225 results about "Data stability" patented technology

The invention discloses a primary and secondary fusion complete ring main unit fault diagnosis method, and particularly relates to the technical field of power distribution fault diagnosis, and the method comprises the steps: collecting multi-path original data, carrying out the frequency domain and time domain combined calibration, carrying out the comprehensive evaluation according to two preset discrimination factors, namely, a data stability deviation amplitude and a multi-path waveform time deviation degree, and obtaining a fault diagnosis result. Two types of feature data sets are constructed subsequently, input data of two interference modeling networks are calculated respectively, then the interference modeling networks are input to output interference grade values, sampling precision dynamic adjustment and fault recognition strategy switching operation are executed according to the interference influence grade values, and diagnosis accuracy and stability in a complex interference environment are improved. According to the method, unified normalization processing of multi-path sensing data is realized, and the sensing accuracy of fault features is improved; through double-factor triggering and feature fusion evaluation, the stability of interference identification is enhanced; and sampling adjustment and strategy switching are executed based on the interference level value, so that the robustness and reliability of diagnosis are improved.

The invention relates to the technical field of cloud computing storage, in particular to a cloud storage dynamic optimization method and system, and the method comprises the following steps: based on a cloud storage data access record, recognizing an access time interval sequence, analyzing the variation amplitude of adjacent time intervals, and calculating the standard deviation of the access time intervals; and determining the access period stability according to the standard deviation and the change amplitude, and classifying the data objects to obtain a data access period stability classification value. According to the method, task scheduling is more accurate by identifying a data access time interval sequence and changes thereof and refining data classification and storage resource allocation, a high-throughput and low-delay storage path is matched, the health condition of a storage node is evaluated, a fault node is identified in advance, data stability is guaranteed, and data copy distribution is adjusted; the storage reliability and stability are improved, the storage path for accessing the intensive data is optimized, the dynamic load balance of the storage resources is realized, and the storage efficiency is improved.

The invention relates to the technical field of quality monitoring, in particular to a virtual simulation and real-time decision-making system for canned fish production quality risks, which is characterized in that data stability and jump accuracy are improved by adopting a filtering mode, a key path area is screened, a conduction trunk is formed, synchronous construction of a mapping and adjusting strategy is realized, and a real-time decision-making result is obtained. By introducing action time, a control time consumption difference value and a node jump amplitude trend multi-dimensional quantitative judgment standard into an execution structure, a feedback recognition mechanism of index fluctuation is enhanced, the abnormal trend response granularity is improved in cooperation with multi-dimensional nested calculation of temperature and pressure parameters, synchronous association of key control parameters and historical operation tracks in a fault chain is complemented, and the reliability of the fault chain is improved. Through starting from a beat logic structure, data cleaning, trend grouping and traceability return are performed alternately, so that a risk propagation chain obtains collaborative modeling among a pressure difference, a temperature difference and a control instruction, and risk path response consistency and intervention precision under a beat driving condition are enhanced.

The invention relates to the technical field of data processing, in particular to a four-fusion integrated terminal with an electric energy quality monitoring function, and the terminal specifically comprises a data collection module which is used for obtaining the parameter value of each monitoring index at each monitoring point; the data analysis module is used for obtaining the data stability degree of each monitoring point according to the change characteristics of each parameter value; the feature acquisition module is used for combining the parameter value of each monitoring index with the data stability degree of each monitoring point to obtain the distribution uniformity degree of each monitoring index; and the abnormity monitoring module is used for screening out target monitoring indexes according to the distribution uniformity, clustering parameter values of the target monitoring indexes by using a K-means algorithm, and monitoring the power quality according to abnormal data in each cluster, so that feature dimension reduction is realized, and the monitoring accuracy is improved. The effect and accuracy of monitoring the power quality by using the K-means algorithm are improved.

