747 results about "Frequency detection" patented technology

The invention relates to the field of chip packaging testing, in particular to a BGA chip packaging testing method. The method comprises the following steps: carrying out full-flow thermal imaging scanning on a BGA chip packaging process, carrying out space temperature gradient diffusion distribution analysis, and constructing a temperature diffusion distribution map of each region; according to the probe array, acoustic scanning is carried out on the BGA chip solder ball contact surface, abnormal frequency detection is carried out, and abnormal acoustic nodes are marked; performing transient temperature mutation detection and potential packaging defect positioning analysis on the temperature diffusion distribution map based on abnormal acoustic nodes, and extracting a potential packaging defect area; and carrying out multi-threshold micro-vibration excitation on the potential packaging defect area, carrying out comprehensive evaluation on the reliability of the defect area structure, and generating a reliability evaluation report of the solder ball contact surface structure. According to the invention, accurate packaging test failure prediction is realized, packaging process parameters are optimized, and chip packaging quality and stability are improved.

The invention relates to the technical field of vehicle safety monitoring, and discloses a non-contact type in-vehicle driver respiratory rate detection method and device. Physiological signal data are collected through a millimeter wave radar, infrared thermal imaging and a microphone array, breathing frequency band signals are extracted through self-adaptive wavelet transform, and breathing signal waveforms are reconstructed through time-frequency joint analysis. A model formed by combining a multi-channel convolutional neural network and a bidirectional gating circulation unit is adopted to extract a respiratory rhythm feature vector, interference is eliminated based on a dynamic time warping algorithm, and a respiratory frequency value is estimated by applying methods such as variational mode decomposition. And processing abnormal points and missing data through a sliding window mechanism, and outputting a stable monitoring result. The invention further provides a sensor data synchronous calibration and model online updating method. According to the invention, the breathing frequency can be detected with high precision in a complex in-vehicle environment, and the safety and intelligent level of the vehicle are improved.

The invention provides a clock signal generating circuit, a wired communication transceiver and electronic equipment, and the circuit outputs a first DC voltage comprising phase difference information and frequency difference information of a reference clock signal and a feedback clock signal through a phase frequency detection module. The pulse generator generates a narrow pulse signal based on the DC voltage and a reference clock signal, the oscillation frequency of each secondary ring oscillator in the cross-coupled ring oscillator is controlled by the first DC voltage, and the cross-coupled loop performs synchronous coupling and phase sequence adjustment processing on each secondary ring oscillator. One output node in the cross coupling ring oscillator receives the narrow pulse signal, frequency and phase locking is completed in an injection locking mode, and the frequency divider is used for conducting frequency division processing on any high-frequency clock signal and outputting a feedback clock signal. Therefore, a high-frequency multi-phase clock signal with low phase noise and high phase precision can be generated by using the low-frequency reference clock signal.

The invention discloses a quadruped inspection robot space positioning method and system based on gravity center sensing, and aims to construct a gravity center potential energy field through foot end contact force and body attitude information, form a gravity center disturbance mode in combination with fluctuation spectrum analysis, and reveal a disturbance propagation mechanism. A reverse compensation technology is adopted to generate an anti-disturbance sequence, a foot end force distribution topology is reconstructed, and active disturbance suppression is achieved; a multi-frequency detection wave beam and coherent superposition technology is introduced, an environment hologram is generated, geometric topology is reconstructed, and a high-precision environment sensing system is established; high and low impedance regions are identified through energy density analysis, and an energy optimal path is constructed in combination with double strategies of potential field bypassing and streamline tracking; establishing an oscillation analysis space for path projection simulation, identifying resonance risk points and determining stabilization parameters; finally, a coordinate reference is generated by adopting a quantum entanglement type multi-anchor-point synchronization mechanism, accurate positioning is realized by fusing environmental geometric information, and a technical closed loop from gravity center dynamics analysis to environmental perception navigation is realized.

