Intelligent operation and maintenance platform for natural gas distributed energy system

Abstract

The invention discloses an intelligent operation and maintenance platform for a natural gas distributed energy system. The platform integrates the functions of real-time monitoring and early warning, intelligent scheduling and optimization, efficient operation and maintenance and management, data integration and sharing and the like. By collecting and analyzing system data in real time, the platform can find and process potential faults in time, the system shutdown event is avoided, meanwhile, the operation strategy of the energy supply equipment is intelligently adjusted according to the load prediction result and the energy efficiency index, and the energy utilization efficiency is improved. Besides, the platform also realizes efficient distribution and tracking of operation and maintenance tasks, reduces the operation and maintenance cost, breaks the barrier of departments, and promotes information sharing and cooperation among different departments. The platform has remarkable advantages in the aspects of improving system stability, reducing operation and maintenance cost, enhancing cross-department cooperation and information circulation, improving safety and reliability and the like, provides powerful technical support and decision basis for operation and maintenance management of the natural gas distributed energy system, and has wide application prospects and popularization values.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance management of natural gas distributed energy systems, and in particular to an intelligent operation and maintenance platform that integrates technologies such as mobile Internet, cloud computing, and big data analysis. Background Art

[0002] With the acceleration of urbanization and the continuous growth of energy demand, natural gas distributed energy systems have been increasingly widely used due to their high energy efficiency, low pollution, and flexible operation. However, the traditional operation and maintenance model has many drawbacks and cannot meet the high requirements of modern energy systems for stability, efficiency, and safety.

[0003] At present, the operation and maintenance of natural gas distributed energy systems mainly rely on manual inspections and regular maintenance. This approach has problems such as slow response, untimely fault detection, and high maintenance costs. At the same time, due to the increase in system complexity and data volume, the traditional operation and maintenance model is difficult to achieve comprehensive monitoring of system status and real-time early warning.

[0004] Although some intelligent operation and maintenance technologies have been applied to energy systems, these technologies often have the following disadvantages:

[0005] Low system integration: Data sharing and interoperability between different devices and systems are poor, resulting in inefficient operation and maintenance.

[0006] Insufficient data analysis capabilities: Traditional data analysis methods have difficulty processing large-scale, complex data sets and are unable to provide accurate fault warnings and optimization suggestions.

[0007] Lack of intelligence in operation and maintenance decisions: Operation and maintenance decisions mainly rely on the experience and intuition of operation and maintenance personnel, and lack scientific decision-making basis and algorithm support. Summary of the invention

[0008] The purpose of the present invention is to provide an intelligent operation and maintenance platform for a natural gas distributed energy system, which can monitor the operating status of the system in real time, provide early warning and intelligent scheduling for faults, improve operation and maintenance efficiency and energy utilization efficiency, and reduce operation and maintenance costs.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] A natural gas distributed energy system intelligent operation and maintenance platform, the platform comprising:

[0011] The data acquisition module collects operating data including but not limited to temperature, pressure, flow and energy consumption in real time through the sensor network deployed in the natural gas distributed energy system, and uploads it to the cloud server through the remote communication module;

[0012] The data analysis module includes a big data analysis engine and machine learning algorithms to pre-process, extract features and recognize patterns on the collected data, evaluate system performance by calculating the energy efficiency index, and use the fault warning model to predict potential faults and generate fault warning information;

[0013] Intelligent scheduling module, including energy management system and load forecasting model. The load forecasting model uses time series analysis or deep learning model to predict future energy demand. The energy management system automatically adjusts energy distribution and scheduling strategies according to load forecast results, energy efficiency index and fault warning information.

[0014] The operation and maintenance management module receives and displays the output results of the data analysis module and the intelligent scheduling module, and provides operation and maintenance task allocation, operation and maintenance progress tracking, and operation and maintenance report generation functions.

[0015] Furthermore, the data acquisition module also includes:

[0016] The data verification unit performs integrity checks on the collected data to ensure that each data point has been correctly recorded;

[0017] The data cleaning unit identifies and corrects errors or outliers in the data, including but not limited to data smoothing, interpolation or elimination, to ensure the accuracy and reliability of the data uploaded to the cloud server.

[0018] Furthermore, the data analysis module calculates the energy efficiency index by the following formula:

[0019]

[0020] Furthermore, the data analysis module also includes an anomaly detection module, which uses at least one of the following algorithms to identify abnormal values ​​in the operating data:

[0021] Statistical clustering analysis, such as K-means or DBSCAN algorithm;

[0022] The isolation forest algorithm based on machine learning detects anomalies in data by building multiple decision trees;

[0023] The above algorithms are combined to form a hybrid model to improve the accuracy and robustness of anomaly detection.

