95 results about "Intelligent adaptive control" patented technology

The invention relates to the technical field of lithium ore conveying, and discloses a lithium ore hopper energy-saving conveying system based on intelligent self-adaptive control. The system comprises a data acquisition module, a self-adaptive control module, an energy-saving optimization module and an execution adjustment module. The data acquisition module acquires multi-dimensional operation data of the lithium ore hopper through a distributed sensor network, and a hopper conveying state database is constructed after standardization processing; the adaptive control module performs dynamic feature extraction on the database, generates a real-time control parameter matrix, establishes an association rule graph of each parameter, and constructs an intelligent adjustment decision model based on the association rule graph; an energy-saving optimization module extracts a key energy consumption feature vector from the real-time control parameter matrix, calculates an energy consumption influence coefficient of each operation parameter, and screens out an optimal energy-saving control sequence in combination with an intelligent adjustment decision model; and the execution adjustment module drives an execution mechanism according to the optimal energy-saving control sequence, and self-adaptive adjustment of hopper conveying parameters is completed.

The invention discloses a network construction type inverter for a photovoltaic energy storage system and a virtual synchronous machine control method. The network construction type inverter comprises an inverter power module and a central controller. The central controller is used for collecting power grid operation data in real time and generating a power grid impedance state vector through online impedance identification; and dynamically adjusting virtual inertia and damping parameters of the inverter power module according to the impedance state vector by using an adaptive mapping function so as to realize cooperative control and circulating current suppression between the modules. Performing stability boundary evaluation based on the adjusted parameters, then performing intelligent early warning of the photovoltaic energy storage system, and smoothly switching the operation mode of the inverter power module; according to the invention, through deep integration of a modular hardware architecture and intelligent adaptive control, the inverter has strong autonomous network construction capability, the problems of weak power grid adaptability and transient stability are effectively solved, and the dynamic response and reliability of a photovoltaic energy storage system are improved.

The invention discloses a glass fiber particle assembly line-based magnetic suction quick-release vibration disc system and a detection method, and relates to the technical field of production and processing, the system comprises a data acquisition module for acquiring data of a vibration light-emitting chassis, a magnetic suction column, an assembly line and particles by means of a distributed sensor network, and transmitting the data to an intelligent self-adaptive control module after signal processing; the intelligent self-adaptive control module realizes intelligent dynamic regulation and control of the parameters of the magnetic suction quick-release vibration disc, optimizes the production process of the glass fiber particles, and automatically generates an accurate control instruction based on a data analysis result by integrating multi-source information and performing deep fusion and analysis, so that the production efficiency of the glass fiber particles is improved. Parameters such as the vibration frequency and amplitude of the vibration light-emitting chassis and the magnetic attraction strength of the magnetic attraction column are adjusted to meet the requirements in different production states, so that the system can quickly respond to changes in the production process, defective particles are reduced, and the production efficiency is improved.

The invention belongs to the technical field of circuit board metal smelting control, and discloses a circuit board metal smelting energy-saving parameter self-adaptive control system and method. The method comprises the following steps: carrying out real-time monitoring and fluctuation analysis on heating circuit voltage collected in unit time in the metal smelting process of the circuit board to obtain a voltage fluctuation level; processing the smelting state data acquired in unit time in the metal smelting process of the circuit board to obtain a smelting state characteristic data set, and identifying a current smelting stage; when the voltage fluctuation grade is greater than or equal to a preset voltage fluctuation grade threshold value, generating an energy-saving strategy control identifier in combination with the current smelting stage and the predicted smelting stage of the next unit time; dynamically adjusting energy-saving control parameters based on the voltage fluctuation grade, the smelting state characteristic data set and the energy-saving strategy control identifier; the intelligent self-adaptive control level of the circuit board metal smelting energy-saving parameters is enhanced, and energy-saving control is changed from passive execution to look-ahead response.

