76 results about "Fuzzy logic algorithm" patented technology

The invention relates to the technical field of motor control, and discloses a permanent magnet synchronous motor control system and method for an electric drive hydraulic source, and the system comprises a data collection module which is used for collecting the state parameters of the electric drive hydraulic source and the operation data of a permanent magnet synchronous motor in real time through multiple sensors; the sensor redundancy and fault-tolerant module is used for performing parallel sampling through hardware dual-redundancy configuration and dual sensors; the self-adaptive fuzzy logic control module is used for dynamically adjusting a rotating speed instruction and a torque instruction of the permanent magnet synchronous motor based on the collected state parameters; a data driving optimization module; and a digital twinning prediction maintenance module. A self-adaptive fuzzy logic control module is used for dynamically generating a motor rotating speed and torque instruction based on parameters such as real-time pressure, flow deviation and change rate, a fuzzy logic algorithm is used for processing a nonlinear time-varying load and quick response, a self-adaptive fuzzy logic control rule weight and a power conversion circuit parameter are optimized through a reinforcement learning algorithm, and the control precision is improved. And dynamic self-optimization of permanent magnet synchronous motor control is realized.

The invention relates to the technical field of energy storage battery equalization adjustment, in particular to an energy storage power station intelligent operation scheduling management system which comprises a battery parameter estimation unit, a dynamic equalization threshold generation unit, an equalization circuit control unit, a data fusion prediction unit and a scheduling strategy generation unit. The battery parameter estimation unit calculates key parameters of a single battery, the dynamic equalization threshold generation unit generates a dynamic equalization threshold through a fuzzy logic algorithm in combination with a battery aging stage, multiple parameters and temperature factors, and the equalization circuit control unit determines an energy transfer strategy according to a model prediction control algorithm. A switch tube is controlled through an adaptive phase shift angle adjustment technology, a fault diagnosis and fault-tolerant control mechanism is provided, the data fusion prediction unit predicts key indexes of the energy storage power station, the scheduling strategy generation unit formulates an optimal operation scheduling strategy, and the ability of the energy storage power station to participate in power grid auxiliary service is improved.

The invention discloses a deep concealed ore deposit detection method based on multi-source geological data fusion, and the method comprises the following steps: 1, carrying out a dynamic multi-source data fusion algorithm: introducing a fuzzy logic algorithm, dynamically adjusting the data fusion weight according to the feature significance of different geological units, and developing a cross-scale feature pyramid network, hierarchical feature extraction and fusion are carried out on different detection depth data; 2, optimizing an intelligent model based on prior knowledge; step 3, constructing a real-time interactive three-dimensional modeling system; 4, establishing an efficient multi-means verification system; and step 5, intelligent integration of the exploration process. According to the method, multi-source data are fused through fuzzy logic dynamic weight adjustment, the mineralization mode and the reinforcement learning optimization model are combined, three-dimensional modeling and multi-means combined verification are updated in real time, and the effects of accurate fusion of the multi-source data, dynamic model adjustment and optimization, efficient and accurate modeling, short verification period and low cost are achieved; and the detection efficiency and scale of the deep concealed ore deposit are improved.

The invention relates to the technical field of forklift control, in particular to an efficient and energy-saving off-road forklift electric drive system and a control method. Multi-source data is collected through a multi-source sensor, and data preprocessing is carried out; outputting a control decision instruction according to a priority decision rule based on the preprocessed data in combination with a fuzzy logic algorithm; in combination with the working condition characteristics and the real-time data, dynamic parameter adjustment is performed on the rotating speed, the torque and the current parameters of the motor by utilizing a reinforcement learning framework, and a PWM duty ratio control instruction is generated; on the basis of a PWM duty ratio control instruction and a hydraulic pressure signal fed back by a hydraulic system, the same-frequency anti-phase compensation torque is calculated, a signal is injected to suppress fluctuation, and torque distribution and hydraulic flow are adjusted through a ramp function; and establishing a hierarchical health early warning mechanism according to the historical data, judging the current early warning level, and outputting a maintenance strategy instruction according to the early warning level. According to the scheme, equipment health management and stability are enhanced through reinforcement learning dynamic optimization and graded early warning.

