4348 results about "Frequency conversion" patented technology

The invention relates to a multi-microphone array beam forming signal enhancement method and device, and the method comprises the steps: obtaining a multi-channel time domain signal collected by a multi-microphone array, and carrying out the time-frequency conversion, and obtaining a plurality of time-frequency domain signals; acquiring a spatial distribution vector of the multi-microphone array, and performing deformation compensation processing on the complex number of time-frequency domain signals to obtain sound field consistency signals; performing sound source direction feature extraction on the sound field consistency signal, and performing complex time-space attention beam forming network processing to obtain a beam forming signal; performing beam direction and gain parameter optimization on the beam forming signal through a preset adversarial environment simulator to obtain a dynamic frequency band enhanced signal; and performing inverse time-frequency conversion on the dynamic frequency band enhanced signal, and performing low-delay optimization to obtain a target quality voice output signal. According to the invention, signal deformation compensation can be carried out more accurately.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention relates to the technical field of tunnel ventilation control, in particular to a tunnel fan energy-saving control system which comprises a multi-parameter sensing module, an intelligent evaluation module, a dynamic prediction module, an energy-saving optimization module, a frequency conversion control module and an alarm emergency module. Wherein the multi-parameter sensing module is used for monitoring various environmental parameters in a tunnel in real time; the intelligent evaluation module is used for calculating the current ventilation demand; the dynamic prediction module is used for predicting the ventilation demand in a period of time in the future; the energy-saving optimization module is used for generating an optimal energy-saving control instruction; the frequency conversion control module is used for adjusting the operation frequency and speed of the fan according to the instruction; and the alarm emergency module is used for sending an alarm signal when an abnormal condition is detected. According to the invention, environmental parameters in the tunnel are monitored and intelligently managed in real time, a fan operation strategy is dynamically optimized, and the system has emergency response capability, so that the ventilation efficiency is remarkably improved, the energy consumption is reduced, and the construction safety is guaranteed.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention provides a disinfection equipment production and processing quality control method and system based on intelligent analysis. Precise control of the drying process is achieved through three-stage collaborative optimization, firstly, hot air distribution parameters and historical energy consumption data of the drying process are integrated, a hot air uniformity and energy efficiency collaborative optimization model is constructed, and a dynamic target parameter system is established. Secondly, temperature field distribution data are collected in real time in the equipment operation process, multi-dimensional dynamic correlation analysis is conducted on the temperature field distribution data and energy consumption data of the heat supply device, heat field abnormal fluctuation and energy consumption correlation characteristics are recognized through a machine learning algorithm, and finally a heating power dynamic adjusting instruction is generated based on an analysis result; and by adopting the synergistic effect of a differential control algorithm and a frequency conversion regulation technology, process requirements are accurately converted into power supply frequency regulation and control signals, and millisecond-level dynamic response and self-adaptive regulation of the heating power are realized. According to the technical scheme provided by the invention, the production and processing quality control efficiency and accuracy of the disinfection equipment can be improved.

The invention relates to the field of machine room water pump control methods, in particular to an efficient machine room water pump frequency conversion control system and method. Modules of intelligent prediction and optimization, dynamic pressure and flow control, multi-sensor fusion, fault prediction and adaptive control, energy management and optimization and the like are integrated; and prospective and self-adaptive operation of the cooling system is realized. The system not only can predict the cooling demand according to historical data and real-time environmental parameters and adjust the working state in advance, but also can monitor and accurately control the pressure and flow of the cooling water system in real time and ensure the cooling efficiency. Through multi-sensor fusion, the system comprehensively monitors the operation state, and in combination with fault prediction and adaptive control, potential faults can be prevented, and the system stability is guaranteed. The energy management and optimization module intelligently dispatches operation of a cooling water tower fan and a cooling water pump, efficient utilization of energy is achieved, and the energy efficiency and reliability of the cooling system are remarkably improved on the whole.

The invention discloses an intelligent frequency conversion control method, device and system based on dynamic data acquisition, and the method comprises the steps: obtaining the electromagnetic parameter waveform of a motor in real time, carrying out the deep analysis of features through a machine learning algorithm, and achieving the recognition of the abnormal recognition precision; synchronously acquiring a rotor position and an evaluation parameter set, executing an exception analysis strategy, generating a risk evaluation coefficient, and accurately locking an exception moment; in the process, through a synergistic effect of quantizing angle deviation, current abnormity and sampling delay, a mathematical framework is provided for fault prediction, and risks are reduced in the acquisition process; and under the condition that the risk assessment coefficient is received, the risk assessment coefficient and a preset assessment threshold are compared and analyzed, and whether a corresponding frequency conversion control instruction is executed or not is selected according to a comparison result, so that high-precision and high-efficiency intelligent frequency conversion control on the motor is realized.

