50 results about "Long term data" patented technology

The invention relates to a conveying belt tearing positioning and tracing system based on RFID and multi-sensor fusion, which effectively overcomes the influence of belt slipping and elastic deformation through multi-sensor fusion and motion compensation, and realizes accurate positioning of a tearing point. Meanwhile, through cross validation of vibration, vision and infrared data, the recognition accuracy is improved to 99.5% or above, false alarms caused by environmental factors (such as dust and moisture) are greatly reduced, stable operation in a severe industrial environment is ensured, and high reliability and low false alarm rate are achieved. And thirdly, the system supports long-term data tracing and intelligent operation and maintenance, and a user can analyze a tearing trend and identify a high-frequency fault area by storing historical data of at least 180 days and providing a visual tool, so that a maintenance plan is optimized, predictive maintenance is realized, and faults are reduced from the source.

The invention discloses a sniffing detection error compensation method, which relates to the technical field of environment detection, and comprises the following steps: S1, data acquisition: collecting detection data and environment parameters of sniffing distinguishers in multiple dimensions, S2, establishment of a sniffing analysis model: extracting features from three aspects of individuals, working states and environments, the method comprises the following steps of: selecting a basic algorithm to construct a model according to personal information, historical detection performance, physiological index statistical values, workload and various environmental parameters, and counting the number of sniffing and distinguishing aiming at a smell adaptation problem, S3, error prediction and coefficient compensation, S4, result correction and S5, data transmission. According to the invention, through data acquisition and establishment of the olfactory analysis model, the physical signs of the olfactory discriminator and the fine parameters of the environment are matched with long-term data accumulation to provide massive and high-quality data for the data-driven olfactory detection error prediction model, and through error prediction, coefficient compensation and result correction, the accuracy of the olfactory detection error prediction model is improved. Therefore, accurate prediction and compensation of the sniff detection error are realized, and the accuracy of sniff detection is improved.

The invention relates to an ambient air quality monitoring data correction system, which belongs to the technical field of computer data processing and modeling, and comprises an environment state judgment module used for acquiring original monitoring data of a micro station, determining a sea fog interference state mark based on the original monitoring data, and sending the sea fog interference state mark to the micro station; the original monitoring data comprises relative humidity and original mass concentration of particulate matters; the first correction module is used for processing the original mass concentration of the particulate matters in the original monitoring data when the sea fog interference state flag is 1 so as to generate first particulate matter correction data; and the second correction module is used for processing the original monitoring data to generate second correction data when the sea fog interference state mark is zero, and technical guarantee is provided for advanced applications which depend on long-term data quality, such as pollution source tracing and environmental governance effect evaluation.

The invention relates to the technical field of wind driven generator blades, and discloses a power increasing and deicing method for wind driven generator blades, which comprises the following steps: S1, analyzing field wind resources and calculating parameters: collecting long-term data by using meteorological data and wind field measurement equipment, analyzing the wind resources, and analyzing and calculating key parameters based on the wind resources; s2, load comparison and strength evaluation: simulating stress conditions of the extended blade at different wind speeds, calculating new load distribution, and comparing a calculation result with an original design load of a unit; s3, on-site blade extension technical improvement is implemented, specifically, the blade tip of an original blade is cut, temperature and flow monitoring equipment is installed at the position of the blade tip, and a blade tip extension section is firmly bonded with the blade root and the blade tip; s4, blade rotation positioning is conducted, specifically, the blades are subjected to pitch changing till the blades are parallel to the ground, the SS faces are downward, and after pitch changing is completed, the blades and the impeller are locked; and S5, polishing and mounting position marking: marking the mounting position of the air guide pipe, and polishing the mounting positions of the air blower and the heater.

