1065 results about "Maintenance plan" patented technology

The invention relates to a power equipment fault prediction method based on multi-source heterogeneous data and a twinborn model, and the method comprises the following steps: S1, obtaining multi-modal data related to the operation of power equipment, constructing a joint feature encoder through an attention mechanism in deep learning, and carrying out the joint modeling of multi-modal information, and obtaining a joint feature representation; s2, based on joint feature representation, adopting a dynamic twin model of self-supervised learning and fusion reinforcement learning, and dynamically updating the equipment health state; s3, constructing a complete-cycle prediction framework based on Transform, capturing long-term dependency and dynamic change characteristics in equipment operation, and identifying trigger factors of faults in combination with causal relationship analysis; and S4, based on the equipment health state and the fault prediction result, optimizing equipment operation parameters by adopting an intelligent operation and maintenance optimization engine, and dynamically generating an optimal operation and maintenance plan in combination with a genetic algorithm. The reliability and the operation efficiency of the power equipment are remarkably improved.

The invention relates to the technical field of digital factories, and discloses an equipment real-time monitoring and fault prediction method and system based on a digital factory. The method comprises the following steps: carrying out iterative filtering interpolation processing on equipment operation data collected by a multi-modal sensor in a digital factory to obtain smooth time sequence data, and constructing a multi-modal feature matrix based on the smooth time sequence data; inputting the multi-modal feature matrix into a joint training generalized Siamese-like wide learning network for model training to obtain an equipment state monitoring model; executing system-level state evaluation and subsystem-level fault prediction calculation based on the equipment state monitoring model, and generating an equipment operation and maintenance strategy; according to the equipment operation and maintenance strategy, a fault risk heat map is generated, a digital twin visualization system is constructed, and an equipment maintenance scheduling scheme is output, the production efficiency and equipment health management are balanced, and collaborative optimization of a maintenance plan and a production plan is achieved.

The invention provides an anomaly detection and maintenance decision optimization method and system for automatic test equipment, and relates to the technical field of automation, and the method comprises the steps: deploying an edge calculation unit and a multi-level sensor network to collect equipment data, carrying out the fault diagnosis through a deep network model, and constructing a maintenance decision model in combination with equipment operation data. The model is input into a quantum enhancement dual-network system to calculate a reward value, and is used for training a maintenance strategy network under a federal reinforcement learning framework. And generating a maintenance resource configuration scheme through a quantum evolutionary algorithm, and generating a maintenance scheme through optimization of the multi-criterion evaluation model and the self-adaptive multi-target differential evolutionary algorithm. According to the scheme, a high-fidelity digital twin model and a multi-task deep transfer learning network are input for verification and updating, updated data are transmitted to a field control system through an industrial Internet of Things platform, and the updated data are recorded by using a block chain technology for updating a deep network model.

A water-turbine generator set voiceprint monitoring and diagnosing method comprises the steps that a multi-channel voiceprint collecting system is adopted, a plurality of voiceprint sensors are arranged at key parts of a water-turbine generator set, and voiceprint signals at different positions are obtained; performing time-frequency analysis on the pre-processed voiceprint signal by using wavelet transform, and extracting voiceprint features to obtain a voiceprint feature vector; if the matching degree of the voiceprint feature vector and the features in the fault feature library exceeds a preset threshold value, determining the fault type and the fault degree; classifying the determined fault type and fault degree by adopting a support vector machine algorithm to obtain a fault diagnosis result; a fault diagnosis result is optimized through a deep learning network, the diagnosis precision is improved, and a final fault diagnosis report is obtained; performing association analysis on the fault diagnosis report and the equipment operation parameters to generate an equipment operation state evaluation report; according to the equipment operation state evaluation report, an equipment maintenance plan is made, and intelligent maintenance of the water-turbine generator set is achieved.