The invention relates to an offshore wind speed prediction method based on a TVFEMD-FE-TCN-Transform model, and the method comprises the steps: obtaining historical offshore wind speed data, carrying out the preprocessing of the data, decomposing the original wind speed data through TVFEMD to obtain a plurality of IMF components, improving the data stability, and carrying out the prediction of the offshore wind speed. According to the method, four types of signals including a high-frequency signal, an intermediate-frequency signal, a low-frequency signal and a trend signal are generated through reconstruction according to IMF component complexity through fuzzy entropy FE, calculation complexity is reduced, a time domain convolutional network TCN is adopted to extract reconstructed signal features, fusion is performed, the reconstructed signal features are input into Transform for wind speed prediction, meanwhile, Transform model parameters are optimized through MWOA, and the wind speed prediction accuracy is improved. Predicting the offshore wind speed by using the optimal parameter combination Transform model to obtain a final offshore wind speed prediction value; according to the method provided by the invention, the problems of insufficient signal decomposition, weak feature extraction capability and low prediction precision of a single prediction model are effectively solved, and powerful support is provided for operation and maintenance management and power grid dispatching of the offshore wind power plant under the condition that the offshore wind speed has intermittent and fluctuation characteristics.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention relates to the technical field of prediction modeling, in particular to an ultra-short-term prediction method and system based on a satellite inversion technology, and the method comprises the following steps: obtaining satellite inversion earth surface solar incident radiation data and ground observation data, calculating the matching degree, classifying and extracting stability indexes according to meteorological conditions, and screening short-term prediction applicable data. An applicable radiation data set is obtained. According to the method, a stability index is calculated by using satellite inversion and ground observation matching degree, an error mapping relation is extracted by combining an error trend and probability density, data stability is enhanced, noise is reduced by numerical adjustment and smoothing processing, prediction continuity is improved, multi-mode meteorological data is fused, dynamic weight optimization is performed, and precision is improved. Photovoltaic prediction is combined with radiation characteristics, error adjustment and abnormity elimination are carried out to reduce extreme fluctuation, regional fragmentation is combined with error feedback to dynamically optimize prediction parameters, adaptability is enhanced, and timeliness and accuracy of short-term prediction are improved.

The invention discloses an internet of things data stable transmission optimization method, system and device and a medium, and relates to the field of internet of things, and the method comprises the steps: generating a plurality of test scenes through a multi-network simulation model; inputting the plurality of test scenes into the target Internet of Things to obtain Internet of Things test data; inputting the Internet of Things test data into the transmission performance prediction model to obtain performance prediction data; inputting the performance prediction data into a data mining and analysis model to obtain data mining information; generating a data stability analysis report according to the data mining information; and determining an abnormal point according to the data stability analysis report to form a detection and maintenance scheme. The stability, the reliability and the operation and maintenance efficiency of the Internet of Things system are effectively improved, and the method has wide application prospects and remarkable engineering practice values.

The invention provides avionic equipment based on high-precision inertial navigation and a signal processing method, relates to the technical field of aviation navigation control, and aims to evaluate the selection of a driving sensor by using environmental complexity, optimize the acquisition quality of inertial data such as angular velocity and acceleration, pixel coordinates and specific optical data in real time, reduce redundancy and improve the precision of navigation. Spectral decomposition and adaptive filtering are utilized, signals are dynamically cleaned according to environment noise of different frequency bands, key features are reserved, high-dimensional feature vectors are generated through spatial-temporal feature reconstruction, the expression ability of low-quality data is enhanced, fusion weights are dynamically adjusted based on the signal-to-noise ratio and consistency indexes, predictive error correction is combined, and long-term drift is eliminated. Hardware support ensures that processing delay is less than 10ms, anti-interference shielding maintains data stability, dependence of traditional navigation on external signals is broken through, long-time high-precision navigation is realized, the method is particularly suitable for aviation secret tasks and civil aviation flight in severe weather, and task success rate and safety are improved.

