An intelligent predictive equipment maintenance system

Abstract

The present application relates to the technical field of equipment maintenance, and more particularly to an intelligent predictive equipment maintenance system, comprising a data acquisition module, a data processing module, a maintenance decision module, a maintenance execution module, a user interaction module and a closed-loop optimization module. The data acquisition module collects equipment operating parameters through the deployment of a sensor array to generate standardized data streams; the data processing module extracts time sequence features and performs state deduction through a virtual model to output fault analysis results; the maintenance decision module calls enterprise resource information for multi-objective optimization to generate a dynamic maintenance plan; the maintenance execution module assists in guiding the execution of maintenance through augmented reality and records data; the user interaction module dynamically displays equipment status and risk information; and the closed-loop optimization module updates model parameters based on feedback data. The system realizes predictive maintenance under data driving through module collaboration, effectively reduces unplanned downtime and resource waste, and improves fault diagnosis accuracy and operation and maintenance efficiency.

Description

Technical Field

[0001] This invention relates to the field of equipment maintenance technology, and in particular to an intelligent predictive equipment maintenance system. Background Technology

[0002] Intelligent predictive maintenance is based on continuous monitoring and data analysis of the operating status of large mechanical equipment. Sensors deployed in key parts of the equipment collect multi-dimensional signals such as vibration, temperature, and acoustics. After preliminary processing by edge computing, the data is transmitted to the cloud or local platform. Machine learning algorithms are used to identify its operating characteristics and weak abnormal patterns, thereby realizing dynamic assessment of the equipment's health status and early warning of potential faults.

[0003] Existing intelligent predictive equipment maintenance suffers from the following technical pain points: Firstly, existing equipment maintenance models primarily rely on reactive, post-incident responses and fixed-cycle prevention, lacking real-time data-driven intelligent prediction capabilities and a collaborative management mechanism for the entire maintenance process. This leads to prolonged unplanned downtime, significant waste of maintenance resources, and insufficient fault diagnosis accuracy in large-scale equipment maintenance scenarios within critical infrastructure sectors such as industrial manufacturing, energy and power, and transportation. For instance, in industrial manufacturing, a sudden bearing failure in critical processing equipment on a production line necessitates a reactive maintenance approach, requiring waiting for the failure to occur before responding, resulting in production interruptions of several hours. Secondly, in the energy and power sector, wind turbines undergo fixed-cycle maintenance, requiring disassembly and inspection even when operating parameters are normal, consuming additional manpower and spare parts. Thirdly, in transportation scenarios, railway locomotives lack intelligent prediction, relying on periodic manual inspections for fault diagnosis, failing to accurately identify early electrical anomalies, impacting operational safety and efficiency. These limitations prevent dynamic optimization of equipment maintenance and result in inefficient resource scheduling. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent predictive equipment maintenance system. This system solves the technical problems of long unplanned downtime, serious waste of maintenance resources, and insufficient fault diagnosis accuracy in industrial scenarios, which are caused by existing equipment maintenance modes relying mainly on reactive response and fixed-cycle prevention, lacking intelligent prediction and full-process collaborative management.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows: This invention provides an intelligent predictive equipment maintenance system, comprising: The data acquisition module is used to collect operating parameters through sensor arrays deployed on equipment components, perform noise reduction and completion processing on the collected raw signals, generate standardized data streams and transmit them, and generate data quality assessment indicators to dynamically adjust the sampling strategy. The data processing module is used to receive the standardized data stream, extract the temporal characteristics of the operating parameters to predict the evolution trend, construct the fault feature space and identify key signs, perform state deduction through the virtual model, and generate analysis results including fault location, classification, risk level and maintenance strategy. The maintenance decision module is used to receive the analysis results, call spare parts inventory and production plan information, perform multi-objective optimization with maintenance cost, downtime and resource utilization as the objectives, generate dynamic maintenance solutions including spare parts list, tool requirements and man-hour estimates, and plan maintenance personnel routes. The maintenance execution module is used to receive the dynamic maintenance plan and generate maintenance work orders, provide augmented reality assistance guidance by scanning and verifying spare parts, record maintenance process data and verify its integrity, and collect equipment operating parameters and maintenance reports after maintenance is completed and provide feedback. The user interaction module is used to dynamically display the health status and risk information of the device, support historical data query and reliability report generation, and adaptively optimize the interface based on user behavior. The closed-loop optimization module is used to receive the maintenance report and equipment operating parameters, update the parameters of the time-series feature prediction model, iterate the maintenance resource scheduling strategy, and dynamically adjust the preventive maintenance threshold. Specifically, the data quality assessment index triggers the data processing module to adjust algorithm parameters, the state inference data of the virtual model constrains the multi-objective optimization process, the deviation in the maintenance plan execution triggers the dynamic maintenance plan re-evaluation, the maintenance process timestamp is used to correct the virtual model parameters, the user query mode is fed back to each module to optimize monitoring and decision-making, and the maintenance report drives the time series feature prediction model update and resource scheduling strategy iteration.

[0006] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the data acquisition module is used for: Equipment operating parameters are collected using vibration, temperature, and current sensors to generate raw signals; The original signal is amplified and filtered to obtain a conditioned signal; Wavelet packet decomposition is performed on the vibration signal in the conditioned signal to extract frequency band energy features, and sliding window smoothing is performed on the temperature signal; The processed signal is encapsulated into the standardized data stream, and a transmission protocol is selected for transmission based on data priority. The signal-to-noise ratio and loss rate of the standardized data stream are calculated to generate the data quality assessment index, and then the sampling frequency and power consumption mode of the sensor are dynamically adjusted.

[0007] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the data processing module is used for: The standardized data stream is received, and the time-series data within it is analyzed using a gated recurrent unit network. Key historical time points are weighted using an attention mechanism. The maximum information coefficient is used to perform feature selection on the weighted time series data to remove redundant parameters; The selected features are input into the virtual model, and the evolution of the device state is simulated by a combination of physical laws and data-driven approaches. The gradient boosting decision tree algorithm is used to perform fault mode matching on the simulation results, and the fault confidence score and risk rating are output to form the analysis results.

[0008] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the maintenance decision module is used for: Receive the analysis results and use spare parts inventory and production plan information from the enterprise system as constraints. A non-dominated sorting genetic algorithm is used to perform multi-objective optimization of maintenance cost, downtime and resource utilization under the constraints, and generate a set of maintenance schemes. By fusing the Dijkstra algorithm and the tabu search algorithm, maintenance personnel paths are planned for the selected maintenance solutions. The expected losses of the maintenance plan are calculated using Monte Carlo simulation, and the weights of decision factors are quantified using the analytic hierarchy process (AHP) to output the dynamic maintenance plan.