The invention relates to a phase-sensitive optical time domain reflectometer based on multi-frequency reference switching. Continuous laser generated by a continuous laser is divided into a local path and a detection path through an optical coupler; the local path is input to the polarization coherent receiving module, the detection path is incident to a light intensity modulator driven by the signal generation circuit module, and the light intensity modulator is converted into a multi-frequency pulse laser sequence with intensity modulation; the multi-frequency pulse laser sequence enters the sensing optical fiber to be measured through the optical circulator; generated Rayleigh backscattered light and local continuous laser are subjected to beat frequency coherent receiving in a polarization coherent receiving module through a circulator; and the signal acquisition circuit adopts a demodulation algorithm to compensate phase drift and sensitivity difference of detection pulse demodulation signals with different frequencies so as to realize dynamic strain sensing. Compared with the prior art, the phase drift and the sensitivity difference between the frequencies are extracted to realize the compensation of the interleaved signals, and the method does not depend on the fixed reference frequency, so that the robustness and the adaptability of the system in a complex environment are remarkably enhanced.

The invention provides a power grid frequency detection method, system and device and a medium, and the method comprises the steps: carrying out the narrowband signal decomposition of an obtained input voltage signal of a power grid through a null-space tracking algorithm, and obtaining a fundamental main mode component of the input voltage signal; performing low-pass processing on the fundamental wave main mode component to obtain a fundamental wave signal of the fundamental wave main mode component; performing discrete Fourier transform on the fundamental wave signal to obtain phase information of the fundamental wave signal; according to the phase information of the fundamental wave signal, calculating phase deviation information of the fundamental wave signal in pre-selected adjacent cyclic waves to obtain frequency estimation information of the power grid; according to the invention, narrow-band decomposition is carried out on an input voltage signal through a null-space tracking algorithm, so that a fundamental main mode can be accurately extracted; and phase deviation information can be determined based on discrete Fourier transform, so that when the interference frequency of the power grid is close to the fundamental frequency, high separation precision can be kept, and the accuracy of power grid frequency estimation can be improved.

The invention relates to an integrated microcavity optical comb repetition frequency and optical frequency locking device and method, and belongs to the technical field of integrated microcavity optical combs. The locking device comprises a pumping module, a power amplification module, a phase modulation module, an electro-optic driving module, an acousto-optic modulation module, an acousto-optic driving module, a band-pass filtering module, a micro-ring resonant cavity and a beam splitting module; the device comprises a soliton detection module, a control module, a repetition frequency detection module, a first feedback control module, an acetylene frequency stabilized laser, a beat frequency module, an optical frequency beat frequency detection module and a second feedback control module. The locking device can achieve full frequency locking of the microcavity optical comb, has the advantages of being high in frequency stability, wide in servo bandwidth, simple and compact in structure, tight in locking, low in noise, high in reliability and low in cost, and can provide a powerful tool for the field of precision measurement.

The invention relates to a SiC crystal ingot facet detection method based on Raman combined with resistivity detection, which comprises the following steps: S1, providing a silicon carbide workpiece, acquiring resistivity rho k of different detection areas on the surface layer of the workpiece based on a preset resistivity detection scanning path, and further preliminarily screening out resistivity abnormal areas; s2, on the basis of a preset Raman detection scanning path, obtaining the edge area on the surface layer of the workpiece, the Raman characteristic peak frequency omega ij of different detection points of the resistivity abnormal area obtained in the S1, a detection image, and an XY coordinate position on an XY plane perpendicular to the height direction of the workpiece; and S3, based on a preset n-rho inverse proportion function model of the doping concentration n and the resistivity rho, and a delta omega-n linear model of the Raman frequency shift delta omega and the doping concentration n, determining and associating the XY coordinate positions of the S2 and the corresponding resistivity rho ij, and constructing a resistivity distribution diagram of the surface layer of the workpiece. The method has the advantages of small detection error and short detection time.

