2663 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 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 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 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 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 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 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.

The invention discloses a soft network system capable of expanding a transmission mode and an implementation method, and the system comprises an equipment resource mapping management unit which automatically generates a unique channel code based on a native hardware interface and establishes a multi-dimensional mapping relation; the heterogeneous interface data acquisition unit is used for monitoring various hardware interfaces in a kernel space in parallel and realizing zero-copy data transmission through a three-buffer lockless annular buffer area; the transmission abstraction and dynamic expansion unit realizes transmission mode modularization in a plug-in form and supports atomic hot plug during operation; the protocol analysis and service processing unit is used for realizing unified scheduling and fault isolation in a single process through a dynamic link mechanism; and the cross-layer monitoring and self-healing unit realizes automatic switching based on a session state machine and a redundant channel. According to the method, the code complexity is reduced from O (N * M) to O (N + M), and the time lt is extended; fault switching is carried out at 1s; and the hardware cost is reduced by more than 40%, so that the method is particularly suitable for power and environment monitoring scenes with more than 1000 devices and more than 15 protocols, and has high creativity.

The invention relates to the technical field of power dispatching, in particular to a power distribution network photovoltaic energy storage collaborative optimization dispatching decision-making system based on big data and artificial intelligence. Comprising a source network load storage full-dimension data acquisition unit; a multi-source heterogeneous data fusion processing unit; the dynamic multi-target intelligent optimization decision-making unit is used for constructing a power distribution network photovoltaic energy storage collaborative scheduling strategy through an improved non-dominated sorting multi-target grey wolf optimization algorithm with adaptive weight adjustment; and a closed-loop control execution unit. According to the invention, photovoltaic, energy storage, load and power grid operation data in the power distribution network are comprehensively captured through the source-network-load-storage full-dimension data acquisition unit, and spatial correlation mapping of topological nodes of the power distribution network in a multi-source heterogeneous data fusion processing process is combined; and constraint conditions such as power balance and node voltage during dynamic multi-objective optimization decision making are included, so that neglect on topology and operation constraints of the power distribution network is effectively made up.

The invention relates to the technical field of automobile sales volume prediction, in particular to an automobile sales volume multi-dimensional prediction model construction system and method. Comprising a data acquisition unit; a data preprocessing unit; a dynamic feature engineering unit; a multi-model collaborative prediction unit; a model training optimization unit; and a prediction result output unit. According to the method, dynamic screening and iterative updating of multi-dimensional prediction features are achieved through the dynamic feature engineering unit, feature importance quantification, real-time contribution degree feedback and feature set stability verification are combined, the prediction feature set can be adaptively adjusted along with the prediction period and data change, interference of low-efficiency features on the prediction process is effectively avoided, and the prediction efficiency is improved. The adaptability of the features and the sales volume prediction demand is improved; according to the invention, a time sequence-non-time sequence dual-channel prediction framework is constructed based on the multi-model collaborative prediction unit, so that the influence of single prediction dimension limitation on the result is reduced, and the stability of sales prediction precision is improved.

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

The invention relates to the technical field of digital twinning equipment management, in particular to a digital twinning scientific research equipment intelligent management system integrating the Internet of Things and artificial intelligence. The system comprises a multi-source heterogeneous data acquisition unit which is used for performing real-time information acquisition on the operation environment and the equipment state of scientific research equipment; the digital twin mapping unit constructs a temperature and humidity coupling dynamic model fusing a temperature dynamic model and a humidity dynamic model by introducing a temperature and humidity coupling coefficient, and maps data collected from multi-source equipment and a laboratory environment to the temperature and humidity coupling dynamic model; the AI multi-dimensional prediction unit predicts the future running state of the equipment and the environmental risk; and the risk early warning unit analyzes the operation state of the air conditioning equipment and the environment coupling risk thereof in real time based on the prediction result of the AI multi-dimensional prediction unit, and sends out an early warning signal before potential abnormity occurs. The method is used for predicting the equipment fault risk in advance, thereby achieving the purposes of advanced prediction and active intervention.