[0009] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the maintenance execution module is used for: Receive the dynamic maintenance plan and generate a maintenance work order to dispatch to the mobile terminal; Maintenance personnel can scan the spare parts' QR code to complete the requisition and verification process and activate augmented reality-assisted guidance. During the maintenance process, image sensors on mobile terminals collect maintenance environment data, and synchronous positioning and mapping algorithms are applied to process the maintenance environment data to generate a 3D model of the maintenance scene. Virtual guidance information is superimposed on the corresponding positions of the 3D model on the display screen of the mobile terminal. The image sensors capture video of the maintenance personnel's movements, and posture recognition algorithms are used to compare the key joint positions in the movement video with a preset standard movement model to detect the standardization of the maintenance movements. Record key data points of the maintenance process onto the blockchain to generate tamper-proof electronic archives; After the repair is completed, a device performance test is triggered, and the test data is packaged with the electronic file into a repair report and fed back.

[0010] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the user interaction module is used for: The data acquisition module, data processing module, maintenance decision module, maintenance execution module and closed-loop optimization module acquire equipment status, alarm information and task progress data, and integrate them into a unified data set; The device geometric data and status parameters in the dataset are parsed, and the 3D graphics library is driven to generate a 3D model of the device based on the parsing results. The device status parameters are mapped to color gradient values ​​to generate a heat map covering the surface of the 3D model. A progressive loading mechanism is used to transmit the 3D model and heat map to the client browser for rendering and display. The alarm information in the dataset is parsed to extract alarm type, alarm source and timestamp features; the correlation rule mining algorithm is used to analyze the correlation between the features, and alarm information with correlation exceeding a preset threshold is merged into an aggregated alarm, thereby reducing the total number of alarms and thus reducing the false alarm rate. It responds to historical data query requests, supports custom indicator configuration and multi-dimensional drill-down analysis, and generates reliability assessment reports.

[0011] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the closed-loop optimization module is used for: The system receives the maintenance report and equipment operating parameters, and updates the parameters of the time-series feature prediction model using a differential privacy-preserving federated learning framework. Using maintenance performance evaluation as a reward signal, the optimal maintenance strategy is explored through a dual-depth Q-network algorithm. A model of the equipment's remaining life is established based on the Wiener process, and the model parameters are dynamically updated using real-time data. Association rule mining technology is used to extract fault evolution patterns from historical maintenance data, which are then used to adjust the preventive maintenance threshold.

[0012] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the analysis results generated by the data processing module include predicted values ​​of equipment performance parameters; The maintenance decision module is specifically used to use the predicted values ​​of the performance parameters as the boundary values ​​of the constraint conditions for a multi-objective optimization problem. The data acquisition module is also used to: automatically increase the sampling frequency of the corresponding sensor when the predicted value of the performance parameter indicates that a specific component has entered a period of high failure incidence.

[0013] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the dynamic maintenance plan generated by the maintenance decision module includes a resource dependency graph; The maintenance execution module is specifically used to: parse the resource dependency graph to determine the task execution sequence, and visualize the sequence through an augmented reality interface; When the deviation of the maintenance plan exceeds a preset threshold, a reassessment request is sent to the maintenance decision module to trigger a plan update.

[0014] Furthermore, in the intelligent predictive equipment maintenance system of the present invention, the user interaction module is also used for: Perform cluster analysis on user query patterns to generate configuration suggestions; The configuration suggestions are sent to the data acquisition module to recommend key monitoring points, to the data processing module to prompt for key monitoring fault modes, and to the maintenance decision module to provide personnel preference scheduling strategy templates. The maintenance decision module is also used to: record users' manual adjustments to maintenance plans, extract expert experience through rule mining algorithms, and use the expert experience to correct the fitness function in multi-objective optimization.

[0015] Beneficial effects of this invention; This invention employs a data acquisition module that deploys a sensor array to collect operational parameters and generate standardized data streams. Combined with data quality assessment indicators, the sampling strategy is dynamically adjusted, improving the accuracy and reliability of data acquisition. The data processing module uses a gated recurrent unit network and attention mechanism to analyze temporal characteristics and performs state deduction through a virtual model, achieving early fault identification and accurate prediction. The maintenance decision module calls upon enterprise system information and uses a multi-objective optimization algorithm to generate dynamic maintenance plans, optimizing maintenance resource allocation and downtime. The maintenance execution module utilizes augmented reality-assisted guidance and blockchain to record the maintenance process, improving maintenance efficiency and operational standardization. The user interaction module integrates multi-source data and provides a visual interface, enhancing system operability and decision support capabilities. The closed-loop optimization module updates model parameters based on federated learning and reinforcement learning, achieving self-learning and continuous optimization of the system. Through bidirectional data flow and collaborative mechanisms, the modules form a complete closed loop from monitoring, decision-making, execution to optimization, effectively overcoming the limitations of reactive response and fixed-cycle maintenance, significantly reducing unplanned downtime and maintenance resource waste, and improving fault diagnosis accuracy and equipment operation and maintenance efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0017] Figure 1 This is a system architecture diagram of an intelligent predictive equipment maintenance system according to the present invention. Detailed Implementation

[0018] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0019] Please see Figure 1 The present invention provides an intelligent predictive equipment maintenance system, comprising: The data acquisition module is used to collect operating parameters through sensor arrays deployed on equipment components, perform noise reduction and completion processing on the collected raw signals, generate standardized data streams and transmit them, and generate data quality assessment indicators to dynamically adjust the sampling strategy. The data processing module is used to receive the standardized data stream, extract the temporal characteristics of the operating parameters to predict the evolution trend, construct the fault feature space and identify key signs, perform state deduction through the virtual model, and generate analysis results including fault location, classification, risk level and maintenance strategy. The maintenance decision module is used to receive the analysis results, call spare parts inventory and production plan information, perform multi-objective optimization with maintenance cost, downtime and resource utilization as the objectives, generate dynamic maintenance solutions including spare parts list, tool requirements and man-hour estimates, and plan maintenance personnel routes. The maintenance execution module is used to receive the dynamic maintenance plan and generate maintenance work orders, provide augmented reality assistance guidance by scanning and verifying spare parts, record maintenance process data and verify its integrity, and collect equipment operating parameters and maintenance reports after maintenance is completed and provide feedback. The user interaction module is used to dynamically display the health status and risk information of the device, support historical data query and reliability report generation, and adaptively optimize the interface based on user behavior. The closed-loop optimization module is used to receive the maintenance report and equipment operating parameters, update the parameters of the time-series feature prediction model, iterate the maintenance resource scheduling strategy, and dynamically adjust the preventive maintenance threshold. Specifically, the data quality assessment index triggers the data processing module to adjust algorithm parameters, the state inference data of the virtual model constrains the multi-objective optimization process, the deviation in the maintenance plan execution triggers the dynamic maintenance plan re-evaluation, the maintenance process timestamp is used to correct the virtual model parameters, the user query mode is fed back to each module to optimize monitoring and decision-making, and the maintenance report drives the time series feature prediction model update and resource scheduling strategy iteration.