The invention discloses an intelligent ultra-wideband positioning data correction method. The method comprises the following steps: S1, generating a preprocessed ultra-wideband positioning data set; s2, training the improved self-supervised learning network model to obtain a positioning feature vector; s3, outputting a dynamic mask parameter pair; s4, outputting the corrected ultra-wideband positioning data; and S5, performing error evaluation on the corrected ultra-wideband positioning data and the original ultra-wideband positioning data, updating the improved self-supervised learning network model and the abnormal mode adaptive clustering mask mechanism according to an error evaluation result, and outputting the corrected ultra-wideband positioning data passing the error evaluation for downstream navigation, tracking or position service application. According to the invention, the data stability and application reliability of downstream navigation, tracking and position service systems are effectively guaranteed.

The invention discloses a method and system for reducing data errors of a dynamic glucometer, and the method comprises the steps: collecting a current signal of fluid between subcutaneous tissues in real time through an implantable sensor, and synchronously recording the working environment parameters of the sensor; carrying out denoising and normalization processing on the current signal, and carrying out temperature compensation and drift correction on the signal according to a working environment parameter; extracting time sequence features and frequency domain features from the signals processed in the step S2; training a machine learning model based on the time sequence feature data and the frequency domain feature data, wherein the machine learning model is used for predicting the blood glucose concentration and identifying abnormal data points; correcting a model prediction result through residual analysis and weighted adjustment, and performing secondary correction on abnormal data points in combination with event data input by a patient; and outputting the optimized blood glucose data to a terminal display device in a time sequence curve form. Through the process, the influence of abnormal state data on blood glucose monitoring is reduced, and the data stability is improved.

The invention relates to the technical field of water level data processing, in particular to a water level data acquisition and processing system based on the Internet of Things, which comprises a trend identification module, a sampling frequency modulation module, a data encapsulation module, an exception elimination module and an instruction linkage module. According to the method, trend recognition is achieved by analyzing the continuous water level change trend, the sampling frequency is dynamically adjusted, so that the sampling interval is matched with the actual water level fluctuation state, the response sensitivity of water level data and the timeliness of data acquisition are improved, and a uniform packaging format is constructed by adopting a timestamp index mechanism and a bidirectional mapping structure; the integrity of a water level data structure and the convenience of index management are enhanced, sudden change breakpoints are recognized through the water level amplitude difference, data are stored in a segmented mode, the data stability and the emergency situation recognition capacity are improved, and the self-adaptive capacity of water level information processing, the maintaining level of data continuity and the coping efficiency of sudden abnormity are overall improved.

The invention discloses a live pig slaughtering whole process management method and system based on a segmentation model, and relates to the technical field of live pig slaughtering management. Fat content and skeleton parameters are obtained through zoning measurement, a prediction result of meat distribution is constructed in combination with real-time environment data, the recognition accuracy and data stability are improved, cutting precision is guaranteed through structural precision in combination with production line capacity and equipment load information, serial numbers are subjected to iterative sorting optimization through a genetic algorithm, and the cutting efficiency is improved. Production line conflicts and resource waste are reduced, the adaptability and dynamic cooperation capability of the whole processing system are enhanced, the beat matching efficiency is improved through path continuity, the cutting deviation and error propagation are reduced, the segmentation sequence is adjusted in combination with equipment load change and conveying rhythm, and data-driven closed-loop management and control from posture recognition to cutting execution stages is achieved; and the precision management, the cutting consistency and the production flow robustness of the slaughter link are effectively improved.

The invention discloses a virtual power plant system integrating multiple resources and a flexible scheduling method of the virtual power plant system, and relates to the technical field of virtual power plant management. Analyzing photovoltaic fluctuation characteristics, energy storage response capability indexes and stability indexes of the load power data corresponding to various types of data, and performing normalization processing; establishing a resource interaction tensor based on the collected data, fusing the power grid frequency deviation and the line load, and constructing a collaborative optimization model containing time delay compensation; updating the power grid frequency and the line load rate with a fixed time window, dynamically solving the collaborative optimization model, and outputting a load regulation quantity and an energy storage target scheduling instruction; and obtaining the power grid impedance characteristics and load distribution parameters of the area to be loaded with the control instruction, analyzing the scene deviation degree of the area, and correcting the scheduling value based on the scene deviation degree. The scheduling precision is improved, and the complex power grid compatibility is improved.

