Equipment operation and maintenance intelligent decision support system based on large model reasoning

Abstract

The invention provides an equipment operation and maintenance intelligent decision support system based on large model reasoning, and aims to improve the accuracy of equipment fault prediction and the intelligent level of operation and maintenance decision. The system obtains equipment operation data in real time through a data acquisition module, performs reasoning analysis on equipment health conditions by using a large-scale pre-trained deep learning model, and predicts the residual service life of equipment and various fault types. The system combines multi-task learning and a multi-objective optimization algorithm, intelligently optimizes operation and maintenance decisions, generates personalized maintenance suggestions, helps operation and maintenance personnel to effectively reduce the downtime of equipment, reduces the maintenance cost, and prolongs the service life of the equipment. The system can be widely applied to the fields of manufacturing industry, energy, traffic and the like, and the accuracy and efficiency of equipment management are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment operation and maintenance management, and in particular to an equipment operation and maintenance intelligent decision support system based on large model reasoning. Background Art

[0002] With the advancement of industrialization, equipment operation and maintenance management has become an indispensable part of modern enterprise operations. Equipment operation and maintenance includes multiple tasks such as equipment monitoring, maintenance, fault diagnosis and performance optimization, with the aim of extending the service life of equipment, improving equipment reliability and reducing downtime. In order to achieve efficient equipment management, it is usually necessary to collect a large amount of equipment operation data and analyze it in order to make accurate operation and maintenance decisions.

[0003] At present, traditional equipment operation and maintenance management methods mainly rely on manual experience or rule-based prediction models. Although these methods can provide some help in some cases, they often face problems such as inaccurate fault prediction, limited data processing capabilities, and low decision-making efficiency. With the increasing complexity of equipment, traditional methods are gradually unable to meet the growing demand for operation and maintenance, resulting in a significant reduction in the ability to predict equipment faults and support operation and maintenance decisions. Although in recent years, some operation and maintenance systems based on machine learning and artificial intelligence have emerged, which have improved the accuracy of fault diagnosis to a certain extent, there are still some problems, such as insufficient generalization ability of the model, lack of multi-task learning ability, and lack of deep optimization of the operation and maintenance decision-making process.

[0004] In response to the above problems, the existing technology urgently needs a new solution that can achieve high-precision prediction of equipment health status, support multi-type fault diagnosis, and intelligently optimize operation and maintenance decisions according to different equipment status. However, in the process of processing equipment fault prediction and decision optimization, the existing intelligent decision support system often cannot effectively combine the multi-dimensional data of the equipment for in-depth analysis and accurate prediction, and also lacks dynamic adjustment and optimization of operation and maintenance decisions. Summary of the invention

[0005] In order to solve the problems of the prior art, an embodiment of the present invention provides an intelligent decision support system for equipment operation and maintenance based on large model reasoning. The technical solution is as follows:

[0006] On the one hand, an intelligent decision support system for equipment operation and maintenance based on large model reasoning is provided, which includes the following modules:

[0007] Data acquisition module, used to collect equipment operation data and environmental data in real time;

[0008] The large model reasoning module is used to perform reasoning analysis on the health status of the device based on the historical data and real-time data of the device through a large-scale pre-trained deep learning model, and output the device health assessment results;

[0009] The fault prediction and multi-task learning module is used to predict various types of equipment faults through multi-task learning technology and output corresponding fault prediction results according to the operating status of the equipment;

[0010] Decision support and optimization algorithm module, which is used to generate optimized operation and maintenance decisions based on equipment health assessment results and fault prediction results combined with multi-objective optimization algorithms, with the goal of minimizing equipment downtime, minimizing maintenance costs, and extending equipment service life;

[0011] Maintenance and optimization suggestion module, which is used to generate personalized equipment maintenance plans based on equipment health assessment results and fault prediction results, including regular inspections, parts replacement suggestions and optimized operation strategies;

[0012] The visualization and report generation module is used to graphically display equipment health assessment results, fault prediction results, and equipment maintenance plans, and generate equipment health reports.

[0013] Furthermore, the data acquisition module obtains the multi-dimensional operation data of the equipment in real time through the wireless sensor network and edge computing nodes, including the equipment's temperature, vibration, pressure, current, voltage, speed and lubricating oil temperature sensor data.

