946 results about "Preventive maintenance" patented technology

The invention discloses a power grid equipment intelligent operation and maintenance method based on a large language model and a knowledge graph, and relates to the technical field of intelligent power grid operation and maintenance, and the method comprises the steps: carrying out the operation and maintenance of power grid equipment through a power grid operation and maintenance knowledge graph constructed through a large language model and a knowledge federation technology, the power grid equipment operation and maintenance comprises one or more of health state evaluation, fault risk prediction and early warning, intelligent operation and maintenance strategy generation and health degree dialogue query of the power grid equipment. According to the invention, a power grid operation and maintenance mode can be effectively promoted to be transformed and upgraded from a traditional manual experience type and a passive maintenance type to a data-driven, intelligent and active preventive maintenance mode. Key intelligent operation and maintenance technical support is provided for building a novel electric power system with new energy as a main body, the novel electric power system is assisted to achieve the development goals of being safer, more efficient, cleaner and lower in carbon, and important industry strategic significance and social contribution are achieved.

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.

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 relates to the technical field of intelligent scheduling, in particular to an enterprise production real-time monitoring and intelligent scheduling system based on artificial intelligence, which comprises a multi-source heterogeneous data fusion unit, a priority resource coupling decision unit, a bottleneck prediction and tracing unit and a scheduling instruction generation unit, the multi-source heterogeneous data fusion unit collects multi-dimensional data such as equipment vibration, temperature, order delivery time and the like in real time and constructs a joint feature vector, and the priority resource coupling decision unit dynamically adjusts task priority and resource allocation through a dual-channel depth Q network to cope with order insertion tasks and equipment health degree fluctuation. The bottleneck prediction and tracing unit predicts production bottlenecks and traces root causes by using a process dependency graph, a multi-modal fusion model and a causal discovery algorithm, and supports preventive maintenance and dynamic scheduling, and the scheduling instruction generation unit synthesizes a preorder result to generate an adaptive scheduling instruction. And enterprise production equipment utilization rate and production efficiency are improved.

The invention discloses a power semiconductor device aging on-line diagnosis method, which relates to the field of power electronics, and comprises the following steps: integrating an intelligent sensor and an aging test circuit, and establishing a multi-physics field coupling simulation model; based on the multi-physics field coupling simulation model, evaluating the aging state of the current device to obtain the health index of the device; based on the multi-dimensional data matrix, constructing an anomaly detection model, and outputting an anomaly detection result; predicting the failure probability and the remaining service life of the semiconductor device according to an abnormal detection result; generating a maintenance suggestion based on the predicted failure probability and remaining useful life of the semiconductor device; according to the method, the maintenance suggestion is generated according to the prediction result, the user is guided to take preventive maintenance measures in time, the accuracy of aging state evaluation is improved, the timeliness of fault early warning is enhanced, and economic losses and technical risks caused by sudden faults are reduced.

The invention relates to the technical field of information resource fusion management, and particularly discloses a fusion and management system for multi-source heterogeneous science and technology information resources. Analyzing an equipment fault chain from an unstructured text of a historical operation and maintenance log, extracting a rated parameter constraint from a structured table of an equipment manual, and collecting an operation feature vector from a real-time sensing data stream to generate a knowledge graph containing N entity relationships; based on an entity attribute constraint rule of the knowledge graph, designing a bidirectional attention mapping network to calculate semantic similarity weights of multi-source data and knowledge nodes, and generating a graph embedding vector set with weight marks through Hadamard product operation; according to the method, the embedded vector set is input into the pre-trained graph neural network model, and the root cause equipment set causing feature offset is positioned, so that efficient fault diagnosis and positioning are realized, decision support is provided for a subsequent preventive maintenance strategy, and the reliability and the operation and maintenance efficiency of the system are improved.

