692 results about "Detection threshold" patented technology

The present invention relates to the field of fault diagnosis technology, specifically to a fault diagnosis method and system for a photovoltaic inverter, comprising the following steps: based on an operating status data set, analyzing a variety of inverter operating data, identifying the volatility of a variety of operating data, extracting statistical features and frequency domain features of the data, adjusting the detection threshold of abnormal data, and generating abnormal fluctuation detection parameters. In the present invention, the abnormal detection threshold is dynamically adjusted by analyzing the operating data, a more sensitive response is made to the data, the accuracy and response speed of the fault warning are improved, the time window segmentation and signal feature extraction are used to timely discover abnormal fluctuations, detect equipment damage or system failures, and the accuracy of fault type identification is improved by combining the comparison and analysis of data features. By analyzing the component types associated with various fault types, the efficiency and accuracy of locating faulty components are improved, and the reliability and economic benefits of the photovoltaic inverter are enhanced.

The invention discloses a coal mine heat energy monitoring and cooling regulation and control method based on the Internet of Things, and relates to the technical field of coal mine heat energy monitoring and cooling regulation and control. The collected temperature data are transmitted to a central monitoring system in real time through an Internet of Things gateway; redundant temperature sensors are arranged at key positions, and data of the temperature sensors in the same area are regularly compared and verified. According to the invention, the monitoring robustness is improved through a redundant temperature sensor and a data fusion algorithm, a monitoring blind area caused by a single-point fault is avoided, and the accident risk is reduced; a self-adaptive learning algorithm is adopted to dynamically optimize a detection threshold value and a compensation strategy, so that the anomaly detection accuracy is improved, and false alarms and missing alarms are reduced; the temperature trend is pre-judged through a predictive control algorithm, cooling is conducted in advance, balance between safety and energy efficiency is achieved, overheating accidents are prevented, and energy consumption is reduced.

The invention discloses a display panel defect detection method and system. The method comprises the following steps: acquiring original image data of a display panel; preprocessing the original image data to generate standardized image data; extracting multi-dimensional defect features based on the standardized image data; inputting the multi-dimensional defect features into a pre-trained defect classification model, and outputting defect types and confidence coefficients; generating a defect distribution map according to the defect type and the confidence coefficient, and adjusting a detection threshold value based on the defect distribution map; and optimizing the defect classification model based on the adjusted detection threshold, and generating a dynamic quality control strategy. The method has the following advantages and effects: the limitation of a single data source is broken through through multispectral fusion and multidimensional features, and surface and internal defects are covered; through dynamic threshold and model optimization, the method adapts to the environmental change of a production line, and guarantees the long-term detection precision.

The invention discloses an Internet of Things intelligent gas meter leakage detection and early warning system and method, and relates to the technical field of gas leakage detection, and the method comprises the following steps: S1, collecting data through multiple sensors; s2, identifying an equipment operation state based on the data set and outputting a state confidence coefficient; s3, a stable monitoring window period is judged, a corresponding strategy is selected to compensate pressure data, and reliability is marked; s4, dynamically generating a detection threshold in combination with the historical mode, the real-time parameters and the data reliability; s5, dynamically adjusting a risk assessment weight according to the confidence coefficient and the reliability, calculating a risk score and determining an early warning level; s6, safety operation is executed according to grades; and S7, updating the model by using process data to realize self-optimization. According to the invention, by deploying a multi-sensor array and adopting a multi-modal signal fusion algorithm, the system can accurately identify the running state of the gas appliance, provides reliable preposition information for subsequent analysis, and overcomes the defect that data of a traditional single sensor is easily interfered.

The invention relates to the technical field of wind power generation. The impeller imbalance detection method based on multi-source data fusion comprises the steps that operation state data of a draught fan unit and environment data of corresponding time are obtained, and a multi-source data set is obtained; preprocessing the multi-source data set to obtain a preprocessed data set; feature parameters in the preprocessed data set are extracted, data fusion is carried out, and a multi-source feature fusion vector is constructed; according to the multi-source feature fusion vector, constructing a time sequence prediction model based on a gating circulation unit, and predicting a theoretical vibration baseline value under the current working condition in real time; and calculating a deviation degree between an actual vibration value and a predicted baseline through residual analysis, dynamically adjusting a detection threshold, and triggering imbalance early warning when the deviation degree exceeds the threshold. The problems that the accuracy of a detection result of a traditional impeller imbalance detection method is affected by various factors, misjudgment and missed judgment are prone to occurring, and the requirement of a modern wind power generation system for high-precision fault detection cannot be met are solved.