[0014] Furthermore, the data acquisition module performs denoising, data cleaning and standardization on the collected raw data, and then inputs it into the large model inference module for analysis and reasoning.

[0015] Furthermore, the large model reasoning module uses a deep learning model to evaluate the health status of the equipment and calculates the remaining useful life (RUL) of the equipment using the following formula:

[0016] RUL=f(HistoryData,SensorData,ModelParameters)

[0017] Among them, RUL represents the remaining service life of the device, HistoryData is the historical data of the device, SensorData is the real-time sensor data of the device, and ModelParameters is the training parameters of the deep learning model.

[0018] Furthermore, the fault prediction and multi-task learning module simultaneously predicts multiple equipment fault types through multi-task learning technology and outputs the prediction results for each fault type.

[0019] Furthermore, the decision support and optimization algorithm module generates optimized operation and maintenance decisions based on the following optimization objectives:

[0020] OptimalDecision = argmin DecisionSpace (C repair ×RepairTime+C downtime ×Downtime)

[0021] Among them, OptimalDecision is the weight of the maintenance cost, DecisionSpace represents the decision space, which includes all possible operation and maintenance decision options; C repair is the weight of the maintenance cost; RepairTime is the maintenance time, C downtime is the weight of the device downtime, and Downtime is the device downtime.

[0022] Furthermore, the maintenance and optimization recommendation module generates personalized maintenance recommendations based on equipment health assessment results, historical maintenance records, and fault prediction results, including regular inspections, parts replacement recommendations, and operation optimization strategies.

[0023] Furthermore, the visualization and report generation module displays the equipment health assessment results, fault prediction results and equipment maintenance plans through a graphical interface, and supports the generation of equipment health reports, which can be exported in PDF or Excel format.

[0024] Furthermore, the large model reasoning module uses a Transformer or GPT deep learning model to perform reasoning and analysis on device data.

[0025] Furthermore, the data acquisition module, fault prediction and multi-task learning module, decision support and optimization algorithm module, maintenance and optimization suggestion module and visualization and report generation module work together through information flow, thereby realizing equipment fault prediction, health assessment and intelligent operation and maintenance decision-making.

[0026] The technical solution provided by the embodiment of the present invention has the following beneficial effects:

[0027] The present invention realizes high-precision prediction and fault diagnosis of equipment operation status through an intelligent decision support system for equipment operation and maintenance based on large model reasoning. Through a comprehensive analysis of multi-dimensional data of the equipment by a deep learning model, potential equipment failures can be discovered in a timely manner and their remaining service life can be predicted, significantly improving the accuracy of fault prediction. In addition, the system adopts multi-task learning technology, which can simultaneously predict multiple types of failures, further enhancing the comprehensiveness and accuracy of fault diagnosis. In terms of operation and maintenance decision-making, combined with a multi-objective optimization algorithm, the system can dynamically optimize the operation and maintenance strategy according to the health status and resource allocation of the equipment, reduce equipment downtime, reduce maintenance costs, and extend the service life of the equipment. Through intelligent operation and maintenance decision support, operation and maintenance personnel can perform equipment maintenance more efficiently, reduce errors caused by human decision-making, and improve the overall reliability and operating efficiency of the equipment, thereby effectively improving the operating efficiency of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 This is a system architecture diagram of an equipment operation and maintenance intelligent decision support system based on large model reasoning according to an embodiment of the present invention.

[0030] Figure 2 This is a workflow diagram of an equipment operation and maintenance intelligent decision support system based on large model reasoning in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0032] The present invention provides an intelligent decision support system for equipment operation and maintenance based on large-scale model reasoning, which aims to solve the problems of inaccurate equipment failure prediction, insufficient operation and maintenance decision support, and unreasonable resource allocation in the prior art by integrating large-scale deep learning models, intelligent decision optimization algorithms, and real-time data collection and analysis. Compared with traditional equipment operation and maintenance management systems, the present invention significantly improves the intelligence level and accuracy of equipment operation and maintenance by introducing advanced technologies such as large-scale model reasoning, multi-task learning, and intelligent decision support. The specific implementation methods of the technical solution are described in detail below.

