Automatic intelligent equipment operation and maintenance control system

Abstract

The invention discloses an operation and maintenance control system for automatic intelligent equipment, and the system comprises an equipment monitoring and data collection module which is used for carrying out the real-time monitoring, data transmission and data storage of the automatic intelligent equipment; the data analysis and anomaly detection module is used for carrying out data preprocessing, data analysis and anomaly detection on the monitored data; the fault diagnosis and positioning module is used for performing fault diagnosis and fault positioning; the maintenance decision and execution module is used for making a maintenance decision and executing the maintenance decision according to the fault diagnosis and positioning result; and the feedback and optimization module is used for recording the maintenance process and optimizing the operation and maintenance control flow according to the maintenance feedback and the data analysis result. According to the invention, intelligent and comprehensive automatic intelligent equipment operation and maintenance control can be carried out.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance control, and in particular to an automated intelligent equipment operation and maintenance control system. Background Art

[0002] Automated intelligent equipment refers to mechanical equipment or systems that use advanced sensing technology, control technology, information technology, artificial intelligence technology, etc. to achieve highly autonomous and intelligent operations. These devices are widely used in various industries to improve production efficiency, accuracy, safety and adaptability; During the operation and maintenance of automated intelligent equipment, the operation and maintenance control system will be used.

[0003] The existing operation and maintenance control system has poor operation and maintenance control effect and low intelligence, which has a certain impact on the use of the operation and maintenance control system. Therefore, an automated intelligent equipment operation and maintenance control system is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to solve the problem that the existing operation and maintenance control system has poor operation and maintenance management effect and low intelligence level, which has brought certain impacts on the use of the operation and maintenance control system, and provides an automated intelligent equipment operation and maintenance control system.

[0005] The present invention solves the above technical problems through the following technical solutions, which include: Equipment monitoring and data acquisition module, which is used for real-time monitoring, data transmission and data storage of automated intelligent equipment; Data analysis and anomaly detection module, which is used to perform data preprocessing, data analysis and anomaly detection on the monitored data; Fault diagnosis and location module: The fault diagnosis and location module is used for fault diagnosis and fault location; Maintenance decision and execution module: The maintenance decision and execution module is used to make maintenance decisions and execute maintenance decisions according to the fault diagnosis and location results; Feedback and optimization module: The feedback and optimization module is used to record the maintenance process and optimize the operation and maintenance control process based on maintenance feedback and data analysis results.

[0006] Furthermore, the specific process of the equipment monitoring and data acquisition module for real-time monitoring, data transmission and data storage of the automated intelligent equipment is as follows: Real-time monitoring: Set up sensors, Internet of Things (IoT) devices and remote monitoring software to monitor automated smart devices and collect various data generated during the operation of the equipment, including operating status, performance parameters, and fault information; Data transmission: Transmit the collected data from the device to the data center or cloud platform; Data storage: Store the received data in the database.

[0007] Furthermore, the specific process of the data preprocessing is as follows: data cleaning Missing value handling: Depending on the nature of the data, choose to fill, delete, or ignore records with missing values; Outlier processing: Identify and process abnormal data points by deleting them, replacing them with the mean / median, or adjusting them according to the data distribution; Duplicate value handling: Check and delete or merge duplicate records.

[0008] Data denoising, smoothing first: For time series data, use methods such as sliding average and exponential smoothing to remove random noise.

[0009] Use filtering techniques, including low-pass filters and high-pass filters, to remove noise based on the frequency characteristics of the signal; Data formatting: unify units to ensure that all related data are measured in consistent units; Data encoding: converting categorical data into numerical data to facilitate processing by machine learning algorithms; Data standardization / normalization: Adjust the data range to make it suitable for specific algorithm requirements and avoid certain features from having too much impact on the results.

