80 results about "Long term data" patented technology

The invention discloses an intelligent management method and system for IT equipment asset maintenance, and relates to the technical field of intelligent management. During system operation, multi-dimensional parameters of IT equipment are collected in real time and transmitted to a data processing module, the collected data are preprocessed, the current state of the equipment is comprehensively analyzed, and the health state coefficient Hstate of the equipment is calculated; the method comprises the following steps: acquiring a current equipment operation state, performing baseline establishment on historical operation data of the equipment, comparing the current state with the historical data, analyzing the performance trend and change of the equipment, calculating to obtain a trend analysis coefficient Ttend, and identifying potential problems and performance reduction trend of the equipment through long-term data accumulation. And calculating a final fault prediction probability index Pfault, comparing the final fault prediction probability index Pfault with a set threshold value, obtaining a grade prediction scheme, generating early warning information, prompting a system administrator to sort and distribute according to the urgency and priority of tasks, automatically generating a comprehensive report by the system, and providing decision support for the administrator.

The invention relates to the technical field of power system monitoring, in particular to an intelligent direct-current power supply whole-course fault recording analysis system which comprises a dynamic threshold self-adaption module, a multi-dimensional data fusion module, an event triggering module, a long-period data accumulation module, a complex boundary condition monitoring module and a fault mode recognition early warning module. Wherein the dynamic threshold adaptive module is used for collecting multi-dimensional data of a direct current power supply system in real time and dynamically adjusting a threshold range of fault detection; the multi-dimensional data fusion module is used for generating a comprehensive system operation state evaluation result; and the event triggering module is used for triggering a fault recording process when an abnormal condition meeting a threshold range condition is detected. According to the invention, through real-time data monitoring, deep learning and fault mode identification and long-term data accumulation, the fault early warning capability and the operation and maintenance efficiency of the DC power supply system are significantly improved, and stable and reliable operation of the system is ensured.

The invention relates to the technical field of temperature regulation and control, in particular to a liquid cooling control method and system of a battery control device, in the liquid cooling control method and system, through real-time collection and area screening of temperature and power data, accurate recognition of a high-temperature area is achieved, a clear cooling requirement partition is provided, and the cooling requirement of a battery is met. A dynamic adjustment strategy of pressure and flow parameters of the liquid cooling pump is optimized through a genetic algorithm, the cooling response speed and control precision are improved while energy consumption and cooling efficiency are effectively balanced, the pump speed and the valve opening degree are adjusted in real time, temperature changes are rapidly responded through automatic control, gradual implementation of a cooling target is ensured, and the cooling efficiency is improved. The hysteresis in the heat management process is reduced, the temperature control effect is continuously evaluated through a fuzzy control algorithm, the cooling intensity is dynamically adjusted according to the monitoring result, efficient cooling is ensured, meanwhile, equalization of temperature distribution is achieved, and a systematic heat management framework is established through long-term data monitoring and continuous optimization of cooling operation. And efficient and stable operation of temperature management is realized.

The invention provides an intelligent learning behavior monitoring and abnormity early warning method and device, and relates to the technical field of education analysis, and the device architecture comprises a multi-modal data collection module, a behavior analysis module, a dynamic abnormity grading early warning module and a processing measure module. The multi-modal data acquisition unit integrates learning platform operation records, facial expression data and physiological data as well as environment and situation information collected by the situation awareness data acquisition unit, so that omnibearing and multi-dimensional data acquisition is realized; the long-time monitoring and personalized model construction unit uses a plurality of data mining algorithms and LSTM in deep learning to combine with an attention mechanism, accurately mines student behavior rules according to long-term data and constructs a personalized model, and can flexibly and accurately judge abnormal behaviors according to student individual conditions and learning situations. Graded early warning is provided for related personnel in time, deterioration of learning problems is effectively prevented, and timeliness and effectiveness of education intervention are improved.

The invention provides an intelligent sturgeon health monitoring method and system based on multi-source data fusion, and the method comprises the steps: firstly, synchronously collecting underwater image data and infrared thermal image data of a sturgeon culture pond in real time, and carrying out the preprocessing of the data; the method comprises the steps that sturgeon behavior features and fish body temperature features are extracted, then fish body positioning is carried out, behavior feature extraction and temperature feature extraction are carried out respectively, feature fusion is carried out on the sturgeon behavior features and the fish body temperature features, finally, the sturgeon fusion features are input into a pre-trained anomaly detection model, and an anomaly detection result is obtained; according to the system, comprehensive, real-time and intelligent monitoring of the sturgeon culture process is achieved by integrating various sensing technologies and an advanced data analysis algorithm, abnormal conditions can be found and processed in time, and scientific basis and optimization suggestions can be provided for culture management through long-term data accumulation and analysis; meanwhile, the sturgeon breeding efficiency and the product quality are also remarkably improved, and the disease risk and the economic loss are reduced.

