequences to the plurality of feature waveforms, the first time series data sequences belonging to a first class; generating a plurality of second feature vectors including feature amounts of the plurality of feature waveforms, based on distances from a plurality of second time series data sequences to the plurality of feature waveforms, the plurality of second time series data sequences belonging to a second class; and updating the plurality of feature waveforms, based on the plurality of first feature vectors, the plurality of second feature vectors, a performance indicator parameter related to a performance indicator for a classification model and a model parameter including weights on the plurality of feature waveforms.

The invention relates to the technical field of intelligent traffic management, and discloses a multi-modal time series data analysis-driven public traffic scheduling optimization method, which comprises the following steps of: acquiring multi-modal traffic data, performing time-space alignment preprocessing, and extracting a feature vector; fusing various feature vectors based on a cross-modal interactive learning method; and performing real-time prediction of the public traffic condition based on the multi-modal fusion feature vector to obtain a public traffic flow prediction value, and making a public traffic scheduling strategy based on the obtained public traffic flow prediction value. According to the method, multiple feature vectors are fused based on a cross-modal interactive learning method, the mutual relation between different types of data is effectively captured, the public transport scheduling strategy is formulated based on the particle swarm optimization algorithm, the global search and local search capabilities are effectively balanced, the performance of the PSO algorithm in the public transport vehicle scheduling problem can be effectively improved, and the public transport vehicle scheduling efficiency is improved. And a more efficient scheduling scheme is realized.

The invention relates to the technical field of medical data evaluation, in particular to a data evaluation and comparison system before and after posterior fossa brain tumor operation rehabilitation, and provides an accurate comparison reference for subsequent recovery prediction and progress monitoring through standardized processing of preoperative physiological data and extraction of key indexes. Through continuous monitoring and comparison of real-time physiological data, abnormal fluctuation can be found in the rehabilitation process, a treatment scheme can be adjusted in time, secondary health risks caused by recovery lag or abnormity are avoided, a hidden Markov model is adopted, a scientific basis is provided for careful division of rehabilitation stages through time series data analysis, and the method is suitable for clinical application and popularization. According to the method and the system, all rehabilitation stages are accurately identified according to the actual state of the patient, and the principal component analysis method and comprehensive evaluation of multi-dimensional data are combined, so that the precision of recovery progress monitoring is improved, challenges possibly encountered by the patient in the rehabilitation process can be identified through analysis of the recovery bottleneck, and the pertinence and effectiveness of a treatment scheme are ensured.

The invention discloses a federated learning time series data analysis method based on shuffling differential privacy, which comprises the following steps that: a central server initializes global model parameters and distributes an initial model to all federated learning clients; each client uses a local time sequence data training model, calculates a gradient, then cuts the gradient, and adds Laplacian noise; the client uploads the disturbed gradients to a shuffling device, and the shuffling device allocates random delay time to each gradient and disrupts an uploading sequence to realize privacy amplification; the central server aggregates the gradients processed by the shuffling device to generate a global gradient, and broadcasts the updated model parameters to the client; repeating the above steps to perform iterative training, and tracking privacy budget consumption in real time through a privacy accounting method until a preset privacy budget threshold value or model convergence is reached; according to the invention, privacy amplification is realized, and noise injection is reduced.

The invention discloses a remote sensing time series data analysis method and system considering geographic object space-time correlation, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring and preprocessing a multi-source multi-temporal remote sensing image and auxiliary data to generate a consistent time sequence data stack; dividing the image into voxels in a three-dimensional time-space domain, carrying out adjacent tracking, identifying a geographic process object with an evolution behavior, and recording the attribute of the geographic process object; judging a space-time topological relation between geographic process objects, and generating a unified space-time relation table through composite reasoning; constructing a geographic process object space-time diagram model by taking the geographic process object as a node and the space-time relationship as an edge, and extracting and standardizing attribute features of the node and the edge; a graph convolutional network and a topology dynamic mechanism are used for joint modeling, and the importance index of each node is calculated. According to the method, closed-loop analysis from data processing, relation modeling and importance evaluation to decision support is realized, and the interpretability of remote sensing time sequence change detection and decision effectiveness are improved.