The invention provides an RFID-based power supply management method for a tilt angle sensor of a power tower, and the method comprises the steps: employing a support vector machine and a decision tree, analyzing the historical operation data of the tower, building a tower fault prediction model, predicting the state of the tower through the tower fault prediction model, and obtaining the fault probability and time of the tower; according to a tower fault prediction result, a preventive maintenance plan is made, priority maintenance is arranged for the tower with the fault probability larger than a preset threshold value, and a power supply strategy is adjusted in advance for the tower predicted to have a fault, and load pressure is reduced; a tower group intelligent power supply management system is adopted, state information and a power supply strategy of a single tower are integrated into a whole power grid, the power supply load of each tower is dynamically adjusted through a load balancing algorithm, and for a tower in a dangerous state, the power supply load of the tower is reduced, and the load is transferred to a tower in a good state.

The invention discloses a real-time monitoring and fault response control device for an industrial and commercial liquid cooling energy storage system, and particularly relates to the technical field of liquid cooling energy storage fault management. Temperature, pressure, vibration and flow data of key components in a liquid cooling system are collected and preprocessed; a time sequence analysis and signal processing technology is utilized to extract feature vectors reflecting key component states from the multi-dimensional operation data set, the saliency of weak abnormal signals is enhanced, and a key feature set containing weak abnormal features is generated; constructing an anomaly recognition model by adopting a long-short-term memory network, inputting a key feature set, outputting an anomaly probability corresponding to the key component, and setting an early warning threshold value of the anomaly probability to trigger early warning; and predicting a fault maintenance time window of the key component based on a Weibull reliability model, and dynamically adjusting the priority of maintaining the key component and executing preventive maintenance based on a fault maintenance time window prediction result and an abnormal probability.

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 relates to the technical field of equipment maintenance scheduling, in particular to an equipment fault repair management system, which comprises a dependency identification and modeling module, a fault state analysis module, a task priority evaluation module, a work order scheduling generation module and a resource scheduling execution module. According to the method, a multi-level dependency topology between devices is constructed through a directed graph traversal algorithm, a trigger relation between physical connection and an operation process is quantitatively analyzed, a classification marking model is established in combination with dynamic parameters such as a performance degradation rate, a fusion influence range and a dependency factor are calculated through scalar superposition, and a priority scoring matrix is dynamically generated. A task queue structure is optimized through a sorting algorithm, a data-driven maintenance decision mechanism is formed, the fault positioning precision is improved, response delay caused by manual intervention is reduced, key node equipment maintenance lag is avoided, the resource configuration efficiency is optimized, the collaboration of fault processing and a production system is strengthened, and formulation of a preventive maintenance strategy is supported.

The invention provides a stamping production line self-organizing production system and method based on a twin intelligent agent, and the twin intelligent agent comprises a data collection layer which is used for collecting the operation state data and production environment parameters of physical equipment; the virtual-real mapping layer is used for constructing a digital twin model of physical equipment and realizing real-time state synchronization and bidirectional control instruction transmission of a physical space and a virtual space; the optimization decision-making layer is used for predicting the performance degradation trend of the equipment based on a deep reinforcement learning algorithm and generating a game parameter adjustment strategy and a preventive maintenance scheme; and the collaborative arbitration layer generates a compromise optimization scheme based on Pareto frontier analysis when the multi-agent strategy conflicts, and the multi-dimensional targets of the production efficiency, the equipment life and the energy consumption are balanced. According to the invention, autonomous task allocation, real-time state monitoring and global resource balance of the stamping production line are realized.

The invention discloses a reliability analysis method and system for a power system, and relates to the technical field of intelligent operation and maintenance of the power system, and the method comprises the steps: collecting data, and constructing a dynamic space-time atlas network of the power system, which comprises time dimension information and space dimension information; feature extraction is carried out based on a spatial-temporal feature learning framework constructed by a dual-channel graph attention network and a gated causal convolution network, and spatial-temporal fusion features containing complete spatial-temporal information are generated by using gated causal convolution and a time attention capture time sequence evolution mode; and converting the space-time fusion features into a local risk state and a global system state through a state feature mapping network, calculating a local component risk index and a system overall reliability index, generating a collaborative decision strategy, and realizing reliability analysis of the power system. According to the method, the intelligent operation and maintenance level of the power system is remarkably improved, the shutdown time and the maintenance cost of the system are reduced, and meanwhile, the system performance is continuously improved through preventive maintenance and an active optimization strategy.