The invention discloses an intelligent control method for power distribution and efficiency optimization of an energy storage power station, and the method comprises the steps: obtaining real-time operation data and historical load records of all PCS units in the energy storage power station, and obtaining a current load change trend through data fusion processing; according to the current load change trend, a fuzzy logic algorithm is adopted to analyze the performance state and the output proportion of the PCS unit, and the initial load distribution weight of each unit is determined; aiming at the optimized distribution scheme, generating a self-adaptive priority ranking rule by adopting a genetic algorithm, judging whether the rule covers real-time state change or not, and obtaining a dynamic ranking list; extracting a high-priority PCS unit from the dynamic sorting list, obtaining an equipment loss index of the high-priority PCS unit, and determining an output increase proportion of a low-loss unit through index comparison processing; and according to the output increase proportion, fusing the resource utilization rate data, and if the utilization rate is lower than a preset threshold value, redistributing idle unit power to obtain overall efficiency improvement configuration.

The present application belongs to the technical field of vehicle mass online estimation, in particular to a vehicle mass online estimation method based on fuzzy logic and weighted least square method, comprising the following steps: 1) obtaining longitudinal acceleration sensor measurement value signal, longitudinal speed signal, engine output torque signal, gear signal, throttle opening signal, brake pedal signal and steering wheel angle signal from vehicle CAN bus; 2) correcting the measurement value of longitudinal acceleration sensor; 3) calculating equivalent driving force and equivalent acceleration; 4) calculating working condition fitness degree by using fuzzy logic algorithm; 5) estimating vehicle mass by using weighted least square method; and 6) suspending and starting vehicle mass estimation. The structure is reasonable, and the accuracy and working condition adaptability of vehicle mass online estimation can be improved during use.

The invention discloses a CO2 well flooding oil casing pressure intelligent control and anti-freezing and anti-blocking system, and relates to the technical field of oil and gas field development engineering.The CO2 well flooding oil casing pressure intelligent control and anti-freezing and anti-blocking system comprises the steps that a controller starts a self-checking program, loads a preset target pressure value, a preset temperature early warning threshold value and a preset ice blocking critical temperature parameter, completes initial configuration, and sends the initial configuration to the controller; zero calibration and precision verification are carried out on the front-end pressure sensor, the rear-end pressure sensor and the temperature sensor, front-end pressure data, rear-end pressure data and temperature data are collected, data filtering and abnormal value removing processing are carried out, and the real-time temperature data and CO2 physical property parameters are obtained. According to the invention, hardware self-inspection and sensor precision calibration are realized through the controller initialization module, the data acquisition module completes multi-source data synchronous acquisition and filtering processing, and the dynamic risk assessment module based on an improved Span-Wagner equation and a fuzzy logic algorithm can accurately calculate a CO2 phase change critical point and output a quantitative risk index. The problems of energy waste and untimely freezing and blocking prevention caused by a traditional fixed threshold value method are solved.

The invention discloses an intelligent monitoring method and system for the microenvironment of a power distribution cabinet, and the method comprises the steps: collecting the multi-parameter data of the microenvironment of the power distribution cabinet in real time through a plurality of sensors disposed in the power distribution cabinet, and enabling the multi-parameter data to at least comprise temperature, humidity, voltage and current; transmitting the multi-parameter data to a processing platform through an internet of things network; on the processing platform, performing data fusion processing on the multi-parameter data to obtain comprehensive environment parameters; based on the comprehensive environmental parameters, performing environmental state evaluation by using a fuzzy logic algorithm, and outputting a security level; based on the time sequence data of the comprehensive environmental parameters, using a time sequence prediction model to carry out fault prediction, and outputting a parameter change trend; and performing anomaly detection based on at least one of the comprehensive environmental parameters, the security level and the parameter change trend, and sending out an alarm signal when an anomaly is detected. The invention aims to realize real-time accurate monitoring of the microenvironment of the power distribution cabinet, early warn various potential risks in time, avoid the situations of terminal strip corrosion, poor contact of a secondary line, short circuit, lightning breakdown, false alarm of a switching signal, false operation of a primary switch and the like caused by the microenvironment problem, improve the power supply reliability and guarantee the safety of a power grid.