The invention discloses a material-environment coupled self-adaptive riveting parameter optimization method and system. The method comprises the following steps: acquiring force data, speed data, position data, environment data and material data in a riveting process; when the force data is greater than a preset force threshold value, adjusting the parameters of the riveting equipment to obtain stable force data; performing fluctuation mode analysis on the stability strength data to obtain an equipment state report; combining the material data with a preset strength prediction model to obtain initial prediction strength; adjusting the initial prediction force according to the comprehensive correction coefficient to obtain the optimal prediction force; and judging whether the equipment parameters need to be adjusted or not according to the predicted optimal force and stable force data to obtain a judgment result, and adjusting the riveting equipment according to the judgment result and the equipment state report. According to the method, the riveting operation stability and the equipment maintenance efficiency can be improved by predicting and adjusting the riveting force in real time and combining long-term data analysis and identification equipment abrasion and material change.

The invention relates to the field of distributed storage, and discloses a distributed storage system, method and equipment for wind power plant data and a medium, which are used for realizing flexible adjustment of a storage strategy and meeting the storage requirement of dynamic change of a wind power plant data value. Comprising the following steps: collecting SCADA system data at edge nodes of a wind power plant, inputting an information entropy module and an access heat analysis module in parallel, fusing to generate a data value evaluation result, inputting the evaluation result into a cellular automaton engine, outputting a cellular state change instruction, summarizing collaborative strategy requests of at least two edge nodes to a regional center node, and carrying out collaborative strategy analysis on the regional center node. And a fair resource allocation scheme is output through the Sharpley value calculation module. And each edge node synchronously receives the scheme, the time sequence database controller generates a final storage instruction, and data distributed persistent storage is completed. According to the method, on the basis of the correlation between the long-term data value evaluation result and the actual use value, the entropy value and the popularity weight coefficient are optimized, and self-adaptive optimization of the system is achieved.

The invention provides a production method of a spicy red oil seasoning, which comprises the following steps: acquiring real-time data of spicy aroma components from spice raw materials through a sensor array in combination with a spectral analysis technology to obtain quantitative data of quality fluctuation of the raw materials; according to the quantitative data of the quality fluctuation of the raw materials, analyzing the relevance between the spicy aroma components and the quality fluctuation by adopting a machine learning model, and determining key parameters of formula adjustment; aiming at the initial parameters for dynamically adjusting the formula, simulating a formula execution effect under consistency constraint in a production process by adopting a digital twinning technology to obtain adjustment parameters for optimizing the formula in real time; if the deviation data of the actual component change exceeds a preset deviation threshold value, adjusting the optimized formula data by adopting a feedback control algorithm, and generating dynamically adjusted formula data; according to the dynamically adjusted formula data, the complete process of formula adjustment is recorded through the data analysis platform, and a digitalized long-term data record of formula management is generated.

The application relates to a multi-unmanned aerial vehicle (UAV) assisted edge computing task offloading method and system, and belongs to the technical field of mobile edge computing task offloading. The method comprises the following steps: constructing a multi-UAV assisted edge computing task offloading system model and an optimization problem model; based on the above model, a Markov decision process under a multi-agent environment is constructed, a reward function taking queue stability and information age minimization as targets is defined, a deep deterministic policy gradient algorithm is adopted, the optimization problem is solved through interaction between the multi-agent and the environment, and an optimal offloading strategy of a ground communication device computing task is obtained. The system comprises a ground communication device and a UAV, the UAV is in communication connection with the ground communication device, and an edge computing server arranged on the UAV is used for realizing the above method. The application not only guarantees the stability of a long-term data queue, but also obtains an optimal information age in an online mode, and solves problems such as communication congestion and poor user experience quality.

This invention discloses a daily runoff prediction method based on the Long Short-Term Component Neural Network (LSTCNet) model. LSTCNet leverages the advantages of convolutional layers to extract features. An attention mechanism (AM) is introduced in the long-term component to automatically select relative times across all time steps of the long-term data. The short-term component utilizes a multi-layer residual structure to fuse multi-level features from the short-term data, effectively improving model performance. Furthermore, a traditional AR model is used as a linear neural network component to enhance the learning of linear dependencies between variables and modify the output prediction values. Experimental results show that the proposed method has good performance in daily runoff prediction; the long-term and short-term components can effectively learn from their respective data, demonstrating the strength of the ensemble model.