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention discloses a power system Internet of Things intelligent detection method, and relates to the technical field of power detection, and the system comprises the following components: S1, data collection and preprocessing, S2, big data analysis and feature extraction, S3, equipment life prediction model construction and training, S4, fault prediction and evaluation, S5, predictive maintenance strategy making, and S6, adaptive adjustment. According to the method, the equipment life prediction model of deep neural network self-learning modeling is adopted, the quantum optimization algorithm is used for optimization, accurate prediction of the residual life and the fault occurrence probability of the power equipment is achieved, and meanwhile, the power equipment state comprehensive evaluation model based on the combination of fuzzy logic and the evidence theory is combined, so that the power equipment state comprehensive evaluation efficiency is improved. The equipment health state grade can be comprehensively evaluated, a scientific basis is provided for formulating a predictive maintenance strategy, a maintenance plan can be reasonably arranged in advance, the maintenance cost is reduced, the service life of equipment is prolonged, and economic benefits and social benefits of a power system are improved.

The invention relates to the technical field of data processing, and discloses a method and system for monitoring the running state of reflow soldering equipment. The method comprises the steps of collecting data through a multi-point sensor, extracting temperature gradient, tension fluctuation and gas concentration characteristics, constructing a state recognition model, calculating a health index and setting an early warning threshold value, constructing a fault precursor extraction model to predict a future state, and finally establishing a maintenance strategy optimization system and generating an equipment maintenance plan based on fault early warning information. Through multi-dimensional data fusion and intelligent analysis, early accurate identification of the abnormal state of the equipment, accurate prediction of the fault development trend and active maintenance decision based on quality influence and cost optimization are realized, so that the welding quality stability is improved, the non-planned downtime is shortened, and the maintenance cost is reduced.

The invention relates to the field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

The invention discloses a new energy equipment health prediction and intelligent maintenance method based on multi-source data fusion, and the method comprises the steps: building a dynamic health state evaluation model through a system in combination with equipment operation parameters and environment variables; monitoring the running state of the equipment in real time, and grading the health degree of the equipment; based on fault prediction of data, a deep learning algorithm is introduced to carry out modeling on time series data, and possible fault occurrence time and types of equipment are predicted by capturing potential trends and change modes in operation of the equipment; a dynamic prediction function is provided, a prediction result is updated in time, and synchronism of a model and an actual equipment state is ensured; and generating an optimized maintenance plan according to the health diagnosis and prediction result. Through innovative design of a multi-source data fusion technology and an intelligent prediction algorithm, a core pain point in operation and maintenance of current new energy equipment is solved, a feasible path is provided for full-life-cycle management of the equipment, and the method has remarkable economic value and industry popularization potential.

The invention relates to the technical field of electric power energy storage system planning analysis, and discloses an electric power system planning operation optimization method, system and device and a storage medium. The method comprises the following steps: collecting electrical and equipment states and environmental parameters for feature extraction, and evaluating the health state of equipment; grouping the distributed energy sources based on health state evaluation and weather early warning information to form a virtual power plant resource pool; performing task decomposition according to the characteristics of the resource pool, and formulating a collaborative scheduling strategy; arranging a maintenance plan according to the equipment state and the scheduling strategy; and optimizing system operation parameters based on the scheduling strategy and the maintenance plan to obtain a balance control scheme. According to the invention, under the condition of considering the health state of the equipment and the influence of extreme weather, the dynamic balance of the minimization of the operation cost of the power distribution network and the maximization of renewable energy consumption is realized through the intelligent arrangement technology of the virtual power plant group.

The invention relates to the technical field of power system scheduling, and discloses a power distribution network simulation scheduling optimization method and system based on artificial intelligence, and the system comprises a data fusion module, a digital twin modeling module, an intelligent prediction module, a strategy optimization module, and a visual scheduling module. The whole scene of the power distribution network is simulated through the digital twin model, the operation state and fault influence of equipment are accurately simulated, a scientific basis is provided for making a maintenance plan, blind maintenance is avoided, and the maintenance and repair cost of the equipment is reduced; meanwhile, by optimizing a load transfer path and distributed power supply output, the network loss rate is reduced, and the utilization efficiency of electric power resources is improved; in addition, the knowledge graph and the LSTM deep learning algorithm are fused, the distribution network topology entity relation network is constructed, and multi-source data are trained, so that the fault prediction accuracy is improved, the power failure risk can be early warned in advance, the conversion from passive first-aid repair to active prevention is realized, and the power failure frequency outside a plan is reduced.