[0024] Furthermore, the intelligent scheduling module also includes an energy storage management function, which includes:

[0025] Energy price forecasting unit, which uses time series analysis or machine learning algorithms to predict future energy price trends;

[0026] The energy storage strategy formulation unit automatically determines the timing and amount of energy storage and release based on energy price forecast results, load forecast results and energy efficiency index to optimize energy costs and energy utilization efficiency;

[0027] The energy storage status monitoring unit tracks the working status and capacity of energy storage equipment in real time to ensure the safety and reliability of the storage process.

[0028] Furthermore, the operation and maintenance report in the operation and maintenance management module calculates the operation and maintenance cost saving rate by the following formula:

[0029]

[0030] Furthermore, the operation and maintenance management module also includes:

[0031] The user interface provides an intuitive operation interface and graphical display, allowing operation and maintenance personnel to remotely monitor the operating status of the natural gas distributed energy system, receive fault warning notifications, and directly execute or adjust operation and maintenance tasks;

[0032] The operation and maintenance task optimization unit automatically optimizes the allocation of operation and maintenance tasks based on the operation and maintenance historical data and current task requirements, thereby improving the operation and maintenance efficiency and response speed;

[0033] The operation and maintenance knowledge base integrates industry standards and best practices to provide problem diagnosis and solution references for operation and maintenance personnel.

[0034] Furthermore, the platform also supports data integration and sharing with other management systems such as ERP and CRM. The specific implementation methods include:

[0035] Standardize data interfaces and provide a unified API interface or data exchange protocol to ensure data interoperability between different systems;

[0036] Data mapping and conversion: data mapping and conversion are carried out according to the data structure and format of each system to achieve seamless data connection;

[0037] The data synchronization mechanism ensures that data between systems are updated in real time to maintain data consistency and accuracy.

[0038] The intelligent operation and maintenance platform of the natural gas distributed energy system of the present invention has the following beneficial effects:

[0039] 1. Improve system stability

[0040] The intelligent operation and maintenance platform can detect and handle potential faults in a timely manner through real-time monitoring and early warning systems to avoid system downtime. At the same time, through intelligent scheduling and optimization systems, the platform can intelligently adjust the operation strategy of energy supply equipment according to load forecast results and energy efficiency index to ensure the stable operation of the system.

[0041] 2. Improve energy efficiency

[0042] The intelligent operation and maintenance platform can accurately evaluate the energy efficiency of the system by collecting and analyzing system data in real time, and make optimization suggestions based on the evaluation results. By optimizing energy allocation strategies and equipment operating parameters, the platform can significantly reduce energy waste and improve energy efficiency.

[0043] 3. Reduce operation and maintenance costs

[0044] The intelligent operation and maintenance platform realizes efficient allocation and tracking of operation and maintenance tasks through an efficient operation and maintenance and management system. At the same time, through data analysis algorithms and intelligent decision support, the platform can reduce unnecessary equipment downtime and maintenance costs, and reduce operation and maintenance costs.

[0045] 4. Strengthen cross-departmental collaboration and information flow

[0046] The intelligent operation and maintenance platform breaks down departmental barriers and promotes information sharing and collaboration between different departments through data integration and sharing systems. This cross-departmental collaboration and information flow improves the overall operational efficiency and management level of the enterprise, and helps to achieve seamless integration of energy management and overall enterprise operation management.

[0047] 5. Improve safety and reliability

[0048] The intelligent operation and maintenance platform uses advanced data encryption and network security technologies to ensure the security and reliability of system data. At the same time, through real-time monitoring and early warning systems, the platform can promptly detect and deal with potential safety hazards to ensure the safe operation of the system.

[0049] In summary, the intelligent operation and maintenance platform of natural gas distributed energy system has significant beneficial effects in improving system stability, improving energy utilization efficiency, reducing operation and maintenance costs, strengthening cross-departmental collaboration and information flow, and improving safety and reliability. The platform provides strong technical support and decision-making basis for the operation and maintenance management of natural gas distributed energy systems, and has broad application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a structural diagram of an intelligent operation and maintenance platform for a natural gas distributed energy system according to the present invention. DETAILED DESCRIPTION

[0051] 1. Platform Overview

[0052] This embodiment provides an intelligent operation and maintenance platform for a natural gas distributed energy system, which integrates functions such as data collection, data analysis, intelligent scheduling, and operation and maintenance management, and aims to achieve comprehensive monitoring, intelligent optimization, and efficient operation and maintenance of the natural gas distributed energy system. 2. Specific implementation methods

[0054] 1. Data acquisition module

[0055] The data acquisition module collects key operating data including but not limited to temperature, pressure, flow, energy consumption, etc. in real time through the sensor network deployed in the natural gas distributed energy system. These data are uploaded to the cloud server through the remote communication module, providing a basis for subsequent data analysis and intelligent scheduling.

