870 results about "Sensor web" patented technology

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention discloses a dam safety monitoring agent system based on a multi-dimensional large model and digital twinning, and belongs to the technical field of safety monitoring of water conservancy and hydropower engineering. According to the method, a cognitive decision kernel is used as a core innovation module, multi-source data is collected by relying on a sky-ground hydraulic engineering integrated sensor network, cross-modal data fusion and anomaly recognition are achieved through a multi-dimensional large model cluster, and multi-physics coupling simulation is completed in combination with a digital twinborn body; the cognitive decision kernel generates an optimal combination scheme through case retrieval and measure unit adaptation degree calculation, a control instruction is output after digital twin rehearsal verification, and finally closed-loop control of dam safety real-time early warning, autonomous decision making and efficient disposal is achieved through an autonomous response mechanism. The problems of data island, passive response, expert dependence and the like of a traditional monitoring system are solved, the dam safety management intelligent level and the emergency response capability are remarkably improved, and the method has wide engineering application value.

The invention provides an alloy bearing unsteady-state damage detection and evaluation method based on digital twinning. The method comprises the following steps: constructing an alloy bearing multi-physical field digital twinning model; the method comprises the following steps: acquiring operation state data of a bearing under an unstable working condition based on a sensor network, preprocessing the acquired data to obtain multi-source features, and fusing the multi-source features to generate a comprehensive health index; inputting the preprocessed data into a digital twin model for forward simulation, generating a prediction observation vector, comparing the prediction observation vector with a sensing measurement value to generate a residual error, and performing damage state assimilation based on the residual error; and based on the assimilated state, predicting a damage evolution trajectory under an unsteady state working condition, and evaluating probability distribution of residual life. According to the method, the reduced-order proxy model for dynamic mode switching is constructed, the mode basis is automatically expanded and the sub-models are switched when the load suddenly changes or the rotating speed jumps, and the problem of feature drift of a traditional fixed basis function model under variable working conditions is effectively solved.

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

The invention relates to the technical field of forestry environment monitoring, and discloses a forestry environment monitoring method and system based on big data analysis. The method comprises the following steps: acquiring multi-dimensional environmental parameters such as soil moisture content, vegetation coverage and meteorological factors through a distributed sensor network, and generating a real-time weight coefficient through a dynamic weight distribution engine; and completing anomaly detection by using the space-time correlation analysis model, and triggering a self-adaptive sampling strategy to perform high-density acquisition on an abnormal region. Real-time data and satellite remote sensing data are integrated through a multi-source data fusion algorithm, a forestry environment state matrix is generated, and a reference threshold is dynamically updated in combination with an incremental learning mechanism. And generating a regulation and control instruction set for soil improvement, vegetation maintenance and disaster early warning according to the updated threshold value, and executing and collecting feedback data by the edge computing node. And comparing feedback data with an expected index through a bidirectional verification mechanism, generating system optimization parameters, and returning the system optimization parameters to a dynamic weight distribution engine, thereby realizing accurate monitoring and efficient regulation and control of a forestry environment.

The invention relates to the technical field of safety monitoring, and discloses a multistage safety early warning and linkage processing system for an electricity-hydrogen complementary energy station, and the system comprises a monitoring collection module which is used for deploying a multi-source sensor network in the electricity-hydrogen complementary energy station, collecting key parameter data in real time, and generating a real-time data set comprising a timestamp, a sensor ID and a parameter value; the anomaly detection module is used for performing multi-dimensional analysis by adopting an anomaly detection algorithm according to the real-time data set, identifying an anomaly mode and outputting a graded early warning signal; the evaluation grading module is used for carrying out dynamic risk evaluation in combination with the early warning signal and the system state, and outputting a quantitative risk grade and a disposal suggestion; the linkage triggering module is used for automatically matching and triggering a corresponding plan according to the risk level and outputting a specific linkage control instruction; an execution feedback module; according to the invention, the emergency response speed is improved, the false alarm rate caused by normal working condition fluctuation is reduced, and fundamental conversion from passive response to active early warning is realized.