This invention relates to the fields of smart city and machine learning technology, specifically a method for continuous self-optimization of urban governance models based on data closed-loop feedback. The scheme involves: acquiring raw waveform data and operating condition reference signals from vibration sensors of urban lifeline facilities; generating a time-frequency energy spectrum matrix through time-frequency transformation; decomposing this matrix into slow-changing and fast-changing components using a machine learning model; removing slow-changing components correlated with long-term operating conditions; labeling the remaining slow-changing components as the first component and the fast-changing components as the second component; generating a frequency-related compensation curve based on the difference between the first component and the historical energy spectrum; performing multiplicative correction; and simultaneously calculating a coupling health index, triggering a calibration work order and updating the historical energy spectrum when the index falls below a threshold. This invention achieves closed-loop optimization of sensor coupling state self-sensing, self-compensation, and self-calibration, significantly improving the long-term data stability and accuracy of urban lifeline monitoring systems.

An AoI optimization method based on a space-air-ground integrated network relates to the technical field of unmanned aerial vehicle communication, and comprises the following steps: 1, dividing a ground sensing area into a plurality of sub-areas configured with an unmanned aerial vehicle, dividing a long-term data collection period into a plurality of equilong basic time slots, and each basic time slot comprises a long time slot and a short time slot which are connected in time sequence; and 2, constructing an AoI optimization model of an air-ground link under a single time slot, and achieving the goal of minimizing the long-term average AoI of ground nodes by optimizing node clustering, energy supply and data transmission time distribution, cluster scheduling and unmanned aerial vehicle trajectory planning. And 3, constructing an AoI optimization model of an air-space link under a single time slot, and realizing the target of reducing the cache backlog of the unmanned aerial vehicle by optimizing an access strategy of the unmanned aerial vehicle and a low-orbit satellite and a transmission bandwidth allocation strategy. According to the invention, the technical problems of limited energy supply of the ground sensing network, high system AoI optimization complexity and difficult network resource scheduling in the prior art can be solved.

This invention discloses a postoperative rehabilitation training and health management system and method for breast cancer patients, belonging to the field of smart healthcare and rehabilitation training technology. The system includes a local smart terminal, a cloud server, and a medical staff terminal. The local terminal integrates a millimeter-wave radar sensor, which generates upper limb point cloud data by transmitting and processing echo signals. A three-dimensional human skeleton model is reconstructed using a deep learning network, and motion parameters such as joint angles and movement trajectories are extracted. The system is then compared with a standard template to calculate the motion completion score and provides real-time corrective feedback. The cloud server deploys a finely tuned large language model, which, combined with a rehabilitation knowledge base, performs enhanced retrieval and generation, providing personalized suggestions based on the patient's rehabilitation history and consultation questions. The rehabilitation plan generation module dynamically adjusts the training plan based on long-term data analysis. This invention achieves a deep integration of non-contact precise perception, real-time motion assessment, and intelligent health management.

The invention discloses a dynamic personal archive optimization method based on continuous feedback, relates to the technical field of personal archive optimization, is used for solving the problems that strategy evolution based on feedback cannot be supported and real-time adjustment means are lacked, and provides a dynamic personal archive optimization method based on continuous feedback by uploading a plurality of candidate archive variants by a user and completing format, size and repeatability verification by a system. The dynamic experiment and optimization module establishes a dobby machine model based on the set, dynamically selects and displays variants by adopting an epsilon-greedy algorithm, collects feedback, calculates and displays a reward value, updates a weight in combination with a smoothing mechanism, avoids local optimum caused by early fluctuation, and improves the dynamic experiment and optimization efficiency. And the personalized archive consultant module constructs a random forest regression model based on long-term data and a tiger machine result, analyzes feature contributions in combination with a Shapley value, generates optimization suggestions, and improves archive attraction and user experience.