[0020] The intelligent predictive equipment maintenance system provided by this invention constructs a complete technical solution from condition monitoring to decision execution and continuous optimization through the collaborative work and data interaction of multiple functional modules. The data acquisition module, as the system's data source, uses a sensor array composed of vibration sensors, temperature sensors, and current sensors deployed on the surfaces of key equipment components to collect raw signals generated during equipment operation at configurable frequencies. Each raw signal is first amplified and filtered by a signal conditioning circuit to eliminate environmental noise interference. Subsequently, wavelet packet decomposition is performed on the vibration signal to extract energy distribution characteristics of different frequency bands, while a sliding window mechanism is used for trend smoothing of the temperature signal. The signals after feature extraction and smoothing are encapsulated into a standardized data stream with a unified format. The system dynamically selects the transmission protocol based on data priority and sends the standardized data stream to the data processing module, simultaneously calculating the signal-to-noise ratio and loss rate of the data stream to generate a data quality assessment index. This index is fed back to the sensor nodes for dynamic adjustment of the sampling frequency and power consumption mode.

[0021] After receiving the standardized data stream from the data acquisition module, the data processing module uses a gated recurrent unit network to perform in-depth analysis of the time-series data of equipment operating parameters. It also employs an attention mechanism to weight key time points in historical data that are highly correlated with equipment performance degradation, enhancing the model's ability to capture long-term dependencies. The module uses the maximum information coefficient to perform feature selection on the weighted time-series data, eliminating redundant parameters irrelevant to fault diagnosis, and inputting the selected key features into the virtual model. The virtual model adopts a fusion of physical laws and data-driven approaches, using digital twin technology to simulate and extrapolate the equipment's operating status in real time, predicting performance parameter trends over a future period. Finally, the module uses a gradient boosting decision tree algorithm to perform fault mode matching on the simulation results, outputting analysis results including fault component location, fault type classification, risk level assessment, and maintenance strategy recommendations.

[0022] After receiving the analysis results from the data processing module, the maintenance decision-making module uses real-time spare parts inventory data, production plan information, and maintenance personnel skill profiles from the Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES) as constraints for a multi-objective optimization problem. The module employs a non-dominated sorting genetic algorithm to search and generate a set of Pareto-optimal maintenance solutions under these constraints, aiming to minimize maintenance costs, reduce downtime, and maximize resource utilization. The optimal maintenance personnel dispatch path is calculated by fusing Dijkstra's algorithm and tabu search. Monte Carlo simulation is used to evaluate the expected losses of different maintenance solutions, and the weights of various decision factors are quantified using the Analytic Hierarchy Process (AHP). The final output is a dynamic maintenance plan including a spare parts list, tool requirements, estimated working hours, and personnel routing.

[0023] After receiving the dynamic maintenance plan generated by the maintenance decision-making module, the maintenance execution module automatically generates a structured maintenance work order and dispatches it to the mobile terminal of the maintenance personnel. Maintenance personnel complete material requisition and verification by scanning the spare parts' QR code, and the mobile terminal activates the augmented reality-assisted guidance function. During maintenance, the terminal's image sensor collects maintenance environment data, and synchronous positioning and mapping algorithms are applied to generate a real-time 3D model of the maintenance scene. Virtual guidance information such as disassembly sequence and torque parameters are overlaid and displayed at the corresponding positions in the model. Simultaneously, the image sensor captures video of the maintenance personnel's operational actions, and a posture recognition algorithm compares the key joint positions in the video with a preset standard action model in real time to check the compliance of the operation. Key node data during the maintenance process is recorded in real-time to a blockchain distributed ledger to generate an immutable electronic file. After maintenance is completed, the system automatically triggers equipment performance testing and packages the test data and electronic file into a maintenance report, which is then fed back to the upstream module.

[0024] The user interaction module acquires real-time equipment status data from the data acquisition module, fault warning information from the data processing module, task progress data from the maintenance decision module, on-site operation records from the maintenance execution module, and model update logs from the closed-loop optimization module, integrating these multi-source heterogeneous data into a unified data set. The module parses the equipment's 3D geometric data and real-time status parameters within the data set, driving a 3D graphics library to generate a visual model of the equipment. It maps status parameters such as temperature and vibration to color gradient values, generating a heatmap overlaid on the model's surface. A progressive loading mechanism transmits the 3D model and heatmap to the client browser for rendering and display. Simultaneously, it parses alarm information within the data set, extracting alarm type, alarm source, and timestamp features. An association rule mining algorithm analyzes the correlation between features, merging multiple alarms with correlation exceeding a preset threshold into aggregated alarms, effectively reducing the total number of alarms and the false alarm rate. The module supports multi-dimensional drill-down queries of historical data and automatically generates equipment reliability assessment reports.

[0025] After receiving maintenance reports and equipment operating parameters from the maintenance execution module, the closed-loop optimization module uses a differential privacy-preserving federated learning framework to distribute the updates of the parameters of the time-series feature prediction model, achieving model performance iteration while protecting data privacy. The module uses maintenance effectiveness evaluation indicators as reward signals and explores optimal maintenance strategies under different equipment states through a dual-deep Q-network algorithm. It establishes a probability model of remaining equipment life based on the Wiener process and dynamically updates model parameters using real-time monitoring data. The module employs association rule mining technology to extract fault evolution patterns from historical maintenance data and dynamically adjusts preventative maintenance thresholds based on these patterns, forming a closed-loop self-learning mechanism from data acquisition, processing, decision-making, execution to optimization. Inter-modules achieve bidirectional data flow through an event-driven architecture. For example, data quality evaluation indicators trigger algorithm parameter adjustments, virtual model deduction results optimize sensor deployment strategies, maintenance execution deviations drive scheme re-evaluation, and user interaction behavior guides adjustments to monitoring priorities, ultimately enabling the system to possess continuous evolution and overall optimization capabilities.