The invention discloses an air compressor frequency conversion and energy saving control method and system based on data analysis, and particularly relates to the technical field of air compressor control. Aiming at the problems of frequency jitter and motor instability of the permanent magnet variable-frequency screw air compressor in a high-frequency air consumption fluctuation scene, the method comprises the following steps of: acquiring exhaust pressure, air consumption flow, motor current, frequency converter output frequency and environment temperature data in real time, performing trend prediction by utilizing a data analysis model, and constructing a multi-dimensional working condition characteristic curve; a gas consumption fluctuation mode is accurately recognized, and predictive judgment is made before frequency fluctuation is about to exceed the limit; according to a prediction result, a frequency converter control strategy is dynamically adjusted, the frequency response rate is buffered in advance, an energy-saving mode is automatically triggered when the load change trend is abnormal, rotating speed control is optimized in real time, and according to the method, the operation stability and response precision of the air compressor are effectively improved, energy consumption is remarkably reduced, and the service life of equipment is prolonged; the method is suitable for the intelligent energy-saving operation requirement in a high-beat and high-precision manufacturing scene.

The invention relates to the technical field of frequency conversion control, and discloses a frequency conversion control method and system for a nuclear power station ventilation system, and the method comprises the steps: collecting the multi-region real-time operation data of the nuclear power station ventilation system, and carrying out the standardization processing, and obtaining a standardized data set; performing fan performance curve fitting and differential pressure field feature mapping on the standardized data set to obtain differential pressure field mapping features; the differential pressure field mapping features are input into an active neural differential pressure field network model for frequency conversion parameter prediction, and target frequency conversion control parameters are obtained; feedforward-feedback combined control is conducted on the target frequency conversion control parameters through the multi-mode frequency conversion controller, the fan rotating speed and the air valve opening value are obtained, the target frequency conversion control parameters can be generated in a self-adaptive mode, the control performance is remarkably improved, and smooth transition of the nuclear power station ventilation system is achieved.

The invention discloses an efficient refrigerating machine room air conditioner energy-saving intelligent control system and method, and relates to the field of air conditioner intelligent control. The method comprises the steps that air conditioner operation parameters, machine room equipment operation data and machine room environment data are collected and preprocessed; building a deep neural network by using a deep Q-learning reinforcement learning algorithm to obtain a thermal load demand of the machine room; introducing a nonlinear dynamic compensation function to calculate an air-conditioner dynamic energy efficiency coefficient and air-conditioner refrigeration power, and calculating the rotating speed of an air-conditioner compressor by referring to air-conditioner operation parameters; a PID (Proportion Integration Differentiation) controller is used for carrying out primary frequency conversion on a machine room air conditioner, and a multi-stage adjusting mechanism is used for correcting a primary frequency conversion result in real time according to machine room equipment operation data, machine room environment data change and frequency conversion conditions. According to the air conditioner energy-saving control method, efficient air conditioner energy-saving control is achieved by combining the deep Q-learning reinforcement learning algorithm, the nonlinear dynamic compensation function and the PID controller.

The invention relates to the technical field of power electronic equipment testing, and discloses a special intelligent testing device and method for a power UPS, and the device comprises a multi-source power supply system which comprises a three-phase program-controlled variable-frequency power supply, a single-phase program-controlled variable-frequency power supply and a DC program-controlled power supply, the output voltage of the three-phase program-controlled variable-frequency power supply is 0-480V and is adjustable, and the output voltage of the single-phase program-controlled variable-frequency power supply is 0-480V; the output voltage of the single-phase programmable variable-frequency power supply is 0-280V and the frequency of the single-phase programmable variable-frequency power supply is 45-64Hz and adjustable; the output voltage of the direct-current programmable power supply is 50-1000V and adjustable; an electronic load system; a signal acquisition system; an embedded control system; and an upper computer software system. A three-phase program-controlled variable-frequency power supply, a single-phase program-controlled variable-frequency power supply and a direct-current program-controlled power supply are integrated to form a multi-source power supply system, a resistive and rectifying dual-mode electronic load is combined, millisecond-level synchronous switching of a voltage level and a load state is realized through an embedded control system, a closed-loop test capability of multi-dimensional performance of the power UPS is formed, and the test efficiency is improved.

