179 results about "Data continuity" patented technology

The invention discloses a coal mine dust diffusion intelligent monitoring method based on a distributed sensing network, and relates to the technical field of coal mine dust diffusion intelligent monitoring, and the method comprises the following steps: deploying a plurality of sensing nodes in a coal mine, each node having a function of collecting environmental data, a distributed sensing network is formed through a wireless or wired communication module, and when all nodes are started, a unique node identifier is issued through a central controller, and self-inspection is carried out. Through bidirectional delay calculation and a progressive fine tuning strategy, high-precision time synchronization is achieved, and it is ensured that timestamps of data such as dust concentration and airflow are accurate and reliable. And data jump caused by large correction is avoided through gradual fine adjustment, data continuity is guaranteed, and the precision of dust diffusion analysis and an early warning model is improved. A redundant node management and automatic switching mechanism improves the fault processing capability of the system, avoids a monitoring blind area, accelerates maintenance response through fault information reporting, and ensures stable operation of a monitoring network.

An intelligent power monitoring management system based on Internet of Things belongs to the technical field of power supervision and comprises a data acquisition module, a communication transmission module, a data processing and analysis module, an edge calculation module, an intelligent control module, an early warning management module and an authority control module. According to the invention, by deploying various types of sensors, real-time acquisition and dynamic monitoring of key parameters of the power system are realized. The system can obtain and analyze data such as voltage, current and power in time, ensures the accuracy and high efficiency of operation of the power system, and effectively avoids energy consumption waste and equipment faults. The edge computing technology is adopted, preprocessing and preliminary analysis are carried out at a data acquisition end, the data processing pressure of a central server is relieved, the bandwidth requirement for data transmission is lowered, and the response speed is increased. Even if the network is interrupted, the edge device can realize short-term data caching, and data continuity is guaranteed. And performing anomaly detection on the power system, identifying potential risks and giving an alarm.

The invention relates to the technical field of land resource management, in particular to a land resource space optimization configuration method and system, and the method comprises the following steps: dividing grid cells based on a land research region, setting the initial land utilization state of each cell, comparing the land suitability of adjacent cells, updating the land utilization type, and determining the land utilization state of each cell; and recursively calculating the change in each time step, and iteratively executing the transfer specification to obtain a land utilization change state sequence. According to the invention, through recursive calculation and land utilization state updating, land utilization dynamic simulation, data continuity and regional adaptability enhancement, vacancy value filling and neural network adjustment, data and environmental factor feedback adjustment, prediction precision and trend judgment capability are improved, and economic, social and ecological benefits are quantified. The land use proportion and spatial layout are optimized, the land resource allocation efficiency is improved, policy adjustment and scene simulation are combined, differentiated decision support is provided, and social fairness and efficient resource utilization are promoted.

The invention discloses a tunnel deformation real-time early warning method and system based on an LSTM-CNN model, and belongs to the technical field of tunnel engineering safety monitoring. The method comprises four steps of data acquisition, spatio-temporal feature fusion processing, deformation prediction and risk assessment, and intelligent early warning and decision support: collecting multi-dimensional monitoring data through a distributed sensor network and recording spatio-temporal labels; after standardized noise reduction, extracting time trend and spatial distribution characteristics by using an LSTM-CNN fusion model, and constructing a time-space sequence data set; outputting a deformation prediction value based on the fusion features, and calculating a deviation degree in combination with a dynamic threshold model; and triggering multi-level early warning according to the deviation degree and performing visual display. The system comprises a data acquisition module, an edge calculation module, a cloud analysis module and an intelligent terminal module, and double-channel redundancy transmission is adopted to guarantee data continuity. Through spatio-temporal feature collaborative mining, dynamic threshold value adaptation and graded early warning, the deformation prediction precision and the risk assessment accuracy are improved, the transmission stability in an extreme environment is guaranteed, and the early warning decision efficiency is optimized.

The invention relates to the technical field of data management, in particular to a pet soft food production data management method and system.The method comprises the following steps of analyzing heat energy and rhythm synchronization based on damp and hot section operation data, judging batch data continuity, extracting fluctuation data to recognize quality inspection deviation, and matching packaging and discharging to analyze response consistency. And abnormal batches are extracted to locate an alarm concentration area. According to the method, by monitoring the heat energy flow and the conveying period, synchronization of the heat load and the putting rhythm is achieved, data consistency and collaboration are improved, data interruption and overlapping are avoided in combination with the batch time difference, fluctuation of indexes such as PH is periodically recognized, the quality inspection gathering time period is determined, packaging and discharging response offset is analyzed, and the equipment collaboration efficiency is quantified; energy records and alarm logs are crossly screened, abnormal batches are accurately positioned, a multi-parameter time sequence coupling analysis chain is constructed, cross-section quality response and traceability are enhanced, and centralized troubleshooting and early warning of the abnormal batches are realized.

