298 results about "Fuzzy rule" patented technology

The invention relates to the field of artificial intelligence and computer science, in particular to a causal structure and latent variable-based adaptive reinforcement learning reasoning migration method, which comprises the following steps of: constructing a causal world model fused with multi-modal observation and a decoupling latent variable space; establishing a hierarchical inference engine comprising an intuition layer, a conventional layer and a planning layer; pre-training a quick response and judicial planning dual-mode strategy and generating an interpretable fuzzy rule base; performing calculation level coarse tuning based on task identification and causal complexity; evaluating the real-time state criticality through an adaptive neural fuzzy system and dynamically switching a decision mode; after the action is executed, the threshold and the rule are subjected to closed-loop optimization, and cross-environment efficient migration is realized by utilizing a causal modularization characteristic. According to the technical scheme, consumption of computing resources is remarkably reduced on the premise that decision precision and safety are guaranteed, and the response speed and cross-scene adaptive capacity of a system on edge equipment are improved.

The invention relates to the technical field of power electronics and power system control, and discloses an intelligent control method and system for a multi-network converter, and the method comprises the steps: collecting the frequency deviation, the power change rate and the voltage amplitude of a power grid in real time, carrying out the filtering and feature extraction, and analyzing the coupling relation, thereby obtaining the parameter coupling strength. And if the intensity exceeds the threshold value, an inertia adjusting signal is generated through a fuzzy logic controller. And calculating a deviation correction value by combining the state evaluation response performance of the converter, and generating an optimization instruction by fusing the frequency deviation. And solving a voltage stability threshold based on the instruction, updating a coupling index, and issuing and executing after verification. And finally, updating the fuzzy rule base by adopting neural network iterative learning according to feedback data, and forming an inertia support frame with self-learning capability. According to the method, the inertia response speed and the operation stability of the multi-network converter system under high-proportion new energy access can be effectively improved, and adaptive cooperative control is realized.

The invention provides a battery cell fault real-time monitoring method and system based on multi-parameter analysis and calculation, and relates to the field of battery safety monitoring. The method comprises the following steps: synchronously acquiring voltage, temperature and internal resistance signals of a battery cell, and preprocessing to obtain original data; and performing voltage correlation analysis, internal resistance-temperature collaborative judgment and isolated forest anomaly detection on the original data in sequence to form a progressive anomaly judgment chain, and extracting multi-dimensional battery cell state features. And dynamically correcting the voltage protection threshold value based on the battery health state attenuation rate and the real-time temperature. And fusing multi-dimensional battery cell state characteristics through a fuzzy rule base, and calculating a comprehensive fault probability to divide a fault probability grade. And triggering a matched response strategy based on the fault probability level. The technical problems that single-parameter monitoring is low in sensitivity and high in false alarm rate, and the real-time monitoring requirement is difficult to meet by depending on historical data are solved.

The invention discloses a storage tank pressurization control method and system based on LNG gas station fuzzy self-adaptive PID control, and aims to solve the technical problems that a traditional pressurization control method of an LNG storage tank is low in control precision, poor in stability and rapid in equipment abrasion. According to the control method, dynamic compensation for the target pressure is introduced, so that the system can aim at a more accurate control target in the initial stage, the steady-state control deviation is reduced, and the control precision is improved. And static pressure head change disturbance caused by liquid level change is overcome through dynamic compensation, the adjusting burden of the controller is reduced, the controller can focus on processing other dynamic disturbance, and therefore the control process is more stable, and overshoot is smaller. Through the design of a dynamic compensation mechanism and a special fuzzy rule base, the system can automatically adapt to various complex working conditions, and fast, stable and overshoot-free pressure control is always kept. Therefore, frequent and large-amplitude action and surge phenomena of the valve are avoided, and the energy consumption of an executing mechanism is reduced.