The invention discloses a lithium iron phosphate battery grading active fire control method and system, and the method comprises the steps: collecting multi-mode perception data through deploying a multi-source sensor network and a BMS data interface, and constructing a battery thermal runaway risk assessment model through employing a Lyapunov fixed time stability theory; a hierarchical prevention and control strategy is dynamically generated in combination with a historical case sensitivity scoring mechanism, and linkage response of first-level early warning heat dissipation, second-level aerosol inhibition and third-level perfluorohexanone fire extinguishing is achieved. According to the invention, the problem that the prior art lacks self-adaptability and BMS deep collaboration is solved, the technical transformation from passive response to active prevention and control is realized, and the accuracy and timeliness of thermal runaway early warning are remarkably improved.

The invention discloses an integrated intelligent management and control method and system for underground water pumping and injection based on the Internet of Things, and the method comprises the steps: carrying out the geological investigation of a target region, constructing a three-dimensional geological model, and planning the layout of a pumping and injection well group and a sensor network according to the three-dimensional geological model; deploying a monitoring network and collecting data based on the layout scheme, and fusing a three-dimensional geologic model to construct a polluted site digital twinborn body; groundwater situation data are extracted through the digital twinborn body, a pollutant migration path and a water level dynamic state are predicted by using an LSTM model, and pollution scene simulation information corrected by geologic features is output; by taking the simulation information as input and coupling geological parameters as constraints, generating a pumping and injection well group cooperative control strategy set by adopting a BLMFO double-layer multi-objective optimization algorithm; and finally, a control instruction is executed, the real-time situation is monitored in the repairing process, strategy change suggestions including the flow adjustment amplitude, the agent optimization scheme and equipment operation parameter correction are judged and pushed, the repairing efficiency is improved, and the method adapts to the complex geological environment.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

The invention relates to the technical field of environment regulation and control, and discloses a multi-parameter environment closed-loop regulation and control method and system based on an FPGA, and the method comprises the steps: unifying monitoring data to a same space coordinate system through the FPGA according to the monitoring data collected by a sensor network disposed in an environment, analyzing the coupling relation between different parameter data, and obtaining fusion data; based on the fusion data, analyzing the environment crop state through a preset crop state recognition model, simulating the influence condition of different parameter adjustment processes on the crop state, and generating a first control instruction set; analyzing the degree of deviation between the environmental parameters in the fused data and a preset target value, calculating the priority of each control instruction in the first control instruction set, and merging and optimizing the control instructions; the environmental parameters are corrected; according to the application, the regulation and control efficiency can be improved, the environmental disturbance and the crop demand change can be quickly responded through FPGA parallel processing, and the environmental regulation and control efficiency and the regulation and control effect are improved.

The invention relates to the cross technical field of artificial intelligence and traffic infrastructure monitoring, discloses an intelligent health monitoring and prediction method and system for a road roadbed, and aims to solve the problems of insufficient monitoring coverage, shallow data mining, low prediction model precision and disjunction of operation and maintenance decisions in the prior art. The method comprises the following steps: collecting roadbed multi-dimensional physical field data through a multi-source sensor network; denoising, abnormity correction, time alignment and feature compression are carried out at the edge end; fusing multi-scale time sequence modeling and spatial correlation analysis to extract health features; predicting a future health state and a risk probability by using an LSTM-Attention model in combination with historical data and environment variables; and triggering graded early warning based on the dynamic threshold and generating a maintenance strategy. According to the technical scheme, high-precision and high-timeliness roadbed health perception and prediction can be realized, and the early warning response speed and the maintenance decision intelligent level are improved.

