74 results about "Fuzzy logic control" patented technology

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention discloses a micro-grid power control method, system and device based on fuzzy modulation and a storage medium, and relates to the technical field of intelligent grid integration of a photovoltaic system and a micro-grid, and the method comprises the steps: firstly obtaining the real-time power data of a photovoltaic system, carrying out the control processing through fuzzy logic, and calculating a reference instruction; according to the reference instruction, hysteresis time is dynamically adjusted through modulation hysteresis control, and an inverter switching signal is generated; converting direct current into alternating current based on the switching signal and a power switching device, injecting the alternating current into the micro-grid, calculating active and reactive power reference values, ensuring that the system operates according to a unit power factor, and giving active power to the reference value; according to the method, the power control precision and adaptability of the micro-grid are improved, excessive switching of the inverter is avoided, loss is reduced, the service life is prolonged, power matching and stable transmission are achieved, reactive loss is reduced, bus voltage is stabilized, harmonic waves can be reduced, the electric energy quality is improved, and efficient and stable operation of the micro-grid is guaranteed.

The invention discloses an internal combustion engine in-cylinder transient self-adaptive exposure control method based on FPGA acceleration, and belongs to the technical field of internal combustion engine endoscopic visualization. In order to solve the technical problem of image overexposure or underexposure caused by violent illumination change in the combustion process in an internal combustion engine cylinder, a real-time exposure detection unit is fused with a camera built-in sensor and an endoscope added sensor to monitor light intensity data; the adaptive exposure control unit accelerated by the FPGA adopts adaptive PID control and fuzzy logic control to generate a parameter adjustment direction, the adaptive PID dynamically adjusts gain based on a machine learning model, and the fuzzy logic outputs a decision according to a fuzzy rule base of brightness, light intensity and errors; the parameter adjusting unit adjusts the aperture size, the shutter speed and the light sensitivity in a linkage mode according to the target and actual gray scale difference value, and millisecond-level multi-parameter exposure control is achieved. Overexposure of a highlight area and loss of details of a low-light area are effectively avoided, and stable and clear images are obtained in the whole combustion process.

The invention relates to the technical field of ship navigation safety, in particular to a real-time ship trajectory tracking and early warning method, which comprises the steps of multi-source data acquisition and preprocessing, trajectory prediction model construction, collision risk assessment, early warning triggering and the like. By integrating AIS, radar and meteorological data, accurate trajectory prediction is realized by adopting Kalman filtering and extended Kalman filtering algorithms, and the method adapts to a complex environment based on dynamic weight factors and fuzzy logic control. Meanwhile, a multi-level early warning mechanism and redundant communication design are provided, and timely transmission of early warning information is ensured. The collision risk assessment model is optimized by using the deep neural network, and the early warning precision is improved. The real-time performance, accuracy and dynamic adaptability of ship trajectory tracking and early warning can be improved, and the efficient and safe requirements of modern shipping are met.

The invention relates to the technical field of charging switching control, in particular to a dynamic switching system for cooperative work of wireless charging and wired charging, which performs node modeling and edge weight propagation on multiple parameters such as magnetic field intensity, current fluctuation ratio, contact resistance and temperature threshold through a graph neural network. Structured expression and dynamic updating of a nonlinear coupling relation among parameters are achieved, abnormal feature aggregation and channel state characterization are more refined, fuzzy reasoning and weight adjustment are conducted on multiple variables such as power output, the heat growth rate, the voltage drop rate and the energy efficiency ratio through fuzzy logic control, the stability of a channel priority sequence is enhanced, and the stability of the channel priority sequence is improved. Through dynamic detection of power curve intersection points and transition section time sequence calculation, continuous power distribution is converted into a smooth function interval, instantaneous energy abrupt change during switching is reduced, closed-loop correction of multi-period signals is achieved, and the system keeps stable operation and energy efficiency output consistency under high-dimensional parameter disturbance.

The invention provides an autonomous cleaning control method of a photovoltaic panel intelligent cleaning robot, and relates to the technical field of automatic control. The autonomous cleaning control method of the photovoltaic panel intelligent cleaning robot comprises the following steps of S1, based on a photovoltaic panel surface environment, S2, based on an original sensor data set, S3, based on a smudginess index distribution diagram, S4, based on a cleaning demand prediction model, S5, based on an optimized cleaning path plan, S6, based on cleaning execution state feedback, and S7, based on an adjusted cleaning parameter set. Surface images and temperature data of the photovoltaic panel are collected through visual and infrared sensors, noise filtering and standardization processing are carried out, data quality and consistency are improved, reliable input is provided for follow-up analysis, a multi-sensor fusion technology is combined with fuzzy logic control to process environmental uncertainty, unified smudginess evaluation indexes are generated, detection accuracy is enhanced, and the method is suitable for large-scale popularization and application. And misjudgment caused by limitation of a single sensor in a traditional method is avoided.

