1216results about "Material capacitance" patented technology

The invention discloses a farmland environment multi-parameter intelligent monitoring system and method based on Internet of Things, particularly relates to the technical field of agricultural Internet of Things monitoring, and is used for solving the problem of stress resistance evaluation distortion caused by neglecting crop physiological recovery hysteresis in the prior art. Collecting soil water content data in real time through an Internet of Things sensor, and extracting a soil water content change rate when a preset threshold value is reached; secondly, constructing a space-time gradient matrix based on different soil layer water deficit rates, and generating a stress cumulative intensity index through principal component analysis and information entropy fusion; then analyzing a coupling relationship between the soil moisture change rate and the stress accumulation effect in the historical stress event on physiological recovery delay to generate a recovery delay coefficient; the physiological state lag degree is evaluated by combining the stomatal conductance change rate ratio of the stress period and the recovery period and the recovery delay coefficient; and finally, dynamically prolonging the irrigation execution time according to the lag degree until the irrigation execution time is lower than a set tolerance value, thereby remarkably improving the reliability of crop stress resistance test data.

The invention provides an abnormal blood sampling inspection data evaluation processing method and system of a blood sampling system. Wherein the diffusion track dynamic state of an anticoagulant-blood contact surface in a blood sampling tube is tracked in real time, dielectric response waveform distortion characteristics on the two sides are synchronously collected through a capacitance sensor array, and diffusion form parameters are generated; curvature sudden change is analyzed to trigger an acoustic fluid device to modulate audio frequency and flow velocity, and a composite disturbance field is formed; performing capacitance axial scanning on the disturbed mixed fluid, extracting phase angle offset and gradient abrupt change point coordinates, and constructing a dielectric anomaly three-dimensional map; matching a standard dielectric model, positioning a coordinate set of a concentration gradient imbalance area and a coordinate set of an eddy current attenuation area, and generating an anticoagulant abnormal index and a mixed defect matrix in combination with phase angle offset difference; and establishing a data anomaly degree evaluation function based on the anomaly index and the defect matrix, dynamically associating a clinical error threshold, and outputting a test evaluation result. According to the invention, anticoagulant diffusion can be monitored in real time, and the mixing quality error can be accurately evaluated.

The invention discloses a tower grounding grid multi-frequency composite wave electric field imaging and soil environment medium diagnosis method, which comprises the following steps of: generating a composite excitation signal through superposition of a low-frequency sine wave and a high-frequency CHIRP square wave, and collecting multi-dimensional data such as voltage / current response, soil temperature and humidity, electric field intensity and the like in combination with a distributed sensor array; separating signal features by using wavelet transform and extracting impedance information of a target frequency point; a multi-grid homotopy algorithm is adopted to realize grounding grid three-dimensional resistivity distribution reconstruction, corrosion spots and abnormal areas are marked, and high-precision positioning of underground conductors is completed through UWB radar or a capacitance detector; meanwhile, based on the information entropy maximization optimization frequency band combination, the grounding grid state is comprehensively evaluated in combination with a multi-parameter fusion model. The method has the non-contact patrol capability, the unmanned aerial vehicle carries equipment to scan the grounding grid, the detection efficiency and convenience are remarkably improved, and an efficient and accurate solution is provided for guaranteeing safe operation of the power grid.

According to one embodiment, a sensor includes a structure. The structure includes a first layer, a first film including a first film region, and a first intermediate layer including a first partial region. The first partial region is between the first layer and the first film region. A volume of the first layer is changeable depending on a detection target around the structure. The first film includes silicon and oxygen. The first intermediate layer includes at least one selected from the group consisting of Pd, Pt and Ti.

The invention provides a farmland soil multi-parameter real-time monitoring system and method based on the Internet of Things. The system comprises a sensor module, a sensor collaborative scheduling module, a data transmission module, a data processing and storage module, an intelligent decision module, a user interaction module and a power supply module. Soil monitoring data, meteorological satellite information and historical yield records are fused, deep learning, a decision tree and a nutrient balance algorithm are combined, an accurate fertilization and irrigation scheme is generated, maintenance decision optimization is achieved, the water and fertilizer utilization efficiency is improved, and resource waste caused by extensive management is reduced; through dynamic sampling, data cross validation, sensor fault self-diagnosis and multi-source data fusion, the system can make a decision, effectively solves the defects of an existing system in the aspects of monitoring precision, data reliability, equipment endurance and decision refinement, and provides an efficient and reliable monitoring and management scheme for precision agriculture.