The invention discloses an internal combustion engine in-cylinder transient self-adaptive exposure control method based on FPGA acceleration, and belongs to the technical field of internal combustion engine endoscopic visualization. In order to solve the technical problem of image overexposure or underexposure caused by violent illumination change in the combustion process in an internal combustion engine cylinder, a real-time exposure detection unit is fused with a camera built-in sensor and an endoscope added sensor to monitor light intensity data; the adaptive exposure control unit accelerated by the FPGA adopts adaptive PID control and fuzzy logic control to generate a parameter adjustment direction, the adaptive PID dynamically adjusts gain based on a machine learning model, and the fuzzy logic outputs a decision according to a fuzzy rule base of brightness, light intensity and errors; the parameter adjusting unit adjusts the aperture size, the shutter speed and the light sensitivity in a linkage mode according to the target and actual gray scale difference value, and millisecond-level multi-parameter exposure control is achieved. Overexposure of a highlight area and loss of details of a low-light area are effectively avoided, and stable and clear images are obtained in the whole combustion process.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

The invention discloses a power coordination control method of a high-temperature gas cooled reactor nuclear steam supply system, which relates to the technical field of nuclear power, and comprises the following steps of: coupling multiple modules in the high-temperature gas cooled reactor nuclear steam supply system through a common conventional island; the key state parameters of the to-be-adjusted module are used as main control variables, and the other modules are used as auxiliary control variables, so that the mutual influence relationship among the modules can be comprehensively considered; reasoning the control quantities based on a preset fuzzy rule to determine a fuzzy control strategy, so that adverse effects among modules can be counteracted, overall parameters are kept in a design value range, and safe and stable operation of the system is guaranteed; flexible adjustment can be performed according to key state parameters of different modules by using a fuzzy control strategy, the power of the reactor is quickly and accurately controlled, the dynamic response capability of the system is improved, and the requirement that the power of the reactor is quickly adjusted along with the load requirement of the steam turbine generator unit is met.

The invention relates to the technical field of constructional engineering and carbon emission management, in particular to a civil engineering carbon footprint accounting method and system. Comprising the following steps: S1, collecting multi-source data of a civil engineering full life cycle; s2, performing fusion processing on the multi-source data, determining a quality score of each data source, dynamically allocating data weights, and establishing a data association relationship; s3, on the basis of the fused data, modeling is carried out on a regional energy structure and a time change trend by utilizing a deep learning model, and the carbon emission coefficients of electric power and energy are dynamically determined; s4, calculating a carbon emission result of each stage according to the carbon emission coefficient; and S5, performing uncertainty quantitative analysis on the carbon emission result, determining a confidence interval of the carbon footprint through random sampling and fuzzy rule reasoning, and generating a full-life-cycle carbon footprint accounting result. According to the method, full-life-cycle dynamic accounting and uncertainty quantitative analysis of the civil engineering carbon footprint are realized, and the accuracy and real-time performance of carbon emission evaluation are improved.

The invention discloses a hazardous chemical substance transportation early warning system based on a fuzzy neural network, and relates to the technical field of traffic transportation and road safety monitoring. According to the hazardous chemical substance transportation early warning system based on the fuzzy neural network, road geometric influence in original data of a sensor is accurately stripped from dynamic response of a vehicle; according to the method, false risk signals caused by road design can be eliminated, the method focuses on instability risks really caused by dynamic factors such as vehicle control and load transfer, and the rollover risk can be judged more essentially and more accurately; converting the preprocessed characteristic quantity into a fuzzy linguistic variable, reasoning by simulating a fuzzy rule of human expert experience, and finally outputting continuous risk indexes and levels; the processing mode not only better conforms to the imprecise characteristic of risk assessment, but also enhances the adaptability and robustness of the system to different vehicle types, loads and road conditions.

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 discloses a holographic communication system-oriented multi-dimensional interaction quality perception and resource optimization scheduling method, which comprises the following steps of: firstly, constructing a system containing objective performance and subjective experience indexes according to a scene, acquiring network, holographic and user layer data, training a subjective experience representation model, and estimating the subjective experience indexes in real time; estimating indexes at the current moment and the next moment, constructing a design vector, and fusing physical, semantic and cognitive layer multi-source knowledge to obtain an enhanced input vector; reasoning the dynamic weight of the index through a utility function and a fuzzy rule, and calculating an interaction quality comprehensive score; screening indexes to be optimized, converting the indexes to be optimized into a multi-objective optimization problem by taking a comprehensive score reaching the standard as a constraint, and searching a compromise optimal scheme; and finally, converting the scheme into a resource configuration scheme to realize self-adaptive scheduling of network and terminal resources. Accurate mapping of objective indexes and subjective experience is realized, scenes and user requirements are dynamically adapted, and immersion experience and communication performance of holographic communication are improved.