The invention relates to the technical field of data processing, and discloses a packaging equipment inspection method and system. The method comprises the steps that packaging process parameters are collected through multiple sensors, and a working condition data set is formed; extracting abnormal features by using spectral analysis, and generating an operation deviation index; quality and state data are compared to establish an association rule, and a fault precursor feature library is obtained; the station coordination is analyzed through time sequence comparison, and a component state evaluation table is formed; calculating the fault probability of the key component, and generating an inspection point sequence; and making an inspection scheme including parameters, standards and periods, and forming a preventive maintenance plan. According to the method, key parameters such as tension, temperature and speed in the packaging process are analyzed, the early characteristics of the abnormal operation state of the equipment are recognized, the routing inspection priority and the maintenance period are determined in combination with historical fault data and component state evaluation, and conversion from passive maintenance to active prevention is achieved.

The invention discloses a building waterproof construction quality monitoring and early warning system based on the Internet of Things, relates to the technical field of building engineering, and realizes omnibearing and real-time monitoring and intelligent early warning of a building waterproof construction area through a multi-layer architecture formed by a data acquisition layer, a data transmission layer, a data processing layer and an application layer. The data processing layer analyzes data features from a time sequence and a spatial distribution dimension through normalization, denoising processing and a multi-head attention mechanism in a Transform model, and accurately recognizes the abnormality and risk trend of a weak point in combination with dynamic threshold and trend analysis, and the application layer determines the risk trend of the weak point through multi-dimensional data visualization, historical playback and real-time interaction. And the early warning module is combined with intelligent reasoning to generate dynamic maintenance suggestions, a maintenance scheme is optimized, the construction quality and the maintenance efficiency are improved, the intelligent level of building waterproof monitoring is improved, risk prediction and accurate maintenance are realized, the management cost is reduced, and the system has wide application value.

The invention discloses an intelligent service system and method for garden management. The intelligent service system comprises an Internet of Things sensing module, an edge computing module, a cloud service module and an application service module. And comprehensive intelligentization and refinement of garden management are realized. Through file recording, multi-module collaborative operation and deep analysis and intelligent decision of big data, the efficiency and quality of garden maintenance are improved, and the tourist experience and the garden safety management level are also significantly enhanced. The system can monitor the health state of plants in real time, accurately predict and prevent diseases and pests, optimize the irrigation strategy and guarantee the optimal growth conditions of the plants. Meanwhile, through the digital twinborn platform, a manager can deduce different maintenance schemes in a virtual environment, scientifically evaluate a long-term effect and make an optimal decision.

The invention relates to the technical field of intelligent traffic engineering, and provides a road guardrail intelligent inspection and maintenance planning method based on machine learning, which breaks through the limitation of a traditional single mode in a detection link, fuses laser radar three-dimensional geometrical characteristics and visual texture information, realizes high-precision identification of defects such as microcracks and hidden corrosion, and improves the inspection efficiency. The detection problem in a complex environment is solved, and the problem that a traditional method is high in omission ratio is solved. In the aspect of dynamic planning, a multi-dimensional state space is constructed based on real-time traffic, weather and defect levels, polling path dynamic optimization and multi-vehicle collaborative operation are achieved through a reinforcement learning algorithm, and the current situations that manual planning is low in efficiency and poor in flexibility are changed. In the aspect of maintenance decision, a time sequence data analysis model is used for accurately predicting guardrail maintenance requirements, a scientific maintenance plan is made in combination with an intelligent scheduling algorithm, and efficient utilization of resources is achieved. According to the method, a complete intelligent operation and maintenance closed loop is constructed, and road guardrail operation and maintenance are promoted to be converted from passive low efficiency to active intelligence.