The invention discloses a wind power plant extreme disaster risk pre-judgment method and device based on extreme value distribution, and the method comprises the steps: collecting the long-term extreme disaster data of more than or equal to 30 years, extracting an annual extreme value sequence, and carrying out the abnormal value processing through the combination of Grubbs detection and meteorological record verification; and selecting a Gunbel or generalized extreme value distribution model according to the disaster type, fitting parameters by using a maximum likelihood method, calculating an extreme value through distribution inversion, comparing the extreme value with an engineering design threshold, and outputting a risk conclusion and a protection suggestion. The method is based on long-term data and probability modeling, the accuracy and scientificity of extreme value prediction are improved, the problem of evaluation deviation caused by short data, rough model and separation from engineering in a traditional method is solved, and the method has good engineering application value.

Techniques of storing and retrieving medical data and information are provided. Medical result information can be stored on a long-term data repository in a shared network, e.g., the Internet. Pseudonymized identifiers of patients can be used to retrieve such data and information.

The present invention discloses an improved traceability method for agricultural production processes based on artificial intelligence. S1: Collect multi-dimensional agricultural production data; S2: Perform data cleaning, noise removal, outlier detection, and format conversion on the multi-dimensional agricultural production data; S3: Divide the multi-dimensional agricultural production data into short-term data, medium-term data, and long-term data according to the time attributes of the multi-dimensional agricultural production data; S4: Divide the multi-dimensional agricultural production data according to the spatial positions of plots, regions, and crop types; S5: Classify the multi-dimensional agricultural production data according to multiple production stages of crops from sowing, fertilizing, watering, weeding, and harvesting; S6: Feed back the information of abnormal agricultural production nodes to the management system; S7: Generate a traceability report; S8: Dynamically adjust the irrigation frequency, fertilization amount, or equipment maintenance plan of crops according to the feedback information. The present invention realizes the flexible management of agricultural production processes and improves the overall efficiency and quality of agricultural production.

The invention relates to a disk CDP-based long-term and short-term data backup storage method and device, and belongs to the field of disaster recovery backup. The method comprises the steps of creating and generating a data mirror image, a bitmap, an I / O log and metadata under a current directory, recording real-time change data by using the files, modifying a directory name of the current directory, deleting the I / O log, obtaining a T0 directory and a file thereof, repeating the previous steps, and generating incremental data according to a subsequently triggered incremental data generation instruction. And sequentially obtaining directories from T1 to TN and files of the directories, and carrying out mirror image rollback on the long-term data through data in the directories from T0 to TN. The device comprises a short-term data directory generation module, a short-term data recording module, a long-term data generation module, a subsequent long-term data generation module and a real-time data storage module. According to the method, long-term and short-term data can be returned through one set of system, the strategy configuration is unified, the storage overhead is low, and one-stop and low-TCO data protection is provided for high-compliance and high-continuity scenes.

The invention discloses a monitoring sensor missing data recovery method based on deep learning, and relates to the technical field of ocean platform health monitoring, and the method comprises the steps: collecting the strain data of a sensor, constructing a data set and a data missing judgment strategy, carrying out the preprocessing of the data set, and dividing the data set into a short-term data missing training set and a long-term data missing test set; constructing a double-branch data processing model based on a bidirectional recurrent neural network and a GRU unit, and inputting two training sets to train the model to obtain a sensor data recovery model; inputting the test set to obtain predicted missing data, and evaluating and optimizing the model according to an error between a predicted value and a true value to obtain an optimized model; inputting the sensor data into the optimized sensor data recovery model, judging the type of missing data, and outputting the missing data of the target sensor; according to the method, the problem of long-term and short-term data missing can be adaptively processed, and missing data can be efficiently and accurately recovered in various data missing scenes.

A threat management facility for an enterprise network provides visualization tools for threat analysis and investigation. While security events may generally be logged in a long term data repository such as a data lake, security events can be transmitted directly to a short term, higher performance data repository for faster visualization when fast response times might be necessary or helpful. In this context, the threat management facility may use time stamps associated with event reporting to select a time-indexed segment of the data lake as a target for supplemental, investigative queries.