The invention discloses a solid powder intelligent weighing system based on fuzzy adaptive PID. The system comprises a material transfer module, a weighing unit, a material information identification module and an intelligent control unit. The material transfer module realizes automatic transfer and positioning of the weighing container; the weighing unit comprises a weighing container, a high-precision sensor and a discharging module, and the discharging module is provided with a material bin and a feeding valve mechanism; the information identification module is used for automatically identifying and inputting material information; the intelligent control unit is internally provided with a fuzzy self-adaptive PID controller, receives weight and material information, identifies valve-flow model parameters on line based on a recursive least square method and performs self-tuning, dynamically adjusts PID parameters according to a fuzzy rule, and outputs an instruction to realize three-stage control of rapid blanking, fuzzy fine adding and inching compensation. Through a parameter self-tuning and segmentation strategy, the method is adaptive to various material characteristics, the efficiency is improved and the static error is eliminated under the condition of ensuring high precision, and safe and traceable intelligent weighing is realized by relying on full-process automation and information tracing.

The invention relates to the technical field of blister thermoforming, and discloses a high-precision controllable thermoforming method of a blister machine, which comprises the following steps: extracting a feature recognition material type, and calculating to obtain a thermal diffusion coefficient; a temperature field is collected in real time, temperature distribution at the next moment is predicted, a fuzzy rule base is established to dynamically adjust PID parameters, a falling risk coefficient is calculated based on the actually measured temperature, and whether airflow compensation is started or not is judged; a mold cavity structure is constructed, and a vacuum degree curve is set in stages; material flow monitoring is conducted on the material, and the vacuum degree is corrected through fuzzy control; analyzing to obtain a coordinate deviation, and executing dynamic compensation; performing real-time correction according to the coordinate deviation, establishing an inter-station coupling coefficient matrix through multi-station cooperative control, and performing temperature compensation; and constructing a multi-dimensional quality evaluation model, performing analysis to obtain a comprehensive quality index, judging whether to trigger a key parameter optimization process or not based on the comprehensive quality index, and performing real-time feedback optimization.

The invention discloses a multi-joint torque control method of a mechanical arm for cleaning foreign matters on a power distribution line. The multi-joint torque control method comprises the following steps: S1, setting an initial value and an expected track of a joint in the mechanical arm; s2, a mechanical arm multi-joint torque control model is constructed based on a fuzzy neural network; s3, performing initialization processing on the fuzzy rule, and determining an initial value of a fuzzy neural network parameter; s4, constructing an error function to calculate an error, and performing adjustment and iterative correction on each parameter; s5, the error and the error change rate are calculated to serve as input of the fuzzy neural network, and the control torque parameter of the mechanical arm serves as output; s6, performing iterative optimization processing on the output parameters of the fuzzy neural network based on a particle swarm and grey wolf hybrid optimization algorithm to obtain a global optimal solution of the output parameters of the fuzzy neural network, and obtaining the joint torque of the mechanical arm; according to the method, the joint output torque can be accurately controlled, the joint position tracking error is reduced, and the overall robustness and stability of a mechanical arm control system are improved.

The invention provides an electronic control hydraulic steering system driving mode switching method, system and device and a medium, and belongs to the technical field of automatic driving control. The method comprises the following steps: constructing a kinetic model of an EHPS system corner torque sensor, constructing a sliding mode observer based on the model, and accurately calculating an estimated value of a driver hand torque by compensating rotational inertia and a damping component of a steering wheel in real time; determining an adjustment factor by using a fuzzy rule according to the vehicle speed and the corner speed, and setting a dynamic threshold by combining the maximum hand torque; judging the hand intervention state of the driver in a self-adaptive time window by comparing the estimated value of the hand torque with a dynamic threshold value; after the manual intervention is confirmed, the target current value of the power-assisted motor and the road curvature in the manual driving mode and the automatic driving mode are fused, a comprehensive target current value in smooth transition is calculated, and finally the comprehensive target current value is converted into electromagnetic torque to complete the driving mode switching. According to the invention, accurate identification of driver intervention intentions and smooth control of a mode switching process are realized.

