222 results about "Fuzzy inference" patented technology

The invention relates to a cargo transportation track real-time tracking method based on the Internet of Things, and the method comprises the steps: carrying out the node collection and multi-source fusion of sensor data (including GPS, RFID, timestamps and the like) of all links of logistics, and employing data standardization and time sequence verification, thereby achieving the consistency processing of different devices and node data. The method is characterized in that standardized trajectory data and breakpoint detection data are stored in a distributed manner by using a block chain non-tampering and multi-party consensus mechanism, third-party multi-source data and a fuzzy reasoning model are fused, and confidence completion is performed on trajectory breakpoints. The system can output a complete and traceable trajectory path, anomaly recognition and responsibility attribution result, and provides a visual and third-party notarization interface. According to the method, the authenticity, the integrity, the space-time consistency and the responsibility traceability of the logistics track data flow are remarkably improved, and the logistics full-link monitoring and risk management capabilities are enhanced.

The invention relates to a fault early warning method, device and equipment for an energy storage system. The method comprises the following steps: acquiring target data of a target parameter; the target data is generated by preprocessing real-time operation data and real-time environment data of the energy storage system; extracting a target feature corresponding to each target parameter based on the target data; calculating a feature confidence interval of each target parameter based on the historical data of the target parameters, and marking suspected abnormal features based on the feature confidence intervals; identifying at least two fault types based on the suspected abnormal features; based on the current environment data, the load power of the energy storage system and the historical operation and maintenance data of the energy storage system, carrying out fuzzy reasoning on the risk membership degree corresponding to each fault type and dynamically adjusting the basic weight coefficient corresponding to each fault type; and determining a comprehensive risk index based on the risk membership degree corresponding to each fault type and the dynamically adjusted dynamic weight coefficient, and performing fault early warning analysis processing based on the comprehensive risk index. The method can improve the accuracy of fault early warning.

The invention relates to the field of resource optimization, and discloses a supply chain full-link-oriented logistics resource optimization and matching method and system, and the method comprises the steps: generating a multi-dimensional logistics state feature vector according to full-link multi-source data of a target supply chain, constructing a multi-target optimization model according to a fuzzy inference rule and a fuzzy weight system, and carrying out the optimization of the multi-target logistics state feature vector; performing preliminary matching degree analysis on the multi-dimensional logistics state feature vector, performing resource allocation on a target supply chain by using a preliminary logistics resource allocation scheme generated according to a comprehensive matching score, and performing weight online learning and dynamic self-adaptive adjustment on a fuzzy weight system according to a performance error value of an actual performance parameter value, so as to obtain a multi-dimensional logistics resource allocation scheme; and obtaining a target matching weight system, optimizing the preliminary logistics resource allocation scheme, and matching the logistics resources of the target supply chain to obtain a target resource matching result. According to the invention, in a dynamic scene of a full link of a supply chain, real-time learning of a multi-target tradeoff relation and adaptive updating of a fuzzy weight can be realized.

The invention discloses a heterogeneous Internet of Vehicles network selection method based on fuzzy logic and a related device. The method comprises the following steps: acquiring network attributes of candidate networks; inputting the network attribute of each candidate network into a membership function to obtain input membership of low, medium and high levels of the network attribute; performing fuzzy reasoning on the input membership degrees of the low, middle and high levels of the network attribute to obtain an aggregation membership degree of the output level of the network attribute; performing defuzzification on the aggregation membership degree of the output level of the network attribute to obtain a network evaluation value of each candidate network; according to the method and the related device, the network selection problem of a V2I communication scene can be solved, the optimal network can be selected, and efficient information transmission with road infrastructures is realized.

The invention discloses a shield cutter cutting load real-time monitoring and adjusting method and system. The method comprises the following steps: constructing a shield cutter distributed sensing network, and collecting strain, vibration and temperature of a cutter; a wireless transmission network is established, and the collected strain, vibration and temperature of the cutter are transmitted in real time; according to the strain, vibration and temperature, received in real time, of the cutter, cutting load parameters are obtained in a multi-source data fusion mode; based on the obtained cutting load parameters, stratum parameter identification is carried out in combination with a deep learning algorithm; in combination with the stratum parameters and the cutting load parameters, a wear state evaluation model based on fuzzy reasoning is established, and the tool wear state is evaluated; and designing a multi-objective optimization function according to the wear state of the cutter and the stratum parameters, solving the multi-objective optimization function, obtaining optimal shield tunneling machine operation parameters, and performing dynamic adjustment. The cutting load of each cutter in shield construction can be monitored in real time, and the cutter parameters can be adaptively adjusted.