The invention discloses a switchable graded sampling pre-construction control method for dust and a detection device. The method comprises the following steps: synchronously acquiring data through multiple types of sensors, constructing a condensation risk prediction model, calculating a condensation risk index RI and dividing risk grades; based on the risk level, dynamically adjusting the target power of the heating wire, the blowback period of the air pump and the airflow speed, and cooperatively triggering an electrode cleaning and trapping filter screen switching program; after cleaning is completed, follow-up control parameters are dynamically adjusted; and closed-loop control is formed. According to the method, the temperature and humidity sensor is arranged in the external environment to obtain background parameters, the airflow velocity sensor is used for sensing the working condition change of the air pump, and a dynamic condensation risk prediction model is established, so that excessive heating is avoided fundamentally, and evaporation loss of semi-volatile substances is prevented; therefore, the accuracy of a dust concentration detection result is remarkably improved in an extreme humidity environment.

The invention discloses a cable insulation moisture content estimation method, system and equipment based on relaxation frequency distribution and a medium, and relates to the technical field of cable insulation moisture content estimation.The cable insulation moisture content estimation method based on relaxation frequency distribution is characterized by comprising the steps that a cable sample is prepared; obtaining a relaxation current sequence by adopting a dielectric response analysis method, calculating relaxation current data by adopting an integral equation theory to obtain a relaxation frequency distribution function, and extracting characteristic parameters; establishing a moisture content estimation empirical formula, and establishing a moisture content estimation model; and estimating the moisture content of the cable sample according to the moisture content estimation model to obtain an estimation result. According to the invention, the cable does not need to be cut or disassembled or a special device does not need to be used for complex testing, and the method is suitable for monitoring a large-range power network. The method has high adaptability and can be widely applied to different scenes of a power system.

The invention provides a Laplace-DLTS stabilization inversion method and system, and the method comprises the steps: applying a pulse bias voltage to a tested semiconductor device, and collecting a transient capacitance signal of the tested semiconductor device; the transient capacitance signal is preprocessed; performing inverse Laplacian transformation processing on the preprocessed transient capacitance signal, and introducing at least one of non-negative constraint, sparsity constraint, physical prior constraint and time window segmentation constraint in an inversion process; carrying out optimization processing on the obtained inversion result, and judging the stability of the inversion result; and extracting a deep energy level trap parameter based on the inversion result, and outputting the deep energy level trap parameter and the processed transient spectrum data. At least one, preferably at least two, constraint conditions are introduced in the inverse Laplacian inversion process, so that the influence of noise and abnormal peaks on the inversion result is effectively inhibited, and the stability, physical consistency and parameter extraction reliability of the inversion are improved.

The invention relates to a deep energy level transient spectrum data processing method based on dictionary learning. The method comprises the following steps: acquiring transient capacitance signals of a sample at different temperatures by using deep energy level transient spectrum equipment; establishing an Arrhenius equation that the defect emissivity changes along with the temperature; the defect emissivity under each group of values is calculated according to all possible values of the coefficient of the exhaustion Arrhenius equation; constructing a dictionary matrix based on the defect emissivity amplitude introducing Arrhenius constraint, and reconstructing a transient capacitance signal by using the dictionary matrix; and constructing a convex optimization problem, solving a dictionary index variable which enables a reconstruction signal of the transient capacitor to be closest to an acquisition signal, and extracting a non-zero solution in the dictionary index variable to obtain a corresponding defect emissivity amplitude spectrum. According to the method, the detection precision of the deep energy level defect can be improved by introducing the Arrhenius constraint relation.

The invention discloses a rock-fill concrete pouring liquid level and compactness monitoring and evaluating method, and relates to the technical field of concrete construction. According to the invention, the compactness sensing probes are uniformly distributed in the warehouse, so that the whole warehouse surface can be comprehensively monitored. The probes are wrapped by high-strength and high-toughness materials, it is ensured that the probes cannot be damaged in the construction process, a three-dimensional Thiessen polygon method is adopted for segmenting the areas in the bin, each compactness sensing probe corresponds to one associated area, the segmentation method can adapt to irregular polygon boundaries, it is ensured that only one representative measuring point exists in each area, and the accuracy of the detection result is improved. Whether the probes are buried by the concrete or densely filled can be judged in real time through the numerical value change of the compactness sensing probes, and the liquid level position of the self-compacting concrete can be calculated according to the states of all the probes.