The invention provides an artificial intelligence-driven energy storage system health state monitoring and early warning mechanism. The mechanism comprises five levels of data acquisition, processing, artificial intelligence analysis, decision support and user interaction. System operation data is collected in real time through a sensor network, and after cleaning and standardization, machine learning and deep learning algorithms are used for analysis. Based on the analysis results, the system provides optimization suggestions, fault diagnosis and predictive maintenance plans, and displays system status and sends alarms through a user interface. According to the invention, intelligent power grid optimization, battery health state monitoring and fault diagnosis early warning are realized, and through multi-source data fusion, real-time artificial intelligence analysis and adaptive learning, the efficiency and reliability of the energy storage system are improved, and the service life of the energy storage system is prolonged. The system can predict the energy demand, evaluate the health state of the battery and identify an abnormal mode, thereby actively making a maintenance plan, reducing accidental shutdown, and providing a comprehensive solution for intelligent management of the energy storage system.

The invention relates to the technical field of intelligent management of storage equipment, and discloses a self-evolution digital twinning storage equipment fault prediction and health management system. The equipment twin modeling module constructs a behavior evolution model based on the vibration spectrum and the energy consumption data, and integrates multi-source data to generate a maintenance scheme; the collaborative awareness module generates a health state evolution table containing health indexes and calibration parameters in combination with the model; the mode mining module analyzes the log identification association factor; the health assessment module calculates a health state offset; the model iteration module updates the evolution table according to the offset; and the prediction execution module triggers fault early warning. The system is additionally provided with an operation instruction comparison unit to process conflicts and optimize scheduling, equipment state evaluation, fault prediction and maintenance optimization are achieved, and the intelligent management level of storage equipment is improved.

The invention provides a large recreation facility operation monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a facility three-dimensional model of a recreation facility; establishing a basic failure model of the recreation facility; based on the model and the three-dimensional model, identifying a key risk part; generating a multi-dimensional health assessment framework; key risk parts are detected, and operation parameters and structure health conditions are monitored; the recreation facility is automatically patrolled, and video images are collected; carrying out deep fusion on the nondestructive testing data, the operation parameters, the structure health condition data and the video images to obtain monitoring data; monitoring data are analyzed, and automatic fault recognition, diagnosis and prediction are achieved; evaluating the remaining service life of the key risk part of the recreation facility, and performing trend analysis in combination with historical data; and performing quantitative evaluation on the health state of the facility, generating dynamic risk early warning and maintenance suggestions, and making a maintenance plan. According to the scheme, the safety and the maintenance efficiency of the equipment are remarkably improved.

The invention provides an AI-based smart old-age care service method, system and device, and a storage medium, and relates to the technical field of smart old-age care. The method comprises the following steps: collecting vital sign data, behavior data and environment data of the elderly, and carrying out standardization processing on past case data to obtain standard case data; performing management fusion on the standard case data, the vital sign data, the behavior data and the environment data to obtain multi-dimensional health data; inputting the multi-dimensional health data into a preset health state evaluation model to obtain health state information; matching the health state information with maintenance knowledge in a preset knowledge base to obtain a plurality of alternative maintenance schemes, and inputting the plurality of alternative maintenance schemes into a preset maintenance effect prediction model to obtain a plurality of maintenance effect prediction results; and taking the alternative maintenance scheme with the highest evaluation score as a target maintenance scheme. Through the alternative scheme, the effect of the pension service is improved.

The present invention relates to the field of geothermal resource development technology, specifically a geothermal well monitoring and maintenance management system and method based on the Internet of Things; the method steps are: through a denoising method combining adaptive fuzzy clustering and deep convolutional neural network, the real signal of sensor data is restored; by generating a check mark, the integrity of the collected geothermal well monitoring data is checked; combined with LSTM time series modeling and deep reinforcement learning for fault prediction and optimization, support vector machine is used for fault type identification, and maintenance strategies are formulated according to the prediction results, the appropriate maintenance type and timing are selected, and resource scheduling optimization is performed to generate a detailed maintenance plan; data and maintenance information are visualized and pushed to on-site staff in real time, and the staff performs operations according to the maintenance plan, and automatically records and updates the health status of the equipment. The present invention improves the monitoring and maintenance efficiency of geothermal wells through intelligent data processing and decision support.