[0033] The technical innovations of the present invention are mainly reflected in the following aspects: First, the system conducts in-depth analysis of the historical data and real-time data of the equipment through large-scale pre-trained models (such as Transformer, GPT, etc.), and realizes high-precision prediction of multiple types of faults. Secondly, the system adopts multi-task learning technology, so that fault prediction is not limited to a single fault type, but can simultaneously identify multiple possible equipment failure modes, which greatly improves the comprehensiveness and accuracy of fault diagnosis. Finally, the present invention optimizes operation and maintenance decisions through reinforcement learning and multi-objective optimization algorithms, making equipment maintenance decisions more intelligent, and being able to dynamically adjust operation and maintenance strategies under different equipment conditions, thereby maximizing equipment service life, minimizing downtime, and optimizing maintenance costs.

[0034] System Architecture

[0035] The equipment operation and maintenance intelligent decision support system of the present invention is composed of a data acquisition module, a large model reasoning module, a fault prediction and multi-task learning module, a decision support and optimization algorithm module, a maintenance and optimization suggestion module, and a visualization and report generation module. Through the collaborative work of these modules, the system can obtain equipment operation data in real time, perform intelligent reasoning analysis, accurately predict fault types and remaining service life, and provide optimized maintenance decisions and personalized maintenance suggestions for operation and maintenance personnel.

[0036] like Figure 1 As shown in the figure, the various modules of the system are connected and collaborated through information flow. The data acquisition module is responsible for collecting equipment operation data and environmental data in real time, and transmitting the processed data to the large model reasoning module for deep learning analysis. The large model reasoning module infers the health status of the equipment through a large-scale pre-trained deep learning model, and outputs the remaining service life of the equipment and fault prediction results. Next, the fault prediction and multi-task learning module combines multiple task objectives to make a comprehensive prediction of the fault type. Then, the decision support and optimization algorithm module generates optimized operation and maintenance decisions based on the status of the equipment and the maintenance resources. The maintenance and optimization recommendation module generates personalized maintenance strategies based on the prediction results and operation and maintenance needs, and presents them to the operation and maintenance personnel and management through the visualization and report generation module.

[0037] Data acquisition and preprocessing module

[0038] The data acquisition module is the first step of the system. It collects multi-dimensional operation data of the equipment in real time through the wireless sensor network (WSN) and edge computing nodes. These data include but are not limited to sensor data such as temperature, vibration, pressure, current, voltage, and historical fault records and operation logs of the equipment. In order to ensure data quality, all collected raw data need to go through preprocessing steps such as denoising, data cleaning, and standardization. The processed data is input into the large model inference module for analysis and reasoning.

[0039] Large model reasoning module

[0040] The large model reasoning module is one of the cores of the present invention. It analyzes the operation data and historical fault records of the equipment based on pre-trained large-scale deep learning models (such as Transformer, GPT, etc.). This module can predict the remaining useful life (RUL) of the equipment and the type of fault by reasoning on the multi-dimensional data of the equipment. The calculation formula for predicting the remaining useful life of the equipment is as follows:

[0041] RUL=f(HistoryData,SensorData,ModelParameters)

[0042] Among them, RUL represents the remaining service life of the device, HistoryData is the historical data of the device, SensorData is the real-time sensor data of the device, ModelParameters is the training parameters of the deep learning model; f is a mapping function learned by a deep learning model, which is usually a nonlinear function trained by a neural network model (such as LSTM, Transformer, etc.). This function receives historical data, real-time sensor data and model parameters as input, and outputs the remaining service life of the device.

[0043] The innovation of this invention lies in that by introducing the reasoning ability of the deep learning model and combining the historical data of the equipment with the real-time data, it can provide a comprehensive and accurate assessment of the status of the equipment and timely discover potential hidden dangers of equipment failure.

[0044] Specifically, the large model reasoning module is responsible for comprehensive analysis of the equipment operation status and historical data, providing high-precision equipment health assessment and fault risk prediction. The module receives multi-dimensional operation data from the data acquisition module, including equipment time series data (such as vibration signals, temperature changes, pressure curves, etc.), environmental data (such as humidity and ambient temperature), and historical operation data (such as fault records and maintenance logs). After denoising, standardization, and feature extraction, these data form input data in a unified format, providing a basis for model reasoning.

