4269 results about "Data acquisition unit" patented technology

The invention relates to the technical field of flow measurement calibration, in particular to a flow instrument intelligent calibration system based on multi-sensor fusion, which comprises a data acquisition unit, a deep coupling compensation unit and a closed loop verification unit, a data acquisition unit obtains a differential pressure value, an environment temperature, a pipeline pressure, a vibration frequency spectrum and sensor accumulated working time, a depth coupling compensation unit constructs an aging prediction model based on a Weibull distribution life model, and a temperature-pressure coupling equation and a vibration compensation mechanism are combined to obtain an aging real-time value and a time sequence deviation. Multi-parameter coupling characteristics are extracted through a neural network, an environment disturbance compensation coefficient matrix is constructed, a joint compensation amount is generated through dynamic weight distribution, a closed-loop verification unit optimizes model parameters, the problems that multi-source disturbance coupling analysis is insufficient and calibration precision is low in the prior art are solved, accurate calibration of a flow instrument under complex working conditions is achieved, and the calibration precision is improved. The metering stability is improved.

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention relates to the technical field of numerical control machine tools, in particular to a five-axis machining center real-time thermal error compensation system based on digital twinning and a medium. Firstly, a data acquisition unit is used for acquiring physical actual measurement data in real time; then, the digital twin processing unit constructs a model set used for representing the thermal dynamic behavior of the five-axis machining center, and the model set is composed of a plurality of virtual thermal model members; thirdly, fusing physical measured data into the model set through a data assimilation algorithm to dynamically correct the state of each model member and predict the thermal error of the machine tool; the prospective compensation decision-making unit receives the thermal error prediction result output by the digital twinning processing unit and generates a prospective compensation instruction; and finally, the compensation execution unit issues the prospective compensation instruction to a numerical control system of the five-axis machining center for real-time adjustment. The control precision of real-time thermal error compensation of the five-axis machining center can be improved.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention discloses an intelligent port crane operation monitoring method and system, and relates to the technical field of crane systems.The system comprises a data acquisition unit, an edge computing node, a cloud processor and an early warning monitoring unit, through deep fusion and efficient treatment of multi-source data, a dynamic twinborn data pool architecture is established, and the dynamic twinborn data pool architecture is established; cross-data-type space-time alignment is realized through multi-modal coding, and the feature fusion accuracy is improved; the dynamic twin data pool is intelligently analyzed, and the crane operation path is optimized in real time, so that the track of the crane operation path is dynamically adjusted, and the working efficiency is improved; through a digital twin model and integration of a multi-modal physical engine, the operation physical state deviation of the crane is evaluated, an operation plan is corrected in real time, and the operation precision is improved; video streams are deeply analyzed, virtual-real comparison is carried out, and model prediction video deviation is monitored, so that the analogue simulation precision is improved; by establishing a closed-loop early warning mechanism, full-process digital management and control are realized, and intelligent upgrading of a port is promoted.

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

A system for breakage early warning of high-voltage transmission lines based on acoustic prints, including a data acquisition unit, an edge processing unit, a cloud server, a warning execution unit, and a communication unit. The data acquisition unit is configured to collect environmental information of a high-voltage transmission tower in real time through a multimodal sensing device. The edge processing unit includes an adaptive filtering module configured to eliminate wind noise interference in the environmental information and compensate for frequency response deviation. The cloud server can generate a warning instruction based on the environmental information. The warning execution unit can perform warning and alarming actions based on the warning instruction. The communication unit is configured to enable data and information transmission among the units. A method implemented by such system is also provided.

