81 results about "Fuzzy inference rules" patented technology

The invention provides a pressure-bearing equipment damage mode identification and risk assessment method and system, and relates to the technical field of safety engineering, and the method comprises the steps: collecting multi-source sensor data and image data, inputting the data into a deep neural network after preprocessing and feature extraction, extracting spatial features through a convolutional layer, and extracting time sequence features through a recurrent neural network. And using the attention mechanism to fuse the features to identify an injury pattern. And then, constructing a multi-level evaluation index system, performing combined weighting by adopting an analytic hierarchy process and an entropy weight method, inputting weights into an improved Bayesian network model based on a D-S evidence theory, dynamically updating a conditional probability table by the model by utilizing a deep neural network and a fuzzy inference rule, and finally obtaining a risk evaluation result. According to the invention, the damage mode of the pressure-bearing equipment can be effectively identified, risk assessment is carried out, and assessment precision and reliability are improved.

The invention discloses a mine ecological environment restoration evaluation method and system based on deep learning, and the method comprises the steps: obtaining related data and evaluation indexes of a mine ecological environment restoration effect, carrying out the fuzzification of the indexes through employing a fuzzy mathematical theory, and obtaining a fuzzy evaluation index set; constructing a fuzzy evaluation index system according to the fuzzy evaluation index set, and obtaining a fuzzy score matrix of each evaluation index and fuzzy information of the fuzzy score matrix; based on fuzzy information of the fuzzy scoring matrix, high-level features of the fuzzy information are extracted; determining different evaluation grades and standards according to the clustering result, and representing the matching degree between the evaluation object and each evaluation grade by adopting a fuzzy membership degree to obtain an evaluation result; establishing a fuzzy reasoning rule base by using a fuzzy reasoning technology; and performing information fusion on the fuzzy scores of the evaluation indexes by adopting a fuzzy integral method, and comprehensively considering the interaction influence and the importance degree difference between the evaluation indexes to obtain a final comprehensive evaluation result of the mine ecological restoration effect.

The invention relates to the technical field of water pump fault diagnosis, discloses a condensate pump shafting vibration fault diagnosis method and device based on multi-parameter analysis and a medium, and solves the problems of sensor signal distortion and noise interference of a condensate pump under high-temperature and high-humidity working conditions through multi-source data fusion and environment adaptive compensation. Dynamic time-frequency domain feature extraction and an improved lightweight residual network are adopted, and efficient decoupling and accurate recognition of multi-fault concurrent features are achieved; the fuzzy inference rule base and the shafting fluctuation propagation model are combined, the fault positioning precision and the early fault detection rate are remarkably improved, scientific and reliable technical support is provided for diagnosis and maintenance of the shafting vibration fault of the condensate pump, and safe and stable operation of factory equipment is effectively guaranteed.

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 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 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 an integrated circuit source measurement unit control method for improving a raccoon algorithm and optimizing fuzzy PID. The method comprises the steps that an output voltage control system of an SMU is set up; acquiring output end voltage data of the SMU system in real time; calculating a voltage error and a voltage error change rate; a fuzzy inference rule base is constructed, fuzzy controller parameters are dynamically adjusted based on the fuzzy rule base according to the voltage deviation value and the voltage error change rate, and then PID parameters are dynamically adjusted; a control signal is generated according to the set PID parameter, and the output voltage of the SMU is adjusted; and S4, optimizing the fuzzy PID voltage controller obtained in the step S4 by adopting an improved raccoon algorithm, selecting a decision variable as a parameter of a fuzzy controller, and assigning the optimized parameter to the fuzzy PID voltage controller to realize control on the output voltage of the SMU. According to the invention, the improved raccoon optimization algorithm and the fuzzy PID controller are combined and applied to the integrated circuit source measurement unit, so that the control performance of the SMU system is improved.