[0045] The large model inference module uses a large-scale pre-trained deep learning model based on the Transformer architecture. Its multi-head attention mechanism can capture the long-term dependencies in the equipment operation data and the complex interactions between features. It gradually extracts high-level semantic features through multi-layer encoders to generate a comprehensive representation of the equipment health status. During the training process, the model first completes pre-training through a large-scale industrial data set to learn general equipment operation modes and feature associations; then it is fine-tuned on the target equipment's operation data to adapt to specific equipment and task requirements.

[0046] The inference results include the equipment health score and remaining useful life (RUL) prediction. The health score reflects the current operating status of the equipment, ranging from 0 to 100. The lower the score, the more abnormal the equipment operation or the potential risk. The remaining useful life prediction provides an intuitive equipment life estimate by comprehensively analyzing the real-time data and historical data of the equipment, helping operation and maintenance personnel to formulate a scientific maintenance plan.

[0047] The module has significant real-time and scalability. It supports millisecond-level real-time reasoning through edge computing nodes to meet the needs of rapid decision-making in industrial sites. In addition, the module supports multiple types of data input, adapts to different devices and application scenarios, and has good versatility and scalability. The innovation of the large model lies in its powerful representation learning ability, which can not only accurately extract the nonlinear relationship between features, but also identify unseen fault modes through the degree of pattern deviation, significantly improving the comprehensiveness and adaptability of the evaluation.

[0048] By integrating time series data, environmental data and historical data for all-round analysis, the large model reasoning module demonstrates excellent accuracy and stability in equipment health assessment and fault prediction. The introduction of this module enables the system to detect potential equipment failures in a timely manner, reduce unplanned downtime, and provide a scientific basis for operation and maintenance decisions, comprehensively improving the level of intelligent equipment management.

[0049] Fault prediction and multi-task learning module

[0050] The fault prediction and multi-task learning module is based on the multi-task learning method, which can simultaneously predict multiple fault types and improve the accuracy of fault diagnosis. Traditional equipment fault prediction usually only focuses on a single type of fault, while the present invention uses multi-task learning technology, so that the system can simultaneously predict multiple possible fault modes such as mechanical faults, electrical faults, and environmental faults. The output of this module includes multiple fault prediction results, as shown below:

[0051] FaultType 1 ,FaultType 2=MultiTaskNetwork(SensorData,HistoryData)

[0052] Among them, FaultType 1 and FaultType 2 They represent different types of fault prediction results. Through multi-task learning, the system can identify and predict multiple fault types at the same time, thereby improving the comprehensiveness and accuracy of fault diagnosis.

[0053] Decision support and optimization algorithm module

[0054] The decision support and optimization algorithm module combines reinforcement learning and multi-objective optimization techniques to dynamically optimize operation and maintenance decisions based on the health status of the equipment, fault prediction results, and operation and maintenance resource conditions. The goal of this module is to minimize equipment downtime, minimize maintenance costs, and extend the service life of the equipment. Its optimization goal can be expressed by the following formula:

[0055] OptimalDecision = argmin DecisionSpace (C repair ×RepairTime+C downtime ×Downtime)

[0056] Among them, OptimalDecision is the weight of the maintenance cost, DecisionSpace represents the decision space, which includes all possible operation and maintenance decision options; C repair is the weight of the maintenance cost; RepairTime is the maintenance time, C downtime is the weight of the device downtime, and Downtime is the downtime of the device. DecisionSpace It means "finding the decision that minimizes the objective function in the decision space", that is, selecting the best one from all possible operation and maintenance decisions.

[0057] Maintenance and optimization suggestion module

[0058] This module provides personalized maintenance recommendations to operators based on equipment health assessment and fault prediction results. The system automatically generates maintenance plans and equipment maintenance cycles based on the equipment's historical maintenance records, operating status, and current fault prediction results. Maintenance recommendations include regular inspections, parts replacement recommendations, and optimized operation strategies to help operators better plan and execute equipment maintenance tasks, thereby reducing the occurrence of faults and improving the overall reliability of the equipment.