[0056] Specific implementation process:

[0057] 1.1 Sensor deployment:

[0058] Deploy sensors at key locations in natural gas distributed energy systems, such as pipelines, equipment interfaces, and energy conversion units;

[0059] Ensure that sensors can accurately measure key parameters such as temperature, pressure, flow, energy consumption, etc.

[0060] 1.2 Data collection and conversion:

[0061] The sensor converts the collected analog signal into a digital signal for subsequent processing;

[0062] The data acquisition unit is responsible for receiving these digital signals and performing preliminary processing and verification to ensure the accuracy and integrity of the data.

[0063] 1.3 Data upload and storage:

[0064] The processed data is uploaded to the cloud server through a remote communication module (such as 4G / 5G, Wi-Fi, wired network, etc.);

[0065] The cloud server is responsible for storing and managing this data, ensuring the long-term preservation and traceability of the data.

[0066] 1.4 Data verification and cleaning:

[0067] On the cloud server, the data is further verified and cleaned to identify and correct errors or outliers;

[0068] This includes operations such as data smoothing, interpolation or elimination to ensure the accuracy and reliability of subsequent data analysis.

[0069] 1.5 Data Integration and Sharing:

[0070] The data acquisition module also integrates and shares data with other management systems (such as ERP, CRM, etc.);

[0071] This helps to achieve seamless integration of energy management and overall enterprise operations management, and improve management efficiency and decision-making level.

[0072] 1.6 Operational data types

[0073] The types of operating data collected by the data acquisition module include but are not limited to the following:

[0074] Temperature data:

[0075] Reflects the operating temperature of each system component, helping to monitor the thermal status of the equipment and potential thermal failures.

[0076] Pressure data:

[0077] Reflects the pressure status of system pipes and equipment, helping to monitor pressure fluctuations and potential pressure leaks.

[0078] Traffic data:

[0079] Reflects the flow of natural gas in the system, helping to monitor flow changes and potential flow imbalance problems.

[0080] Energy consumption data:

[0081] Reflecting the energy consumption of each device in the system helps to evaluate energy utilization efficiency and identify potential energy-saving opportunities.

[0082] Other relevant data:

[0083] It also includes equipment status data, fault alarm data, environmental parameter data, etc., which help to fully understand the system's operating status and potential problems.

[0084] In summary, the data acquisition module provides basic data support for the intelligent operation and maintenance platform of the natural gas distributed energy system by deploying sensor networks, collecting key operation data, uploading to the cloud server, and performing verification and cleaning. At the same time, the collected operation data types are rich and diverse, which helps to comprehensively monitor and analyze the operation status of the system.

[0085] 2. Data Analysis Module

[0086] The data analysis module includes a big data analysis engine and machine learning algorithms to conduct in-depth mining and analysis of the collected data.

[0087] Energy Efficiency Index (EEI) calculation:

[0088]

[0089] The index is used to evaluate the energy utilization efficiency of natural gas distributed energy systems and help operation and maintenance personnel understand the actual operating status of the system.

[0090] Fault warning model:

[0091] The fault warning model is based on historical fault data and current operation data, and uses machine learning algorithms such as support vector machine (SVM) and neural network to predict potential fault points and generate fault warning information, which helps operation and maintenance personnel take measures in advance to avoid faults.

[0092] Specific implementation process:

[0093] 2.1 Energy Efficiency Index (EEI) Calculation

[0094] 2.1.1 Data preparation:

[0095] Acquire actual energy output data and theoretical maximum energy output data of the natural gas distributed energy system from the data acquisition module;

[0096] Ensure the accuracy and completeness of data and handle missing or abnormal data.

[0097] 2.1.2 Calculation of EEI:

[0098] Use the formula to calculate the energy efficiency index;

[0099] The "actual energy output" in the formula refers to the amount of energy actually generated by the system in a certain period of time, and the "theoretical maximum energy output" refers to the maximum energy output that the system can achieve under ideal conditions.

[0100] 2.1.3 Result analysis:

[0101] Analyze EEI value to understand the energy efficiency of the system;

[0102] Compare EEI values ​​with historical data or industry standards to evaluate system performance;

[0103] According to the changing trend of EEI value, the future energy utilization efficiency of the system is predicted to provide a basis for operation and maintenance decision-making.

[0104] 2.2 Fault warning model

[0105] 2.2.1 Data collection and preprocessing:

[0106] Obtain historical fault data and current operation data from the data acquisition module;

[0107] Perform preprocessing operations such as cleaning, denoising, and normalization on the data to improve data quality.