The invention relates to the technical field of solar power generation, in particular to a charging and discharging management system of a solar street lamp, which comprises a data acquisition unit, an illumination intensity prediction unit, a fuzzy reasoning unit, a charging optimization unit, a discharging optimization unit and a battery management unit. Historical data of street lamps are collected and preprocessed through a data acquisition unit, and a first data set is generated; the illumination intensity prediction unit applies an SARIMA model to predict future illumination intensity; the fuzzy reasoning unit generates a membership function according to the first data set and the predicted illumination intensity; the charging optimization unit constructs a fuzzy inference rule base by using a membership function and calculates the current charging power; the discharge optimization unit divides discharge time periods according to the first data set and calculates discharge power; the battery management unit updates the remaining capacity of the storage battery according to the charging power and the discharging power and evaluates the energy storage effect, the energy management effect of the solar street lamp can be improved, and therefore the adaptability of charging and discharging management of the solar street lamp is improved.

The invention relates to the technical field of injection mold monitoring, and discloses an injection mold cavity state monitoring method, system, equipment and medium, the injection mold cavity state monitoring method comprises the following steps: multiple sensors collect mold state data in real time, and the mold state data are preprocessed and fused to generate a comprehensive data set; training a machine learning model based on historical samples, and updating model parameters in real time through an incremental learning algorithm; analyzing current data by adopting dynamic fuzzy logic reasoning, and outputting a state evaluation result; real-time data and results are uploaded to the cloud for deep analysis and distributed storage; a user feedback mechanism is integrated, and model parameters and a fuzzy inference rule base are optimized. According to the method, model parameters are updated in real time through an incremental learning algorithm, a multi-parameter nonlinear coupling relation is analyzed in combination with dynamic fuzzy logic reasoning, and based on cloud collaboration and a user feedback closed-loop mechanism, self-adaptive monitoring and continuous optimization of the injection mold state are achieved, and the anomaly detection precision and the system robustness are 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 relates to the technical field of data management, in particular to a variable universe fuzzy PID water treatment parameter dynamic optimization method based on SSA. The method comprises the following steps: firstly, according to a dynamic weight parameter generated by a multi-target SSA algorithm, optimizing a fuzzy reasoning parameter and a scaling factor, dynamically adjusting a search step size in combination with a self-adaptive step size adjustment strategy to obtain an optimized parameter so as to support data management and optimization of a water treatment system; then, dynamically updating a fuzzy inference rule through a reinforcement learning algorithm according to a real-time state, and screening out optimization parameters suitable for management optimization; and finally, performing analysis and short-term trend prediction on real-time input data, integrating quantization deviation into a fitness function, and performing prospective adjustment and correction on the screened optimization parameters so as to improve the accuracy of parameter management. The method can improve the management efficiency and optimization effect of water treatment parameters, and is suitable for intelligent management and supervision of a water treatment system.

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 provides a motor parameter self-learning fuzzy control method and system for a servo driver, and the method comprises the steps: carrying out the combined sampling and interference elimination processing of multi-source operation data in an industrial field, and obtaining the correlation information of a motor structure and load change. And performing multi-level reduction and dynamic expansion of the fuzzy inference rule base based on the associated information to obtain variable membership function configuration. And performing iterative correction on the torque floating data of the motor in the high-speed operation and local load sudden change stages according to the configuration to obtain updated key motor parameters. And triggering a closed-loop control signal of the servo driver according to the updated motor parameters, and performing multi-path output processing to obtain a real-time control instruction. According to the method, the stability and the response precision of the motor under different loads and operation states are improved by adaptively adjusting the control strategy of the servo driver, and the problems of inaccurate motor parameters and poor adaptability in a traditional control method are solved.

The invention relates to the technical field of slope engineering, and discloses a slope deformation damage scale prediction model construction method and device, and the method comprises the steps: defining m fuzzy factors affecting slope damage, and carrying out the quantification of the fuzzy factors; defining a fuzzy set of quantized fuzzy factors to obtain m fuzzy factor sets; performing rule base construction on the m fuzzy factor sets to obtain a rule base of fuzzy inference rules; and performing model construction based on the rule base by adopting a fuzzy neural network, and constructing a prediction model. According to the method, by introducing the fuzzy set theory and the fuzzy logic tool, the fuzzy factors in slope deformation and damage are quantified, the adaptability of the prediction model to the complex slope environment is improved, and the precision and reliability of the prediction result are improved; the theoretical model and monitoring data are effectively combined, the slope state can be dynamically evaluated in real time, a scientific decision basis is provided, precautionary measures can be taken in advance, and the slope instability risk can be reduced.

