24 results about "Fuzzy inference rules" patented technology

The invention discloses a method and a system for adjusting the internal environment of a mobile laboratory cabin, particularly relates to the technical field of environment control, and aims to solve the problem that the existing environment adjusting method adopts a fixed control target and cannot adapt to differentiated environment requirements in a multi-task scene of a mobile laboratory. The method comprises the following steps: acquiring a current task identifier of a mobile laboratory, identifying a corresponding experiment task execution stage, and dynamically generating an inter-parameter collaborative constraint relation matrix based on a fuzzy inference rule base; a regulation and control demand vector is generated by collecting a multi-dimensional environmental parameter measurement value and combining a pre-stored environmental parameter expected value; calculating a regulation and control conflict risk entropy value based on the matrix and the vector, and performing risk judgment; when the risk tolerance is exceeded, a roundabout regulation and control strategy is generated as an optimization guide parameter; finally, an environment control instruction is generated in a coordinated mode according to the optimized guiding parameters and sent to an executing mechanism; the dynamic adaptation of the environmental parameter regulation and control strategy and the experiment task is realized, and the precision and reliability of multi-parameter cooperative control are improved.

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 relates to the technical field of molten salt conveying, and discloses a molten salt spherical tank heat storage and heat exchange gas pressure conveying regulation and control system and method.The method comprises the following steps that multi-source parameters are read in real time, the current pressure difference is calculated to be combined with the temperature to fit the real-time viscosity value, the pressure difference, the viscosity and the valve opening degree are used for dynamically updating the theoretical calculation value of viscosity time lag, and the viscosity time lag is calculated. Historical data of time lag, pressure difference and valve opening are stored, a time window sample is formed, a time lag change rule is learned through an LSTM network, model weight parameters are updated online, a current working condition sequence is input, and a predicted value of future time lag is output. According to the method, the influence of fluid transmission time lag on pressure control is reduced, valve actions are synchronized with actual requirements through dynamic prediction and a fuzzy compensation mechanism, overshoot or undershoot of pressure is avoided, the anti-interference capability and robustness are enhanced by combining an online learning prediction model and a fuzzy inference rule base, and the pressure control precision is improved. And a scheme integrating dynamic response quality and long-term stable operation 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.

The invention discloses a driving right distribution method and device based on man-machine sharing, equipment and a storage medium. The driving right distribution method based on man-machine sharing comprises the steps that current deviation information of a vehicle in the current driving process is acquired; determining a current target torque according to the current deviation information, wherein the current target torque is used for controlling the vehicle to correct the current deviation information; and determining the driving right according to the current deviation information, the current target torque and a preset fuzzy inference rule. According to the scheme, the accuracy of driving right distribution can be improved.

The application relates to the technical field of intelligent control of mining equipment, and in particular to a pick-type pick tooth rock breaking load space-time evolution analysis method and system. The method comprises the following steps: collecting multi-dimensional load signals in a rock breaking process through a sensor array; establishing a digital representation surface based on pick tooth geometric morphology and sensor coordinates; generating a load estimation value of any point on the surface by using a spatial interpolation algorithm; mapping to the surface to form a continuous space-time load distribution field; extracting rock breaking efficiency and pick tooth load state characteristic parameters; and generating equipment parameter optimization guidance information through a fuzzy inference rule base. The application solves the problem that traditional methods cannot represent the continuous space-time evolution of the load, realizes visual analysis of the load distribution in the rock breaking process and self-adaptive regulation and control of the equipment parameters, and significantly improves the rock breaking efficiency and the service life of the pick tooth.

The present application relates to the field, disclose a kind of parallel power supply efficiency optimization method and system, the output current of each power module is collected in real time by the current sensor of each power module, while collecting the total output current of parallel power supply system and the input voltage, output voltage of each power module;The current sharing error of each power module is calculated, the proportional coefficient, integral coefficient and differential coefficient of fuzzy PID controller are adjusted by fuzzy inference rule;The voltage regulator of power module is adjusted by control signal;According to the input voltage, output voltage and output current of each power module, the efficiency of each power module is calculated, according to the efficiency and load condition of each power module, the best working point of each power module is determined, the input power of power module is adjusted, to complete the efficiency optimization of entire parallel power supply;The present application can adjust the working state of power module in real time under different working conditions, has strong adaptability and stability.