The invention relates to the technical field of host operation and maintenance, in particular to a cross analysis digital twin intelligent operation and maintenance method and system based on artificial intelligence. The method comprises the following steps that multi-mode sensing data and central air conditioner structure data are obtained, multi-mode air conditioner operation sensing is conducted, and multi-mode operation sensing data are obtained; carrying out feature weight dynamic assignment on the multi-modal operation sensing data to obtain a space-time interaction feature matrix; a real-time updating digital twinborn model is constructed based on the central air conditioner structure data; quantitatively evaluating the equipment degradation degree based on the real-time updated digital twin model to obtain a fault critical point prediction matrix; and performing double-layer game fault risk assessment on the fault critical point prediction matrix, and executing dynamic collaborative optimization of host operation parameters to obtain an intelligent operation and maintenance scheme of the central air conditioner. According to the invention, the operation and maintenance efficiency and the fault detection accuracy can be improved.

The invention provides a wind power mixed tower damage prediction method and system based on digital twinning. The method comprises the steps of obtaining multi-source data of a wind power mixed tower; wherein the multi-source data comprises structure response data, environment load data and historical operation and maintenance data; based on a building information model and finite element model fusion technology, constructing a digital twinborn model of the wind power mixing tower; inputting the multi-source data into a digital twinborn model to realize real-time mapping of the wind power mixed tower and the digital twinborn model, and obtaining real-time structure state data of the wind power mixed tower through the digital twinborn model; inputting the multi-source data and the real-time structure state data into a pre-trained LSTM-GRU hybrid neural network model, and predicting a damage evolution trend of the wind power hybrid tower in combination with an attention mechanism; and on the basis of the damage evolution trend, a maintenance scheme of the wind power mixed tower is generated by using a multi-target particle swarm algorithm, so that balanced optimization of maintenance cost, power generation loss and risk level is realized, and the operation and maintenance efficiency and safety of the wind power mixed tower are integrally improved.

The invention relates to the technical field of big data analysis, in particular to a contact defect detection system and method based on big data, and the system comprises a multi-dimensional data capture module, a time sequence analysis module, a trend evaluation and anomaly recognition module, a future state prediction module, and a fault classification and diagnosis module. According to the invention, by deeply capturing and screening contact operation state data, the accuracy of input data is ensured, abnormal interference of contact resistance is avoided, the data validity is improved, current impact fluctuation points are identified, the sensitivity to operation abrupt change is enhanced, the accuracy of time sequence analysis is ensured, long-term offset is tracked, the wear identification capability is improved, and the future state is accurately predicted. The method is advantaged in that maintenance plan precision is improved, contact pressure reduction trend is analyzed, fault classification is refined, defect identification accuracy is improved, data screening, trend tracking, offset characteristic calculation and fault classification are optimized, accurate detection is realized, and equipment reliability and operation safety are improved.

The invention provides an airport luggage system speed reducer fault prediction and diagnosis method based on digital twinning, which relates to the technical field of fault prediction and comprises the steps of acquiring real-time operation data of a speed reducer, establishing a digital twinning model and updating the model in real time, constructing a multi-dimensional feature vector and then performing feature extraction and fusion through deep learning, identifying a fault type and evaluating the degree. And optimizing model parameters by using a particle swarm optimization algorithm to establish a residual life prediction model, generating early warning information and determining a maintenance scheme. Accurate prediction and early warning of faults of the speed reducer are realized, the maintenance cost is reduced, and the operation reliability of an airport luggage system is improved.

The invention relates to the technical field of industrial monitoring, in particular to a fan running state comprehensive monitoring system and a noise and current detection method thereof, and the system comprises a noise data acquisition module, a vibration noise correlation analysis module, a running current acquisition module, an abnormal current detection module, an equipment fault diagnosis module and a performance degradation prediction module. According to the method, the sound data are collected, the frequency and amplitude changes are analyzed, the types of various sound sources are recognized, abnormity caused by mechanical parts is found in the early stage, association analysis of vibration data and sound data is combined, understanding of the physical state of the fan is enhanced, and by monitoring current data and the fan blade rotating speed in real time, the fan blade rotating speed can be accurately detected. Abnormal detection of the current data and evaluation of the current stability are realized, a data basis is provided for fault diagnosis, the operation efficiency and the performance degradation trend of the fan are evaluated in combination with the current data, the service life and the maintenance period of equipment are effectively predicted, and the operation strategy and the maintenance plan of the fan are optimized.