[0026] An array of vibration, temperature, and current sensors collects equipment operating parameters in parallel. The raw signals are processed by amplification and filtering circuits to form conditioned signals. For vibration signals, wavelet packet decomposition is used to extract frequency band energy distribution characteristics, while for temperature signals, a sliding window averaging method is used to eliminate instantaneous fluctuations. The processed signals are packaged into standardized data streams according to a preset format, and 5G or LoRa transmission protocols are selected based on the urgency of the data. Finally, quality assessment indicators are generated by calculating the signal-to-noise ratio and data loss rate, and the sensor sampling frequency is dynamically adjusted to balance accuracy and energy consumption.

[0027] The gated recurrent unit network extracts features from time-series data in the standardized data stream. The attention mechanism automatically identifies historical time points strongly correlated with equipment degradation and assigns weights. The maximum information coefficient algorithm performs correlation analysis on the weighted features and removes redundant parameters to reduce computational complexity. The filtered features are input into the virtual model for state inference, and the equipment evolution path is simulated by combining physical constraints and data-driven methods. The gradient boosting decision tree algorithm performs multi-class matching on the simulation results and outputs a fault type confidence and risk level assessment report.

[0028] The maintenance decision module calls on the enterprise system to obtain spare parts inventory and production plan as optimization constraints, and uses a non-dominated sorting genetic algorithm to solve the multi-objective Pareto solution set of maintenance cost, downtime and resource utilization. The Dijkstra algorithm and the tabu search algorithm are combined to calculate the optimal personnel dispatch path, and Monte Carlo simulation is used to evaluate the expected loss of the plan. The analytic hierarchy process quantifies the weight of decision factors, and finally generates a dynamic maintenance plan including spare parts list and time estimate.

[0029] After receiving a repair work order, the mobile terminal completes spare parts verification and activates augmented reality navigation by scanning a QR code; the image sensor collects repair environment data, and the synchronous positioning and map building algorithm generates a 3D scene model in real time, with virtual guidance information overlaid and displayed at the corresponding location; the posture recognition algorithm compares the repair actions with the joint positions of the standard model, and the blockchain records key operation nodes; after the repair is completed, performance testing is automatically triggered, and an electronic report including test data is generated.

[0030] The user interaction module aggregates data from multiple modules to form a unified format collection; the 3D graphics library parses device geometric data to generate a model; and the state parameters are mapped to color gradients to form a heatmap. A progressive loading mechanism enables smooth rendering on the browser side, and an association rule mining algorithm analyzes the correlation of alarm features and merges alarms that exceed the threshold to reduce false alarms. It supports multi-dimensional drill-down queries of historical data and automatically generates reliability analysis reports.

[0031] The closed-loop optimization module uses a differential privacy federated learning framework to update the prediction model parameters, and a dual-deep Q-network uses maintenance performance as a reward signal to explore the optimal strategy; the Wiener process establishes a probability model of the equipment's remaining lifespan, and the parameters are dynamically corrected based on real-time data; association rule mining technology extracts fault patterns from historical data and adaptively adjusts the preventive maintenance threshold.

[0032] The predicted performance parameters output by the data processing module serve as boundary conditions for multi-objective optimization. When the predicted value indicates that a component is entering a period of high failure incidence, the data acquisition module automatically increases the sampling frequency of the corresponding sensor, forming a dynamic adjustment mechanism that drives the monitoring strategy based on the prediction results.

[0033] The resource dependency graph generated by the maintenance decision module is parsed into a task sequence by the maintenance execution module, and the execution process is visualized in the augmented reality interface. When the actual execution deviation exceeds the threshold, a reassessment request is sent to the decision module to trigger real-time updates of the solution.

[0034] User query patterns are used to generate configuration suggestions through cluster analysis, recommending key monitoring points for the data acquisition module, key monitoring fault modes for the data processing module, and personnel preference strategies for the maintenance decision-making module. Manual adjustment records are used to extract expert experience through rule mining and dynamically correct the multi-objective optimization fitness function. Vibration sensors, temperature sensors, and current sensors acquire equipment operating parameters in parallel. The raw signals are amplified and filtered to form conditioned signals. Wavelet packet decomposition is used to extract frequency band energy distribution characteristics for vibration signals, and sliding window averaging is used to smooth temperature signals. The processed signals are encapsulated into standardized data streams according to a preset format, and the transmission protocol is dynamically selected based on data priority. Data quality assessment indicators are generated by calculating the signal-to-noise ratio and loss rate, and the sensor sampling frequency is dynamically adjusted to optimize data acquisition efficiency.

[0035] The gated recurrent unit network receives standardized data streams and analyzes time-series data. An attention mechanism assigns weights to key historical time points. The maximum information coefficient algorithm performs correlation analysis on the weighted features and removes redundant parameters to improve feature quality. The filtered features are input into a virtual model, and the device state evolution is simulated by combining physical laws and data-driven methods. The gradient boosting decision tree algorithm performs fault mode matching on the simulation results and outputs fault confidence scores and risk ratings to form the analysis results.

[0036] The maintenance decision module calls on the enterprise system to obtain spare parts inventory and production plan information as constraints, and uses a non-dominated sorting genetic algorithm to solve a multi-objective optimization problem of maintenance cost, downtime and resource utilization. The Dijkstra algorithm and the tabu search algorithm are combined to calculate the optimal maintenance personnel path, and Monte Carlo simulation is used to evaluate the expected loss of the solution. The analytic hierarchy process is used to quantify the weight of decision factors, and finally output a dynamic maintenance solution.

[0037] After receiving the dynamic repair plan, the mobile terminal generates a repair work order. Repair personnel scan the spare parts QR code to complete the verification and activate augmented reality-assisted guidance. The image sensor collects repair environment data, and the synchronous positioning and map building algorithm generates a 3D scene model. Virtual guidance information is overlaid and displayed at the corresponding location. The posture recognition algorithm compares the repair actions with the joint positions of the standard model, and the blockchain records key operation nodes. After the repair is completed, a performance test is triggered, and a repair report including test data is generated.

[0038] The user interaction module aggregates device status, alarm information, and task progress data from various modules and integrates them into a unified data set; the 3D graphics library parses device geometric data to generate 3D models, and maps status parameters to color gradients to form heatmaps; a progressive loading mechanism enables smooth rendering on the browser side; an association rule mining algorithm analyzes the correlation of alarm features and merges alarms that exceed thresholds to reduce false alarm rates; and it supports multi-dimensional drill-down queries of historical data to generate reliability assessment reports.

[0039] The closed-loop optimization module receives maintenance reports and equipment operating parameters, and uses a differential privacy-preserving federated learning framework to update the parameters of the time-series feature prediction model; the dual-deep Q-network algorithm uses maintenance effect evaluation as a reward signal to explore the optimal strategy; the Wiener process establishes a probability model of the equipment's remaining lifespan, and the parameters are dynamically updated in real time; the association rule mining technology extracts fault evolution patterns from historical data and dynamically adjusts the preventive maintenance threshold.