The invention relates to the technical field of resource optimization, in particular to an unmanned aerial vehicle distribution task real-time optimization scheduling management system, which comprises a bat sound wave calculation module for performing echo positioning frequency conversion calculation on the distance between the current position of an unmanned aerial vehicle and a target distribution point, a time window and a carrying weight upper limit value through a bat algorithm, and obtaining a task sound wave waveform matrix. According to the invention, the application of the bat algorithm enables the unmanned aerial vehicle to accurately calculate the task search space when the unmanned aerial vehicle executes the distribution task, and avoids the resource waste caused by a static distribution mode. The task search vector is formed based on the comparison operation of the task waveform amplitude and the distribution distance, so that the unmanned aerial vehicle can quickly adjust the task priority in a dynamic environment, and the execution accuracy of the distribution task is improved. The task group is optimized through the subspace decomposition operation, so that the unmanned aerial vehicle can execute multiple tasks in a parallel mode, the load pressure of a single unmanned aerial vehicle is reduced, and the cooperation efficiency of the overall tasks is improved.

The invention relates to the technical field of optimization control, in particular to an operation parameter optimization control method and system for an air condenser, and the method comprises the steps: firstly obtaining environment data, processing the environment data to obtain a self-adaptive environment monitoring value, and constructing a non-uniform frequency conversion strategy according to the self-adaptive environment monitoring value to control the optimization frequency of the operation parameters of the air condenser; then obtaining structural features and operation features of the air condenser, obtaining a first to-be-optimized parameter vector according to a principal component analysis method, obtaining the first to-be-optimized parameter vector, and carrying out sensitivity analysis to obtain a second to-be-optimized parameter vector; and then the optimization parameter prediction model predicts a parameter value vector of the second to-be-optimized parameter vector, the distributed parameter optimization control model sets a local agent of the to-be-optimized parameter, the parameter value vector of the second to-be-optimized parameter vector is acquired, and parameter optimization control is performed through a reinforcement learning algorithm. According to the method, through optimal control over the operation parameters, the parameter optimization speed and stability are improved, and the energy consumption of the air condenser is reduced.

The invention discloses a high-voltage circuit for an electric flame stove. The high-voltage circuit comprises a PFC circuit, a high-frequency conversion circuit, a boosting transformer, an energy coupling circuit and a voltage doubling circuit which are electrically connected in sequence. According to the EMI filter circuit, common-mode interference and differential-mode interference are filtered out, and pure alternating current input is provided for a back-end PFC circuit; two chips are adopted in the PFC circuit, so that the PFC circuit 300 finally realizes the consistency of the current waveform and the voltage phase; in the other embodiment of the invention, double chips are adopted, so that'fault linkage protection 'is realized; stable discharging of multiple groups of positive and negative electrodes is achieved through multiple groups of single energy coupling capacitors and independent voltage doubling rectifier bridges which are connected in parallel.

The invention discloses a variable-frequency ash removal device for improving photovoltaic power generation efficiency, and a control system of the variable-frequency ash removal device can dynamically adjust the output frequency and amplitude of a signal generator according to cleaning requirements. Particularly, when different types of dust are cleaned, the system can automatically adjust the vibration frequency, so that the cleaning process is more accurate. Large-particle dust is effectively removed through low-frequency and large-amplitude vibration, and small-particle dust is finely cleaned through high-frequency micro-vibration. The dynamic adjustment of the frequency and the amplitude ensures that each dust type can be thoroughly removed, and the problem of poor effect caused by fixed frequency in a traditional cleaning method is avoided.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention relates to the technical field of variable frequency speed regulation, in particular to an automatic water pump control strategy and energy efficiency optimization method and system.The method comprises the following steps of obtaining water pump outlet flow data, calculating flow acceleration, determining the pressure gradient direction, matching the hydraulic power trend, calculating frequency instruction deviation and setting the adjustable range; lagging and current rising mark inertia disturbance, power frequency limitation is set, a compensation value is generated, the frequency rate is dynamically adjusted in combination with all factors, and the frequency conversion adjusting priority is generated. According to the method, through matching of the flow acceleration and the pressure gradient, the sensing capacity for dynamic changes of fluid is improved, frequency adjustment of a frequency converter is optimized in combination with frequency deviation calculation, energy consumption increase caused by lag is reduced, and based on correlation analysis of the motor rotating speed change rate and current fluctuation, inertia disturbance accurate recognition is achieved; the voltage compensation timeliness is improved by combining bus voltage dynamic monitoring and a reverse compression strategy, the frequency of the frequency converter is dynamically adjusted by combining multiple parameters, and the response speed and the energy efficiency are improved.

