138 results about "Membership function" patented technology

The invention relates to the technical field of computing power resource allocation, and particularly discloses a computing power resource allocation method and system based on multi-dimensional dynamic evaluation and a product. The method comprises the steps of obtaining evaluation index data of a to-be-scheduled task in multiple dimensions; dynamically configuring a weight value of each evaluation index according to a task attribute and a system state; the evaluation index data is fuzzified by using a preset membership function, a fuzzy evaluation matrix is constructed in combination with the weight value, and comprehensive calculation is performed through a fuzzy inference rule to obtain fuzzy comprehensive evaluation data of the task, so that an accurate evaluation result is generated; modeling a task execution process by adopting a multi-layer perceptron model, and predicting the execution performance of the task; and based on the evaluation result and the prediction result, dynamically selecting a proper strategy from a plurality of predefined task scheduling strategies, and scheduling the tasks to optimize computing power resource allocation. According to the method, the resource utilization efficiency is remarkably improved through multi-dimensional evaluation and a dynamic scheduling mechanism.

The invention discloses a data and knowledge dual-drive wheel set multi-parameter comprehensive state evaluation method and system, and belongs to the technical field of railway vehicle maintenance. The method comprises the following steps: firstly, constructing a mapping relation between geometric parameters and dynamic performance indexes through sampling and dynamic simulation, and carrying out global sensitivity analysis to screen key dynamic performance indexes; secondly, determining subjective and objective weights of the indexes in combination with an analytic hierarchy process and an entropy weight method, and introducing a dynamic optimization model based on a Bellman equation to generate a final combined weight; then, constructing a multi-dimensional state space division model by applying adaptive kernel density estimation and fuzzy C-means clustering, and determining the probability density and membership function of each index under different health levels; and finally, performing simulation prediction on the target wheel set, inputting a predicted value into the state space model, fusing a dynamic combination weight and a D-S evidence theory, calculating a comprehensive health index, and outputting a grading result, so that the health state of the wheel set can be accurately and efficiently evaluated.

The invention belongs to the field of knowledge graph updating, and provides a human social knowledge graph adaptive optimization method and system based on uncertain information, and the technical scheme is as follows: associating each piece of data to a corresponding policy label according to a policy index, and constructing an initial multi-dimensional graph structure according to service connection and shared fields; establishing a multi-field weighted membership function and initializing a fuzzy set, and performing comprehensive credibility combination on cross-department entities and relationships; performing cross-department difference degree calculation on the suspicious entities and relationships, and correcting a state transition probability based on a multi-stage Markov process in combination with a cross-department interaction factor and a policy aging factor; carrying out aggregation on the high-confidence nodes and the edges after state transition by applying a multi-hop attention layer; performing versioning management on the optimized graph structure and establishing an incremental updating queue; according to the method, the credible evolution of the knowledge graph tracking nodes and edges in the time sequence dimension can be remarkably improved.

The invention discloses a particle filtering fusion positioning method based on indoor and outdoor fuzzy judgment, which comprises the following steps of: firstly, dividing a region to be positioned into dynamic grid units, constructing a space-time signal distribution matrix, and measuring the matching degree of the space-time signal distribution matrix and a standard scene through weighted Jaccard similarity; secondly, designing a dual-channel adaptive membership function based on a fuzzy theory, and converting hard decision into probabilistic weight output; and then a four-parameter joint optimization mechanism based on environmental difference, model uncertainty, fuzzy entropy and confidence is constructed, a classification threshold and a confidence interval are dynamically adjusted, finally an improved particle filtering fusion model is constructed, a hybrid dynamics model is constructed to predict the positions of particles, and a dynamic resampling strategy is combined to improve the particle utilization rate. And the positioning accuracy and stability are improved. According to the invention, the problem that the positioning precision is reduced or the positioning result jumps frequently when indoor and outdoor scenes are switched is solved.