[0040] The predicted values ​​of equipment performance parameters generated by the data processing module serve as the boundary values ​​of the constraints in a multi-objective optimization problem. When the predicted values ​​indicate that a specific component is entering a period of high failure incidence, the data acquisition module automatically increases the sampling frequency of the corresponding sensor to achieve prediction-driven adaptive monitoring.

[0041] The resource dependency graph generated by the maintenance decision module is parsed into a task execution sequence by the maintenance execution module, and the sequence is visualized in the augmented reality interface. When the deviation of the maintenance plan execution exceeds the preset threshold, a re-evaluation request is sent to the maintenance decision module to trigger a plan update.

[0042] The user interaction module performs cluster analysis on user query patterns to generate configuration suggestions, recommending key monitoring points for the data acquisition module, key monitoring fault modes for the data processing module, and personnel preference scheduling strategies for the maintenance decision module. The maintenance decision module records user manual adjustment records, extracts expert experience through rule mining algorithms, and corrects the multi-objective optimization fitness function.

[0043] This intelligent predictive equipment maintenance system constructs a complete technical solution from condition monitoring to decision execution and continuous optimization through the collaborative work and data interaction of multiple functional modules. The data acquisition module, as the system's data source, uses a sensor array composed of vibration sensors, temperature sensors, and current sensors deployed on the surfaces of key equipment components to collect raw signals generated during equipment operation at configurable frequencies. Each raw signal first undergoes amplification and filtering preprocessing by a signal conditioning circuit to eliminate environmental noise interference, resulting in a conditioned signal. Subsequently, wavelet packet decomposition is performed on the vibration signal in the conditioned signal to extract energy distribution characteristics of different frequency bands, while a sliding window mechanism is used for trend smoothing of the temperature signal. The signals after feature extraction and smoothing are encapsulated into a standardized data stream with a unified format. The system dynamically selects the transmission protocol based on data priority and sends the standardized data stream to the data processing module, simultaneously calculating the signal-to-noise ratio and loss rate of the standardized data stream to generate a data quality assessment index. This index is fed back to the sensor nodes for dynamic adjustment of the sampling frequency and power consumption mode.

[0044] After receiving the standardized data stream from the data acquisition module, the data processing module uses a gated recurrent unit network to perform in-depth analysis of the time-series data of equipment operating parameters. It also employs an attention mechanism to weight key time points in historical data that are highly correlated with equipment performance degradation, enhancing the model's ability to capture long-term dependencies. The module then uses the maximum information coefficient to perform feature selection on the weighted time-series data, eliminating redundant parameters irrelevant to fault diagnosis. The selected key features are input into the virtual model. The virtual model uses a fusion of physical laws and data-driven approaches, employing digital twin technology to simulate and extrapolate the equipment's operating status in real time, predicting performance parameter trends over a future period. Finally, the module uses a gradient boosting decision tree algorithm to perform fault mode matching on the simulation results, outputting analysis results including fault component location, fault type classification, risk level assessment, and maintenance strategy recommendations.

[0045] After receiving the analysis results from the data processing module, the maintenance decision-making module uses real-time spare parts inventory data, production plan information, and maintenance personnel skill profiles from the Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES) as constraints for a multi-objective optimization problem. The module employs a non-dominated sorting genetic algorithm to search and generate a set of Pareto-optimal maintenance solutions under these constraints, aiming to minimize maintenance costs, reduce downtime, and maximize resource utilization. The optimal maintenance personnel dispatch path is calculated by fusing Dijkstra's algorithm and tabu search. Monte Carlo simulation is used to evaluate the expected losses of different maintenance solutions, and the weights of various decision factors are quantified using the Analytic Hierarchy Process (AHP). The final output is a dynamic maintenance plan including a spare parts list, tool requirements, estimated working hours, and personnel routing.

[0046] After receiving the dynamic maintenance plan generated by the maintenance decision-making module, the maintenance execution module automatically generates a structured maintenance work order and dispatches it to the mobile terminal of the maintenance personnel. Maintenance personnel complete material requisition and verification by scanning the spare parts' QR code, and the mobile terminal activates the augmented reality-assisted guidance function. During maintenance, the terminal's image sensor collects maintenance environment data, and synchronous positioning and mapping algorithms are applied to generate a real-time 3D model of the maintenance scene. Virtual guidance information such as disassembly sequence and torque parameters are overlaid and displayed at the corresponding positions in the model. Simultaneously, the image sensor captures video of the maintenance personnel's operational actions, and a posture recognition algorithm compares the key joint positions in the video with a preset standard action model in real time to check the compliance of the operation. Key node data during the maintenance process is recorded in real-time to a blockchain distributed ledger to generate an immutable electronic file. After maintenance is completed, the system automatically triggers equipment performance testing and packages the test data and electronic file into a maintenance report, which is then fed back to the upstream module.

[0047] The user interaction module acquires data such as equipment status, alarm information, and task progress from the data acquisition, data processing, maintenance decision-making, maintenance execution, and closed-loop optimization modules, integrating the diverse and heterogeneous data into a unified data set. The module parses the equipment's 3D geometric data and real-time status parameters within the data set, driving a 3D graphics library to generate a visual model of the equipment. It then maps status parameters such as temperature and vibration into color gradient values ​​to generate a heatmap overlaid on the model's surface. A progressive loading mechanism transmits the 3D model and heatmap to the client browser for rendering and display. Simultaneously, the module parses alarm information within the data set, extracting alarm type, alarm source, and timestamp features. It uses association rule mining algorithms to analyze the correlation between features, merging multiple alarms with correlation exceeding a preset threshold into aggregated alarms, effectively reducing the total number of alarms and the false alarm rate. The module supports multi-dimensional drill-down queries of historical data and automatically generates equipment reliability assessment reports. The user interaction module also performs cluster analysis on user query patterns, generates configuration suggestions, and sends these suggestions to the data acquisition module to recommend key monitoring points, to the data processing module to prompt key monitoring fault modes, and to the maintenance decision module to provide personnel preference scheduling strategy templates.

[0048] After receiving maintenance reports and equipment operating parameters from the maintenance execution module, the closed-loop optimization module uses a differential privacy-preserving federated learning framework to distribute the updates of the parameters of the time-series feature prediction model, achieving model performance iteration while protecting data privacy. The module uses maintenance effectiveness evaluation indicators as reward signals and explores the optimal maintenance strategy under different equipment states through a dual-deep Q-network algorithm. It establishes a probability model of remaining equipment life based on the Wiener process and dynamically updates the model parameters using real-time monitoring data. The module employs association rule mining technology to extract fault evolution patterns from historical maintenance data and dynamically adjusts preventative maintenance thresholds based on these patterns, forming a closed-loop self-learning mechanism from data acquisition, processing, decision-making, execution to optimization.