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 an Internet of Things service transmission method for a low-orbit satellite mobile communication system, which relates to the technical field of Internet of Things and comprises the following steps: starting a satellite base station, dividing resources, carrying out initial resource configuration and broadcasting a message by a terminal; the Internet of Things terminal receives the broadcast message, carries out primary connection with the satellite base station, and sends a handshake packet to the satellite base station to carry out intention identification and service parameter configuration; dynamically adjusting a spectrum allocation strategy according to a prediction result, and generating an optimized spectrum allocation scheme and a resource scheduling instruction; performing related state information switching based on the optimized spectrum allocation scheme and the resource scheduling instruction; after receiving the service data uploaded by the terminal, the data center judges whether a confirmation signal needs to be sent to the Internet of Things terminal in combination with the switched state information; according to the invention, the direct communication link is established through the inter-satellite link, so that the data continuity and the low-delay characteristic in the switching process are ensured.

The invention relates to the technical field of earthquake prediction, in particular to a crustal stress azimuth earthquake prediction method based on a transverse wave speed change rate, which comprises the following steps: calculating a speed value of a transverse wave and a propagation distance, extracting a difference value to generate a change rate set, calculating an offset rate, screening a continuous overrun section, identifying a disturbance path section, and performing interpolation restoration to generate a purification sequence. Extracting forward and reverse propagation trends and counting direction identifiers, and generating a crustal stress main stress direction map; according to the method, a change rate set is generated through a sliding window segmentation speed sequence, data continuity is enhanced, local features are reserved, a sudden change area is screened and accurately positioned in combination with a continuous offset rate, multi-path lithology difference identification and disturbance repair are carried out, non-structural interference is eliminated, and forward and reverse path cross validation is carried out to screen a direction consistent section, so that the discrimination robustness is improved; the velocity direction is dynamically associated with the structure trend, synchronous analysis of the stress field and fault activity is realized, multi-stage processing is combined with dynamic threshold and direction verification, and the derivation precision and timeliness are improved.

The invention relates to the technical field of information management, in particular to a safety production informatization data management method and system, and the method comprises the following steps: obtaining a synchronous monitoring record, screening field integrity and numerical value qualified information, marking and classifying abnormal items, filing qualified and abnormal data, constructing a time sequence, additionally recording and correcting abnormal contents, and storing the abnormal contents. Weight hierarchical storage information is extracted, low-frequency data is compressed, a field structure is optimized, and a management scheme is generated. According to the method, the monitoring information is preliminarily screened according to field integrity and numerical standard, the data distribution efficiency is improved, abnormal information is classified and labeled, the structure recognition precision is enhanced, missing supplementary pushing and numerical correction are realized through time sequence comparison, the data continuity is guaranteed, hierarchical storage is performed according to information weight, attributes are clearly reserved, and low-frequency data compression and integration are performed; and the storage burden is relieved, the field structure is automatically expanded and rearranged, the format uniformity and the management specification are improved, and refined processing and efficient circulation of data are realized.

The invention discloses a UWB (Ultra Wideband) ranging error suppression method fusing a black-wing plinary algorithm and an information entropy adaptive adjustment mechanism. The method comprises the following steps: firstly, identifying a non-line-of-sight signal by combining a ranging residual error, a first-order difference and a sliding window strategy; abnormal values are removed based on residual errors, missing data are compensated through segmented three-time Hermite interpolation, and data continuity and smoothness are guaranteed; calculating Shannon information entropy by using sliding window kernel density estimation, dynamically quantifying residual distribution uncertainty, adaptively adjusting Kalman gain, and realizing weight real-time adjustment; meanwhile, the initial parameter configuration of the filter is optimized by adopting a black-wing algorithm, and the convergence and the overall stability of adaptive filtering are improved. Aiming at the problems of measurement abnormity and precision reduction caused by the fact that UWB distance measurement is easy to be shielded and interfered in the NLOS environment, the method can effectively inhibit distance measurement errors, overcomes the limitation that a traditional method depends on manual parameter adjustment, and provides powerful technical support for high-precision UWB distance measurement in the NLOS environment.