The invention discloses an identification and positioning method for power cable defects and short-circuit faults. The defect identification and positioning method comprises the following steps: establishing a power cable transmission line simulation model and a partial discharge simulation model by using electromagnetic transient simulation software; obtaining a partial discharge signal according to the power cable transmission line simulation model and the partial discharge simulation model, and identifying the partial discharge signal based on an SSA-BP algorithm; performing preliminary positioning on the power cable by adopting an ultrasonic, high-frequency and ultrahigh-frequency detection method; accurate positioning of power cable defects is realized through an infrared heat and sound wave positioning principle. According to the method, a simulation analysis method is adopted to obtain a sample, and then accurate identification of a cable fault is realized through a BP neural network (SSA-BP) optimized based on a sparrow search algorithm. And meanwhile, accurate cable fault positioning is realized aiming at different fault positions.

The invention relates to the technical field of information management, in particular to a smart agriculture precision big data information management system, which comprises a growth index extraction module, a stage data clustering module, a nutrient fluctuation identification module, an agricultural material strategy generation module and a scheduling information scheduling module. According to the method, the acceleration criterion is constructed based on the continuous period leaf area index change sequence, transition node identification is completed in combination with the photosynthetically active radiation utilization rate and the nitrogen absorption rate change trend, accurate positioning of crop growth stage change time points is achieved, the stability of stage classification in a space region is enhanced, and the accuracy of crop growth stage classification is improved. The agricultural material putting grade is judged and the fertilization frequency and dosage are blended by analyzing a root zone soil nutrient concentration difference value and a rate fluctuation frequency detection mode in combination with moisture content and root activity conditions, and operation period adjustment is guided in a task schedule in cooperation with a meteorological element fluctuation rate. The precision of farmland crop stage identification and the timeliness of nutrient fluctuation response are integrally improved.

The invention relates to the technical field of wireless communication, in particular to an OTA upgrading method and system for Bluetooth sound equipment. The method comprises the following steps: acquiring to-be-upgraded firmware data and currently played audio data, and acquiring power amplifier current feedback data of a sound speaker by sending a double-frequency detection signal containing 21kHz and 22kHz; acquiring equipment transmission capability confirmation data according to a silent response mode of the sound equipment to the preset handshake signal; and calculating a nonlinear distortion coefficient of the loudspeaker according to the intensity of the 1kHz difference frequency component in the power amplifier current feedback data, and obtaining real-time channel evaluation data based on current waveform deviation analysis. According to the method, the nonlinear distortion degree of the loudspeaker is accurately evaluated through the 1kHz difference frequency component generated by the 21kHz and 22kHz dual-frequency detection signals, so that the reliability of data transmission is more accurately predicted.

An energy storage system (ESS) includes an energy storage unit, a power conditioning system (PCS), a frequency detection circuit, and a controller. The controller performs first / second frequency adjustment control on the PCS, adjusts a weight of the first / second frequency adjustment control based on a frequency difference between a frequency of an output voltage of the power conditioning system and a target frequency, controls the frequency of the output voltage of the power conditioning system to be the target frequency, and detects that islanding occurs in the power conditioning system. After islanding occurs in the power conditioning system in the energy storage system, the controller can adjust the frequency of the output voltage of the power conditioning system in which islanding occurs to the target frequency, so that islanding can be detected and an anti-islanding protection action can be completed.

The invention discloses a membrane bioreactor membrane pollution prediction method and system, and relates to the technical field of pollution detection. The method comprises the following steps: setting a frequency detection strategy, and connecting a membrane bioreactor to obtain a membrane feedback signal based on the frequency detection strategy; performing feedback signal fluctuation time sequence characteristic analysis based on the membrane feedback signal to obtain a feedback signal characteristic; the feedback signal features are combined with reactor operation parameters to be imported into a mechanism hybrid prediction model, mechanism constraint is carried out through a mechanism constraint layer according to the feedback signal features, data analysis is carried out on time series data features on the basis of the mechanism constraint through a data driving prediction layer, and a membrane state prediction result is obtained; and performing membrane pollution state analysis and progress prediction according to the membrane state prediction result, and generating a membrane pollution prediction data chain. The technical problems that in the prior art, membrane pollution prediction depends on an empirical threshold, so that recognition lags and precision is insufficient are solved, and the technical effect of improving membrane pollution prediction accuracy and real-time performance is achieved.