The invention relates to the technical field of vehicle control, and provides a vehicle chassis drive-by-wire method and device, electronic equipment and a readable storage medium. The method comprises the steps of obtaining current actual state data of a vehicle at a current moment and historical state data of a plurality of continuous historical moments before the current moment; jointly inputting the current actual state data and the historical state data into a pre-trained prediction model to obtain a state change prediction value of the vehicle at the future moment relative to the current moment; calculating a plurality of state error amounts between the state change prediction value and a preset expected state change amount; inputting the plurality of state error amounts into a preset fuzzy controller, and performing fuzzy reasoning based on a preset fuzzy rule base in the fuzzy controller to generate a fuzzy control signal; performing conversion processing on the fuzzy control signal to obtain a control instruction for the vehicle chassis drive-by-wire system; and controlling the action of the vehicle chassis drive-by-wire execution mechanism according to the control instruction.

The invention discloses a multi-input multi-output fuzzy neural network control method and system for a pseudo-ginseng planting greenhouse, and the method comprises the steps: obtaining original parameters including greenhouse temperature, humidity, illumination, water and fertilizer concentration, pseudo-ginseng growth state and water flow velocity, and carrying out the standardization of the original parameters, and obtaining input parameters; inputting the parameter into a three-layer back propagation neural network pre-training model, and outputting initial adjustment coefficients of six control quantities such as the opening degree of the sunshade; constructing a fuzzy rule base based on a parameter coupling scene, inputting a preliminary adjustment coefficient to obtain a fuzzy output quantity, and performing defuzzification through a gravity center method to obtain a corrected adjustment coefficient; a control instruction is calculated in combination with an equipment rated range, and closed-loop control is realized through dynamic adjustment. The system automatically operates through a multi-parameter acquisition module, an equipment control output module and the like. The application breaks through the traditional single-input single-output limitation, reduces the parameter fluctuation by more than 50%, adapts to the whole growth cycle of pseudo-ginseng, improves the utilization rate and yield of water and fertilizer, reduces the labor cost, and has remarkable economic benefits.

The invention relates to the technical field of ship hoisting control, in particular to a ship hoisting heaving active control system based on hydraulic compensation, and adopts the technical scheme as follows: precise advanced prediction of heaving motion of a ship in future 2-5 seconds is realized by integrating a motion sensing and prediction module and adopting a time sequence prediction model based on a long short-term memory network; a core control decision module adopts a model prediction control strategy fused with multi-source data to uniformly optimize multiple targets such as load stability, energy consumption and equipment wear; internal and external state changes of the system are sensed in real time through a system state monitoring and self-adaption module, control parameters are adjusted on line based on a fuzzy rule base, and the system is endowed with self-adaption capacity for coping with load changes, sea condition fluctuation and component performance degradation in the whole life cycle; a closed volume control scheme that a servo motor directly drives a two-way metering pump is adopted in the hydraulic execution and compensation module, throttling loss in a traditional valve control system is abandoned, and efficient conversion between hydraulic energy and mechanical energy is achieved.

The invention provides a closed-loop control method for viscosity in a pregelatinized starch production process, and relates to the technical field of intelligent control, which comprises the following steps: acquiring real-time viscosity data and real-time temperature data of pregelatinized starch slurry, calculating a viscosity deviation value, calculating a temperature compensation coefficient by adopting an improved Arrhenius viscosity-temperature equation, and calculating the viscosity deviation value according to the temperature compensation coefficient. A compensated viscosity deviation value is obtained, the compensated viscosity deviation value and the change rate of the compensated viscosity deviation value are input into a double-variable fuzzy controller, and the stirring speed adjusting amount is calculated according to a fuzzy rule; according to the compensated viscosity deviation value and the change rate of the viscosity deviation value, an incremental PID control algorithm is adopted to calculate the adjusting amount of the opening degree of a steam valve, and cooperative control over steam heating and mechanical shearing is formed. According to the method, accurate compensation of the nonlinear relation between the viscosity and the temperature is achieved, by introducing a dynamic correction factor and a multi-protection mechanism, adaptability and stability under complex working conditions are enhanced, and the regulation and control efficiency and the product quality stability in the pregelatinized starch production process are effectively improved.