The invention provides a grounding wire state monitoring and early warning system and method based on a random forest algorithm, relates to the technical field of electric power safety monitoring, and solves the limitation problems of low efficiency, poor data continuity, inaccurate early warning judgment and the like in the existing scheme. In the system, a multi-source sensor network acquires multi-dimensional operation data corresponding to a grounding wire and transmits the data to a data acquisition terminal; the data acquisition terminal performs feature extraction processing to generate a feature vector set and uploads the feature vector set to the cloud data processing platform; the cloud data processing platform uses a random forest classification model, based on the feature vector set, realizes dynamic feature weight adjustment through feature importance analysis, constructs a hierarchical decision tree structure, identifies and outputs a grounding wire fault mode, and issues the grounding wire fault mode to the local early warning terminal; and the local early warning terminal provides early warning information for the ground wire site by adopting a graded and differentiated early warning mode. According to the invention, high-precision early warning and predictive maintenance of the state of the grounding wire are effectively realized.

The invention discloses a drainage pipe network data cleaning and intelligent repairing method fusing multiple models. The method comprises the following steps that S1, original monitoring data flow is collected; s2, carrying out the preliminary detection and elimination of the abnormity based on IQR; s3, performing upstream and downstream multi-source feature extraction and XGBoost weight analysis; step S4, multivariable depth autoregression prediction based on PatchTST is carried out; s5, generating and publishing continuous high-quality data; according to the method, the key technical problems of various abnormal types, incomplete abnormal detection, low cleaning and repairing precision, poor data continuity and trend consistency, high system integration and real-time processing difficulty and the like commonly existing in the actual collection and transmission process of the online monitoring data of the drainage pipe network are solved.

The invention discloses a multi-source geological disaster information intelligent processing method, and particularly relates to the technical field of geological disaster monitoring. The method comprises the following steps: collecting multi-source monitoring data such as remote sensing images, surface deformation, meteorology, underground water level and seismic micro-motion, and preprocessing to form a feature vector set; establishing a credibility weight matrix based on data continuity and consistency evaluation; main disaster-inducing factor combination features are extracted through an adaptive feature extraction model; constructing a disaster evolution graph in combination with historical disaster data; in the continuous monitoring process, when the node attribute variation rate in the atlas exceeds a threshold value and an abnormal path chain is formed, geological disaster early warning output is triggered; according to the method, heterogeneous data fusion and disaster factor dynamic modeling can be realized, and disaster prediction accuracy and response efficiency in a complex environment are improved.

The present application provides a method, apparatus, device, and storage medium for determining the buoyancy production term of atmospheric turbulent kinetic energy. The method for determining the buoyancy production term of atmospheric turbulent kinetic energy obtains the wind speeds at several moments through a wind profiler radar within a specified time length, determines the dissipation rate and the pulsating components corresponding to the wind speed in different coordinate axes directions according to the wind speeds at several moments, determines the turbulent trend term, the shear production term, and the turbulent transport term according to the pulsating components corresponding to different coordinate axes directions. Using a wind profiler radar to collect wind speeds is beneficial to improving the spatial resolution, data continuity, and expanding the detection range of data, determining more accurate dissipation rate, turbulent trend term, shear production term, and turbulent transport term, so as to be able to more accurately deduce the buoyancy production term of atmospheric turbulent kinetic energy according to the dissipation rate, turbulent trend term, shear production term, and turbulent transport term, ensuring the accuracy of the buoyancy production term.

The invention provides a multi-source data fusion correction method for a marine observation station, which belongs to the technical field of marine observation, and comprises the following steps of: establishing a hierarchical interpolation correction model comprising a weighted moving average algorithm and a space collaborative interpolation algorithm, and constructing a marine environment adaptive threshold adjustment mechanism based on a closed-loop feedback system; a Kalman filter is combined with a multi-factor coupling drift prediction model of a long-short-term memory network, a progressive degradation fusion strategy switched according to environmental conditions is designed, and a biological pollution gradual change detection correction model adopting a liquid neural network structure is constructed. A multi-source data quality evaluation system based on data continuity, numerical stability, consistency and historical reliability is established, high-precision self-adaptive fusion correction of the marine environment multi-source sensor data is achieved, and the technical problem that high-precision self-adaptive fusion correction of the marine environment multi-source sensor data is difficult to achieve is solved.