[0010] Furthermore, the specific process of the data analysis is as follows: Conduct descriptive statistical analysis and calculate statistics to describe the distribution and characteristics of the data. Statistics include mean, median, standard deviation, and quartiles. Draw charts to visualize data and discover patterns and trends in data, including histograms, box plots, and scatter plots; Correlation analysis, identifying the associations between different variables and understanding which factors may affect the operating status of the equipment; Use correlation coefficients to quantify the relationship between variables; Machine learning models that apply cluster analysis to identify different operating states or abnormal patterns of equipment; Use classification or regression models to predict the future state or performance of a device.

[0011] Furthermore, the specific process of the anomaly detection is as follows: setting an anomaly threshold, based on a statistical method: using a standard deviation or IQR method to set the threshold; Model-based approach: train a model to predict normal behavior and treat points where the prediction error exceeds a certain threshold as abnormal; Distance-based method: Calculate the distance from a data point to its nearest neighbor, and consider points with too large a distance as anomalies; Trigger the anomaly detection mechanism to monitor the data flow in real time. When the data exceeds the set threshold, the anomaly detection mechanism is triggered, and different alarm levels are set to respond differently according to the severity of the anomaly. Generate abnormal alarms and send alarm notifications to relevant personnel, including specific information about the abnormality, time of occurrence, and possible causes; Provides a visual interface to help personnel quickly understand abnormal situations and the current status of equipment.

[0012] 6. The automated intelligent equipment operation and maintenance control system according to claim 1 is characterized in that: the specific process of the fault diagnosis and fault location module for fault diagnosis and fault location is as follows: Fault diagnosis: Combine the fault database and expert system resources to analyze abnormal alarms and diagnose the cause and type of equipment failure; Fault location: Determine the specific location and impact range of the fault based on the fault diagnosis results.

[0013] Furthermore, the maintenance decision and execution module formulates and executes maintenance decisions according to the fault diagnosis and location results in the following specific processes: Make maintenance decisions, evaluate the impact of failures, estimate the impact of failures on equipment performance, production processes and safety, and determine the urgency of maintenance; Determine maintenance goals: clarify the main goals of maintenance, including restoring equipment performance, extending equipment life, and improving production efficiency; Develop maintenance plans and maintenance time: determine the best maintenance time based on production plans and equipment downtime windows; Maintenance content: Based on the fault diagnosis results, identify the parts that need to be repaired or replaced and the parameters that need to be adjusted; Risk assessment: Evaluate the risks and potential safety hazards that may be encountered during maintenance and develop countermeasures; Select maintenance strategy: Select appropriate maintenance strategy based on equipment type, fault nature and maintenance cost factors; Develop emergency plans: To cope with emergencies, develop emergency plans to ensure that problems encountered during maintenance can be responded to and properly resolved quickly.

[0014] Execute maintenance decisions: Execute maintenance, remote operation and maintenance: For equipment that supports remote monitoring and control, perform fault diagnosis, parameter adjustment, and software upgrades through the remote operation and maintenance platform; On-site maintenance: For equipment that requires on-site operation, maintenance personnel must perform on-site maintenance in accordance with maintenance plans and programs; Verify the effect: After the maintenance is completed, test and verify the equipment to ensure that the fault has been repaired and the equipment performance has returned to normal; Record key data and observations during the maintenance process to provide a basis for subsequent analysis and improvement.

[0015] Furthermore, the feedback and optimization module is used to record the maintenance process and optimize the operation and maintenance control process according to the maintenance feedback and data analysis results. The specific process is as follows: feedback and optimization, maintenance feedback, recording key information and data in the maintenance process, including fault resolution time, maintenance cost, and maintenance effect; Process optimization: Optimize the operation and maintenance control process based on maintenance feedback and data analysis results.