The invention relates to the technical field of temperature control, and discloses an intelligent welding temperature control method and system based on multi-sensor fusion, and the method comprises the steps: obtaining a welding spot temperature distribution matrix, contact pressure data and wetting angle data; performing environment interference identification and compensation to obtain an environment correction temperature matrix; performing thermal resistance calculation to obtain a dynamic contact thermal resistance value; identifying a solder phase change state identifier; according to the method, the control gain parameters are adjusted in real time according to the solder phase change state identification and the dynamic contact thermal resistance value, intelligent self-adaption of the control strategy is achieved, and the problem that adaptability of fixed parameter control in different welding stages is insufficient is solved.

The invention discloses a mountainous area sudden change wind field active control wind tunnel simulation method based on deep learning, and relates to the technical field of wind tunnel experiment research, and the method comprises the steps: obtaining a target wind field parameter in a mountainous area target region, constructing an MLP input vector according to the target wind field parameter in combination with wind tunnel real-time sensor data, a deep learning control model comprising an MLP forward mapping model and a Transform time sequence feedback model is established, the MLP forward mapping model generates a multi-fan array control signal according to the MLP input vector, a multi-fan array is controlled to generate a simulated wind field, the Transform time sequence feedback model generates a multi-fan array control correction signal according to a wind field difference value between the simulated wind field and a target wind field, and the multi-fan array control correction signal is controlled to generate a multi-fan array control correction signal according to a wind field difference value between the simulated wind field and the target wind field. The simulated wind field parameters are dynamically corrected in real time, and target wind field parameters are approached; according to the method, high-fidelity dynamic reproduction and intelligent adaptive control of the complex wind field in the mountainous area are realized through MLP feedforward generation and Transform feedback correction.

The invention provides an energy-saving compressor intelligent self-adaptive control system based on multi-sensor fusion, which belongs to the technical field of industrial compressor intelligent control and comprises a data acquisition module, a signal conditioning module, a multi-sensor information fusion module, an intelligent control decision module, an execution driving module and a system power supply management module. A state display alarm and remote communication module can be additionally arranged. Multiple sensors collect operation and environment parameters of the compressor, and after conditioning, a comprehensive state evaluation result is generated through D-S evidence theory or fuzzy logic fusion; the intelligent module generates a control instruction based on a load prediction algorithm and a fuzzy PID self-tuning strategy, and the execution module drives the compressor to act; the power supply module supports solar power supply and multi-power supply switching. The system overcomes the limitation that a traditional compressor is single in sensing, rigid in control and the like, comprehensive sensing, accurate evaluation and self-adaptive energy-saving control are achieved, the operation stability is improved, and the energy consumption and the operation and maintenance cost are reduced.

The invention relates to the technical field of robot control, and discloses a robot hybrid intelligent adaptive control method and system adopting reinforcement learning, and the method comprises the steps: collecting environment perception parameters, and constructing a dynamic environment model; setting and testing a robot execution mechanism dynamic model, and obtaining motion characteristics of the robot execution mechanism; integrating the dynamic model into the dynamic environment model; calculating a task demand parameter and a theoretical control load, and generating a control quantity in combination with motion characteristics; a driving unit action instruction is generated based on the control quantity and control is carried out; and evaluating the execution effect. The system comprises a dynamic environment model construction module, an execution mechanism dynamic model construction module, a model fusion module, a task parameter calculation module, an action instruction generation module, an action control module and an execution effect evaluation module. According to the invention, high-precision self-adaptive control of robot operation in a complex dynamic environment is realized, and environmental adaptability and task execution efficiency are improved.

The invention relates to the technical field of intelligent spraying, in particular to an intelligent spraying control method and system suitable for the end face of a large bridge. The method comprises the following steps: positioning the initial operation position of the bridge end surface spraying equipment based on machine vision and a multi-sensor calibration method; constructing a bridge end face spraying model, and obtaining an optimal spraying path based on a path generation algorithm; intelligent adaptive control is conducted on a spraying mechanism based on multi-sensor data fusion, and the position posture of an executing mechanism is adjusted through a dynamic angle compensation algorithm; a multi-degree-of-freedom stable operation control algorithm is used for controlling the speed and the action period of an executing mechanism; and a multi-mechanism joint control algorithm is used for controlling linkage control of the executing mechanism and the spray head. Aiming at the core problems of low manual operation efficiency, large quality fluctuation, heavy and limited traditional equipment, prominent potential safety hazards and the like in railway bridge end face waterproof construction, systematic technical breakthrough is realized through integration of electromechanical integration innovation and an intelligent algorithm.