The invention provides an efficient processing and report generation method, system and equipment for tide level data and a medium. The method comprises the following steps: S1, obtaining original tide level data and carrying out data continuity analysis; s2, de-noising processing is carried out on the tide level data to obtain processed tide level data; s3, performing interpolation processing on the processed tide level data to obtain interpolated tide level data; s4, extracting tide level characteristic parameters from the normalized tide level data after interpolation correction; s5, performing identification and lunar calendar conversion on related Gregorian calendar dates; and S6, generating a standardized report based on the extracted tide characteristic parameters. Through automatic data denoising, interpolation, feature extraction and other processing, the processing efficiency and accuracy of the tide level data are greatly improved, and the defects of manual processing are effectively overcome; meanwhile, by means of an intelligent report generation mode, the analysis result of the tide level data is more visual and readable, and a user can conveniently and rapidly obtain needed key information.

The present invention discloses a method for predicting errors of digital temperature and humidity meters in workshops based on machine learning, and belongs to the field of industrial metrology technology. The technical method proposed in the present invention accurately predicts the measurement error of the meter under test by introducing data smoothing, peak extraction, filtering and outlier processing, and establishing a multivariate linear regression model; the measurement calibration cycle is dynamically adjusted according to the predicted error, which not only ensures the accuracy of the measured data, but also realizes error prediction. The method of the present invention effectively solves the metrology problems caused by small amount of data, misaligned data time series, poor data continuity, unclear performance indicators, and poor abnormal response on site, and combines time series and adaptive adjustment algorithms to improve the credibility of model prediction. The above technical method can reduce human errors in on-site measurements, the algorithm responds in real time, saves manpower, promotes the construction of digital workshops, and ensures the mathematical basis support for measurement.

The invention discloses a data processing method and system based on a monitoring bracelet, and the method comprises the steps: obtaining physiological monitoring data of a monitored object through a built-in chip module of the monitoring bracelet; performing abnormal interval identification and abnormal interval classification on the physiological monitoring data; according to the classification of all the abnormal intervals and the features of the abnormal intervals under each classification, constructing adaptive features of the monitoring object; performing parameter mapping through the self-adaptive features of the monitoring object to generate parameters of a data supplement model; and after data clearing is carried out on each abnormal interval, data supplementation is carried out by utilizing the data supplementation model after parameter assembly. Feature differences of individuals in different abnormal states can be reflected, and personalized data analysis is realized; more reasonable supplementary data is generated for different abnormal characteristics; the generated data is ensured to be consistent with normal data in numerical characteristics and dynamic trends, and the data continuity and physiological rationality are effectively improved.

The invention relates to the technical field of wireless communication, and discloses an uplink wireless channel segmentation transmission method, which comprises the following steps that: a base station calculates the maximum frequency offset and time offset change rate based on a satellite orbit and an elevation angle, determines the acceptable maximum uplink segmentation duration, and configures segmentation parameters of an NPRACH (Non-Physical Random Access Channel) and a public NPUSCH (Non-Physical Uplink Shared Channel) through system broadcast; the terminal performs segmented transmission according to the configuration, inserts the maximum gap between the segments, pauses the transmission, and delays the data; the base station synchronously reserves gap resources to avoid conflicts; the terminal reports the gap capability in milliseconds, and the base station combines the gap for generating the special NPUSCH and the segment configuration and issues through the RRC, so that the overall data continuity and demodulation performance are ensured. According to the invention, the continuity, synchronization accuracy, demodulation performance and stability of NPRACH and NPUSCH segmented transmission in a high-dynamic NTN scene are improved by introducing millisecond-level interval capability reporting, standardized gap insertion and data delay.

The invention discloses a metro vehicle supervision manufacturing multi-station data traceability control system, and relates to the technical field of industrial data traceability, and the system comprises a data collection module which obtains the original manufacturing data of each station of metro vehicle manufacturing; the feature extraction module is used for processing the original manufacturing data and generating behavior fingerprint vectors and station features; the identity analysis module is used for carrying out feature matching calculation and outputting a candidate component identity set and matching similarity; the logic verification module is used for carrying out logic consistency verification and determining the identity of the target component; and the traceability control module establishes a mapping relation with the original manufacturing data based on the identity of the target component, generates a unique traceability node, and writes the traceability node into a multi-station traceability data chain. According to the method, the problems of identification failure, data link breakage and affiliation errors caused by station switching or equipment sharing are solved, supervision data continuity and identity recognition accuracy are improved, and reliable support is provided for vehicle full-life-cycle data tracing.