[0059] Visualization and Reporting Module

[0060] The visualization and report generation module displays the health status of the equipment, fault prediction results, maintenance history and other information through a graphical interface, helping operation and maintenance personnel to quickly understand the equipment status and make scientific decisions. The system will automatically generate equipment health reports based on the real-time data of the equipment, fault prediction results and maintenance recommendations, and provide them to operation and maintenance personnel and management in PDF, Excel and other formats.

[0061] Figure 2 The workflow of the equipment operation and maintenance intelligent decision support system based on large model reasoning is demonstrated. The collaborative work of each module realizes the whole process of equipment health status assessment, fault prediction and operation and maintenance decision optimization. The specific description is as follows:

[0062] S1. Data collection and preprocessing:

[0063] Input: real-time operating data of the equipment (including sensor data such as temperature, vibration, pressure, current, voltage, etc.) and environmental data.

[0064] Processing: The data acquisition module collects multi-dimensional data in real time through wireless sensor networks (WSNs) and edge computing nodes, and pre-processes it, including denoising, data cleaning, and standardization operations to ensure data quality.

[0065] Output: Processed high-quality operation data is transmitted to the large model inference module.

[0066] S2. Equipment health reasoning analysis:

[0067] Input: pre-processed real-time equipment operation data and historical fault records.

[0068] Processing: The large model reasoning module performs reasoning analysis on the input data based on the deep learning model to evaluate the health status of the equipment and predict its remaining useful life (RUL). The analysis results include the current health status of the equipment and the predicted operational risk level.

[0069] Output: Health assessment results and remaining service life prediction results are passed to the fault prediction and multi-task learning module.

[0070] S3. Multi-task failure prediction:

[0071] Input: Health assessment results and equipment operation data.

[0072] Processing: The fault prediction and multi-task learning module uses multi-task learning technology, combined with health assessment results, to simultaneously predict multiple possible fault types (such as mechanical faults, electrical faults, etc.). The prediction results for each fault type are accompanied by a corresponding probability or risk level.

[0073] Output: The prediction results of various types of faults, including fault types and corresponding risk levels, are transmitted to the decision support and optimization algorithm module.

[0074] S4. Operation and maintenance decision optimization:

[0075] Input: Failure prediction results, health assessment data, and historical equipment maintenance records.

[0076] Processing: The decision support and optimization algorithm module generates optimized operation and maintenance strategies based on input data and combined with multi-objective optimization algorithms. The strategy goals include reducing equipment downtime, reducing maintenance costs, and extending equipment life.

[0077] Output: Operation and maintenance optimization suggestions, including inspection cycles, key component replacement plans, and operation adjustment suggestions, are delivered to the maintenance and optimization suggestion module.

[0078] S5. Generate maintenance and optimization suggestions:

[0079] Input: Operation and maintenance optimization suggestions and equipment historical maintenance records.

[0080] Processing: The maintenance and optimization suggestion module generates personalized maintenance plans based on the operation and maintenance decision results.

[0081] The program includes:

[0082] Schedule for regular inspections;

[0083] Parts replacement program;

[0084] Equipment operation optimization strategy.

[0085] Output: Complete maintenance proposal, transferred to the visualization and report generation module.

[0086] S6. Visualization and report generation:

[0087] Input: Health assessment, failure prediction, and maintenance recommendation data.

[0088] Processing: The visualization and report generation module displays the health status of the equipment, fault prediction results and maintenance suggestions through a graphical interface, providing intuitive analysis results for operation and maintenance personnel. At the same time, the module supports the generation of maintenance reports (PDF or Excel format) for easy archiving and sharing.

[0089] Output: Graphical display and exported equipment health reports for operation and maintenance personnel and management to review.

[0090] Through this process, the system realizes a closed-loop operation from data collection to optimized decision-making, effectively improving the equipment operation and maintenance efficiency and prediction accuracy, while providing scientific and executable decision-making support for operation and maintenance personnel.