[0108] 2.2.2 Feature extraction:

[0109] Extract fault-related features from preprocessed data, such as abnormal changes in key parameters such as temperature, pressure, flow, and energy consumption;

[0110] The feature selection algorithm is used to screen out the features that have an important impact on fault warning.

[0111] 2.2.3 Model training:

[0112] Select machine learning algorithms such as support vector machines (SVM) and neural networks as the basis of fault warning models;

[0113] Use historical failure data as a training set to train and optimize the model;

[0114] The performance of the model is evaluated through methods such as cross-validation to ensure the accuracy and generalization ability of the model.

[0115] 2.2.4 Fault warning:

[0116] Input the current operation data into the trained fault warning model to obtain the fault warning result;

[0117] According to the early warning results, potential fault points are identified and fault early warning information is generated;

[0118] Push fault warning information to operation and maintenance personnel in a timely manner so that they can take preventive measures to avoid failures.

[0119] 2.2.5 Model update and optimization:

[0120] As new data continues to accumulate, the fault warning model is regularly updated and optimized;

[0121] Retrain the model using new data to improve its accuracy and adaptability;

[0122] The performance of the model is continuously monitored and evaluated to ensure its effectiveness in practical applications.

[0123] In summary, the data analysis module achieves in-depth mining and analysis of the operating data of the natural gas distributed energy system by calculating the energy efficiency index and building a fault warning model. This not only helps operation and maintenance personnel understand the actual operating status of the system, but also predicts potential fault points in advance, providing strong support for the stable operation and efficient operation and maintenance of the system.

[0124] 3. Intelligent scheduling module

[0125] The intelligent scheduling module includes an energy management system and a load forecasting model.

[0126] Load forecasting model:

[0127] The load forecasting model uses time series analysis (such as the ARIMA model) or deep learning models (such as the LSTM network) to predict energy demand in the future based on historical load data and current environmental factors (such as weather, holidays, etc.). This helps operation and maintenance personnel to reasonably arrange energy supply and improve energy utilization efficiency.

[0128] Energy Management System:

[0129] The energy management system automatically adjusts the energy distribution and scheduling strategies based on load forecast results, energy efficiency index and fault warning information through optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.). For example, during periods of peak energy demand, it prioritizes scheduling of efficient and stable energy supply equipment; during periods of low energy demand, it reasonably arranges equipment inspection and maintenance to reduce operation and maintenance costs.

[0130] Specific implementation process:

[0131] 3.1 Implementation process of load forecasting model

[0132] 3.1.1 Data collection and preprocessing

[0133] Obtain historical load data from the data acquisition module, including energy demand data for different time periods (such as day, week, month, year);

[0134] Collect data on current environmental factors, such as weather conditions (temperature, humidity, wind speed, etc.), holiday information, economic activity levels, etc.;

[0135] The data was cleaned and preprocessed to remove outliers, fill missing values, and perform normalization.

[0136] 3.1.2 Feature Selection and Engineering

[0137] Based on domain knowledge and data correlation analysis, key factors that affect load forecasting are selected as features;

[0138] Perform engineering processing on features, such as extraction of time features (such as hours, weeks, months, etc.), quantification of environmental factors (such as conversion of temperature into Celsius or Fahrenheit), etc.

[0139] 3.1.3 Model selection and training

[0140] According to the data characteristics and forecasting requirements, select a time series analysis model (such as ARIMA model) or a deep learning model (such as LSTM network) as the load forecasting model;

[0141] Use historical load data and environmental factor data as training sets to train and optimize the model;

[0142] The model parameters are adjusted through cross-validation, grid search and other methods to improve the prediction accuracy.

[0143] 3.1.4 Load forecasting and result analysis

[0144] Input the current environmental factor data into the trained load forecasting model to obtain the energy demand forecast results for a period of time in the future;

[0145] Analyze the accuracy and reliability of forecast results and compare them with historical data or industry standards;

[0146] According to the changing trend of the prediction results, the future energy demand of the system is predicted, providing a basis for the scheduling strategy of the energy management system.

[0147] 3.2 Implementation process of energy management system

[0148] 3.2.1 Data Integration and Analysis

[0149] Obtain relevant data from the load forecasting model, energy efficiency index calculation module and fault warning model;

[0150] Integrate and analyze data to understand the system's energy demand, energy efficiency, and potential failure points.

[0151] 3.2.2 Scheduling Strategy Formulation and Optimization

[0152] Formulate energy distribution and dispatch strategies based on load forecast results, energy efficiency index and fault warning information;

[0153] Use optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) to optimize scheduling strategies to ensure that energy demand is met while reducing operation and maintenance costs;

[0154] Consider the impact of different time periods, different weather conditions, different energy prices and other factors on the scheduling strategy, and formulate flexible scheduling plans.