This disclosure describes systems, methods, and devices related to multi-link device (MLD) data continuity. An MLD device may set up one or more links with a station multi-link device (STA MLD), wherein the STA MLD comprises one or more logical entities defining separate station devices. The MLD device may transmit a data packet associated with a traffic identifier (TID) to the STA MLD. The MLD device may determine that the data packet was not received by the STA MLD. The MLD device may retransmit the data packet to the STA MLD. The MLD device may increment a retransmit counter every time the data packet is retransmitted. The MLD device may refrain from transmitting a second data packet until the data packet is dropped or successfully received by the STA MLD.

The invention relates to the technical field of mine ventilation, and provides a mine ventilation intelligent prediction method based on a cloud computing platform, which is characterized in that in data acquisition and processing, an innovative sensor deployment and subnet cooperation mechanism is adopted, and data continuity and reliability are guaranteed; the data transmission network adopts the design of double-base-station hot standby, optical fiber double-ring network and the like, the stability is far better than that of a traditional network, and data loss is basically eradicated; in the aspect of prediction model, a unique CFD physical model and lightweight neural network hybrid architecture can accurately identify working conditions and switch prediction modes, the prediction precision is improved compared with that of a traditional single model, and the advantages are obvious under complex working conditions; the safety and fault processing mechanism is complete, data safety is ensured through data encryption, authority grading, remote disaster recovery and the like, rapid switching compensation can be achieved during faults, multiple regulation and control modes are supported, and different scene requirements are met.

The invention relates to the technical field of self-checkout, in particular to a restaurant self-checkout method and system based on intelligent tray binding, and the method comprises the following steps: capturing a contact track through installing a sensor at the edge of a tray, judging the attribution of the track, marking the tray to be matched with a seat, obtaining a time period classification food mapping table through a time sequence group, and storing the time period classification food mapping table; and extracting a payment request and identifying delay to obtain an abnormal payment record list, updating the payment interface display structure and outputting an interface payment mode updating list. According to the invention, through tray induction identification, seat matching is realized, consumption sources are clearly identified, data confusion is avoided, a consumption sequence is constructed through track merging, data continuity is improved, time frame mapping is associated with food information, data structure linkage is enhanced, payment behaviors are identified and delayed, processing efficiency is improved, and payment interfaces are dynamically sorted according to abnormal frequency and call amount. The operation fluency and the system stability are optimized, and the settlement efficiency and the payment process stability in the peak period are comprehensively improved.

The invention discloses a PLC and MES simulation interaction protocol conversion gateway realization method, and relates to the field of data interaction, and the method comprises the following steps: 1, collecting an original data flow of a PLC side heterogeneous industrial bus protocol and an interaction instruction of an MES side manufacturing execution layer protocol in real time, dynamically scanning the data flow through an abnormality diagnosis engine, and carrying out the dynamic scanning of the data flow through an abnormality diagnosis engine; identifying missing fields, transmission timeout or numerical value border crossing abnormal states in the PLC data, and generating an abnormal feature vector set carrying timestamps and associated point position labels; 2, constructing and pre-training an anomaly prediction model through a deep learning algorithm, and inputting an anomaly feature vector to the model; trend prediction and confidence evaluation of missing data are realized through a pre-training deep learning model, quantitative credibility support is provided while data continuity is ensured, it is ensured that an MES system can still operate continuously based on credible simulation data when equipment is abnormal, and the risk of production stagnation caused by forced interruption of a traditional gateway is avoided.

The embodiment of the invention discloses a method and device for extracting cross-page table data. The method comprises the following steps that a page object in OFD is analyzed, and text information and line segment information are extracted; classifying the extracted line segments into a horizontal line segment group and a vertical line segment group, and combining the line segment data and the text data of the first page and the second page to form a unified line segment data group and a text data group; identifying a table structure from the merged line segment data set, forming an intersection point grid by detecting intersection of horizontal line segments and vertical line segments, and constructing rectangular cells based on adjacent intersection points to form a cell set; and extracting a text associated with each cell by judging whether the rectangular boundary of each text character is located in the cell boundary or not. According to the embodiment of the invention, the text in the cross-page table is extracted by vectorizing the identification table, the complex page layout, the variable table structure and the potential data continuity problem can be processed, and the consistency and the accuracy of the extraction result are further improved.