The present invention proposes a breathing frequency detection method based on time-frequency analysis of Hilbert-Huang transform. First, a WiFi link with strong environmental perception sensitivity is selected to construct a channel state information (CSI) ratio model. Secondly, the filtered CSI ratio time series is projected, and candidate sets of different breathing mode signals are generated by combining amplitude and phase information. Thirdly, the selected breathing modes are subjected to signal variational mode decomposition (VMD) and time-frequency analysis of Hilbert-Huang transform, so as to remove non-human breathing frequency components. On this basis, reconstruction is carried out, and the reconstructed signals are fused using principal component analysis (PCA). Finally, the breathing frequency is calculated through a false peak detection algorithm. The breathing frequency detection algorithm designed by the present invention not only expands the sensing range and eliminates "blind spots" while removing time-varying phase offsets and high-frequency noise and selecting the optimal subcarriers, but also improves the breathing frequency detection accuracy, providing a more accurate detection method for non-contact breathing frequency detection.

The invention provides a battery charging and discharging pulse frequency detection method and system, and the method comprises the steps: dividing a pulse current signal of a target battery during charging and discharging into a plurality of pulse signal segments, and determining the static deviation of the current of the target battery during charging and discharging based on the local current characteristics of each pulse signal segment; determining the time domain positioning error of the time interval through the static offset and the pulse width of the pulse current signal; monitoring the surface temperature of the target battery during charging and discharging, and determining the disturbance characteristics of the temperature of the target battery during charging and discharging to the current according to all the monitored temperatures and the pulse current signal; and performing pulse identification on the pulse current signal according to all the time domain positioning errors and disturbance characteristics to obtain an effective pulse time mark sequence of the target battery during charging and discharging, and determining an effective pulse frequency of the target battery during charging and discharging based on the effective pulse time mark sequence. According to the scheme, the influence of current signal drift on pulse frequency detection in the battery charging and discharging process can be reduced.

The invention relates to the technical field of battery pack linkage, and discloses a multi-string battery pack voltage collection and protection linkage method and device and computer equipment, and the method comprises the steps: carrying out the data collection of each series unit in a multi-string battery pack, and obtaining basic monitoring data; performing junction temperature calculation on power loss and thermal resistance characteristics of the MOSFET devices in each series unit to obtain real-time junction temperature data; carrying out oscillation frequency detection on the grid voltage change of the MOSFET device in each series unit to obtain grid oscillation frequency data, and carrying out thermal resistance oscillation coupling detection to obtain a dynamic protection threshold parameter; performing temperature compensation and cross interference elimination on the voltage acquisition channel of each series unit to obtain target voltage data; according to the anti-oscillation locking protection method, the problem that the MOSFET fails due to overheating is effectively prevented, the coordinated protection among the series units is ensured, and the influence of single-point failure on the whole battery pack is avoided.

The invention belongs to the technical field of gas detection and recognition, and particularly relates to a mixed gas component acoustic qualitative recognition method based on a deep learning network and a network training method. Testing a plurality of groups of sound velocities and sound absorption coefficients corresponding to different sound wave frequencies of the to-be-identified gas under the conditions of preset temperature and pressure, and then calculating to obtain effective heat capacities corresponding to the plurality of groups of to-be-identified gas according to the plurality of groups of sound velocities and sound absorption coefficients; decomposing the effective heat capacity according to a mixed gas acoustic relaxation decoupling model to obtain a gas relaxation information parameter; and taking the gas relaxation information parameter as the input of the trained mixed gas vibration characteristic frequency detection network, namely outputting the gas type in the to-be-identified gas. The invention provides a new way for identifying gas types, and the model is high in identification accuracy and wide in application scene.