[0155] 3.2.3 Energy distribution and scheduling execution

[0156] Send the optimized dispatching strategy to each device or subsystem of the natural gas distributed energy system;

[0157] Monitor the execution of scheduling strategies to ensure that equipment distributes and schedules energy according to predetermined strategies;

[0158] During periods of peak energy demand, priority is given to dispatching efficient and stable energy supply equipment; during periods of low energy demand, equipment overhaul and maintenance are reasonably arranged.

[0159] 3.2.4 Performance Monitoring and Evaluation

[0160] Continuously monitor and evaluate the performance of the energy management system;

[0161] Collect system operation data and analyze changes in indicators such as energy efficiency, equipment operation status, and failure rate;

[0162] Adjust and optimize the energy management system based on the evaluation results to improve the stability and reliability of the system.

[0163] In summary, the intelligent dispatching module realizes the intelligent dispatching and optimized management of the natural gas distributed energy system through the collaborative work of the load forecasting model and the energy management system. This not only helps to improve energy utilization efficiency, but also reduces operation and maintenance costs, providing strong support for the stable operation and efficient operation and maintenance of the system.

[0164] 4. Operation and maintenance management module

[0165] The operation and maintenance management module is used to receive and display the output results of the data analysis module and the intelligent scheduling module, and provide functions such as operation and maintenance task allocation, operation and maintenance progress tracking, and operation and maintenance report generation.

[0166] Operation and maintenance task allocation:

[0167] The operation and maintenance management module automatically or manually assigns operation and maintenance tasks to corresponding operation and maintenance personnel based on the output results of the data analysis module and the intelligent scheduling module. When allocating tasks, factors such as the skill level, geographical location, and urgency of the operation and maintenance personnel are taken into consideration to ensure efficient execution of operation and maintenance tasks.

[0168] Operation and maintenance progress tracking:

[0169] The operation and maintenance management module tracks the execution of operation and maintenance tasks in real time, including task start time, completion time, executor and other information, which helps operation and maintenance personnel to understand the task progress in a timely manner and adjust work plans.

[0170] Operation and maintenance report generation:

[0171] The operation and maintenance management module automatically generates an operation and maintenance report based on the execution status of operation and maintenance tasks and data analysis results; the report content includes but is not limited to energy efficiency index, fault warning information, operation and maintenance task execution status, operation and maintenance cost saving rate, etc.

[0172] The operation and maintenance cost saving rate is calculated by the following formula:

[0173]

[0174] This indicator is used to evaluate the effectiveness of the intelligent operation and maintenance platform in reducing operation and maintenance costs.

[0175] Specific implementation process:

[0176] 4.1 Operation and maintenance task allocation

[0177] 4.1.1 Receive output results:

[0178] The operation and maintenance management module receives data such as energy efficiency index and fault warning information from the data analysis module;

[0179] Receive information such as energy distribution and scheduling strategies, equipment status, etc. from the intelligent scheduling module.

[0180] 4.1.2 Task Requirements Analysis:

[0181] Analyze the operation and maintenance requirements of the current system based on the received data, such as fault handling, equipment maintenance, energy optimization, etc.;

[0182] Assess the urgency, importance, and complexity of the task.

[0183] 4.1.3 Operation and maintenance personnel matching:

[0184] Match the most suitable operation and maintenance personnel to perform the current task based on their skill level, experience, geographical location and other factors;

[0185] Considering the urgency of the task, priority will be given to operation and maintenance personnel who can respond quickly.

[0186] 4.1.4 Task Assignment and Notification:

[0187] Automatically or manually assign maintenance tasks to selected maintenance personnel;

[0188] Send task information to operation and maintenance personnel through system notifications, text messages, emails, etc., including task description, execution requirements, completion time, etc.

[0189] 4.2 Operation and maintenance progress tracking

[0190] 4.2.1 Task execution record:

[0191] When the operation and maintenance personnel perform tasks, the system records the task start time, execution process, problems encountered, and other information;

[0192] The system updates the task status in real time, such as in progress, completed, pending, etc.

[0193] 4.2.2 Progress monitoring and reminders:

[0194] The operation and maintenance management module tracks the execution of operation and maintenance tasks in real time and displays the task progress bar or Gantt chart;

[0195] According to the task completion time and progress, reminder information is automatically sent to the operation and maintenance personnel to ensure that the task is completed on time.

[0196] 4.2.3 Exception handling:

[0197] If an abnormal situation occurs during the execution of a task, such as equipment failure or material shortage, the operation and maintenance personnel can submit an abnormality report through the system;

[0198] The system automatically sends exception reports to relevant personnel or departments so that timely measures can be taken to solve the problem.