The invention discloses a measuring result stability analysis method and system of a metering instrument, and relates to the technical field of time sequence data analysis. The method comprises the steps of measurement data flow alignment, measurement result stability baseline feature construction, three-state feature decomposition and measurement result stability analysis of the metering instrument. Extracting repeatability features, trend features and structural features of the measurement result, and constructing a measurement result stability baseline feature vector; a three-state feature decomposition method based on baseline constraint is adopted to decompose the stability change of the measurement result into a drift feature, a noise feature and a jump feature; comprehensively analyzing the stable state and the change trend of the measurement result of the metering instrument based on the baseline feature and the tri-state feature decomposition result; according to the method, the stability of the measurement result of the metering instrument can be subjected to refined and cause-distinguishable analysis under the complex working condition and the multi-state operation condition, and a reliable basis is provided for the evaluation of the operation state of the instrument and the judgment of the credibility of the measurement result.

The invention relates to a fault prediction method and system for parameter fine tuning and adaptive structure pruning based on business influence driving, and belongs to the technical field of artificial intelligence, time series data analysis and intelligent operation and maintenance. Comprising the following steps: S1, business influence data modeling: integrating multi-source operation and maintenance data and historical business fault event data, and constructing a business influence quantitative model; s2, efficient fine tuning of service influence guide parameters: loading the pre-training fault prediction model, and performing efficient fine tuning of the parameters on the basis of service influence signals; s3, business influence driven adaptive structure pruning; S4, dynamic reasoning and alarm generation: realizing a dynamic reasoning mechanism of a pruned fault prediction model, and generating a structured and business value oriented fault prediction alarm according to a reasoning result; and S5, performing closed-loop optimization and continuous evolution. According to the invention, the configuration accuracy of the operation and maintenance resources is improved.

The invention discloses a frequency converter abnormal state detection method based on time series data analysis, which belongs to the technical field of electric power, and comprises the steps of: finally generating a structured abnormal event through acquisition, preprocessing, feature extraction, anomaly detection, anomaly level judgment and trend analysis of data of a multi-source sensor; accurate prediction and early warning of the operation state of a complex system are achieved, the technical problems of high real-time performance, low data redundancy and multi-source feature fusion of abnormal monitoring of the frequency converter are solved, redundant data are reduced, the data volume is reduced, the communication bandwidth pressure is reduced, it is ensured that high-frequency, low-frequency and event data are aligned on the same time axis, and the accuracy of abnormal monitoring of the frequency converter is improved. Abnormal data are transmitted preferentially, abnormal information is guaranteed to be complete and reliable, suspicious anomalies are screened rapidly, local and remote alarms are generated according to the abnormal levels, an abnormal database and a periodic summary report are stored, and operation and maintenance and remote monitoring are facilitated.

The invention provides a full-period follow-up visit patient risk dynamic management method based on time series data analysis. The method comprises the steps of performing standardization processing on multi-source follow-up visit data of a patient; data acquisition is dynamically optimized through an adaptive strategy based on reinforcement learning, and multi-scale time sequence features are extracted; performing dynamic risk assessment by using the attention mechanism enhanced double-layer LSTM model, and outputting a risk probability; and calculating a comprehensive risk index by integrating the risk probability, the change trend and the volatility, and realizing dynamic risk layering and automatic intervention based on a rule engine. According to the method, a traditional fixed acquisition mode is converted into a dynamic optimization process capable of responding to patient risks, equipment states and system loads in real time, on the premise that high-risk patient monitoring is guaranteed, system resource consumption is remarkably reduced, and long-term optimal balance of data quality, system performance and resource overhead is achieved.