[0049] The modules achieve bidirectional data flow and collaboration through an event-driven architecture. For example, data quality assessment indicators trigger the data processing module to adjust algorithm parameters; virtual model state deduction data constrains the multi-objective optimization process of the maintenance decision module; if the deviation of the maintenance plan execution exceeds a preset threshold in the maintenance execution module, a reassessment request is sent to the maintenance decision module to update the dynamic maintenance plan; timestamp data of the maintenance process is used to correct the parameters of the virtual model; user query patterns analyzed by the user interaction module are fed back to each module to optimize monitoring and decision-making priorities; and maintenance reports drive the closed-loop optimization module to update the parameters of the time-series feature prediction model and iterate the maintenance resource scheduling strategy. The predicted values ​​of equipment performance parameters generated by the data processing module are used by the maintenance decision module as boundary values ​​of constraints for the multi-objective optimization problem; when the predicted performance parameter value indicates that a specific component is entering a high-incidence period of failure, the data acquisition module automatically increases the sampling frequency of the sensors in the corresponding part. The dynamic maintenance plan generated by the maintenance decision module includes a resource dependency graph; the maintenance execution module parses this resource dependency graph to determine the task execution sequence and visualizes the sequence through an augmented reality interface. The maintenance decision-making module also records users' manual adjustments to maintenance plans, extracts expert experience through rule mining algorithms, and uses this expert experience to correct the fitness function in multi-objective optimization. Ultimately, this enables the system to have a continuously evolving overall optimization capability.

[0050] In the data acquisition module, the specific data processing path for extracting frequency band energy distribution features by performing wavelet packet decomposition on the vibration signal is as follows: First, the conditioned vibration signal obtained after amplification and filtering preprocessing is used as input, and a specific wavelet basis function is used to perform multi-level decomposition on the signal. Each level of decomposition divides the signal into low-frequency approximation coefficients and high-frequency detail coefficients. Second, the coefficients of each node at the lowest level are reconstructed, and the energy value of each frequency band signal is calculated. Finally, the energy values ​​of all frequency bands are normalized to form a frequency band energy feature vector characterizing the vibration state of the equipment. This feature vector is then encapsulated into a standardized data stream. The specific data processing path for trend smoothing of the temperature signal using a sliding window mechanism is as follows: The conditioned temperature signal is arranged in chronological order, and a fixed-length window is defined and slid across the time series. For the temperature data points within the window, their arithmetic mean is calculated as the smoothed temperature value at the center of the window. The window slides sequentially and the calculation is repeated to finally generate a smoothed temperature time series curve to eliminate random fluctuations and highlight the temperature change trend. This smoothed temperature data is also encapsulated into a standardized data stream.

[0051] In the data processing module, the specific data processing path for deep analysis of time-series data of equipment operating parameters using a gated recurrent unit network is as follows: the received standardized data stream is sliced ​​according to time steps and organized into sample sequences containing multiple time steps as network input; the update gate in the network controls the proportion of hidden state information from the previous moment flowing into the current state, and the reset gate controls the degree of forgetting past information; the sequence data is processed cyclically through the gating mechanism, and the final output is the final hidden state vector that can capture long-term dependencies. The specific data processing path for feature selection of weighted time-series data using the maximum information coefficient is as follows: the multi-dimensional time-series feature data after attention mechanism weighting is used as input; for each pair of feature variables, their mutual information value is calculated using a grid partitioning method, and the grid partitioning method that maximizes the mutual information value is found; the maximum mutual information value is normalized to obtain the maximum information coefficient value between the two features; all feature pairs are traversed, and their maximum information coefficient with the target fault label is calculated; features with coefficient values ​​lower than a preset threshold are judged as redundant parameters and removed, thereby selecting the key feature subset most relevant to fault diagnosis. The specific data processing path for fault mode matching of simulation results using the gradient boosting decision tree algorithm is as follows: the performance parameter prediction sequence obtained after the virtual model state deduction is used as the feature input, and the known historical fault types are used as labels; the algorithm initializes a basic predictor, and then trains multiple decision trees in a serial manner. The learning objective of each new tree is the residual between the prediction results of all previous tree combinations and the real fault labels; by continuously fitting the residuals to reduce the loss function, the prediction results of all trees are finally weighted and summed to obtain the fault mode classification result and the corresponding fault confidence score.

[0052] In the maintenance decision-making module, the specific data processing path for multi-objective optimization using the non-dominated sorting genetic algorithm is as follows: maintenance cost, downtime, and resource utilization are constructed as three objective functions that need to be optimized simultaneously; spare parts inventory, production plan, etc., are encoded into individual chromosomes as constraints; after initializing a population, the performance of each individual in the population on the three objective functions is calculated, and individuals are non-dominated sorted according to Pareto dominance and crowding degree is calculated; individuals are selected for crossover and mutation operations based on sorting and crowding degree to generate offspring populations; the parent and offspring populations are merged, and non-dominated sorting and elite selection are repeated until the termination condition is met, finally outputting a set of Pareto optimal maintenance solutions that cannot be improved simultaneously on the three objectives. The specific data processing path for planning maintenance personnel routes using a fusion of Dijkstra's algorithm and tabu search algorithm is as follows: First, using the current location of the maintenance personnel and the locations of each piece of equipment to be repaired as nodes, and the road network as edges, Dijkstra's algorithm is used to calculate the shortest path distance from the starting point to all other nodes, forming an initial shortest path matrix. Then, multiple maintenance task points and their time window constraints are constructed as a path optimization problem, which is solved using tabu search algorithm. Its neighborhood search operation calls the shortest path matrix to calculate the path length, and avoids repeated searches through the tabu list. Finally, the maintenance personnel access sequence with the shortest total travel time and satisfying the constraints is output.

[0053] In the maintenance execution module, the specific data processing path for generating a 3D model of the maintenance scene in real time using the synchronous localization and mapping algorithm is as follows: Video frames and depth information of the maintenance environment are continuously acquired through the image sensor of the mobile terminal; feature points are extracted from each frame and matched with the feature points of the previous frame; the terminal's pose change is estimated using visual odometry; simultaneously, the successfully matched feature points are fused with the depth data to gradually construct and update a 3D point cloud map of the environment; finally, the consistency between pose estimation and the map is optimized through closed-loop detection to generate an accurate 3D scene model that can be used to overlay virtual guidance information. The specific data processing path for detecting operational compliance using the pose recognition algorithm is as follows: The video of maintenance personnel's actions captured by the image sensor is divided into continuous image frames; a human keypoint detection model is used for each frame to locate the 2D coordinates of the human joints; the joint coordinates of multiple consecutive frames are connected to form a sequence of key joint positions describing the temporal changes of the actions; this sequence is dynamically time-normalized and compared with a predefined standard maintenance action template sequence, and the similarity distance between the two is calculated. If the distance exceeds a preset threshold, the current action is determined to be non-compliant.