[0199] 4.3 Operation and maintenance report generation

[0200] 4.3.1 Data collection and collation:

[0201] The operation and maintenance management module collects data such as the execution status of operation and maintenance tasks and data analysis results;

[0202] Organize and analyze the data, and calculate indicators such as operation and maintenance cost savings rate.

[0203] 4.3.2 Report template design:

[0204] Design operation and maintenance report templates according to business needs, including report title, content structure, chart style, etc.;

[0205] Make sure the report is clear, intuitive, and easy to understand.

[0206] 4.3.3 Report generation and export:

[0207] Automatically generate operation and maintenance reports based on templates and data;

[0208] Provides report export function, supporting PDF, Excel and other formats.

[0209] 4.3.4 Report review and release:

[0210] Operation and maintenance managers review the generated reports to ensure the accuracy of data and the completeness of reports;

[0211] After the review is passed, the report will be released to relevant personnel or departments for their reference and decision-making.

[0212] In summary, the operation and maintenance management module realizes the functions of operation and maintenance task allocation, operation and maintenance progress tracking, and operation and maintenance report generation by receiving and displaying the output results of the data analysis module and the intelligent scheduling module; this not only improves the efficiency and quality of operation and maintenance work, but also provides strong support for the stable operation and efficient operation and maintenance of the system. At the same time, by calculating indicators such as the operation and maintenance cost saving rate, the effect of the intelligent operation and maintenance platform in reducing operation and maintenance costs can be evaluated, providing data support for optimizing operation and maintenance strategies.

[0213] 3. Platform Advantages

[0214] 1. Real-time monitoring and early warning advantages

[0215] Instant insight into system status: Through data collection and analysis modules, the platform can capture key parameters of natural gas distributed energy systems in real time, such as equipment operating status, energy consumption, environmental parameters, etc., providing the operation and maintenance team with an instant and comprehensive system view;

[0216] Accurate fault warning: Using advanced data analysis algorithms, the platform can identify signs of system anomalies and issue fault warnings in advance, allowing the operation and maintenance team to take action before problems occur, avoiding or reducing downtime and ensuring continuous and stable operation of the system;

[0217] Improve system reliability: Real-time monitoring and early warning mechanisms help to promptly detect and address potential problems, reduce the occurrence of sudden failures, and thus significantly improve the overall reliability and stability of the system.

[0218] 2. Advantages of intelligent scheduling and optimization

[0219] Flexible response to load changes: Based on load forecast results, the platform can intelligently adjust the operation strategy of energy supply equipment to ensure sufficient supply during peak energy demand and optimize equipment operation during low energy demand to reduce unnecessary energy consumption.

[0220] Improve energy efficiency: Through real-time monitoring and optimization of the energy efficiency index, the platform can identify and optimize inefficient links in energy use, such as unreasonable energy allocation and equipment overload, thereby improving the energy utilization efficiency of the entire system;

[0221] Promote sustainable development: Intelligent scheduling and optimization functions help reduce energy waste and carbon emissions, which is in line with the current green and low-carbon development trend and helps companies achieve sustainable development goals.

[0222] 3. Efficient operation and management advantages

[0223] Optimize the allocation of operation and maintenance resources: The operation and maintenance management module can intelligently allocate operation and maintenance tasks based on factors such as the skill level of the operation and maintenance personnel, geographical location, and task urgency, ensuring efficient use of operation and maintenance resources;

[0224] Improve operation and maintenance efficiency: By tracking the operation and maintenance progress in real time, the platform can provide the operation and maintenance team with a clear task view, helping the team to respond to problems quickly, shorten processing time, and improve operation and maintenance efficiency;

[0225] Reduce operation and maintenance costs: Intelligent operation and maintenance management helps reduce unnecessary equipment downtime and maintenance costs. At the same time, it predicts potential failures through data analysis, reduces the occurrence of sudden maintenance events, and further reduces operation and maintenance costs.

[0226] 4. Data integration and sharing advantages

[0227] Realize seamless information connection: The platform supports data integration and sharing with other management systems (such as ERP, CRM, etc.), so that energy management data can be seamlessly connected with the overall operation management data of the enterprise, providing comprehensive decision-making support for management;

[0228] Promote cross-departmental collaboration: Data integration and sharing functions help break down departmental barriers, promote information sharing and collaboration between different departments, and improve the overall operational efficiency of the enterprise;

[0229] Enhance scientific decision-making: By integrating data from multiple parties, the platform can provide management with more comprehensive and accurate business insights, helping management make more scientific and reasonable decisions.