The invention relates to the technical field of fault diagnosis, in particular to an automatic anomaly detection method for a high-speed rotating transmission device, which comprises the following steps of: acquiring angular acceleration, rotating inertia and strain data of the high-speed rotating transmission device, calculating a transient strain gradient and matching a rotating speed change rate, adjusting a strain deviation range according to the change of the rotating inertia, and detecting the anomaly of the high-speed rotating transmission device. And screening strain abrupt change points to obtain a rotation strain matching result. According to the method, the dynamic strain response interval is set, the strain deviation range is adjusted, the strain abrupt change points are screened, the frequency response of the equipment is monitored in real time and compared with the normal frequency, so that continuous monitoring of the frequency deviation rate is more accurate, the frequency deviation rate jointly acts on identification and clustering of abnormal characteristics, the accuracy and the real-time performance of fault diagnosis are improved, and the fault diagnosis efficiency is improved. By calculating the deviation degree of the abnormal characteristics and the normal track, the abnormal detection threshold can be adjusted, potential faults can be identified and processed more effectively, the maintenance cost of the high-speed rotating transmission device is reduced, and the operation efficiency is improved.

The invention discloses an operation data management system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: 1, collecting multi-source data in real time through an intelligent sensor and industrial equipment, achieving time synchronization, and ensuring data alignment; step 2, cleaning, labeling and standardizing the collected data, extracting feature vectors of the data, and generating multi-modal feature vectors through dynamic weighted fusion of an attention mechanism; step 3, calculating a reconstruction error based on an auto-encoder to identify an abnormal state, quantifying the contribution degree of each feature to the abnormity in combination with an SHAP algorithm, and positioning key influence factors; and step 4, dynamically adjusting the detection threshold by adopting reinforcement learning to reduce the false alarm and missing alarm rate, and evaluating the prediction confidence by quantifying the model error and the data error to realize the dynamic adjustment of the early warning priority. According to the invention, the condition that abnormal data and false alarm of the abnormal data are difficult to effectively warn in the prior art can be improved.

The invention relates to an electric power internet of things operation and maintenance management method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining sensor data, carrying out the cleaning and time alignment of the sensor data, and obtaining standard operation data; based on a historical data window corresponding to the standard operation data, calculating a mean value and a standard deviation of the historical data, and further generating a dynamic detection threshold value; comparing the standard operation data of the current time node with a dynamic detection threshold value, and screening out potential abnormal data; calculating an abnormal trend factor corresponding to the potential abnormal data, and determining an abnormal development trend of the potential abnormal data according to the abnormal trend factor; and determining a corresponding causal influence weight based on a preset causal relationship network, and calculating a fault score according to the causal influence weight and the abnormal trend factor, so as to generate an early warning report according to the fault score. The method has the effect of improving the management efficiency of the power equipment.

The invention relates to a data access prediction scheduling method and system based on a dynamic threshold value, and belongs to the technical field of data processing. The method comprises the following steps of: dynamically adjusting an abnormal detection threshold in an access behavior by utilizing reinforcement learning and an error feedback mechanism, realizing preposition identification of potential high-frequency access data by combining abnormal point classification processing and access trend prediction, and generating a scheduling request to drive cache preheating or interface priority scheduling of the data. According to the method, access behavior modeling, anomaly recognition, trend prediction and strategy feedback mechanisms are combined, interface access optimization requirements in a mass data storage system are met, and the key problems that access behaviors are unpredictable, scheduling response lags and the like are solved.

The invention discloses a millimeter wave radar meteorological target detection method based on deep learning, and relates to the technical field of meteorological radar processing. The method comprises the following steps: acquiring a millimeter wave radar original signal by using a transmission control protocol for preprocessing; the distance and the angle between an object and an antenna are measured through a millimeter wave radar, so that three-dimensional space coordinates of the measured object are obtained; converting the three-dimensional space coordinates through time alignment and a space coordinate system, and projecting the three-dimensional space coordinates into a visual coordinate system; pre-training a feature extractor, analyzing a data label by using ECMWF, and then performing end-to-end fine tuning; and automatically optimizing the detection threshold according to the signal-to-noise ratio, and outputting target meteorological classification, meteorological intensity and meteorological motion prediction. According to the method, the detection threshold is automatically optimized according to the signal-to-noise ratio, target classification, intensity estimation and motion vector prediction are output, weather weak signal leak detection is avoided, and the target tracking capability and the extreme weather generalization capability are improved.