Methods, systems, and computer program products for implementing a soil analysis process. A set of soil data may be received at a device from one or more soil sensors. The one or more soil sensors may be coupled to a portion of an agricultural machine configured to be in contact with soil. The device may be configured to analyze soil when a portion of the one or more soil sensors is in contact with the soil. Spectral data and soil conductivity and / or capacitance data may be obtained based on the collected set of soil data. Analyzed soil data may be determined based on the spectral data and soil conductivity and / or capacitance data. The analyzed soil data may be provided to a user device configured to display a visual representation of at least part of the analyzed soil data.

The invention provides a virtual measurement method and device, and belongs to the technical field of semiconductor manufacturing. The method comprises the steps that surface topography parameters of M1 wafers in a wafer set are measured, a first measurement result of the M1 wafers is obtained, the wafer set comprises N continuous wafers in the same crystal bar, and the M1 wafers are wafers distributed on the crystal bar at intervals in the N wafers; the first measurement result is input to a pre-trained virtual measurement model, a second measurement result of M2 wafers is obtained, and the M2 wafers are wafers except the M1 wafers in the N wafers; and combining the first measurement result and the second measurement result to obtain a measurement result of the wafer set. And the accuracy of the measurement result is improved while the productivity resources are released.

The invention belongs to the technical field of constructional engineering detection, and discloses a building exterior wall water seepage measurement method and system.The method comprises the steps that humidity sensor arrays are arranged on the surface and inside an exterior wall, multi-point humidity original data are collected, abnormity correction and noise reduction are conducted, and a complete preprocessing data set is obtained; inputting the data into a diffusion modeling module, carrying out time sequence analysis and spatial distribution estimation, generating a water seepage diffusion characteristic matrix and extracting key diffusion parameters, calculating a predicted value in a penetration depth estimation model based on a parameter set, constructing a water seepage distribution curve, calling a risk judgment module, and comparing a predicted result with a safety threshold. And outputting the risk level. The system comprises a data acquisition and preprocessing module, a diffusion modeling and feature extraction module and a penetration estimation and risk assessment module. According to the invention, dynamic monitoring and grading risk assessment of external wall water seepage can be realized, and a reliable basis is provided for building maintenance and safety management.

Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

The invention discloses a two-phase cold plate liquid cooling dryness real-time measurement method and system based on data fusion, and relates to the technical field of liquid cooling data processing, and the method comprises the following steps: S1, collecting multi-mode liquid cooling working condition data in real time, carrying out the data preprocessing, carrying out the fusion of original dryness according to three types of dryness signals, and evaluating the health degree of signal fusion; s2, dynamically distributing adaptive signal weights for each type of dryness signals to obtain high-precision fusion dryness, and performing anomaly diagnosis; s3, the high-precision fusion dryness is optimized, flow adjustment is carried out, meanwhile, dead zone compensation of flow adjustment is carried out, and flow compensation correction is carried out; and S4, monitoring a flow regulation process and abnormal information in real time, and performing high-precision visual feedback and operation and maintenance optimization of the fusion dryness. The problems that an existing two-phase cold plate liquid cooling system generally adopts a single dryness detection mode and is prone to being interfered by the environment and lagging in response, so that the dryness measurement precision is insufficient, and the reliability and the heat dissipation efficiency of cooling capacity adjustment are affected are solved.

The invention relates to the technical field of flexible sensors, in particular to a wireless passive multi-mode flexible LC sensor and a manufacturing method and application thereof. The invention provides a wireless passive multi-mode flexible LC sensor. The wireless passive multi-mode flexible LC sensor comprises a flexible substrate, an LC resonant sensing array arranged on the surface of the flexible substrate and a reading coil used for multi-mode signal wireless receiving. Wherein the LC resonant sensing array comprises LC resonant humidity, gas and pressure sensing units which can work independently; and the capacitive humidity, gas and pressure sensitive layers are respectively arranged on the capacitive end interdigital electrode of each sensing unit. The sensor provided by the invention is based on the LC resonant sensing array, can realize multi-channel signal synchronous detection and decoupling of pressure, humidity and gas, has the characteristics of no external power supply, wireless data transmission, ultrahigh sensitivity and multi-parameter independent identification, remarkably improves the sensing capability of wearable equipment in a complex environment, and is suitable for popularization and application. The method has high sensitivity and good repeatability.