The invention relates to a method and system for adjusting the rotating speed of a brushless electronic fan, and relates to the field of new energy automobile heat management. Collecting vehicle condition interference parameters; forming target temperatures corresponding to the three-electricity system based on the three-electricity system dynamic heat production parameters, the vehicle condition interference parameters and the scene rules; current temperatures corresponding to the three electric systems are obtained in real time; determining a temperature deviation duration based on the current temperature and the target temperature, and forming a deviation temperature value and a deviation change rate; determining an adjustment coefficient according to the deviation temperature value, the deviation change rate, the temperature deviation duration and a fuzzy logic algorithm; obtaining the current fan speed, and determining a target fan speed based on the adjustment coefficient and the current fan speed; forming a fan adjusting scheme; and executing fan parameter adjustment operation according to the fan adjustment scheme. The method has the effects of improving the accuracy and adaptability of adjusting the rotating speed of the fan, optimizing the heat dissipation capability of the three-electric system and prolonging the service life of equipment.

The invention relates to the technical field of logistics data management and industrial cloud platforms, and discloses a logistics data management method and system based on a cloud platform. The method comprises the following steps: acquiring multi-source data and preprocessing the multi-source data to obtain a cleaned data set; processing the data by adopting a fuzzy logic algorithm to obtain an uncertainty quantitative index; when the index exceeds a threshold value, integrating the road condition and demand data based on a Bayesian network, and generating a prediction deviation correction value; a transportation plan is updated in real time, and an optimized route is obtained; a dynamic visualization report is generated after the decision accuracy is further verified; and finally, an adjustment instruction is output through closed-loop control. The method is especially suitable for transportation optimization of cold-chain logistics in complex scenes such as haze, and can effectively improve the accuracy and real-time performance of a transportation decision and the cargo fresh-keeping quality.

The invention discloses an agricultural frequency conversion monitoring method and system fusing LSTM-GRU time sequence prediction and fuzzy logic, relates to the technical field of agricultural intelligent monitoring, and solves the problems of energy waste and easy missing of data at a critical moment caused by adoption of a fixed data acquisition frequency during agricultural precise monitoring in the prior art. The method comprises the following steps: collecting farmland environment data, predicting future farmland environment data by using an LSTM-GRU model, evaluating an environment change state based on a prediction result sampling fuzzy logic algorithm, if an evaluation result shows that the environment is stable, reducing the data collection frequency, otherwise, improving the data collection frequency, and realizing frequency conversion control of data collection. Therefore, the monitoring precision and the energy consumption overhead can be dynamically balanced under different environmental conditions.

The invention relates to the technical field of new energy vehicles, and provides a thermal management method and system of an electric vehicle and the electric vehicle. The electric automobile comprises a refrigerant module, a battery temperature control loop communicated with the refrigerant module through a first electronic expansion valve and a passenger compartment air conditioner loop communicated with the refrigerant module through a second electronic expansion valve. The thermal management method of the electric automobile comprises the steps that multi-dimensional parameters describing a battery and the environment are obtained; obtaining a first initial opening degree and a second initial opening degree based on a fuzzy logic algorithm and a rule base according to the multi-dimensional parameters; acquiring a first weight corresponding to the first initial opening degree and a second weight corresponding to the second initial opening degree according to the driving mode; and closed-loop adjustment is conducted on the weighted opening degree through a PID algorithm, the first control opening degree of the first electronic expansion valve and the second control opening degree of the second electronic expansion valve are obtained, and the first electronic expansion valve and the second electronic expansion valve are controlled according to the first control opening degree and the second control opening degree.