The invention discloses a target object targeting agent monitoring method and device based on AI visual identification, belongs to the technical field of termite monitoring, and aims to solve the problems of high manual dependence degree, high misjudgment rate, low data transmission efficiency and the like in traditional target object monitoring. A slow-release targeting agent and a solar hybrid power supply system are arranged in the device, and low-power-consumption endurance is supported. According to the core scheme, vibration and infrared sensor signals are fused, and image acquisition is triggered through feature analysis; recognizing a target object through background difference, a multi-scale image and feature matching, and dynamically optimizing a detection threshold value; after video acquisition, activity intensity is calculated in combination with target tracking and gridding analysis, and transmission is optimized based on intensity and quota; light supplement is automatically adjusted according to ambient light, and image quality is ensured through pixel compensation and brightness adjustment. According to the scheme, a closed-loop system with multi-mode accurate triggering and intelligent identification is constructed, and the intelligence and efficiency of termite monitoring are improved.

The invention discloses a data processing method and device, equipment and a medium. The method comprises the steps that a detection threshold value is determined based on a first access traffic log; determining an access behavior for the service domain name based on the number of domain name requests determined by the access traffic indicated by the second access traffic log and a detection threshold; when the access behavior aiming at the service domain name belongs to the abnormal access behavior, taking a behavior determination time point corresponding to the abnormal access behavior as a critical time point, and taking a third access traffic log obtained based on the critical time point as an abnormal access traffic log; when a normal traffic feature determined based on the first access traffic log is obtained, determining an abnormal traffic feature of the service domain name based on the abnormal access traffic log, and performing feature comparison on the abnormal traffic feature and the normal traffic feature to obtain an attack feature of the service domain name; a protection policy for the service domain name is determined based on the attack feature. According to the invention, the security and stability of the business server can be maintained.

The invention relates to the technical field of ecological system service function evaluation. The invention relates to a frequency offset correction algorithm in HPLC (High Performance Liquid Chromatography) + HRF (High Radio Frequency) dual-mode communication. The method comprises the following steps: S1, collecting phase sequence data from an HPLC communication link, collecting frequency offset estimation data and corresponding confidence data from an HRF communication link, and adaptively adjusting a preset detection threshold value of the phase sequence data according to the confidence data; s2, performing pulse abnormal sample point detection and elimination on the phase sequence data according to the adjusted preset detection threshold value; according to the method, the detection threshold is dynamically adjusted through the confidence data, so that the anomaly detection process can be matched with the current interference environment in real time, the integrity of the phase sequence data is maintained, and the problem of insufficient adaptability of a fixed detection threshold in an electromagnetic interference dynamic change scene is solved; and a more reliable phase data basis is provided for subsequent interpolation compensation and frequency offset estimation, so that the frequency offset correction precision of the dual-mode communication system is improved.

This disclosure introduces an automated computer system designed to detect fire and smoke in video frames. Within this system, an object detection model is trained to recognize fire and smoke patterns. In a post-training evaluation phase, the system utilizes the model to determine an instance-level detection threshold from a validation set, which dictates the minimum confidence score required for a fire or smoke designation. In addition, a frame-level detection rule is defined, considering both the cumulative count of detected fire or smoke instances and their associated confidence scores. During inference, the system processes video frames through the model, designates detected patterns with bounding boxes, and subsequently applies the instance-level threshold and frame-level rule. When a sequence of frames fulfills a predetermined criterion, the system activates an alarm.