The invention relates to the field of industrial intelligent control, in particular to a real-time production efficiency optimization system based on an artificial intelligence technology, and the system comprises an equipment fault prediction module which is used for collecting multi-modal time sequence data, and predicting the equipment fault probability based on a deep learning network; the process parameter adjusting and optimizing module is in communication connection with the equipment fault prediction module and is used for receiving the fault prediction probability and dynamically adjusting process parameters based on a reinforcement learning algorithm; the federated learning and incremental training module is in communication connection with the equipment fault prediction module and the process parameter tuning module, and is used for realizing collaborative optimization and privacy protection of a local model and a global model, greatly improving the fault prediction accuracy, reducing the false alarm rate from 20% to 5% or below, prolonging a prediction window from 10 minutes to 30 minutes or above, and improving the prediction efficiency. A sufficient preventive maintenance time window is provided for a production system; and the process parameter optimization effect is remarkably improved, and the comprehensive efficiency improvement space of the equipment is expanded to 8-12% from the traditional 5%.

The invention discloses a numerical control machine tool motor operation monitoring method and system based on data analysis, which are used in the field of motor monitoring and fault diagnosis, and the method comprises the steps: collecting working condition data corresponding to the operation process of a numerical control machine tool motor; establishing a temperature change trend model, and predicting temperature change conditions of the motor of the numerical control machine tool under different working conditions; combining the extracted frequency and amplitude with the prediction result of the temperature trend model, and judging the current operation state of the numerical control machine tool motor; and setting a threshold value of the working condition data, comparing a classification analysis result with a preset threshold value, and automatically triggering a multi-stage early warning mechanism when monitoring that the working condition data of the motor of the numerical control machine tool exceeds the threshold value. According to the method, preventative maintenance is realized, sudden faults are avoided, the motor is ensured to operate under efficient and stable conditions by determining the optimal electrical working range and real-time monitoring data, energy consumption is reduced, the service life of equipment is prolonged, and intelligent motor state analysis and prediction are realized.

A system and method for simultaneously running a plurality of air quality sensor units using a centralized station and software to test for field performance upon factory return from field installations, automatically identify preventative maintenance requirements, and automatically calibrate the sensor units and generate quality metric reporting. The centralized station and software include a computer, a manifold, tubes, and cables. A test gas is supplied, wherein the manifold distributes the test gas. Once powered and supplied with test gas, the sensor units are tested simultaneously. The computer runs software that compares the test data collected from the sensor units to determine if any of the sensor units fail standards or produce data that deviates from a mean average range. Sensor units that do not require maintenance are calibrated using test gasses to generate calibration coefficients which are stored within the sensors' firmware.

The invention discloses a method and system for predicting the health state of equipment based on large model AGENT capability. According to the method, equipment vibration, temperature, pressure, current and other data are acquired in real time through a multi-mode sensor, and data fusion and self-supervised learning are realized in combination with virtual data generated by digital twin simulation, so that equipment operation characteristics are extracted, and an equipment health index is calculated. According to the method, the health index and the change rate thereof are comprehensively considered, the equipment fault risk is dynamically evaluated, meanwhile, a dynamic weight self-adaptive updating mechanism is introduced, model parameters are optimized on line, and it is ensured that the model keeps high-precision prediction in long-term operation. The system can be widely applied to various heterogeneous devices, realizes full-process closed-loop management from data acquisition, intelligent analysis to risk early warning, and provides a scientific basis for device maintenance and preventive overhaul, so that the device failure rate is reduced, and the service life of the device is prolonged.

