238 results about "Membership function" patented technology

The invention belongs to the technical field of power distribution network ground fault line selection, and provides a single-phase ground fault line selection method and system based on zero-sequence current mode decomposition, and the technical scheme is as follows: obtaining zero-sequence current and nth harmonic current; calculating the total harmonic distortion rate of the current according to the zero-sequence current and the nth harmonic current, and determining the maximum and minimum value of the distortion rate of the zero-sequence current; if the maximum value of the zero-sequence current distortion rate is far greater than the minimum value, the outgoing line has a fault, the zero-sequence transient current signal of each line is decomposed by using full-noise-assisted aggregation empirical mode decomposition to obtain an IMF component of each line, and the IMF component of each line is calculated; linear Gaussian fuzzy information granulation is carried out on the IMF component of each line to obtain a membership function of linear Gaussian fuzzy information particles, a fuzzy entropy is solved according to the membership function, and a single-phase earth fault line is determined according to the fuzzy entropy; if the maximum value of the zero-sequence current distortion rate is equal to the minimum value, the bus breaks down. And the fault type can be quickly and accurately judged.

The invention discloses a multi-dimensional collaborative AI training processor benchmark evaluation device and method. According to end-to-end performance evaluation, TTA and throughput indexes are obtained through a standardized training process; the operator support degree evaluation adopts a dynamic parameter generation technology to evaluate the speed-up ratio and the floating point performance utilization rate of calculation-intensive and memory-access-intensive operators; the energy consumption evaluation calculates a power consumption delay product based on steady-state power consumption monitoring. And finally, integrating multi-dimensional indexes through an analytic hierarchy process model and a fuzzy theory, realizing level mapping by adopting a four-section membership function, and finishing multi-level aggregation evaluation in combination with matrix operation. According to the method, three dimensions of performance, operator support degree and energy efficiency are creatively integrated, the problems of single evaluation index and high environment dependence of the heterogeneous AI processor are solved through automatic container deployment, dynamic parameter generation and steady-state power consumption monitoring technologies, and a comprehensive and quantifiable evaluation basis is provided for processor model selection.

The invention relates to the technical field of network security management, in particular to a network security management system based on big data, which comprises a data acquisition and integration unit, a dynamic threshold setting unit, a fuzzy comprehensive evaluation unit, a machine learning optimization unit and a control unit. The acquisition process has intelligent characteristics and adopts a block chain to cache data, the dynamic threshold setting unit constructs a model to adjust a threshold by combining a hidden Markov model, kernel density estimation and a reinforcement learning algorithm for different network crime types, and the fuzzy comprehensive evaluation unit determines a membership function and distributes weights by applying a fuzzy mathematical algorithm. And the machine learning optimization unit uses historical data to train and update the model, assists in case-related account determination, effectively solves the problems of missed determination and misjudgment caused by a fixed threshold value in traditional account risk assessment, and improves the accuracy of network criminal account risk assessment.

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 relates to a hydroelectric generating set mirror plate fault judgment method based on analytic hierarchy process and fuzzy membership, which comprises the following steps: selecting symptom indexes reflecting faults, and systematically analyzing and constructing a hierarchical structure model in a fault type-symptom index association form by using the analytic hierarchy process; a triangular-semi-trapezoidal membership function is used as a basis, a membership function is constructed according to a fault condition level, a symptom index membership matrix is calculated according to a deviation degree between a symptom index real-time value and a health reference value, a fault type fuzzy judgment matrix is further obtained, and a fault type fuzzy judgment matrix is obtained according to a maximum membership principle. And obtaining a diagnosis result whether the hydroelectric generating set has a mirror plate levelness standard exceeding fault. According to the invention, through systematic analysis, objective weight distribution, dynamic reference comparison and a fuzzy determination method, the accuracy of fault determination is significantly improved.