The invention relates to the technical field of engineering management, and particularly provides an engineering detection data block chain evidence storage method and system based on dynamic weight, and the method comprises the steps: collecting current engineering detection data for a detection object, the current engineering detection data comprising a plurality of feature parameters of different evaluation dimensions of the detection object in a current period; determining the credibility weight configuration of the current engineering detection data based on a fuzzy logic algorithm, and monitoring the object parameters of the detection object in real time, so as to adjust the credibility weight configuration under the condition that the object parameters accord with preset changes, carrying out evidence storage adjustment on the current engineering detection data based on the new credibility weight configuration; wherein the credibility weight configuration is used for determining the evidence storage processing mode corresponding to the current engineering detection data. Through multi-dimensional dynamic weight evaluation and evidence storage strategy adaptation, the block chain resource utilization efficiency can be optimized, and meanwhile it is ensured that data credibility evaluation is always matched with actual quality.

The invention belongs to the technical field of fans, and particularly discloses a control method and system for an efficient axial flow fan based on the adjustable blade angle, and the method comprises the steps that the air volume, pressure data and environment variables in the operation process of the axial flow fan are collected in real time through a sensor; mapping the collected data into specific demand indexes under different working conditions, wherein the specific demand indexes comprise target air pressure, target air volume and an allowable fluctuation range; according to the obtained variable working condition demand indexes, a fuzzy logic algorithm is adopted to process overall blade angle distribution, and a macroscopic mode of segmented adjustment is determined; initial angle values of adjacent segments are extracted from the determined macroscopic mode, and if it is judged that the difference between the initial angle values exceeds a preset threshold value, an intermediate angle sequence with smooth transition is obtained through interpolation calculation; obtaining an obtained intermediate angle sequence, and applying a neural network model to predict a potential vibration problem for an airflow non-uniform region to obtain an optimized segmented angle correction value; the problem that in the prior art, a draught fan is poor in stability under variable working conditions is solved.

This invention belongs to the field of laboratory safety monitoring technology, and particularly relates to a method and system for real-time intervention of experimental operation violations based on multimodal behavior recognition. It includes collecting multimodal operation data during the experiment; constructing a database of standard operating procedures (SOPs); identifying the currently executed action; comparing the currently executed action with data in the SOP database in multiple dimensions; calculating a deviation score based on the comparison results; and, combined with preset hazard coefficients, environmental coefficients, and personnel coefficients, using a fuzzy logic algorithm to determine the risk level of the experimental violation, thus achieving real-time intervention for experimental operation violations. This invention is based on the deployment and data acquisition of a multimodal sensor network, performing multidimensional modeling of standard operating procedures, and then using a real-time behavior recognition algorithm for multidimensional comparison and violation determination, thereby initiating corresponding intervention measures to promptly eliminate safety hazards, resulting in significant social and economic benefits.

The invention relates to a warehouse material unmanned management method and device based on digital twinning, equipment and a medium. According to the method, a twin data pool of a storehouse is generated through cooperation of a multi-source sensor and a digital twin model; extracting spatial features by using a convolutional neural network, capturing time sequence behaviors by using a sliding window method, and constructing a situational feature vector; human operation intentions are analyzed through a long short-term memory network, a system decision state is deduced through a hidden Markov model, and man-machine collaboration intention consistency evaluation is generated by combining the similarity of the human operation intentions and the system decision state; historical decision data and a fuzzy logic algorithm are fused, and a bidirectional dynamic credibility score is quantitatively calculated; based on the situation features, the intention consistency and the credibility score, a right and responsibility allocation scheme is dynamically generated through reinforcement learning, and a decision traceability report is generated through an interpretable algorithm in task execution. The self-adaptive adjustment of the man-machine decision boundary, the accurate response of risk control and the interpretability guarantee of the operation process in a complex storehouse scene are realized.