The invention provides a model and data driven low-orbit navigation enhanced satellite clock error forecasting method and equipment, and the method comprises the steps: carrying out the preprocessing of original clock error data of a low-orbit satellite: converting clock error time domain data into frequency domain data through time-frequency conversion, and employing a quartile method to recognize and eliminate frequency domain outliers and corresponding time domain abnormal values; constructing a frequency domain energy model and extracting periodic terms, including performing power spectral density analysis on the preprocessed clock error frequency domain data, and adaptively extracting significant periodic components of the low earth orbit satellite clock error through a threshold value; establishing a polynomial low earth orbit satellite clock error forecasting model considering periodic term correction and forecasting a clock error sequence to obtain a corresponding fitting residual error sequence and a clock error forecasting value; and normalizing the fitting residual error sequence, inputting the fitting residual error sequence into the gating circulation unit neural network, training and forecasting by adopting a window sliding input mode, and outputting a residual error prediction result. According to the method, the interpretability of the forecasting result can be effectively improved while the low earth orbit satellite clock error forecasting precision and stability are improved.

The invention discloses a zero-sequence pilot-frequency excitation method for a double-circuit line in an incomplete power failure environment, which comprises the following steps of: applying zero-sequence pilot-frequency signal excitation on a power failure line by using a pilot-frequency power supply as an excitation signal source of a zero-sequence network of the double-circuit line, different-frequency induced current and voltage are measured at secondary sides of a current transformer and a voltage transformer at two sides of the operation line, and different-frequency zero-sequence voltage and different-frequency zero-sequence current in the double-circuit line zero-sequence network are obtained; simultaneously solving a zero-sequence network equation, and calculating mutual impedance between double circuits; performing frequency conversion to obtain the power frequency mutual impedance of the line; and finally, carrying out simulation verification by constructing a non-full-stop double-circuit line zero-sequence mutual inductance impedance measurement system. According to the method, the multi-path coupling caused by the existing power frequency interference method and the accuracy problem caused by the signal measurement error of the zero sequence increment method are solved, the test calculation can be accurately completed under the complex interference working condition, and the safety risk caused by the single-phase reclosing operation is avoided.

The invention discloses a configurable multi-slave-station frequency conversion communication system based on Modbus communication. The configurable multi-slave-station frequency conversion communication system comprises an upper computer, a Modbus communication module and a lower computer, wherein the upper computer communicates with the lower computer through the Modbus communication module, and the upper computer constructs and sends a command frame to the lower computer according to a Modbus communication protocol specification; after receiving the command frame of the upper computer, the lower computer analyzes the command frame, executes corresponding operation according to a command frame analysis result, encapsulates a response frame and returns the response frame to the upper computer; and after receiving the response frame of the lower computer, the upper computer analyzes the response frame to analyze the required data. Compared with the prior art, the system disclosed by the invention has the advantages of high flexibility, high reliability, resource saving, high efficiency, easy expansion, high data transmission speed, high expandability and the like.

The invention discloses a sewage treatment information intelligent management method, system and equipment based on the Internet of Things, and relates to the technical field of sewage treatment information management, and the method comprises the steps: 1, collecting multi-source water quality data, initializing sensor equipment, and carrying out the primary processing and caching of the data; step 2, carrying out edge intelligent prediction on pollutant concentration and pollution load sudden increase; step 3, establishing a prediction and control response model, introducing a control strategy generation mechanism based on a rule engine and priority tradeoff, and calculating a dosing dynamic sensitivity value; 4, adjusting frequency conversion of a fan, a dosing module and a reflux pump, and feeding back, collecting and uploading in real time; and step 5, performing artificial intelligence strategy center verification and a digital twinborn simulation strategy, analyzing multi-plant load scheduling and implementing optimization measures, and performing visual interaction in real time. The problem of energy and medicament waste caused by lack of a feed-forward pre-judgment mechanism is solved.