The invention relates to the technical field of industrial equipment state monitoring and fault diagnosis, in particular to a trend fault prediction method based on cooperation of a dynamic mode and a threshold value. According to the method, a theoretical prediction interval dynamically changing along with a load is generated in real time by establishing nonlinear mapping between working conditions and key parameters, parameter drift interference caused by working condition fluctuation is effectively eliminated, a real-time health baseline of equipment is quantified in combination with maintenance records, and the width of an early warning threshold value is cooperatively adjusted according to feature similarity and the health level. Self-adaptive monitoring of different aging stages of the whole life cycle is realized, mode matching is performed by utilizing multi-dimensional feature vectors and fusing physical field information, deviation severity and form similarity are comprehensively evaluated through fuzzy reasoning, abnormity is locked in advance according to high feature goodness of fit when a numerical value does not seriously exceed a limit, and a real-time monitoring result is obtained. Early weak symptoms are accurately captured, abnormal sources are output, and the diagnosis precision under variable working conditions is remarkably improved.

The invention discloses a park micro-grid energy prediction and correction method based on a fuzzy language function. According to the method, a park micro-grid photovoltaic power generation capacity is taken as a prediction target, illuminance, temperature and air visibility are taken as core indexes to construct a park micro-grid photovoltaic power generation system prediction model, and the characteristics of high calculation speed and high responsiveness of a fuzzy language function are creatively utilized to track system prediction errors and error change rates. Core parameter indexes of the model are corrected in real time after fuzzy reasoning calculation, so that the model always tracks real-time environment characteristics to realize evolution, the park microgrid energy state is effectively predicted, a basis is provided for power dispatching and the like of the whole system, the charging and discharging time sequence of an energy storage system is guided, and the energy efficiency is improved. Therefore, a scientific solution is provided for the problem.

The invention relates to a kitchen waste sewage accurate aeration control method, system and device based on fuzzy PID and deep learning and a medium, and the method comprises the steps: collecting parameters, and processing the parameters to obtain preprocessed data; predicting the oxygen demand through a deep learning time sequence prediction model, and generating a dissolved oxygen set value interval in combination with multi-target reinforcement learning; based on the deviation and the deviation change rate of a dissolved oxygen set value interval and a real-time measured value, an evolutionary fuzzy reasoning system is used for adjusting gain parameters of an aeration controller, PID control quantity is output and converted into an aeration equipment driving signal, and multi-area aeration intensity dynamic distribution is implemented in combination with a hydrodynamic mapping relation and preprocessed data. Through the synergistic effect of the technologies, the core defects of single sensing dimension, prediction lag, control target conflict, extensive execution and the like of a traditional method are systematically solved, and collaborative optimization of degradation efficiency, energy consumption economy and equipment operation life in the kitchen waste sewage treatment process is realized.

A predictive fuzzy PID control method for an intelligent greenhouse includes: acquiring greenhouse real-time data, where the real-time data includes temperature data and humidity data; obtaining fuzzy quantity, control quantity and error data based on the real-time data, where the error data includes an error value and an error change rate; obtaining a target value based on the error data and a predictive functional control; controlling the target value and the error data for fuzzy inference, determining and adjusting parameters, and generating a control signal based on the parameters; and controlling greenhouse equipment adjustment based on the control signal to enable an output value of the equipment to approach the target value, and repeating the above steps to achieve continuous greenhouse environment control. This method achieves more accurate and efficient control of environmental parameters such as temperature, humidity, and illumination, and provides an optimal growing environment for plants.