The invention relates to the technical field of electronic cigarette control, and particularly discloses an electronic cigarette self-adaptive precise temperature control system based on fuzzy PID control, which comprises a temperature acquisition module, a disturbance quantization module, a fuzzy reasoning module, a parameter self-adaptive PID control module and a power driving module. By quantizing the disturbance intensity and adjusting PID parameters in real time based on a fuzzy rule, the system can adaptively enhance the anti-interference capability and the steady-state precision. The system has quick response to sudden disturbance, control oscillation possibly caused by frequent parameter adjustment is avoided, optimal balance between response speed and control stability is achieved, and the system is particularly suitable for portable equipment such as electronic cigarettes with complex and changeable working conditions. According to the scheme, rapid, stable and overshoot-free accurate control over the atomization temperature is achieved, and the use experience and consistency of the electronic cigarette are improved.

The invention relates to the technical field of industrial control systems, and particularly discloses a selenium-rich water equipment water yield control system based on neural fuzzy control. The system comprises a data acquisition module, a neural fuzzy control module, an actuator driving module and an online learning and parameter adjustment module, fuzzy rules and membership parameters are dynamically optimized through a neural network, a gradient descent algorithm and a working condition memory unit are combined, high-precision self-adaptive control over the water yield is achieved, and the response speed and stability of the system are improved.

The invention provides a multi-temperature-zone welding temperature intelligent control method and system based on deep learning, and relates to the technical field of welding temperature control, and the method comprises the steps: obtaining multi-temperature-zone real-time temperature data, carrying out the feature extraction and time sequence analysis, carrying out the temperature change prediction, dividing a control level, calculating a temperature deviation value, generating an initial control parameter, and calculating the temperature deviation value; and iteratively optimizing the parameter group through an adaptive weight algorithm, carrying out sliding window analysis on historical temperature data, and carrying out temperature compensation based on proportional integral control and a fuzzy rule.

The invention discloses a rail transit charging and discharging service life prolonging method based on an energy storage system health state attenuation model, and belongs to the technical field of rail transit energy management. The method comprises the following steps: acquiring data through a multi-dimensional sensor to construct a coupling tensor, extracting a main coupling singular value sequence, and establishing an advanced prediction model of an instantaneous attenuation rate and a system health state; dynamically setting a target threshold value based on a real-time monitoring result, and solving an optimal charging and discharging parameter boundary in the digital twin through double-engine simulation optimization; adopting the Pontryagin minimum principle to generate a reverse reference trajectory meeting the life constraint; and finally, quantizing the track into a pulse sequence, introducing power grid fluctuation and health attenuation rate as adjusting variables, and executing after dynamically adjusting pulse parameters through a fuzzy rule. Advanced sensing of the health state of the energy storage system and accurate optimization control of the charging and discharging process are achieved, and the service life of the system is remarkably prolonged.

The invention discloses an active vibration absorber control strategy for vibration at the tail end of a flexible arm, and relates to the technical field of mechanical arm control, and the active vibration absorber control strategy comprises the following steps: building a three-dimensional model for a two-degree-of-freedom flexible mechanical arm, describing the joint flexibility of a system by using a linear spring, and representing the rigidity coefficient of a flexible joint by using the elastic coefficient of the linear spring; fuzzy RBF adaptive PID control is trained in combination with a real object, the identification precision of the model is improved, and vibration suppression is realized; in the fuzzy control, input variables are fuzzified by simulating the thinking mode of human beings, reasoning is carried out in combination with expert experience rules, and finally a system of an accurate dynamic model is established. According to the control strategy, the limitation of a traditional control technology based on a model during control is reduced, dynamic hysteresis nonlinearity generated in actual work is effectively corrected, the control precision is improved, fuzzy rule parameters are adjusted through modal analysis in combination with system characteristics, and the control precision is improved. Therefore, the vibration suppression force synthesis in different scenes is more accurate, and the vibration suppression effect is better.

