19 results about "Fuzzy prediction" patented technology

A power transformation equipment oil temperature prediction and abnormity early warning method based on a fuzzy algorithm comprises the following steps that real-time operation parameters and oil temperature historical data of power transformation equipment are collected, the data are preprocessed, and a high-quality input data set is constructed; constructing a fuzzy inference system considering multiple input factors, and setting a fuzzy membership function and an inference rule base; according to the fuzzy prediction result and the real-time oil temperature change, a self-adaptive dynamic safety threshold model is compared, a potential abnormal trend is identified, and automatic grading early warning is carried out according to the overtemperature grade; and corresponding exception type identifiers and disposal suggestions are generated and are pushed to the operation and maintenance platform through the communication module, so that remote operation and maintenance scheduling and response control are supported, and intelligent cooperative processing is realized. According to the invention, intelligent prediction of the oil temperature change trend under the influence of multiple factors is realized, the capability of sensing temperature rise abnormity in advance is improved, the accuracy of sensing the operation state of the equipment and the timeliness of early warning response are also remarkably improved, and the risk of equipment failure caused by the abnormal oil temperature is reduced.

This invention discloses a method, apparatus, and device for evaluating the quality of medical document images, relating to the field of medical technology. The method includes: acquiring a medical document image to be evaluated; detecting text boxes containing text content within the image; inputting the text boxes into a text fuzzy prediction model to obtain a fuzzy prediction result for the text boxes; and determining the quality of the medical document image based on the fuzzy prediction result. This method can accurately evaluate the quality of medical documents that are partially clear and partially blurred.

This application relates to the field of stationary time series analysis and prediction technology, and discloses a fuzzy prediction method for non-stationary time series, including: determining the fuzzy posterior probability corresponding to the current sequence; updating the fuzzy parameters of the fuzzy set based on the fuzzy posterior probability; generating a rule base associated with the updated fuzzy parameters; performing probabilistic defuzzification prediction based on the rule base to obtain the prediction result; updating the model parameters of the Gaussian mixture model based on the prediction error set corresponding to the prediction result; determining the KL divergence corresponding to the prediction error set based on the parameters in the model parameters, the first half error set corresponding to the first half window, and the second half error set corresponding to the second half window; wherein the current window consists of a first half window and a second half window; the prediction error set consists of the first half error set and the second half error set; and performing prediction based on the KL divergence to obtain the output sequence. This solution can meet the practical prediction needs of non-stationary time series.

The application discloses a kind of high-resolution radar echo extrapolation prediction methods of fusion satellite data, specifically as follows, first, input history radar echo sequence pretreatment of previous T time, including denoising, normalization processing, data set segmentation, obtain cleaned data;Then, by deterministic modeling method (SimVP), obtain the fuzzy prediction sequence of future T length, then variational autoencoder (VAE) respectively original radar echo image and fuzzy prediction sequence are mapped to low-dimensional latent space, and two-stage diffusion modeling is carried out on this basis;For the first stage, utilize space-time converter (ST-Translator) to extract the space-time evolution characteristics of radar echo;Second stage first input corresponding time satellite data of previous T time, pretreatment is carried out, including normalization processing, feature selection, data set segmentation, obtain cleaned data, adopt multi-source fusion denoising network Fsrformer, dynamically adjust the influence of satellite data in diffusion process, to make full use of satellite information;Finally, the output result of two stages is inversely transformed to pixel space, and the high-resolution radar echo extrapolation prediction result of future T length is obtained.The application can effectively reduce the consumption of computing resources, improve the precision and detail fidelity of short-term precipitation prediction.

The present application belongs to the technical field of ship working condition prediction, and discloses a rotating speed prediction method for a ship gas-electric hybrid power system. A ship gas-electric hybrid power propeller rotating speed prediction model is constructed based on an adaptive neuro-fuzzy inference system (ANFIS); a difference between the obtained predicted rotating speed and the actual rotating speed is obtained; an improved rotating speed prediction model is constructed using the obtained difference and the used rotating speed information; and future rotating speed prediction is performed through the constructed improved rotating speed prediction model. The present application uses the initially constructed rotating speed prediction model to obtain the first-step predicted rotating speed, and provides the difference between the predicted rotating speed and the actual rotating speed for the improved prediction model; the improved rotating speed prediction model is constructed according to the obtained difference and the initial rotating speed information, so as to improve the rotating speed prediction accuracy and achieve the real-time prediction effect within a given time step.