The invention discloses an oil abrasive particle enrichment chip based on sound control and an LIBS (laser-induced breakdown spectroscopy) detection system and method.The enrichment chip is internally provided with an enrichment flow channel, and enrichment of oil abrasive particles is achieved through sound field driving; the detection system comprises a driving device, an enrichment chip, a detection chip, a pulse pump and an LIBS spectral analysis module, the driving device is used for sending out an electric signal with a preset frequency to excite piezoelectric ceramics to form a preset sound field in an enrichment flow channel, and oil abrasive particles are directionally gathered under the action of the sound field; the pulse pump is used for pumping the oil liquid enriched by the enrichment chip into the detection chip; the detection chip is used for carrying out secondary dispersion on the enriched oil liquid; the LIBS spectrum analysis module is used for carrying out breakdown detection on the oil liquid dispersed by the detection chip so as to obtain oil liquid spectrum data. Through a closed-loop system of sound field dynamic enrichment, pulse pump time sequence conveying and LIBS high-frequency detection, real-time continuous analysis of oil abrasive particle components is achieved, and the limitation of a traditional static sampling mode is broken through.

The invention discloses a battery changing station battery digital intelligent management system, and relates to the technical field of battery changing station battery management, and the system comprises a charging strategy optimization module which integrates historical battery changing data, a power grid load and time-of-use electricity price information, employs a time sequence prediction algorithm to predict a battery changing demand, and carries out the optimization of a charging strategy according to the battery changing demand; the battery performance monitoring module is used for carrying out dynamic frequency detection on the battery of the battery swap station by adopting a grading detection strategy and carrying out real-time parameter abnormity monitoring on the battery of the battery swap station; and the dynamic tracking and scheduling module is used for distributing coding information for the battery swap station batteries, updating the coding information in real time and performing dynamic tracking and scheduling on the battery swap station batteries according to the coding information. Through combination of the charging strategy optimization module, the battery performance monitoring module and the dynamic tracking scheduling module, full life cycle management of the battery of the battery swap station is realized, and the operation efficiency and the resource utilization rate of the battery swap station are improved.

The invention relates to the technical field of impedance matching, and discloses an impedance matching method and device, external line gateway equipment, a storage medium and a computer program product, the method is applied to the external line gateway equipment, the external line gateway equipment is connected with a telephone network for use, and the method comprises the following steps: sending a multi-frequency detection signal to the telephone network based on a preset period, echo signal attributes corresponding to the collected echo signals are obtained; feature extraction is carried out according to the echo signal attribute to obtain a feature vector, the normalized feature vector is input into a preset neural network prediction model to obtain a prediction impedance value, and the preset neural network prediction model is obtained by training the sample impedance value and the sample feature vector; and obtaining a target impedance value based on a preset smooth transition algorithm and the predicted impedance value, and performing impedance matching according to the target impedance value. Since the corresponding target impedance value is generated in real time for impedance matching, the equipment can adapt to different telephone network line characteristics for impedance matching.

The invention discloses an MEMS resonator low ripple error temperature measurement system based on voltage feedback compensation, and the system comprises a trans-impedance amplifier, an analog-to-digital converter, a frequency detection module, a PI controller, a variable gain amplifier, a Schmitt trigger, a first digital-to-analog converter, a biasing circuit, mode switches S1 and S2, and a voltage feedback compensation loop. The compensation loop comprises an amplitude detection module, a lookup table, a second digital-to-analog converter and an adder; the amplitude detection module receives the digital signal from the analog-to-digital converter and outputs a vibration amplitude signal in real time; the lookup table receives the vibration amplitude signal, inquires and outputs a corresponding digital compensation voltage, and converts the digital compensation voltage into an analog voltage through a second digital-to-analog converter; the adder adds the analog voltage and a fixed bias voltage to generate a dynamic bias voltage and applies the dynamic bias voltage to a drive electrode of the MEMS resonator. According to the system, an amplitude-driving end bias voltage feedback compensation loop is introduced, so that the periodic ripples of the resonant frequency are effectively suppressed.