The invention relates to the technical field of fire control management, and discloses an intelligent fire control door management method and system based on the Internet of Things, and the method comprises the following steps: S1, collecting multi-environment data of a fire control door; s2, performing standardization processing to obtain multi-environment data in a unified format, and establishing a feature matrix; s3, determining the importance degree of each item of data in the feature matrix to obtain a comprehensive weight; s4, constructing a membership function, and outputting a fire risk level; s5, determining the fire stage of the current environment; s6, selecting a final risk response strategy through a fuzzy game method; and S7, controlling the action of a fire exit door according to the selected final risk response strategy. According to the method, the information entropy and the analytic hierarchy process algorithm are combined to determine the importance degree of the multi-environment data and calculate the comprehensive weight of the fire risk, so that the assessment is more objective and comprehensive, overlarge deviation of a single model is avoided, and the problems of bias and poor applicability of assessment are solved.

The determination of a production index for a selected well using a fuzzy logic and neural network model (a “fuzzy-neural model”). Input data may be obtained from one or more producing wells and preprocessed for use in training and testing. The preprocessed data may be fuzzified into fuzzy values using fuzzy sets, membership functions, and a rule base. The neural network may be trained using the fuzzy values from the fuzzification to output the production index. The trained fuzzy-neural model may then be used to determine a production index for new data from the selected well.

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 driving danger prediction method and device based on an adaptive fuzzy neural network. The method comprises the steps that original variables used for reflecting the vehicle state are collected, dimension reduction processing is conducted on the original variables, and key variables are obtained; constructing a driving danger prediction model based on an adaptive fuzzy neural network; the genetic algorithm is utilized to optimize the preceding component parameters and the following component parameters of the driving danger prediction model, and the preceding component parameters are optimized according to the selection, crossover and mutation operation of the genetic algorithm, and the central position and the standard deviation of a membership function are optimized; the optimization of the consequent parameters comprises the step of optimizing linear combination parameters in the fuzzy inference rule; and inputting vehicle state data obtained in real time into the optimized driving danger prediction model, and outputting a driving danger value used for representing driving danger. According to the method, the model adaptability can be improved, the parameter optimization efficiency is improved, the accuracy of model prediction is improved, and accurate and real-time driving risk assessment is realized.

The invention discloses a generator set multi-target maintenance optimization scheduling method considering renewable energy access. The method comprises the following steps: establishing an uncertain model of renewable energy output and load; considering an economy target, a reliability target and a renewable energy consumption target, selecting a minimum total cost, a minimum standby capacity sum of squares and a minimum wind curtailment and light curtailment amount for comprehensive evaluation, and constructing a multi-target unit maintenance optimization model; converting a system power balance constraint and a reserve capacity constraint into a deterministic constraint with clear equivalence; selecting a membership function of each target based on a fuzzy theory, and converting the multi-target unit maintenance optimization model into a single-target unit maintenance optimization model; and solving the single-target unit maintenance optimization model converted based on the fuzzy theory by using an improved ant colony algorithm, and obtaining the maintenance and operation conditions of each unit while obtaining the maximum satisfaction degree. The method is applied to a power grid to arrange maintenance scheduling of the generator set, and economic benefits, reliability and renewable energy consumption are improved.

The invention provides a method for establishing a precise ammonia injection fuzzy control model based on an LSTM neural network. The method comprises the following steps: S1, analyzing historical data, cleaning, designing a membership function and formulating a fuzzy rule table; s2, constructing a control model by using an LSTM neural network algorithm to obtain an error between a predicted value and an actual value and an error change rate; and S3, comparing a prediction result of the model with an actual output result. According to the invention, an advanced neural network technology and a flexible fuzzy control strategy are fused, so that accurate adjustment of ammonia spraying of the SCR denitration system is realized. A fuzzy expert control system is built, an error, a change rate range of the error and valve opening variation corresponding to different errors and error change rates are obtained by analyzing historical data of ammonia water adjustment, and a fuzzy discourse domain and a fuzzy rule table are designed in combination with a fuzzy control principle and a system working condition. And fuzzy reasoning and fuzzy judgment are carried out on real-time data of the system, and the increment of the ammonia water regulating valve under the current working condition is calculated and regulated.

