601 results about "Baseline data" patented technology

The invention provides a lower limb and gravity center dynamic balance posture early warning method based on a body measurement all-in-one machine, and the method comprises the steps: obtaining a pelvis forward inclination angle, an arch height and a gravity center position under a standard standing posture, taking the pelvis forward inclination angle, the arch height and the gravity center position as individualized baseline data, and carrying out the statistical analysis of the baseline data, and generating a dynamic balance reference value reflecting the normal balance capability of an individual; on the basis of the dynamic balance reference value, in combination with a gravity center forward trend and a lower limb muscle compensation mode, a balance offset index in standing posture change is calculated through a time sequence analysis algorithm, and a dynamic balance risk assessment result is obtained; and if a dynamic balance risk assessment result exceeds a danger threshold, clustering analysis is carried out on associated parameters of the pelvic forward inclination angle and the arch height, grouping processing is carried out on the associated parameters, and potential reasons causing balance capability degradation are identified.

The invention relates to the technical field of bolt tightening, and provides an intelligent bolt tightening control method and system based on axial force measurement. Comprising the steps of collecting bolt identification and process parameter data, collecting temperature measurement data, and obtaining sound velocity compensation parameters and elastic modulus compensation parameters. And zero-load preloading data are collected and processed to generate zero-load baseline data, and a self-calibration model is obtained. Axial force data and ultrasonic echo data are collected, and an axial force fusion estimation result is obtained. And comparing an axial force fusion estimation result with target axial force data. And when in the target interval, fine twisting control is carried out based on model predictive control and a micro-stepping strategy, and the controllable damping unit is driven to carry out energy absorption and stopping. And acquiring an axial force fusion estimation result when the target axial force is reached, processing the data in the holding stage to obtain an axial force rebound check result, and if the axial force rebound check result exceeds the limit, performing secondary twisting control. According to the scheme, refinement and traceability of the bolt tightening process are achieved.

The invention relates to a nurse handover management system and method, and relates to the technical field of data processing, and the system comprises a real-time monitoring module which is used for constructing an individualized baseline database of a patient; the data integration module is used for outputting a multi-dimensional structured data set; the intelligent analysis engine is used for identifying clinical state change information of the patient and generating a corresponding risk early warning score; the decision support module is used for generating targeted nursing intervention suggestions; the interactive shift exchange platform is used for generating a structured shift exchange abstract based on the risk early warning score, the clinical change information and the nursing intervention suggestion, and dynamically displaying the structured shift exchange abstract through a mixed reality technology; and the closed-loop optimization module is used for receiving shift-change feedback information and subsequent execution data of the shift-change party, evaluating the completeness of shift-change information, updating a nursing plan, and optimizing and adjusting analysis and recommendation parameters of the system. The system can improve the accuracy and efficiency of nurse handover management.

The invention discloses an aero-engine group health evaluation method based on multi-working-condition dynamic clustering, and belongs to the field of aero-engine health state evaluation. The method comprises the following steps: firstly, carrying out clustering analysis on set parameters in engine operation data, and carrying out merging processing on small-scale abnormal clusters to obtain a working condition category division result; secondly, constructing a health baseline data set, carrying out standardized preprocessing on sample data in a working condition category division result, and carrying out nonlinear dimensionality reduction to obtain a low-dimensional feature data set; thirdly, performing clustering analysis on the low-dimensional feature data set by adopting a Gaussian mixture model, and calculating an average mahalanobis distance between a sample of each clustering category and a health reference center to obtain multi-level health levels corresponding to different clustering categories; and finally, through fusing the membership soft probability and the sample individual mahalanobis distance, constructing a continuous health score and obtaining a health grade determination interval. According to the method, health state characteristics under different working conditions can be effectively identified, individual difference modeling and group transverse comparison evaluation are supported, and the accuracy is improved.