The invention discloses a non-contact screening system for motion coordination based on computer vision, which relates to the field of computer vision, and comprises the following steps: acquiring a human body motion image through a non-contact camera, extracting human body key point coordinates frame by frame by using a deep learning algorithm after noise reduction and distortion correction, and constructing a time sequence data set; performing smooth jitter removal and interpolation processing to form standardized data; calculating track, rhythm and stability multi-dimensional motion coordination quantitative characteristics based on the data, and performing comparative analysis with a personal historical database to identify a motion function degradation trend; and based on age groups and scenes, customizing screening parameters, coordinating workflows of the components, and realizing full-process automatic screening. The method has the advantages that invasive and cross infection risks are avoided through non-contact acquisition, accurate and objective assessment of the exercise coordination is realized by means of deep learning and time series data analysis, exercise function degradation can be early warned, and an efficient and reliable scheme is provided for various exercise coordination screening scenes.

The invention provides an enterprise production data processing system based on artificial intelligence big data analysis. The system comprises a real-time data acquisition module; the streaming data processing engine is connected with the real-time data acquisition module and used for efficient processing based on the real-time data stream; the time sequence data analysis module is connected with the streaming data processing engine and is used for capturing a long-term dependency relationship among data and realizing accurate prediction of equipment faults; the image data analysis module is connected with the streaming data processing engine and is used for improving the accuracy and efficiency of product quality detection and carrying out product quality inspection image analysis; and the association relationship analysis module is connected with the streaming data processing engine and is used for constructing an association graph of the equipment process materials and mining hidden relationships. By applying the technical scheme of the invention, the problems of insufficient real-time performance, data processing capability and intelligent degree in the prior art can be effectively solved, especially in the aspects of real-time data processing, deep intelligent analysis and adaptive optimization.

The application discloses an optical instrument performance detection system and method based on multi-source data fusion analysis, and relates to the technical field of optical instrument detection, to solve the technical problem that in the prior art, the signal characteristics of environmental light interference and product defects are prone to confusion, and specifically, through the cooperative work of an instrument self-calibration module, a calibration evaluation unit, an environmental light interference detection unit and a health state prediction unit, a full-process intelligent detection system is constructed, the application of multi-source data fusion technology breaks through the limitation of traditional single data detection, multi-modal feature matching, time series data analysis, grid fine monitoring and other technologies improve the accuracy and comprehensiveness of detection, accurate differentiation of environmental light interference and product defects reduces misjudgment and missed judgment, and the proactive maintenance of the hardware health state reduces downtime loss.

This invention discloses a method and system for stability analysis of metrological instrument measurement results, relating to the field of time series data analysis technology. The method includes measurement data stream alignment, construction of baseline features for measurement result stability, three-state feature decomposition, and stability analysis of metrological instrument measurement results. Repeatability, trend, and structural features of the measurement results are extracted to construct a baseline feature vector for measurement result stability. A three-state feature decomposition method based on baseline constraints is used to decompose the stability changes of the measurement results into drift, noise, and jump features. Based on the baseline features and the three-state feature decomposition results, a comprehensive analysis of the stability state and changing trends of the metrological instrument measurement results is performed. This invention enables refined and causally distinguishable analysis of the stability of metrological instrument measurement results under complex operating conditions and multi-state operation, providing a reliable basis for instrument operating status assessment and measurement result reliability judgment.

The application discloses a traffic sequence prediction method based on multivariate time series data analysis, adopts a random graph diffusion attention mechanism to extract global and local spatial features of a traffic sequence, uses time attention to extract time features, improves prediction accuracy, reduces memory usage of a model, and improves the effect of the model on long-term prediction. The traffic sequence prediction method based on multivariate time series data analysis solves the problems of insufficient short-term prediction accuracy, high calculation complexity, large memory occupation, and insufficient lightweight of the existing model while maintaining the accuracy of long-term prediction.