The invention provides an intelligent self-adaptive control method and system for sewage aeration, and is applied to the field of intelligent control of activated sludge process sewage treatment. The method comprises the steps that inlet water quality parameters are collected through a sensor array, abnormal fluctuation is recognized through a support vector machine, the sudden increase probability of pollutant concentration is calculated by combining a neural network with a historical mode, and when a threshold value is exceeded, a response delay index is calculated based on inlet water flow and biochemical kinetic parameters; generating an oxygen injection sequence according to the real-time dissolved oxygen data so as to regulate and control aeration equipment, and performing closed-loop feedback regulation according to the real-time dissolved oxygen data; by means of the scheme, pollution impact can be predicted in advance, system lag can be compensated, accurate matching of oxygen supply and loads on the time sequence is achieved, the quality stability of outlet water is effectively improved, and energy consumption is reduced.

The invention provides a tunnel construction method, system and device based on intelligent self-adaptive control and a storage medium, and the method comprises the steps: obtaining environment perception data of an unexcavated area of a tunnel, constructing and dynamically updating a virtual geologic model of the unexcavated area of the tunnel according to the environmental perception data based on a digital twinborn technology, and predicting to obtain a potential geologic risk; generating a corresponding support scheme based on the virtual geologic model and the potential geologic risk so as to implement advanced or radial support operation; actual tunneling data in the excavation process are obtained, and target control parameters of a current tunneling cycle are dynamically generated through advanced analog simulation based on the virtual geologic model and the actual tunneling data; and based on the deviation between the target control parameter and the actual tunneling data, dynamically adjusting the tunneling speed of the tunneling mechanism and the rotating speed of the cutterhead through a simulated PID algorithm. According to the self-adaptive tunnel construction method, high-precision construction control can be achieved on the premise that construction safety is guaranteed, and the tunneling efficiency is improved.

The invention provides a display screen energy consumption optimization system based on adaptive control. The system improves the energy efficiency ratio of a display screen and the visual experience of a user. Firstly, ambient light data, user viewpoint data and display content data are captured through a sensing module, and an ambient light gradient map, a user watching weight matrix and a content feature tensor are generated; then, the hybrid control module carries out spatial alignment on the multi-modal data through a region mapping unit and a dual-channel control unit, establishes a light environment model of a region of interest, and generates a self-adaptive control strategy; and finally, the execution module implements two-stage energy consumption optimization based on the control strategy, and constructs a bidirectional feedback loop to feed back real-time energy consumption data and a user visual comfort index to the hybrid control module for strategy iterative optimization. According to the invention, through multi-mode sensing fusion, intelligent adaptive control and a closed-loop feedback mechanism, the energy consumption optimization effect and the system adaptability of the display screen are remarkably improved.

The invention provides a self-adaptive anti-interference circuit for an industrial customized keyboard and a control method, power supply noise, surge and transient drop in an industrial environment are eliminated through a power supply anti-interference module, multi-protocol compatible communication is realized through a data anti-interference module, a double-insurance mechanism of a data path is formed, and the self-adaptive anti-interference circuit is applied to the industrial customized keyboard. A feedback link is formed through the data acquisition module and the adjustment control module, and real-time data support is provided for dynamic adjustment; besides, through intelligent self-adaptive control, a closed-loop control engine is formed, a magnetic bead array is activated to filter high-frequency interference, and a USB protocol is switched to ensure communication quality and power-off protection of an ESD event. And the anti-interference capability, the communication reliability and the electrostatic protection capability of the keyboard in an industrial scene are improved.