The invention relates to the technical field of biomechanical measurement and analysis, in particular to a home textile recommendation method and system based on child health data monitoring and analysis. The method comprises the following steps: collecting an original point cloud coordinate set of a sleeping posture of a child; extracting a cervical curvature parameter group according to the extracted cervical curvature parameter group of the skeleton key point spatial distribution diagram; performing cervical vertebra and body position analysis according to the cervical vertebra curvature parameter group to obtain cervical vertebra curvature-body position holographic data; collecting a static pressure baseline data set according to the cervical curvature-body position holographic data; performing body position-pressure correlation analysis according to the static pressure baseline data set to obtain a body position-pressure mapping relation table; and generating a pressure distribution index map according to the body position-pressure mapping relation table. According to the method, the physical sign data, the pressure distribution and the physiological curvature change of the children in the natural sleep state are comprehensively collected and analyzed, so that high-precision, whole-process and personalized healthy home textile recommendation is realized.

The invention relates to the technical field of electrical fault detection, in particular to a mobile power supply fault detection method and system based on intelligent diagnosis, and the method comprises the following steps: collecting voltage and current data of a mobile power supply in a normal operation state, and generating an electrical performance baseline data set through recording the timestamp of the data and a corresponding electrical performance index; and based on the electrical performance baseline data set, extracting time sequence features of voltage and current to obtain electrical performance feature data. According to the invention, based on voltage and current data acquisition in a normal state of the mobile power supply, a baseline data set covering timestamps and electrical performance indexes is established, and detail information of electrical performance changes is captured by extracting time sequence features such as peak values, mean values and standard deviations; meanwhile, frequency spectrum features are accurately extracted by means of fast Fourier transform, the intensity and phase information of frequency components is mastered, then signals are decomposed through wavelet transform, and richer electrical performance anomaly information under multiple scales is obtained.

The embodiment of the invention discloses an equipment health assessment method and device based on multi-dimensional data fusion and dynamic weight, and relates to an intelligent monitoring and health assessment technology of an equipment complex system in the field of equipment health management. The method comprises the following steps: describing the change condition of health parameters along with the increase of equipment storage life from the angles of the regression trend of the health parameters along with time, the incidence relation among different health parameters and the probability distribution of health parameter data, and accurately reflecting the difference between health parameter monitoring data and baseline data. A dynamic weight updating method in the whole storage process is provided, and the weights of different health parameters in the process of calculating the health indexes are scientifically calculated. Therefore, the problem that health parameter monitoring data are scarce due to the fact that the equipment cannot be frequently powered on for testing in the actual storage and use process is effectively solved, and the assessment accuracy of the health state of the equipment is improved under the condition that the monitoring data are limited.

Systems, methods, and apparatus are provided for automated identification of baseline data and changes in state in a wireless communications spectrum, by identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and determining information about the baseline data and changes in state based upon the measured and analyzed data in near real time, which is stored on each apparatus or device and / or on a remote server computer that aggregates data from each apparatus or device.

The invention discloses a land utilization dynamic change monitoring method and system based on multi-modal remote sensing data, and relates to the technical field of multi-modal remote sensing data analysis. The system comprises a data acquisition and preprocessing module, a consistency baseline construction module, a real-time data analysis module and an abnormality diagnosis and processing module. The data acquisition and preprocessing module realizes space-time alignment of multi-source data; the consistency baseline construction module divides land utilization types based on geographic features and establishes a baseline database; the real-time data analysis module screens suspected abnormal areas through difference indexes; the abnormity diagnosis and processing module distinguishes data quality abnormity and emergency abnormity based on the characteristic association relation, the data quality abnormity triggers data restoration and verification, and the emergency abnormity marks a high-priority area and outputs position information. According to the system, through multi-modal data fusion and correlation analysis, high-precision monitoring and abnormal intelligent classification of land utilization changes are realized.

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

The invention provides a multi-source network equipment fault mode identification and positioning method and system, and relates to the technical field of computer fault diagnosis, and the method comprises the steps: collecting equipment hardware data and software data, generating a fault feature vector, collecting adjacent equipment information, and constructing a communication relation graph and a link abnormity probability distribution graph; determining fault source equipment and probability through correlation analysis; according to the method, the processing instruction is generated, and the baseline data is updated, so that the computer network fault can be accurately identified and quickly positioned, the fault processing efficiency is improved, and the network maintenance cost is reduced.