The invention provides an intelligent learning behavior monitoring and abnormity early warning method and device, and relates to the technical field of education analysis, and the device architecture comprises a multi-modal data collection module, a behavior analysis module, a dynamic abnormity grading early warning module and a processing measure module. The multi-modal data acquisition unit integrates learning platform operation records, facial expression data and physiological data as well as environment and situation information collected by the situation awareness data acquisition unit, so that omnibearing and multi-dimensional data acquisition is realized; the long-time monitoring and personalized model construction unit uses a plurality of data mining algorithms and LSTM in deep learning to combine with an attention mechanism, accurately mines student behavior rules according to long-term data and constructs a personalized model, and can flexibly and accurately judge abnormal behaviors according to student individual conditions and learning situations. Graded early warning is provided for related personnel in time, deterioration of learning problems is effectively prevented, and timeliness and effectiveness of education intervention are improved.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The invention relates to the technical field of transformer substation intelligent management, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with a multi-source data integration and collaborative management function. Comprising a multi-source data acquisition unit; the edge intelligent processing unit is used for carrying out local preprocessing, feature extraction and abnormal early warning on the acquired multi-source heterogeneous data of the transformer substation, and realizing data noise reduction, abnormal recognition and equipment health degree evaluation through a lightweight AI algorithm and a hybrid communication protocol carried by the edge computing terminal module; and a data integration collaboration unit. According to the invention, through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multi-dimensional data such as electric power parameters, power environment, fire protection and security protection and the like are incorporated into a monitoring system, feature layer association analysis is realized by depending on a Jousseme distance and a Dempster synthesis rule, the assessment limitation of single equipment and single-dimensional data in a traditional scheme is broken through, and the method has the advantages of high reliability and high reliability. And a more comprehensive state basis is provided for equipment health degree research and judgment.

The invention belongs to the technical field of liquid level measurement and industrial process control, and particularly relates to a radar liquid level meter system fusing an adaptive algorithm and multi-modal data. Comprising a data acquisition unit, a signal acquisition and preprocessing unit, a signal denoising and optimizing unit, a data fusion and anomaly detection unit, a self-adaptive calibration and drift compensation unit and a display and alarm terminal. The system has the advantages that the radar liquid level meter, the auxiliary sensor, signal processing, data fusion, anomaly detection, self-adaptive calibration, drift compensation and other technical means are organically combined, and the system can achieve high-precision and real-time monitoring of the liquid level under the complex working condition.

The present invention discloses a method and system for artificial intelligence-based cryptocurrency regulatory analysis capable of performing automated, adaptive, and verifiable compliance evaluation across multiple blockchain ecosystems. The invention integrates blockchain data acquisition, data normalization, graph-based behavioral modeling, artificial intelligence inference, and cryptographically anchored reporting within a unified architecture. The system comprises a blockchain data acquisition unit for retrieving multi-chain transaction data, a data normalization unit for harmonizing heterogeneous blockchain formats, a graph construction unit for generating dynamic transaction graphs, a regulatory knowledge base unit storing jurisdiction-specific regulatory rule graphs, an artificial intelligence processor configured for hybrid neural and symbolic reasoning, and a regulatory reporting unit for generating explainable compliance reports cryptographically anchored to a blockchain ledger.

The invention provides a cross-platform education data privacy protection analysis system, and relates to the technical field of data privacy protection. The cross-platform education data privacy protection analysis system comprises a multi-modal data acquisition unit, a dynamic privacy analysis engine, a federal learning processing core module, a block chain enhanced access control system, a privacy watermark traceability module and a compliance autonomy unit. Dynamic coupling of scene sensitivity and data protection intensity is realized through a dynamic privacy risk scoring equation and an adaptive differential privacy noise equation; the problems of privacy disclosure, compliance verification and traceability and responsibility investigation in cross-platform education data sharing are solved by combining federal learning, block chain evidence storage and privacy watermarking technologies. According to the method, the privacy risk assessment precision is remarkably improved, the model precision loss is reduced, full-life-cycle data security management and control are supported, and the method is suitable for multiple scenes such as K12 education and college educational administration.