The application discloses a kind of equipment fault diagnosis methods based on time series data analysis in industrial production in industrial scene, steps include: the data in time series database generated when industrial production system runs is preprocessed;Effective sample is extracted from the data after pre-processing, and multiple measuring point sample time is synchronized;Effective sample data is analyzed according to the characteristics of occurrence frequency, occurrence time, duration, and the correlation of the measuring points is obtained, so as to extract relevant measuring points;According to the data of relevant measuring points, the fault attribution factor graph is inferred.The application uses the data mining method related to big data to mine the correlation of equipment failure from massive sensor data, uses the related method in the field of artificial intelligence, carries out single-source, multi-source time series analysis on historical sensor data, locates one or more fault positions corresponding to abnormal data in combination with the correlation relationship mined.

The invention belongs to the technical field of disease and pest early warning, and discloses a change detection disease and pest early warning method fusing multispectral time sequence images and deep learning, which comprises a data acquisition module, a data preprocessing module, a model design module, a disease and pest recognition module and an early warning module, the data preprocessing module comprises an automatic preprocessing process and a high-quality cloud mask algorithm, the model design comprises an input layer, a feature extraction module, a time sequence modeling module, a change detection head and an output layer, and the early warning module comprises time sequence data analysis and uncertainty analysis. The early-stage, accurate and automatic early warning capability is realized; experimental results show that the method shows excellent performance in a plurality of crops and areas, and has good popularization and application values.

This deep learning-based method for predicting lung cancer recurrence using time-series data, comprises the steps in which: an analysis device receives, from a patient, inputs of static data collected before surgery and time-series dynamic data collected after the surgery; the analysis device pre-processes the static data and basic dynamic data among the time-series dynamic data; the analysis device inputs, into a trained prediction model, the pre-processed static data, the pre-processed basic dynamic data, and a medical image analysis sentence included in the time-series dynamic data; and the analysis device predicts the possibility of lung cancer recurrence of the patient within a predetermined period on the basis of a value output by the prediction model.

Artificial intelligence for time-series data analytics is provided. A first time-series data set is provided to a pre-trained recurrent neural network trained based on a second time-series data set. A prediction of a ground truth state of the first time-series data set is received therefrom. The first time-series data set is provided to a dynamical recurrent neural network trained based on the second time-series data set and the pre-trained recurrent neural network. A noise-reduced prediction of a ground truth system state of the first time-series data set is received therefrom. An estimate of sensor noise is read. The estimate of sensor noise is generated based on the second time-series data set and the pre-trained recurrent neural network. A prediction of a state of the system is generated based on the pre-trained recurrent neural network, the dynamical recurrent neural network, and the estimate of sensor noise.

The application provides a method for extending time series database analysis capability, and relates to the field of data analysis, which comprises the following steps: constructing a time series data analysis system for providing time series data analysis service independently of a time series database system, and extending, upgrading and maintaining the time series data analysis system without affecting the normal operation of the time series database system; adding an adaptive interface in the time series data analysis system, and calling different time series data analysis models; establishing an analysis operator for the time series data analysis service in the time series database system, and the analysis operator calling the corresponding time series data analysis model through the adaptive interface to complete time series data analysis in the execution of an SQL query process, so that interaction between the time series database system and the time series data analysis system which are isolated from each other in the processing flow is realized, and the complexity of the analysis and processing process is shielded.

Systems and methods are provided for implementing cloud distributed database capacity planning and / or adjustment, using time-series data analysis. In various embodiments, a computing system may be used to analyze collected throughput data associated with consumption of provisioned throughput resources of a distributed cloud database over one or more past periods by an entity. Based on the analysis, a set of predicted throughput data may be determined or generated for the entity over a future upcoming period. In some cases, based on a determination that adding physical partitions would be required, the computing system may adjust the set of predicted throughput data to reduce or minimize a number of physical partitions to be added. The provisioned capacity of the distributed cloud database may then be dynamically adjusted based at least in part on one of the unadjusted or adjusted set of predicted throughput data.