The invention discloses a building carbon metering method, equipment and medium based on edge calculation, and the method comprises the steps: deploying a plurality of sensors in a building, and collecting the data of power consumption, gas use, environment temperature and humidity, indoor and outdoor gas components, and equipment working states in real time; an edge computing device is utilized to perform preprocessing and real-time carbon emission calculation on acquired data, and meanwhile, the edge computing device and a cloud end perform cooperative processing, so that long-term data storage and deep analysis are realized. The system can intelligently optimize and make decisions, provide dynamic adjustment suggestions, and reduce energy consumption and carbon emission of the building. In addition, the method further comprises a carbon emission monitoring and alarming mechanism and strict security and data protection measures, so that the security and privacy protection of data are ensured. The method has the beneficial effects that carbon emission of the building can be effectively monitored and managed through the method, and real-time data support and optimization suggestions are provided for building managers, so that an intelligent building management target is realized, and sustainable development and environmental protection are supported.

The invention relates to the technical field of optical disk storage, in particular to an optical disk box storage management system based on hierarchical management, which comprises a data analysis module, a weight calculation module, a strategy making module, a medium selection module, a dynamic adjustment module and a performance evaluation module. According to the method, the optical disk data is deeply analyzed, and the access frequency, the file number and the modification frequency of the data are recorded, so that the storage requirement and the change of a data access mode can be more effectively identified, the accurate recording and weight distribution of data attributes and the sensitivity analysis of optical disk files are realized, and a more detailed storage management strategy is constructed; storage positions and priorities can be adjusted according to actual use conditions of data, real-time data requirements are responded, a mechanism is adjusted in real time to improve response speed and operation efficiency of a system, continuous adaptability and efficiency of a storage strategy are ensured through regular performance evaluation, and reliability and safety of long-term data storage are guaranteed.

The invention provides a high-speed traffic flow prediction method based on long and short period Transform fusion trend and seasonal characteristics, and belongs to the field of data unsupervised prediction. The invention provides a high-speed traffic flow prediction method based on long and short period Transform fusion trend and seasonal characteristics, aiming at the problems that time-space relevance exists among variables in traffic flow data and short-term and long-term information cannot be fully combined when a traditional method is used for processing the data, and the invention provides the high-speed traffic flow prediction method based on the long and short period Transform fusion trend and seasonal characteristics. According to the method, a Transform attention mechanism is innovatively combined with diffusion convolution, the spatial-temporal correlation of short-period data is deeply mined, meanwhile, the trend of Transform is fused with seasonal characteristics, and the long-term dependency relationship in different periods is effectively captured. And finally, fusing the prediction results of different period data by using three weight moments, and fully considering the characteristics of short-term data and long-term data. When the network model is applied to high-speed traffic flow prediction, the prediction accuracy can be improved, and the effectiveness of the method is verified by experimental results.

The invention discloses an improved agricultural production process traceability method based on artificial intelligence. The method comprises the following steps: S1, collecting multi-dimensional agricultural production data; s2, performing data cleaning, noise removal, abnormal value detection and format conversion on the multi-dimensional agricultural production data; s3, dividing the multi-dimensional agricultural production data into short-term data, medium-term data and long-term data according to time attributes of the multi-dimensional agricultural production data; s4, dividing the multi-dimensional agricultural production data according to land parcels, areas and crop type space positions; s5, classifying the multi-dimensional agricultural production data according to a plurality of production stages of crops from sowing, fertilizing, watering, weeding and harvesting; s6, information of the abnormal agricultural production nodes is fed back to the management system; s7, generating a traceability report; and S8, dynamically adjusting the irrigation frequency, the fertilization amount or the equipment maintenance plan of the crops according to the feedback information. According to the invention, flexible management of the agricultural production process is realized, and the overall efficiency and quality of agricultural production are improved.

