38 results about "Fuzzy prediction" patented technology

The invention discloses a non-energy-storage off-grid photovoltaic hydrogen production system and a control method. The control method comprises five steps of environmental data acquisition and preprocessing, photovoltaic cell output power estimation, electrolytic cell power management, converter control parameter optimization and system performance evaluation and adaptive adjustment. The method comprises the following steps: firstly, processing environmental data by adopting an isolation forest algorithm, Kriging interpolation and an empirical mode decomposition method, and extracting time sequence features; and then combining with the estimated photovoltaic output power. And then the genetic algorithm and dynamic programming are utilized to realize electrolytic cell power optimization distribution. And then converter control parameters are optimized through subspace identification, pole assignment and a hybrid optimization algorithm. And finally, system performance evaluation and adaptive adjustment are realized by adopting a sliding time window method, singular spectrum analysis and a fuzzy prediction control method. The technical problems of the energy-storage-free photovoltaic hydrogen production system in the aspects of environmental data processing, power estimation, energy management, control optimization and the like are solved, and the operation efficiency and reliability of the system are improved.

The invention discloses a high-resolution radar echo extrapolation prediction method fused with satellite data, and the method specifically comprises the following steps: firstly, inputting historical radar echo sequence preprocessing at a previous T moment, including denoising, normalization processing and data set segmentation, and obtaining cleaned data; then, through a deterministic modeling method (SimVP), a fuzzy prediction sequence of a future T duration is obtained, then a variational auto-encoder (VAE) maps an original radar echo image and the fuzzy prediction sequence to a low-dimensional potential space, and two-stage diffusion modeling is carried out on the basis; in the first stage, a space-time converter (ST-Translator) is used to extract space-time evolution characteristics of radar echoes; in the second stage, satellite data at the corresponding time of the previous T moment is input, preprocessing including normalization processing, feature selection and data set segmentation is carried out, cleaned data is obtained, and the influence of the satellite data is dynamically adjusted in the diffusion process by adopting a multi-source fusion denoising network Fsrform so as to make full use of satellite information; and finally, inversely transforming output results of the two stages into a pixel space to obtain a high-resolution radar echo extrapolation prediction result of the future T duration. According to the invention, computing resource consumption can be effectively reduced, and the precision and detail fidelity of short temporary rainfall prediction are improved.

The invention discloses a welding process parameter intelligent optimization system based on digital twinning, which relates to the technical field of welding intelligent manufacturing and comprises a plurality of modules, a data holographic acquisition module, a multi-modal sensor array, an advanced algorithm for noise reduction to obtain accurate data, a digital twinning model building module, a digital twinning model building module, a digital twinning model building module, a digital twinning model building module and a digital twinning model building module, a digital twinning model building module and a digital twinning model building module. In combination with a multi-physics field coupling equation and a microstructure evolution model, deep simulation is realized, a data transmission synchronization module uses terahertz and software to define a network fusion architecture, the transmission quantity is reduced through edge federation transfer learning, data consistency is ensured by dual mechanisms, an optimization algorithm fusion module fuses multiple algorithms, and the data transmission efficiency is improved. The parameter adjustment feedback module updates the model in real time according to feedback by means of multi-agent cooperation and fuzzy prediction control. By means of digital twinning, the welding process is comprehensively monitored and accurately simulated, parameters are intelligently optimized, the welding quality stability is improved, the production efficiency is improved, the frontier technology is fused, and intelligent development of the welding industry is promoted.

The invention discloses a fuzzy model predictive control method for a coal-fired boiler-steam turbine system, and mainly relates to the technical field of intelligent environmental protection. Comprising the following steps: S1, constructing a fuzzy prediction model by using Takagi-Sugeno fuzzy logic; s2, constructing a control efficiency evaluation index, and establishing a dual-channel event triggering mechanism; s3, constructing a model prediction controller, constructing an objective function based on the prediction model, and solving an optimal control signal at each sampling moment; s4, performing offline training on the fuzzy prediction model through actual data of the boiler-steam turbine system to obtain a prediction model for a control system; s5, on the basis of a dual-channel event triggering mechanism, updating the prediction model according to real-time data through an online updating strategy; according to the method, the steam drum pressure, the generated power and the steam drum water level can be efficiently and accurately controlled, the calculation overhead can be effectively reduced, and the real-time performance and the stability of a control system are improved.