This invention discloses a method and system for amphibious vehicle navigation perception fusion in complex environments, relating to the field of amphibious vehicle waterborne perception technology. The method includes the following steps: acquiring point cloud data in different multimodal regions; preprocessing the point cloud data, with different regions including the water surface region, the above-water region, and the below-water region; performing feature-level fusion on the multimodal point cloud data and assigning comprehensive credibility to the fused feature-level targets; using a fuzzy logic algorithm to perform decision-level fusion on the feature-level targets to generate the final comprehensive environmental perception result; and performing environmental modeling, path analysis, and collision risk assessment based on the final comprehensive environmental perception result. This invention utilizes the complementary advantages of deep collaborative sonar and millimeter-wave radar to achieve stable and accurate detection of obstacles on, below, and above water in complex environments.

The invention relates to the technical field of tourism safety management, and discloses a tourism safety early warning system and method based on artificial intelligence. According to the system, accurate early warning is realized through cooperation of multiple modules. The data acquisition module uses a multi-source heterogeneous data fusion algorithm to integrate multiple types of data such as tourist positioning, meteorology, terrain and the like in a target area; the risk assessment module extracts multi-dimensional risk features and outputs a safety risk probability value by means of a space-time attention mechanism deep learning model; the dynamic adjustment module optimizes an early warning trigger threshold and a response strategy by using a reinforcement learning algorithm; the early warning generation module is combined with a fuzzy logic algorithm to generate multi-stage early warning signals and is associated with emergency resources; and the user interaction module converts early warning into multi-language prompts through a natural language processing technology and pushes the multi-language prompts to tourists and a management platform. Wherein the risk assessment module outputs a probability value through specific input and model training. According to the system, intelligent monitoring and early warning of tourism safety risks can be realized, and the tourism safety management level is improved.

The invention belongs to the technical field of vocal music training, and provides a singing expiration strength stability training system for breath flow control, comprising: a hardware sensing module for collecting airflow dynamic data, respiratory muscle group electrophysiological data and respiratory kinematics data of a user; the data processing control center is used for receiving and processing the airflow dynamic data, the respiratory muscle group electrophysiological data and the respiratory kinematics data to obtain a processing result; the multi-mode feedback module outputs real-time feedback to the user through visual, auditory and tactile multiple channels based on the processing result; according to the method, heterogeneous data of airflow dynamics, electromyography and respiratory kinematics are synchronously collected and fused, song audios are analyzed in combination with an artificial intelligence algorithm to generate a scene-based target curve, and a personalized training baseline is established based on accurate decision and multi-modal sensory collaborative feedback of a fuzzy logic algorithm; the efficient, accurate and personalized improvement of the stability of the exhaling force of singing is realized.

The present invention pertains to the technical field of motorcycles. Disclosed is a motorcycle safety guarding early warning system, comprising: a data acquisition module, used for acquiring real-time operating data of a motorcycle; a risk coefficient fitting module, used for inputting the acquired real-time operating data into a pre-constructed accident prediction model to output a risk coefficient; a fuzzy probability output module, which uses a fuzzy logic algorithm to evaluate the risk coefficient, and calculates the probability of an accident; and an early warning module, which uses a preset safety threshold, and triggers early warning when the probability of accident occurrence is greater than or equal to the safety threshold. Thus, too frequent false alarms are avoided while the sensitivity of early warning is ensured, improving the applicability and reliability.

The invention discloses a self-mixing small material feeding intelligent mistake proofing system, and relates to the technical field of industrial automation control. The method is used for solving the problems of mistaken feeding, overfeeding and unauthorized operation in a raw material feeding link. The system comprises a scanning module, a verification module, a monitoring feeding module and a control module. Acquiring identification information of the raw material barrel and the storage tank through a scanning module and generating a raw material matching confidence coefficient; the verification module fuses the matching confidence coefficient and the operator authority level based on a fuzzy logic algorithm, and calculates a feeding authorization coefficient; the monitoring and feeding module establishes a weight change rate curve according to the authorization coefficient, detects the sudden change characteristic of the feeding rate and generates abnormal feeding risk parameters; and the control module adopts a finite-state machine control algorithm to realize graded response based on the risk parameters, and performs feeding duration adjustment, intermittent operation or emergency stop control on different risk grades, so that intelligent mistake proofing and safety management and control in the feeding process are realized.