The invention discloses a bearing real-time anomaly detection method based on an adaptive network fuzzy inference system and related equipment, and the method comprises the steps: determining a first feature index group and a second feature index group according to a first sensor signal in a bearing data set and an anomaly detection result, designing a fuzzy inference rule, calculating an abnormal score of the first feature index group, taking the second feature index group as input of an adaptive network fuzzy inference system, and training the system according to the abnormal score; and extracting a third feature index group of the bearing to be detected, inputting the third feature index group into the trained adaptive network fuzzy inference system for inference, and completing anomaly detection according to a negative anomaly feature value prediction result obtained by inference and a preset decision rule. According to the embodiment of the invention, bearing anomaly detection can be realized by combining the fuzzy reasoning system and the adaptive network fuzzy reasoning system, and the prediction accuracy and the operation efficiency are relatively high. The method can be widely applied to the technical field of bearing anomaly detection.

The invention provides a road recognition method and device based on fuzzy logic and geographic information and a medium, belongs to the field of intelligent traffic, and realizes dynamic recognition and slicing of road states through cooperative work of vehicle-mounted driving assistance map equipment and a TCU (traffic control unit). The method specifically comprises the following steps: installing equipment and ensuring that a TCU receives electronic map information; acquiring front road information in real time during driving and storing the information as a distance / gradient array; establishing a fuzzy inference rule to define nine road states; carrying out smooth processing and change rate calculation on the gradient data; the input fuzzy system outputs road state division; calculating the length of each state slice; and the processing flow is repeated during dynamic updating. Through real-time road state division, a vehicle control strategy can be accurately matched, and the driving safety and comfort are improved. A dynamic updating mechanism adapts to road condition changes, and energy consumption management is optimized; the fuzzy inference rule is compatible with complex road condition recognition, the robustness of the system is enhanced, and a reliable decision basis is provided for intelligent driving.

The invention provides a fuzzy self-adaptive Q learning control method and system for sewage treatment, and the method comprises the steps: obtaining the dissolved oxygen concentration as a system state, and constructing a nonlinear optimization problem; performing dynamic adjustment on proportion, integral and differential coefficients by adopting a Mamdani type fuzzy inference rule in combination with fuzzy logic, performing defuzzification through a centroid method to obtain adaptive PID parameters, and forming a fuzzy adaptive control strategy; an online Q learning framework is further constructed, a Q function is approximated by using the evaluation network, a network generation strategy adjustment amount is executed, and network weight is optimized based on a Bellman equation and a particle swarm algorithm; finally, fuzzy control and Q learning output are fused, control input is generated through a coupling coefficient to adjust an oxygen transfer coefficient, and tracking control over the dissolved oxygen concentration is achieved. According to the invention, the tracking control precision of the dissolved oxygen concentration and the system operation stability can be improved.

The invention belongs to the technical field of wind power generation, and particularly relates to a wind power blade icing state monitoring method and system based on fuzzy reasoning. The method comprises the following steps: acquiring non-icing historical operation data of a unit, averaging according to a wind speed interval by referring to an IEC standard, and fitting performance characteristics of the unit at different wind speeds; sCADA data of a unit in nearly four hours are acquired and cleaned, and whether the data are enough for monitoring is judged; averaging the cleaned data according to a fixed time period, dividing the data into two groups above a rated wind speed and below the rated wind speed, and respectively calculating performance average deviations; and in combination with a corresponding icing grade fuzzy inference rule, calculating two groups of icing grades by adopting a Mamdani inference algorithm, and weighting according to the number of the two groups of corresponding time periods to obtain an average blade icing grade of nearly 4 hours. The system comprises a performance feature fitting module, a data processing module, a performance average deviation calculation module and a blade icing grade calculation module.