[0016] Compared with the prior art, the present invention has the following advantages: the automated intelligent equipment operation and maintenance control system can realize comprehensive monitoring, intelligent analysis and efficient operation and maintenance of the equipment, improve the reliability and stability of the equipment, reduce the operation and maintenance costs, and improve the overall operational efficiency of the enterprise. Through equipment monitoring and data collection, the automated intelligent equipment operation and maintenance control system can grasp the operating status of the equipment in real time, provide strong data support for subsequent fault prediction, diagnosis and maintenance, effectively perform data preprocessing, analysis and anomaly detection, improve the reliability and operating efficiency of the equipment, and improve the accuracy and efficiency of fault diagnosis by combining resources such as fault libraries and expert systems; through scientific fault location methods, the specific location and impact range of the fault can be accurately determined, providing strong support for maintenance work; through scientific and standardized maintenance decision-making and execution processes, it can ensure that the equipment is maintained in a timely and effective manner, improve the reliability and service life of the equipment, and provide strong guarantees for the production operations of the enterprise, making the system more worthy of promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0018] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0019] like Figure 1 As shown, this embodiment provides a technical solution: an automated intelligent equipment operation and maintenance control system, comprising: Equipment monitoring and data acquisition module, which is used for real-time monitoring, data transmission and data storage of automated intelligent equipment; Equipment monitoring and data collection is one of the core links of the automated intelligent equipment operation and maintenance control system. It ensures that the operation and maintenance team can grasp the operating status of the equipment in real time and provide a data basis for subsequent fault prediction, diagnosis and maintenance. The following is a more detailed description of this link: Equipment Monitoring Purpose: To monitor the operating status and performance parameters of the equipment in real time so as to detect potential problems or abnormal conditions in a timely manner.

[0020] means: Sensor: Various sensors installed on the equipment are responsible for collecting various physical quantities of the equipment, such as temperature, pressure, flow, vibration, etc. These physical quantities directly reflect the operating status of the equipment.

[0021] Internet of Things (IoT) devices: In addition to traditional sensors, IoT devices are also widely used in equipment monitoring. These devices usually integrate sensors, communication modules and certain processing capabilities, and can autonomously collect data and transmit it to a data center or cloud platform through the network.

[0022] Remote monitoring software: The remote monitoring software installed on the server in the operation and maintenance center or cloud can receive real-time data from IoT devices and display it in a graphical and visual way, making it convenient for operation and maintenance personnel to intuitively understand the operating status of the equipment.

[0023] Data collection Purpose: To collect various data generated during the operation of the equipment, including operating status, performance parameters, fault information, etc.

[0024] Way: Active collection: Through the preset collection frequency and rules, the system actively obtains data from sensors or IoT devices. This method can ensure the real-time and accuracy of the data.

[0025] Passive reception: In some cases, devices or IoT devices may actively send data to the data center or cloud platform, such as fault alarms, status change notifications, etc. At this time, the system needs to be able to passively receive this data and process it accordingly.

[0026] Data Transfer Purpose: To transfer the collected data from the device to the data center or cloud platform for subsequent analysis and processing.

[0027] Way: Wired transmission: Data is transmitted to a data center or cloud platform through wired media such as cables and optical fibers. This method usually has higher transmission speed and stability, but the deployment cost is high and it is not flexible enough.

[0028] Wireless transmission: Data is transmitted to the data center or cloud platform through wireless communication technologies such as WiFi, Bluetooth, LoRa, NB-IoT, etc. This method is flexible to deploy and has low cost, but may be limited by signal coverage and transmission speed.

[0029] Data storage Purpose: Store the received data in the database for subsequent data analysis, fault prediction and diagnosis.

[0030] Considerations: Storage capacity: Based on the number of devices and the frequency of data collection, the storage capacity of the database should be reasonably planned to ensure that data can be stored for a long enough time.

[0031] Data security: Use encryption technology, access control and other security measures to protect data from illegal access or tampering.

[0032] Data accessibility: Design a reasonable database architecture and query interface to ensure that operation and maintenance personnel can easily access and query the required data.

[0033] Through equipment monitoring and data collection, the automated intelligent equipment operation and maintenance control system can grasp the operating status of the equipment in real time, providing strong data support for subsequent fault prediction, diagnosis and maintenance; Data analysis and anomaly detection module, which is used to perform data preprocessing, data analysis and anomaly detection on the monitored data; Data analysis and anomaly detection are key to maintaining equipment health, improving production efficiency, and preventing failures. Below I will elaborate on the specific steps and methods of data preprocessing, data analysis, and anomaly detection.