The invention provides a defect detection model parameter construction method, a controller, a medium and a product. The method comprises the following steps: acquiring a plurality of product images; inputting the plurality of product images into a deep learning model to obtain a reasoning result, the reasoning result comprising a defect category; setting an initial strict threshold value, an initial balance threshold value and an initial loose threshold value according to a reasoning result; and based on the defect category, correcting the detection parameters of the deep learning model according to the initial strict threshold value, the initial balance threshold value and the initial loose threshold value to obtain corrected defect detection model parameters. The corrected defect detection model parameters are obtained based on the model reasoning result and analysis of actual production requirements, so that the defect detection model has the flexible adjustment capability of defect detection in the actual production process, the detection threshold value can be adjusted in real time according to customer requirements, and it is ensured that the detection result meets the specific quality standard, and the detection efficiency is improved. And the dynamic requirements of different batches are met, and accurate and efficient detection is realized.

The invention discloses a signal detrending method based on sliding window statistics and dynamic wave crest correction. The method comprises the step of realizing accurate extraction of signal trend and fluctuation characteristics through a double-window dynamic adjustment mechanism of a mean value window and a standard deviation window. A detection threshold value is dynamically adjusted by analyzing the change trend of the historical wave crest amplitude, and the real-time tracking capability of signal fluctuation is ensured; meanwhile, different parameter configurations are preset for different application scenes, the problem of missing detection or misjudgment generated when the amplitude of a fixed threshold algorithm changes suddenly is solved, and the wave crest recognition accuracy is improved. Based on a dynamic threshold algorithm, wave peak start and end points are automatically identified, a linear interpolation technology is adopted to calculate slope of each interval, fitting parameters are dynamically adjusted through residual analysis, and finally low-frequency trend terms and high-frequency characteristic components are effectively separated through difference operation of original signals and fitting signals. The problem of fitting deviation caused by non-uniform sampling can be effectively solved.

The invention provides a network information operation and maintenance system based on AI multi-modality, and belongs to the technical field of network information operation and maintenance. Heterogeneous operation and maintenance data such as network flow, equipment state, audio alarm and thermal imaging are acquired through an acquisition unit, and various features are extracted in parallel by using a multi-modality feature extraction module to form a unified multi-dimensional feature vector; a hierarchical anomaly detection architecture is established, lightweight and deep anomaly detection models are deployed on an edge side and a cloud end respectively, different modal features are intelligently fused through an attention mechanism by adopting a multi-modal feature fusion model, and a detection threshold is optimized in real time according to a network state by using an adaptive threshold dynamic adjustment Bayesian algorithm. And a multi-modal fusion decision engine is constructed to perform weighted fusion on the anomaly detection result and dynamically adjust the modal weight, so that the technical problem of insufficient accuracy of multi-modal operation and maintenance data fusion processing is solved.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

The invention relates to the technical field of robot navigation, and discloses a multi-sensor fusion navigation method and system of a robot. The method comprises the following steps: collecting point cloud data of a laser radar sensor and motion data of an IMU six-axis sensor; executing generalized likelihood ratio test and sliding window interval search on the point cloud data and the motion data to obtain a zero-speed abnormal interval, and creating a detection threshold value for the current ground environment according to the zero-speed abnormal interval; performing spatial superposition on the position coordinates of the zero-speed abnormal interval and a laser radar scanning blind area to obtain an obstacle distribution map; cleaning control parameters are adjusted based on the detection threshold value, a re-planning cleaning path is generated based on the obstacle distribution map and the cleaning control parameters, the method can actively identify the potential trapped area and re-plan the cleaning path, and the autonomous navigation ability and cleaning efficiency of the intelligent cleaning robot are improved.

The invention discloses an abnormal data monitoring method and device based on artificial intelligence, and the method comprises the steps: 1, dividing an original data stream through a sliding window, extracting statistics, time sequence and change rate features, and dynamically screening features adaptive to data distribution based on an SHAP value; 2, constructing a double-flow model, capturing a global isolated mode by adopting an improved isolated forest in a static flow, capturing time sequence dependence on the basis of LSTM-AE in a dynamic flow, and fusing two-flow scores through performance-driven dynamic weight distribution; 3, combining a density peak value algorithm with historical density attenuation weighting, and dynamically adjusting an abnormal threshold value; 4, realizing low-delay incremental learning through a double-trigger mechanism and experience playback; 5, multi-granularity interpretation is generated, manual annotation feedback is supported, feature engineering and model training are integrated, and a'detection-interpretation-feedback-optimization 'closed loop is formed; high-adaptability anomaly monitoring is realized through dynamic feature screening, double-flow fusion detection, threshold value self-adaption and man-machine collaborative optimization.