The invention discloses a high arch dam global deformation analysis method and system fusing InSAR and measuring point data, particularly relates to the technical field of radar interference deformation monitoring, and is used for solving the problem of inaccurate shielding failure compensation. According to the method, data stability is guaranteed by obtaining InSAR image data and extracting sight line direction displacement in combination with coherence indexes, a key measuring point deformation sequence is collected, an InSAR result is calibrated based on stable reference points, data consistency is improved, a projection correction coefficient is constructed by using a dam body three-dimensional structure model, the sight line displacement is inversely solved into structural normal displacement, and the accuracy of the dam body three-dimensional structure model is improved. Obtaining an initial deformation value of the real structure meaning; according to the method, a spatial coupling map of measuring points and InSAR pixels is constructed for a shielding failure area, deformation compensation of the failure area is completed based on an edge weight, finally the deformation compensation result is remapped to a three-dimensional structure model, measuring point data restoration is performed on a high arch dam, and structural plane-level deformation visualization and trend analysis are realized more quickly and more accurately.

The invention discloses a data transmission method, a server system, equipment and a medium, and relates to the technical field of data transmission, and the method comprises the steps: determining a sampling pulse corresponding to first target data, obtaining a plurality of sampling pulses in a segment through the segmentation processing of the sampling pulse, and taking the plurality of sampling pulses as target sampling pulses, so as to improve the accuracy of collecting effective data. According to the method, interference signals caused by corresponding jitter instability conditions under serial port hot plugging are avoided, and the target sampling pulses obtained after segmentation processing are used for keeping data stability and avoiding high possibility of doping of the interference signals at the same time. And determining the second target data based on the target sampling pulse, and improving the anti-interference performance of the second target data under the bit. The technical problems that normal serial port use cannot be maintained when jitter interference occurs in the hot plug process, and even the whole machine crashes can be solved, and the technical effect of improving the anti-interference performance of data is achieved.

The invention is suitable for the technical field of semiconductor testing, and provides a data acquisition method and device based on a DDIC chip, and testing equipment, and the data acquisition method comprises the steps: receiving a test pattern, controlling the DDIC chip to output a corresponding gray scale voltage signal, and achieving the comprehensive testing of the response capability of the chip in a full gray scale range; secondly, a target acquisition link composed of functional units including impedance conversion, single-ended to differential conversion, programmable gain amplification, filtering, analog-to-digital conversion and the like is utilized, so that the signal driving capability and the anti-interference capability are effectively improved, meanwhile, high-frequency noise and nonlinear distortion are reduced, and the basic quality of sampled data is guaranteed; on the basis, sampling optimization processing is carried out on the sampling data based on the FPGA unit, and the data stability is improved. According to the scheme, through high-precision transmission of the signal link, the data acquisition precision of the DDIC chip is improved, and then the technical requirements of high-precision DDIC chip testing are met.

The invention discloses a method for determining the demulsification speed of emulsified asphalt, and relates to the technical field of online detection and intelligent monitoring of road engineering materials, and by integrating a lightweight time sequence prediction model and a self-adaptive processing mechanism, the practicability and reliability of the determination of the demulsification speed of the emulsified asphalt are remarkably improved; in the aspect of portability, an edge model based on a gating circulation unit structure is adopted, so that the dependence on computing resources is reduced, the method can be operated on handheld equipment, the problem that traditional laboratory equipment is heavy is avoided, and construction personnel can conveniently and quickly deploy the equipment on site; the real-time performance is enhanced, the adaptive sliding window dynamically adjusts the window length and step length according to the data stability to ensure that the inference frequency is matched with the demulsification process, and the model can immediately respond to environmental changes such as temperature fluctuation and output a demulsification speed estimated value without obvious delay in combination with an online learning mechanism, so that the paving and compacting opportunities are guided; and construction interruption is reduced.