The invention discloses a first view angle online action segmentation method based on uncertainty, and relates to the technical field of video action analysis. According to the method, a strategy of dynamically changing the network according to uncertainty is adopted, and efficient and accurate online action segmentation under the condition of information loss in the future is realized. According to the invention, a method for scenario context according to an uncertainty principle is adopted. The method comprises two key parts: 1, a context aggregation converter which combines a joint auto-encoder and an auto-regression design to enhance contextualization of frame-level features and effectively overcome limitations caused by lack of subsequent contexts in a streaming video; and 2, action contextualization based on evidence: quantifying uncertainty in a model decision by using evidence deep learning, and correcting fuzzy prediction based on a previous context. According to the mode, the context scene information is supplemented, and the problems in the prior art can be effectively solved. The method can be widely applied to scenes related to first person view angle video action analysis, such as human body behavior monitoring based on wearable equipment, character action understanding in augmented reality and virtual reality scenes and the like.

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.

The invention relates to the technical field of motor driving control, and discloses a three-phase permanent magnet synchronous motor dynamic flux fuzzy prediction torque control method. According to the method, a three-phase two-level permanent magnet synchronous motor mathematical model is established, and a flux weighting factor is dynamically adjusted in combination with fuzzy control, so that the torque and flux linkage of the motor are accurately controlled. Through two-step prediction and cost function optimization, an optimal voltage vector is selected, and in combination with a dead-beat torque control principle, an output pulse signal is optimized. According to the method, the real-time performance and robustness of the system are improved, the torque ripple and flux linkage fluctuation are effectively restrained, the total harmonic distortion of the output current is reduced, and the method is suitable for efficient and stable operation of the motor under the complex working condition.

The invention discloses an anti-interference fuzzy prediction control method based on a linear matrix inequality. The method comprises the following steps: 1) establishing a system state equation of a cleaning robot with noise; 2) designing a fuzzy controller according to a T-S fuzzy method; 3) converting an anti-interference fuzzy prediction control problem into a solution minimization problem; and 4) solving a minimization problem based on a linear matrix inequality method. According to the invention, the model is reasonable and has certain robustness in switching control of various states; according to the method, the cleaning robot can adapt to various different working states, the system is kept stable, meanwhile, the robustness of the system can be kept under the condition that noise disturbance exists, and the working efficiency of the cleaning robot is improved.

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.

The invention discloses a hybrid power generation system energy prediction method based on multi-field unified modeling, and belongs to the technical field of power system energy control, and the method comprises the steps: selecting system working parameters, and constructing constraint conditions; constructing dynamic working models of the power generation system, wherein the dynamic working models comprise a hydrogen fuel cell working model and a lithium battery dynamic model; deploying a WSN module to collect working parameters of key parts of the hybrid power generation system in real time, and establishing a standard data set by adopting a dynamic filtering algorithm; classifying the standard data set into a corresponding cluster by adopting a clustering space input algorithm; and establishing a fuzzy prediction model based on a four-layer neural network, extracting related data from the standard data set according to a membership function, taking the related data as input of the fuzzy prediction model, and obtaining an energy prediction value. According to the method, adaptive energy prediction is realized through updating iteration, the energy distribution efficiency is improved, and accurate prediction of multi-field energy in the hybrid power generation system can be realized.

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 invention discloses an additional damped oscillation suppression method for an AC / DC hybrid power system containing a network construction type HVDC. The method comprises the following steps: acquiring data information of a target AC / DC hybrid power system containing the network construction type HVDC; establishing a model of the VSC-HVDC system; establishing a mathematical model of the network-building type inverter station; establishing a mathematical model of two regional alternating current systems; and a model prediction controller is established to control the established model, and additional damped oscillation suppression of the target AC / DC hybrid power system containing the network construction type HVDC is completed. The invention also discloses a system for realizing the additional damped oscillation suppression method for the AC / DC hybrid power system containing the network construction type HVDC. According to the method, modeling is carried out on the target system, and fuzzy prediction control is adopted to control the model, so that suppression of additional damped oscillation of the AC / DC hybrid power system containing the network construction type HVDC can be realized, and the reliability and the accuracy are higher.