The invention relates to the technical field of mine production operation management, in particular to a mine production operation intelligent decision-making system based on multi-source heterogeneous data fusion. A data preprocessing module; a data fusion modeling module; an intelligent decision module; and a decision execution feedback module. In the scheme, an edge computing terminal is deployed through a data acquisition unit, a multimode communication technology is integrated, data of an underground sensor, electromechanical equipment and the like are accessed in real time, and the data transmission reliability is ensured through a communication quality evaluation formula; the business system interconnection unit maps heterogeneous fields of systems such as ERP, WMS and the like into global unified business keys by means of semantic middleware, and semantic ambiguity is eliminated. The data preprocessing module integrates the dispersed data into a standardized data set through intelligent cleaning, format conversion and three-dimensional association, and provides data support for decision making.

The invention relates to the technical field of intelligent regulation and control of fusion data, in particular to a rolled finished product-based size precision intelligent regulation and control system and method, which comprises a data acquisition unit, a data fusion unit, a deep learning prediction unit and a compensation execution unit, multi-source data such as temperature gradient, roll gap fluctuation and tension spectrum are collected in real time through a sensor array, thermal expansion compensation correction is carried out through a data fusion unit based on a rolling elasticoplastic deformation equation, an anti-interference coupling feature vector is generated by using adaptive Kalman filtering, and a deep learning prediction unit is used for predicting the rolling elasticoplastic deformation through a dual-channel space-time convolutional network. The thickness error and the width error are predicted by combining the constraint of a rolling force balance equation, the compensation execution unit dynamically generates the roll gap adjusting amount and the rolling force compensation amount according to a three-level response mechanism, phase lag compensation current is injected to suppress resonance, the high-end steel yield and the rolling precision are improved, and the rolling quality is improved. And an intelligent closed-loop control scheme is provided for high-precision rolling under complex working conditions.

The invention relates to the field of concrete detection, and discloses a concrete structure health real-time monitoring system based on multi-source sensing fusion, and the system comprises a data collection unit which carries out the collection of the physical field data of a concrete structure through a sensor array, and obtains the multi-source sensing data; performing structure response difference analysis on the multi-source sensing data to obtain concrete structure response difference data; according to the response difference data of the concrete structure, performing spatial and temporal distribution density analysis on a microcrack propagation path to obtain microcrack propagation spatial and temporal distribution density data; a data evaluation unit; multi-scale damage evolution path simulation is carried out through the structural damage time-space correlation characteristic data to obtain multi-scale damage evolution path data, the sensor array is used for collecting multi-source sensing data, structural response difference analysis is carried out, tiny changes of a concrete structure can be accurately captured, potential structural problems can be found in time, and the construction efficiency is improved. And a high-quality data basis is provided for subsequent analysis.