The application discloses a weather radar echo type identification method and device, a medium and equipment. The application obtains preset polarization radar observation data, accurately calculates the distribution characteristics of ocean clutter by using a statistical method, and further constructs a membership function, thereby providing a quantitative basis for a fuzzy logic algorithm. Through fuzzy logic algorithm processing, the membership degree of each pixel belonging to a preset echo category is obtained, and finally the echo category is determined according to the membership degree. The application solves the problem that the echo type of the weather radar cannot be accurately identified in the prior art.

The invention belongs to the technical field of stock bin systems, and particularly relates to a high-reliability stock bin system which comprises a bin body, an equipment table is arranged on the outer wall of the bottom of the bin body, a movable connecting mechanism is arranged between the equipment table and the bin body, and side plates are fixedly connected to the two sides of the outer wall of the top of the equipment table; a lifting block is arranged on the outer wall of the side plate, a height adjusting mechanism is arranged between the lifting block and the side plate, a clamping mechanism is arranged between the lifting block and the bin body, and a transverse plate is fixedly connected to the top of the lifting block. After data is transmitted to the electric control equipment, the fuzzy logic algorithm module quickly generates a taper angle adjusting instruction, so that the taper angle is dynamically matched with the granularity, humidity and other characteristics of the material, the material guide groove of the material guide roller is driven by the first motor to rotate, the material is further guided to flow orderly, the problems of dry powder bridging, wet material retention and the like are greatly reduced, and the production efficiency is improved. The adaptability and the anti-blocking capability of the system to diversified materials are improved.

The invention provides an intraoperative tracking method and system for a surgical instrument, and relates to the technical field of surgical instrument calibration and tracking, and the method comprises the steps: collecting intraoperative multi-source data, and carrying out preoperative calibration; according to a preoperative calibration result, establishing an initial transformation relation between the coordinate systems; respectively filtering the visual data and the kinematics data through an extended Kalman filter based on the initial transformation relation to obtain an instrument pose estimation result; correcting the instrument pose estimation result through a fuzzy logic algorithm in combination with the statistical characteristics of the visual measurement residual error and the kinematics residual error to obtain a fused pose result; updating the three-dimensional position and posture of the surgical instrument in real time based on the fused posture information; mapping the updated three-dimensional position and attitude into an intraoperative digital model; and based on the updated three-dimensional position and posture, in combination with a historical tracking trajectory, tracking the surgical instrument in real time until the operation is finished, and outputting the three-dimensional position and posture of the surgical instrument.

The invention relates to the technical field of dynamic air distribution, in particular to a dynamic air distribution control method and system for a supercritical direct-current pulverized coal boiler. A fuzzy logic algorithm is used for judging the combustion state, and a corresponding air distribution strategy adjusting instruction is sent; a pruning strategy optimization model is constructed, the air distribution strategy adjustment instruction serves as the pruning strategy optimization model, and the optimal solution set of the ratio of primary air to secondary air, the air speed and nozzle design parameters is dynamically adjusted; the dynamic optimal solution set is fed back to a data monitoring module, a deep Q network algorithm is used for continuously optimizing a control strategy, and a self-adaptive control closed loop is formed; setting a safety boundary threshold value, judging whether the detected temperature and pressure exceed the preset threshold value or not, and if yes, automatically switching an emergency control plan. The optimal solution set of the ratio of primary air to secondary air, the air speed and nozzle design parameters is dynamically calculated through the CPPO model, so that the air distribution strategy is scientific and reasonable, and the combustion efficiency and the heat efficiency of the boiler are effectively improved.

The application relates to the technical field of engineering management, and particularly provides an engineering detection data blockchain storage method and system based on dynamic weights, which comprises the following steps: collecting current engineering detection data of a detection object, wherein the current engineering detection data comprises a plurality of characteristic parameters of different evaluation dimensions of the detection object in a current period; determining a credibility weight configuration of the current engineering detection data based on a fuzzy logic algorithm, and performing real-time monitoring on object parameters of the detection object, so that the credibility weight configuration is adjusted when the object parameters meet a preset change, and the current engineering detection data is adjusted and stored based on a new credibility weight configuration; wherein the credibility weight configuration is used for determining a storage processing mode corresponding to the current engineering detection data. Through multi-dimensional dynamic weight evaluation and storage strategy adaptation, the application can optimize the utilization efficiency of blockchain resources, and ensure that the data credibility evaluation is always matched with the actual quality.