[0054] In the user interaction module, the specific data processing path for analyzing the correlation between alarm features using association rule mining algorithms is as follows: Each alarm in the historical alarm information dataset is considered a transaction, and features such as alarm type and alarm source are considered items in the transaction; by scanning the dataset, the frequency of occurrence of all individual items (features) is counted, and all frequent itemsets that meet the minimum support threshold are generated; through join and pruning steps, candidate k+1 itemsets are generated from the frequent k itemsets, and their support is calculated again by scanning the dataset to filter out frequent itemsets; based on the finally obtained frequent itemsets, their confidence and lift indicators are calculated to mine strong association rules such as "when alarm type A occurs, alarm source B also occurs at the same time", and highly related alarms are merged into aggregated alarms according to these rules.

[0055] In the closed-loop optimization module, the specific data processing path for updating the parameters of the time-series feature prediction model using a federated learning framework with differential privacy protection is as follows: multiple device terminals train local copies of the time-series feature prediction model using their respective maintenance reports and operating parameter data; before sending the local model parameter updates to the central server, carefully designed random noise that meets the definition of differential privacy is added to the update vector; the central server aggregates the noisy parameter updates from all terminals, calculates the average update value, and uses it to update the global model; this process achieves collaborative iterative optimization of model parameters while protecting the data privacy of each terminal. The specific data processing path for exploring the optimal maintenance strategy using the dual-deep Q-network algorithm is as follows: Equipment state is encoded as environmental state; executable maintenance actions (such as adjusting monitoring frequency and triggering checks) are used as the agent's action space; the algorithm maintains two Q-networks with identical structures but different parameter update frequencies: the current network and the target network; the agent selects actions based on the Q-value output of the current network and interacts with the environment to obtain rewards (maintenance effectiveness evaluation) and new equipment states; the interaction experience is stored in a replay buffer, and batch data is sampled from it. The parameters of the current network are updated by minimizing the temporal difference error between the outputs of the current and target networks. The parameters of the target network are periodically synchronized from the current network, thereby stabilizing the training process and ultimately learning the mapping strategy from equipment state to optimal maintenance actions.

[0056] Embodiment 1 of the present invention: In an industrial manufacturing scenario, for key processing equipment such as CNC machine tools on the production line, the system of the present invention deploys vibration sensors, temperature sensors and current sensors through a data acquisition module to collect the operating parameters of the spindle bearing in real time; after the original signal is amplified and filtered, wavelet packet decomposition is used to extract the vibration frequency band energy features, and the temperature signal is smoothed by a sliding window to generate a standardized data stream that is transmitted to the data processing module. The data processing module uses gated recurrent unit network analysis to analyze time-series data, weights historical key points through an attention mechanism, filters features based on the maximum information coefficient, inputs the data into a virtual model for state extrapolation, predicts the remaining bearing life, and generates a fault risk level. The maintenance decision module calls upon spare parts inventory and production plans from the enterprise resource planning system, uses a non-dominated sorting genetic algorithm to optimize maintenance costs and downtime, generates dynamic maintenance plans, and plans personnel routes. The maintenance execution module receives work orders via mobile terminals, scans QR codes to verify spare parts, uses augmented reality to guide maintenance personnel in performing disassembly operations, uses posture recognition algorithms to detect the standardization of actions, and records process data on the blockchain. The user interaction module aggregates equipment status and alarm information, generates a 3D heatmap to display risk distribution, and uses association rule mining to reduce false alarms. The closed-loop optimization module updates the prediction model parameters based on maintenance reports and adjusts preventative maintenance thresholds through Wiener processes. This system achieves closed-loop management from fault prediction to maintenance execution, reducing unplanned downtime and improving diagnostic accuracy.

[0057] Embodiment 2 of this invention: In the energy and power sector, this system is applied to the operation and maintenance of wind turbines. The data acquisition module deploys multimodal sensors on the generator bearings and gearbox to collect vibration and temperature signals. After signal conditioning and feature extraction, the data is sent as a standardized data stream via an adaptive transmission protocol. The data processing module uses a gradient boosting decision tree algorithm to classify faults in the virtual model's simulation results, identify early electrical anomalies, and output risk warnings. The maintenance decision module combines wind speed prediction and grid dispatch information, uses Monte Carlo simulation to assess the expected losses of maintenance plans, quantifies decision weights using the analytic hierarchy process (AHP), and generates an optimized maintenance plan. The maintenance execution module guides high-altitude operations through an augmented reality interface, uses synchronous positioning and map building algorithms to generate a 3D environment model, and overlays virtual guidance to display bolt torque parameters. The user interaction module supports multi-dimensional queries of historical data and generates a reliability report showing the generator's health trend. The closed-loop optimization module uses a federated learning framework to update the model, avoiding data leakage, and adjusts the inspection cycle based on maintenance results. This system overcomes the waste of fixed-cycle maintenance, achieves dynamic resource scheduling, and improves operational efficiency.

Claims

1. An intelligent predictive equipment maintenance system, characterized in that, include: The data acquisition module is used to collect operating parameters through sensor arrays deployed on equipment components, perform noise reduction and completion processing on the collected raw signals, generate standardized data streams and transmit them, and generate data quality assessment indicators to dynamically adjust the sampling strategy. The data processing module is used to receive the standardized data stream, extract the temporal characteristics of the operating parameters to predict the evolution trend, construct the fault feature space and identify key signs, perform state deduction through the virtual model, and generate analysis results including fault location, classification, risk level and maintenance strategy. The maintenance decision module is used to receive the analysis results, call spare parts inventory and production plan information, perform multi-objective optimization with maintenance cost, downtime and resource utilization as the objectives, generate dynamic maintenance solutions including spare parts list, tool requirements and man-hour estimates, and plan maintenance personnel routes. The maintenance execution module is used to receive the dynamic maintenance plan and generate maintenance work orders, provide augmented reality assistance guidance by scanning and verifying spare parts, record maintenance process data and verify its integrity, and collect equipment operating parameters and maintenance reports after maintenance is completed and provide feedback. The user interaction module is used to dynamically display the health status and risk information of the device, support historical data query and reliability report generation, and adaptively optimize the interface based on user behavior. The closed-loop optimization module is used to receive the maintenance report and equipment operating parameters, update the parameters of the time-series feature prediction model, iterate the maintenance resource scheduling strategy, and dynamically adjust the preventive maintenance threshold. Specifically, the data quality assessment index triggers the data processing module to adjust algorithm parameters, the state inference data of the virtual model constrains the multi-objective optimization process, the deviation in the maintenance plan execution triggers the dynamic maintenance plan re-evaluation, the maintenance process timestamp is used to correct the virtual model parameters, the user query mode is fed back to each module to optimize monitoring and decision-making, and the maintenance report drives the time series feature prediction model update and resource scheduling strategy iteration.