The invention relates to the technical field of data monitoring analysis, and discloses a centrifugal machine data remote monitoring analysis method and system based on a cloud computer, and the method comprises the following steps: collecting centrifugal machine operation index data, carrying out time stamp synchronization, carrying out the recognition and elimination of abnormal values in the data, and carrying out the data integration through data normalization processing, and generating synchronous monitoring data. According to the invention, the data frequency domain analysis of the centrifugal machine is implemented on the cloud platform, the vibration signal frequency spectrum and energy distribution are accurately identified, the efficient marking and trend analysis of abnormal data are realized, the detailed degree and response speed of abnormal detection are enhanced, and the monitoring of the running state of the centrifugal machine is more accurate; by combining real-time trend analysis and historical fault data, the accuracy of fault prediction is improved, the classification of fault modes is more scientific, the situations of false alarm and missing alarm are effectively reduced, the timeliness and pertinence of preventive maintenance measures are enhanced, and the running efficiency and safety of the centrifugal machine are remarkably improved.

The invention discloses an industrial internet-based equipment maintenance and management method, and relates to the technical field of industrial equipment maintenance and management. According to the method, the operation parameters in the set time interval are regularly extracted for comprehensive evaluation, in the aspect of operation parameter evaluation, the fault temperature index and the fault abnormal sound index are calculated, and the corresponding weight coefficient is set for calculating the fault evaluation index, so that the equipment fault risk degree can be more accurately quantified, the possible faults of the equipment can be predicted in advance, and the fault risk of the equipment can be accurately evaluated. A basis is provided for taking maintenance measures in time, loss caused by sudden equipment failures is reduced, performance parameters of prediction and evaluation equipment which does not trigger failure maintenance signaling are deeply analyzed to calculate a predictive evaluation index, and the predictive evaluation index is compared with a reference threshold index to trigger preventive maintenance signaling, so that the failure maintenance signaling is prevented from being damaged. The condition of excessive maintenance or insufficient maintenance is avoided, timely intervention can be performed when the equipment performance begins to decline but no serious fault is caused, the maintenance cost is reduced, and the service life of the equipment is prolonged.

The invention discloses an equipment preventive maintenance and diagnosis method based on multi-dimensional data verification, relates to the technical field of equipment preventive diagnosis, and solves the technical problems that fault reasons are difficult to comprehensively and deeply analyze, and maintenance measures are lack of pertinence due to the fact that the fault reasons are often misjudged or cannot be positioned. According to the method, the fault diagnosis model is constructed, the adaptation algorithm is adopted for different types of data, the diagnosis accuracy of the model to equipment faults and the adaptability of the model to complex working conditions are improved, the equipment fault type and the abnormal state can be rapidly and accurately recognized, and the fault diagnosis accuracy is improved through the methods of fault phenomenon matching, parameter correlation analysis, historical data deep backtracking and the like. According to the method, fault reasons can be accurately determined, the performance degradation trend and potential risks of the equipment can be effectively identified in combination with a hybrid similarity measurement method, correlation among data is further mined through a secondary analysis mechanism, the potential risks are accurately pre-judged in combination with historical data, and real preventive maintenance is achieved.

The invention relates to the field of wind power plants, in particular to a wind power cluster preventive maintenance strategy intelligent generation method and system. The method comprises the following steps: collecting multi-source heterogeneous data of a wind power cluster in real time, including SCADA system operation data, CMS monitoring data and environmental sensor data; performing standardization processing and abnormal value elimination on the collected data, and constructing a wind power equipment operation state database; a wind power equipment health state evaluation model is established based on a deep learning algorithm, the equipment operation state is analyzed in real time, and the potential fault risk is predicted; generating a preventive maintenance strategy by adopting a multi-objective optimization algorithm according to a fault risk prediction result in combination with historical maintenance records of the equipment and operation and maintenance resource scheduling information; and issuing the generated maintenance strategy to a wind power cluster operation and maintenance system to realize intelligent execution of preventive maintenance. The problems of high data fusion difficulty and low processing efficiency in the prior art are solved.