The invention discloses a low-voltage cabinet distributed control method and system based on multi-source data fusion, and relates to the technical field of distributed control. The method comprises the following steps: extracting topological structures of an equipment layer, a loop layer and a system layer from a distributed low-voltage cabinet set to obtain the topological structure of the equipment layer, the topological structure of the loop layer and the topological structure of the system layer; obtaining an equipment physical interface parameter set; obtaining a feeder loop state feature set; performing adjacency state analysis on the equipment physical interface parameter set to obtain an adjacency state matrix set; and taking the feeder loop state feature set and the adjacent state matrix set as real-time low-voltage cabinet working conditions, matching the real-time low-voltage cabinet working conditions with a pre-constructed working condition distributed control library, determining a target distributed control scheme, and performing distributed control on a distributed low-voltage cabinet set. The technical problems that in the prior art, low-voltage cabinet distributed control is poor in flexibility and poor in working condition adaptability are solved, and the technical effect of achieving intelligent self-adaptive control based on the real-time working condition is achieved.

The invention discloses a multi-stage uniform stirring preparation device and method for multi-element solid waste paste, belongs to the technical field of metal mine tailing filling, and solves the problems that an existing paste preparation method is not high in stirring applicability for the multi-element solid waste paste, the stirring efficiency is poor, and slurry is not uniform. The device comprises a three-stage gradient uniform stirring mechanism, a dynamic rheological regulation and control module and an intelligent adaptive control unit, multi-stage uniform stirring and multiple stirring modes are utilized, corresponding stirring modes are adopted for materials with different particle sizes, multi-element solid waste generated by a mine is fully utilized, discharge of the mine solid waste is greatly reduced, the cost of a mine filling process is reduced, and finally paste filling slurry which is uniform in stirring and good in performance is formed. The method is suitable for recycling mine solid wastes such as metal and nonmetal and goaf treatment.

The invention relates to the technical field of photovoltaic power stations, and particularly discloses a neural network enhanced photovoltaic power station multi-modal data monitoring and early warning method and system. The method comprises the following steps: performing multi-modal data acquisition on renewable energy equipment under an extreme environment condition; parameters in the environment subsets are fused, influence coefficients are calculated, and fusion vectors are obtained; predicting an equipment performance trend and judging a strategy adjustment direction to obtain a decision instruction; extracting more gradient features to update response demand features; recalculating the influence coefficient, predicting the performance trend of the equipment and judging the adjustment direction of a new strategy; if the new strategy adjustment direction indicates the specific load scene, updating and generating a new decision instruction; and adaptively adjusting and optimizing operation through a threshold value to obtain a final control scheme for optimizing the tiny fluctuation of the equipment under the extreme environment condition. The operation stability and efficiency of renewable energy equipment of a photovoltaic power station in an extreme environment can be remarkably improved, and intelligent self-adaptive control is achieved.

The invention discloses an intelligent self-adaptive control bionic flapping wing flight experiment data acquisition system and method, and belongs to the technical field of bionic aircraft testing and intelligent control, and the system comprises a hardware system integration module, a wind tunnel environment simulation module, a three-degree-of-freedom motion control module, a flexible sensor array module and a reinforcement learning interaction module. According to the intelligent self-adaptive control bionic flapping wing flight experiment data acquisition system, the upper computer is used as a control center, and a multifunctional visual interface is integrated; the multi-fan wind tunnel is linked with the three-degree-of-freedom platform to reproduce an airflow-attitude coupling relationship and improve the suitability of the two; data-training-optimization integration is realized, and system compatibility is improved.

This invention discloses a decapping device for wafer cleaning rod processing, comprising: a support structure including a support platform with two parallel support rails on the support platform; a clamping structure including multiple first sliders slidably connected to the upper ends of the support rails, with two first sliders on the same axis fixedly connected to the same first mounting plate at their upper ends, and a clamping component for fixing the wafer cleaning rod provided at the upper end of the first mounting plate; and a decapping structure including multiple second sliders slidably connected to the upper ends of the support rails, with the second sliders correspondingly disposed outside the first sliders. This invention, through the deep integration of precision mechanical structure design and intelligent adaptive control system, not only achieves a leap from manual to automated wafer cleaning rod decapping operations, but also endows the device with the core capability to cope with complex working conditions and ensure processing quality through real-time sensing and feedback control.