The invention relates to the technical field of low-grade gold ore recovery, and discloses a photoelectric intelligent sorting-based low-grade gold ore comprehensive recovery method, which comprises the following steps of: firstly, collecting physical characteristic data such as ore particle size distribution, color characteristics and density, calculating a sorting path and parameters, and evaluating a processing demand to generate a target sorting strategy; combining historical sorting data and ore grade fluctuation data, making and optimizing a preliminary sorting plan through clustering analysis, a random forest algorithm and the like, and generating a personalized sorting scheme; according to the final implementation scheme, the ore recovery rate index is monitored in real time through a sensor, the light intensity threshold value and the separation speed are dynamically adjusted through a long-short-term memory network, a fuzzy logic control system and the like, and a comprehensive recovery scheme is generated. According to the method, by means of multiple intelligent algorithms and a real-time feedback mechanism, the sorting precision and the ore recovery rate are improved, and efficient comprehensive recovery of the low-grade gold ore is achieved.

The invention relates to the technical field of numerically-controlled machine tool monitoring, in particular to a numerically-controlled machine tool monitoring method and system, terminal equipment and a storage medium, and the numerically-controlled machine tool monitoring method comprises the following steps: based on key parts of a numerically-controlled machine tool, monitoring tiny wear or deformation by adopting a time domain reflection method and a frequency domain reflection method; and the collected ultrasonic signals are analyzed in combination with a convolutional neural network, phase changes and abnormal features in the signals are identified, data fusion processing is carried out, and wear identification indexes are generated. According to the method, the advanced monitoring and analysis technology is adopted, comprehensive optimization of the performance of the numerical control machine tool is achieved, the time domain reflection method and the frequency domain reflection method are combined with convolutional neural network analysis, the detection precision of tiny abrasion or deformation is improved, dynamic optimization is achieved on clamping force adjustment through fuzzy logic control, the method adapts to different machining conditions, and the machining precision of the numerical control machine tool is improved. Workpiece damage is reduced, the machining quality is improved, and the machining precision is improved through deep diagnosis and dynamic compensation strategies of multi-axis synchronous errors.

The invention relates to the technical field of flow metering, in particular to a temperature return control method of an intelligent control valve. The method comprises the following steps: receiving a temperature return control strategy sent by a user and a set value of specified temperature return, and judging an initial control parameter set according to the temperature return control strategy; real-time operation data are collected from the temperature sensing module and the flowmeter according to the sampling period, the return water temperature and flow at the current moment are obtained, and the return temperature deviation is calculated; calculating a control target value of the intelligent control valve by utilizing a model fuzzy logic predictive control algorithm based on the temperature return deviation; and the valve opening degree is adjusted based on the control target value, and the control parameters are dynamically corrected through the temperature return control strategy for self-adaptive temperature return control. According to the method, through a composite algorithm of fusing fuzzy logic control and model predictive control (MPC), on the basis that preliminary control signals are generated through fuzzy reasoning, a nonlinear system model is combined to carry out multi-step prediction on the future return water temperature change trend.

The invention provides an anesthesia auxiliary system supporting personalized administration and risk early warning. A hardware adaptation layer of the anesthesia auxiliary system adopts a general protocol and a customized driving mode; the data layer realizes acquisition, cleaning, storage and sharing of multi-source data, constructs an anesthesia whole-process data pool, and provides data support for algorithm operation; the core function layer utilizes an algorithm to run through the whole process of decision making, early warning and control to realize intelligent anesthesia management; and the interaction layer adopts a professional mode and concise mode double-interface design, so that the use requirements of different scenes are met, and the operation efficiency is improved. A multi-modal data acquisition-decision support-drug infusion control-risk early warning-data tracing full-closed-loop software system is designed, a layered decoupling architecture is adopted, and anesthesia equipment and a hospital information system are in butt joint through a standardized interface. According to the core improvement point, multi-modal data acquisition is supplemented, a data closed loop is perfected, algorithms such as off-line reinforcement learning, LSTM look-ahead early warning and fuzzy logic control are mainly integrated, and data driving and knowledge driving double-wheel decision making is achieved.