The invention discloses an automobile tubular part inner wall supporting type laser welding device and technology, and relates to the technical field of automobile tubular part machining, the automobile tubular part inner wall supporting type laser welding device comprises a pipe welding base, an inner wall self-adaption supporting unit and a vertical displacement adjusting assembly, radial guide bases are connected to the two sides of the pipe welding base, and a synchronous servo motor is installed on the outer side of the pipe welding base; an internal driving screw rod and a guide sliding rod drive a stroke frame and a bearing supporting plate to achieve radial positioning of the supporting unit, the inner wall self-adaptive supporting unit comprises a driving cabin and a synchronous power source, transmission is achieved through a lead screw, a transmission fluted disc and a vortex transmission cylinder, a sliding adjusting sleeve is pushed, a supporting rod drives an arc face attaching lining plate to open and attach to the inner wall of the tubular part, and the tubular part is fixed. A vertical displacement adjusting assembly is arranged in a frame supporting beam, a variable frequency motor and a fine adjustment motor drive a guide screw rod and a screw rod correspondingly, alignment of a laser welding head is adjusted, a welding slag collecting groove in the center of a base and a detachable storage box are convenient to remove slag, and the welding machine is suitable for welding of tubular parts of multiple specifications.

The invention relates to the technical field of frequency conversion, in particular to a radio frequency microwave signal modulation method and system, and the method comprises the following steps: detecting the ratio of the time domain peak interval to the frequency domain main sideband energy of a radio frequency signal, inputting the time domain peak interval and the frequency domain main sideband energy into a cross-correlation algorithm for synchronism analysis, extracting the periodic characteristics of a time domain peak sequence, and obtaining a frequency domain peak sequence; and generating an energy coupling factor based on a difference operation result of the frequency domain energy density gradient value and the time domain peak interval. According to the method, periodic characteristics are analyzed and extracted through the ratio of peak spacing to main sideband energy to generate an energy coupling factor, a main frequency point offset and an energy tilt vector are fused to generate a harmonic tendency parameter, an interference source path is positioned by combining a frequency trajectory vector, and disturbance levels are divided by clustering energy and harmonic parameters. And a conflict direction is pre-judged and stop band parameters are pre-loaded by grey prediction of a main sideband frequency difference value trend, and a closed-loop regulation and control system is constructed to improve the anti-drift capability and reduce the spectrum conflict risk.

The invention provides a heat supply digital twinborn intelligent regulation and control method, and relates to the technical field of intelligent regulation and control, and the method comprises the steps: executing millisecond-level high-concurrency numerical simulation according to a dynamic digital twinborn body, so as to obtain a simulation result; based on the simulation result, coupling the electricity price fluctuation data and the weather prediction data, performing future heat supply system multi-working-condition deduction, and dynamically optimizing a heat source unit start-stop combination and pipe network valve opening strategy to obtain a deduction result and an optimization strategy; generating an equipment regulation and control instruction set according to the deduction result and the optimization strategy; and an equipment regulation and control instruction set is issued to an edge computing node, a variable-frequency circulating pump and an electric control valve executing mechanism are controlled, and dynamic closed-loop regulation of the flow and the pressure of the heat supply pipe network is achieved. According to the invention, the pipe network flow and pressure regulation response time can be shortened to a second level.

The invention relates to the technical field of communication network fault diagnosis, and discloses a communication information processing method and device. The method comprises the steps that a voiceprint sensor collects an original signal, and a quantum coding time-frequency matrix is generated through time-frequency conversion and quantum coding; the matrix and real-time circuit diagram data are fused, and topology-fault joint features are generated through feature alignment and wavelet-convolution joint extraction; analyzing the features to reconstruct a node relation graph, and generating real-time updated circuit diagram data through graph optimization; loading an incremental parameter adapter initialization graph neural network based on the data, and executing fault propagation deduction to output fault coordinates and confidence data; generating an incremental parameter adapter according to confidence data federal optimization; and analyzing the fault coordinate matching topology library based on the adapter, and associating the maintenance knowledge base to output a visual repair suggestion. According to the method, fault dynamic deduction is realized, the cross-line generalization ability is enhanced by an incremental learning mechanism, bandwidth occupation is reduced by edge-cloud cooperation, and the fault positioning efficiency and accuracy are remarkably improved.