The invention discloses a charging pile thermal runaway intelligent protection method and system based on edge calculation, and relates to the technical field of charging pile thermal runaway protection. Comprising the following steps: S1, collecting thermal runaway multi-mode sensing data in real time, and carrying out data preprocessing; a charging pile thermal runaway multi-mode abnormal state is judged, and an abnormal feature data packet is generated; s2, multi-modal abnormal feature vectors are constructed, the thermal runaway risk probability is evaluated, and thermal runaway risk grading protection of the charging pile is carried out; s3, the response effect of thermal runaway risk grading protection is quantified, and thermal runaway risk grading protection is adjusted; and S4, monitoring and feeding back the thermal runaway risk, and optimizing algorithm parameters and a thermal runaway risk grading protection strategy. The problems that an existing charging pile thermal runaway protection system is difficult to adapt to complex working conditions, multiple in false alarm and missing alarm, lagging in response and insufficient in protocol compatibility and data stability, and consequently the thermal runaway risk is difficult to recognize and protect timely and accurately are solved.

The invention discloses a sea fog visibility prediction method based on an SLSTM-SVM model, and aims to overcome the defects of an existing sea fog visibility prediction method, the sea fog visibility prediction method comprises the following steps: firstly carrying out smooth stabilization processing on an LSTM network to obtain an SLSTM, and then replacing a softmax layer in the SLSTM with an SVM classifier to construct a model. Four types of hydrological data such as rainfall and water level and five types of meteorological data such as air temperature and air pressure form a sequence input model, an SVM classifier predicts and outputs a value through SLSTM layer smoothing and stabilization processing and data deep mining, parameters are updated through back propagation, and iterative training is carried out until errors reach the standard. Meanwhile, an autocorrelation function is adopted to evaluate data stability, and a Z-score method is utilized to normalize the data. The method can quickly converge the network weight to a globally optimal solution, is high in prediction accuracy, and has great application potential in long-term sea fog visibility prediction.

The invention relates to the technical field of remote meter reading, in particular to an intelligent monitoring method and system for an internet of things ultrasonic water meter, and the method comprises the following steps: obtaining three-period pressure difference, calculating absolute difference, accumulating and judging the variation range, if the variation range is exceeded, collecting a flow velocity sequence, calculating standard difference, judging stability, adjusting high frequency and sampling; according to the state extraction data, calculating a first order difference parity ratio to judge the credibility, if the credibility is low, performing weighted substitution on front and rear values, and judging fluctuation to recover the initial frequency by using a substitution value. According to the method, the state change of the pipeline is recognized through accumulation of periodic pressure difference values, the data fluctuation recognition capability is enhanced, the sampling frequency is dynamically adjusted by introducing the flow velocity fluctuation degree to improve the response speed, the data credibility is judged through a first-order difference ratio, and the data stability and continuity are improved through low-credibility periodic weighted correction; and the sampling recovery state is judged by comparing the previous and later periods, so that the sampling frequency is timely homed, a self-adaptive closed-loop dynamic mechanism is constructed, and the data accuracy and continuity under the complex working condition are enhanced.

The invention relates to the technical field of cloud data synchronization, in particular to a diabetes management-oriented cloud data synchronization method, which comprises the following steps: based on a blood glucose monitoring record, unifying a time zone and verifying a device serial number, generating a terminal version identifier, comparing a cloud version to screen missing entries, and identifying time abnormality and a device source. And forming a version difference mark, judging a missing range and a conflict identifier, updating a synchronization state after sorting, and counting the number of synchronization entries. According to the method, the problems of additional recording of historical data and version conflicts in data synchronization are effectively solved by introducing a dynamic rollback mechanism and timestamp difference comparison, clock differences between devices are automatically detected and adjusted in the synchronization process, timely updating and consistency of data are ensured, invalid synchronization operations are reduced through an intelligent conflict resolution mechanism, and the synchronization efficiency is improved. The accuracy and timeliness of data in diabetes management are ensured, and the data stability and consistency in a multi-terminal environment are improved.