[0034] Data preprocessing Data preprocessing is the cornerstone of data analysis. Its goal is to improve data quality and make the data more suitable for subsequent analysis and modeling. The following are the main steps of data preprocessing: Data cleaning Missing value handling: Depending on the nature of the data, choose to fill (such as mean, median, mode filling, or using a predictive model), delete, or ignore records with missing values.

[0035] Outlier handling: Identify and handle data points that deviate significantly from other observations, which may be due to data entry errors or measurement errors. Treatment methods include deletion, replacement with mean / median, or adjustment based on data distribution.

[0036] Duplicate value handling: Check and delete or merge duplicate records.

[0037] Data Denoising Smoothing: For time series data, methods such as sliding average and exponential smoothing can be used to remove random noise.

[0038] Filtering technology: such as low-pass filters and high-pass filters, which remove noise based on the frequency characteristics of the signal.

[0039] Data formatting Standardize units: Ensure that all relevant data is measured in consistent units.

[0040] Data encoding: Convert categorical data (such as device status) into numerical data to facilitate processing by machine learning algorithms.

[0041] Data standardization / normalization: Adjust the data range to make it suitable for specific algorithm requirements and avoid certain features from having too much impact on the results.

[0042] Data analysis Data analytics aims to extract valuable information from data and identify equipment operating patterns and potential problems.

[0043] Descriptive statistical analysis Calculate statistics (such as mean, median, standard deviation, quartiles, etc.) to describe the distribution and characteristics of the data.

[0044] Draw charts (such as histograms, box plots, scatter plots, etc.) to visualize data and discover patterns and trends in the data.

[0045] Correlation analysis Identify correlations between different variables to understand which factors may affect the operating status of the equipment.

[0046] Use correlation coefficients (e.g., Pearson correlation coefficient, Spearman rank correlation coefficient) to quantify the association between variables.

[0047] Machine Learning Models Apply cluster analysis (such as K-means, hierarchical clustering) to identify different operating states or abnormal patterns of equipment.

[0048] Use classification or regression models (e.g., decision trees, random forests, neural networks) to predict the future state or performance of a device.

[0049] Anomaly Detection Anomaly detection is the process of identifying points or subsets in data that do not conform to normal patterns or expected behavior.

[0050] Setting anomaly thresholds Based on statistical methods: thresholds are set using methods such as standard deviation, IQR (interquartile range), etc.

[0051] Model-based approach: Train a model to predict normal behavior and consider points where the prediction error exceeds a certain threshold as anomalies.

[0052] Distance-based method: Calculate the distance from a data point to its nearest neighbor, and consider points with too large a distance as anomalies.

[0053] Triggering anomaly detection mechanism Monitor data streams in real time and trigger anomaly detection mechanisms when data exceeds set thresholds.

[0054] You can set different alarm levels and respond differently based on the severity of the anomaly.

[0055] Generate exception alerts Send alarm notifications to relevant personnel, including specific information about the abnormality, time of occurrence, possible causes, etc.

[0056] Provides a visual interface to help personnel quickly understand abnormal situations and the current status of equipment.

[0057] Through the above steps, data preprocessing, analysis and anomaly detection can be effectively performed to improve the reliability and operation efficiency of the equipment.

[0058] Fault diagnosis and location module: The fault diagnosis and location module is used for fault diagnosis and fault location; Troubleshooting Definition and Importance Fault diagnosis is the process of monitoring the operating status of equipment, discovering abnormalities and analyzing the causes. Its importance lies in timely discovering and solving equipment failures to ensure the normal operation of the equipment and avoid production interruptions and safety accidents.

[0059] Methods and Techniques Sensory diagnosis method: Observation: Check whether the appearance of the equipment is damaged and whether the connection is normal.

[0060] Hearing: Listen to see if the equipment is running normally and determine if there are any abnormal sounds.

[0061] Touch: Touch the device surface temperature and vibration to determine the device operating status.

[0062] Tool diagnosis method: Use professional tools (such as multimeter, oscilloscope, gas leak detector, etc.) to test the equipment and determine the fault location and cause.