The application discloses a kind of power transformer health state determination methods, including obtaining the attribute data of power transformer;Attribute data is normalized and corresponding grey correlation coefficient is calculated;The weight value of attribute data information is calculated using analytic hierarchy process, and grey correlation degree value is calculated;The attribute of power transformer is divided into health grade and the priority of each health grade is determined by comparison;Health grade is associated with grey correlation degree value to construct fuzzy logic control rule;The health state determination of actual power transformer is carried out using fuzzy logic control rule.The application also discloses a kind of system for realizing the power transformer health state determination method.The application obtains and processes the data of power transformer, and predicts the state of power transformer based on analytic hierarchy process and grey correlation analysis scheme, so that the application can not only realize the health state determination of power transformer, but also has high reliability and good accuracy.

The invention provides an intelligent energy-saving building ventilation system self-adaptive to environmental changes, and relates to the technical field of intelligent buildings. The system comprises a hardware layer and a functional layer, the hardware layer comprises a sensor group, an actuator and a controller, and the functional layer is composed of a plurality of program modules operated by the controller. Wherein the data sensing and processing module is responsible for collecting indoor and outdoor environment parameters and preprocessing the data; the deep learning prediction module predicts a future environmental parameter trend and system energy consumption by using historical environmental data, an equipment operation state and external environmental data on the basis of an LSTM (Long Short Term Memory) network; the fuzzy logic control module combines the real-time environment data and the prediction data, and generates a preliminary control instruction according to a fuzzy rule. According to the system, through cooperative work of all the modules, intelligent adjustment and energy-saving optimization of the building ventilation system are achieved, energy consumption is reduced, and meanwhile the indoor comfort degree and the air quality are remarkably improved.

This invention discloses a digital identification system and method for high-pressure testing instrument detection data. The system includes: a data acquisition and conversion module for acquiring and converting analog signals; a data preprocessing module for noise removal and anomaly detection; a key parameter extraction module for extracting and optimizing key parameters; a data analysis and identification module for analyzing data and issuing early warnings of anomalies; a feedback adjustment module for optimizing test parameters; and a display and control module for displaying data, analysis results, and alarm prompts. This invention combines sliding window time series analysis, Kalman filtering, temporal convolutional neural networks, and adaptive fuzzy logic control algorithms to intelligently analyze and process the detection data of high-pressure testing instruments, enabling fault early warning, precise optimization, real-time adjustment, and maintenance decision-making. The system features high precision, strong real-time response capabilities, and significant optimization and maintenance effects, significantly improving data processing capabilities and decision-making efficiency during high-pressure testing.

This invention discloses a maximum power point tracking (MPPT) control system and method for a pump-driven turbine power generation system. It calculates output power by acquiring DC-side voltage and current, and also acquires rotational speed. Within each MPPT cycle, the operating state is identified by the power-speed slope feature G: when operating conditions change rapidly, the weight of the disturbance observation branch is increased; when approaching the maximum power point, the weight of the adaptive scale fuzzy logic control branch is enhanced. The two branches achieve branch current fusion according to the weights constructed using (|G|), outputting the q-axis stator current increment. A dq current closed loop is used to output a PWM drive signal to control the machine-side converter, adjusting the electromagnetic torque of the permanent magnet synchronous generator, thereby achieving efficient MPPT under time-varying head / flow conditions. This invention relies only on measurable power and speed signals, requiring no precise hydraulic model or data-intensive training, resulting in low computational load, ease of embedded implementation, improved energy recovery efficiency, and suppression of power fluctuations and torque pulsations.

An intelligent centrifugal separation system and method for protease production, a process parameter monitoring module is used for real-time acquisition of fermentation broth temperature, separation cavity pressure, drum running vibration amplitude and feed liquid level as an initial process data set; the data processing module is connected with the process parameter monitoring module, the data processing module is used for preprocessing the collected initial process data set, and an input feature vector is obtained; the parameter optimization module is connected with the data processing module, the parameter optimization module uses an expert rule base to adjust the operation parameters in real time based on a fuzzy logic control model; the execution control module is used for receiving the instruction of the parameter optimization module, and is used for dynamically adjusting the centrifugal separation operation according to the optimized parameters. The overall structure is simple, the process parameter monitoring, data processing, fuzzy logic control and execution adjustment are organically combined, and the intelligent control of the centrifugal separation operation in the protease production process is realized.