The invention discloses an equipment performance prediction method based on digital twinning, particularly relates to the field of digital twinning, is used for solving the problems of hidden fluctuation and burst interference in equipment operation data, and comprises the following steps: dividing the equipment operation data into different clusters through clustering analysis; irrelevant fluctuation and interference signals are accurately identified and removed by combining a fluctuation trajectory stability index and a sudden-change derivative acceleration index, so that the data input into the model can reflect the real state of the equipment more accurately; hidden fluctuation caused by equipment microscopic change, external interference or environmental fluctuation is stripped, so that the interference of the change which is not easy to perceive on a prediction result is avoided; according to the process, the data quality is remarkably improved, the influence of noise which is difficult to perceive in a short time on the stability of the model is prevented, fluctuation and jumping fluctuation in the prediction process are effectively reduced, and it is ensured that the model keeps high prediction reliability and consistency in a complex equipment operation environment and long-term data.

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.

Disclosed is a stroke-based differentiable rendering for both representation of spatiotemporal sensor data and unsupervised spatiotemporal events detection wherein we encode DAS waterfall data into a structured latent space based on parameterized brushstrokes. The structured brushstroke representation can (1) suppress background noise and distracting clutters from the original waterfall data, (2) allow easy leverage of geometrical prior knowledge for physics-informed pattern recognition. Guided by multiple specially designed targets that emphasize different aspects of the original data, the optimized strokes not only preserve the salient information, but also align well with the original data in terms of spatial and temporal coordinates. As a results, it also provides pixel-level annotation as a byproduct. Based on long term DFOS data and cumulative statistics, we can further localize landmarks (such as traffic lights, manholes, etc.) from the waterfall data. These landmarks can be used for cable mapping.

The invention discloses a railway line environment potential safety hazard intelligent identification system, which mainly comprises a sensor module, a data processing unit and an alarm and feedback system, and is characterized in that the sensor module comprises a video monitoring camera, an environment sensor (such as a temperature sensor, a humidity sensor, a vibration sensor and a noise sensor), a laser radar and a gas detection sensor; the system is used for collecting multi-dimensional data along the railway, the safety monitoring efficiency and the early warning capability of the environment along the railway are remarkably improved through the application of the system, and high efficiency of informatization management and hidden danger rectification is achieved. Through an intelligent recognition algorithm, the system can automatically detect and recognize potential safety hazards along the railway, the dependence on manual inspection is reduced, and the emergency response speed and optimization of resource allocation are improved. In addition, the data storage and history backtracking functions of the system support long-term data analysis, and continuous improvement of railway safety management is facilitated.

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 embodiment of the invention provides a generation power and electric quantity prediction method of a wind power plant and electronic equipment. The method comprises the following steps: acquiring target data, wherein the target data comprises short-term data, medium-term data and long-term data; respectively inputting the short-term data, the medium-term data and the long-term data into a first sub-model, a second sub-model and a third sub-model to obtain a first prediction result, a second prediction result and a third prediction result; and according to the first prediction result, the second prediction result and the third prediction result, determining the generation power and the electric quantity of the wind power plant. According to the embodiment of the invention, short-term data, medium-term data and long-term data are analyzed at the same time, change modes on different time scales are captured, and the generated power of the wind power plant and the comprehensiveness of electric quantity prediction are improved; by considering the data change of multiple time scales, the machine learning model can keep stable accuracy in different time ranges, and adapts to short-term fluctuation and long-term trend.

The invention relates to the technical field of equipment control, and provides a target equipment control method and device, electronic equipment and a storage medium. The method comprises the following steps: collecting current operation parameters of target equipment, and cleaning the current operation parameters to obtain current operation characteristics; the current operation features are input into a target fusion model for processing, a demand prediction value of work of the target equipment in the next period is obtained, the target fusion model comprises a target long-term model and a target short-term model, the target long-term model is obtained based on long-term data training, and the target short-term model is obtained based on short-term data training; the target short-term model comprises historical short-term models arranged according to a time sequence, and the adjacent previous historical short-term model is obtained by training the adjacent later historical short-term model based on the short-term data, determining a control parameter of the target equipment based on the demand prediction value, and controlling the target equipment to operate based on the control parameter. The problem that a system prediction result is inaccurate in the prior art is solved.

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 present invention belongs to the field of business analysis technology, and specifically is a business intelligence report and analysis system, including a data acquisition module, a data analysis module, an evaluation module and a decision support module; the data acquisition module is used to extract and organize front-end product information, and also includes collecting information on products to be compared related to the front-end products; the front-end product information is used to generate a front-end product marketing report; the data analysis module is used to analyze the influence relationship between the front-end product and the products to be compared, including calculating the correlation R c And according to the correlation R c Determine related products; through correlation calculation, calculate the correlation of the products to be compared that may be related to the front-end products in turn, obtain a quantitative correlation supported by long-term data, and then use the lower limit value of the first threshold interval to dynamically adjust the remaining products to be compared to perform a second screening, so as to determine the related products.

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.