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 provides a pipettor automatic calibration positioning and accuracy evaluation method based on photoelectric sensing, and relates to the technical field of pipettor calibration, and the method comprises the following steps: arranging an annular photoelectric sensor array to collect interference fringe phase difference, liquid level reflection light signals and surface acoustic wave signals, and adopting a five-step phase shift method to obtain a three-dimensional displacement vector; dynamic calibration is achieved through liquid level morphological parameter analysis in combination with adaptive Kalman filtering, numerical simulation is conducted on the liquid drop forming process through time-frequency analysis and a fluid mechanics model, and finally the accuracy is evaluated through adaptive fuzzy reasoning and Bayesian estimation. According to the invention, high-accuracy automatic calibration and evaluation of the pipettor are realized.

The invention discloses an automatic instrument fault prediction method based on fuzzy logic, and belongs to the technical field of industrial automation and equipment fault diagnosis. Comprising the following steps: generating a preprocessed sensor data set; constructing a feature space fusing multi-dimensional information; constructing a three-layer fuzzy reasoning architecture comprising an input layer, a reasoning layer and an output layer; mapping membership function parameters and rule weights in the three-layer fuzzy reasoning architecture into a parameter optimization space comprising a parameter adjustment action, a rule weight optimization action and a reasoning structure adjustment action; self-adaptive adjustment of the membership function parameters is achieved; and calculating the fault probability according to the current optimized membership function parameter, and evaluating the optimization effect of the current parameter configuration in combination with the multi-target reward function. According to the method, through combination of multi-time window dynamic monitoring, multi-type membership function design and a strict hard-soft constraint mechanism, accurate evaluation and stable prediction of the equipment fault probability are realized.

The embodiment of the invention provides a network security information consistency representation method and device based on ontology modeling, and the method comprises the steps: classifying various entities related to network space security, and the relationships and attributes of the entities, and constructing an ontology model of a hierarchical structure; concept conflicts and ambiguity of multi-source data in the ontology model are eliminated through context binding, multi-view reconstruction and fuzzy reasoning; data is injected in real time through a heterogeneous data source to trigger ontology updating, and an ontology model is updated through a collaborative reasoning engine and an atomization updating operation set; and performing cross-level consistency verification on the static structure and the dynamic behavior in the ontology model, and verifying the robustness of the ontology model through a antagonism test. According to the scheme, the heterogeneity, conflict and dynamic variability of multi-source data in the field of network security are effectively solved, so that more accurate and robust security management and decision are realized.

The invention relates to an intelligent control method, system, equipment and medium for automatic discharging mining tunneling cutting picks, and the method comprises the following steps: monitoring the flowing state of coal rock slag in a spiral discharging channel in real time, collecting a channel pressure difference signal and a cutting pick vibration signal, and generating discharging state characteristic parameters; blocking risk quantification is carried out on the characteristic parameters, and a real-time blocking index is generated through weighted fusion calculation; based on the index, a cutting head rotating speed optimization adjustment amount is generated through a fuzzy reasoning mechanism; and finally, generating a rotating speed control instruction through dynamic amplitude limiting processing and sending the rotating speed control instruction to a variable-frequency driving system. By means of the method, real-time sensing and dynamic self-adaptive adjustment of the blockage risk of the discharging channel are achieved, the problem that the discharging channel is blocked due to coal rock property changes is effectively solved, the discharging efficiency and the stability of tunneling operation are improved, and the service life of cutting teeth is prolonged.

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 relates to the technical field of medical information processing, in particular to an orthodontic treatment effect evaluation method. Comprising the following steps: acquiring and preprocessing multi-dimensional data in real time to obtain preprocessed multi-dimensional data; performing enhancement fusion processing on the preprocessed multi-dimensional data by using a similarity-based adaptive transformation enhancement fusion algorithm to obtain comprehensive multi-dimensional data; evaluating and analyzing the comprehensive multi-dimensional data by using an orthodontic treatment effect evaluation algorithm based on fuzzy reasoning to obtain an evaluation result; and evaluating the orthodontic treatment effect based on the evaluation result. The technical problems that multi-dimensional data processing is not accurate enough and evaluation accuracy is low in orthodontic treatment are solved.