The invention relates to the technical field of fermentation process control, and discloses a coffee fermentation tank intelligent interlayer system based on multi-mode temperature control, which comprises a three-layer composite structure composed of an outer spiral flow channel heat exchange layer, a middle nanometer phase change energy storage buffer layer and an inner solid heat conduction balance layer, the multi-physical field fusion sensing module is laid on the wall surface of the inner layer and can synchronously decouple a two-dimensional temperature field and a strain field; and the hierarchical predictive control module adopts a model predictive control algorithm. During working, the sensing module acquires a temperature field of a tank wall in real time and analyzes a fermentation dynamic state from a strain field; based on the multi-dimensional information, the control module cooperatively regulates and controls the three-layer composite structure through an upper-layer optimization decision and a lower-layer real-time execution architecture. Through multi-mode cooperative control and multi-physical field fusion perception, rapid, accurate, uniform and intelligent adaptive control of the fermentation temperature is realized, and the quality and consistency of coffee fermentation are significantly improved.

The invention relates to the technical field, and specifically discloses an LED lamp intelligent adaptive control method and device, and the method comprises the following steps: obtaining real-time air humidity and real-time surface temperature; calculating a condensation risk coefficient of condensation of water vapor on the outer surface of the lampshade; if the condensation risk coefficient is larger than a preset risk threshold value, a brightness adjustment process is executed, and the brightness adjustment process comprises a pre-adjustment stage and an operation adjustment stage; in the pre-adjustment stage, the LED lamp is controlled to illuminate at the pre-drying brightness, and the pre-drying brightness is larger than the conventional operation brightness of the LED lamp; and in the operation adjustment stage, the light transmittance of light rays emitted by the LED lamp after passing through the outer surface of the lampshade is calculated, and the output brightness of the LED lamp is adjusted based on the light transmittance. According to the invention, the problems of optical interference and visual comfort damage when the humidity is too high for a long time in unventilated spaces such as underground garages and the like can be solved.

The invention belongs to the technical field of gateway adaptive control, and discloses a CAN bus gateway intelligent adaptive control method. The method comprises the following steps: extracting infotainment domain feature data from message metadata of an infotainment domain CAN subnet, generating a network intrusion diagnosis score, and mapping the score into a network intrusion threat level; based on the driving scene and the network intrusion threat level, generating a reasonable instruction set and an unreasonable instruction set through an instruction setting model; if the new control instruction is matched reasonably, releasing the new control instruction, if the new control instruction is matched unreasonably, intercepting the new control instruction and counting abnormal counts, if the new control instruction is matched unreasonably, generating a comprehensive rationality score based on the network intrusion threat level, the driving scene, the information entertainment domain feature data and the new control instruction, and releasing or intercepting the new control instruction and counting abnormal counts according to the comprehensive rationality score. Generating an exception instruction when the exception count reaches a preset maximum exception frequency; according to the method, the control authority can be intelligently adjusted in different driving contexts, and forged control frames or abnormal instructions are accurately blocked.

The application relates to the technical field of all-in-one machine control, in particular to a five-axis drive control all-in-one machine control method, system, equipment and medium. The application detects a power supply state through a power management module and performs filtering, each separated inverter module is initialized in parallel to obtain an enable signal; target position instructions are generated based on user input and dynamic constraint parameters, real-time position and speed instructions are calculated; a five-axis servo system is driven to execute movement and feedback adjustment; current fluctuation characteristics and position deviation change rates are monitored in real time, when a source abnormality is detected, only the inverter module corresponding to the abnormal axis is turned off; after replacing the abnormal module, the parameters are automatically loaded and the inter-axis synchronization compensation is optimized through adaptive configuration. The application reduces electromagnetic interference through distributed filtering, reduces maintenance cost through modular fault isolation, and realizes system reliability through intelligent adaptive control.