The application discloses an engine air-fuel ratio control method, and relates to the technical field of engine control, and comprises the following steps: obtaining the accurate value of a first actuator adjustment increment and the accurate value of a second actuator adjustment increment through the accurate value of a mixer outlet pressure deviation value, the accurate value of a change rate of the mixer outlet pressure deviation value and the accurate value of an air flow and fuel flow ratio deviation value; and adjusting the air flow and fuel flow ratio according to the accurate value of the first actuator adjustment increment and the accurate value of the second actuator adjustment increment. The application adjusts the first actuator opening degree and the second actuator opening degree through fuzzy inference rules and fuzzy algorithms, and realizes the control of the engine air-fuel ratio.

The application discloses a damaged ancient Zhuang character detection and recognition method based on fuzzy logic and belongs to the technical field of ancient character intelligent recognition. First, the damaged ancient Zhuang character image is collected, and preprocessing is completed through adaptive denoising, stroke loss detection and completion; then, multi-dimensional fuzzy features such as stroke integrity, contour similarity, structural connectivity and texture consistency are extracted, corresponding membership functions are constructed, and membership values are calculated; fuzzy processing, rule matching and defuzzification processing are completed based on a fuzzy inference rule base, and the damage degree grade and candidate recognition confidence are output; then, high-dimensional semantic features are extracted through a deep learning model, and the fuzzy inference result is weighted and fused to obtain the final recognition result; finally, the modern character mapping database of ancient Zhuang characters is queried to output the interpretation. The application combines fuzzy logic and deep learning, effectively processes uncertain problems such as stroke loss and contour fuzziness, significantly improves the recognition accuracy and robustness of damaged ancient Zhuang characters, is suitable for damaged scenes such as epigraphy, ancient books and cliff inscriptions, and provides a reliable technical scheme for the digital protection of ancient Zhuang character cultural heritage.

The invention discloses an underground water level early warning method and system. The method comprises the following steps: acquiring an underground water level time sequence data set of all stations in a target area; a hierarchical clustering algorithm based on dynamic time warping is adopted to recognize spatial heterogeneity of underground water level changes, and all stations are classified; for each type of stations, acquiring the groundwater level mean value and the water level change rate of each type of stations as input variables, and standardizing the input variables; performing fuzzification processing on the standardized input variables through a Gaussian membership function to generate multiple pieces of input variable fuzzy data; performing fuzzy reasoning on the multiple pieces of input variable fuzzy data according to a preset fuzzy reasoning rule to obtain multiple pieces of output variable fuzzy data; the multiple pieces of output variable fuzzy data are converted into underground water level risk coefficients through a gravity center method; and determining the underground water level early warning level of the corresponding type of station according to the underground water level risk coefficient of each type of station.

The embodiment of the invention provides a meteorological window selection method and device and a computer readable medium. The method comprises the steps that at least one meteorological element of a target task and state grading standards corresponding to different meteorological states of each meteorological element are acquired; for any target meteorological element, counting meteorological variables corresponding to the target meteorological element in the current preset time period, and generating a current meteorological sequence; predicting a predicted meteorological state corresponding to the next preset time period based on the current meteorological sequence; and based on the fuzzy membership degree corresponding to the predicted meteorological state of each target meteorological element in the at least one meteorological element, analyzing the performability of the target task according to a fuzzy inference rule, and generating a meteorological window. According to the embodiment of the invention, the Markov chain model and the fuzzy inference rule are combined for selecting the meteorological window; the problem that the long-term meteorological window selection precision of a traditional method under complex meteorological conditions is not enough is solved, and the meteorological window selection precision is improved.

The invention relates to the technical field of driving behavior intelligent evaluation, in particular to a driving behavior evaluation method and system based on big data, and the method comprises the steps: extracting an absolute position coordinate, an instantaneous velocity vector and an acceleration vector of a vehicle to form an original track set; and calculating a speed change rate, an acceleration change rate and a track transverse offset, and constructing a multi-dimensional feature matrix. And inputting the matrix into a driving style classification model combining a long short-term memory network and an attention mechanism, outputting a driving style probability distribution vector, accurately capturing key characteristics of a driving time sequence, and presenting a dynamic transition state of the driving style. And determining a style label in combination with a preset threshold value, calling historical driving behavior baseline data to generate a deviation index containing a deviation direction and amplitude, and outputting an evaluation level through operation of a fuzzy inference rule base. According to the method, personalized characterization of the driving behavior deviation can be realized, so that the evaluation result better fits the individual operation habit of the driver.