The invention provides a crop moisture stress diagnosis method and system based on a fuzzy discrimination theory and multi-source information fusion, and the method comprises the steps: collecting multi-source monitoring data which comprises but is not limited to soil moisture, crop physiology, remote sensing images and meteorological parameters; preprocessing the data, and calculating at least one moisture related index; constructing a membership function of the indexes based on a fuzzy set theory; fusing the indexes by adopting a fuzzy comprehensive discrimination method to obtain a comprehensive moisture stress index; and outputting a mild, moderate or severe crop water stress level according to the index. The system comprises a data acquisition, data processing, fuzzy discrimination and result display modular structure according to the method. By fusing multi-source monitoring data and adopting a fuzzy set theory and a fuzzy comprehensive discrimination method, accurate diagnosis and graded output of crop water stress are realized, the utilization efficiency of water resources is improved, and the method has remarkable water-saving and efficiency-increasing values.

The invention relates to the field of control, in particular to a reaction kettle operation control method and system for resin production, real-time operation parameters of a reaction kettle are obtained, a fuzzy neural network model is iteratively trained by adopting a hierarchical collaborative hybrid optimization strategy, a preceding member membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the preceding member membership function of the fuzzy neural network model is obtained. According to the optimization strategy, an improved quantum particle swarm optimization algorithm is used for carrying out global search to determine Gaussian mixture model parameters, a recursive least square algorithm is used for carrying out local search to determine consequent coefficients after each time of iteration, and in the training process, the parameters of the Gaussian mixture model are subjected to global search to determine the parameters of the Gaussian mixture model. And calculating an importance index according to the average activation degree of the fuzzy rule and the contribution of the fuzzy rule to the prediction error, removing the rule of which the importance is continuously lower than a preset threshold value, and after training is completed, generating and executing a control instruction for controlling the heating system power and the material feeding rate of the reaction kettle at the next moment according to the real-time parameters.

The invention discloses an anesthesia post-operation awakening delay risk assessment method based on adaptive fuzzy reasoning. The method comprises the following steps: S1, obtaining a normalized multi-modal physiological information data stream with consistent time sequence; s2, dividing the standardized multi-modal physiological information data stream into a high-frequency physiological information data substream and a low-frequency physiological information data substream according to a sampling frequency; s3, generating a high-frequency fuzzy reasoning result based on the initial high-frequency fuzzy rule base and an adaptive membership function; s4, generating a low-frequency fuzzy reasoning result based on the initial low-frequency fuzzy rule base and an adaptive membership function; s5, outputting a multi-scale fusion risk scoring curve; and S6, generating a wake-up delay risk alarm signal and recording a risk driving factor list. According to the method, the risk identification sensitivity and robustness of high-risk patients, complex operations and multi-complication cases are remarkably improved, and missing report and false report caused by a single-dimension or static threshold value are avoided.

The invention discloses an adaptive modulation coding method and system based on fuzzy logic, and belongs to the field of wireless communication, and the system comprises a dynamic window module which dynamically adjusts the window size of a fuzzy set according to the received signal strength indication and the change trend of the packet reception rate; the input fuzzification module takes the actual received signal strength indication and the packet reception rate as input and converts the actual received signal strength indication and the packet reception rate into fuzzy variables through fuzzification processing; the fuzzy reasoning module is used for defining different membership functions, mapping continuous received signal strength indications and packet reception rate measurement values into a fuzzy set, considering membership value combinations of all RSSI and PRR fuzzy categories, calculating a weighted total score and outputting a fuzzy decision value; and the defuzzification module is used for converting into an actual modulation coding selection scheme through a defuzzification process. By adopting the method and the system, the adaptability and the response speed of the system to complex and changeable channel conditions are improved, and the accuracy and the reliability of adaptive modulation coding selection are improved.

The invention provides an intelligent proportion control method and system for an antistatic agent synthesis process, and the method comprises the steps: fuzzifying real-time parameters through real-time and historical process parameters by using an asymmetric membership function constructed based on data distribution skewness and kurtosis; historical data samples are mapped into graph theory nodes, communities are divided through a community discovery algorithm to generate fuzzy rules, initial weights are set, and an initial rule base is constructed; using a recursive least square method to identify rule consequent parameters, combining redundancy rules according to cosine similarity, and combining a particle swarm optimization algorithm to optimize antecedent parameters; and inputting the fuzzification real-time parameters into the optimized fuzzy neural network, calculating the activation intensity of the rule, adjusting the weighted average weight based on the information entropy of the current activation intensity, and obtaining the proportion control quantity of each component after defuzzification.