This invention belongs to the field of circuit board metal melting control technology. It discloses an adaptive control system and method for energy-saving parameters in circuit board metal melting. The method includes: real-time monitoring and fluctuation analysis of the heating circuit voltage collected per unit time during the circuit board metal melting process to obtain the voltage fluctuation level; processing the melting state data collected per unit time during the circuit board metal melting process to obtain a melting state characteristic data set and identifying the current melting stage; when the voltage fluctuation level is greater than or equal to a preset voltage fluctuation level threshold, generating an energy-saving strategy control identifier by combining the current melting stage with the predicted melting stage for the next unit time; and dynamically adjusting the energy-saving control parameters based on the voltage fluctuation level, the melting state characteristic data set, and the energy-saving strategy control identifier. This application enhances the intelligent adaptive control level of energy-saving parameters in circuit board metal melting, enabling energy-saving control to shift from passive execution to proactive response.

The invention relates to the technical field of environmental engineering, and discloses a sewage filtering method and system based on multi-crop collaboration.The sewage filtering method based on multi-crop collaboration.The sewage filtering method based on multi-crop collaborationcomprises the steps that an original sewage sample is obtained, a multi-factor comprehensive evaluation method is adopted for analysis and treatment, and a multi-crop collaborationpollutant removal mechanism model is established; a multi-level system structure is constructed, and a three-dimensional hydraulic modeling method is adopted for analysis and calculation; a multi-sensor fusion monitoring network is constructed, and a data fusion algorithm is adopted for processing and analysis; system parameters are optimized and adjusted by adopting an intelligent self-adaptive control strategy; regularly adding a microbial agent to carry out system biological enhancement and dynamic regulation and control optimization, and establishing a stable microbial ecological system; all functional units are integrated into a modular system, and performance verification is carried out based on a stable microbial ecological system to obtain purified water; the system has the advantages of being high in treatment efficiency, stable in operation, high in intelligent level and low in construction cost.

The application relates to the technical field of intelligent sensors, and discloses a Bluetooth earphone automatic control method and system.The method comprises the following steps: acquiring external environment original sound signals and user motion trajectory data; performing sound signal frequency domain conversion and noise reduction to obtain a pure acoustic data set; extracting a frequency spectrum feature to construct a matrix, analyzing background volume, and dividing interference levels; smoothing motion data to extract behavior features, and matching to obtain a current user behavior state; triggering noise reduction mode switching if a standard is met, setting an environment control strategy; generating environment adaptation parameter adjustment volume to obtain a volume adjustment value; converting into a voltage current signal to form earphone output configuration; generating a control signal to be applied to hardware, optimizing output timing, and obtaining a scene-adapted driving signal.The method can realize intelligent adaptive control under complex environments and dynamic user states.

This invention relates to the field of robot control technology, and discloses a hybrid intelligent adaptive control method and system for robots employing reinforcement learning. The method includes: collecting environmental perception parameters to construct a dynamic environment model; setting and testing a dynamic model of the robot's actuators to obtain their motion characteristics; integrating the dynamic model into the dynamic environment model; calculating task requirement parameters and theoretical control load, and generating control quantities based on the motion characteristics; generating drive unit action commands based on the control quantities and implementing control; and evaluating the execution effect. The system includes modules for dynamic environment model construction, actuator dynamic model construction, model fusion, task parameter calculation, action command generation, action control, and execution effect evaluation. This invention achieves high-precision adaptive control of robots operating in complex dynamic environments, improving environmental adaptability and task execution efficiency.