The invention relates to the technical field of fan control, and discloses a fan rotating speed adjusting method and system based on a dynamic pollution load. The method comprises the following steps: collecting a target PM2.5 concentration, a target VOC concentration and a target microorganism concentration of each monitoring point, and calculating a comprehensive pollution load index of each monitoring point; traversing boundary critical points in all the monitoring points based on the comprehensive pollution load index and generating a boundary identification vector; the real-time pressure drop of the air inlet side and the air outlet side of the filter element in each fan is collected, and the pressure difference compensation gain is calculated; calculating a target reference rotating speed for all monitoring points in the responsible sector of each fan; and calculating the average pollution load index of the responsible sector of each fan, executing fuzzy reasoning to obtain a rotating speed increment, and generating a PWM duty ratio according to the target reference rotating speed and the rotating speed increment to drive each fan. The technical problems of space pollution response lag and energy consumption imbalance are solved, and the performance of the air purification system is improved.

The application discloses a digital twin pre-rehearsal method and system for tower crane collision risk, and relates to the technical field of collision risk analysis. Firstly, a unified time tag snapshot is generated by using cloud edge double clocks and weight synchronization. Secondly, the snapshot is cached according to the survival degree and the invalid frame is resampled. Thirdly, the time synchronization deviation and the spatial position deviation are fused into a synchronous reliable coefficient through fuzzy reasoning. Then, the reliable coefficient is used to drive the physical scheduling model to predict the trajectory, generate the safety envelope, monitor the difference and adaptively shrink, adjust the multi-tower operation window by combining the time slot insertion algorithm, and finally, the fixed-length data packet and the hash chain are used to prevent tampering and archive key quantities, and the sliding forgetting model and reinforcement learning are used to update the sampling step length, trade-off weight and envelope flexibility online. The method realizes second-level prediction, instant scheduling and self-evolution, and can reduce the multi-tower collision risk of high-rise buildings and take into account the construction efficiency.

This invention provides a fuzzy inference method and system for the initial phase of a low-speed engine crankshaft. The method includes: constructing an angle measuring device; calculating the state of each magnetic induction sensor in each phase interval based on the angle measuring device to form a state list; acquiring the actual state of the magnetic induction sensor and comparing it with the state list to obtain the phase interval corresponding to the actual state; and calculating the initial phase of the low-speed engine crankshaft, thereby realizing fuzzy inference of the initial phase of the low-speed engine crankshaft. This invention, based on a combination of multiple magnetic induction sensors, determines the state of the sensors in each phase interval to form a state list, compares and matches the actual state of the sensors with the state list to obtain the phase interval, and calculates the initial phase of the crankshaft, thus realizing fuzzy inference of the initial phase of the crankshaft. This is used to determine the initial phase angle of the crankshaft of an electronically controlled diesel engine after the system is powered on, so that the electronic control system can obtain the initial crankshaft position before the initial start control of the diesel engine.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment process parameter adjusting method and system based on fuzzy reasoning, and the method comprises the steps: obtaining to-be-treated sewage and a historical treatment log set, carrying out data cleaning on the historical treatment log set to obtain a cleaned log set, and obtaining a membership function set, performing fuzzy classification based on the membership function set and the cleaned log set to obtain a fuzzy historical data set, constructing a fuzzy rule base, performing water quality detection on to-be-treated sewage to obtain real-time water quality data, summarizing the real-time water quality data and a process parameter set to obtain a real-time treatment log, and adding the real-time treatment log to a historical treatment log set. And on the basis of the updated processing log set and the standard real-time water quality data, sewage treatment process parameter adjustment based on fuzzy reasoning is completed. The intelligent level and stability of parameter adjustment of the sewage treatment process can be improved.

This invention discloses an adaptive impedance control method and device for a teleoperated robot system. The method is based on a fuzzy logic-based intelligent control mechanism. It dynamically generates a correction update rate for online compensation adaptive terms through fuzzy inference, based on real-time acquired master-slave state information and preset control parameters. This correction update rate, real-time state information, and preset parameters are integrated, and the core dynamic equations of the preset impedance control model are solved to calculate the robot's target position within the current control cycle. This method, based on the robot following the operator's hand movements, superimposes a position compensation amount optimized by the adaptive impedance control model. When problems such as operator hand tremor or network transmission delay cause a sudden increase in contact force, making it impossible to effectively track the desired contact force, the adaptive impedance control device adjusts the contact force, achieving refined teleoperation control.