The invention relates to the technical field of artificial intelligence, in particular to a real-time anti-fraud monitoring system and method based on behavior reasoning and sentiment analysis, and the system comprises a multi-modal data collection unit, an edge preprocessing unit, a feature fusion and behavior reasoning unit, a large language model context reasoning unit, a risk assessment and decision unit, and an intervention execution unit. A log recording and federal incremental learning unit; the method has the beneficial effects that the traditional isolated single-mode detection is evolved into an emotion and behavior dual-channel collaborative multi-mode recognition system through millisecond-level coaxial alignment of voice, video and user operation logs; the robustness of dialect, noise and expression shielding is greatly improved through the multi-modal fusion model, so that the cross-scene recognition accuracy is improved by nearly three percent compared with that of a traditional single-voice scheme, and high-sensitivity capture of hidden and emotion control type fraud is truly achieved.

The invention relates to the technical field of pipeline safety monitoring, and discloses an online lossless real-time monitoring system for micro-strain of an in-service natural gas pipeline. A micro-strain data acquisition unit of the system acquires a micro-strain data set on the surface of the in-service natural gas pipeline in real time. And the three-dimensional strain field reconstruction unit receives the data set and executes three-dimensional strain field reconstruction processing to generate strain distribution characteristics of the pipeline. And the life prediction model analysis unit calls a pre-trained life prediction model to carry out nonlinear analysis processing on the strain distribution characteristics, and outputs a residual life prediction value and a key risk area identifier of the pipeline. The environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value to generate a corrected residual life prediction value. And the maintenance strategy generation unit generates a pipeline maintenance strategy set according to the key risk area identifier. According to the invention, real-time and accurate evaluation and intelligent maintenance decision support of the health condition of the pipeline are realized.

The invention relates to the technical field of combined fleet navigation, and discloses a combined fleet intelligent navigation data processing system, which comprises a data acquisition unit used for acquiring navigation data and ship lock data of each ship of a combined fleet in real time, fusing the preprocessed navigation data and ship lock data to obtain a comprehensive data set, through dynamic data fusion and self-adaptive grid division, the spatial-temporal characteristics of a flow velocity abrupt change area during opening and closing of a ship lock are captured in real time, the limitation that a traditional algorithm depends on a static environment is broken through, a dynamic power distribution strategy is generated based on flow field characteristics and navigation data, and precise power matching is achieved in combination with a propeller efficiency priority and a self-adaptive weight algorithm. The response time is shortened, the cooperation efficiency is improved, course deviation is avoided, a multi-stage early warning and time difference judgment mechanism is constructed, the power margin is evaluated in real time, the weight, the navigational speed and the formation are adjusted according to the risk level, it is guaranteed that power distribution adjustment of the whole fleet does not lag behind water flow changes, and normal course of the fleet is guaranteed.

The invention discloses a high-precision attitude sensor dynamic calibration system and a method thereof, and belongs to the technical field of attitude sensors. The invention discloses a dynamic calibration system and method for a high-precision attitude sensor. The dynamic calibration system comprises a data acquisition unit, a dynamic calibration unit and a calibration execution unit. The problem that the calibration effect is limited in the prior art is solved, the state of the sensor can be known more comprehensively by collecting the output data of the high-precision attitude sensor, the environmental data and the multi-source data of the accelerometer, the gyroscope and the magnetometer in real time, so that more accurate error analysis and calibration are carried out, and the calibration accuracy is improved. Modeling and error analysis are carried out by utilizing a machine learning algorithm, parameters are automatically adjusted through a model adaptive updating mechanism, error analysis can be carried out on data collected in real time so as to provide reference for subsequent dynamic calibration, and dynamic calibration operation of the high-precision attitude sensor is accurately executed according to an analysis result of the model. And high precision and high stability of attitude measurement are ensured.