The invention discloses an underwater robot motion control method based on fuzzy active disturbance rejection control. As the basis of underwater robot motion control research, firstly, a kinetic model is established for an underwater robot; and then an active disturbance rejection controller is used as a basic controller. In order to avoid the problems of phase lag and long adjustment time caused by a tracking differentiator, the linear active disturbance rejection controller designed by the invention does not use the tracking differentiator (TD), and only keeps a linear extended state observer (LESO) and a linear state error feedback rate (LSEF). Finally, in order to adapt to the problem that the underwater robot faces control model change when executing different operation tasks, a fuzzy controller is added on the basis of the improved linear active-disturbance-rejection controller, parameters in the linear active-disturbance-rejection controller are dynamically adjusted through a fuzzy inference rule, and therefore the control model change is avoided. Therefore, optimal control parameters can be adjusted for robot motion control under different conditions.

The invention relates to the field, and discloses a parallel power supply efficiency optimization method and system, and the method comprises the steps: collecting the output current of each power supply module in real time through a current sensor of each power supply module, and collecting the total output current of a parallel power supply system, and the input voltage and output voltage of each power supply module; calculating a current sharing error of each power supply module, and adjusting a proportionality coefficient, an integral coefficient and a differential coefficient of a fuzzy PID controller through a fuzzy inference rule; the voltage regulator is used for regulating the power supply module through the control signal; calculating the efficiency of each power supply module according to the input voltage, the output voltage and the output current of each power supply module, and determining the optimal working point of each power supply module and adjusting the input power of each power supply module according to the efficiency and the load condition of each power supply module so as to complete the efficiency optimization of the whole parallel power supply; according to the invention, the working state of the power supply module can be adjusted in real time under different working conditions, and the method has high adaptability and stability.

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 invention discloses a bidirectional research and judgment method for power distribution network faults in combination with an incidence matrix and dynamic Bayesian, and the method comprises the following steps: constructing the incidence matrix through an actual topological structure of a power distribution network, and constructing a fault research and judgment matrix in combination with a fault information matrix; fault criteria under the conditions of node fault information missing report and fault information false report are established respectively, so that fault feeder identification from top to bottom is realized; constructing a dynamic Bayesian network according to an actual topological structure of the power distribution network and a'station-line-transformer-household 'relationship, and reasoning the fault probability of each region; constructing a membership function and a fuzzy inference rule to obtain a fuzzy inference system for adjusting the fault probability; and finally inferring the most probable fault area, and realizing fault area research and judgment from bottom to top. According to the method, on the basis of a traditional matrix algorithm, the fault criterion is improved by constructing the causal incidence matrix, the feeder line identification problem under the conditions of fault information missing report and fault information false report is effectively solved, the calculation amount is reduced, and the fault positioning accuracy is improved.

The invention discloses a power distribution network self-healing capability evaluation method and device and a storage medium, and the method comprises the steps: building key indexes of the self-healing capability of a power distribution network, the key indexes comprising self-healing reliability, self-healing recovery speed, self-healing rate and self-healing benefit; defining a fuzzy set corresponding to each key index, and constructing a membership function corresponding to each fuzzy set; setting a fuzzy inference rule, and evaluating the self-healing capability of the power distribution network according to the fuzzy inference rule, the fuzzy set and the membership function to obtain membership degrees of a plurality of fuzzy intervals; and carrying out defuzzification processing on the membership degrees of the plurality of fuzzy intervals to obtain a self-healing capability evaluation result of the power distribution network. According to the method, various uncertain factors influencing the self-healing capability evaluation are comprehensively considered, so that the accuracy of the self-healing capability evaluation of the power distribution network can be effectively improved, the uncertainty in the fault recovery process of the power distribution network is effectively handled, and the overall stability and the operation efficiency of the power distribution network are improved.