This invention relates to the field of speed control technology, specifically to an intelligent lamination and tension control system and method for multi-ribbed belt production lines in washing machines. The system includes: a multi-dimensional state information extraction module, a real-time collaborative control module, a control performance evaluation module, and a fuzzy rule self-tuning module. In this invention, by calculating tension error and lamination position deviation, a basis for dynamic adjustment is provided, eliminating reliance on preset fixed parameters. By invoking a fuzzy logic inference engine, tension error, error change rate, and position deviation are fused and processed to achieve synchronous and precise control of the transmission roller speed and the position of the correction mechanism. This effectively replaces the slow-responding mechanical tension device, solving the problems of tension fluctuation and lamination deviation. Furthermore, a long-term comprehensive performance index is introduced to evaluate the control effect. When performance deteriorates, a gradient descent algorithm is immediately triggered to self-tune the fuzzy rule parameters, continuously optimizing the control strategy and ensuring the system maintains high stability and high accuracy.

The invention relates to the technical field of industrial real-time control software, in particular to a tea processing equipment self-adaptive temperature control system based on fuzzy PID, and the system comprises the steps: obtaining a temperature parameter, a moisture content parameter and a color characteristic quantity; calculating a deviation signal, a deviation change rate and a water content change rate; calculating a model drift vector according to the acquired parameters and scaling an optimization domain space in a sparrow search algorithm; performing online global optimization on the PID control parameters, and if a preset condition is met, superposing a penalty weight in a target function, and outputting reference PID parameters; monitoring a sudden change point identification switching moment; at the switching moment, smooth interpolation switching is carried out on the multiple sets of fuzzy rule matrixes, and PID parameter correction increments are output; and generating an adaptive control signal based on the reference PID parameter, the PID parameter correction increment, the deviation signal and the deviation change rate, and driving an execution mechanism to perform process control. And self-adaptive industrial control is realized by sensing the change of physical property parameters.

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 present application relates to a method for detecting loosening of ceramic dewatering elements for papermaking based on vibration spectrum analysis, aiming to solve the problems of difficult early identification of loosening and weak anti-working condition interference ability of ceramic dewatering elements in the running of the paper machine wire section. The scheme synchronously collects vibration signals and multi-dimensional working condition parameters, generates a structured frequency spectrum through time-frequency analysis and power spectrum density processing, and then fuses the knowledge base to match the reference structure template, realizing the topological edit distance quantization of the measured and reference frequency spectrum. Combined with the fuzzy rule engine, the different loosening degrees are dynamically weighted and inferred, and the positioning accuracy is improved through graph structure evolution trajectory analysis and high-resolution order tracking. The technology has high robustness and early loosening identification ability, which is beneficial to improve the intelligentization and intelligent decision level of the health monitoring of the clean production line ceramic elements.

The invention discloses a distributed photovoltaic access power distribution network collaborative optimization system and computer equipment, and the system comprises a data collection and processing unit, a coupling relation analysis unit, a coupling relation processing unit, a local network frame regulation and control unit and a multi-element scene evaluation unit. The system aims at analyzing a strong coupling relationship among three key factors of site selection, access capacity and equipment compensation configuration when distributed photovoltaic is accessed to a power distribution network, decoupling the strong coupling relationship by using a fuzzy rule, and regulating and controlling a local network frame by using a differential evolution algorithm. According to the invention, by coordinating and optimizing the site selection, the access capacity and the equipment compensation configuration of the distributed photovoltaic access, when the distributed photovoltaic is accessed to the power distribution network, the power distribution network can operate stably in an economical and efficient manner, and the utilization rate of distributed photovoltaic resources can be maximized.

The invention relates to an environmental sanitation unmanned vehicle numerical control cleaning mechanism load adaptive adjustment method and system, and the system comprises a data collection module which comprises a pressure sensor, a weight sensor and an encoder; the improved ANFIS control module is used for receiving the road surface hardness, the garbage loading capacity and the rolling brush rotating speed, calling an improved WNN algorithm to optimize membership function parameters of the ANFIS, starting a fuzzy rule self-learning mechanism based on the optimized membership function to update a fuzzy rule base, and outputting the fuzzy rule base; an optimal motor torque parameter and an optimal suction cup negative pressure parameter are calculated through ANFIS forward reasoning and are transmitted to the numerical control execution module; and the numerical control execution module is used for receiving the optimal motor torque parameter to drive the cleaning motor to output the corresponding torque so as to adjust the rotating speed of the rolling brush, receiving the optimal suction cup negative pressure parameter and controlling the suction cup negative pressure by adjusting the power supply voltage of the vacuum pump. According to the invention, the real-time adaptive adjustment requirement in a complex operation scene can be met.