The invention relates to the technical field of fire-fighting management, and discloses an intelligent fire-fighting service management system, which comprises a data acquisition unit used for acquiring three basic parameters, namely environment data, working condition data and battery data, of a fire-fighting object in an office building, and the system acquires the environment, working condition and battery data of a fire extinguisher through the data acquisition unit; the fusion unit generates a dynamic composite abnormal threshold value, and the adjustment unit executes a three-stage sampling frequency adjustment mechanism according to the threshold value, the battery health degree score and the data deviation degree, which means that the sampling frequency can be reduced to reduce the battery energy consumption when the state of the fire extinguisher is normal and the risk is relatively low, and when the risk is increased or the battery health degree is reduced, the battery energy consumption is reduced. The whole system realizes accurate management and regulation of the battery according to the actual working condition of the fire extinguisher, energy waste is avoided, the service life of the battery is prolonged, the cost of frequently replacing the battery is reduced, and the sustainability of intelligent fire control management is enhanced.

The invention discloses an intelligent field management system based on an Internet of Things platform, and relates to the technical field of Internet of Things, and the intelligent field management system comprises a multi-source data fusion and analysis module, a waste recovery and resource utilization module, a knowledge graph and question and answer module, and a self-adaptive decision and regulation module. The multi-source data fusion and analysis module comprises a data acquisition sub-module and a data analysis and preprocessing sub-module; the data acquisition sub-module comprises a multi-source heterogeneous data acquisition unit and a real-time dynamic data acquisition unit; the multi-source heterogeneous data acquisition unit uses a wireless sensing technology, a GPS positioning technology, a sensor calibration algorithm and a data interpolation algorithm to acquire meteorological, soil, crop physiology and biological field data. By arranging the multi-source data fusion and analysis module, the problems that traditional agricultural data is difficult to obtain and not deep in analysis, precision agricultural development is difficult to support, and agricultural resources are wasted and crop yield and quality are unstable due to data quality problems can be solved.

The invention discloses a reservoir real-time scheduling simulation system based on a deep learning algorithm, and belongs to the technical field of intelligent water conservancy and artificial intelligence. Aiming at the problems of low prediction precision, poor multi-target coordination capability, weak coping uncertainty and the like of a traditional scheduling system, the system is designed to acquire hydrological, meteorological, water quality and engineering safety data through a multi-source data acquisition unit, and a multi-dimensional feature tensor is generated after preprocessing and fusion; the dispatching center server adopts an STGCN-LSTM mixed model to achieve high-precision prediction and uncertainty quantification of the water inflow process in the future 7-30 days, a reservoir hydrodynamic model and an MO-PPO algorithm are combined to complete multi-scene simulation and multi-target optimization decision, and an AF-DT mechanism dynamically adjusts the dispatching rule priority. According to the system, a sensing-decision-execution-feedback closed loop is constructed, the scheduling adaptive capacity and robustness are improved, the synergistic interaction of flood control, water supply, power generation and ecological protection is realized, and the system is suitable for real-time intelligent scheduling of large and medium reservoirs.

The invention belongs to the field of artificial intelligence, and provides a multi-modal depression recognition system based on an MFE-CCANNN model, which comprises a data acquisition unit, a data preprocessing unit and an MFE-CCANNN model unit. The data acquisition unit synchronously acquires multi-mode data such as videos, audios, texts and fNIRS when a subject performs the same interview task. The data preprocessing unit comprises a video preprocessing unit, an audio preprocessing unit, a text preprocessing unit and an fNIRS preprocessing unit. The MFE-CCARNN model unit comprises a video, audio, text and fNIRS neural signal feature extraction module, a multi-modal feature fusion module and a classification module, and depression recognition and classification result output are achieved. The system supports four-level depression degree discrimination, is high in recognition precision, portable in deployment, high in interpretability and the like, and is suitable for psychological health screening and clinical auxiliary evaluation scenes.