The invention relates to a data compression storage method and device, computer equipment, a readable storage medium and a program product, and relates to the technical field of big data. The method comprises the following steps: in response to a compression storage instruction for target data, obtaining performance parameters of a data storage service for compressing and storing the target data; performing fuzzification processing on the performance parameter to obtain a target fuzzy quantity of the performance parameter; performing fuzzy reasoning according to the target fuzzy quantity and a fuzzy reasoning rule to obtain a first fuzzy set; the fuzzy inference rule is used for describing a corresponding relationship between the target fuzzy quantity and each compression algorithm; performing defuzzification processing on the first fuzzy set to obtain a target compression ratio; and based on a compression algorithm matched with the target compression ratio, performing compression storage on the target data through the data storage service. By adopting the method, the data storage efficiency can be improved.

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 present application relates to the technical field of solar power generation, in particular to a kind of solar street lamp's charge-discharge management system, including data acquisition unit, illumination intensity prediction unit, fuzzy inference unit, charging optimization unit, discharge optimization unit and battery management unit.The present application generates first data set by data acquisition unit collection and pre-processes the historical data of street lamp;Illumination intensity prediction unit applies SARIMA model to predict future illumination intensity;Fuzzy inference unit generates membership function according to first data set and predicted illumination intensity;Charging optimization unit constructs fuzzy inference rule base using membership function, calculates current charging power;Discharge optimization unit divides discharge period according to first data set and calculates discharge power;Battery management unit updates battery remaining capacity according to charging power and discharge power, and evaluates energy storage effect, which can improve the energy management effect of solar street lamp, thereby improving the adaptability of solar street lamp's charge-discharge management.

This invention belongs to the field of automotive braking technology, specifically relating to a method for assessing and warning of instability risk in automotive braking systems and a corresponding automotive vehicle. The method includes: calculating characteristic values ​​during vehicle braking. l t ; Calculate the proportion of flutter energy during vehicle braking or t ;according to l t and or t The precise probability of vehicle flutter during braking is obtained based on a fuzzy algorithm. P t ;judge P t Is it greater than or equal to the calibrated probability? P 0; when P t ≥ P At time 0, an early warning is issued regarding the risk of vehicle braking chatter instability; otherwise, no warning is required. The core advantages of this invention are: 1) It theoretically solves for real-time characteristic values ​​that characterize the stability of the vehicle braking system, while simultaneously collecting brake disc angular velocity signals to calculate the proportion of chatter energy; 2) It establishes fuzzy inference rules that integrate the mechanism model and measured data to assess the risk of vehicle braking chatter instability, providing technical support for the accurate prediction and control of vehicle braking chatter in engineering.

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 invention discloses a water conservancy project safety risk early warning method based on deep learning, and the method comprises the steps: S1, obtaining the monitoring data of a high-flow-rate reinjection well, and carrying out the normalization processing; s2, constructing a fuzzy logic reasoning model based on a Kailey manifold fuzzy reasoning algorithm, and determining an initial reasoning path; s3, based on a seepage Lagrange optimization model, optimizing a reasoning path in a high-dimensional Kailey manifold space; s4, calculating a seepage critical value, adjusting a fuzzy inference rule, and dynamically updating an inference path based on time sequence analysis; s5, performing historical data projection, and calculating the evolution trend of the seepage risk based on a time scale transformation method; s6, updating the reasoning path calculation weight according to different water injection pressures and permeability; and S7, based on a calculation result of the reasoning path, adjusting a calculation structure of the reasoning model. According to the invention, long-term reliable operation of water conservancy project risk early warning is effectively ensured.

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 disclosure provides a kind of shielded mask mark detection method, comprising: S1, the mask mark image collected is converted into gray scale chart;Wherein, the image collected is at least partially shielded;S2, gray scale chart is carried out image segmentation, and non-mark part is filled as white;S3, the gray scale median of gray scale chart is obtained, as initial binary threshold value the gray scale chart is carried out binary processing;Carry out outline detection, and obtain the multiple minimum circumscribed rectangle of image outline;S4, according to the long and wide features of mask mark minimum circumscribed rectangle, determine fuzzy inference rule, obtain the probability that each minimum circumscribed rectangle is actual shielded mark circumscribed rectangle;S5, whether there is probability greater than preset threshold in probability;If not, adaptive adjustment binary threshold value, repeat S3-S5;If yes, determine the minimum circumscribed rectangle of maximum probability, as first detection target;S6, according to first detection target, determine first center coordinates, complete the positioning detection of mask mark.

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.