The invention discloses an equipment fault prediction method in a power transmission and transformation system, and relates to the technical field of power system operation and maintenance, and the fault prediction method comprises the following steps: data collection: collecting the data of power transmission and transformation equipment through a sensor network, and forming a multi-source heterogeneous data set; data preprocessing: performing abnormal value elimination, missing value filling and standardization processing on the multi-source heterogeneous data set; feature extraction: mining associated features and time-space evolution laws of data of different dimensions by adopting a multi-modal fusion network based on an attention mechanism; performing fault prediction, and constructing a dynamic threshold early warning model in combination with an equipment aging curve; and correcting the result to generate a final fault prediction report. The equipment fault prediction method in the power transmission and transformation system solves the problems that traditional prediction depends on single data, the fixed threshold value false alarm is high, and the advance is insufficient, improves the fault prediction accuracy and reliability, and is suitable for operation and maintenance early warning of power transmission and transformation equipment such as transformers and circuit breakers.

The invention discloses a double-machine cooperative hoisting operation guiding system and method based on Beidou positioning, and the system comprises a coordinate data implantation unit which is used for pre-storing a standardized coordinate template with environment compensation parameters; the sensor network is used for acquiring crane position, load and environment data in real time; the crane positioning and guiding unit is used for generating a multi-dimensional environment correction factor, dynamically comparing coordinates, weighting and calculating deviation, and planning a synchronous walking path through a multi-objective optimization algorithm; and the hoisting control unit is used for constructing a synchronous control model containing moment balance and kinematics constraint and adjusting hoisting parameters by adopting a closed-loop algorithm. According to the method, millimeter-level positioning guiding and double-machine load self-adaptive balancing can be achieved, segment inclination and wind disturbance influences are effectively restrained, hoisting synchronization precision and operation safety are remarkably improved, high-steady-state anti-inclination control can be completed without newly adding hardware, and the method has high engineering practicability and popularization value.

The invention relates to the technical field of quality regulation and control, in particular to a self-adaptive pile forming quality regulation and control system and method for soft foundation treatment pile construction. The method comprises the steps that a construction parameter acquisition unit acquires key construction parameters in real time through a multi-type sensor network; the data preprocessing and feature extraction unit is used for preprocessing the collected key construction parameters and extracting key feature parameters reflecting the construction stability and the pile forming uniformity; and the pile-forming quality judgment and deviation recognition unit is used for comparing the key characteristic parameters with a preset pile-forming quality threshold model in real time, judging the quality state of the pile-forming process and outputting a deviation category and a deviation strength index. According to the method, the limitation that judgment only depends on a single parameter threshold in the prior art is broken through, and a multi-mode coupling resonance intensity evaluation algorithm is provided for solving the problem that nozzle abrasion is high in concealment and great in influence.

The invention discloses a practical training evaluation method and system based on multi-modal data fusion. The method comprises the following steps: firstly, constructing a practical training task topology based on a knowledge graph, mapping elements such as knowledge points into nodes, and defining a relationship through directed edges; in response to task starting, dynamically deploying a multi-modal data fusion agent pre-loaded with a targeted model and a rule; in the operation process, the agent synchronously collects and processes original data from the machine vision and sensor network in real time, and a standardized feature flow is generated; then, on-line space-time correlation and reasoning are carried out on the multi-modal features based on a fusion strategy, and a task scene understanding model is dynamically constructed and maintained; after the task is completed, packaging a program code, a report and process abstract data exported by the model to form an enhanced practical training result package; and finally, multi-dimensional automatic comparison is carried out by calling the rule base and the case model, and an evaluation result is generated. According to the invention, intelligent perception and comprehensive evaluation of the whole practical training operation process are realized.

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 building energy consumption subentry metering optimization regulation and control method and system, and relates to the technical field of building energy consumption management. The method comprises the following steps: collecting multi-source data through a sensor network, performing event-level segmentation processing based on a power transition event to obtain an energy consumption sub-sequence, determining the category and confidence of an energy consumption unit through an interpretable classification model, and performing non-stationary correction to obtain a stabilized sub-item energy consumption sequence; constructing a temperature condition embedded sequence prediction model, and performing scale calibration and time sequence consistency correction in combination with building features to obtain a calibrated sub-item energy consumption prediction model; when the predicted deviation exceeds the threshold, a multi-strategy regulation process is triggered, an optimal control strategy is dynamically selected, and a regulation instruction is generated; and carrying out transfer learning updating on the prediction model based on virtual-real consistency feedback. According to the invention, closed-loop optimization from fine perception and accurate prediction to intelligent regulation and control is realized, and the accuracy of building energy consumption metering and the intelligent level of regulation and control are effectively improved.