[0230] In summary, the intelligent operation and maintenance platform for the natural gas distributed energy system provided in this embodiment realizes comprehensive monitoring, intelligent optimization and efficient operation and maintenance of the natural gas distributed energy system by integrating functions such as data collection, data analysis, intelligent scheduling and operation and maintenance management. The platform has significant advantages in improving energy utilization efficiency, reducing operation and maintenance costs, and improving system stability and reliability.

[0231] Specific application example 1:

[0232] Intelligent operation and maintenance platform for natural gas distributed energy system in a data center

[0233] 1. Project Background and Requirements

[0234] As a critical information infrastructure, a data center has extremely high requirements for the stability and efficiency of energy supply. The data center uses a natural gas distributed energy system that integrates power supply, cooling and heating to meet its complex energy needs. However, the traditional operation and maintenance model has problems such as slow response speed, untimely fault detection, and low energy utilization efficiency. Therefore, the data center introduced an intelligent operation and maintenance platform to improve the stability and operation and maintenance efficiency of the system.

[0235] 2. Functions of the Intelligent Operation and Maintenance Platform

[0236] Real-time monitoring and early warning system

[0237] Data collection: The platform collects key parameters of the natural gas distributed energy system in real time through various sensors and monitoring equipment, such as equipment temperature, pressure, vibration, energy consumption, etc.

[0238] Data analysis and early warning: Using advanced data analysis algorithms, the platform processes and analyzes the collected data in real time, identifies signs of system abnormalities, and issues fault warnings in advance. The warning information includes the fault type, location, possible causes, and recommended treatment measures.

[0239] Intelligent scheduling and optimization system

[0240] Load forecasting: Based on historical data and machine learning algorithms, the platform can accurately predict the energy needs of data centers, including electricity, cooling and heating needs.

[0241] Energy efficiency index calculation: Based on the useful energy output by the system and the total energy input by the system, the platform calculates the energy efficiency index in real time to evaluate the energy utilization efficiency of the system.

[0242] Intelligent scheduling strategy: Based on the load forecast results and energy efficiency index, the platform intelligently adjusts the operation strategy of energy supply equipment, including equipment start and stop, load distribution, etc., to optimize the energy distribution strategy and improve energy utilization efficiency.

[0243] Efficient operation and management system

[0244] Operation and maintenance task allocation: The platform intelligently allocates operation and maintenance tasks based on factors such as the skill level of the operation and maintenance personnel, geographical location, and task urgency, ensuring efficient use of operation and maintenance resources.

[0245] O&M progress tracking: The platform tracks the execution of O&M tasks in real time, including task start time, end time, processing results, etc., so that management can understand O&M progress and problem handling in a timely manner.

[0246] Operation and maintenance cost analysis: The platform compares the operation and maintenance costs before and after the adoption of the platform and calculates the operation and maintenance cost savings rate to evaluate the economic benefits of the platform.

[0247] Data integration and sharing system

[0248] System integration: The platform supports data integration with other management systems (such as ERP, CRM, etc.) to achieve seamless integration of energy management and overall enterprise operations management.

[0249] Data sharing: The platform provides data sharing functions, enabling different departments to share energy management data and promote cross-departmental collaboration and information flow.

[0250] 3. Application Effect

[0251] Improved system stability

[0252] Through real-time monitoring and early warning systems, the platform has successfully warned of multiple potential failures and avoided system downtime. For example, in a generator overheating warning, the platform issued a warning message in a timely manner, and the operation and maintenance personnel responded quickly and took measures to avoid generator damage and system downtime.

[0253] Improved energy efficiency

[0254] Through the intelligent scheduling and optimization system, the platform intelligently adjusts the operation strategy of energy supply equipment according to load forecast results and energy efficiency index. For example, during peak power consumption, the platform gives priority to starting energy-efficient equipment and reduces the operating time of inefficient equipment, thereby improving energy efficiency. According to statistics, after adopting the platform, the energy efficiency of the data center has increased by about 10%.

[0255] Reduced operation and maintenance costs

[0256] Through an efficient operation and maintenance and management system, the platform achieves efficient allocation and tracking of operation and maintenance tasks, reducing operation and maintenance costs. For example, the platform intelligently analyzes operation and maintenance data and finds that a certain device frequently fails, and recommends preventive maintenance. After the operation and maintenance personnel take measures based on the platform's recommendations, the failure rate of the device is significantly reduced, and the operation and maintenance costs are also reduced accordingly. According to statistics, after adopting the platform, the operation and maintenance costs of the data center have been saved by about 20%.