The invention relates to the field of computer servers, and discloses a modularized computing power and storage integrated server which comprises a computing module, a storage module, a bus module, a control module and an optimization module. The calculation module and the storage module support modular hot plug through a standardized interface, and can dynamically adjust configuration according to task requirements; the bus module is used for realizing high-speed data transmission between the calculation module and the storage module and supporting dynamic allocation of bandwidth priority; the control module generates a resource extension suggestion and a bandwidth allocation strategy based on fuzzy reasoning; and the optimization module performs offline optimization on the fuzzy rule base and membership function parameters of the control module through a genetic algorithm so as to improve the overall performance of system regulation and control. According to the method, elastic expansion and dynamic management of computing and storage resources are achieved, the resource utilization rate, the bandwidth allocation efficiency and the system operation stability are improved, and the method is suitable for high-performance computing and large-scale storage tasks with complex loads and multi-scene requirements.

In an automated insulin delivery device, fuzzy logic may be applied to the responding to the possibility of a user taking additional action that may affect the blood glucose concentration. Fuzzy sets may be defined for empirically derived different likelihoods of the user taking such additional action based on correlated factors. A membership function may be provided for each fuzzy set. The membership function may provide a probability of membership in the set based on a parameter. Each fuzzy set may have a response that is reflective of the likelihood of additional user action associated with the fuzzy set. The responses by the AID device to each of these cases may reflect the probability of each such case occurring as evidenced by empirical data.

The invention discloses an electronic equipment intelligent control method and system based on the Internet of Things, and relates to the technical field of Internet of Things data analysis, and the method comprises the steps: analyzing the contribution degree of each data category to a control target, and considering the contribution or association conditions of different data categories corresponding to different control targets; and a reliable basis is provided for subsequent data fusion and equipment intelligent control. Kalman filtering parameters are set according to the contribution degree of the data category to the control target, and the Kalman filtering parameters are jointly set according to the attribute of the data category and the contribution degree. A fuzzy set and a membership function are defined on the basis of the fusion features, the type of the membership function is selected according to the features of the fusion features, and the pertinence of fuzzy reasoning is improved. And fuzzy reasoning is performed according to the fuzzy control rule to generate the control instruction, so that the adaptability and accuracy of intelligent control of the equipment are improved, normal, automatic and safe operation of intelligent equipment is ensured, and the working efficiency of the equipment is improved.

The invention discloses a fuzzy self-adaptive PID (Proportion Integration Differentiation) control method for a high-temperature environment of a small closed space. According to the method, a fuzzy logic controller and PID controller cooperative control architecture based on the variable universe theory is constructed, and PID parameters are adjusted on line by utilizing a fuzzy inference rule by collecting the temperature deviation and the change rate of the closed space in real time. The fuzzy rule base design adopts a variable universe theory, a membership function and a universe range are dynamically adjusted according to a system state, and the adjustment direction and amplitude of PID parameters are accurately mapped. A real-time monitoring mechanism based on feedback of a temperature sensor is introduced, high-frequency sampling is carried out on the temperature of the space, and timeliness and accuracy of control signals are ensured. According to the method, by setting a temperature threshold value and an overshoot limiting mechanism, the phenomena of temperature overshoot and oscillation are effectively avoided, and the stability and reliability of the system are improved. According to the invention, the temperature can be quickly and accurately controlled in a high-temperature environment of a complex small closed space, and the temperature fluctuation range is obviously reduced.

The invention provides a hail probability and size identification algorithm based on fuzzy logic and semi-supervised learning, and aims to improve the accuracy of hail early warning and reduce the false alarm rate. According to the algorithm, the probability comprehensive recognition of fuzzy logic and the Self-training semi-supervised learning technology are combined, and the probability and size of hail occurrence are accurately recognized through deep analysis of dual-polarization radar data and meteorological sounding data. The method comprises the specific steps that firstly, the heights of a 0 DEG C layer and a-20 DEG C layer are calculated through polynomial fitting, then a fuzzy logic algorithm is combined with expert experience to determine a membership function and weight of characteristic parameters, and the probability of hail occurrence is calculated; and then, a semi-supervised learning algorithm is adopted to further refine identification of the hail size. A weak classifier is adopted, and a Self-training iteration process is carried out, so that labeled samples are expanded, and the classification accuracy is improved. A final evaluation result shows that the algorithm is superior to a traditional method in the aspects of the hail recognition rate, the false alarm rate, the accuracy rate and the like, has high application value and can provide effective support for forecasting and early warning of hail weather.