[0091] Technical Effects

[0092] Through the intelligent decision support system of the present invention, the operation and maintenance process of the equipment becomes more efficient, intelligent and accurate. First, the introduction of large-scale pre-trained models makes the equipment health assessment more accurate, and fault prediction discovers potential problems in advance, greatly improving the accuracy of fault prediction. Secondly, multi-task learning technology enables the system to simultaneously identify multiple types of equipment failures, comprehensively improving the fault diagnosis capability. Finally, the optimization algorithm module can dynamically adjust the operation and maintenance strategy under different equipment conditions through multi-objective decision optimization, thereby minimizing downtime and maintenance costs, and extending the service life of the equipment. The system not only provides intelligent decision support for equipment operation and maintenance personnel, but also effectively reduces the maintenance cost of equipment and improves the reliability of equipment.

[0093] Through the above implementation, the present invention provides an equipment operation and maintenance intelligent decision support system based on large-scale model reasoning, which significantly improves the accuracy and intelligence level of equipment operation and maintenance decisions through large-scale data analysis and deep learning technology, and effectively solves the problems in the prior art.

[0094] Application Examples

[0095] In order to more clearly illustrate the practical application effect of the system of the present invention, the following takes the operation and maintenance management of key production equipment of a manufacturing enterprise as an example to introduce the application process of the equipment operation and maintenance intelligent decision support system based on the present invention.

[0096] Application Background:

[0097] A large-scale machining equipment is used on the production line of a manufacturing company. This equipment is crucial to product quality and production efficiency. However, the equipment is prone to abnormal vibration, overheating and other problems during long-term operation, resulting in sudden shutdowns, which not only increases maintenance costs but also affects production plans. Traditional manual inspection methods cannot accurately predict the time when equipment failures will occur, resulting in relatively blind maintenance.

[0098] System application process:

[0099] 1). Data collection and preprocessing:

[0100] Through the wireless sensor network (WSN) and edge computing nodes on the equipment, the system collects multi-dimensional operating data of the equipment in real time, including spindle vibration signals, operating temperature, feed speed, current and voltage, etc.

[0101] The data acquisition module performs denoising, data cleaning and standardization on these real-time data, and combines them with the historical operation records and fault logs of the equipment to form a complete input data set.

[0102] 2) Health status assessment:

[0103] The data is input into the large model inference module. Based on historical data and real-time data, the system conducts a comprehensive assessment of the current health status of the equipment through a pre-trained deep learning model.

[0104] The results showed that the health status score of the device was 75 (out of 100), indicating that the equipment had certain minor hidden dangers, but would not affect normal operation for the time being.

[0105] 3). Multi-task fault prediction:

[0106] The fault prediction and multi-task learning module predicts the possible fault types and risk levels of the equipment based on the evaluation results:

[0107] Mechanical failure: spindle bearing wear, risk level is medium.

[0108] Abnormal temperature: The efficiency of the equipment cooling system decreases and the risk level is low.

[0109] The system also predicted that if no action was taken, wear problems on the equipment's spindle bearings could cause equipment downtime within 30 days.

[0110] 4) Operation and maintenance decision optimization:

[0111] The decision support and optimization algorithm module generates the following operation and maintenance recommendations based on the fault prediction results and the historical maintenance records of the equipment:

[0112] Arrange inspection and replacement of equipment spindle bearings before the next production cycle is completed;

[0113] At the same time, clean and maintain the cooling system to improve cooling efficiency;

[0114] Adjust the equipment operating speed from the current 1200RPM to 1000RPM to reduce the bearing load.

[0115] 5). Generation of maintenance and optimization suggestions:

[0116] The system generates a detailed maintenance plan based on the optimized operation and maintenance decisions, including:

[0117] Inspection cycle: It is recommended to check the spindle vibration once a week;

[0118] Replacement parts: Replace the spindle bearings at the next maintenance and prepare spare parts in advance;

[0119] Optimization strategy: Reduce equipment speed to reduce load until maintenance is completed.

[0120] 6). Visualization and report generation:

[0121] The maintenance plan and equipment health assessment results are intuitively displayed to the operation and maintenance team through a visual interface. At the same time, the system generates a PDF-formatted equipment health report that records the equipment's operating status, fault prediction results, and maintenance recommendations in detail for easy archiving and management.