The invention provides a projection-near-end agricultural instance segmentation method and system based on capacity limitation. The method comprises the following steps: establishing a capacity-calculation contract, and constraining model parameter drift and frame rate; an orthogonal residual aggregation module is introduced into the feature space, and the edge consistency gradient is enhanced through orthogonal disturbance in a Cayley form; introducing an anisotropic offset correction module into a geometric space, and utilizing conical cutting and group-level low-rank sharing to stabilize gallery type scene sampling; an overlapping prototype clamping module is introduced into an output space, and fuzzy prediction is inhibited through near-end updating and exclusive loss. According to the method, the real-time reasoning requirement of embedded equipment is met, and meanwhile, the segmentation precision and connectivity of crop rows, travelable areas and obstacles in an unstructured environment are remarkably improved.

The invention provides a multi-model fusion resident electricity consumption prediction method, which comprises a data acquisition step, a first model construction step, an input data judgment step, a first prediction step, a second prediction step and an electricity consumption calibration step. The power consumption of the electric appliance can be predicted through the fuzzy prediction model, and when a user only inputs necessary option data and a specific time period, the power consumption of the electric appliance can be predicted through the fine prediction model, so that the data required to be manually input by the user is greatly reduced, the automation is improved, and the user experience is improved. Different models can accurately predict the electricity consumption for different input data, and in addition, for the electric appliance which is used for more than 15 minutes each time and has constant power, the electricity consumption of the electric appliance can be calibrated by acquiring 24-hour 96-point load data of the electric appliance. The method has the advantages that the automation level of residential electricity consumption prediction and the accuracy of energy consumption estimation are improved.

The invention provides an interlocking control system and method for a blow-off valve of a blast furnace. The system comprises a gas pipe, the blast furnace, a hot blast stove, a fan and an anti-surge valve which are connected in sequence, the TRT unit, the bypass valve group and the regulating valve group are arranged on the gas pipe in parallel; the pressure sensing element is arranged at the top of the blast furnace; the control unit is electrically connected with the TRT unit, the bypass valve group, the regulating valve group, the anti-surge valve and the pressure sensing element respectively; wherein the TRT unit comprises a turbine stationary blade with an adjustable opening degree; the bypass valve group comprises a first bypass valve and a second bypass valve which are arranged in parallel; the regulating valve group comprises regulating valves A, B, C and D which are arranged in parallel; the control unit comprises a fuzzy PID prediction module and an interlocking logic module. The method effectively solves the problems that an existing gas system is weak in processing capacity, an adjusting valve set is delayed in response and limited in adjusting capacity, so that when the blast furnace condition is abnormal, the top pressure peak value is difficult to effectively control through an automatic wind reducing measure, and finally non-planned opening of a blow-off valve is easily caused.

The invention discloses a ventilation system energy-saving control method and system based on fuzzy prediction, and the method comprises the steps: capturing a heat storage state of an enclosure structure in real time through a nonlinear observation technology, carrying out the vectorization of the heat storage state into a thermal momentum feature representing the evolution trend of a temperature field, and endowing a fuzzy controller with the physical pre-sensing capability for physical environment evolution, and response lag compensation caused by the thermal capacitance effect of the building structure is realized. According to the scheme, the limitation that system inertia is simplified through a traditional algorithm is broken through, physical energy efficiency verification is carried out by introducing multi-dimensional self-adaptive fuzzy reasoning of thermal momentum compensation and combining the fan pressure flow characteristics and the surge boundary, the prediction precision under the dynamic fluctuation working condition of the temperature field is remarkably improved, and the prediction efficiency is improved. And temperature overshoot and energy consumption loss caused by response time sequence mismatch are effectively eliminated. Finally, deep coupling of control logic and building thermodynamic characteristics is achieved, and the dynamic robustness and the comprehensive energy-saving efficiency of the ventilation system are greatly optimized while the indoor comfort degree is guaranteed.