[0063] Intelligent diagnosis method: using modern scientific and technological means, through sensors, data collection and intelligent analysis systems, real-time monitoring of equipment operating parameters, analysis of fault causes, and rapid location of fault sites.

[0064] Experience diagnosis method: Based on the long-term experience accumulated by maintenance personnel, combined with the equipment operation history and performance, the cause and location of the fault can be quickly determined. By comparing similar cases, the accuracy and efficiency of fault diagnosis can be improved.

[0065] Combined resources: Combined with resources such as fault databases and expert systems, abnormal alarms are analyzed to diagnose the causes and types of equipment failures. These resources can provide historical fault cases, fault solutions, etc., to help maintenance personnel locate faults more quickly and accurately.

[0066] Fault Location Definition and Purpose Fault location is the process of determining the specific location and impact range of a fault based on fault diagnosis. Its purpose is to locate the fault point more accurately so that targeted maintenance measures can be taken.

[0067] Tips and Examples Observation method: By observing the operating status and abnormal phenomena of the equipment, a preliminary judgment can be made on the area where the fault may occur.

[0068] Auscultation method: Use tools such as a stethoscope to listen to the sound of the equipment when it is running, and determine the fault location by the changes in the sound.

[0069] Touch method: Touch the surface of the device to feel the changes in parameters such as temperature and vibration to help locate the fault point.

[0070] Isolation method: Isolate equipment components or systems one by one to determine the specific location of the fault. For example, you can disconnect the power supply or connection line of a component and observe the changes in the equipment's operating status.

[0071] Data analysis: Combine the data collected during the fault diagnosis process (such as vibration data, temperature data, current data, etc.) and analyze them through data analysis software or expert systems to determine the specific location and impact range of the fault.

[0072] Trial run and software check: By trial running a part of the equipment or system, observe whether the fault phenomenon still exists, so as to narrow down the scope of the fault. At the same time, use software check tools to check the equipment's configuration data, program logic, etc. to locate the fault point.

[0073] Maintenance decision and execution module: The maintenance decision and execution module is used to make maintenance decisions and execute maintenance decisions according to the fault diagnosis and location results; Maintenance decision-making is the process of formulating reasonable and effective maintenance plans and programs based on the diagnosis results and the actual condition of the equipment after fault diagnosis and location. This process is crucial to ensure the long-term stable operation of the equipment, reduce downtime and improve production efficiency.

[0074] Formulate steps Assess the impact of the failure: First, assess the impact of the failure on equipment performance, production process and safety, and determine the urgency of maintenance.

[0075] Determine maintenance goals: Clarify the main goals of maintenance, such as restoring equipment performance, extending equipment life, and improving production efficiency.

[0076] Create a maintenance plan: Maintenance time: Determine the best maintenance time based on production plan and equipment downtime window.

[0077] Maintenance content: Based on the fault diagnosis results, identify the parts that need to be repaired or replaced, the parameters that need to be adjusted, etc.

[0078] Required resources: List the human, material, financial and other resources required for maintenance, including maintenance personnel, spare parts, tools, equipment, etc.

[0079] Risk assessment: Evaluate the risks and safety hazards that may be encountered during the maintenance process and develop response measures.

[0080] Select maintenance strategy: Select appropriate maintenance strategy, such as preventive maintenance, corrective maintenance, and corrective maintenance, based on factors such as equipment type, nature of failure, and maintenance cost.

[0081] Develop emergency plans: To cope with emergencies, develop emergency plans to ensure that problems encountered during maintenance can be responded to and properly resolved quickly.

[0082] Approval and release: Submit the maintenance plan and program to the relevant department for approval, and after approval, formally release it to relevant personnel for execution.

[0083] Maintenance execution Definition and method Maintenance execution is the process of repairing, maintaining and debugging equipment in accordance with maintenance plans and programs through remote operation and maintenance or on-site maintenance to repair equipment failures and restore normal operation of the equipment.

[0084] Execution steps Preparation phase: Confirm maintenance tasks and safety requirements.

