1583 results about "Maintenance plan" patented technology

The invention provides an intelligent maintenance decision-making method and system for a numerical control machine tool based on digital twinning, and relates to the technical field of equipment maintenance, and the method comprises the steps: obtaining the structural data and simulation precision requirements of the numerical control machine tool, determining the optimal grid size and distribution parameters of a finite element model, obtaining operation conditions, and adding the operation conditions to a multi-physics coupling modeling module, generating a machine tool digital twinborn model, fusing the machine tool digital twinborn model with the physical field, generating a machine tool operation degradation model, and optimizing to obtain a self-evolution digital twinborn model; obtaining machine tool operation parameters, performing preprocessing to obtain multi-modal perception data, extracting multi-view potential features, determining potential correlation features, and obtaining a machine tool health state evaluation result; the method comprises the following steps: generating a multi-level fault prediction result, determining a candidate maintenance scheme, adding the candidate maintenance scheme and the multi-level fault prediction result into a knowledge graph construction module, generating a semantic association network, and solving to obtain an optimal maintenance decision in combination with a multi-objective optimization algorithm.

The invention provides an intelligent auxiliary diagnosis and maintenance method and system based on multi-path recall, and relates to the technical field of fault processing, and the method comprises the steps: collecting equipment operation data, extracting and enhancing time sequence features after preprocessing, constructing a feature dependency graph, and generating a state feature vector. And carrying out fault feature recall by using the multi-path recall diagnosis network, fusing to generate a fault propagation map, and further generating a fault diagnosis result. And finally, generating a preliminary maintenance scheme based on a fault diagnosis result and the equipment state transition probability matrix, and optimizing the maintenance scheme by adopting a hierarchical reinforcement learning method in combination with a historical case and a fault propagation map to generate an optimal maintenance scheme. According to the invention, the accuracy of fault diagnosis and the effectiveness of a maintenance scheme can be improved, and the equipment maintenance cost is reduced.

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.

AI-driven predictive maintenance system (100) for industrial plants, including: a multitude of sensors (101) configured to collect real-time operational data from components of the industrial plant, the data including at least one of the following elements: vibration, temperature, pressure and rotational speed; a preprocessing unit (102) configured to filter and clean the operational data to remove noise and inconsistencies; a deep learning engine (103) comprising a neural network model (103a) trained on historical operational data and failure events, wherein the deep learning engine is configured to analyze the real-time operational data and detect anomalies that indicate an impending equipment failure; a prediction module (104) configured to generate risk values ​​based on the detected anomalies, the risk values ​​indicating the probability of equipment failure within a specific time frame; a maintenance optimization unit (105) configured to recommend a maintenance plan based on the generated risk assessments, thereby minimizing downtime and optimizing equipment maintenance; a user interface (106) configured to display the status of the equipment, risk assessments and recommended maintenance measures for the operators.

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 petroleum drilling and production equipment management method and system based on big data analysis, and relates to the technical field of equipment management.The method comprises the steps that operation parameters and environment parameters of equipment are collected in real time by installing various sensors; based on the historical operation data of the equipment, calculating a dynamic baseline value of the operation parameters of the equipment, and setting a normal fluctuation range; the method comprises the following steps: analyzing operation parameters of a plurality of devices in the same operation area, calculating a correlation degree among the devices, and identifying a correlation mode among the devices; when the operation parameters of the equipment exceed the dynamic baseline range and the relevance between the equipment is abnormally changed, judging that the equipment is abnormal, and triggering early warning; and according to an abnormal detection result, the importance of the equipment and the operation condition, evaluating the maintenance priority, and making an optimized maintenance plan. According to the method, by introducing calculation of the dynamic baseline value and recognition of the association mode between the devices, early warning can be given out in time when the parameters are abnormal and the association changes, and the accuracy of anomaly detection is 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 BIM (Building Information Modeling)-based building energy efficiency optimization system and method, particularly relates to the field of building energy efficiency management, and is used for solving the problems of dynamic optimization and collaborative maintenance of building equipment aging and energy efficiency reduction. Initial attenuation data, historical operation data and environmental influence parameters of the equipment are embedded into the data, and dynamic adjustment and real-time tracking of the aging state of the equipment are realized in combination with a sensor network and edge calculation. The maintenance period of the equipment is predicted based on a random forest regression algorithm, a collaborative maintenance plan is generated in combination with physical position information in the BIM, and load optimization and time sequence coordination during multi-equipment maintenance are ensured. By calculating the dynamic adaptability nonlinear response coefficient and the performance regeneration residual nonlinear deviation coefficient, the load adjustment opportunity is accurately judged, the load distribution between the equipment is optimized, the overall energy efficiency of the building is effectively improved, and the full-life-cycle management and energy efficiency optimization of the building equipment are realized.