The invention discloses a data processing method and system of a chromatographic instrument, and relates to the technical field of data processing, and the method comprises the following steps: collecting whole-course original detection signals and external interference signs generated in a chromatographic analysis process, carrying out time alignment under a unified time scale, generating an interference rhythm index, and calculating the interference rhythm index; the reference is used as a unified reference for subsequent signal comparison; and based on the interference rhythm index, performing phase drift comparison on the acquired signal, calculating the phase difference between the detection signal and the interference signal, drawing a beat frequency wandering graph, extracting candidate signal segments of pseudo-period interference according to beat frequency change, and generating a pseudo-period candidate segment list. According to the invention, by establishing the interference rhythm index and comparing the interference rhythm index with the phase drift, the time decoupling of the detection signal and the interference signal is realized, and the sampling anti-interference capability is improved; and through dynamic control of a false peak characteristic fingerprint draft and a misjudgment reason table, signal rhythm correction and peak identification optimization are completed, so that the detection accuracy and the data stability are improved.

The invention discloses a dynamic biomarker analysis method and system based on big data, and relates to the technical field of data analys.The dynamic biomarker analysis method comprises the steps of obtaining dynamic biomarker data, obtaining a batch effect removing method corresponding to each dynamic biomarker data according to the dynamic biomarker data, and obtaining a batch effect removing method corresponding to each dynamic biomarker data based on the batch effect removing method; and processing the dynamic biomarker data to obtain biomarker processing data. According to the method, the data comparability is ensured and the reliability of subsequent analysis is improved through a data attribute intelligent matching batch removal effect method, the batch removal effect is verified through the differential gene number and the overlapping rate, the stability of the data is ensured, the biomarker data types related to the target disease are selected through the biomarker feature set, and the accuracy of the biomarker data types related to the target disease is improved. According to the method, the data calculation amount is reduced, the data analysis efficiency is improved, and a more comprehensive risk assessment basis is provided for clinic by constructing the regulation and control network containing the molecular interaction relationship.

The invention relates to the field of data analysis, in particular to a database information acquisition and analysis method for pharmacological analysis of heat stroke, which comprises the following steps: determining a data state according to data diversity and data stability of rat input data, and determining a data processing mode according to the data state; in analog data supplementation, determining a supplementation mode according to a comparison result of the effective standard index proportion and a preset effective standard index proportion; in the keyword analysis, a use state is determined according to the matching frequency and the spacing distance of the effective keywords, and a corresponding search mode is determined according to the use state; in the effective keyword search process, determining a page distribution state according to the page similarity and the information relevancy of the target website, and determining an acquisition adjustment mode as track simulation adjustment or search simulation adjustment according to the page distribution state; the searching and matching efficiency of database information acquisition and analysis in pharmacological analysis related to heat stroke is improved.

The invention discloses a data fusion dynamic operation state evaluation method and device, and relates to the technical field of operation state evaluation methods, and the method comprises the steps: collecting original data from a plurality of sensors, monitoring equipment or an external system, and carrying out the normalization processing and abnormal value elimination operation of the collected original data, obtaining a preprocessed standardized data sequence; based on the preprocessed standardized data sequence, the dynamic confidence value of each data source at the current moment is calculated, the dynamic confidence value is comprehensively determined by three factors of historical consistency, environmental adaptability and data stability of the data sources, and a dynamic weight value corresponding to each data source is obtained; based on the dynamic weight value, performing weighted fusion operation on the standardized data sequence in a set time window, introducing a time decay factor to enhance the attention on recent data, and obtaining a fused state feature vector; and taking the fused state feature vector as an input.