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 switch cabinet thermal fault detection method and system, a terminal and a storage medium, and relates to the technical field of switch cabinet fault detection, and the method comprises the steps: collecting the current temperature of a switch cabinet observation window; inputting the acquired current temperature of the observation window of the switch cabinet into the trained fuzzy prediction model, and calculating to obtain the occurrence condition of a thermal fault in the switch cabinet; the trained fuzzy prediction model is a model capable of detecting the occurrence condition of the thermal fault in the switch cabinet based on the input temperature of the observation window of the switch cabinet; the thermal fault occurrence condition in the switch cabinet is the thermal fault occurrence condition of an isolation knife switch contact contact part, a switch trolley contact contact part, a conducting bar lap joint part and a cable head connection part in the switch cabinet. According to the invention, the internal thermal fault of the switch cabinet can be detected by collecting the temperature of the observation window of the switch cabinet, the limitation of insufficient detection of the internal deep thermal fault by an infrared nondestructive detection technology is overcome, and the detection efficiency of the thermal fault of the switch cabinet is improved.

The invention discloses a sewage nitrogen concentration prediction method based on artificial intelligence, and the method specifically comprises the steps: employing the historical data of ammonia nitrogen or total nitrogen, and combining a deep learning algorithm to obtain an ammonia nitrogen or total nitrogen historical data prediction value set; determining an auxiliary variable, constructing a relation model of ammonia nitrogen and the auxiliary variable as an ammonia nitrogen fuzzy prediction model, constructing a relation model of total nitrogen and the auxiliary variable as a total nitrogen fuzzy prediction model, and obtaining an ammonia nitrogen or total nitrogen fuzzy prediction concentration value set; constructing an ammonia nitrogen accurate prediction model or a total nitrogen accurate prediction model; and obtaining an accurate prediction value of ammonia nitrogen or total nitrogen. According to the method, a more comprehensive and accurate prediction model is constructed according to the historical data rule of ammonia nitrogen or total nitrogen, the strong association relationship with ammonia nitrogen or total nitrogen and the association relationship of other water quality parameters. Compared with a single variable or simple combination mode in the prior art, the method can capture the trend of water quality change more accurately, and the accuracy of a prediction result is improved.

The invention discloses an outburst danger early warning analysis method and system suitable for being applied to a heading machine, and relates to the technical field of coal mine safety. The method comprises the following steps: acquiring a working face coal seam image, a personnel image, gas concentration, coal seam stress, wind speed, a ground stress impact energy peak value and coexistence rate acquired by acquisition equipment of an outburst early warning electric control platform additionally arranged on the heading machine through wireless communication; preprocessing and analyzing working face coal seam and personnel images to obtain personnel states and bearing strength of soft rock and hard rock, calculating change rates of gas concentration and wind speed, and comparing coal seam stress with the bearing strength of the soft rock and the hard rock to calculate stress intensity; according to the method, the impact energy peak value and the coexistence rate are jointly brought into consideration of an adaptive fuzzy prediction model based on collaborative decision making, data change correlation is analyzed to evaluate the outburst early warning level, the personnel state and the outburst early warning level are analyzed in combination with a mine G-IS technology, a knowledge graph and a rule engine, and an optimal emergency scheme is matched. And real-time monitoring and early warning coping of outburst dangers are realized.

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 a DoS attack-oriented cloud control system modeling and event triggering prediction method under an interval type-2 T-S fuzzy model framework, and the method specifically comprises the steps: S1, monitoring the conditions of complex information transmission and the like of a cloud end and a physical end in a cloud control network system framework; establishing a cloud control system difference equation taking the interval type-2 T-S fuzzy model as a description language; s2, performing statistics, classification and linearization on the state of the fuzzy cloud control system by adopting a type-2 fuzzy set theory and a time division segmentation analysis method, and giving an attack model and regional parameters; s3, designing a dynamic event triggering mechanism with an exponential decay characteristic, reducing the data transmission quantity, and improving the bandwidth and energy utilization rate; s4, proposing an event type fuzzy prediction controller, and establishing a prediction model, a performance index and a constraint condition; s5, constructing a switching control law and a fuzzy optimization algorithm, and obtaining optimal parameters; and S6, the prediction stability of the interval type-2 T-S fuzzy system is ensured, and time-zone-division modeling and on-demand control are realized.