The invention discloses an intelligent precision part full-process machining control system, and relates to the technical field of machining control, and the system analyzes each machining influence parameter of a precision part based on a superior sequence diagram method, and determines the weight value of each machining influence parameter of the precision part for machining monitoring control; monitoring each machining influence parameter of the precision part in real time, and performing real-time early warning on the machining of the precision part by combining the weight value of each machining influence parameter of the precision part; constructing a machining qualification detection model of the precision part based on the historical machining sample of the precision part, and identifying a qualification detection threshold value of the machining qualification detection model; and performing intelligent detection on each batch of processing results of the precision parts by using the processing qualification detection model and the qualification detection threshold, and outputting a precision part processing detection report. Through intelligent full-process control, the machining process is optimized, the machining precision is improved, the machining quality is ensured, and early warning and potential problem correction are conducted in time.

The invention discloses a non-uniform clutter and interference cooperative suppression target fusion detection method and system, and relates to the technical field of broadband radar signal processing, and the method comprises the steps: carrying out the unitary transformation of test data, a clutter covariance matrix, a target coordinate matrix and an interference coordinate matrix based on the skew symmetry of the clutter covariance matrix; constructing a distance extension target Gradient detection statistical magnitude under the condition of a known clutter skew symmetry covariance matrix; calculating the maximum likelihood estimation of the clutter skew symmetry covariance matrix; based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the distance expansion target Gradant detection statistic, constructing a target detection statistic of non-uniform clutter and interference cooperative suppression; performing target fusion detection on the target detection unit based on the target detection statistical magnitude and a preset detection threshold; according to the invention, the technical problems of complex construction process and high calculation complexity of the target detector in the prior art are solved.

The invention provides a dynamic threshold anomaly detection method and system based on deep semantic reconstruction and an isolated forest, and belongs to the technical field of crossing of network security and artificial intelligence. A sliding window segmentation and dynamic zero padding mechanism is adopted to unify texts of any length into standardized segmented data; the method comprises the following steps: constructing a nine-layer depth auto-encoder (four encoding layers, a middle layer and four decoding layers), learning multi-dimensional semantic features of normal data, calculating a reconstruction error matrix of each data segment, extracting error distribution statistics (mean value, variance, skewness and kurtosis), performing unsupervised clustering on the extracted error features by adopting an improved isolated forest algorithm, and obtaining a clustering result; a dynamic detection threshold value is automatically generated, and after semantic reconstruction and feature analysis are carried out on real-time data, whether the real-time data are abnormal or not is judged in combination with the dynamic detection threshold value; a self-adaptive anomaly detection model is constructed through conjoint analysis of a dynamic threshold generation mechanism and deep semantic features, and dependence on artificial experience is reduced while high detection precision is kept.

The invention discloses a self-adaptive adjustment method for a time sequence anomaly detection threshold, which dynamically estimates an anomaly upper bound according to anomaly score distribution in a time sequence local window, realizes self-adaptive adjustment of the detection threshold, improves the anomaly detection accuracy, reduces the false alarm rate and the omission ratio, and improves the detection accuracy. And good robustness and practicability are shown.

The invention discloses a GNSS (Global Navigation Satellite System) signal spoofing detection method, device and equipment, a storage medium and a program product. The method comprises the following steps: tracking and monitoring a plurality of GNSS satellite signals, calling a tracking loop correlator to carry out autocorrelation operation to obtain the autocorrelation power of each satellite signal, constructing the AP detection quantity of each satellite signal based on the autocorrelation power, verifying the Gaussian property of the AP detection quantity, and carrying out moving average filtering processing on the AP detection quantity conforming to Gaussian distribution to obtain the AP detection quantity of each satellite signal. The method comprises the following steps: acquiring Gaussian distribution characteristic information of a GNSS satellite signal, constructing an AP-MA detection quantity, determining a detection threshold according to the Gaussian distribution characteristic information, detecting the AP-MA detection quantity based on the detection threshold, and judging whether the GNSS satellite signal has deception jamming or not; the AP detection quantity is constructed based on the self-correlation power to carry out signal spoofing detection, so that the detection range and the detection precision are effectively improved, and the timeliness and the stability of spoofing detection are ensured under the condition of not increasing additional equipment and algorithm complexity.