This invention discloses a bidirectional fault assessment method for distribution networks combining correlation matrices and dynamic Bayesian methods. The method includes the following steps: constructing a correlation matrix based on the actual distribution network topology, and combining this with a fault information matrix to construct a fault assessment matrix; establishing fault criteria for both missed and false fault reports, thereby achieving top-down fault feeder identification; constructing a dynamic Bayesian network based on the actual distribution network topology and the "station-line-transformer-customer" relationship to infer the fault probability of each region; constructing membership functions and fuzzy inference rules to obtain a fuzzy inference system for adjusting fault probabilities; and finally inferring the most likely fault region, achieving bottom-up fault region assessment. Based on traditional matrix algorithms, this invention improves fault criteria by constructing a causal correlation matrix, effectively solving the feeder identification problem under conditions of missed and false fault reports, reducing computational load, and improving the accuracy of fault location.

The invention relates to the technical field of intelligent control of mining equipment, in particular to a spatio-temporal evolution analysis method and system for rock breaking loads of pickaxe-shaped cutting picks. The method comprises the steps that a multi-dimensional load signal in the rock breaking process is collected through a sensor array; establishing a digital representation curved surface based on the geometric morphology of the cutting pick and the coordinates of the sensor; generating a load estimation value of any point on the curved surface by using a spatial interpolation algorithm; mapping to a curved surface to form a continuous space-time load distribution field; extracting characteristic parameters of rock breaking efficiency and a loading state of the cutting pick; and generating equipment parameter optimization guidance information through the fuzzy inference rule base. According to the method, the problem that continuous spatio-temporal evolution of the load cannot be represented by a traditional method is solved, visual analysis of load distribution in the rock breaking process and self-adaptive regulation and control of equipment parameters are achieved, the rock breaking efficiency is remarkably improved, and the service life of the cutting pick is remarkably prolonged.

The invention relates to an emergency scheduling method and system for an electric vehicle to participate in power supply recovery of a power distribution network, and the method comprises the steps: determining a quotation interval through the accounting of the own cost and the estimation of the cost of an opposite side, constructing a multi-round price negotiation model of dynamic yielding according to a dynamic yielding negotiation mechanism, and determining a final price; determining a fuzzy inference rule according to the charge state and time flexibility, constructing an electric vehicle user response intention fuzzy model, and determining a user response intention; calculating the total discharge capacity of the vehicle, establishing a multi-objective optimization function, and solving to obtain an electric vehicle discharge plan; the optimization objectives of the multi-objective optimization function include minimum economic loss in a power failure area, maximum user discharge compensation and longest continuous power supply of an important load; and according to the final price, the user response willingness and the electric vehicle discharge plan, an emergency scheduling method is obtained through integration. According to the invention, benefit balance between the power grid and the user can be realized, the utilization efficiency of electric vehicle resources is improved, and power failure loss is significantly reduced.

The invention relates to the technical field of intelligent control, and discloses a greenhouse environment control method and system for phalaenopsis. The method comprises the following steps: acquiring temperature, humidity and illumination signals, and generating a real-time environment state data set; analyzing the real-time environment state data set by adopting a fuzzy inference rule, and calculating a coupling effect of temperature, humidity and illumination to obtain temperature regulation and control demand data; whether the blade area temperature exceeds a first preset threshold value or not is judged, if yes, root heating power is adjusted, and heating power distribution is optimized; dynamically adjusting the power output of the bottom heating plate and the air heating equipment, and obtaining the real-time change trend data of the temperature of each region; according to comparison between the temperature real-time change trend data and a preset temperature threshold value in the growth period of the butterfly orchid, the heating power is calculated again, and the regulation and control instruction is updated; and according to the updated regulation and control instruction data, combining external environment data, and adopting time sequence analysis to predict temperature fluctuation. According to the invention, the efficiency and precision of greenhouse environment control are improved.