The invention discloses an underground pipeline three-dimensional deformation quantitative inspection method, which comprises the following steps: processing a multi-source sensing sequence to generate a baseline data packet; the method comprises the following steps of: performing first-stage on-line correction, acquiring a medium state agent quantity by applying micro-excitation sampling combined by a micro-amplitude angle and double power to laser, and performing joint inversion to obtain section geometric data subjected to medium correction by utilizing a forward model with endogenous refraction and scattering correction items; second-stage scale robustness processing is carried out, in a unified optimization framework, motor current-based dynamic consistency constraint, geometric conservation constraint and wellhead external reference strong constraint are fused, and a globally consistent pose track is solved; and performing three-dimensional reconstruction and deformation quantification according to the corrected section data and the pose track. According to the invention, the accuracy of geometric measurement of the pipeline and the global consistency of the pose track in a complex environment such as a humid environment can be improved.

The invention relates to a wind power plant current collection line fault early warning method, system and device and a medium, and belongs to the technical field of line fault detection of intelligent power grids, and the method comprises the steps: dividing a 35KV current collection line of a wind power plant into a plurality of sections; respectively judging whether each section has a fault or not; if yes, determining the section as a fault section; if not, performing dynamic deviation calculation on the current environment data of the section and the environment baseline data to obtain a dynamic environment feature vector; calculating a dust retention prediction growth rate and a dust retention evaluation index; determining the section with the dust retention evaluation index exceeding a preset threshold value as a dust retention risk section; and fusing the multi-dimensional feature vectors of the dust accumulation risk section, generating a comprehensive fault feature vector, performing hidden arc fault prediction, obtaining a hidden arc fault risk probability, determining a risk early warning level, and further generating risk early warning information and a risk maintenance strategy. According to the invention, rapid positioning of dominant faults and accurate prediction of hidden faults of the current collection line can be realized.

The invention discloses a pneumatic joint sealing performance intelligent test method and system based on dynamic pressure spectrum and multi-parameter fusion, and belongs to the technical field of pneumatic element sealing detection. According to the method, a dynamic pressure spectrum simulating an actual working condition is applied to a tested joint, pressure, temperature and ultrasonic signals are synchronously acquired, environmental thermodynamic interference is eliminated by adopting a temperature real-time compensation algorithm, and pressure change purely caused by leakage is separated by deducting system baseline data; and finally, fusing double characteristic parameters of the pressure leakage rate and the ultrasonic energy, and comprehensively evaluating the sealing performance through an intelligent judgment model. The system comprises a gas circuit tool module, a multi-sensing module and a control processing module, can effectively solve the problems that a traditional static pressure maintaining method is greatly influenced by temperature, transient leakage cannot be detected and the like, has the advantages of being high in detection precision, high in anti-interference capability, capable of simulating real working conditions and the like, and remarkably improves the test reliability and efficiency.

The invention discloses a container anomaly detection method, electronic equipment and a storage medium. Acquiring a system call log, network flow data and a resource utilization rate in the container, and comparing the system call log, the network flow data and the resource utilization rate in the container with respective baseline data to obtain abnormal behavior alarm data; performing time-space alignment on the abnormal behavior alarm data and the process tree topological structure, and calculating an edge weight between process nodes by analyzing a mapping relationship between the abnormal behavior alarm data after time alignment and the process nodes in the process tree topological structure based on a triggering condition probability; a weighted causal graph is constructed through the mapping relation between the abnormal behavior alarm data and process nodes in the process tree topological structure and the edge weight between the process nodes; a root cause node with in-degree of 0 is extracted from a weighted causal graph, a root cause propagation path is backtracked along a causal edge, multi-modal data can be fused, the false alarm rate is reduced, and evolution of attack behaviors is reflected in real time.

The invention discloses a new energy charging pile remote control method considering a battery health state. The method comprises the following steps of obtaining battery full life cycle data, charging pile real-time working condition data and charging area power grid dynamic data, and performing preprocessing to generate battery health baseline data, pile body operation reference data and power grid dispatching reference data; extracting current health state parameters of the battery based on the health baseline data of the battery, calculating a charging adaptation coefficient in combination with the operation reference data of the pile body, analyzing the redundant capacity of the power grid, and constructing a charging control decision model; outputting an initial charging parameter combination based on the charging control decision model, performing health protection correction, and generating a basic remote control instruction; and issuing the basic remote control instruction to the target charging pile to calculate an instruction execution deviation degree, and performing dynamic iterative optimization to generate a new energy charging pile remote control scheme. According to the invention, collaborative optimization of safety and high efficiency of new energy vehicle charging and battery charging protection can be realized.