The invention relates to the technical field of power system automation, and particularly discloses an intelligent fault diagnosis and analysis system and method for power secondary equipment, and the method comprises the following steps: constructing a sensor network, collecting the operation data of a power system, and extracting the characteristics related to the fault of the power secondary equipment in the operation data; constructing a fault diagnosis model of the power secondary equipment, and obtaining faults of the power secondary equipment in the monitoring time period; constructing an association period according to the fault occurrence time point, generating coordinate points according to the fault occurrence sequence in the association period, and clustering the coordinate points to obtain a cluster; and according to the density of the clustering clusters, strong correlation faults of the faults are obtained, and when the faults occur, prompt information is sent to maintain the strong correlation faults corresponding to the occurring faults in advance. According to the invention, the real-time accurate diagnosis, strong correlation early warning and interpretable visual root cause analysis of the power secondary equipment fault are realized, and the safe operation of the system is effectively guaranteed.

The invention relates to the technical field of hoisting equipment monitoring, and discloses a hoisting equipment digital real-time monitoring system based on the Internet of Things. A dynamic load analysis module of the system collects multi-dimensional operation parameters in real time through distributed edge computing nodes; the risk situation assessment module executes tensor decomposition operation on the parameters, extracts feature vectors and generates a three-dimensional risk map; the self-adaptive safety control module dynamically adjusts the working state of the equipment according to the risk map; the digital twin mapping module is used for realizing time-space synchronous mapping of real-time parameters and a three-dimensional model and outputting holographic running state projection; and the cloud collaborative diagnosis module fuses the historical fault case library to generate a preventive maintenance strategy and returns the preventive maintenance strategy. The system can comprehensively monitor the equipment state, accurately assess the risk, realize dynamic safety control and preventive maintenance, and improve the safety and reliability of the operation of the hoisting equipment.

The invention relates to the technical field of fracturing pump truck monitoring, and discloses a fracturing pump truck monitoring system based on acoustic fingerprints and deep learning, and the system comprises a plurality of sensor modules which are used for collecting multi-dimensional operation state data of a fracturing pump truck and comprise sensors used for detecting sound, vibration, temperature, pressure, flow and electrical parameters; the data acquisition and preprocessing module is used for carrying out noise reduction processing and feature extraction on the data; and the anomaly detection module is used for detecting the operation anomaly of the fracturing pump truck based on the acoustic fingerprint model. According to the method, the technology based on acoustic fingerprints and deep learning is adopted, and the fault diagnosis precision and preventive maintenance capability of the fracturing pump truck monitoring system are remarkably improved. Meanwhile, the system can adapt to different environments through data analysis and self-adaptive learning, and the operation efficiency and the response speed are remarkably improved through improved user interaction and data visualization functions.

The invention discloses an equipment management method based on data acquisition, and relates to the technical field of equipment management, and the method comprises the following steps: S1, selecting and configuring a sensor; s2, establishing a data acquisition protocol; s3, deploying data acquisition hardware; s4, designing and applying a data storage system; s5, performing data cleaning and preprocessing; s6, deploying an analysis model; s7, establishing a real-time monitoring system; s8, optimizing a maintenance strategy; and S9, performing regular evaluation and optimization. According to the equipment management method based on data acquisition, the running state of the equipment is monitored in real time through real-time data acquisition and analysis, and potential faults and performance problems are found in time. In combination with an advanced data processing technology, such as a machine learning analysis model, future behaviors of the equipment can be learned and predicted from historical data, so that preventive maintenance is realized, and risks of equipment failure and production interruption are reduced. In addition, a condition-based maintenance policy replaces traditional periodic maintenance.

The invention relates to the technical field of equipment operation monitoring, in particular to a medical equipment intelligent early warning system based on the Internet of Things, which comprises an abnormal accumulation risk assessment module, a core component degradation analysis module, a workload analysis module, an equipment health state monitoring module and a dynamic alarm grading module. According to the invention, the equipment operation parameters including X-ray tube current fluctuation, cooling system temperature change and the like are collected by using the Internet of Things technology, the accumulated influence of abnormal parameters is calculated through accumulated data, the long-term degradation curve of the equipment is effectively fitted, the potential long-term degradation trend of the equipment can be predicted, the possibility of preventive maintenance is enhanced, and the maintenance efficiency is improved. By dynamically monitoring the working load and calculating the influence of the working load on the equipment state, the service life of the equipment is prolonged, the maintenance cost is reduced, the accuracy of fault detection is improved, the resource configuration is optimized, and greater economic and safety values are created for medical institutions.