The invention relates to the technical field of flood purification treatment, and discloses a flood percolation and purification system based on artificial intelligence. A flood data acquisition unit of the system acquires physical characteristics and chemical components of flood in real time, extracts multi-dimensional water quality indexes, identifies pollution concentration abnormal areas through a neural network, and generates an infiltration medium pollution load distribution diagram; a percolation layer state analysis unit calls high-load area data, recognizes percolation layer blockage and saturation segments in combination with historical percolation efficiency, and generates a percolation efficiency attenuation interval; the purification medium regulation and control unit extracts the purification efficiency and the regeneration state according to the attenuation interval, judges the medium failure condition and generates a purification medium dynamic adjustment strategy; and the purification process execution unit calls the regulation and control parameters, detects the pressure change and the pollutant interception amount, identifies the interlayer synergistic effect strength, and generates a purification medium backwashing instruction to control the start and stop of a purification layer high-pressure backwashing pump after reaching the standard.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention relates to the field of hydroelectric generation automation control, discloses an intelligent flow-pressure double closed-loop control method and system for a water inlet ball valve of a water turbine, and aims to solve the technical problems that flow stability and pressure fluctuation suppression are difficult to consider, dynamic response is delayed and working condition adaptability is poor in existing single-loop control. The method comprises the steps that inlet pressure and flow data are collected in real time through a pressure sensor and an ultrasonic flowmeter, a cascade structure of outer ring flow control and inner ring pressure control is constructed in combination with working condition parameters such as the rotating speed of a unit and the opening degree of a guide vane, and a working condition self-adaptive weight distribution module is introduced to dynamically adjust the double-ring gain coefficient; meanwhile, a feed-forward compensation mechanism and an optimal efficiency curve database are fused, and dynamic matching of set values and advanced response of load instructions are achieved. The system comprises a data acquisition unit, a double-closed-loop control unit, a weight distribution unit, an electro-hydraulic servo execution mechanism and a safety interlocking module, and supports microsecond-level gain updating and high-precision valve position control. According to the technical scheme, the adjusting precision and the response speed of the water turbine under the complex working condition are remarkably improved, the actual measurement efficiency is improved by 2.5%-4.8%, the adjusting time is shortened to be within 3 seconds, the positioning precision reaches + / -0.5 degree, and safe and stable operation of the system is guaranteed.

The invention relates to the technical field of lime kiln control, and discloses a lime kiln partition intelligent temperature control system and a storage medium, and the system comprises a data collection unit which is used for collecting state data of each region of a lime kiln; the working condition recognition unit recognizes working conditions based on the state data; the temperature control unit is provided with a PID (Proportion Integration Differentiation) control strategy and a prediction control strategy, and the opening degrees of the fuel regulating valves and the air regulating valves in different areas in the lime kiln are calculated according to the corresponding control strategies selected on the basis of working conditions; the discharging control unit is provided with a balance control strategy, the balance control strategy obtains the current discharging speed through the current productivity of the lime kiln and the material level height in the lime kiln, and the execution unit correspondingly controls the lime kiln according to the control quantity. The method effectively solves the problems of difficulty in multi-area coordination, poor working condition adaptability, high energy consumption and the like in a traditional control mode, and has the remarkable advantages of improving the temperature control precision, ensuring the production stability and reducing the energy consumption.

The invention provides a semi-aviation transient electromagnetic acquisition device and method for calibrating data drift in real time, and relates to the technical field of data calibration, and the device comprises an electromagnetic data acquisition unit which receives a secondary induction signal generated by a ground transmitting end through a transient electromagnetic hollow induction coil disposed in a coil storage cabin of an unmanned aerial vehicle, and transmits the secondary induction signal to an unmanned aerial vehicle; self-adaptive gain control is carried out according to the signal intensity to obtain real-time electromagnetic data; the flight control unit monitors the swing amplitude and frequency of the coil in real time according to the inclination angle and acceleration data fed back by the sensor in the coil storage cabin, and carries out flight attitude adjustment and control; the drift calibration unit adopts a static reference mode for calibration at the beginning stage of flight; and switching to a dynamic reference mode in the flight process, and carrying out self-adaptive calibration on the real-time data stream based on a sliding window. According to the invention, drift calibration of traditional software post-processing is migrated to a hardware acquisition stage, so that synchronous completion of data acquisition and drift compensation is realized.

The invention belongs to the technical field of new energy charging, and particularly relates to a light storage and charging integrated new energy charging station which comprises a photovoltaic power generation module, an energy storage module, a charging module and an intelligent control unit. The photovoltaic power generation module predicts power generation power through an LSTM neural network in combination with meteorological data, and dynamically adjusts a prediction time window based on a weather type; the energy storage module is directly connected with the photovoltaic direct current side to improve energy efficiency; the charging module obtains the SOC / SOH state of the vehicle and the user demand through the data acquisition unit, and generates a priority coefficient by using a scoring model. The system dynamically distributes photovoltaic power, energy storage power and power grid power through a demand grading module, and when it is detected that the photovoltaic output deviation exceeds a threshold value or the energy storage SOC is insufficient, a power redistribution and compensation mechanism is automatically triggered. According to the invention, efficient utilization of renewable energy sources, intelligent scheduling of an energy storage system and differentiated supply of charging services are realized, and the problems of low photovoltaic consumption rate and response lag of a traditional charging station are solved.