[0085] Gather the necessary tools, spare parts, and other resources.

[0086] Ensure site safety and take protective measures.

[0087] Perform maintenance: Remote operation and maintenance: For devices that support remote monitoring and control, fault diagnosis, parameter adjustment, software upgrades and other operations can be performed through the remote operation and maintenance platform.

[0088] On-site maintenance: For equipment that requires on-site operation, maintenance personnel must perform on-site maintenance according to the maintenance plan and program, including disassembly of faulty parts, replacement of spare parts, parameter adjustment, cleaning and maintenance, etc.

[0089] Verify results: After maintenance is completed, test and verify the equipment to ensure that the fault has been repaired and the equipment performance has returned to normal.

[0090] Record key data and observations during the maintenance process to provide a basis for subsequent analysis and improvement.

[0091] Sorting and feedback: Sorting out tools, spare parts and other resources used in the maintenance process and returning them to designated locations.

[0092] Clean up the site and restore the environment around the equipment.

[0093] Feedback maintenance results and problems encountered to relevant departments and personnel for follow-up tracking and improvement.

[0094] Documentation: Write maintenance reports that detail the maintenance process, maintenance results, problems encountered, and solutions.

[0095] Keep maintenance reports on file for later review and reference.

[0096] Through scientific and standardized maintenance decision-making and execution processes, we can ensure that equipment is maintained in a timely and effective manner, improve equipment reliability and service life, and provide strong support for the company's production operations.

[0097] Feedback and optimization module: The feedback and optimization module is used to record the maintenance process and optimize the operation and maintenance control process based on maintenance feedback and data analysis results.

[0098] Maintenance feedback: record key information and data during the maintenance process, including fault resolution time, maintenance cost, maintenance effect, etc.

[0099] Process optimization: Based on maintenance feedback and data analysis results, the operation and maintenance control process is continuously optimized to improve operation and maintenance efficiency and accuracy.

[0100] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0101] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0102] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An automated intelligent equipment operation and maintenance control system, characterized in that: include: Equipment monitoring and data acquisition module, which is used for real-time monitoring, data transmission and data storage of automated intelligent equipment; Data analysis and anomaly detection module, which is used to perform data preprocessing, data analysis and anomaly detection on the monitored data; Fault diagnosis and location module: The fault diagnosis and location module is used for fault diagnosis and fault location; Maintenance decision and execution module: The maintenance decision and execution module is used to make maintenance decisions and execute maintenance decisions according to the fault diagnosis and location results; Feedback and optimization module: The feedback and optimization module is used to record the maintenance process and optimize the operation and maintenance control process based on maintenance feedback and data analysis results.

2. The automated intelligent equipment operation and maintenance control system according to claim 1, characterized in that: The specific process of the equipment monitoring and data acquisition module for real-time monitoring, data transmission and data storage of automated intelligent equipment is as follows: Real-time monitoring: Set up sensors, IoT devices and remote monitoring software to monitor automated smart devices and collect various data generated during the operation of the equipment, including operating status, performance parameters and fault information; Data transmission: Transmit the collected data from the device to the data center or cloud platform; Data storage: Store the received data in the database.

3. The automated intelligent equipment operation and maintenance control system according to claim 1, characterized in that: The specific process of data preprocessing is as follows: Data cleaning Missing value handling: Depending on the nature of the data, choose to fill, delete, or ignore records with missing values; Outlier processing: Identify and process abnormal data points by deleting them, replacing them with the mean / median, or adjusting them according to the data distribution; Duplicate value processing: check and delete or merge duplicate records; Data denoising, smoothing first: For time series data, use methods such as sliding average and exponential smoothing to remove random noise; Use filtering techniques, including low-pass filters and high-pass filters, to remove noise based on the frequency characteristics of the signal; Data formatting: unify units to ensure that all related data are measured in consistent units; Data encoding: converting categorical data into numerical data to facilitate processing by machine learning algorithms; Data standardization / normalization: Adjust the data range to make it suitable for specific algorithm requirements and avoid certain features from having too much impact on the results.