The application discloses a wind turbine blade icing blur early warning method and system, and belongs to the technical field of wind turbines. The method comprises the following steps: real-time monitoring of key factors; establishing a blade icing risk assessment model based on fuzzy logic; establishing a fuzzy inference rule table of the blade icing risk assessment model; inputting the real-time monitoring key factor data into the blade icing risk assessment model to obtain an abstract risk level; converting the abstract risk level into the membership degree of the output result; using a weighted average method to demystify the abstract risk level to obtain an accurate blade icing risk value and an accurate corresponding blade icing risk level; and issuing an early warning according to the blade icing risk level. The application solves the problems of existing wind turbine blade icing detection technology, such as response lag and high false alarm rate, and guarantees the safe operation of the wind turbine.

The invention discloses a tracked vehicle man-machine co-driving steering control method based on fuzzy control, belongs to the field of man-machine co-driving steering control, is suitable for man-machine co-driving control of tracked vehicles, and comprises the steps of constructing a driver steering control model and designing a man-machine steering control strategy. Setting a fuzzy variable and a membership function; constructing a hierarchical fuzzy inference rule, and obtaining fuzzy output through the hierarchical fuzzy inference rule according to the set fuzzy variable and membership function; performing defuzzification processing on the fuzzy output to obtain an output variable, namely a driver driving weight; and according to the driving weight of the driver, the final steering control quantity is obtained through weighted calculation. According to the method, the system control problem with uncertainty and nonlinearity is processed based on fuzzy logic, the driving weight is designed by comprehensively considering various factors of a man-machine co-driving control strategy, flexible transition of the control right is achieved, and various complex driver states and driving environments are better adapted through gradual and fuzzy transition.

The invention relates to the technical field of heat exchangers, in particular to a heat exchanger self-adaptive control method and system based on load prediction. The method comprises the following steps: firstly, acquiring historical, environmental and plan data, and predicting a future thermal load; secondly, the predicted value is used as a feed-forward signal to calculate a feed-forward control quantity so as to offset main interference in advance; meanwhile, the outlet temperature deviation and the change rate are monitored in real time, PID controller parameters are self-tuned on line through a fuzzy inference rule, and the feedback control quantity is calculated; and finally, fusing the feedforward control quantity and the feedback control quantity to drive an execution mechanism. The corresponding system comprises a load prediction module, a feed-forward calculation module, a feedback control module, a signal synthesis module and the like. Advanced compensation of the lagging link is achieved through feedforward, optimal control under all working conditions is ensured through parameter self-adaption, the defects that a traditional control method is large in lagging and poor in adaptability are overcome, the rapidity, stability and precision of heat exchanger control are remarkably improved, and meanwhile the excellent energy-saving effect is achieved.

The invention provides a power equipment operation and maintenance early warning method and system based on AI and Internet of Things big data. The method comprises the following steps: uniformly mapping power equipment operation data to a standardized feature space; an AI algorithm is used for learning boundary features of normal operation of equipment in a standardized feature space and an association rule between heterogeneous equipment, a self-adaptive adjustment mechanism of an early warning trigger threshold value is established, and a multi-dimensional condition combination is dynamically set; constructing a high-dimensional feature vector of a multi-dimensional parameter, and determining an accurate trigger boundary under different parameter combinations by using an AI algorithm; flexible matching and linkage triggering of early warning conditions among heterogeneous devices are achieved through a fuzzy inference rule base, and when a device operation boundary changes, the influence of the device operation boundary on the early warning conditions of the associated heterogeneous devices is automatically analyzed, and a multi-dimensional triggering condition combination is dynamically optimized. According to the invention, high efficiency, accuracy and intelligence of operation and maintenance of the power equipment can be realized, and the overall stability and early warning response capability of the system are improved.