This invention discloses a cross-individual epilepsy prediction domain generalization method based on feature decoupling. This method extracts various time-frequency features commonly used in epilepsy prediction and effectively fuses them using a spatiotemporal multi-scale convolutional neural network, fully capturing short-term and long-term dependencies and local and global spatial structures in EEG signals. Furthermore, a feature decoupling strategy divides the fused features into domain-invariant features and domain-related features, and adversarial training is used to suppress individual information contained in the domain-invariant features, thereby improving the model's generalization ability on data from unknown subjects. This invention significantly improves the accuracy of cross-subject epilepsy prediction, reduces reliance on long-term data collection from new subjects, and provides a practical technical path for the real-world application of epilepsy prediction.

The invention relates to a method (100) for providing long-term data collection, comprising the following, which is performed by a base station (10) of a communication network (1): - Sending (101) an information message (300) regarding the availability of a long-term data collection function, which provides long-term data collection via a dedicated interface to user equipment (20) in the communication network (1), wherein the information message (300) includes an indication of the availability of the long-term data collection function, - Receiving (102) from the user equipment (20) a user request based on the sent (101) information, wherein the user request includes the indication that references the availability of the long-term service function, - Providing (103) the long-term data collection of the long-term service function at the dedicated interface based on the received (102) user request.

This invention relates to the field of distributed storage, and discloses a distributed storage system, method, device, and medium for wind farm data. It enables flexible adjustment of storage strategies to meet the storage needs of dynamically changing wind farm data value. The system includes collecting SCADA system data at wind farm edge nodes, inputting the data into an information entropy module and an access heat analysis module in parallel, fusing the data to generate a data value assessment result, inputting the assessment result into a cellular automaton engine, outputting cellular state change instructions, aggregating collaborative strategy requests from at least two edge nodes to a regional central node, and outputting a fair resource allocation scheme via a Shapley value calculation module. Each edge node synchronously receives the scheme, and a time-series database controller generates the final storage instruction, completing the distributed persistent storage of the data. This invention optimizes the entropy value and heat weight coefficient based on the correlation between long-term data value assessment results and actual use value, achieving adaptive system optimization.

The invention provides a lake ecological flow processing method and a lake ecological flow processing device based on a remote sensing and water balance model, which are used for establishing a set of lake ecological flow standardization processing architecture based on a remote sensing and water balance equation, effectively solving the limitation that the traditional method seriously depends on actually measured hydrological data, and reducing the calculation cost. Meanwhile, compared with the characteristic that long-term data accumulation is needed in a traditional method, the timeliness is remarkably enhanced, so that the application requirements of low cost and rapid evaluation can be met, and good data support is provided for lake water resource development and utilization management and control and lake ecological management work.

The application discloses a kind of based on deep learning's monitoring sensor missing data recovery method, it is related to ocean platform health monitoring technical field, including: the strain data of acquisition sensor, constructs data set and data missing judgment strategy, data set is preprocessed and is divided into short-term and long-term data missing training set and test set;Bi-branch data processing model based on bidirectional recurrent neural network and GRU unit is constructed, two kinds of training set are input to train the model and obtain sensor data recovery model;Input test set obtains the missing data of prediction, according to the error of predicted value and true value the model is evaluated and optimized to obtain optimized model;Sensor data is input into the optimized sensor data recovery model, judges missing data type, and outputs the missing data of target sensor;The application can adaptively handle long-term and short-term data missing problem, ensure that missing data can be efficiently and accurately recovered under various data missing scenarios.

A data hierarchical storage system of a vehicle remote monitoring system is provided with a hot data layer, adopts distributed storage, and is used for storing nearly real-time data, holding high-concurrency write-in and low-delay query, and ensuring real-time monitoring and quick response; a temperature data layer is arranged, column type storage is adopted, the temperature data layer is used for storing medium-term data, and query performance and storage cost are balanced through compression and partitioning technologies; a cold data layer is arranged, object storage or a distributed file system is adopted, and the cold data layer is used for storing long-term archived data and supports batch access and offline analysis. Through the arrangement of the hot data layer, the warm data layer and the cold data layer, the overall storage cost is remarkably reduced, the situation that all data occupy high-performance storage resources for a long time is avoided, the SSD consumption problem is solved, meanwhile, the data storage and analysis process is simplified through the mode, data redundant replication is avoided, and the data management efficiency is improved; and finally, data lake construction of vehicle enterprises can be supported, and unified data management and long-term data value mining are realized.