[0257] Strengthening cross-departmental collaboration

[0258] Through the data integration and sharing system, the platform breaks down departmental barriers and promotes information sharing and collaboration between different departments. For example, the finance department can use the platform to understand energy consumption and operation and maintenance costs, providing a basis for budget preparation; the operation and maintenance department can use the platform to understand the equipment operation status and fault warning information, and take timely measures to ensure the stable operation of the system. This cross-departmental collaboration and information flow method improves the overall operational efficiency and management level of the enterprise.

[0259] In summary, the application of the natural gas distributed energy system intelligent operation and maintenance platform in a data center has achieved remarkable results, improving system stability, energy utilization efficiency, reducing operation and maintenance costs, and strengthening cross-departmental collaboration and information flow. The platform provides strong support for energy management in data centers and similar scenarios, and has broad application prospects and promotion value.

[0260] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A natural gas distributed energy system intelligent operation and maintenance platform, characterized in that: The platform includes: The data acquisition module collects operating data including but not limited to temperature, pressure, flow and energy consumption in real time through the sensor network deployed in the natural gas distributed energy system, and uploads it to the cloud server through the remote communication module; The data analysis module includes a big data analysis engine and machine learning algorithms to pre-process, extract features and recognize patterns on the collected data, evaluate system performance by calculating the energy efficiency index, and use the fault warning model to predict potential faults and generate fault warning information; Intelligent scheduling module, including energy management system and load forecasting model. The load forecasting model uses time series analysis or deep learning model to predict future energy demand. The energy management system automatically adjusts energy distribution and scheduling strategies according to load forecast results, energy efficiency index and fault warning information. The operation and maintenance management module receives and displays the output results of the data analysis module and the intelligent scheduling module, and provides operation and maintenance task allocation, operation and maintenance progress tracking, and operation and maintenance report generation functions.

2. The intelligent operation and maintenance platform for a natural gas distributed energy system according to claim 1, characterized in that: The data acquisition module also includes: The data verification unit performs integrity checks on the collected data to ensure that each data point has been correctly recorded; The data cleaning unit identifies and corrects errors or outliers in the data, including but not limited to data smoothing, interpolation or elimination, to ensure the accuracy and reliability of the data uploaded to the cloud server.

3. The intelligent operation and maintenance platform for a natural gas distributed energy system according to claim 1, characterized in that: The data analysis module calculates the energy efficiency index by the following formula:

4. The intelligent operation and maintenance platform for a natural gas distributed energy system according to claim 1, characterized in that: The data analysis module also includes an anomaly detection module, which uses at least one of the following algorithms to identify outliers in the operating data: Statistical clustering analysis, such as K-means or DBSCAN algorithm; The isolation forest algorithm based on machine learning detects anomalies in data by building multiple decision trees; The above algorithms are combined to form a hybrid model to improve the accuracy and robustness of anomaly detection.

5. The intelligent operation and maintenance platform for a natural gas distributed energy system according to claim 1, characterized in that: The intelligent scheduling module also includes an energy storage management function, which includes: Energy price forecasting unit, which uses time series analysis or machine learning algorithms to predict future energy price trends; The energy storage strategy formulation unit automatically determines the timing and amount of energy storage and release based on energy price forecast results, load forecast results and energy efficiency index to optimize energy costs and energy utilization efficiency; The energy storage status monitoring unit tracks the working status and capacity of energy storage equipment in real time to ensure the safety and reliability of the storage process.

6. The intelligent operation and maintenance platform for a natural gas distributed energy system according to claim 1, characterized in that: The operation and maintenance report in the operation and maintenance management module calculates the operation and maintenance cost saving rate using the following formula:

7. The intelligent operation and maintenance platform for a natural gas distributed energy system according to claim 1, characterized in that: The operation and maintenance management module also includes: The user interface provides an intuitive operation interface and graphical display, allowing operation and maintenance personnel to remotely monitor the operating status of the natural gas distributed energy system, receive fault warning notifications, and directly execute or adjust operation and maintenance tasks; The operation and maintenance task optimization unit automatically optimizes the allocation of operation and maintenance tasks based on the operation and maintenance historical data and current task requirements, thereby improving the operation and maintenance efficiency and response speed; The operation and maintenance knowledge base integrates industry standards and best practices to provide problem diagnosis and solution references for operation and maintenance personnel.

8. The intelligent operation and maintenance platform for a natural gas distributed energy system according to claim 1, characterized in that: The platform also supports data integration and sharing with other management systems such as ERP and CRM. The specific implementation methods include: Standardize data interfaces and provide a unified API interface or data exchange protocol to ensure data interoperability between different systems; Data mapping and conversion: data mapping and conversion are carried out according to the data structure and format of each system to achieve seamless data connection; The data synchronization mechanism ensures that data between systems are updated in real time to maintain data consistency and accuracy.

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