The invention provides a transformer fault detection method, device and equipment, a medium and a program product. In the method, according to acquired dissolved gas concentration data, dissolved gas ratio data, dissolved gas Duwal triangle data and model detection accuracy data, a first fuzzy prediction model, a second fuzzy prediction model and a third fuzzy prediction model, the dissolved gas concentration data, the dissolved gas ratio data, the dissolved gas Duwal triangle data and the model detection accuracy data are acquired; and determining a first fault type probability vector, a second fault type probability vector and a third fault type probability vector. And inputting the first fault type probability vector, the second fault type probability vector and the third fault type probability vector into a classification model to obtain a detection result which is a fault-free or target fault type. According to the scheme, fault detection is carried out on the transformer through the dissolved gas concentration data, the dissolved gas ratio data, the dissolved gas Duwal triangle data and the model detection accuracy data, and the detection accuracy is effectively improved.

The present application provides a method, apparatus, electronic device, and storage medium for querying the IP address location, which relates to the field of communication technologies. The method includes: receiving a target IP address sent by a client, and converting the target IP address into binary data to obtain an IP address to be queried; querying whether the IP address to be queried exists in a pre-established IP database; in the case where the IP address to be queried does not exist, inputting the IP address to be queried into a pre-established fuzzy prediction model for the fuzzy prediction model to make a prediction to obtain the location information corresponding to the target IP address; wherein, the fuzzy prediction model is obtained by training a machine learning model with the existing IP addresses stored in the pre-established IP database and the location information corresponding to the existing IP addresses. The above method improves the efficiency of querying the IP address location.

The invention provides a hydraulic cylinder fault prediction and diagnosis method based on a short memory grey model, and belongs to the technical field of fault prediction and diagnosis. According to the technical scheme, the method comprises the following steps that 1, a hydraulic cylinder fault prediction and diagnosis short memory grey model is constructed; 2, converting the hydraulic cylinder short memory grey model into an algebraic model; 3, collecting input and output data of the hydraulic cylinder in real time, and identifying model parameters in real time in combination with a deep learning algorithm; and 4, establishing a model parameter fault membership function, and performing fuzzy prediction and diagnosis on the hydraulic cylinder fault according to the parameter membership. The method has the advantages that the system response model is established from the input and output response characteristics of the hydraulic cylinder, the response model is cut off, the short memory gray model is obtained, gray model parameters are identified in real time through input and output data, and faults of the hydraulic cylinder are predicted and diagnosed in real time according to the gray model parameters.

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 present invention discloses an evaporation energy-saving optimization method and system based on intelligent control, and relates to the field of data processing technology. The method comprises: collecting the composition information and evaporation amount information of the evaporation liquid to be energy-saving evaporated, and obtaining the characteristic information of the evaporation liquid; performing evaporation energy-saving fuzzy prediction according to the characteristic information of the evaporation liquid, and obtaining the fuzzy energy-saving coefficient; combining the fuzzy energy-saving coefficient, constructing an evaporation energy-saving function that uses mechanical vapor recompression for evaporation energy-saving control; optimizing the evaporation energy-saving control parameters according to the evaporation energy-saving function, and obtaining the optimized evaporation energy-saving control parameter space; in the optimized evaporation energy-saving control parameter space, selecting the optimal evaporation energy-saving control parameters according to the fuzzy energy-saving coefficient, and performing evaporation energy-saving control. The technical problems of high energy consumption and poor energy-saving effect in the traditional evaporation process are solved, and the technical effect of reducing energy consumption and improving energy-saving efficiency is achieved through mechanical vapor recompression technology and intelligent control algorithm.

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.