The invention discloses a multi-channel analog-to-digital converter for multi-channel frequency division multiplexing and a conversion circuit and method thereof, and the frequency division multiplexing multi-channel analog-to-digital conversion circuit comprises an analog frequency conversion module which is used for receiving N paths of modulation clock signals, and generating N paths of modulation clock signals according to the modulation clock signals, n paths of input signals are modulated to preset frequencies of the input signals respectively, and N paths of modulation signals are obtained; wherein N is greater than or equal to 2, and N is a positive integer; the frequency bands where the N paths of modulation signals are located are not overlapped; the analog adder is used for combining the N paths of modulation signals to obtain one path of analog signal; the input end of the analog-digital conversion module is connected with the analog-digital conversion module; the digital logic control module is used for receiving the N paths of modulation clock signals and separating the digital signals into N paths of digital signals which are output in parallel. Multi-channel analog-to-digital conversion can be realized by adopting one analog-to-digital converter, the hardware volume, cost and power consumption can be reduced, and the analog-to-digital conversion rate and precision can be improved.

The invention discloses a high-throughput satellite communication terminal system, which comprises a radio frequency channel unit, a signal processing unit, a terminal processing unit, a power supply management unit and an anti-electromagnetic interference module, and is characterized in that the radio frequency channel unit carries an agile device and a power amplifier and is used for realizing bidirectional conversion between a baseband signal and a radio frequency signal; communication frequencies of a Ka frequency band, a valley value of 5GHz and a peak value of 40GHz are supported, a zero intermediate frequency receiver structure is adopted, image frequency interference is eliminated through one-time frequency conversion to a baseband, and a high-integration design that a radio frequency signal directly enters a frequency mixer after passing through an antenna, a radio frequency filter and a low-noise amplifier is realized. According to the high-throughput satellite communication terminal system, the coverage problem of traditional communication in a blind area of a power grid is solved, safe, efficient and stable transmission of power data is realized, the emergency communication capability and the equipment environment adaptability are remarkably improved, and key technical support is provided for constructing global visual monitoring and intelligent scheduling of the digital power grid.

The invention relates to the technical field of tunnel ventilation and environmental protection, in particular to an efficient and energy-saving tunnel ventilation and dust removal integrated system. Data analysis and decision making; ventilation control; dust removal operation; and sewage treatment. The intelligent sensing module is installed in the tunnel, environment parameters of different positions can be accurately obtained in real time and transmitted to the central control system, the efficient and energy-saving draught fan is selected, the frequency conversion and speed regulation device is installed, energy consumption is reduced, and by arranging the air filtering device and the dust falling device, the dust falling efficiency is improved. The dust collection device and the dust blocking device arranged at the tunnel portal effectively control the air environment quality in the tunnel, sewage purification and cyclic utilization are achieved through sewage treatment, and the system is high in intelligent degree, can be flexibly adjusted according to the actual situation and is suitable for popularization and application. The problems that a traditional ventilation and dust removal mode is high in energy consumption, poor in effect, lack of intelligent control and the like are effectively solved, the health of constructors is guaranteed, and pollution to the surrounding environment is reduced.

The invention discloses a wavelength-adjustable high-power picosecond all-solid-state laser. The microchip seed source adopts a passive Q-switching principle, can directly generate laser with the pulse width as narrow as 300ps, is high in seed laser efficiency, good in beam quality and large in energy, can synchronously output TTL (transistor-transistor logic) level, and is used for controlling the pulse duration and delay of a 808nm QCW pumping source; the optical isolator protects the front-stage microchip solid laser from being damaged by returned light of the rear-stage amplifier; the single-rod two-way end pump amplifier amplifies laser pulse energy output by the microchip solid laser; the light beam shaper comprises a Galileo laser beam shrinking system and a soft edge diaphragm and is used for shaping light beams; the single-rod one-way end pump amplifier amplifies the pulse energy of the preceding-stage laser; and the frequency multiplier performs frequency conversion on the output laser, and outputs wavelength-adjustable picosecond laser with millifocus magnitude, high power and high beam quality.

The invention discloses an efficient time sequence analysis method and device based on a convolutional neural network and a medium. Time sequence modeling analysis results for different downstream tasks are obtained through the steps of original data reading, sample standardization, adaptive time-frequency conversion, frequency domain component extraction and fitting, time domain data reconstruction and the like. In the time sequence analysis method, a time-frequency conversion and time domain data reconstruction step is realized through a dynamically adjustable wavelet analysis component, and a frequency domain data fitting step efficiently models distribution of frequency components in original data through a convolutional neural network with low time and space complexity. Raw data reading and sample standardization provide a starting point for time series analysis for different tasks. The method is based on data driving, has no special requirement for data, is high in universality, can maintain high modeling precision while greatly reducing time cost and hardware resource overhead, and has high theoretical property and practicability.