The invention relates to the technical field of lubricating oil quality monitoring, in particular to a lubricating oil quality on-line monitoring system and method, and the system comprises a data collection module which collects lubricating oil parameters in real time through a sensor array; the communication transmission module is used for constructing a data flow network and supporting multi-level interaction; the data analysis and diagnosis module is used for realizing state evaluation and fault early warning; the user interaction and visualization module provides a human-computer interaction interface and decision support; and the intelligent decision module realizes monitoring-maintenance closed-loop control, gives an alarm in time and realizes full-link intelligent control triggered by a maintenance action. According to the method, the prediction precision and the early warning capability of the degradation trend of the oil system are effectively improved through the improved autoregression moving average model, the real-time state of the oil is accurately reflected by dynamically adjusting the alarm threshold value, and the fault positioning precision is greatly improved through the space matching algorithm.

The invention relates to the technical field of tea processing, in particular to an intelligent processing device for tea. The device comprises a frame body, a fermentation box and a spraying mechanism installed on the frame body, a bottom plate is arranged in the fermentation box, a fermentation cavity is formed by the bottom plate and the upper end of an inner cavity of the fermentation box, two vertical partition plates are arranged in the fermentation cavity, and the fermentation cavity is divided into a main fermentation cavity and two sub-cavities, and the bottoms of the main fermentation cavity and the sub-cavities are communicated. According to the tea leaf humidifying device, when the humidity sensor detects that the humidity of the tea leaves at the bottom of the main fermentation cavity is insufficient, the push plate is synchronously started again to push the tea leaves at the bottom to the branch cavities on the two sides, and targeted humidification is conducted in cooperation with the atomization spraying heads corresponding to the branch cavities in position; the tea leaves relative to the upper layer in the main fermentation cavity naturally fall into the vacant area of the lower layer, and along with the circulation of humidity detection and the action of the push plate, the tea leaves can complete the position exchange of the upper layer and the lower layer in the dynamic circulation of the main fermentation cavity and the sub-cavities.

The invention relates to the technical field of circuit board detection, in particular to a printed circuit board drilling quality detection method and device, and the method comprises the steps: injecting sinusoidal current signals with preset frequencies into a plurality of test points of a to-be-detected drilling region of a printed circuit board, extracting impedance values under each frequency component, and calculating the drilling quality of the printed circuit board; constructing an equivalent impedance spectrum containing a real part and an imaginary part according to the impedance value; according to the interlayer structure and the equivalent impedance spectrum of the printed circuit board, calculating equivalent resistance and equivalent capacitance of the drilling area to be measured to form a characteristic parameter set; comparing the data of each grid point in the characteristic parameter set with a preset threshold value, and integrating the grid points exceeding the preset threshold value to generate a defect state mark; and performing electrical topology constraint on the defect state mark to generate a defect positioning result. According to the invention, hidden defects in the drill hole and interlayer structures can be deeply detected, the electrical performance of the drill hole can be comprehensively evaluated, and accurate defect positioning and state marking can be provided.

A measuring system for characterizing a multilayered structure with layer-by-layer different ohmic properties, having a sensor module, an electrical switching device, and a measuring electronics unit. The sensor module has more than two planar coils arranged in a stack, and the electrical switching device is contacted with the planar coils and is configured to connect these coils to the measuring electronics unit such that the coils are switchable between a measuring configuration and a reference configuration. In the measuring configuration, the planar coils at least partially form an optionally inductive or capacitive measuring sensor, with the detection area extending outside the sensor module for determining a property of a low and / or high-resistance layer of the structure. In the reference configuration, the planar coils at least partially form an optionally inductive or capacitive reference sensor, with the detection area extending essentially inside the sensor module for determining a sensor module property.

The invention discloses a method for detecting and analyzing the content of liquid flavor substances in a beverage, and belongs to the technical field of beverage quality detection. The method comprises the following steps: obtaining a flavor substance dielectric response signal and a surface acoustic wave propagation signal of a beverage sample, and carrying out correlation mapping integration to form a structured original data set; preprocessing data by adopting ensemble empirical mode decomposition, integrating features through a Bayesian network multi-source data fusion algorithm, and constructing an optimized content calculation model based on a gradient boosting tree algorithm; performing time sequence consistency and dimension matching verification on the data set, and inputting the data set into a model to generate a prediction result; and extracting a related content value, carrying out deviation analysis on the related content value and a preset standard range, calculating a signal feature contribution weight, and outputting a detection report containing standard judgment and deviation traceability. According to the method, the physicochemical characteristics of the flavor substances can be comprehensively represented, accurate prediction and deviation traceability are realized, the comprehensiveness, reliability and efficiency of detection are improved, and powerful support is provided for beverage quality control.