The application discloses a kind of based on trend prediction's combined harvester cleaning system control method, comprising the following steps: construct fuzzy predictor, according to expert experience library, predict the future prediction trend of the control action of harvester cleaning system to cleaning performance;The language trend of the fuzzy inference output of fuzzy predictor is equalized into real utility value;According to real utility value, construct total utility target function J (U), control action sequence when J (U) maximization is solved.The application advantage is to predict the influence trend of " control action to impurity rate / loss rate" using fuzzy rule base, and the trend is converted into symbolic reward / penalty in MPC optimization target, so that robust, smooth cleaning parameter adaptive adjustment is realized under the condition of model and sensing limitation.

The invention discloses a rice transplanter operation row accurate identification and self-adaptive tracking control method and system, and belongs to the technical field of agricultural machinery intellectualization. The method comprises the following steps: accurately identifying seedling rows through an improved YOLOv8n visual algorithm, wherein a P2 small target detection layer is added, a C2f module is reconstructed by adopting RFAConv, and an SPPF module is replaced by utilizing a Focal Modulation module; performing seedling row positioning point clustering and navigation line extraction based on a detection result, and converting image coordinates into world coordinates through a BP neural network model; and performing path tracking by adopting fuzzy prediction function control based on feedback linearization, performing accurate linearization on a non-linear motion model of the rice transplanter through feedback linearization, and dynamically adjusting a weighting coefficient by utilizing prediction function control taking Morlet wavelet as a primary function and a fuzzy controller. According to the method, the problems of difficulty in seedling row identification and poor path tracking precision in a complex paddy field environment are effectively solved, and the automation level and the operation quality of the rice transplanter are remarkably improved.

The invention belongs to the technical field of ocean energy storage and power systems, and particularly relates to a seabed gravity-buoyancy hybrid energy storage device based on variable buoyancy adjustment. Comprising a suspension platform arranged below the water surface, a liftable gravity block, a flexible buoyancy bag body arranged in the gravity block, a vertical guide rail, a seabed base and an intelligent control system. The core of the device is that seawater is injected or discharged into the buoyancy bag body through the gas-liquid adjusting system, compressed air is filled or released, and the net buoyancy of the gravity block is accurately adjusted in real time, so that the circulation of energy storage descending and energy release ascending is realized along the guide rail under the synergistic effect of the gravity and the buoyancy. The intelligent control system adopts a fuzzy-predictive cooperative control algorithm to dynamically coordinate the torque of the winch and the buoyancy adjusting rate, so that efficient and stable conversion between buoyancy and gravitational potential energy is realized. The device is reliable in structure, high in energy efficiency, high in environmental adaptability and suitable for large-scale energy storage of open sea islands and offshore floating platforms.

This invention relates to a triangular fuzzy prediction method for power load based on MEEMD and optimal combination integration, which overcomes the information loss caused by the fact that power load prediction values ​​are usually real-value sequences compared to existing technologies. The invention includes the following steps: acquiring the dataset; decomposing the dataset; performing single-model prediction; constructing a triangular fuzzy optimal combination prediction model; and obtaining the triangular fuzzy prediction results for power load. This invention performs empirical mode decomposition on the raw power load data, constructs triangular fuzzy numbers, and uses three models—XGBoost, MSVR, and MLP—for combined prediction. It utilizes the MEEMD algorithm and optimal combination integration technology to improve prediction accuracy and mitigate prediction risks.

The invention relates to the technical field of insulation tool detection, and provides an insulation tool detection power frequency withstand voltage test method, which realizes dynamic evaluation and risk prediction of insulation performance by applying preset high voltage to an insulation tool and collecting electrical signals in real time in combination with a multi-modal data processing and intelligent analysis algorithm. The method specifically comprises the following steps: acquiring voltage, current and related environmental parameters of an insulation tool, and performing feature extraction and anomaly identification on a signal by using an adaptive fuzzy prediction algorithm; performing clustering analysis on the abnormal waveform through a Gaussian mixture model, and calculating an insulation risk probability of the tool; and outputting a comprehensive risk assessment and health status report in combination with historical data and a real-time measurement result. According to the method, the insulation performance of the insulation tool can be continuously monitored and accurately judged without shutdown, and the high-voltage operation safety and the equipment management efficiency are remarkably improved.