2. The intelligent predictive equipment maintenance system according to claim 1, characterized in that, The data acquisition module is used for: Equipment operating parameters are collected using vibration, temperature, and current sensors to generate raw signals; The original signal is amplified and filtered to obtain a conditioned signal; Wavelet packet decomposition is performed on the vibration signal in the conditioned signal to extract frequency band energy features, and sliding window smoothing is performed on the temperature signal; The processed signal is encapsulated into the standardized data stream, and a transmission protocol is selected for transmission based on data priority. The signal-to-noise ratio and loss rate of the standardized data stream are calculated to generate the data quality assessment index, and then the sampling frequency and power consumption mode of the sensor are dynamically adjusted.

3. The intelligent predictive equipment maintenance system according to claim 2, characterized in that, The data processing module is used for: The standardized data stream is received, and the time-series data within it is analyzed using a gated recurrent unit network. Key historical time points are weighted using an attention mechanism. The maximum information coefficient is used to perform feature selection on the weighted time series data to remove redundant parameters; The selected features are input into the virtual model, and the evolution of the device state is simulated by a combination of physical laws and data-driven approaches. The gradient boosting decision tree algorithm is used to perform fault mode matching on the simulation results, and the fault confidence score and risk rating are output to form the analysis results.

4. The intelligent predictive equipment maintenance system according to claim 3, characterized in that, The maintenance decision module is used for: Receive the analysis results and use spare parts inventory and production plan information from the enterprise system as constraints. A non-dominated sorting genetic algorithm is used to perform multi-objective optimization of maintenance cost, downtime and resource utilization under the constraints, and generate a set of maintenance schemes. By fusing the Dijkstra algorithm and the tabu search algorithm, maintenance personnel paths are planned for the selected maintenance solutions. The expected losses of the maintenance plan are calculated using Monte Carlo simulation, and the weights of decision factors are quantified using the analytic hierarchy process (AHP) to output the dynamic maintenance plan.

5. The intelligent predictive equipment maintenance system according to claim 4, characterized in that, The maintenance execution module is used for: Receive the dynamic maintenance plan and generate a maintenance work order to dispatch to the mobile terminal; Maintenance personnel can scan the spare parts' QR code to complete the requisition and verification process and activate augmented reality-assisted guidance. During the maintenance process, image sensors on mobile terminals collect maintenance environment data, and synchronous positioning and mapping algorithms are applied to process the maintenance environment data to generate a 3D model of the maintenance scene. Virtual guidance information is superimposed on the corresponding positions of the 3D model on the display screen of the mobile terminal. The image sensors capture video of the maintenance personnel's movements, and posture recognition algorithms are used to compare the key joint positions in the movement video with a preset standard movement model to detect the standardization of the maintenance movements. Record key data points of the maintenance process onto the blockchain to generate tamper-proof electronic archives; After the repair is completed, a device performance test is triggered, and the test data is packaged with the electronic file into a repair report and fed back.

6. The intelligent predictive equipment maintenance system according to claim 5, characterized in that, The user interaction module is used for: The data acquisition module, data processing module, maintenance decision module, maintenance execution module and closed-loop optimization module acquire equipment status, alarm information and task progress data, and integrate them into a unified data set; The device geometric data and status parameters in the dataset are parsed, and the 3D graphics library is driven to generate a 3D model of the device based on the parsing results. The device status parameters are then mapped to color gradient values ​​to generate a heat map covering the surface of the 3D model. A progressive loading mechanism is used to transmit the 3D model and heatmap to the client browser for rendering and display; The alarm information in the dataset is parsed to extract alarm type, alarm source and timestamp features; The association rule mining algorithm is used to analyze the correlation between the features, and alarm information with a correlation exceeding a preset threshold is merged into a single aggregated alarm, thereby reducing the total number of alarms and thus reducing the false alarm rate. It responds to historical data query requests, supports custom indicator configuration and multi-dimensional drill-down analysis, and generates reliability assessment reports.

7. The intelligent predictive equipment maintenance system according to claim 6, characterized in that, The closed-loop optimization module is used for: The system receives the maintenance report and equipment operating parameters, and updates the parameters of the time-series feature prediction model using a differential privacy-preserving federated learning framework. Using maintenance performance evaluation as a reward signal, the optimal maintenance strategy is explored through a dual-depth Q-network algorithm. A model of the equipment's remaining life is established based on the Wiener process, and the model parameters are dynamically updated using real-time data. Association rule mining technology is used to extract fault evolution patterns from historical maintenance data, which are then used to adjust the preventive maintenance threshold.

8. The intelligent predictive equipment maintenance system according to claim 7, characterized in that, The analysis results generated by the data processing module include predicted values ​​of equipment performance parameters; The maintenance decision module is specifically used to use the predicted values ​​of the performance parameters as the boundary values ​​of the constraint conditions for a multi-objective optimization problem. The data acquisition module is also used to: automatically increase the sampling frequency of the corresponding sensor when the predicted value of the performance parameter indicates that a specific component has entered a period of high failure incidence.

9. The intelligent predictive equipment maintenance system according to claim 8, characterized in that, The dynamic maintenance plan generated by the maintenance decision module includes a resource dependency graph. The maintenance execution module is specifically used to: parse the resource dependency graph to determine the task execution sequence, and visualize the sequence through an augmented reality interface; When the deviation of the maintenance plan exceeds a preset threshold, a reassessment request is sent to the maintenance decision module to trigger a plan update.

10. The intelligent predictive equipment maintenance system according to claim 9, characterized in that, The user interaction module is also used for: Perform cluster analysis on user query patterns to generate configuration suggestions; The configuration suggestions are sent to the data acquisition module to recommend key monitoring points, to the data processing module to prompt for key monitoring fault modes, and to the maintenance decision module to provide personnel preference scheduling strategy templates. The maintenance decision module is also used to: record users' manual adjustments to maintenance plans, extract expert experience through rule mining algorithms, and use the expert experience to correct the fitness function in multi-objective optimization.

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