The invention relates to the technical field of fault detection, in particular to an ultralow-temperature refrigerator refrigeration fault detection method. Information is collected through the information sensing layer, external sensing data and refrigerating system data are output, and the defect of a single external sensor is overcome; the external sensing unit and the refrigeration sensing unit respectively perform targeted acquisition, and when an external sensor is abnormal, fault detection can be realized through refrigeration system data, so that the stability and reliability of detection are improved, and the problem of detection failure caused by sensor faults is avoided; according to the method, the data stability index is obtained by identifying the external sensing data, the data health signal is obtained by performing health assessment on the data stability index, the external sensing abnormal total value and the refrigerating system abnormal total value are comprehensively analyzed, abnormal levels are divided, the corresponding fault alarm strategy is generated, the fault severity degree can be matched, and the fault alarm accuracy is improved. False report and missing report are reduced, and safe operation of the refrigerator is guaranteed.

The invention discloses an engineering material extrusion control system based on performance constraint, and relates to the related technical field of engineering manufacturing, and the system comprises the following steps: obtaining performance analysis data of an engineering material; performing data stability probability analysis, and determining constraint performance based on the data trend stability probability; performing support degree relation operation of decomposition parameters by using a process target result to obtain a process core control parameter; establishing a mapping relation between the constraint performance and the flow core control parameters; and optimizing control parameters when the control target is maximum to obtain extrusion control information, and sending the extrusion control information to a control center to perform extrusion operation process control. The technical problems of unstable product quality and low production efficiency caused by difficulty in real-time monitoring and accurate control of key parameters in the extrusion process in the existing engineering material extrusion control are solved, automatic adjustment of extrusion parameters is realized, and the technical effects of improving the stability of the product quality and the production efficiency are achieved.

The invention relates to the technical field of air pollution detection, in particular to a flue gas detector and a calibration detection method thereof, and the flue gas detector comprises a concentration fluctuation analysis module, an abnormal data screening module, an error interval collection module, a parameter trend analysis module and a period stability judgment module. According to the method, abnormal data and equipment drift influence are dynamically screened, an error change interval is actively collected, and through linkage comparison of multi-source parameter trends and separation of working condition disturbance, instrument abnormality and periodic offset signals, automatic screening of concentration data and operation parameters under multiple links is achieved, feature recognition and abnormal partition are associated, and the accuracy of the system is improved. The sampling effectiveness, the data stability and the instrument parameter change trend under the continuous detection working condition are managed in a layered mode, a traceable dynamic calibration chain is formed, the smoke detection consistency and the data exception handling capacity in the complex emission environment are remarkably improved, and an intelligent judgment mechanism of data grading rejection and trend aggregation is perfected.

The invention belongs to the technical field of medical image processing, and provides a multi-modal medical image analysis system, medium and equipment for gastrointestinal disease recognition, and the technical scheme is as follows: extracting multi-scale texture features and structural features of a gastrointestinal endoscopy color image and a gastrointestinal endoscopy OCT depth image respectively, and fusing to obtain a cross-modal feature sequence; based on the feature sequence added with the position code, learning a spatial corresponding relation of different modal data on blood vessel morphology and gastrointestinal tract wall interlayer structure; obtaining a multi-scale global context feature sequence and a local detail feature sequence based on the aligned cross-modal blood vessel and gastrointestinal tract wall interlayer structure features, and fusing the multi-scale global context feature sequence and the local detail feature sequence to obtain fused multi-modal features; obtaining an adjusted dense deformation field based on the fused multi-modal features; and performing coarse registration and fine registration based on the adjusted dense deformation field to obtain a registered gastrointestinal endoscopy color image and a registered gastrointestinal endoscopy OCT depth image. The anti-interference capability is strong, and the data stability is high.