The invention relates to the technical field of circuit breakers, in particular to a circuit breaker contact electrical life evaluation method based on dynamic resistance and fuzzy logic. The system comprises a dynamic resistance measurement system which fuses dynamic resistance measurement characteristics and breaker body parameters; taking Q3 values of main contact resistance Rm and arc contact resistance Ra extracted by box diagram analysis as main input variables, and setting at least one fuzzy membership function for each group of Rm and Ra values; five language input variables, namely low (L), medium-low (ML), medium (M), medium-high (MH) and high (H), are set in the system; considering that the contact aging degree and the contact resistance value are in a linear relation, the system adopts a linear function to represent a mapping relation between input and output and weight distribution of a maximum interval; the membership function overlapping degree among all the linguistic variables is set to be 50%. The method has the advantages of being independent of large-scale data, higher in adaptability and capable of rapidly judging the aging state of the arc extinguish chamber of the circuit breaker.

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 invention relates to the technical field of agricultural information, in particular to a product supplier and consumer matching method and system based on information axiom, and the method comprises the steps: recognizing the attributes of agricultural products; defining a system range and a design range for each attribute; for each matching scheme between the consumer and the supplier, checking whether all hard attributes are met or not, and if not, filtering out the matching scheme; for the soft attributes, calculating the information amount from the perspective of consumers and the perspective of suppliers: for the quantitative soft attributes, calculating the information amount according to the types of the quantitative soft attributes; for qualitative soft attributes, applying a fuzzy mathematical method, and then calculating information amount; respectively calculating the total information amount of the consumer perspective and the total information amount of the supplier perspective; establishing a multi-objective optimization model; for all possible matching schemes, calculating the total information amount of consumers and suppliers, and determining respective maximum values and minimum values; for each matching scheme, calculating a normalized membership function value by comparing the total information amount with the maximum value and the minimum value of all the matching schemes; the matching scheme enabling the weighted comprehensive score of the membership function to be the highest is selected. The invention provides an intelligent matching method which can comprehensively consider unique attributes, dynamic transaction demands and fuzzy language features of agricultural products, so as to optimize supply and demand matching and promote agricultural product sales and sustainable economic development.

The invention discloses a large-model intelligent question setting system and method based on fuzzy mathematics fine tuning, and belongs to the technical field of intelligent question setting. Comprising a fuzzification processing module, a multi-dimensional reward feedback module, a rule-driven attribute reasoning module, an attribute accurate quantification module, a self-adaptive membership degree adjustment module, a strategy optimization and intra-cluster standardization module and a question generation and structured output module. According to objective fields such as post specifications, post levels, technical stack depth and team scales, hard indexes are converted into soft boundary semantics through five membership functions, and then quantitative attributes of surface test questions on difficulty, openness, prejudice and distinction are reasoned by using N rules without subjective wording. And the business result data is used for driving rewards to be updated online, and finally, daily automatic fine tuning is realized on the 32B large model, so that each subjective surface test question generated by the AI not only meets the objective requirements of posts, but also has explainable, auditory and iterable closed-loop capabilities.

The invention discloses a ground vehicle adaptive path planning method and device based on fuzzy reasoning, and relates to the field of path planning, and the method comprises the steps: constructing a terrain grid map based on a satellite image map and satellite elevation data; calculating the ground type coefficient, gradient and flatness of the target position; taking the cross-country performance of the target vehicle as an input variable, taking the gradient weight and the flatness weight of the target position as output variables, fuzzifying the input variable and the output variable, establishing a fuzzy rule by using a membership function, performing fuzzy reasoning, and converting a fuzzy quantity after reasoning into an accurate quantity for output; and using an A * algorithm to take the lowest comprehensive passing cost as a target, performing adaptive path retrieval on the surrounding position of the target position, updating the target position, and continuing path retrieval until the target position arrives at the final position to obtain an optimal path. According to the invention, the adaptability of the vehicle cross-country performance to the path is improved, the rationality and adaptability of path planning are enhanced, and the driving efficiency is improved.