The disclosure provides a method for diagnosing vehicle faults based on an environment-adaptive Bayesian network. The method can comprise receiving an input of a faulty symptom of a vehicle into a vehicle fault detection system; selecting an optimal weighted association fault tree model from a library comprising a plurality of weighted association fault tree models, based on the faulty symptom; mapping the optimal weighted association fault tree model to an environment-adaptive Bayesian network; calculating a credibility of each leaf node within the environment-adaptive Bayesian network; and generating a fault ranking and a fault status information based on the credibility of each leaf node within the environment-adaptive Bayesian network, and initiating a multimedia fault alert. The method for diagnosing vehicle faults demonstrated of the disclosure exhibits remarkable adaptability to varying scenarios and facilitates rapid and accurate diagnosis of vehicle faults, supporting reliable operation and preventive maintenance of vehicles.

The invention discloses a ship heat exchanger heat transfer performance simulation method, particularly relates to the field of heat transfer, and comprises the steps of multi-source data acquisition, characteristic parameter calculation, heat transfer stability diagnosis, comprehensive heat transfer degradation evaluation, structure reliability prediction and system efficiency comprehensive evaluation. Multi-dimensional data synchronous acquisition is realized through a multi-source sensor network, a dynamic coupling analysis model is constructed to accurately calculate the heat transfer stability and the comprehensive degradation index, a three-level early warning mechanism is established to improve the state evaluation accuracy, turbulence attenuation characteristics and vibration data are fused to predict the structural reliability, and the reliability of the system is improved. And closed-loop management and control is formed by combining an efficiency index triple evaluation system, so that the spanning from single alarm to preventive maintenance is realized, the health management capability of the system is comprehensively enhanced, and full-life-cycle dynamic optimization support is provided for the ship heat exchanger.

The invention discloses an artificial intelligence-driven industrial Internet of Things equipment fault prediction system, and relates to the technical field of industrial Internet of Things and predictive maintenance, an edge-cloud collaborative architecture is adopted, real-time acquisition, preprocessing and online fault prediction of industrial equipment sensing data are realized, the system uses a Transform neural network to construct a hierarchical spatio-temporal model, and the fault prediction of the industrial equipment sensing data is realized. Time sequence data are processed in a segmented mode through a sliding window method, a causal reasoning enhancement mechanism is integrated, an industrial equipment causal atlas is constructed, attention masks are generated, a model is focused on key features, and therefore prediction accuracy and interpretability are improved, meanwhile, a closed-loop continuous optimization mechanism is established by the system, and prediction efficiency is improved. And an edge fault prediction result and actual operation feedback are uploaded to a cloud, a causal atlas and model parameters are updated, adaptive optimization of the model is realized, and the system provides efficient, accurate and explainable decision support for preventive maintenance of industrial equipment.

The invention discloses a dynamic environment monitoring system data change analysis early warning method integrating an AI large model. According to the embodiment of the invention, the AI large model is used to analyze the environmental data collected by the plurality of sensors, and the preset alarm threshold is adjusted according to the analysis result. Therefore, when it is determined that one or more pieces of environmental data exceed the adjusted threshold according to the analysis result, the risk continuity is further determined according to the change trend of the environmental data generated by the AI large model, and the current risk is further determined according to the risk continuity, so that alarm information is generated. Therefore, according to the early warning method provided by the embodiment of the invention, the trend of the environmental data can be analyzed and predicted in advance, the accuracy and timeliness of warning are improved, the preventive maintenance capability of the system is enhanced, and the warning strategy can be continuously optimized through the self-learning function of the AI model to adapt to the change of the environment.