The invention discloses a fiber composite material based on hydrogen bond self-repairing and conductive self-diagnosis and a preparation method of the fiber composite material, and belongs to the technical field of intelligent diagnosis and self-repairing fiber reinforced resin-based composite materials and preparation of the intelligent diagnosis and self-repairing fiber reinforced resin-based composite materials. According to the method, the problem that the self-healing effect cannot be timely obtained on the internal damage of the material due to the fact that the damage area cannot be accurately positioned and identified before the damage of the existing fiber composite material is repaired is solved. According to the invention, a quantum tunnel effect and a three-dimensional conductive skeleton are constructed by using a micro-nano multi-stage conductive filler composed of a nano carbon nanotube, micron flake graphite powder and unidirectional short carbon fiber, a damage occurrence position is realized based on an electro-mechanical corresponding relation, a self-sensing function is given, and accurate positioning of the damage is realized (the error is less than 1mm). A multiple reversible hydrogen bond system formed by acrylamide, polyethyleneimine and epoxy resin is further utilized, the self-repairing effect of the composite fiber material is achieved, and the test result shows that the self-repairing efficiency of at least 90% or above is achieved at the normal temperature.

The invention discloses a detection method for rapid real-time drug effect evaluation and application of the detection method. The detection method comprises the following steps: providing a quantitative corresponding relation between a same-generation calibration change value of electrical characteristics caused by physiological changes such as cell same-representative characteristics and the like and the cell proliferation ability; adhering to-be-detected cells to the surface of the same membrane electrode; applying the medicine to be detected, and observing the same generation measurement change value of the electrical characteristics in a cell proliferation cycle; and based on the quantitative corresponding relationship, obtaining the drug effect of the corresponding real-time proliferation characteristic evaluation drug. According to the detection method provided by the invention, the physiological changes such as the same-representative characteristics of the adherent cells are monitored through the membrane electrode, and after the quantitative corresponding relation between the change indexes of the same-representative characteristics of the cells and the proliferation capacity of the cells is established, the cell proliferation capacity is detected; the change of the cell proliferation capacity can be predicted through physiological changes such as the same representative characteristics of the cells before proliferation and apoptosis of the cells, so that the judgment period of the influence of the medicine on the cells can be greatly shortened, and the progress of medicine evaluation and research and development can be remarkably accelerated.

The invention discloses an asphalt aging degree nondestructive testing method and system, and belongs to the technical field of road material detection. The method comprises the following steps: firstly, preparing standard asphalt samples with different aging degrees, calibrating a real aging degree in combination with a destructive test, measuring broadband dielectric spectrums at a plurality of temperature points, and constructing a multi-temperature-range standard dielectric spectrum-aging degree database; comprehensive dielectric characteristic parameters containing fixed-point dielectric characteristics at a specific temperature point and evolution dielectric characteristics changing along with temperature are extracted from the database; training an artificial neural network model by using the characteristic parameters, establishing a nonlinear mapping relation with the aging degree, and forming an aging degree prediction model; and finally, performing lossless multi-temperature-range dielectric detection on a to-be-detected asphalt sample, extracting the same characteristics from the obtained dielectric data, inputting the same characteristics into the prediction model, and calculating the aging degree.

The invention discloses a multi-station cooperative efficient production process for carbon fiber pipe automatic weaving, and the process comprises the following modules: a multi-dimensional tension monitoring module which is used for deploying a fiber grating sensor array along a fiber path, and collecting the axial / transverse tension data of a fiber bundle in real time; the environment compensation module integrates a temperature and humidity sensor and an air pressure monitoring device, and constructs an environment disturbance model according to the following specific formula: Finv = 0.05 deltaT + 0.03 deltaH + 0.02 deltaP; the dynamic regulation and control actuator adopts a micro-displacement platform driven by piezoelectric ceramics; and the intelligent decision control module is used for controlling the dynamic regulation and control actuator to carry out tension control by obtaining the measurement data of the environment compensation module and the monitoring data of the multi-dimensional tension monitoring module, and the control formula is G = 0.2 mu m / N. Through four innovations of multi-dimensional perception, environment compensation, high-speed execution and intelligent decision making, precision and self-adaption of carbon fiber tension control are achieved, and the method is particularly suitable for carbon fiber pipe production work with strict mechanical property requirements.