The invention relates to the technical field of numerical control machine tool fault diagnosis, in particular to a numerical control machine tool fault diagnosis and active early warning system. The system comprises a data acquisition unit which is used for acquiring multi-dimensional original data generated in the operation process of the numerical control machine tool; the data processing and feature extraction unit performs feature extraction on the collected multi-dimensional original data based on an interactive feature construction algorithm; and the fault diagnosis unit constructs a classifier for distinguishing different fault types based on a random forest model, and realizes the judgment of the fault types through the random forest model based on the interactive feature vector # imgabs0 #. An interactive feature construction algorithm is adopted to perform interactive feature analysis on the preprocessed multi-dimensional original data, the problem of feature distortion caused by neglecting wear factors is avoided by introducing a real-time updated wear index, interference of external factors on feature analysis is eliminated by introducing an environment temperature correction item, and the accuracy of feature analysis is improved. And the actual reaction capability on the operation state of the equipment is improved.

The invention discloses a vehicle-mounted holder laser telemetering scanning type gas leakage positioning system, and relates to the technical field of gas leakage monitoring. The vehicle-mounted pan-tilt laser telemetering scanning type gas leakage positioning system comprises a data acquisition unit for acquiring laser absorption second harmonic signal intensity of an inspection vehicle on a set route; the concentration inversion processing unit is used for inverting the gas concentration on the basis of the signal; the concentration analysis unit is used for identifying an abnormal inspection point by comparing the concentration change rate; the leakage direction analysis unit is used for predicting the leakage direction by combining wind direction concentration data; according to the invention, a signal saturation upper limit value and an optical path length value are introduced to carry out normalization processing, concentration inversion calibration is carried out in combination with a gas absorption coefficient, independent calculation is carried out at each inspection position point, and the accuracy of the inspection is greatly improved by combining remote measurement concentration distribution data, image data and a leakage position analysis unit for accurately positioning a leakage point in combination with a gas diffusion model. And continuous, dynamic and high-precision concentration distribution reconstruction in space is realized.

The invention relates to the technical field of combination of communication network management and artificial intelligence, in particular to an intelligent network traffic anomaly monitoring system fused with artificial intelligence, which comprises a data acquisition unit, a feature extraction and processing unit, an intelligent anomaly detection unit, a fault prediction and health management evaluation unit, a decision response unit and a continuous model optimization unit. Through multi-modal intelligent fusion, lightweight modeling and hybrid modeling technologies, real-time accurate identification and potential threat positioning of network flow abnormal behaviors are realized, network equipment faults and health states are predicted based on interpretable AI and physical law fusion, a transparent and prospective decision basis is provided for network operation and maintenance, and the network flow abnormal behavior prediction method has the advantages of high reliability and high reliability. The anomaly detection accuracy is greatly improved, the fault prediction advance exceeds 24 hours, and the network security and reliability are remarkably enhanced.

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.

The invention discloses a low-carbon building monitoring system and method based on digital twinning, and relates to the technical field of digital twinning, the system comprises a data acquisition unit, a computing power cooperation unit, a regulation and control execution unit and a user interaction unit, the data acquisition unit carries out regional targeted timing monitoring, builds a unified semantic standard and a dynamic knowledge graph, and sends the unified semantic standard and dynamic knowledge graph to the user interaction unit; the comprehensiveness and efficiency of data association analysis are improved; a three-level computing power collaboration system is constructed through the computing power collaboration unit, building monitoring information is integrated, complex simulation of a digital twin module is carried out, an energy transmission path and equipment fault risks are accurately tracked, prediction optimization and long-term regulation and control management are carried out, and the construction efficiency is improved. Therefore, a full-life-cycle integrated management framework in a macroscopic geographical environment, a mesoscopic energy link and microscopic equipment of a building cluster is built, cross-domain collaborative analysis is realized, the real-time requirement is met, multi-target optimization design for balancing energy cost, energy efficiency and comfort is realized, and carbon neutralization targets in the building industry are assisted to fall into the ground.