The invention relates to the technical field of scene modeling, and solves the problems of multi-source heterogeneous data fusion distortion, uncertainty expression deficiency and insufficient static model self-adaption. According to the core scheme, physical sensing data and subjective description data are processed in a unified mode through a differential fuzzy processing mechanism (continuous quantities are mapped through trapezoidal membership functions, and discrete quantities are mapped through semantic rules); a dynamic weight superposition strategy is adopted, data collaboration is achieved based on a spatial incidence matrix, and dynamic optimization of an additional aging attenuation factor is achieved; synchronously outputting a deterministic model framework and a fuzzy boundary thermodynamic diagram during scene reconstruction; and establishing a real-time feedback closed loop optimization membership function parameter and weight strategy. The innovation points are as follows: semantic distortion is eliminated by a confidence conversion mechanism, an adaptive superposition operator switches an operation mode according to a confidence threshold, and three-dimensional semitransparent rendering visualization uncertainty distribution is realized. The method has the advantages that the multi-source fusion semantic distortion rate is reduced by 62%, the disaster early warning accuracy rate is improved by 55%, and the continuous operation precision attenuation rate of the model is controlled below 3%.

The invention discloses a software quality evaluation method based on a fuzzy theory. The method comprises the following steps: obtaining a quality factor set according to a Meiger software quality model; determining an evaluation grade set, and dividing the evaluation grade set into five evaluation grades; constructing a membership function by adopting a kernel density estimation method; constructing a judgment matrix through the triangular fuzzy number and calculating a defuzzification weight; and converting the score into a fuzzy synthesis value through fuzzy synthesis operation, and generating a fuzzy comprehensive evaluation score through weighted average and normalization processing. According to the method, the fuzzy theory and the Meicole model are combined innovatively, and uncertainty in subjective and objective evaluation is processed by applying fuzzy mathematics. The objectivity, scientificity and repeatability of an evaluation result are effectively improved through a membership function and a weight set constructed based on historical data of similar software. The method is suitable for quality evaluation and optimization decision making of various complex software systems.

The embodiment of the invention provides a decision negotiation method and device based on a depth deterministic policy gradient. The method comprises the following steps: receiving negotiation scene information input by a first negotiation party; wherein the negotiation scene information comprises a plurality of negotiation topics, a preference value interval for each negotiation topics and a preference weight value for each negotiation topics; for each negotiation topic, creating a invention for the negotiation topic according to the preference value interval and the preference weight value of the negotiation topic; wherein the utility model comprises corresponding trapezoidal fuzzy membership functions when negotiation values are in different preference value intervals, and an overall satisfaction function for performing weighted summation on the trapezoidal fuzzy membership functions of all negotiation dimensions; a first reference value of the first negotiation party is predicted through a pre-trained Actor-Critic network; and receiving a first negotiation value input by the first negotiation party according to the first reference value, and sending the first negotiation value to a second negotiation party, thereby facilitating determination of the negotiation value.

The invention relates to the technical field of automatic control, and discloses a sliding mode control buffeting suppression method based on an interval type-2 fuzzy rule. Dynamic balance of buffeting suppression and robustness is realized through deep cooperation of an interval type-2 fuzzy logic system and sliding mode control: an interval membership function is utilized to accurately capture an uncertainty boundary of the system, a double-layer fuzzy reasoning mechanism is combined to cooperatively optimize switching gain and boundary layer thickness, and high-precision tracking is maintained under strong disturbance; the control signal high-frequency oscillation energy is monitored in real time to drive parameter reconstruction, and buffeting is eliminated from the source; the modular design can be directly embedded into an industrial control system, abrasion of an executing mechanism is remarkably reduced, the service life of equipment is prolonged, and the inherent contradiction between dynamic response and stability of a traditional method is solved.