The invention discloses a plateau alpine region well logging information automatic updating system, relates to the technical field of well logging information, and collects an underground well logging state record set, dynamically adjusts a sampling strategy according to energy consumption and data characteristics, guarantees stable transmission and recombination of key data when a network is abnormal, and performs consistency check and missing completion on the data. According to the system, logging data can be automatically collected, dispatched, transmitted and complemented in the plateau and alpine regions, and through multi-source sensing, self-adaptive sampling, dual-channel communication and intelligent prediction, the real-time monitoring of the logging data can be realized, and the real-time monitoring of the logging data can be realized. The extreme environment problems of low-temperature network disconnection and insufficient power supply are effectively solved, data continuity and response timeliness are guaranteed, remote updating and closed-loop feedback are supported, high robustness and intelligent level are achieved, and the limitation that operation is unstable and response is not timely under the extreme condition in the prior art is broken through.

The invention discloses a landfill methane long-term monitoring static box device and method, and relates to the technical field of landfill methane emission monitoring. The device comprises a box body with an opening in the bottom, a gas conveying unit, a gas mixing unit, a one-way low-pressure relief valve, a detection module, a power supply unit and a control system. The method comprises a deployment stage, an initialization stage and a monitoring stage, and the methane emission rate can be calculated by automatically injecting air, mixing gas and collecting and transmitting data. According to the device and the method, automatic and long-term continuous monitoring is realized, multi-dimensional parameters are synchronously acquired, the monitoring efficiency and the data continuity are improved, the problems that a traditional static box depends on manpower, the data dimension is single and the like are solved, and data support is provided for an emission reduction strategy.

The invention provides a real estate operation management system based on digital twinning, which belongs to the technical field of real estate operation management, and comprises a data acquisition unit, a digital twinning modeling unit, an intelligent analysis unit, an intelligent investment attracting unit, an operation monitoring center unit and a customer experience platform unit, the digital twin modeling unit is connected with the intelligent analysis unit, the intelligent analysis unit is respectively connected with the intelligent investment attracting unit and the operation monitoring center unit, and the intelligent investment attracting unit and the operation monitoring center unit are both connected with the customer experience platform unit. According to the method, comprehensive mapping and dynamic optimization, data driving, real-time simulation optimization, predictive maintenance and resource dynamic allocation from a physical space to a virtual space are achieved, data continuity supports asset value long-term management from design and construction to operation maintenance, digital twin energy consumption optimization assists commercial land carbon production emission reduction by 30%, and the method is suitable for large-scale industrial production. And carbon transaction is participated to create additional benefits.

The invention belongs to the technical field of electronic measurement and control, and particularly relates to a seamless switching method for a VI source voltage range, and the system comprises a digital controller, a digital-to-analog converter, a programmable gain amplifier, an isolation switch module, a voltage maintaining and buffering module, a power amplifier, and a feedback measurement network. The digital controller is configured to receive a range switching instruction, manage the working state of the VI source, execute digital closed-loop voltage regulation and coordinate actions of all hardware components in the range switching process; a digital voltage reference generation module, a measuring range switching control logic module, a loop adjusting module and a high-precision clock synchronization unit are integrated in the digital controller. According to the invention, seamless switching of the output voltage can be realized, the test efficiency is obviously improved, the protection of the tested device is enhanced, and the data continuity and accuracy are improved.

The invention belongs to the technical field of environmental protection ecological monitoring, and particularly relates to an intelligent ecological environmental protection monitoring method and system based on artificial intelligence, and the method comprises the steps: integrating and constructing a multi-source data collection network, introducing a Q-Learning algorithm to dynamically adjust the sampling and cruising frequency, and achieving the multi-dimensional data collection; data abnormal values are identified and eliminated, and heterogeneous data are processed in a standardized mode; designing a cross-modal fusion network containing a coordinate attention mechanism, extracting various types of data features, and weighting by using an attention weight coefficient to generate a unified environment state vector; designing an intelligent analysis algorithm, intelligently identifying a pollution source, tracing the pollution source in combination with a graph attention network, and dynamically adjusting an early warning threshold in combination with a PPO algorithm; and optimizing a self-adaptive monitoring strategy, detecting a sensor fault through a self-encoder, and repairing missing data by adopting a K-nearest neighbor interpolation method to maintain data continuity. According to the invention, the coverage range of ecological monitoring can be effectively expanded, the data intelligent processing capability is enhanced, and the early warning accuracy and adaptability are improved.