This invention provides a primary frequency regulation method for a wind-storage integrated system based on multivariable fuzzy logic control, comprising: establishing a wind-storage integrated system model, dividing wind speed regions, and establishing a multivariable fuzzy logic control method. The primary frequency regulation method for a wind-storage integrated system based on multivariable fuzzy logic control provided by this invention can realize power allocation for wind power and energy storage participating in primary frequency regulation, fully leverage the frequency regulation advantages of DFIG and energy storage, and improve the frequency stability of power systems with high wind power penetration.

The invention belongs to the technical field of current sharing control methods, and particularly relates to a silicon controlled rectifier bridge intelligent current sharing control method and system, and the method comprises the steps: collecting the total output current of each rectifier bridge and the current of each bridge arm in real time; based on a fuzzy logic control algorithm, taking the bridge arm current deviation and the rectifier bridge current deviation as input variables, and calculating the trigger angle compensation amount of each bridge arm; superposing the trigger angle compensation quantity with a reference trigger angle calculated by an excitation regulation controller to generate a final trigger pulse signal, and sending the final trigger pulse signal to a corresponding silicon controlled rectifier; based on the long-term trigger angle compensation amount data of each bridge arm, state evaluation and early warning of the silicon controlled rectifier are carried out, and the state evaluation comprises test stage characteristic matching rate evaluation and operation stage performance degradation evaluation. Through the dead zone design and low-speed adjustment characteristics of the fuzzy control algorithm, the interference to the transient process of the system is reduced while the current sharing effect is ensured.

The present application relates to the field of information technology, and relates to a foot massage control method based on a massage shoe wearing device and a massage shoe, and the method comprises the following steps: acquiring initial three-dimensional model data of a foot, and extracting a foot acupoint reference position coordinate from the initial three-dimensional model data; if the real-time offset exceeds a preset threshold, a position correction algorithm is used to adjust the reference position coordinate to obtain an updated acupoint target point; starting from the updated acupoint target point, a foot physiological signal in a massage process is acquired, and the physiological signal is processed to judge an improvement degree of a foot blood circulation state; according to the improvement degree of the foot blood circulation state, a fuzzy logic control method is used to calculate a massage intensity correction value to obtain a personalized massage intensity parameter; the final massage rhythm sequence is used to drive an executing mechanism to apply massage to the foot acupoint, and real-time user physiological response data is acquired to iteratively optimize subsequent adjustment.

The invention discloses a core circuit for realizing accurate modulation and demodulation based on differential signal coupling design, and the core circuit comprises a differential signal coupling module which is used for converting an input signal into a differential positive signal and a differential negative signal, and outputting the differential positive signal and the differential negative signal; the signal acquisition module is used for receiving the differential positive signal and the differential negative signal and outputting an acquisition signal; the self-adaptive filtering module is used for receiving the acquisition signal and outputting a filtering control signal; the fuzzy logic control module is used for receiving the acquisition signal and outputting a mode control signal; the threshold value control module is used for outputting a protection trigger signal based on the abnormal threshold value; and the modulation-demodulation module is used for adjusting working parameters based on the filtering control signal and the mode control signal. Therefore, the signal imbalance can be accurately compensated by combining the differential signal with the self-adaptive filtering module, modulation and demodulation can be dynamically optimized through the fuzzy logic control module, and the modulation and demodulation accuracy is remarkably improved.

The invention discloses a silicone oil fan clutch for controlling the inclination angle of cooling ribs based on fuzzy logic. The silicone oil fan clutch comprises a clutch shell, the cooling ribs, a track ring, a driven gear, a driving gear, a motor, a temperature sensor, a motor fixing plate, a driving shaft, a driving disc, a working cavity, a valve block and a positioning pin. The temperature sensor collects the temperature of silicone oil in the working cavity in real time, after collected temperature signals are processed through a fuzzy logic control algorithm, target inclination angle parameters and control instructions used for adjusting the heat dissipation ribs are output, and therefore the motor is driven to adjust the inclination angle change of the heat dissipation ribs relative to the clutch shell through the track ring gear transmission set. According to the real-time temperature of the silicone oil in the working cavity, self-adaptive dynamic regulation and control are carried out on the heat dissipation efficiency, and the device has the advantages of being high in applicability, stable and reliable in work, controllable in heat dissipation efficiency and the like.