The invention discloses a Z-axis accurate control method for a linear motor type head of a high-speed chip mounter, and the method comprises the steps: firstly constructing a mathematical model of a Z-axis linear motor of the high-speed chip mounter in a D-Q-axis coordinate system, and then calculating a final current iqfinal according to a torque feed-forward compensation value and a speed feed-forward value, so as to improve the speed following performance; a reference position Pf and a reference speed Vf are used as input variables, a triangular membership function is used for describing gradual change characteristics of the input variables and output quantities in a certain range, and a Z-shaped membership function is used for processing characteristics of the input variables and the output quantities in boundary conditions and rapid changes. Fuzzy output obtained through fuzzy reasoning is converted into accurate output control quantity through a gravity center method, and the accurate output control quantity serves as a feedforward control coefficient of the Z-axis linear motor. According to the method, the control problem caused by the fact that a linear motor type machine head is limited in space, the Z-axis linear motor is small in power and a control object is complex in a high-speed chip mounter is solved through an algorithm; the equipment performance is improved.

The invention provides a geotechnical engineering geological disaster prediction method and system for mining exploitation, and relates to the technical field of geotechnical engineering, and the method comprises the steps: S1, obtaining multi-dimensional data from different regions of a mining area in real time through a plurality of data collection devices, S2, carrying out the denoising, abnormal value correction, space-time alignment and missing value supplementation of the multi-dimensional data obtained in S1, and obtaining a prediction result; and the dynamic time window technology is adopted to process the time sequence data, so that the data under different time scales can be effectively synchronized, and accurate alignment and dynamic updating of the data are ensured. According to the geotechnical engineering geological disaster prediction method and system for mining exploitation, the fusion problem of multi-source heterogeneous data is effectively solved by combining the advantages of fuzzy logic and a neural network through an adaptive neural network-fuzzy hybrid algorithm. The fuzzy logic model carries out modeling on spatial and temporal changes of different data sources, and the influence degree of each data source on disaster prediction is accurately evaluated by dynamically adjusting parameters of a membership function.

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.

The invention relates to the technical field of avionics, and discloses a friend behavior prediction and multi-platform autonomous decision-making method and system during communication interruption, and the method comprises the steps: constructing a multi-level fuzzy inference tree, and carrying out the fuzzy processing of multi-source heterogeneous input information, and forming a plurality of fuzzy sets; decomposing the complex input relation into a plurality of subsystems based on a hierarchical fuzzy reasoning mechanism, and obtaining a subtask response benefit value through layer-by-layer reasoning; input variables are fuzzified based on a triangular membership function, and a potential task allocation scheme of the friend formation is predicted in combination with a hierarchical rule reasoning mode. According to the method, centerless, low-dependence and high-robustness multi-platform autonomous task allocation can be realized, and the collaborative decision-making capability and task execution efficiency of formation members in a complex environment under an incomplete information condition are improved.

Methods for determining the reliability of a technical system using fault tree analysis, comprising the following steps: S1 - Defining basic events of fault tree analysis; S2 - for each basic event, defining a fuzzy linguistic variable and a set of membership functions; S3 - at least partially based on real-time embedded software, defines a minimal cutting set for linguistic fault tree analysis; S4 - at least partially based on the minimum intersection theorem, determining the reliability of the technical system. Furthermore, the invention relates to a control unit, vehicle system, a computer program product and a computer-readable medium for executing the method.

The application relates to a T-S fuzzy semantic intelligent liver cancer staging method and system. The method comprises the following steps: S1. liver cancer feature extraction, key liver cancer features are extracted from obtained digital radiology diagnosis reports of patients to form a data set; S2. liver cancer feature fuzzification, a plurality of fuzzy semantic sets are used for fuzzy representation, and relevant membership functions are designed; S3. T-S fuzzy semantic model construction, comprising the following steps: designing a T-S fuzzy semantic rule, solving the membership degree of each rule, identifying the antecedent parameter membership function, identifying the consequent parameter, and outputting the T-S fuzzy semantic model; S4. liver cancer intelligent staging and performance evaluation verification, the liver cancer categories in the Chinese CNLC standard are subjected to digital label processing, a training set and a test set are divided, and the staging performance of the intelligent staging model is verified. The T-S fuzzy semantic intelligent liver cancer staging method can divide the liver cancer into Ia, Ib, IIa and IIb-IV, and is more accurate in grading and staging the liver cancer. The application greatly reduces the estimation error and classification error of the liver cancer state.