The invention provides a tumor treatment effect prediction method based on multi-modal data, and relates to the field of medical artificial intelligence. The method comprises the following steps: collecting magnetic resonance image data, histopathological section images and clinical baseline data of a tumor patient and a treatment response evaluation record after neoadjuvant chemotherapy; the collected image information is preprocessed; feature extraction is carried out based on the preprocessed image information; screening a feature subset with the highest prediction value from the extracted features; constructing a radiopathomics fusion model by using the feature subset with the highest prediction value; evaluating the radiopathomics fusion model; and predicting the tumor treatment effect by using the evaluated radiopathomics fusion model. The problems that an existing tumor neoadjuvant chemotherapy (NACT) curative effect prediction method is low in accuracy, poor in generalization ability, lack of multi-modal data fusion and insufficient in model interpretability are solved.

The invention discloses a sand and dust pollution health risk early warning terminal and early warning method, and the method comprises the steps: obtaining multi-source input data, such as meteorological parameters, sand and dust pollutant concentration and geographical environment, in real time, and carrying out the preprocessing through a data fusion algorithm, thereby obtaining a standardized data set; based on the data set, utilizing a trained time sequence prediction model to output a sand and dust pollution level prediction value of a future preset time length; meanwhile, an exposed crowd sensitivity index and regional health baseline data containing the disease incidence rate and the meteorological sensitive crowd proportion are constructed; calculating a comprehensive health risk index through a dynamic health risk assessment model in combination with the predicted value, the sensitivity index and the baseline data; and when the index exceeds a preset threshold dynamically adjusted based on the incidence peak range in the baseline data, generating a graded early warning signal and issuing the graded early warning signal in real time, thereby realizing accurate early warning of the dust pollution health risk.

The invention discloses a concrete structure tall formwork supporting system stress deformation actual measurement method, and relates to the technical field of constructional engineering structure safety, the method comprises the following steps: determining initial stress distribution reference data through a pre-established supporting system geometric model database, and determining final construction adjustment guidance data; according to the final digital construction scheme, a construction safety assessment report is automatically generated, final conclusion data of safety assessment is determined, a result is fed back to the design optimization module through the data transmission module, and the unsolved uneven stress or deformation problem is extracted from the feedback data; obtaining input parameters of a new round of design optimization, and obtaining a continuously improved design iteration result; according to the actual measurement method for stress deformation of the high and large formwork supporting system of the concrete structure, the design rationality and construction safety of the high and large formwork supporting system are improved, and intelligent and digital whole-process management and control are achieved.

The invention relates to the technical field of pipe network detection, in particular to a target detection method, system and device based on image recognition and a medium, and the method comprises the following steps: obtaining a data packet in a mechanical mining process; preprocessing the data packet to obtain a target point cloud; performing target positioning of the underground pipe network according to the preprocessed data to obtain a target area; a baseline database of the underground pipe network in the target area is obtained, target detection is conducted on the change trend of the baseline database along with the time sequence, and health data are obtained; according to the acquired health data and the data packet, acquiring detection data of the target area; through time synchronization packaging and semantic fusion of heterogeneous sensor data, a target point cloud containing space-time correlation characteristics is constructed, and progressive monitoring of the cable health state is realized in combination with time sequence analysis of a dynamic baseline database. The problems of leak detection and false detection of cable detection in a complex environment are effectively solved.

The invention discloses a machine vision emotion recognition and interaction system and method based on multi-modal fusion, and the method comprises the steps: obtaining the multi-modal emotion expression data of a user and the basic information data of the user, constructing a user emotion feature initial recognition model, carrying out the calculation through the user emotion feature initial recognition model, and obtaining a user emotion feature recognition result; obtaining an original emotion recognition result; based on the original emotion recognition result, historical recognition deviation data in the long-term interaction process of the user is obtained and preprocessed, and a processed individual emotion baseline data set is obtained; optimizing the user emotion feature initial recognition model by using the individual emotion baseline data set, and constructing a user feature adaptive emotion recognition model; and performing multi-modal emotion recognition prediction based on the user feature adaptive emotion recognition model. According to the method, the emotion discrimination accuracy in a multi-user recognition scene is remarkably improved, and the problem of misrecognition caused by expression differences among the users is solved.