The invention belongs to the technical field of liquid flow energy storage control, and particularly relates to a liquid flow energy storage control method and system, and the method comprises the steps: collecting the frequency of a power grid in real time, and calculating the frequency deviation and the frequency change rate; obtaining the reference adjustment amount of the virtual inertia and the damping coefficient by using symmetric fuzzy logic control; constructing a frequency disturbance multi-dimensional direction factor according to the frequency deviation and the frequency change rate, and obtaining a final adjustment amount in combination with the reference adjustment amount; and performing online updating on the virtual inertia and the damping coefficient of the virtual synchronous machine according to the final adjustment amount. According to the method, the asymmetric enhanced control strategy is constructed, so that the supporting capacity of the liquid flow energy storage system on power grid frequency reduction is improved, and the stability and safety of the power grid are effectively improved.

The invention discloses a multi-objective optimization control method and system for a wind storage electric field to participate in power grid frequency modulation. Aiming at the problem that the frequency stability of a power system is reduced due to increase of wind power permeability, on the basis of a wind storage combined frequency modulation technology and comprehensively considering energy storage economic cost and a frequency curve of the power system, firstly, off-line multi-target optimization is performed on control parameters of a wind storage electric field participating in frequency modulation of the power system by using a sparrow search algorithm; secondly, on-line self-correction based on working conditions is carried out on the control parameters based on fuzzy logic control, so that the control parameters can adaptively change along with the change of the working conditions; compared with a traditional control method, on the basis of optimizing the parameters, the control parameters are made to change in a self-adaptive mode along with the working conditions, the control parameters are optimized, and meanwhile the defect that the control effect of fixed parameter control under certain working conditions is not good is effectively overcome; and the frequency curve of the power system after the frequency event is effectively optimized on the basis of considering the economical efficiency of the energy storage battery.

The application provides an intelligent control and data analysis method and system for gas purification in an adsorption tank. The multi-dimensional environmental data in the adsorption tank is collected in real time to obtain a multi-dimensional environmental data set. Based on the multi-dimensional environmental data set, the time series analysis and machine learning algorithm are used to predict the concentration change trend of the gas components and the performance degradation of the adsorption material, and the abnormal detection algorithm is applied to identify potential abnormal working conditions to obtain a comprehensive prediction result. According to the comprehensive prediction result, a multi-objective optimization algorithm and a fuzzy logic control theory are used to generate an optimized operation parameter setting. The optimized operation parameter setting is applied to the actual operation of the adsorption tank, the implementation effect is monitored, and the actual operation effect is fed back to the prediction model to generate a more accurate prediction model. The technical scheme provided by the application can improve the data analysis effect of gas purification in the adsorption tank.

The invention discloses a quality detection and metrological verification system for a safety valve of special equipment. The system comprises a data acquisition module used for acquiring data acquired by a multi-source sensor and preprocessing the data to obtain standard data; the optimization module is used for adjusting a pre-stored detection standard process through a fuzzy logic control algorithm according to the standard data to obtain an optimized detection standard process; the adaptive module is used for obtaining detection data collected by the multi-source sensor when quality detection is executed based on standard data, comparing the detection data with the optimized detection standard process, marking the detection data exceeding the detection standard process and feeding back the detection data to the optimization module to readjust the detection standard process; and the report generation and feedback module is used for generating a verification report with a metering traceability conclusion according to a comparison result of the self-adaptive module after the quality detection is completed. According to the invention, quality detection and metrological verification can be completed without dismounting the safety valve and stopping for inspection, and the detection efficiency is greatly improved.

This invention discloses a silicone oil fan clutch based on fuzzy logic control of the tilt angle of the heat dissipation ribs. The clutch includes a clutch housing, heat dissipation ribs, a track ring, a driven gear, a driving gear, a motor, a temperature sensor, a motor mounting plate, a drive shaft, a drive disc, a working chamber, a valve plate, and a positioning pin. The temperature sensor collects the silicone oil temperature in the working chamber in real time. After processing the collected temperature signal through a fuzzy logic control algorithm, it outputs target tilt angle parameters and control commands for adjusting the heat dissipation ribs. This drives the motor to adjust the tilt angle of the heat dissipation ribs relative to the clutch housing through the track ring gear transmission group. This invention adaptively and dynamically regulates the heat dissipation efficiency based on the real-time temperature of the silicone oil in the working chamber, offering advantages such as strong applicability, stable and reliable operation, and controllable heat dissipation efficiency.