The invention discloses a gas extraction dynamic regulation and control system and method based on three-dimensional modeling, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: constructing an underground multi-source sensor network, and collecting underground gas extraction multi-modal data flow; the method comprises the following steps: acquiring an underground gas historical extraction data set, performing dynamic early warning analysis in combination with an underground gas extraction multi-modal data flow, and establishing a gas emission abnormity early warning model and an extraction system failure early warning model; self-adaptive fusion modeling is carried out, and a three-dimensional model of the extraction drilling system is generated; and the gas emission abnormity early warning model and the extraction system failure early warning model are mapped to the extraction drilling system three-dimensional model for extraction regulation and control analysis to determine target gas extraction control parameters, and extraction closed-loop regulation and control are carried out. The technical problem of monitoring and regulation response lag caused by gas extraction data islanding in the prior art is solved, and the technical effects of real-time sensing, intelligent early warning and accurate regulation in the gas extraction process are achieved.

The invention relates to the technical field of intelligent identification of a grounding grid step voltage abnormal area, and specifically discloses an intelligent identification method of a grounding grid step voltage abnormal area. Comprising the steps of multi-source sensing data acquisition, data sliding filtering processing, dynamic threshold parameter generation, voltage gradient matrix construction, collaborative judgment result generation and abnormal identification threshold optimization. Multi-source sensing data is comprehensively acquired by using a distributed sensor network, noise interference is effectively removed through sliding filtering processing, then a dynamic threshold parameter is generated in combination with historical operation data and real-time environment parameters, meanwhile, a voltage gradient matrix reflecting potential field spatial characteristics is constructed, an abnormal area is judged and accurately identified through cooperation of the two, and then the abnormal area is accurately identified. According to feedback information, an optimization direction is analyzed, an abnormal identification threshold value is dynamically adjusted, a complete intelligent optimization closed loop is formed, double verification and accurate identification of the state of the grounding grid are achieved, and the intelligent management level is improved.

A digital twin holography monitoring and self-healing system for wind turbines, characterized by the fact that it comprises sequentially connected components: a physical plane, including the body of the wind turbines (1), the multi-source sensor network deployed on it and the execution device; a data acquisition and transmission layer for acquiring and transmitting data from the aforementioned multi-source sensor network; Digital twin model layer for creating and running virtual twins that are synchronized with the physical entity layer, including geometric models, physical models, behavioral models, rule models, and data model fusion modules; Holographic monitoring and diagnostic layers for visualizing plant condition, health assessment, fault prediction, and diagnosis based on the digital twin layer; and A fault self-healing regulation layer serves to independently generate and execute regulation strategies based on diagnostic results, which are returned to the execution organization of the physical entity layer.

The invention relates to the technical field of sensor networks and Internet of Things, in particular to a data center air quality intelligent early warning method and system based on a sensor network. The method comprises the following steps: constructing a self-organizing cooperative network by deploying multiple types of sensor nodes in a key area of a data center, and collecting and correcting multi-dimensional air data in real time; the data reliability is improved through inter-node dynamic weight fusion and local anomaly recognition; establishing an air parameter and machine room structure correlation model by utilizing space mapping and time sequence analysis, and identifying a micro-scale diffusion trend; a self-adaptive dynamic threshold mechanism is constructed, and threshold rolling optimization is realized in combination with historical statistics and real-time feedback; and generating a graded alarm strategy based on multi-stage early warning judgment and triggering conditions, and continuously self-optimizing early warning precision and response efficiency through closed-loop feedback. According to the invention, all-around, intelligent and high-reliability early warning and regulation and control of the air quality of the data center are realized.