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 provides a self-adaptive interval type-2 fuzzy logic enhanced intelligent manufacturing control method, which comprises the steps that a fuzzy set influencing a target system is predicted through a particle swarm optimization algorithm, the fuzzy set comprises one or more dimensions, and each dimension determines a numerical range capable of generating corresponding influence through an upper membership function and a lower membership function; activating a corresponding fuzzy reasoning rule in response to the obtained fuzzy set output by the particle swarm optimization algorithm, performing fuzzy reasoning based on upper and lower membership functions in the fuzzy set, and obtaining a reasoning set; and performing defuzzification on the reasoning set to obtain a control instruction, and controlling the target system to perform intelligent manufacturing based on the control instruction. According to the method, the capability of representing and processing uncertainty in the intelligent manufacturing control process can be improved, so that a more accurate decision instruction is provided to improve the product quality and the production performance.

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 relates to an intra-region multi-point energy storage scheduling method, system, device and medium, and the method comprises the steps: obtaining the state of charge, health degree and available cycle life related real-time data of each new energy station energy storage system in a region; according to the fuzzy reasoning model and the real-time data, a preset membership function is adopted, and the charge state, the health degree and the membership degree of the available cycle life are obtained. And determining a rule base in the fuzzy inference model based on the state of charge, the health degree, the membership degree of available cycle life, the operation condition of the energy storage system and related technical standards, and classifying the energy storage systems of the new energy stations in the region according to the rule base in the fuzzy inference model to obtain a classification result. And in response to the received scheduling instruction, scheduling each new energy station energy storage system in the region according to a preset scheduling rule based on the classification result, and obtaining a scheduling result. The efficiency of the system is improved, and the problem of low utilization rate of multi-point energy storage in the existing region is solved.

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 belongs to the technical field of PID control, and particularly relates to a PID parameter adaptive algorithm, system and program based on a multi-dimensional fuzzy rule. A PID parameter adaptive algorithm based on a multi-dimensional fuzzy rule comprises the following steps: normalizing system error data and an error change rate by taking a maximum change value as a normalization factor; performing fuzzification processing on the normalized error data and the normalized error change rate by adopting a triangular membership function of a plurality of linguistic variables to obtain fuzzy error data and a membership distribution matrix of the fuzzy error change rate; orthogonal design is carried out on the multiple linguistic variables, and a fuzzy rule base is constructed; and according to fuzzy error data in the membership degree distribution matrix and the distribution of the fuzzy error change rate, traversing the fuzzy rule base, forming a plurality of matched mapping results, performing weighted average calculation, and performing fixed amplitude limiting and dynamic amplitude limiting to obtain a final proportional gain # imgabs0 #, a final integral gain # imgabs1 # and a final differential gain # imgabs2 #.

The invention relates to a THI millimeter wave cloud radar clear sky echo filtering method, device and equipment, and the method comprises the steps: carrying out the marking of clear sky echoes or meteorological echoes through employing the dual-mode characteristics of space-time physical information combination for sample data of each channel in a sample data set; marking the sample data of each channel, constructing a corresponding fuzzy logic membership function according to the marked sample data of each channel, obtaining membership degrees of clear sky echoes and meteorological echoes from the base data of different channels in the original THI data obtained through actual measurement by using the corresponding fuzzy logic membership functions, and inputting the membership degrees of the channels into a trained classification identification model, and identifying clear sky echoes and meteorological echoes in the original THI data to realize filtering of the clear sky echoes. By adopting the method, clear sky echoes can be accurately filtered.