The application discloses a kind of data acquisition method and system for equipment service management platform, it is related to data acquisition technical field, it is proposed that a kind of intelligent screening reduction sequencing is used to solve the problem of low data acquisition efficiency, waste of computing resources, by collecting data call frequency, data fluctuation value, data effective period length and data area coverage range value, establish data analysis model, carry out logistic regression calculation, obtain screening evaluation coefficient, and compare with preset screening threshold value, according to the comparison result, obtain the equipment data paragraph of priority screening, then according to the data similarity of each priority screening equipment data paragraph, determine data screening paragraph, according to data screening paragraph, according to data screening paragraph call frequency user end real-time average upload speed and data screening paragraph in different area data distribution frequency degree A set of fuzzy rules are formulated to carry out fuzzy reasoning, determine data screening paragraph call sequencing result.

This invention discloses a multi-parameter coupling-based deep peak-shaving full-condition optimization control method for coal-fired power units. Addressing the problems of existing technologies, this invention constructs a multi-parameter coupling index system, calculates the comprehensive coupling strength between each parameter and the load, and selects core parameters. Dynamic clustering is used to finely divide the full-condition operation from 20% to 100% rated load into multiple operating zones. An LSTM-GRU dual-channel deep learning prediction model is established for each operating zone. A three-layer hierarchical control architecture is designed, comprising load optimization scheduling, boiler-turbine coordination control, and combustion optimization execution. The boiler-turbine coordination layer uses multivariate model predictive control and online self-tuning of multi-objective weights through fuzzy inference. The combustion execution layer uses the NSGA-III multi-objective evolutionary algorithm to optimize air distribution. A constraint-adaptive tuning mechanism based on safety margin is established to form a closed-loop iterative optimization across all operating conditions. This invention can significantly improve the load response speed, main parameter stability, and combustion economy of the unit during wide-load operation.

The invention belongs to the technical field of ocean engineering, and particularly discloses a multi-objective optimization design method for an ocean vehicle based on hierarchical design, which comprises the following steps of: S1, defining design variables, optimization objectives and constraint conditions, and constructing a parameterized model of the vehicle; s2, sensitivity analysis is conducted on the design variables, and the design variables are divided into high-sensitivity design variables and low-sensitivity design variables according to sensitivity ranking; s3, processing the high-sensitivity design variables by a multi-objective optimization method based on adaptive sequential sampling to obtain an optimal combination of the high-sensitivity design variables; s4, optimizing the low-sensitivity design variables by utilizing a fuzzy reasoning Taguchi method to obtain an optimal combination of the low-sensitivity design variables; and completing optimization of the aircraft according to the optimal combination of the high-sensitivity design variables and the optimal combination of the low-sensitivity design variables. According to the method, through technical innovation such as variable layering, adaptive sampling and staged optimization, the bottleneck of a traditional modeling and optimization method on high-dimensional complex problems is effectively broken through, and the method has clear technical advantages and wide engineering adaptability; and reliable and efficient technical support is provided for subsequent parameter sensitivity analysis, uncertainty quantification and rapid design iteration.

The invention belongs to the technical field of artificial intelligence, and relates to a water immersion identification method and device, computer equipment and a storage medium, the method comprises the following steps: receiving real-time water immersion data collected by water immersion data collection equipment, the real-time water immersion data comprising real-time sensor data and real-time image data; performing state estimation processing on the real-time sensor data according to a Kalman filtering algorithm to obtain a water immersion state estimation value; performing visual verification processing on the real-time image data according to an image recognition technology to obtain visual water immersion confidence; performing fuzzy reasoning processing according to a fuzzy rule, the water immersion state estimation value and the visual water immersion confidence to obtain a fuzzy reasoning result; performing defuzzification processing on the fuzzy reasoning result to obtain an accurate alarm confidence coefficient; and confirming a water immersion identification result according to the accurate alarm confidence coefficient. According to the invention, through fusion analysis and intelligent reasoning of multi-source information, accurate identification and reliable early warning of the water immersion state are realized.