The invention relates to the technical field of transformer equipment protection and operation control, and provides a method and a system for suppressing excitation inrush current and phase jump of transformer switching, which are characterized in that an excitation inrush current suppression mechanism is integrated into a control system of an energy storage converter, and sine wave pulse width modulation is adopted to generate an output voltage waveform. At the moment of switching of the transformer, the controller firstly detects the instantaneous current change of the output end in real time, and when it is monitored that the current value rapidly rises and overcurrent occurs, the modulation depth coefficient is automatically adjusted by adopting a fuzzy logic control algorithm according to an error interval. The modulation depth coefficient is used as a control parameter to determine the amplitude of the output voltage so as to influence the current input intensity. And then, according to a feedback current value, if the detected current is reduced below a rated value, gradually increasing the PWM pulse duty ratio, so that the output voltage is gradually recovered to a normal level, meanwhile, ensuring the continuity and smooth transition of a voltage phase, and finally realizing a control closed loop of the transformer from a switching-on state to a stable operation state.

The application provides a shelter hospital power supply optimization method and system based on micro-grid technology, wherein, through monitoring the state information of each power consumption unit in the shelter hospital, the embodiment of the application analyzes the monitoring data by using a deep reinforcement learning algorithm, generates a dynamic adjustment strategy of a distributed power supply, evaluates and optimizes the charging and discharging strategy of an energy storage system by combining a multi-objective optimization algorithm, adjusts the power supply configuration scheme by using an adaptive fuzzy logic control system according to the internal environmental parameters of the shelter hospital, the working characteristics of the power consumption unit and the patient comfort requirement, forms an energy consumption control strategy, applies a time series prediction model to predict the power demand change trend in combination with historical power consumption data and weather forecast information, generates a scheduling plan, and records and verifies the power transaction process by using a blockchain technology to generate a power distribution scheme, and the application realizes the operation and management of the power system of the shelter hospital through comprehensive monitoring, intelligent analysis and adaptive control, and ensures reliable power supply.

The application provides a precipitation estimation method, system, device and storage medium applied in low-altitude economic services, and the method comprises the following steps: acquiring dual-polarization radar echo data and environmental parameters in a low-altitude atmosphere layer and taking the data and the parameters as inputs of a least mean square algorithm, combining a fuzzy logic control algorithm to dynamically adjust weight coefficients of an adaptive filter, using the adaptive filter with the adjusted weight to suppress noise of the dual-polarization radar echo data, and obtaining filtered radar echo data; extracting key precipitation characteristic parameters from the filtered radar echo data and calculating radar original precipitation estimation values; acquiring observation data of a ground meteorological station and performing fusion processing to generate regional precipitation reference data; using the regional precipitation reference data, determining correction coefficients by using a regularized linear regression method, correcting the radar original precipitation estimation values by using the correction coefficients, and outputting final precipitation estimation values. The application effectively improves the precipitation estimation precision in a low-altitude scene.

The invention relates to a multi-discriminator cooperative anti-interference pseudo code tracking method based on fuzzy control, and belongs to the technical field of satellite navigation and spaceflight measurement and control. The method specifically comprises the following steps: step 1, carrying out de-spreading and integral clearing processing on a received signal; step 2, using two bilateral slope anti-interference discriminator groups with different correlator distances to carry out pseudo code phase discrimination on the signal output in the step 1 to obtain a phase error estimation value; step 3, evaluating the dynamic size of the current carrier and the multi-access interference intensity; step 4, performing adaptive weighted fusion on the identification result based on fuzzy logic control; and 5, performing filtering processing by using the final identification result, and generating a pseudo code sequence.

The application discloses an intelligent display control method and system for liquid crystal panel image recognition driving, and relates to the technical field of liquid crystal panels. The method comprises the following steps: collecting user image data of a liquid crystal panel through a camera and preprocessing the user image data to obtain a user image of the liquid crystal panel; inputting the user image of the liquid crystal panel into an improved VGG-Face model to output a user age prediction value; collecting environment data of the liquid crystal panel, and based on the environment data of the liquid crystal panel and the user age prediction value, performing display adjustment on the liquid crystal panel through a fuzzy logic control method to obtain a first display adjustment result; collecting user feedback data, training a gradient boosting tree model by taking the user feedback data as a training set to obtain a trained gradient boosting tree model, inputting the first display adjustment result into the trained gradient boosting tree model, and outputting a second display adjustment result, so that intelligent display control of the liquid crystal panel is realized.