Technologies directed to smart farming are described. A multimodal soil sensor device includes an elongated housing having first and second portions. An antenna is located in or above the first portion and coupled to a wireless communications component. The wireless communications component causes the antenna to radiate or receive electromagnetic energy to communicate with a second device. The multimodal soil sensor device includes one or more multimodal soil sensors to measure one or more first measurements of a first sensing modality and one or more second measurements of a second sensing modality different than the first sensing modality. The wireless communications component wirelessly sends measurement data, including the first measurements and the second measurements, to the second device via the antenna.

A measuring instrument according to an exemplary embodiment includes a base substrate having a disk shape, a plurality of first sensors arranged along a peripheral edge of the base substrate, a circuit substrate fixed on the base substrate, and a cover fixed to the circuit substrate or the base substrate to cover the top of the circuit substrate. The plurality of first sensors measure capacitance between the plurality of first sensors and a first object disposed beside the base substrate. An expansion rate of the base substrate is smaller than an expansion rate of the circuit substrate. The expansion rate of the circuit substrate is smaller than an expansion rate of the cover.

The invention relates to the technical field of testing equipment, and particularly discloses an insulating oil dielectric strength testing device which comprises a dustproof detection box, one side of the dustproof detection box penetrates through and is fixedly connected with an observation window, the top of the dustproof detection box is fixedly connected with a dehydration device, and the bottom of the inner wall of the dustproof detection box is fixedly connected with a first supporting seat. The bottom of the testing oil cup is communicated with an oil outlet pipe, the bottom of the testing oil cup penetrates through and is rotationally connected with a cleaning device, the parts, located on the two sides of the testing oil cup, of the bottom of the inner wall of the dustproof detection box are fixedly connected with impurity removing devices, and the parts, located on the two sides of the testing oil cup, of the bottom of the inner wall of the dustproof detection box are fixedly connected with distance adjusting devices; the dielectric strength testing device for the insulating oil is provided with the dehydration device, so that the dielectric strength of the insulating oil is prevented from being reduced due to the existence of water, the accuracy of a test result is improved, and the impurity removal device is arranged, so that the test result is prevented from being influenced by impurities or generated foam in the insulating oil.

The invention belongs to the technical field of non-metallic material nondestructive testing, and particularly relates to a method for evaluating the defect detection capability of a coplanar capacitive sensor. According to the defect detection capability evaluation method, defects under different buried depths and sizes are scanned, the distortion rate of a detection signal is calculated, and a preset threshold value is given to judge whether the defects are detected or not; and then a visual chart is drawn, and evaluation of the defect detection limit capability of the coplanar capacitive sensor is realized. The invention discloses a coplanar capacitive sensor defect detection capability evaluation method. The method comprises the following steps: determining a positive sensitivity region, a negative sensitivity region and a zero sensitivity region of a coplanar capacitive sensor; constructing to obtain a non-metallic material defect test group; establishing an electrostatic field simulation model; solving a distortion rate D of the detection signal; drawing a relation curve of detection signal distortion rates D under different burial depth positions and different defect sizes; giving a preset threshold value A, and recording a comparison result; and drawing a comparison result in a visual chart.

The invention relates to the technical field of quality detection, and provides a method for detecting the quality of an aminobutyric acid extract in peanut sprouts, which comprises the following steps: performing specific recognition on aminobutyric acid by a molecular imprinting sensor, and calculating the impurity contribution by combining a preset peanut sprout interference database and partial least square regression; the interference of complex matrix impurities in the peanut bud extract can be specifically eliminated, and the problem that the specificity is insufficient when a traditional method is used for detecting a complex sample is solved; the molecularly imprinted sensor and the quartz crystal microbalance are connected in series through the microfluidic channel, and resonance frequency detection is triggered by using capacitance variation, so that a coherent process is formed from identification to quality analysis of sample detection, and the detection efficiency is improved; the corrected frequency offset, the phase angle and the temperature are input into the recurrent neural network, accurate output of the concentration of the aminobutyric acid is achieved by integrating multi-dimensional detection information, compared with a single parameter detection method, the real content of the aminobutyric acid in an extract can be reflected more comprehensively, and a reliable basis is provided for quality judgment.