The invention provides an unmanned autonomous underwater vehicle (UUV) toughness design framework considering multi-source uncertainty, which comprises four key stages: an initial design stage: optimizing physical parameters and control parameters of a UUV by adopting a multidisciplinary optimization method to obtain initial optimal performance; in the absorption stage, performance changes caused by multi-source uncertainty factors in the task execution process are monitored in real time, the performance degradation condition is analyzed, and the uncertainty type and degree are identified; in the recovery stage, based on the data in the absorption stage, a control strategy is dynamically adjusted through an intelligent optimization algorithm, and the performance of the UUV in the uncertain environment is recovered; in the evolution stage, the uncertainty evolution rule is analyzed through long-term data, physical and control parameters of the UUV are optimized, and the comprehensive performance of the whole application period is improved. According to the method, the environmental adaptability and real-time control capability of the UUV are improved, the design process is optimized, the task endurance time is prolonged, and the method is suitable for the fields of ocean observation, national defense safety, ocean resource development and the like.

The application provides a lake ecological flow processing method and device based on remote sensing and water balance model, which is used for establishing a lake ecological flow standardized processing architecture based on remote sensing and water balance equation, effectively solving the limitation that the traditional method seriously depends on measured hydrological data, and reducing the calculation cost. At the same time, compared with the characteristics that the traditional method needs long-term data accumulation, the timeliness is also significantly enhanced, so as to meet the application demand of low-cost and rapid evaluation, and provide good data support for lake water resource development and utilization control and lake ecological management.

The invention belongs to the field of artificial intelligence, and relates to a risk trend data pushing method and device, equipment and a medium, and the method comprises the steps: obtaining crop multi-source data from a multi-source database, carrying out the coupling with an initial meteorological prediction model, and constructing a meteorological risk assessment model. Acquiring monitoring images and meteorological data of crops in the target plot, and determining the current growth period of the crops. And outputting risk degree indexes and risk driving factor analysis results through evaluation of the meteorological risk evaluation model by using the growth period data and the meteorological data. And correcting the full-period risk threshold according to the growth period to obtain a target risk threshold. And obtaining meteorological prediction data of a target time period, and combining the above results to perform prediction by using a time sequence prediction model to obtain risk trend data. And extracting user information of the target plot from the crop multi-source data, and pushing the risk trend data to a corresponding user terminal. The method can be applied to the business fields of financial insurance, agricultural management and the like, and the accuracy of crop risk trend data pushing can be improved.

The invention discloses a phytoplankton in-situ flow imaging device and an imaging method, and belongs to the technical field of marine ecology and water quality monitoring, and the phytoplankton in-situ flow imaging device comprises an optical imaging module, a fluid control module, a data processing module and a communication module which are fixedly arranged in a sealed cavity of an outer shell. According to the in-situ detection device, optical triggering is not needed, the structure is simple, full-fluid microscopic images can be continuously collected, automatic monitoring and high-throughput quantitative analysis of phytoplankton and suspended particles are achieved, the remote interaction operation capacity is achieved, and reliable long-term data support is provided for marine ecological monitoring.

The present invention relates to the field of temperature control technology, and specifically to a liquid cooling control method and system for a battery control device. In the present invention, through real-time collection and regional screening of temperature and power data, accurate identification of high-temperature areas is achieved, clear cooling demand zoning is provided, and a dynamic adjustment strategy of liquid cooling pump pressure and flow parameters is optimized by genetic algorithm. While effectively balancing energy consumption and cooling efficiency, the cooling response speed and control accuracy are improved, and the pump speed and valve opening are adjusted in real time. Through automatic control, the temperature change is quickly responded to to ensure the gradual realization of cooling goals and reduce the lag in the thermal management process. The temperature control effect is continuously evaluated by fuzzy control algorithm, and the cooling intensity is dynamically adjusted according to the monitoring results to ensure efficient cooling while achieving balanced temperature distribution. Through long-term data monitoring and continuous optimization of cooling operations, a systematic thermal management framework is established to achieve efficient and stable operation of temperature management.