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 structure health monitoring, in particular to a basement prestress bottom plate health monitoring system based on the internet of things, which comprises a stress distribution analysis module, a defect diffusion analysis module, a damage characteristic evaluation module and a safety monitoring evaluation module. According to the method, accurate stress data collection and real-time monitoring are realized by capturing spatial position information and node stress values, so that stress change is carefully tracked and evaluated, stress conduction and edge weight between monitoring points are analyzed, the internal stress distribution state of the structure is accurately revealed, the defect diffusion range and path are simulated, and the damage trend is predicted in time; according to damage characteristic evaluation, through analysis of stress mutation and material strain, key data support is provided, maintenance decision making and maintenance plan optimization are effectively guided, threshold value classification and early warning implementation of safety monitoring evaluation are effectively guided, the recognition capacity of an overproof stress concentration area is improved, and the reliability of the system is improved. And the overall safety and stability of the structure are ensured.

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 provides an intelligent maintenance decision-making method and system based on bridge big data, and belongs to the technical field of bridge safety. The intelligent maintenance decision-making method comprises the steps of 1, collecting basic information, a detection report, a maintenance record and real-time state data of a bridge; 2, key features influencing the bridge performance are mined from the data collected in the step 1, the current state of the bridge is analyzed and evaluated, and the change trend of the bridge condition in a period of time in the future is predicted; 3, generating a plurality of possible maintenance strategy schemes based on the result of the step 2; step 4, carrying out cost benefit analysis on the maintenance strategy scheme generated in the step 3; 5, a multi-objective optimization algorithm is utilized, bridge performance improvement, maintenance cost, construction time and traffic influence factors are comprehensively considered, and an optimal scheme is selected from the maintenance strategy schemes evaluated in the step 4; and 6, formulating a detailed maintenance plan according to the optimal strategy and implementing the detailed maintenance plan.

The invention relates to the technical field of power systems, in particular to a power transmission and transformation network power distribution station room monitoring device based on the Internet of Things, which comprises a data acquisition module, a data preprocessing module, a heterogeneous data fusion power grid health assessment module, a fault prediction and alarm module and an intelligent maintenance module, the data acquisition module acquires real-time operation data and environmental data of power grid equipment; the data preprocessing module is used for preprocessing the acquired operation data and environment data; the heterogeneous data fusion power grid health assessment module assesses the health condition of the power grid in real time; the fault prediction and alarm module identifies potential faults of power grid equipment in advance and generates fault early warning signals. And the intelligent maintenance module automatically generates an equipment maintenance plan based on the generated fault early warning signal. According to the invention, quick response can be realized under different operation conditions, false alarms are reduced, and stable operation of a power grid is effectively guaranteed.

The invention provides an embedded device online test and remote maintenance method and system, and relates to the technical field of device maintenance, and the method comprises the steps: collecting multi-source heterogeneous data, selecting a sampling period based on Shannon entropy, employing wavelet transform and fractal theory to extract features, inputting an anomaly detection model to generate an anomaly degree vector, and carrying out the detection of the anomaly degree vector; extracting features by using a capsule neural network and performing fault type probability distribution prediction; calculating system state indexes by combining a phase-space reconstruction technology; inputting a causal reasoning network to generate a fault diagnosis report; retrieving a maintenance scheme based on the fault diagnosis report; and the maintenance effect is evaluated through the twin neural network, and the maintenance instruction is iteratively optimized according to the effect until the maintenance effect reaches the standard.

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 an intelligent management platform suitable for a power plant, and relates to the technical field of power plant management, and the platform employs an intelligent scheduling system and a DDPG algorithm, and dynamically adjusts a power generation strategy according to the real-time equipment health score, residual life prediction, and the change of energy price and load demand. The power plant can quickly make adjustments when the load demand changes sharply, preferentially dispatch efficient and low-emission equipment, improve the overall energy efficiency and reduce energy waste, and through integration of a sensor network and edge computing, key operation parameters of the equipment are collected in real time, and a dynamic maintenance plan is generated based on health scores and residual life prediction of the equipment. The equipment about to break down is preferentially processed, the random forest model is used for health state evaluation and fault probability prediction, the health score of the equipment can accurately reflect the running state of the equipment, the maintenance time is dynamically adjusted in combination with the load condition of the equipment, and excessive maintenance or maintenance lag is avoided.

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 discloses an industrial data acquisition and management method based on the Internet of Things, and relates to the technical field of data management, and the method comprises the following steps: carrying out the demand analysis of an industrial environment and a production process, and determining to-be-monitored industrial equipment indexes in the industrial environment and the production process, namely an equipment performance index and a production performance index; according to the industrial data acquisition and management method based on the Internet of Things, equipment performance indexes and production performance indexes are monitored in real time, so that the reliability of equipment is remarkably improved, the accidental downtime is shortened, and through comprehensive analysis of an equipment state evaluation index, a fault analysis evaluation index and a production performance evaluation index, the reliability of the equipment is improved. The method can predict possible faults and performance reduction of the equipment, so as to take preventive maintenance measures in advance, not only can reduce production interruption caused by equipment faults, but also can improve the efficiency and effect of maintenance work by optimizing a maintenance plan.

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 provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

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.