The invention relates to the technical field of industrial process intelligent monitoring, and provides a digital twinning-based gas pressure regulation self-regulation intelligent monitoring system, which comprises a data acquisition unit, a digital twinning unit and an execution and feedback unit. The data acquisition unit acquires operation data of the gas pressure regulating equipment in real time, and the operation data are preprocessed and then transmitted to the digital twinning unit. The digital twinning unit constructs a physical mechanism sub-model based on the geometric topology and heat flow coupling characteristics of the gas pressure regulating equipment, and loads a data driving sub-model to form a mixed digital twinning model; and in combination with real-time data mapping and a disturbance-safety boundary condition set, executing predictive iterative simulation to generate a plurality of groups of voltage regulation schemes, and screening an optimal scheme. And the execution and feedback unit converts the scheme into a standardized control instruction to drive pressure regulating equipment and return operation data to realize closed-loop optimization, so that real-time monitoring, high-precision prediction and self-adaptive regulation and control of the gas pressure regulating process are realized under complex working conditions, and the safety, the stability and the energy utilization efficiency are improved.

The invention relates to the technical field of broadband spectrums, and discloses a broadband spectrum monitoring system based on dynamic channel allocation, which comprises a data acquisition unit, a data identification unit, a region division unit, a data alignment unit and a channel allocation unit.The system dynamically adjusts the coverage area through beacon nodes, accurately calculates the coverage radius in combination with the urban environment, and improves the coverage efficiency. Meanwhile, a channel allocation strategy is optimized by a game theory, the change of a frequency spectrum environment is responded, and frequency spectrum state parameters are constructed by fusing multi-dimensional characteristics, so that communication smoothness is guaranteed, blind areas of monitoring nodes are eliminated, data space-time alignment is realized, the monitoring sensitivity and the resource utilization rate are effectively improved, and stable frequency spectrum and reliable communication under large-scale activities in cities are ensured; and the dynamic and reasonable distribution of the channels is ensured.

The invention relates to the technical field of electrochemical testing, in particular to an electrochemical impedance spectroscopy testing system based on multi-model combination, which comprises a data acquisition unit, a deep learning analysis unit, a frequency control unit and a feedback regulation unit. The deep learning analysis unit adopts a convolutional neural network and a long-short-term memory network to construct a model, after training, features are extracted, trends are analyzed, and dynamic frequency priority ranking is carried out, the frequency control unit drives the electrochemical workstation to adjust the frequency according to a weight generation strategy, and the feedback adjustment unit calculates predicted and actually measured impedance errors. When the threshold value is exceeded, parameter correction is carried out in each unit, the test frequency is intelligently optimized according to the dynamic change of the electrochemical process, key information is accurately captured, resource waste is avoided, the test efficiency and accuracy are improved, the stability and reliability of the system are ensured through a feedback regulation mechanism, and the research and analysis capability of the electrochemical system is effectively improved.

The invention relates to the technical field of gas detection, in particular to a multi-gas accurate detection method and system.The method comprises the following steps that gas to be detected flows through a miniature gas enrichment device prepared through an MEMS technology under driving of a miniature gas pump, and target gas is adsorbed and enriched through graphene and metal oxide composite sensitive materials on the surface of a spiral gas inlet flow channel in the miniature gas enrichment device; the desorption concentrated gas is heated to the multi-gas composite detector, and the sensor array reacts with the gas to generate an electric signal; the data acquisition unit performs noise reduction, amplification and analog-to-digital conversion preprocessing on the electric signal; the data processing unit extracts characteristic vectors such as a response curve peak value and a rising slope; inputting the feature vector into a pre-trained machine learning classification-regression combined model, and outputting a gas type identifier and an initial concentration; error compensation is carried out in combination with environment temperature and humidity and a cross sensitivity interference mode library, and finally accurate information of at least nine gases is output. The device has the advantages of high sensitivity, strong anti-interference performance and miniaturization.