[0122] Application effect:

[0123] Through the intelligent decision support of this system, the company successfully completed the replacement of the spindle bearing and the optimization of the cooling system before the equipment had a serious failure. The results show that:

[0124] The accuracy of fault prediction reaches 95%, avoiding sudden equipment downtime.

[0125] Repair costs were reduced by approximately 20% and equipment downtime was reduced by more than 30%.

[0126] The work efficiency of the operation and maintenance team has been significantly improved.

[0127] Summarize:

[0128] This example shows that the equipment operation and maintenance intelligent decision support system based on the present invention can detect potential equipment failures in advance through real-time data collection and intelligent analysis, and provide scientific maintenance suggestions, thereby significantly improving the operating efficiency and reliability of the equipment.

[0129] This example further demonstrates the application effect of the system of the present invention in actual scenarios, reflecting its advantages in fault prediction and operation and maintenance optimization.

[0130] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent decision support system for equipment operation and maintenance based on large model reasoning, characterized in that: Contains the following modules: Data acquisition module, used to collect equipment operation data and environmental data in real time; The large model reasoning module is used to perform reasoning analysis on the health status of the device based on the historical data and real-time data of the device through a large-scale pre-trained deep learning model, and output the device health assessment results; The fault prediction and multi-task learning module is used to predict various types of equipment faults through multi-task learning technology and output corresponding fault prediction results according to the operating status of the equipment; Decision support and optimization algorithm module, which is used to generate optimized operation and maintenance decisions based on equipment health assessment results and fault prediction results combined with multi-objective optimization algorithms, with the goal of minimizing equipment downtime, minimizing maintenance costs, and extending equipment service life; Maintenance and optimization suggestion module, which is used to generate personalized equipment maintenance plans based on equipment health assessment results and fault prediction results, including regular inspections, parts replacement suggestions and optimized operation strategies; The visualization and report generation module is used to graphically display equipment health assessment results, fault prediction results, and equipment maintenance plans, and generate equipment health reports.

2. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The data acquisition module obtains the multi-dimensional operation data of the equipment in real time through wireless sensor networks and edge computing nodes, including the equipment's temperature, vibration, pressure, current, voltage, speed and lubricating oil temperature sensor data.

3. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The data acquisition module performs denoising, data cleaning and standardization on the collected raw data, and then inputs it into the large model inference module for analysis and reasoning.

4. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The large model inference module uses a deep learning model to evaluate the health status of the device and calculates the remaining useful life (RUL) of the device using the following formula: RUL=f(HistoryData,SensorData,ModelParameters) Among them, RUL represents the remaining service life of the device, HistoryData is the historical data of the device, SensorData is the real-time sensor data of the device, and ModelParameters is the training parameters of the deep learning model.

5. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The fault prediction and multi-task learning module uses multi-task learning technology to simultaneously predict multiple equipment fault types and output the prediction results for each fault type.

6. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The decision support and optimization algorithm module generates optimized operation and maintenance decisions based on the following optimization objectives: OptimalDecision=argmin DecisionSpace (C repair ×RepairTime+C downtime ×Downtime) Among them, OptimalDecision is the weight of the maintenance cost, DecisionSpace represents the decision space, which includes all possible operation and maintenance decision options; C repair is the weight of the maintenance cost; RepairTime is the maintenance time, C downtime is the weight of the device downtime, and Downtime is the device downtime.

7. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The maintenance and optimization recommendation module generates personalized maintenance recommendations based on equipment health assessment results, historical maintenance records, and fault prediction results, including regular inspections, parts replacement recommendations, and operation optimization strategies.

8. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The visualization and report generation module displays equipment health assessment results, fault prediction results and equipment maintenance plans through a graphical interface, and supports the generation of equipment health reports, which can be exported in PDF or Excel format.

9. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The large model reasoning module uses a Transformer or GPT deep learning model to perform reasoning and analysis on device data.

10. The equipment operation and maintenance intelligent decision support system based on large model reasoning according to claim 1 is characterized in that: The data acquisition module, fault prediction and multi-task learning module, decision support and optimization algorithm module, maintenance and optimization suggestion module and visualization and report generation module work together through information flow to achieve equipment fault prediction, health assessment and intelligent operation and maintenance decision-making.

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