The invention discloses an intelligent fire prevention and control and operation and maintenance optimization method and system for an electric power supply network, and the method comprises the steps: collecting temperature, humidity and smoke concentration data in real time through a sensor network, generating a comprehensive environment index data flow, and obtaining a fire initial signal through abnormality judgment; calling a medium selection model, and outputting an optimal environment-friendly insulating medium and release parameters thereof by querying an environment-friendly medium and insulating characteristic parameter library and performing matching calculation based on the equipment type; utilizing a response speed optimization algorithm to generate an accurate release instruction sequence containing a release path, a dose and a time sequence; controlling the medium atomization device to synchronously release the medium according to the instruction; and collecting equipment protection and power supply continuity data, performing risk assessment to generate an efficiency index, and updating the prevention and control strategy library to optimize a subsequent decision. Through intelligent decision making and closed-loop optimization, power supply continuity and equipment safety are guaranteed to the maximum extent while rapid and accurate fire extinguishing is achieved, and transformation from post-disaster disposal to operation and maintenance level prevention and control is completed.

The invention provides an energy consumption optimization method and system for mine ventilation, relates to the technical field of mine ventilation, and solves the technical problems that an existing mine ventilation system is too high in energy consumption and uneven in air distribution. The method comprises the following steps: acquiring environment data in real time through a sensor network; simulating air volume distribution in a mine by using a digital twinborn model, and inputting the environmental data into a space-time diagram convolutional network to predict a ventilation demand; performing global optimization by adopting a dynamic weight multi-objective optimization algorithm according to the air volume distribution and the ventilation demand, and generating a multi-fan cooperative control instruction; the control instruction is issued to a frequency converter and a PLC, fan rotating speed adjustment is executed, and a fault diagnosis mechanism is integrated to achieve redundancy switching; and feeding back the execution effect to the digital twinborn model for iterative optimization. The method is used for ventilation system design, operation and maintenance links in various underground mineral exploitation scenes.

The invention provides an asphalt mixture production quality defect traceability analysis method, which comprises the following steps of: acquiring time sequence process parameters such as raw material attributes, mixing temperature and paving speed and quality defect characteristics such as segregation degree, void ratio and compaction degree through a distributed sensor network and an image system, and performing filtering, denoising, standardization and characteristic zooming on the parameters; constructing a unified defect-parameter association data set; historical data and expert experience are utilized to establish a process semantic network and an extensible meta-path template, a defect propagation path is dynamically activated, node embedding and path weight are optimized through online comparative learning of a neural network, and defect cause chain visualization and future trend prediction are realized. According to the invention, the accuracy of defect tracing and the quality control efficiency of the production process are improved.

The invention discloses a photovoltaic cable health state assessment method and system, and the system comprises a signal collection module which is disposed at a key node of a cable, and comprises an ultra-wideband pulse sensor, an infrared thermometer, and an illumination sensor; the edge processing module is used for constructing a state characteristic curve and identifying a target sample time interval; the cloud analysis engine is used for executing defect contribution simulation value extraction and thermal-electric coupling model iterative optimization; according to the scheme, multi-dimensional signals such as current, voltage, partial discharge pulse, temperature and illumination intensity are synchronously collected through a distributed sensor network, a multi-parameter evaluation system including amplitude, phase and temperature is constructed, and the health state level, the defect position distribution and the simulation value change trend are displayed through multi-dimensional signal fusion and multi-parameter analysis. And the detection accuracy and comprehensiveness are improved.

The invention relates to the field of mine disaster prediction, and discloses an AI-based mine disaster prediction and risk assessment method, which comprises the steps of deploying a multi-source sensor network which comprises a microseismic monitoring sensor, a gas concentration sensor, a stress sensor and a hydrological sensor, the data acquisition module is used for acquiring micro-seismic waveform data, gas concentration time sequence data, stress data and hydrological parameter data of a mining working face, a roadway and a geological structure area; and performing space-time alignment processing on the microseismic waveform data, the gas concentration time sequence data, the stress data and the hydrological parameter data to generate a space-time aligned heterogeneous data set, and performing encryption feature interaction with an external system through a federated learning framework. According to the method, a multi-modal deep learning model is constructed, microseismic waveforms, gas concentration time sequences and geologic structure space correlation characteristics are extracted, rock mechanics equation constraint conditions are injected in model training, and the recognition capacity of rock stratum fracture precursor signals and the disaster chain coupling process is remarkably enhanced.