4. The automated intelligent equipment operation and maintenance control system according to claim 2, characterized in that: The specific process of the data analysis is as follows: Conduct descriptive statistical analysis and calculate statistics to describe the distribution and characteristics of the data. Statistics include mean, median, standard deviation, and quartiles. Draw charts to visualize data and discover patterns and trends in data, including histograms, box plots, and scatter plots; Correlation analysis, identifying the associations between different variables and understanding which factors may affect the operating status of the equipment; Use correlation coefficients to quantify the relationship between variables; Machine learning models that apply cluster analysis to identify different operating states or abnormal patterns of equipment; Use classification or regression models to predict the future state or performance of a device.

5. The automated intelligent equipment operation and maintenance control system according to claim 2, characterized in that: The specific process of anomaly detection is as follows: setting anomaly threshold, based on statistical methods: using standard deviation and IQR methods to set thresholds; Model-based approach: train a model to predict normal behavior and treat points where the prediction error exceeds a certain threshold as abnormal; Distance-based method: Calculate the distance from a data point to its nearest neighbor, and consider points with too large a distance as anomalies; Trigger the anomaly detection mechanism to monitor the data flow in real time. When the data exceeds the set threshold, the anomaly detection mechanism is triggered, and different alarm levels are set to respond differently according to the severity of the anomaly. Generate abnormal alarms and send alarm notifications to relevant personnel, including specific information about the abnormality, time of occurrence, and possible causes; Provides a visual interface to help personnel quickly understand abnormal situations and the current status of equipment.

6. The automated intelligent equipment operation and maintenance control system according to claim 1, characterized in that: The specific process of the fault diagnosis and fault location module for fault diagnosis and fault location is as follows: Fault diagnosis: Combine the fault database and expert system resources to analyze abnormal alarms and diagnose the cause and type of equipment failure; Fault location: Determine the specific location and impact range of the fault based on the fault diagnosis results.

7. The automated intelligent equipment operation and maintenance control system according to claim 1, characterized in that: The specific process of the maintenance decision and execution module making maintenance decisions and executing maintenance decisions according to the fault diagnosis and location results is as follows: Make maintenance decisions, evaluate the impact of failures, estimate the impact of failures on equipment performance, production processes and safety, and determine the urgency of maintenance; Determine maintenance goals: clarify the main goals of maintenance, including restoring equipment performance, extending equipment life, and improving production efficiency; Develop maintenance plans and maintenance time: determine the best maintenance time based on production plans and equipment downtime windows; Maintenance content: Based on the fault diagnosis results, identify the parts that need to be repaired or replaced and the parameters that need to be adjusted; Risk assessment: Evaluate the risks and potential safety hazards that may be encountered during maintenance and develop countermeasures; Select maintenance strategy: Select appropriate maintenance strategy based on equipment type, fault nature and maintenance cost factors; Develop emergency plans: Develop emergency plans to deal with emergencies and ensure that problems encountered during maintenance can be responded to and properly resolved quickly; Execute maintenance decisions: Execute maintenance, remote operation and maintenance: For equipment that supports remote monitoring and control, perform fault diagnosis, parameter adjustment, and software upgrades through the remote operation and maintenance platform; On-site maintenance: For equipment that requires on-site operation, maintenance personnel must perform on-site maintenance in accordance with maintenance plans and programs; Verify the effect: After the maintenance is completed, test and verify the equipment to ensure that the fault has been repaired and the equipment performance has returned to normal; Record key data and observations during the maintenance process to provide a basis for subsequent analysis and improvement.

8. The automated intelligent equipment operation and maintenance control system according to claim 1, characterized in that: The feedback and optimization module is used to record the maintenance process and optimize the operation and maintenance control process according to the maintenance feedback and data analysis results. The specific process is as follows: feedback and optimization, maintenance feedback, recording key information and data in the maintenance process, including fault resolution time, maintenance cost, and maintenance effect; Process optimization: Optimize the operation and maintenance control process based on maintenance feedback and data analysis results.

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