The invention relates to the technical field of spectrum baseline correction, and provides a spectrograph baseline correction method and system based on improved empirical mode decomposition, and the method comprises the following steps: employing a sliding window smoothing method, and carrying out the preprocessing of obtained measurement spectrum data; an improved empirical mode decomposition algorithm is adopted, fitting is carried out on the preprocessed measurement spectrum data to obtain baseline data, and the improved empirical mode decomposition algorithm adopts least square fitting to construct upper and lower boundary envelopes of the spectrum data; subtracting the baseline data obtained by fitting from the obtained measurement spectrum data to obtain the measurement spectrum data after the baseline is removed; and sequentially carrying out abnormal point detection and removal, spectral power calibration and wavelength calibration on the measured spectral data after baseline removal to obtain spectral data after baseline correction. The spectral baseline is identified by improving empirical mode decomposition, the stability and boundary adaptability of mode decomposition are enhanced, and a more accurate baseline fitting result is obtained.

The invention discloses a PLC behavior measurement method, device and equipment based on trusted 3.0 and national cryptographic algorithms, and relates to the field of safety control in an industrial control system.The method comprises the steps that a hardware trust root module installed on a PLC in advance is used for conducting trusted verification on the PLC during starting, and the module is constructed based on the trusted computing 3.0 standard; after verification succeeds, a behavior feature vector of the PLC operation state is obtained based on trusted computing 3.0, wherein the vector comprises at least one of an integrity verification parameter, a resource consumption parameter, an operation parameter and an associated parameter; generating behavior measurement data based on trusted computing 3.0, and measuring a vector through an Euclidean distance algorithm in combination with behavior baseline data; comparing the measurement result with a preset Euclidean distance threshold value, encrypting the measurement result by adopting a domestic key algorithm, and transmitting a comparison result to a security management platform; according to the method, trusted computing 3.0 (TC3.0) and the national cryptographic algorithm are combined, accurate behavior measurement is carried out on the operation state, and the equipment safety and the attack resistance can be enhanced.

The invention relates to the technical field of child cognitive function evaluation, and particularly discloses an intelligent child cognitive function evaluation method and device and a medium, and the method comprises the steps: collecting real-time behavior physiological coupling response data of a tested child under the stimulation of color and digital dual tasks through a multi-modal stimulation collection device, and obtaining a cognitive response chain dynamic model; according to the cognitive reaction chain dynamic model, performing performance quantification on the joint distribution of the reaction accuracy and the reaction time of the child under the stimulation of color and digital dual tasks to obtain a cognitive performance quantitative index; meanwhile, self-adaptive calibration of the individual cognitive baseline is carried out to obtain individual cognitive baseline data; and finally obtaining a personalized course recommendation matrix. The method not only is helpful for evaluating the current cognitive ability level of children, but also can provide reference for self-adaptive calibration of individual cognitive baselines. The method is crucial for recognizing cognitive deviation and formulating a personalized training plan. Adjustment can be carried out according to the specific cognitive requirements of children, and the effectiveness and pertinence of training can be improved.

The invention relates to the technical field of concrete detection, in particular to a concrete protective layer void detection method and system based on a detachable piezoelectric ceramic sensor and an electromechanical impedance resonance technology. The detection method comprises the following steps: temporarily fixing a detachable piezoelectric ceramic sensor on a detection point on the surface of a concrete structure; applying an alternating-current sweep-frequency voltage to excite the piezoelectric ceramic sensor, and obtaining an electromechanical impedance signal frequency spectrum; the electromechanical impedance signal frequency spectrum is analyzed, the frequency of a local defect formant is obtained, and according to the frequency of the local defect formant, geometric parameters of a void area are estimated through a defect size and frequency relation model; and moving the sensor and repeatedly detecting other detection points. The detection method has high efficiency and flexibility, and detection can be realized in any environment; the single-point detection speed is high, the efficiency is high, baseline data is not needed for detection, the anti-interference performance is high, and rapid positioning and quantitative evaluation of void damage can be achieved.