The invention relates to the technical field of wind turbine generator state monitoring, in particular to a wind turbine generator state monitoring method and system based on multi-source heterogeneous data fusion, and the system comprises a data collection unit, a dynamic noise processing unit, a damage feature analysis unit and a health state output unit. A data acquisition unit synchronously obtains blade strain, unit rotating speed and environment vibration signals through hardware timestamp alignment, and a dynamic noise processing unit removes rotating speed coupling noise by using a two-parameter coupling model, a temperature-pitch angle three-dimensional correction curved surface and closed-loop feedback in combination with variable step blanking and local band elimination protection. The damage feature analysis unit extracts microcrack features by adopting multi-scale wavelet packet decomposition and kurtosis detection, and performs cross validation by fusing a vibration mode confidence factor, and the health state output unit generates a topological graph containing a crack position, an expansion trend and an alarm confidence level, so that the problems of insufficient multi-source data fusion and false and missing judgment of damage are solved, and the safety of the system is improved. And the monitoring precision is improved.

The invention provides a medicine bottle array detection system based on vision and sensing fusion, and relates to the technical field of data processing, and the system comprises the steps: obtaining the spatial distribution data of a vision unit and the posture state data of a sensing unit, fusing the position parameters and state parameters of each bottle body, building a multi-dimensional data node under a unified time index, calculating the spatial aggregation degree of the medicine bottle array to obtain an array quantity, combining the aggregation change of each time point into an evolution chain table, carrying out joint operation on the difference result and the state parameter of adjacent nodes in the evolution chain table, calculating the dynamic coupling degree of the medicine bottle array to obtain a sequence potential energy value, and dividing the sequence potential energy values of the different numerical value intervals into a plurality of level nodes, and performing consistency analysis and anomaly detection on features of the different level nodes to obtain detection result data. By detecting the state of the medicine bottles and the arraying direction, bottle toppling early warning is achieved.

The invention discloses a bridge girder erection machine structure health intelligent management system and an intelligent management method thereof. The system comprises a state data acquisition unit, a data preprocessing module, a structure state modeling module, a state prediction and residual life estimation module, an early warning and control module, a self-learning and optimization module and a system integration module. The method comprises sensor deployment, data preprocessing, structural state modeling, state prediction and residual life estimation, early warning and control, self-learning and optimization and system integration. Structural state monitoring data are collected in real time through a sensor, a structural state model is constructed after preprocessing, a future state is predicted in combination with time sequence prediction and a mechanism model, and the remaining safe life and risk points are estimated. And the system triggers multi-stage early warning according to a prediction result, executes an active maintenance control strategy, and continuously improves a prediction model through a self-learning and optimization module. According to the invention, real-time health management, remote operation and full-life-cycle risk control of the bridge girder erection machine structure can be realized.

The invention relates to the field of building construction, and discloses a BIM model dynamic update-driven steel structure construction real-time accurate early warning method, which comprises the following steps: deploying a sensor array at a construction site for collecting various data of a steel structure, the various data including actual size, installation position, stress strain, temperature and humidity data; capturing various data of a construction area through a three-dimensional laser scanner and an image recognition technology; the invention also discloses a BIM model dynamic update-driven steel structure construction real-time accurate early warning system. The system comprises a data acquisition unit, a three-dimensional scanning unit, a data processing unit, a risk assessment unit, an early warning mechanism unit and a feedback correction unit. According to the method, the construction deviation is fed back in real time, the BIM model is dynamically updated, and risk assessment is performed by integrating multiple factors, so that the construction precision and safety are improved, the precise monitoring and management of the construction process are ensured, and manual errors and delayed response are avoided.