The invention discloses an active protection control system and method of a motor controller and an electric vehicle. Comprising a phase current frequency detection module, a voltage conversion module, a driving chip, a control module, a high-voltage alternating-current electricity taking module and a power module, the phase current frequency detection module is used for detecting a real-time phase current frequency value of a three-phase winding of the motor in real time; the control module is used for storing a phase current frequency limit value and outputting an enable signal of the voltage conversion module; the high-voltage alternating current power taking module is used for obtaining at least one phase of alternating current in the three-phase alternating current and supplying power to the voltage conversion module; the voltage conversion module is used for receiving the voltage conversion enable signal, starting working, converting at least one phase of alternating current into working voltage required by the power module, and outputting the working voltage to a fault pin of the driving chip and a grid electrode of a driving tube of an upper bridge arm or a lower bridge arm in the power module; the active short-circuit protection of the motor in a galloping or overspeed state is realized, and the overvoltage damage of the motor controller is avoided.

The invention belongs to the technical field of medical detection, and particularly relates to an intelligent specimen submission device and management system based on the Internet of Things, and the device comprises a specimen partition placement module, a timing signal sending module, a duration detection module, a frequency detection module, and a data analysis unit. And through partition design, classified management of the specimens is realized. Urgent specimens are placed on the upper layer and are more conspicuous, workers preferentially process the specimens, and missing detection or delay is reduced. A manual misoperation path is blocked through hardware partition, and compared with a pure label management system, the partition design enables the sample misplacement rate to be greatly reduced. The data analysis unit is used for monitoring the states of the specimens in real time and transmitting the states to the corresponding working modules, differential management is adopted for different specimen placement conditions, the manual conveying cost for specimen inspection is greatly reduced, specimen state tracking is achieved, it is ensured that the specimens are processed preferentially, and the management efficiency is improved.

The invention discloses a pulling and lifting cable frequency detection and cable force measurement method and system based on a millimeter wave radar, and belongs to the field of pulling and lifting cable force measurement. The method comprises the steps that vibration signals are collected through the millimeter wave radar to obtain complex data streams, time-frequency analysis is conducted on the complex data streams, and an interference model is established; the method comprises the following steps: reconstructing a noise-reduced vibration signal by adopting adaptive filtering, extracting candidate vibration frequencies through power spectral density estimation and spectral peak detection, calculating corresponding stability scores, and obtaining a first verification factor used for evaluating wind disturbance and a second verification factor used for evaluating vibration frequency deviation; according to the method, through multi-source sensing data fusion and self-adaptive signal processing, complex environment interference is effectively overcome, non-contact high-precision cable force measurement is achieved, and the method has the outstanding advantages of being high in anti-interference capacity, high in measurement reliability and the like.

A performance testing device for a quartz crystal resonator comprises a testing box, an opening is formed in the top of the testing box, the opening is sealed through an upper cover, a transversely-arranged testing plate is installed in the middle of the interior of the testing box, the testing plate is fixedly connected with the testing box through a plurality of supporting springs, and vibration equipment is installed at the bottom of the testing plate. The top of the test plate is provided with a contact pin used for being matched with a quartz crystal resonator, the contact pin is externally connected with a crystal oscillator detection frequency instrument through a wire, a pressing plate is arranged right above the contact pin, the test plate is provided with a vertically arranged guide rod, the pressing plate is transversely and movably sleeved on the guide rod, the guide rod above the pressing plate is provided with a support plate, and the support plate is arranged on the support plate. A pressing bolt matched with the pressing plate is arranged on the supporting plate in a screwed mode, and a reset spring matched with the pressing plate is further arranged on the guide rod below the pressing plate in a sleeved mode. According to the invention, the actual vibration environment can be accurately simulated, so that the influence of vibration at different temperatures on the performance of the quartz crystal resonator can be detected.

The invention discloses a broadband low-power-consumption receiving and transmitting shared phase shifter which comprises a differential signal common port, a frequency sampling and control circuit, a dual-mode orthogonal generation circuit and a passive vector modulation synthesis circuit. The differential signal common port comprises a common input / output port interface of RF + and RF-differential radio frequency signals; the frequency sampling and control circuit is composed of a frequency sampling circuit, a frequency detection circuit and a logic control unit. The dual-mode orthogonal generation circuit is composed of a frequency selection change-over switch, a multi-phase filter, a coupling transformer, a matching network 1 and a matching network 2. The passive vector modulation synthesis circuit is composed of two passive vector modulation arrays and two combining / power dividing circuits.