The invention discloses a small and micro enterprise loan availability evaluation method, which belongs to the technical field of data processing, and integrates unstructured operation and semi-structured behavior data, establishes a dynamic data confidence evaluation matrix, and starts artificial recheck to guarantee data quality. And constructing a three-layer fuzzy membership function system, and generating a standardized score by using an improved interval type-2 fuzzy set and an alpha-cut algorithm. Defining an interest rate sensitive dimension, generating an initial interest rate hierarchy through an improved spectral clustering algorithm, and predicting operation fluctuation and adjusting parameters by using a double-layer LSTM (Long Short Term Memory). And through IPC cross validation, carrying out residual analysis on the fuzzy data and on-site collected data, generating a joint confidence interval through D-S evidence fusion, and evaluating loan availability and outputting a suggestion report on the basis of the joint confidence interval. According to the method, the existing evaluation data processing problem is effectively solved, evaluation scientificity, accuracy and comprehensiveness are improved, a reliable decision basis is provided for financial institutions, and accurate customer layering and product adaptation are assisted to be achieved.

The invention provides a dry-type transformer health state assessment method and system based on an improved fuzzy analytic hierarchy process and an abrupt change series method, and belongs to the technical field of transformer state assessment. A health state assessment index system is established, and a health state comment set is constructed according to the health state deterioration degree of a transformer; dynamically adjusting the weight, and determining the index weight of each layer; calculating the membership degree of each index, and combining the membership degree with the weight matrix to obtain a total fuzzy evaluation matrix; calculating to obtain a system total mutation membership function value; and determining the health state grade of the dry-type transformer according to the maximum membership degree principle by adopting a weighted average method and integrating the evaluation results of the two. According to the method, the interaction and synergistic effect between indexes are considered, and the defects of existing single-angle evaluation are overcome; the method considers the influence of operation time, dynamically adjusts the weight, accurately reflects the influence degree of each index in different operation stages on the health state of the transformer, and achieves the accurate and comprehensive evaluation of the health state of the dry-type transformer.

The present invention relates to the technical field of quality management, and specifically provides a quality management method based on standard data. Specifically, it includes: constructing a dynamic standard database; collecting production data in real time; adopting a hybrid anomaly detection and dynamic interpolation strategy to handle data missing and noise, generating a standardized matching code based on hash and polynomial ring operations to ensure data consistency; determining quality deviations based on a dynamic threshold model and fuzzy logic, quantifying the deviation amount by combining the weighted Euclidean distance, realizing hierarchical early warning through a membership function, and triggering response measures according to the early warning level; using a scoring model and non-linear prediction to evaluate the effectiveness of measures and screening optimization plans; a closed-loop feedback mechanism continuously updates model parameters and the knowledge base. Through dynamic data integration, hybrid detection algorithms and matching code design, the present invention effectively solves the problems of inflexible static thresholds, single anomaly detection, and poor data consistency in traditional quality management, and significantly improves the detection accuracy and system robustness.

The invention discloses a three-dimensional sonar target detection method and device based on fuzzy decoupling, and the method comprises the steps: S1, reducing errors caused by different collection devices through maximum and minimum standardization based on a preprocessing module of an original point cloud; s2, performing fuzzy feature decoupling coding on the sonar original point cloud based on prior knowledge, establishing a point cloud decoupling membership function, and coding the discrete point cloud into a feature sequence by using a coding function; and S3, performing feature extraction on the two encoded decoupling features through a VFE module to obtain BEV features. S4, the BEV features are subjected to Gaussian thermodynamic diagram prediction through a backbone network and decoded into a first-stage proposal. And S5, combining the first-stage proposal with two-stage refinement based on Markov prediction to obtain a final three-dimensional calibration frame and the category and score thereof. Aiming at the problem of weak heterogeneity of a target in a three-dimensional sonar point cloud target identification task, the challenge of three-dimensional sonar weak heterogeneity identification is effectively solved through fuzzy decoupling feature coding, point-based Gaussian thermodynamic diagram prediction and Markov prediction-based two-stage refinement.