The invention discloses an optical storage and charging dynamic power distribution control method and system based on AI prediction, and the method comprises the steps: collecting photovoltaic, energy storage, charging, power grid and environment multi-dimensional operation data through a sensor network, and obtaining a historical and real-time data set through preprocessing; training an AI joint prediction model by using the historical data set, and outputting a photovoltaic output and charging load demand prediction result; inputting system operation constraint condition parameters to delimit an optimization boundary; solving a power instruction reference value of each power module through a multi-objective optimization algorithm in combination with a prediction result and a constraint condition to realize global optimal distribution; and correcting the reference value based on the hardware operation parameters and the deviation between the predicted value and the real-time value, issuing an instruction, monitoring operation, and triggering fault protection. The method effectively solves the problems that a traditional system is rigid in power dispatching, low in photovoltaic consumption rate, large in energy storage life loss, remarkable in power grid impact, insufficient in prediction precision, high in energy transmission loss and weak in multi-target coordination.

The invention relates to the technical field of community management, in particular to an intelligent community service management method and system based on cloud computing, and the method comprises the following steps: S1, collecting community multi-dimensional data in real time through a heterogeneous data collection terminal; s2, constructing an edge-cloud cooperative processing framework; s3, implementing intelligent video analysis; s4, realizing data-driven resource scheduling; and S5, establishing an intelligent storage system. According to the invention, omnibearing perception of a community environment is realized through a multispectral camera and a distributed sensor network, data processing efficiency is improved by adopting an optimized lightweight algorithm and an edge-cloud collaborative architecture, and a visual decision platform is constructed in combination with adaptive resource scheduling and an intelligent analysis technology. A closed-loop management system integrating real-time monitoring, intelligent early warning and accurate operation and maintenance is formed, the community management efficiency is improved, the operation cost is reduced, stable operation of the system is guaranteed through multiple safety mechanisms, and an intelligent solution is provided for modern community services.

The invention relates to the technical field of bridge engineering maintenance decision, and discloses a bridge maintenance decision method based on big data. The method comprises the steps of collecting bridge monitoring data through a sensor network, performing hierarchical processing on an original data stream, identifying and packaging abnormal fluctuation, trend change and a periodic mode, and forming a structured health event sequence. And dynamically constructing and updating the digital sign profile of the bridge in combination with the design parameters and the material degradation model. And when a decision is triggered, simulating a performance evolution path under multi-factor coupling based on the profile, generating a future maintenance scene set, and matching and assembling a preliminary plan from historical cases and the knowledge graph. And outputting a final maintenance instruction list sorted according to priorities by performing resource and time coupling analysis and multi-target tradeoff on all plans. According to the method, deep analysis and prospective simulation of the bridge state are realized, and the intelligence, accuracy and performability of maintenance decision making are improved.

The invention discloses a microgrid-oriented inverter transient switching control method and system, and relates to the related field of microgrid control, and the method comprises the steps: constructing a multi-source sensor network in a target microgrid, and collecting the operation data flow of the microgrid in real time based on a PGA module; state evaluation fitting is carried out by combining the operation characteristics of the micro-grid and the historical operation data of the micro-grid, a micro-grid operation state evaluation model is established, state evaluation is carried out on the operation data flow of the micro-grid, and the current operation state of the micro-grid is determined; performing transient switching judgment and control strategy analysis on the current running state of the micro-grid according to the inverter switching condition to obtain a target inverter control strategy; inverter transient switching is executed, and inverter output parameters and micro-grid power balance parameters are monitored for feedback optimization control. The technical problem that the accuracy and the stability of transient switching control of an existing micro-grid inverter are insufficient is solved, and the technical effect of optimizing the accuracy and the stability of transient switching of the inverter is achieved.