The invention discloses a 2-FSK (Frequency Shift Keying) signal frequency amplitude detection method based on an FPGA (Field Programmable Gate Array), which relates to the field of communication systems and comprises the steps of signal acquisition, frequency analysis, amplitude spectrum conversion and amplitude estimation, frequency estimation, data output, signal preprocessing and frequency detection result calibration. In the acquisition step, signals are acquired by an eight-channel synchronous or sequential sampling technology; in the analysis step, frequencies are analyzed by a different-point FFT algorithm; in the conversion step, amplitude spectrums are obtained by processing spectrum conversion; in the estimation step, frequencies are calculated, verification and correction are carried out by a phase difference method; in the preprocessing step, a hardware filter is used to preprocess a signal, and in the calibration step, a detection result is calibrated and optimized according to a calibration formula. The method is high in detection precision, strong in real-time performance, high in detection speed, good in adaptability, low in cost, small in power consumption, beneficial to system integration and miniaturization and wide in application prospect in the field of communication and the like.

The invention discloses a highway geological disaster emergency monitoring system and method, and belongs to the technical field of geological disaster monitoring. The system comprises a main control device, and an earth surface displacement rate detection device, a slope internal displacement detection device, a pore water pressure detection device, an acoustic emission event frequency detection device, a soil pressure increment detection device and a strategy generation device which are electrically connected with the main control device. The system realizes grading evaluation of geological disaster risks through a multi-stage detection mechanism: firstly, detecting earth surface deformation characteristics, and if the earth surface deformation characteristics are abnormal, judging that the earth surface deformation characteristics are first-stage risks and starting deep soil deformation and hydrogeological condition detection; if the indexes are abnormal at the same time, judging that the risk is a secondary risk, and starting a soil body fracture process and stress mutation feature detection; and if the risk is abnormal again, determining that the risk is third-level risk. And the system automatically generates corresponding emergency disposal strategies according to different risk levels. According to the invention, accurate recognition and graded response of highway geological disaster risks are realized, and the monitoring efficiency and the emergency disposal capability are improved.

The invention relates to the technical field of image processing, in particular to an intelligent adaptive aeration management and control method and system based on deep learning, and the method comprises the steps: collecting inlet water flow, inlet water oxygen demand and inlet water ammonia nitrogen value, carrying out the division, obtaining a divided aeration region set, extracting divided aeration regions, obtaining detection data, collecting the detection data, obtaining a detection data set, and carrying out the image collection. The method comprises the steps of obtaining an aeration initial image, calculating actually measured mixed concentration and a regional settlement value, obtaining an actually measured mixed concentration set and a regional settlement value set, calculating initial frequency, calculating optimal frequency based on the initial frequency, a detection data set, the actually measured mixed concentration set and the regional settlement value set, and completing intelligent adaptive aeration control according to the optimal frequency. According to the invention, the aeration stability can be improved.

The present invention relates to the field of communication and sensing, and specifically to a communication and sensing integrated device based on a seven-core optical fiber interferometer and an application method therefor. The application method comprises: a first beam splitter splitting, into reference light and signal light, a light source emitted by a narrow linewidth laser; performing modulation by means of a first coherent optical transmitter and a second coherent optical transmitter; amplifying the light by means of a first amplifier and a second amplifier, and then enabling the light to pass through a multi-core optical fiber from a fan-in end, and to output from a fan-out end; performing beam splitting by means of a second beam splitter and a third beam splitter, and performing demodulation by means of a first coherent receiver and a second coherent receiver; and finally, a phase-locked loop performing interference elimination on preliminarily-demodulated reference light and preliminarily-demodulated signal light to obtain finally-demodulated reference light and finally-demodulated signal light. The present invention uses a phase-locked loop to further eliminate disturbance noise of reference light and signal light. Therefore, noise interference is eliminated in continuous low-frequency detection, thereby improving the communication and sensing effect.