The invention discloses a structure construction deviation automatic identification and safety evaluation method, which comprises the following steps: acquiring multi-modal original signals such as space measurement, environment, equipment operation and images from multiple positions of a structure construction site, and establishing a trend drift feature distribution model through time sequence alignment, normalization, noise reduction and feature extraction; then through combination with generative artificial intelligence, virtual samples of historical and extreme working conditions are expanded, and construction of a mixed working condition data pool is realized; through distribution drift analysis and multi-target parameter optimization, a deviation detection threshold value is dynamically and adaptively adjusted, parameters and a judgment result are pushed to a digital twinning and safety control platform in real time, and meanwhile, whole-process log and performance archiving are carried out, so that the sensitivity, adaptability and system safety of structure construction deviation detection are improved, and the construction safety is improved. And the method has relatively high data fusion efficiency and algorithm generalization ability.

The invention discloses a narrow environment high-precision laser inertial navigation and SLAM (Simultaneous Localization and Mapping) method based on adaptive parameters. According to the method, firstly, radar and IMU data are collected and preprocessed to form a unified input data stream; obtaining a pose prediction result by using the data stream, constructing an observation model, and obtaining a laser observation residual error; calculating a characteristic value proportion in the matching process of the point cloud and the sub-map, and judging a geometric degradation scene; when degradation is detected, adaptively adjusting a voxel filtering radius and a loopback detection threshold value; fusing the pose prediction result and the laser observation residual error by adopting a Kalman filtering model to obtain an optimal pose estimation result, and carrying out loopback detection and back-end map optimization under a dynamic threshold value to correct accumulated drift and update a voxel map; and finally outputting a continuous pose track and a three-dimensional voxel map. According to the method, the robustness and the positioning precision of the laser inertial navigation fusion SLAM in narrow scenes such as pipe galleries, tunnels and chemical plants are effectively improved.

The invention discloses a fault detection method for a low-voltage AC system containing a distributed power supply based on SOGI and wavelet, and the method comprises the steps: carrying out the filtering of a fault signal through a second-order generalized integrator SOGI, generating an orthogonal signal, and calculating the amplitude; performing wavelet packet decomposition on the amplitude signal, and extracting a high-frequency detail component as a fault feature; and dynamically adjusting a detection threshold according to the permeability of the distributed power supply, and judging a short-circuit state based on the high-frequency detail component. According to the method, alternating current side short circuit fault detection under different fault types under different direct current system source load access conditions is realized, and the rapidity of a fault detection algorithm in a high-permeability scene is improved. The method is beneficial to improving the efficiency, reliability and universality of short-circuit fault identification.

The invention discloses a multi-channel NDIR infrared gas sensor detection data analysis system and method, and relates to the technical field of gas detection.The method comprises the steps that infrared light intensity of each gas channel is collected to serve as original data, and a fluctuation value of the original data is calculated to serve as an original fluctuation value; acquiring historical temperature and humidity data, fitting a linear relationship between the historical temperature and humidity data and the historical infrared light intensity data, and calibrating the original data based on a correction coefficient obtained by fitting to obtain correction data and a corresponding calibration fluctuation value; calculating a variable coefficient of the correction data, and setting a detection threshold value for each gas channel in combination with the calibration fluctuation value so as to screen out effective original data; distributing an initial weight for each gas channel, dynamically adjusting the weight based on the variable coefficient of the effective data, and processing the adjusted weight through a smoothing algorithm to obtain a smooth weight; and converting the effective original data into gas concentration data, and carrying out weighted fusion on the gas concentration data and the smoothed weight so as to output final gas concentration data.

The invention relates to the technical field of radars, and provides a frequency diversity array multi-input multi-output radar target detection system. The system comprises a detection statistic construction module, a detection optimization module, a detection threshold determination module and a target judgment module. Wherein the detection statistic construction module is used for constructing a detection statistic; according to the detection statistics, constructing a semi-definite programming optimization model; the detection optimization module is used for constructing a semi-definite programming optimization model according to the detection statistics; the detection optimization module is also used for solving the optimization model to obtain an optimal value of the detection statistics; and the target judgment module is used for comparing the detection statistic optimal value with a detection threshold so as to detect whether a target exists or not, so that the target detection probability and the anti-interference capability of the frequency diversity array multi-input multi-output radar in a complex environment are improved.