This invention discloses an adaptive prediction method for ship motion based on fuzzy inference and Bayesian optimization. The method first constructs an adaptive fuzzy prediction system, outputting interpretable point prediction values ​​based on inputs of ship motion-related physical quantities. Next, a parameterized confidence interval model is established and controlled by upper and lower bound parameters. Then, an optimization objective function is constructed through real-time data acquisition. Finally, the confidence interval parameters are dynamically corrected using Bayesian optimization, and the final prediction result with adaptive confidence intervals is output. This invention integrates the good interpretability of fuzzy inference systems with the efficient adaptability of Bayesian optimization, achieving accurate online quantitative assessment of the uncertainty of ship motion response. It also possesses advantages such as high computational efficiency and embedding into real-time systems, significantly improving the navigation safety and intelligent decision-making level of ships in complex sea conditions.

The invention relates to the technical field of archive management, and discloses a real-time mapping digital twin archive repository management system. The system monitors and captures multidimensional environmental parameters in a storehouse in real time, and generates a calibrated environmental basic data set after noise filtering and abnormal value elimination; dynamically updating the position and state of the entity object in the digital twin map in combination with a pre-built three-dimensional model, and generating a real-time digital twin map with consistent time and space; analyzing an environment state by applying a fuzzy logic reasoning mechanism, and evaluating an environment risk level and a processing priority of each region according to a fuzzy rule base; according to an evaluation result, dynamically adjusting a safe operation threshold value of the environment parameter to form a dynamic threshold value configuration scheme; deducing a future short-term environment change track by using a fuzzy prediction method; optimizing an operation strategy of the environment regulation and control equipment according to the prediction report, and generating an optimal control strategy set; and finally executing the control instruction, driving an execution mechanism to act, and correcting deviation through feedback data.

The invention discloses a dynamic load prediction method and system based on solar power generation, and relates to the technical field of data processing, and the method comprises the steps: obtaining a prediction time interval, obtaining a first processing time period, obtaining a second processing time period, and obtaining a first load sequence and a second load sequence; obtaining a first fuzzy trend variable and a second fuzzy trend variable, judging whether the first fuzzy trend variable and the second fuzzy trend variable are the same in number or not, and if yes, obtaining a fuzzy predictor of the to-be-predicted time point; obtaining a first stability variable according to the first load sequence, obtaining a second stability variable according to the second load sequence, and obtaining a dynamic compensation amount according to the first stability variable and the second stability variable; and obtaining dynamic load prediction data of the to-be-predicted time point according to the fuzzy prediction quantity and the dynamic compensation quantity. The method has the advantages that prediction speed and reliability are balanced, prediction errors are reduced, and dynamic self-adaption is achieved.

The present application relates to the technical field of building load prediction, in particular to a cross-scale modular integrated building load interval prediction method and system, the method comprising: constructing a modular integrated fuzzy model, constructing a single-input single-output fuzzy prediction module corresponding to each input variable, training the fuzzy model using a building load multi-dimensional dataset to obtain an initial modular integrated fuzzy model; dividing the input space based on the initial modular integrated fuzzy model, constructing a local modular integrated fuzzy model for a multi-dimensional subspace with poor prediction effect, and integrating the initial global model and the local model to construct a cross-scale fuzzy model; performing quantile regression modeling based on the output results of the cross-scale fuzzy model under different quantile points to determine the lower quantile boundary and the upper quantile boundary of the building load prediction result respectively, and constructing a building load interval prediction model. The present application can improve the adaptability and accuracy of building load interval prediction.

The application discloses a kind of vehicle-mounted hydrogen storage bottle gas hydrogen filling limit condition prediction methods, it is related to the technical field of hydrogen energy and fuel cell, in view of the existing filling strategy safety boundary fuzzy, the problem of low prediction efficiency, the method first obtains the geometric and physical parameters of hydrogen storage bottle;Then establish fluid-structure coupling numerical model, the lumped parameter model of hydrogen zone is coupled with the one-dimensional unsteady heat conduction model of solid wall surface and is calculated in batches, to construct filling process data set;Then based on the data set, adopt and optimize XGBoost algorithm to construct prediction model;Finally, under the premise of setting filling temperature safety threshold, the limit value of filling parameter is predicted by model reverse, and the quantitative relationship between each limit parameter is polynomially fitted.The application considers the high fidelity and calculation efficiency of modeling, can give the quantitative safety filling boundary, effectively improve filling rate and reduce precooling energy consumption.