The invention discloses an intelligent power grid operation and maintenance system based on federated learning and edge calculation, and the system comprises an intelligent electric meter enhancement module which is disposed at a power grid monitoring node and is used for collecting electric energy quality parameters and environment data in real time; the edge calculation layer is used for carrying out data preprocessing, power quality parameter anomaly detection and building a federated learning model; the secure communication framework is used for establishing a hybrid communication network and multi-level security protection, carrying out routing decision and carrying out encrypted transmission on interactive data among the modules; and the end analysis platform is used for integrating the multi-source heterogeneous data of the power grid, predicting the state of the power grid and generating a maintenance plan. According to the invention, comprehensive monitoring and predictive maintenance of the operation state of the power grid are realized.

The invention discloses a property maintenance prediction system based on big data, and the system comprises the steps: monitoring equipment and environment in real time through an intelligent sensor, transmitting the data to a cloud or local storage through a wireless network, and carrying out the historical data processing and cleaning through a distributed architecture; predicting the equipment fault risk through machine learning, establishing a customized prediction model for different equipment, analyzing the life cycle and predicting the service life of the equipment; for old equipment, constructing a life extension model by using historical fault data; the system generates intelligent decision suggestions, pushes equipment health states to property management personnel, automatically formulates maintenance plans and priorities, and dispatches maintenance personnel; during fault prediction, early warning is given out in real time, related personnel are reminded through a short message or an APP, meanwhile, maintenance operation is recorded, historical data are analyzed, and future maintenance plans and budget are optimized; the system dynamically adjusts the life prediction model of the old equipment by accumulating historical data, and ensures the accuracy and efficiency of equipment health management.

The invention discloses a boiler fault self-diagnosis method and related device, and the method comprises the steps: S1, collecting boiler operation parameters, generating virtual data in combination with a digital twinborn model, and constructing a multi-modal monitoring data set; s2, carrying out preprocessing and anomaly detection on the data set by utilizing an edge computing node, and obtaining a preliminary anomaly signal and a feature vector; s3, uploading the abnormal signal and the feature vector to a cloud end, and performing simulation verification through a digital twin engine; s4, inputting the feature vector and a verification result into a hybrid enhancement diagnosis model, and outputting a fault type and a probability; s5, reasoning a fault source and a propagation path in combination with the knowledge graph according to the fault type and the probability; and S6, generating a maintenance scheme based on the fault information, and optimizing the diagnosis model by using operation and maintenance feedback. According to the method, the boiler operation parameters are collected and combined with the digital twinborn model to generate the virtual sensor data, the multi-modal monitoring data set is constructed, synchronous monitoring of multiple data is achieved, and one-sidedness of single-parameter monitoring is avoided.

The invention relates to the technical field of intelligent control, in particular to a barrier gate remote monitoring system and method based on the Internet of Things, and the method comprises the steps: building a dual-channel communication link through a 4G module and a Bluetooth / WiFi module, achieving the synchronous collection and uploading of the current of a barrier gate motor, a position sensor and environment data, deploying an LSTM model at an edge calculation layer, and carrying out the remote monitoring of a barrier gate. Time sequence prediction and abnormal deviation quantification are carried out on the operation state, video acquisition is triggered, vehicle retention and rod body deformation are identified in combination with a lightweight YOLOv5 model, abnormal event grading judgment is realized, spare part inventory is associated according to event types, and a maintenance scheme is dynamically generated. An intelligent work order including a thermodynamic diagram, a video frame and a prediction curve is constructed by using a three-dimensional visualization template, evidence storage processing is completed through a block chain module, and finally optimal personnel scheduling and order dispatching are executed according to a maintenance scheme. According to the method, multi-source data fusion modeling, visual linkage anomaly recognition and full-link credible operation and maintenance are realized.