The present application relates to a kind of video enhancement inference method, device, equipment and medium based on fuzzy control, method includes: initialization multiple different scale pre-training video inference model;Define the fuzzy semantic set of input variable and output variable and membership function;Based on prior knowledge, construct fuzzy rule table;Real-time acquisition input data and complete fuzzy inference, and accurate score is obtained by solving fuzzy output by barycenter method;According to the score value range and anti-shake strategy, the optimal video inference model of next frame is dynamically selected.This method makes full use of the continuity characteristics of target between video frames, realizes the dynamic balance of hardware resources and inference accuracy, the utilization rate of GPU is obviously improved, the target detection rate is also obviously improved, and the temperature is stable within the safety threshold, suitable for real-time video scene such as traffic monitoring, unmanned aerial vehicle inspection.

The invention provides a precast beam concrete defect pouring parameter determination method and device, equipment and a medium. The precast beam concrete defect pouring parameter determination method comprises the steps of obtaining an image of a precast beam, and obtaining a pixel region of a concrete surface defect and a confidence coefficient corresponding to each defect category through an instance segmentation model; determining the physical area of the concrete surface defect based on the mapping relation between the pixel region and the actual size of the precast beam; taking the confidence coefficient of each defect category as a defect category membership degree, and determining a pouring effect membership degree based on the pouring effect evaluation value of the previous pouring period; according to the defect category membership degree and the pouring effect membership degree, the pouring coefficient in the pouring period is determined through fuzzy reasoning; determining concrete weight parameters for pouring concrete surface defects based on the physical area and the pouring coefficient; and obtaining defect images before and after the pouring so as to determine a pouring effect evaluation value of the pouring period.

The application discloses a kind of resource-restricted scene under bidirectional fuzzy inference sensor signal anomaly detection method, and industrial sensor multivariate signal anomaly detection technical field, the method formulates the abnormal identification mechanism based on " neighborhood-global " mode difference learning;Design the customization double-layer Fuzzy-KAN small volume network guided by this mechanism;Joint bidirectional learning strategy and four parallel Fuzzy-KAN network constructs bidirectional fuzzy inference anomaly detection model, executes " neighborhood-global " context mode difference learning in fuzzy domain from " history to future " and " future to history " two directions in Fuzzy-KAN network directly, identifies time point level anomaly in multivariate sensor signal under zero label.This application anomaly detection method realizes three goals of high detection speed, high detection precision and low resource consumption on the industrial equipment of limited resources, promotes the landing application of intelligent model in real industrial scene.

The invention belongs to the technical field of industrial process control, and particularly relates to a color master batch filtering pressure constant adjusting method based on proportional integral derivative control, and the method comprises the steps: obtaining the current pressure, the current flow and the target pressure, and calculating the pressure deviation and the deviation change rate; based on the current pressure and the current flow, stage parameters representing the filtering resistance state are obtained; extracting low-frequency drift characteristics of the pressure deviation on a time sequence and calculating a deviation degree according to the low-frequency drift characteristics; performing fuzzy reasoning on the pressure deviation and the deviation change rate to obtain an original increment; adjusting the original increment according to the stage parameters and the deviation degree to obtain a target increment; and superposing the target increment to the historical instruction to obtain a flow instruction, and outputting the flow instruction to the pumping device for execution. According to the invention, stage parameters and low-frequency drift characteristics cooperate to drive the regulatory factors, and constant control of the filter pressure of the color master batch in the whole period is realized.

The present application relates to the technical field of data processing, in particular to a wireless satellite chain real-time early warning data processing method and system for landslides. It includes the following steps: collecting data affecting the occurrence of landslides, using historical data to learn time series patterns, selecting key time series features from different time windows, and finally assigning weights to each key time series feature; according to the real-time data affecting the occurrence of landslides, combined with the selected key time series features and the weights of the key time series features, a prediction model is generated, and using fuzzy reasoning rules, the probability is combined with the key time series features to evaluate the risk level in real time. In the wireless satellite chain real-time early warning data processing method and system for landslides, the most valuable features for landslide prediction can be more effectively selected, the correlation between these features is strengthened by calculating the interaction information between them, thereby improving the accuracy of the landslide prediction model.