The application discloses a soft sealing frictionless ball valve intelligent adaptive control method, and relates to the technical field of valve control, which comprises the following steps: in step one, a perturbation is superimposed on the seat section to determine the instant of the contact seat by judging the residual point of the stiffness mutation and the non-contact stroke reference curve, and the position of the contact seat and the local stiffness are output; in step two, the pressure difference, temperature and seat ring material are collected, the minimum necessary compression load is calculated, and the upper limit of the allowable torque-angle curve is generated; in step three, the smooth closing trajectory is executed with the position of the contact seat as the starting point, the minimum necessary compression load is reached under the amplitude limitation, and the process is stopped, and the contact energy and sealing confirmation are recorded; in step four, the breathing type pressure maintaining is implemented in the maintaining stage, the seat ring health index is formed, and the material-temperature correction coefficient is written back. The method can reduce overload and underload, stabilize zero visible leakage, reduce energy consumption and prolong the service life of the soft seat, improve the predictability of operation and maintenance and the consistency of parameters, and support online diagnosis and maintenance decision.

The invention belongs to the technical field of pyrolysis parameter self-adaptive control, and discloses a multi-mode sensing circuit board pyrolysis parameter self-adaptive control system and a multi-mode sensing circuit board pyrolysis parameter self-adaptive control method. The method comprises the following steps: carrying out feature extraction on spectral signals of R circuit boards to obtain a fluorescence spectrum feature vector set; performing feature extraction on the reflection spectrum data of the R circuit boards to obtain a near-infrared reflection spectrum feature vector set; performing feature extraction on the surface images of the R circuit boards to obtain a circuit board image feature vector set; evaluating the R circuit boards based on the fluorescence spectrum feature vector set, the near-infrared reflection spectrum feature vector set and the circuit board image feature vector set to obtain predicted manufacturing times and aging evaluation grades of the R circuit boards; performing intelligent self-adaptive control on pyrolysis parameters of the circuit board; according to the method, the circuit board pyrolysis parameters can be accurately matched with circuit board decomposition requirements under different manufacturing backgrounds and aging states, and the precision of circuit board pyrolysis parameter regulation and control is improved.

The utility model relates to the technical field of oil development, in particular to an ultrasonic demulsification and dehydration device for crude oil. An ultrasonic demulsification and dehydration device for crude oil comprises an ultrasonic dehydration device, an inlet of the ultrasonic dehydration device is connected with an oil inlet pipeline, and an outlet of the ultrasonic dehydration device is provided with a water outlet and an exhaust port; a moisture collector is also arranged at the upper part in the ultrasonic dehydration device and is connected with the exhaust port; one end of the ultrasonic vibration device is located outside the ultrasonic dehydration device, and the other end of the ultrasonic vibration device sequentially penetrates through the ultrasonic dehydration device and the moisture collector to be located in the ultrasonic dehydration device; a flow metering device and an ultrasonic controller are arranged on the oil inlet pipeline; and the ultrasonic vibration device is connected with the ultrasonic controller. The device adopts an ultrasonic dehydration technology, realizes rapid and efficient dehydration of crude oil in an oil field through efficient and intelligent self-adaptive control, simplifies the operation process, improves the oil-water separation effect, and is green, energy-saving and environment-friendly.

The invention relates to the technical field of electric vehicle heat dissipation, discloses a flat stepping type electric vehicle battery pack heat dissipation method, and relates to the technical field of electric vehicle heat dissipation. According to the method, vibration energy generated in the running process of the flat stepping type electric vehicle is used for driving a heat dissipation assembly to generate airflow motion, an upward heat dissipation airflow channel is formed, airflow is guided to gaps among batteries in a battery pack, and comprehensive and uniform heat dissipation of the battery pack is achieved. The method comprises the four steps of collecting vibration energy, converting the vibration energy into heat dissipation power, forming an airflow channel and achieving uniform heat dissipation. A temperature sensing and adjusting function, a vehicle speed linkage control function, a waterproof sealing control function and a system maintenance function are further designed, and intelligent self-adaptive control of the heat dissipation system is achieved. Compared with the prior art, the technical problems of uneven heat dissipation of the battery pack and particularly insufficient heat dissipation of the lower part of the battery are solved, a heat dissipation power source does not need to be additionally arranged, and the energy consumption is reduced.