The application provides a formation pressure prediction method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining well logging data of a target formation; performing seismic inversion according to the well logging data of the target formation to obtain an inversion result; constructing a first pressure prediction model of the target formation corresponding to an undercompaction cause, a second pressure prediction model of the target formation corresponding to a hydrocarbon generation and pressure increase cause, and a third pressure prediction model of the target formation corresponding to a tectonic extrusion cause according to the inversion result; and fusing the first pressure prediction model, the second pressure prediction model and the third pressure prediction model by using a fuzzy logic algorithm to obtain a pressure prediction result of the target formation. The method realizes complex cause formation pressure prediction based on multi-information joint, and improves the drilling pre-prediction accuracy of formation pressure under complex geological conditions.

The invention discloses a charging method and system for vehicle-mounted equipment, and the method comprises the steps: taking a voltage fluctuation rate, a current harmonic distortion rate and a temperature rise slope as the input of an improved fuzzy logic algorithm, and outputting a charging mode coefficient; determining a charging mode according to the residual electric quantity of the equipment and the charging mode coefficient, and charging the equipment in the corresponding charging mode; in the charging process, carrying out voxel grid division on the interior of the equipment to obtain thermal image data of the equipment, and converting the thermal image data into an infrared image; and identifying the high-risk hot spot area of the infrared image by using a preset abnormal thermal mode identification model, and carrying out heat dissipation on the high-risk hot spot area. According to the method, fine perception of multi-dimensional characteristics of the power supply state of the equipment can be realized, the dynamic adaptability to complex working conditions is improved, the local hot spot area of the equipment can be accurately identified, local heat dissipation can be performed in a targeted manner, the local overheating phenomenon in the equipment charging process can be effectively avoided, and the heat dissipation efficiency is remarkably improved.

The invention discloses a dynamic precise metering control method and system, and relates to the technical field of chemical reaction control. The method comprises the following steps: acquiring temperature, pressure and raw material purity data in a reaction container, and fusing the data to obtain a real-time reaction condition vector; after comparing with a preset standard vector and dynamically adjusting, inputting a fuzzy logic algorithm model to process the interaction between components to obtain a preliminary proportion adjustment coefficient; key component proportions are extracted, and a refining proportion scheme is obtained through PID algorithm optimization; the raw material injection amount is adjusted in real time, the reaction state is monitored, the fuzzy logic model is iteratively updated based on the comparison result of the intermediate product quality index and the target purity, the by-product inhibition factor is introduced, and the proportion is continuously adjusted through the PID model until the product purity is stable and the production efficiency is optimal. According to the invention, the dynamic optimization control of the ratio of the multi-component raw materials is realized, and the reaction precision and the product quality are effectively improved.

The invention relates to the technical field of power grid control, particularly discloses a starting control method and system suitable for a full-direct-current collection network construction type energy storage isolated network, and aims to solve the problems of single-point fault, low reliability and the like of existing centralized control. According to the method, the voltage, frequency fluctuation and power change rate of a direct current bus are monitored, a fuzzy logic algorithm is combined with multiple parameters to judge whether the system is in a silent state, and if the system is in the silent state, each energy storage unit calculates a priority score based on own state information through distributed negotiation to select a main starting unit; the main starting unit executes an adaptive soft starting algorithm to establish a DC bus rated voltage, and the slave unit monitors the starting state of the slave unit, reselects the main unit through a fault-tolerant reselection mechanism when a preset condition is not met, and accesses the bus in sequence and switches to a network construction mode to complete isolated network construction if the preset condition is met. Distributed control is adopted, single-point dependence is avoided, the reliability and adaptability of isolated network starting are improved, and efficient and stable starting of the system is ensured.