The application provides a CLAHE image blur enhancement method based on a mixed membership function. The method improves the single membership function in the existing CLAHE method based on the fuzzy theory into a mixed membership function, dynamically fuses the exponential and linear membership functions in the threshold clipping process, realizes the differential processing of the flat area and the area rich in details, and thus adaptively adjusts the clipping threshold, so as to solve the problems of the insufficient histogram equalization capacity, the easy amplification of noise and the poor edge retention effect of the prior art. Compared with the traditional method, the image enhancement quality of the method is significantly improved under multiple scenes.

The present invention discloses a method, apparatus, device, and medium for handling the criticality of charging equipment. The method includes: determining criticality measurement factors and fuzzy levels of the criticality measurement factors for the fault type based on the historical operation records of the charging equipment; determining fuzzy level membership intervals for the fuzzy levels of the factors within a trapezoidal membership function; generating the trapezoidal membership function based on the correlation between the fuzzy levels of the factors and the criticality values; determining the membership fuzziness and total fuzziness of the membership relationships corresponding to the fuzzy levels of the factors based on the fuzzy level membership intervals; and determining the gray correlation degree of the fault type based on the membership fuzziness and total fuzziness of the fuzzy levels of the factors. Embodiments of the present invention can accurately measure the criticality of faults.

This invention relates to a knowledge and data fusion-driven, interpretable primary headache auxiliary identification method. The method includes: collecting patient information and preprocessing it to obtain a multi-dimensional feature vector; inputting the multi-dimensional feature vector into an interpretable primary headache auxiliary identification model to obtain a probability distribution of headache identification results; outputting the headache type with the highest probability and its identification confidence, and generating a risk warning; wherein, the hierarchical fuzzy logic layer of the interpretable primary headache auxiliary identification model, based on the multi-dimensional feature vector, calculates the fuzzy membership degree of each feature when it belongs to different headache types according to the differentiated membership function of different headache types, obtaining fuzzy membership degree features; the KAN feature fusion network fuses the fuzzy membership degree features with the original vector to obtain classification features; the classifier outputs the probability distribution of headache types based on the classification features. Compared with existing technologies, this invention provides a solution for intelligent headache diagnosis that combines accuracy, robustness, and interpretability.

The application discloses a voltage sag region online identification method and system based on a dynamic fuzzy rule base, and belongs to the field of power system power quality analysis and protection. The method comprises the following steps: calculating initial voltage sag critical points on each line in a power system to generate an initial voltage sag region AOV; acquiring voltage amplitude deviation, phase jump rate and fault point electric distance data of a load point, and performing fuzzy processing on the data by using a membership function to obtain a membership set, inputting the membership set into a TSK fuzzy reasoning model based on a dynamic updating rule base to output a critical point correction amount; and adaptively updating a region boundary according to the initial voltage sag critical points and the critical point correction amount to output an accurate voltage sag region. By introducing a dynamic fuzzy reasoning and online updating mechanism, the application effectively improves the accuracy and adaptability of voltage sag region identification, and solves the problems that existing methods cannot comprehensively consider multiple influence factors and lack online updating capability.

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 boiler soot blowing control method and a related device, and relates to the technical field of boiler operation control. Performing image processing on the initial image of the boiler heating surface to obtain a processed image, performing mapping operation on pixel coordinates of the processed image and actual reference coordinates of the boiler heating surface to obtain a mapping result, and obtaining a region image corresponding to the soot blower based on the mapping result, carrying out dust deposition identification operation on the regional image to obtain a dust deposition identification result, calculating regional attribute information of the regional image according to the dust deposition identification result, determining a fuzzy membership function boundary value corresponding to the regional attribute information, constructing a self-adaptive fuzzy membership function by using the fuzzy membership function boundary value, and obtaining a self-adaptive fuzzy membership function; and determining a fuzzy quantity corresponding to the regional attribute information by using an adaptive fuzzy membership function, determining a soot blowing parameter of a soot blower corresponding to the regional image according to the fuzzy quantity corresponding to the regional attribute information and the fuzzy rule base, and controlling the corresponding soot blower to perform soot blowing operation according to the soot blowing parameter.