The disclosed method includes: determining a subsurface space of interest; arranging a source sensor array relative to a receiver sensor array of a Surface Distributed Acoustic Sensing (S-DAS); transmitting, from the source sensor array, a first set of seismic signals indicating baseline measurements; receiving, using the receiver sensor array, the baseline measurements and thereby generate baseline data; injecting fluid into the subsurface space of interest; transmitting, using the source sensor array, a second set of seismic signals indicating fluid plume measurements; receiving, using the receiver sensor array, the fluid plume measurements and thereby generate fluid plume data; determining, based on the baseline data and the fluid plume data, time-shift data and thereby generate fluid plume extent data; formatting the fluid plume extent data into data values that characterize an extent or rate of spread of the fluid plume; and visualizing the extent or rate of spread of the fluid plume.

The present disclosure includes devices methods and systems for detecting draper belt tears in a work machine. The method may include receiving belt operation data from one or more sensors. The method may include receiving belt condition baseline data. The method may include includes determining a belt active condition based at least in part on the belt condition baseline data and belt operation data. The method may include determining a change in active condition based at least in part on the belt operation data and the belt condition baseline data. The method may include adjusting one or more belt operation parameters based at least in part on the determination of the change in active condition.

The invention relates to the technical field of optical current transformer detection, and provides an optical current transformer real-time calibration and health diagnosis method and system, and the method comprises the steps: carrying out the harmonic analysis of an initial digital signal sequence obtained through the conversion of a continuous modulation optical signal of a target optical current transformer, and obtaining harmonic component data; physical state evaluation is carried out according to the higher harmonic component data and preset health state baseline data to generate a physical state evaluation result, nonlinear disturbance compensation is carried out according to an error control signal generated based on the higher harmonic component data, and after a linear working point is reached, nonlinear disturbance compensation is carried out; and calculating an electrical measurement value according to the output target fundamental component data, and performing comprehensive analysis according to the physical state evaluation result and the electrical measurement value to generate a diagnosis report. According to the invention, through a measurement calibration and state diagnosis mechanism based on harmonic analysis, while the electrical performance measurement precision is effectively improved, reliable quantitative diagnosis and accurate traceability of different physical faults in the equipment are realized.

The invention discloses a service network flow analysis system capable of giving an alarm in a second level, improving the abnormal response speed of a key link and reducing the risk of network attack, and the system comprises a flow collection module which collects flow data from a router / switch port of the key link through an SNMP protocol; the data processing module is used for cleaning, aggregating and analyzing the collected flow data to generate a flow index; the data storage module is used for storing historical data, baseline data, user information and alarm records; the baseline early warning module dynamically generates a workday / holiday flow threshold according to the historical data, compares the current flow with the flow threshold in real time, and triggers an alarm; the alarm module is used for giving an alarm and carrying out fault positioning when traffic abnormity or port state abnormity is detected; and the report query module provides a multi-dimensional report query interface and analyzes historical data. The invention further discloses a service network flow analysis method implemented by adopting the service network flow analysis system.

The invention relates to the technical field of agricultural product safety detection, in particular to an agricultural product veterinary drug residue high-throughput detection method and system based on spectrum peak analysis. The method comprises the following steps: acquiring multiple batches of spectral data, and performing cross-batch pre-alignment according to the multiple batches of spectral data to obtain cross-batch alignment data; performing background baseline estimation according to the cross-batch alignment data to obtain background baseline data; congestion detection area extraction is carried out according to the cross-batch alignment data to obtain congestion detection area data; performing common-peak decoupling according to the congestion detection area data to obtain common-peak decoupling data; performing residual image construction on the common peak decoupling data according to the background baseline data to obtain residual image data; performing image feature extraction according to the residual image data to obtain image feature data; and performing residual judgment according to the graph feature data to obtain residual judgment data. According to the method, the identification accuracy of spectrum peak pseudo-congestion and residual abnormity in a high-throughput mixed agricultural product detection scene is improved.